Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Optimal Scheduling of Energy Storage Systems with Private Ownership Based on a Stochastic-Robust Hybrid Optimization Model in Energy and Ancillary Services Markets Optimal Scheduling of Energy Storage Systems with Private Ownership Based on a Stochastic-Robust Hybrid Optimization Model in Energy and Ancillary Services Markets 1 12 8074 10.22075/jme.2023.26339.2226 FA Mohammad Farahani MSc, Department of Electrical Engineering, Arak University of Technology, Arak, Iran 0000-0003-4848-5046 Abouzar Samimi Assistant Professor, Department of Electrical Engineering, Arak University of Technology, Arak, Iran Hossein Shateri Assistant Professor, Department of Electrical Engineering, Arak University of Technology, Arak, Iran Journal Article 2022 02 21 In this paper, the problem of participation of a Battery Energy Storage (BES) in the Day-Ahead Market (DAM) and Real-Time Market (RTM) in three cases including Joint Energy and Reactive Power Market (JERPM), Energy Market (EM) and Energy and Reserve Market (ERM) is modeled based on a hybrid Stochastic Robust Optimization (SRO) model and it tries to maximize the profit of the BES owner in the face of uncertainty pertaining to the market prices. In the proposed model, in the first step, the decision maker or BES owner predicts energy, reserve and reactive power prices in each market according to historical network information. In the second step, by determining the uncertainty interval of prices for DAM and RTM, robust optimization is implemented aiming at maximizing the profit of the BES owner using a model of forming a robust counterpart of the objective function and dual theory. In the next step, by defining some different scenarios for the robust budget, Stochastic Programming (SP) assigns a probability to each scenario. Then, the final profit of each market is calculated through probabilistic weighting, and finally the market with more profit is opted as considered participation market. The proposed formulation based on the Mixed Integer Nonlinear Programming (MINLP) model is implemented in the GAMS software environment and the results demonstrate the maximum profitability of BES in EM (21% more) and also show participation in providing ancillary services, such as reserve to provide security in incidents and reactive power to maintain stability and reduce cost and losses, despite the decrease in profit, leads to neutralizing the negative impact of uncertainties in the BES profit. . In this paper, the problem of participation of a Battery Energy Storage (BES) in the Day-Ahead Market (DAM) and Real-Time Market (RTM) in three cases including Joint Energy and Reactive Power Market (JERPM), Energy Market (EM) and Energy and Reserve Market (ERM) is modeled based on a hybrid Stochastic Robust Optimization (SRO) model and it tries to maximize the profit of the BES owner in the face of uncertainty pertaining to the market prices. In the proposed model, in the first step, the decision maker or BES owner predicts energy, reserve and reactive power prices in each market according to historical network information. In the second step, by determining the uncertainty interval of prices for DAM and RTM, robust optimization is implemented aiming at maximizing the profit of the BES owner using a model of forming a robust counterpart of the objective function and dual theory. In the next step, by defining some different scenarios for the robust budget, Stochastic Programming (SP) assigns a probability to each scenario. Then, the final profit of each market is calculated through probabilistic weighting, and finally the market with more profit is opted as considered participation market. The proposed formulation based on the Mixed Integer Nonlinear Programming (MINLP) model is implemented in the GAMS software environment and the results demonstrate the maximum profitability of BES in EM (21% more) and also show participation in providing ancillary services, such as reserve to provide security in incidents and reactive power to maintain stability and reduce cost and losses, despite the decrease in profit, leads to neutralizing the negative impact of uncertainties in the BES profit. .

https://modelling.semnan.ac.ir/article_8074_b3219199942de5373d10f7e325b8d9f3.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 A Modified Single-Phase Single-Stage Boost Inverter A Modified Single-Phase Single-Stage Boost Inverter 13 26 8069 10.22075/jme.2023.30104.2415 FA Hadi Khoramdel PhD student, Faculty of Engineering, University of Mohaghegh Ardabili, Ardabi, iran Farzad Sedaghati Associate Professor, Faculty of Engineering, Energy Management Research Center, University of Mohaghegh Ardabili, Ardabi, iran Hossein Shayeghi Professor, Faculty of Engineering, Energy Management Research Center, University of Mohaghegh Ardabili ,Ardabi, iran Hadi Dolati MSc Student, Faculty of Electrical and Computer Engineering, University of Tabriz,Tabriz,, iran Journal Article 2023 03 05 This paper presents a modified single stage single phase inverter with four switches. The proposed inverter has important features such as continuous input current, voltage buck and boost with single stage conversion, short circuit safety and operation in various duty cycles same as qZSI and qSBI converters. In the rest of the paper, the suggested inverter operation mode and its switching method are described first and then, the steady state analysis is presented. To validate the merits of the proposed inverter, a comparison of the converter with similar topologies is provided. To verify the proper operation of the converter and also, to prove the presented theoretical calculations, a prototype of the introduced topology has been implemented in the laboratory. Test measurement results of the suggested inverter in 150 W with 10kHz frequency is derived. The obtained results and waveforms from test measurement present proposed inverter operation, steady state analysis and performance. . This paper presents a modified single stage single phase inverter with four switches. The proposed inverter has important features such as continuous input current, voltage buck and boost with single stage conversion, short circuit safety and operation in various duty cycles same as qZSI and qSBI converters. In the rest of the paper, the suggested inverter operation mode and its switching method are described first and then, the steady state analysis is presented. To validate the merits of the proposed inverter, a comparison of the converter with similar topologies is provided. To verify the proper operation of the converter and also, to prove the presented theoretical calculations, a prototype of the introduced topology has been implemented in the laboratory. Test measurement results of the suggested inverter in 150 W with 10kHz frequency is derived. The obtained results and waveforms from test measurement present proposed inverter operation, steady state analysis and performance. .

https://modelling.semnan.ac.ir/article_8069_afa9347e0e766858f5b3cd47512535de.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Modelling and Extraction of Current Harmonic Components based on Instantaneous Power Theory for Shunt Active Filter under Weak Grid Modelling and Extraction of Current Harmonic Components based on Instantaneous Power Theory for Shunt Active Filter under Weak Grid 27 44 8086 10.22075/jme.2023.30169.2421 FA Farzad Sajadi Master of Power Electronics and Electrical Machines, Arak University of Technology, Arak, iran Mohammad Pichan Assistant Professor, Faculty of Electrical Engineering, Arak University of Technology, Arak, iran Adel Zakipour Assistant Professor, Faculty of Electrical Engineering, Arak University of Technology, Arak, iran Journal Article 2023 03 13 The most important part in the suitable performance of the active power filter is possible by precise harmonic extraction of load current. Current harmonic extraction is possible in both time and frequency domain. However, time domain methods have higher speed and low complexity. One of the most popular methods in this field is the Instantaneous Power Theory method but, the main problem is when the grid voltage is not ideal. Therefore, this paper provides a method based on the Instantaneous Power Theory that shows very good performance with the least complexity of implementation in all grid voltage states, whether asymmetry, distorted, or both at the same time. To examine the performance of the proposed method, the three -phase filtration is simulated in the MATLAB/Simulink environment under different grid voltage conditions and finally the experimental results are provided in the laboratory environment. The results verify the effectiveness of the proposed method where the THD% is decreased from 25% to 5%. . The most important part in the suitable performance of the active power filter is possible by precise harmonic extraction of load current. Current harmonic extraction is possible in both time and frequency domain. However, time domain methods have higher speed and low complexity. One of the most popular methods in this field is the Instantaneous Power Theory method but, the main problem is when the grid voltage is not ideal. Therefore, this paper provides a method based on the Instantaneous Power Theory that shows very good performance with the least complexity of implementation in all grid voltage states, whether asymmetry, distorted, or both at the same time. To examine the performance of the proposed method, the three -phase filtration is simulated in the MATLAB/Simulink environment under different grid voltage conditions and finally the experimental results are provided in the laboratory environment. The results verify the effectiveness of the proposed method where the THD% is decreased from 25% to 5%. .

https://modelling.semnan.ac.ir/article_8086_97f389cfbf36c92195c96e4862d7c004.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 A New Heterostructure Junctionless Tunnel Field Effect Transistor with Silicon-on-Nothing Technique for DC Parameter Improvement A New Heterostructure Junctionless Tunnel Field Effect Transistor with Silicon-on-Nothing Technique for DC Parameter Improvement 45 53 8360 10.22075/jme.2023.29572.2392 FA Amin Vanak Doctoral student, Department of Electrical Engineering, College of Technical and Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran. Amir Amini Associate Professor, Department of Electrical Engineering, College of Technical and Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran. Journal Article 2023 01 11 In this paper, a novel heterostructure junctionless tunnel field effect transistor with silicon-on-nothing technology (SON HS-JLTFET) is proposed. The proposed device has two advantages over conventional JLTFET. First, one decade of increment in the ON current is achieved and subthreshold swing is improved by 10%. In this device, InAs is used in the source region of SON HS-JLTFET which has a lower energy band gap than Si to achieve thinner tunneling barrier width. Hence, more electron can tunnel from source to channel. As a result, it provides improvements in drain current and subthreshold swing. The second advantage is that the ambipolar current reduction due to the use of SON technique. In fact, in this technique, air is considered as the gate dielectric which results in decrement in the electric field in the drain/channel junction. This reduced electric field causes increasing the width of the tunneling barrier which results in lower ambipolar current in the drain/channel junction. . In this paper, a novel heterostructure junctionless tunnel field effect transistor with silicon-on-nothing technology (SON HS-JLTFET) is proposed. The proposed device has two advantages over conventional JLTFET. First, one decade of increment in the ON current is achieved and subthreshold swing is improved by 10%. In this device, InAs is used in the source region of SON HS-JLTFET which has a lower energy band gap than Si to achieve thinner tunneling barrier width. Hence, more electron can tunnel from source to channel. As a result, it provides improvements in drain current and subthreshold swing. The second advantage is that the ambipolar current reduction due to the use of SON technique. In fact, in this technique, air is considered as the gate dielectric which results in decrement in the electric field in the drain/channel junction. This reduced electric field causes increasing the width of the tunneling barrier which results in lower ambipolar current in the drain/channel junction. .

https://modelling.semnan.ac.ir/article_8360_080881cdefd13ee7e8c21691a8336212.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 A Classifier Based on K-Nearest Neighbors Using Weighted Summation of Reconstruction Errors A Classifier Based on K-Nearest Neighbors Using Weighted Summation of Reconstruction Errors 55 68 8365 10.22075/jme.2023.30380.2437 FA Rassoul Hajizadeh Machine Learning and Deep Learning Research Laboratory, Faculty of Engineering Modern Technologies, Amol University of Special Modern Technologies, Amol, Irans Mohammad Ali Hosseinzadeh Faculty of Engineering Modern Technologies, Amol University of Special Modern Technologies, Amol, Iran Journal Article 2023 04 15 In this paper, a classifier is introduced based on the nearest neighbor classifier and the reconstruction error for data classification. In the proposed method, first, K nearest data points (neighbors) from each category in the training data are calculated for the test data point. Then, the reconstruction of the test data is performed based on different numbers of nearest neighbors (from one to K) in each category, and the reconstruction error is calculated separately for each number of neighbors. In the next step, for each category, the error is calculated as the weighted sum of the errors obtained from all reconstructions. The weight of the reconstruction error is proportional to the number of neighbors involved in it, so the reconstruction error is multiplied by the number of neighbors. Finally, the test data belongs to the category with the lowest overall error. This process allows a combination of K nearest neighbor classifiers to play a role in data classification. In this paper, 10 datasets from the UCR time series database and five datasets from the UCI classification database are used to evaluate the proposed method. The results of these evaluations show that the proposed method significantly improves the performance of the minimum reconstruction error based KNN classifiers, achieving approximately 5% better recognition rate for some K values and an average recognition rate improvement of about 1.6% for all K values (from 2 to 15). . In this paper, a classifier is introduced based on the nearest neighbor classifier and the reconstruction error for data classification. In the proposed method, first, K nearest data points (neighbors) from each category in the training data are calculated for the test data point. Then, the reconstruction of the test data is performed based on different numbers of nearest neighbors (from one to K) in each category, and the reconstruction error is calculated separately for each number of neighbors. In the next step, for each category, the error is calculated as the weighted sum of the errors obtained from all reconstructions. The weight of the reconstruction error is proportional to the number of neighbors involved in it, so the reconstruction error is multiplied by the number of neighbors. Finally, the test data belongs to the category with the lowest overall error. This process allows a combination of K nearest neighbor classifiers to play a role in data classification. In this paper, 10 datasets from the UCR time series database and five datasets from the UCI classification database are used to evaluate the proposed method. The results of these evaluations show that the proposed method significantly improves the performance of the minimum reconstruction error based KNN classifiers, achieving approximately 5% better recognition rate for some K values and an average recognition rate improvement of about 1.6% for all K values (from 2 to 15). .

https://modelling.semnan.ac.ir/article_8365_8451b213fccf92c975ac688e0c677a2a.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT 69 83 8366 10.22075/jme.2023.30503.2443 FA Bahman Sanjabi Master's degree in Computer Architecture Engineering, Department of Computer Engineering and Information Technology, Razi University, Iran Mahmood Ahmadi Associate Professor, Department of Computer Engineering and Information Technology, Razi University, Iran Journal Article 2023 04 29 oday, due to the considerable benefits of the Internet of Things (IoT) in various fields such as smart homes, industry, cars, agriculture, etc., its application is very widespread. Due to this, the security of these networks is receiving more and more attention. One of the methods of providing security in networks as well as IoT network is intrusion detection systems. Traditional intrusion detection systems are not very efficient for use in the Internet of Things, so the use of new methods is required. One of these methods is intrusion detection systems based on machine learning and deep learning that have been considered in this area. They are trained in machine learning and deep neural network learning to detect attack patterns. There are important parameters for setting up a machine learning network, and choosing the right value for these parameters has a great impact on system accuracy. In this paper, a method is presented that uses meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and gray wolf to find the optimal hyperparameters for the deep learning network and the intrusion detection system is created based on these hyperparameters. This method was implemented using the Tensorflow and keras libraries and tested on the KDDCup99, UNSW-NB15 and Bot-IoT datasets. The results showed that the proposed method can detect attacks with a high accuracy of 99%. . oday, due to the considerable benefits of the Internet of Things (IoT) in various fields such as smart homes, industry, cars, agriculture, etc., its application is very widespread. Due to this, the security of these networks is receiving more and more attention. One of the methods of providing security in networks as well as IoT network is intrusion detection systems. Traditional intrusion detection systems are not very efficient for use in the Internet of Things, so the use of new methods is required. One of these methods is intrusion detection systems based on machine learning and deep learning that have been considered in this area. They are trained in machine learning and deep neural network learning to detect attack patterns. There are important parameters for setting up a machine learning network, and choosing the right value for these parameters has a great impact on system accuracy. In this paper, a method is presented that uses meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and gray wolf to find the optimal hyperparameters for the deep learning network and the intrusion detection system is created based on these hyperparameters. This method was implemented using the Tensorflow and keras libraries and tested on the KDDCup99, UNSW-NB15 and Bot-IoT datasets. The results showed that the proposed method can detect attacks with a high accuracy of 99%. .

https://modelling.semnan.ac.ir/article_8366_982118b276761e251a8104e4ea1c48ee.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 VNR_CCP: A New Approach to Congestion Control Using Virtualization Technique and Switch Migration in SDN VNR_CCP: A New Approach to Congestion Control Using Virtualization Technique and Switch Migration in SDN 85 98 8355 10.22075/jme.2023.28836.2357 FA Mohammad Reza Jenabzadeh PhD Student, Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran Vahid Ayatollahitafti Assistant Professor, Department of Computer Engineering, Taft Branch, Islamic Azad University, Taft, Iran Mohammad Reza Mollakhalili Assistant Professor, Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran Mohammad Reza Mollahoseini Ardakani Assistant Professor, Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran Journal Article 2022 10 28 By separating the data layer from the control layer in the software defined network and the possibility of centralized and programmable management, many limitations and common problems in traditional networks can be solved or improved. One of the existing problems in these networks is the issue of congestion and its control. In software defined networks, the use of information under the supervision of domain controllers and the collection of network statistics can be useful in controlling or preventing congestion. When an SDN switch node is subjected to many requests, the network becomes congested, and to solve this problem, the controller can use network virtualization and switch migration, taking into account the free resources available in the switches and links. In this paper, a software-based network approach for congestion control and optimal resource management called VNR_CCP is presented. In this approach, an attempt has been made to control congestion by calculating the nodes and links profit to search for congestion and request the virtual network to reduce the existing load and manage resources. The result of the simulation using the NS2 simulator shows that the proposed approach has better performance compared to the similar method. It was concluded that the throughput has increased by 4.3%, the delay has decreased by 5.3%, and the average cost has decreased by 26% compared to the similar method. . By separating the data layer from the control layer in the software defined network and the possibility of centralized and programmable management, many limitations and common problems in traditional networks can be solved or improved. One of the existing problems in these networks is the issue of congestion and its control. In software defined networks, the use of information under the supervision of domain controllers and the collection of network statistics can be useful in controlling or preventing congestion. When an SDN switch node is subjected to many requests, the network becomes congested, and to solve this problem, the controller can use network virtualization and switch migration, taking into account the free resources available in the switches and links. In this paper, a software-based network approach for congestion control and optimal resource management called VNR_CCP is presented. In this approach, an attempt has been made to control congestion by calculating the nodes and links profit to search for congestion and request the virtual network to reduce the existing load and manage resources. The result of the simulation using the NS2 simulator shows that the proposed approach has better performance compared to the similar method. It was concluded that the throughput has increased by 4.3%, the delay has decreased by 5.3%, and the average cost has decreased by 26% compared to the similar method. .

https://modelling.semnan.ac.ir/article_8355_c9a30d29770c93b7ade9ca8a032435c7.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Application of Monte Carlo Simulation (MCS) and Fuzzy Finite Element (FFEM) for Investigating the Uncertainty of Seepage in Homogeneous Earth Dams Application of Monte Carlo Simulation (MCS) and Fuzzy Finite Element (FFEM) for Investigating the Uncertainty of Seepage in Homogeneous Earth Dams 99 114 8358 10.22075/jme.2023.29331.2377 FA Milad Kheiry PhD graduate, Factuly of Civil Engineering, University of Tabriz,Tabriz, Iran. Farhoud Kalateh Factuly of Civil Engineering, University of Tabriz, Tabriz, Iran. Journal Article 2022 12 16 In the analysis of saturated and semi-saturated soil media, the use of the finite element method results in more realistic analyses than deterministic methods due to the random nature of porous media properties. The purpose of this research is to investigate the impact of uncertainty in the prediction of seepage flow through earth dams using the Fuzzy Monte Carlo Simulation (FMCS) new hybrid algorithm, which is implemented with the help of the finite element method and monte carlo simulation. In this study, a computer program was used for Finite Element Analysis (FEA), which was definitively checked after validation with experimental results. Monte Carlo iteration loops were then used for probabilistic mode. The Fuzzy Finite Element Method (FFEM) was executed assuming the probability of the soil for four variables: soil permeability (Kx/Ky), water height ratio (Hd/Hu), horizontal width of downstream slope ratio to base width (Bd/B), and horizontal width of downstream slope ratio to horizontal upstream width (Bd/Bu). The results of this research show that the fuzzy membership function is linear-symmetric for Kx/Ky variables and linear-asymmetric for two geometric variables, Bd/B and Bd/Bu. Additionally, the membership function was extracted for Hd/Hu . In the analysis of saturated and semi-saturated soil media, the use of the finite element method results in more realistic analyses than deterministic methods due to the random nature of porous media properties. The purpose of this research is to investigate the impact of uncertainty in the prediction of seepage flow through earth dams using the Fuzzy Monte Carlo Simulation (FMCS) new hybrid algorithm, which is implemented with the help of the finite element method and monte carlo simulation. In this study, a computer program was used for Finite Element Analysis (FEA), which was definitively checked after validation with experimental results. Monte Carlo iteration loops were then used for probabilistic mode. The Fuzzy Finite Element Method (FFEM) was executed assuming the probability of the soil for four variables: soil permeability (Kx/Ky), water height ratio (Hd/Hu), horizontal width of downstream slope ratio to base width (Bd/B), and horizontal width of downstream slope ratio to horizontal upstream width (Bd/Bu). The results of this research show that the fuzzy membership function is linear-symmetric for Kx/Ky variables and linear-asymmetric for two geometric variables, Bd/B and Bd/Bu. Additionally, the membership function was extracted for Hd/Hu .

https://modelling.semnan.ac.ir/article_8358_528b0079d44caaae72d066e38f8c84dc.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Improving Performance of Germanium-Based Vertical Tunneling Field Effect Transistor Using GaAs as Channel Improving Performance of Germanium-Based Vertical Tunneling Field Effect Transistor Using GaAs as Channel 115 122 8350 10.22075/jme.2023.26379.2231 FA Shoaib Babaei Tooski Assistant Professor, Department of Electrical Engineering, Hamedan University of Technology, Hamedan, Iran 0000-0002-6322-3397 Mohammad Javad Rezaei MSc, Department of Electrical Engineering, Hamedan University of Technology, Hamedan, Iran Seyed Manoochehr Hoseini Professor, Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran Journal Article 2022 03 04 In this paper, Germanium-based vertical tunneling transistors are investigated and the electrical properties of the transistor in two modes of Germanium utilization as well as Gallium Arsenide as the channel are compared. The simulation of this transistor was performed by Silvaco software using non-local tunneling model. The results show that more ON-current, less OFF-current and less bipolar current at negative gate voltage are the advantages of using Gallium Arsenide instead of Germanium as the channel. In the following, the channel parameters are changed and the effect of their change on the behavior of the transistor is studied. Increasing the channel length reduces the Off-current and increases the On-current to Off-current ratio, as well as reducing the sub-threshold slope. On the other hand, increasing the channel width reduces On-current to Off-current ratio and increases the sub-threshold slope. The On-current to Off-current ratio increases with increasing channel length and decreasing channel width, and increases to 1.5 × 10 + 15. . In this paper, Germanium-based vertical tunneling transistors are investigated and the electrical properties of the transistor in two modes of Germanium utilization as well as Gallium Arsenide as the channel are compared. The simulation of this transistor was performed by Silvaco software using non-local tunneling model. The results show that more ON-current, less OFF-current and less bipolar current at negative gate voltage are the advantages of using Gallium Arsenide instead of Germanium as the channel. In the following, the channel parameters are changed and the effect of their change on the behavior of the transistor is studied. Increasing the channel length reduces the Off-current and increases the On-current to Off-current ratio, as well as reducing the sub-threshold slope. On the other hand, increasing the channel width reduces On-current to Off-current ratio and increases the sub-threshold slope. The On-current to Off-current ratio increases with increasing channel length and decreasing channel width, and increases to 1.5 × 10 + 15. .

https://modelling.semnan.ac.ir/article_8350_afbceb4e5fab2209ac72c4368aca4292.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Analysis of Transformer Insulation Risk Due to Back Flashover Lightning on High Voltage Substations by Considering the Effect of Environmental Pollution Analysis of Transformer Insulation Risk Due to Back Flashover Lightning on High Voltage Substations by Considering the Effect of Environmental Pollution 123 139 8375 10.22075/jme.2023.31377.2505 FA Faridoddin Safaei PhD Student, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran Mohsen Niasati Associate Professor of Electrical Engineering Department, Semnan University, Semnan, Iran Journal Article 2023 07 27 The improved limiting parameter method of Monte Carlo is used in this study to estimate the impact of back-flashover (BF) due to lightning and provide an evaluation criterion for the insulation risk of the transformer in the high-voltage substation. In order to avoid the computational burden of the transient-state simulation, the Monte Carlo (MC) simulation method is combined with the limiting parameter method while taking into account the environmental conditions governing the high-voltage substation. On the other hand, depending on its amplitude and duration, any stress brought on by an excess of voltage causes destructive structural effects. Insulating behavior may be different before or after applying stress. Additionally, it is necessary to consider how the presence of environmental pollutants affects on BF lightning overvoltage amplitude. Therefore, the voltage-time-dependent strength accumulation characteristic has been developed in this study based on the transformer's non-self-healing behavior when exposed to various insulation stresses. By selecting appropriate distribution of expected strokes to estimate insulation risk, the finite area MC method that is being proposed calculates the insulation risk of the transformer based on the transient overvoltage that appears at the transformer terminals. Also discussed is the relationship between BF lightning and the contamination level of the insulation surface under the stresses brought on by lightning strikes. In this manner, the insulation coordination of the transformer can be known with the least number of calculations by using the structural data of the substation, the lines connected to it, and the transformer. The simulation results presented in this study were performed in a real sample network using the field and experimental data. The results showed an 18 percent increase in insulation risk considering the effect of environmental condition. . The improved limiting parameter method of Monte Carlo is used in this study to estimate the impact of back-flashover (BF) due to lightning and provide an evaluation criterion for the insulation risk of the transformer in the high-voltage substation. In order to avoid the computational burden of the transient-state simulation, the Monte Carlo (MC) simulation method is combined with the limiting parameter method while taking into account the environmental conditions governing the high-voltage substation. On the other hand, depending on its amplitude and duration, any stress brought on by an excess of voltage causes destructive structural effects. Insulating behavior may be different before or after applying stress. Additionally, it is necessary to consider how the presence of environmental pollutants affects on BF lightning overvoltage amplitude. Therefore, the voltage-time-dependent strength accumulation characteristic has been developed in this study based on the transformer's non-self-healing behavior when exposed to various insulation stresses. By selecting appropriate distribution of expected strokes to estimate insulation risk, the finite area MC method that is being proposed calculates the insulation risk of the transformer based on the transient overvoltage that appears at the transformer terminals. Also discussed is the relationship between BF lightning and the contamination level of the insulation surface under the stresses brought on by lightning strikes. In this manner, the insulation coordination of the transformer can be known with the least number of calculations by using the structural data of the substation, the lines connected to it, and the transformer. The simulation results presented in this study were performed in a real sample network using the field and experimental data. The results showed an 18 percent increase in insulation risk considering the effect of environmental condition. .

https://modelling.semnan.ac.ir/article_8375_648372bee5ec700cef3a54ef2efd2e2a.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 A New Fault Detection and Classification Scheme in MTDC Grids with Hybrid Cable and Overhead Transmission Line A New Fault Detection and Classification Scheme in MTDC Grids with Hybrid Cable and Overhead Transmission Line 141 154 8377 10.22075/jme.2023.31417.2509 FA Zahra Moravej Professor, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran Amir Imani PhD Student, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran Mohammad Pazoki Associate Professor, School of Engineering, Damghan University, Damghan, Iran Journal Article 2023 08 02 The growth of exploitation of distributed generation sources (DGs) such as offshore wind farms makes DC networks an interesting alternative to conventional AC grids. But protection of DC lines is one of the main challenges of these grids especially in hybrid non-homogenous corridors including underground cables and overhead lines. In this paper, a new single-end time domain-based protection scheme for fault detection and classification is presented with remarkable features such as easy implementation, low computation burden, low sampling frequency, no setting parameters requirement, and also appropriate performance in noisy conditions. To validate the proper performance of the proposed scheme, several scenarios are simulated including internal and external DC and AC faults, and severe load variations in EMTDC/PSCAD software environment. Also, some hybrid line scenarios such as line length variation, OHL or Cable length changes, and increasing the number of line segments are investigated. The result shows desirable performance in various conditions. The growth of exploitation of distributed generation sources (DGs) such as offshore wind farms makes DC networks an interesting alternative to conventional AC grids. But protection of DC lines is one of the main challenges of these grids especially in hybrid non-homogenous corridors including underground cables and overhead lines. In this paper, a new single-end time domain-based protection scheme for fault detection and classification is presented with remarkable features such as easy implementation, low computation burden, low sampling frequency, no setting parameters requirement, and also appropriate performance in noisy conditions. To validate the proper performance of the proposed scheme, several scenarios are simulated including internal and external DC and AC faults, and severe load variations in EMTDC/PSCAD software environment. Also, some hybrid line scenarios such as line length variation, OHL or Cable length changes, and increasing the number of line segments are investigated. The result shows desirable performance in various conditions.

https://modelling.semnan.ac.ir/article_8377_23dad7b6cc43ac7d23ba4bb8740e9344.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Epidemic Modeling and Flattening the Infection Curve in Social Networks Epidemic Modeling and Flattening the Infection Curve in Social Networks 155 165 8363 10.22075/jme.2023.30259.2425 FA Mohammadreza Doostmohammadian Assistant Professor, Faculty of Mechanical Engineering, Semnan University, Semnan, Iran Soraya Doustmohamadian Assistant Professor, Semnan University of Medical Sciences, Semnan, Iran Najmeh Doostmohammadian Assistant Professor, Semnan University of Medical Sciences, Semnan, Iran Azam Doustmohammadian Assistant Professor, Gastrointestinal and Liver Diseases Research Center, Iran University of Medical Sciences, Tehran, Iran Houman Zarrabi Assistant Professor, Iran Telecom Research Center (ITRC), Tehran, Iran Hamid R. Rabiee Professor, Faculty of Computer Engineering, Sharif University of Technology, Tehran, iran Journal Article 2023 03 26 The main goal of this paper is to model the epidemic and flattening the infection curve of the social networks. Flattening the infection curve implies slowing down the spread of the disease and reducing the infection rate via social-distancing, isolation (quarantine) and vaccination. The nan-pharmaceutical methods are a much simpler and efficient way to control the spread of epidemic and infection rate. By specifying a target group with high centrality for isolation and quarantine one can reach a much flatter infection curve (related to Corona for example) without adding extra costs to health services. The aim of this research is, first, modeling the epidemic and, then, giving strategies and structural algorithms for targeted vaccination or targeted non-pharmaceutical methods for reducing the peak of the viral disease and flattening the infection curve. These methods are more efficient for nan-pharmaceutical interventions as finding the target quarantine group flattens the infection curve much easier. For this purpose, a few number of particular nodes with high centrality are isolated and the infection curve is analyzed. Our research shows meaningful results for flattening the infection curve only by isolating a few number of targeted nodes in the social network. The proposed methods are independent of the type of the disease and are effective for any viral disease, e.g., Covid-19. . The main goal of this paper is to model the epidemic and flattening the infection curve of the social networks. Flattening the infection curve implies slowing down the spread of the disease and reducing the infection rate via social-distancing, isolation (quarantine) and vaccination. The nan-pharmaceutical methods are a much simpler and efficient way to control the spread of epidemic and infection rate. By specifying a target group with high centrality for isolation and quarantine one can reach a much flatter infection curve (related to Corona for example) without adding extra costs to health services. The aim of this research is, first, modeling the epidemic and, then, giving strategies and structural algorithms for targeted vaccination or targeted non-pharmaceutical methods for reducing the peak of the viral disease and flattening the infection curve. These methods are more efficient for nan-pharmaceutical interventions as finding the target quarantine group flattens the infection curve much easier. For this purpose, a few number of particular nodes with high centrality are isolated and the infection curve is analyzed. Our research shows meaningful results for flattening the infection curve only by isolating a few number of targeted nodes in the social network. The proposed methods are independent of the type of the disease and are effective for any viral disease, e.g., Covid-19. .

https://modelling.semnan.ac.ir/article_8363_1d8c1157cb2ef8bb369fc0251e4f5636.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Numerical Study on the Effects of Extended Surfaces on the Performance of a Coaxial Geothermal Heat Exchanger Numerical Study on the Effects of Extended Surfaces on the Performance of a Coaxial Geothermal Heat Exchanger 167 175 8085 10.22075/jme.2023.30329.2433 FA Naser Bakhshi MSc Student, Department of Energy, Faculty of New Sciences and Technologies, Semnan University, Semnan, Iran Saman Rashidi Assistant Professor, Department of Energy, Faculty of New Sciences and Technologies, Semnan University, Semnan, Iran Roohollah Rafee Associate Professor, Faculty of Mechanical Engineering, Semnan University, Semnan, Iran Journal Article 2023 04 07 In this study, the effect of expanded surfaces on the performance of a vertical coaxial geothermal heat exchanger has been investigated. The fluid (water) with an initial temperature of 3.5 ºC and a volumetric flow rate of 0.0008 m3/s enters from the annular area and exits from the middle pipe. The simulation is done for the heat absorption mode. The SST k-ω turbulence model is used for simulation of the turbulent flow. The thermal performance of the heat exchanger has been improved by placing ribs and dimples with several different geometries on the surface of the outer tube. Triangular surfaces showed better temperature output than other geometries. The temperature difference between the inlet and outlet of the fluid in the heat exchanger with a dimple and a triangular rib with a dimple depth of 5 mm has increased by 6.5%. The highest pressure drop is related to the heat exchanger with a dimple and a triangular tooth with a dimple depth of 5 mm, the value of which is 10.9 kPa. Different values of the local Nusselt number in the annular region of the heat exchanger have been calculated for different depths. The simple heat exchanger has the highest average Nusselt number in the studied range, and the average Nusselt number for this type of heat exchanger is 57.15. In this study, the effect of expanded surfaces on the performance of a vertical coaxial geothermal heat exchanger has been investigated. The fluid (water) with an initial temperature of 3.5 ºC and a volumetric flow rate of 0.0008 m3/s enters from the annular area and exits from the middle pipe. The simulation is done for the heat absorption mode. The SST k-ω turbulence model is used for simulation of the turbulent flow. The thermal performance of the heat exchanger has been improved by placing ribs and dimples with several different geometries on the surface of the outer tube. Triangular surfaces showed better temperature output than other geometries. The temperature difference between the inlet and outlet of the fluid in the heat exchanger with a dimple and a triangular rib with a dimple depth of 5 mm has increased by 6.5%. The highest pressure drop is related to the heat exchanger with a dimple and a triangular tooth with a dimple depth of 5 mm, the value of which is 10.9 kPa. Different values of the local Nusselt number in the annular region of the heat exchanger have been calculated for different depths. The simple heat exchanger has the highest average Nusselt number in the studied range, and the average Nusselt number for this type of heat exchanger is 57.15.

https://modelling.semnan.ac.ir/article_8085_a6d7a4cfb823273f065f1e1e66e813a3.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Mechanical Properties Analysis of a Monolayer Biphenylene at Different Temperatures Mechanical Properties Analysis of a Monolayer Biphenylene at Different Temperatures 177 187 8372 10.22075/jme.2023.31122.2485 FA Mohammad Amin Hemmatpour Khotbesara MSc. Student, Department of Mechanical Engineering, University of Mohaghegh Ardabili, Iran Masoud Ajri Assistant Professor, Department of Mechanical Engineering, University of Mohaghegh Ardabili, Iran. Majid Samadiyan MSc. Student, Department of Mechanical Engineering, University of Mohaghegh Ardabili, Iran. Journal Article 2023 07 02 In this study, the mechanical behavior of the newest allotrope of carbon called biphenylene network (BPN) has been investigated using molecular dynamics simulations. The structure of BPN consists of four, six, and eight-membered carbon rings hybridized with sp2. In this study, the interatomic potential is considered to be AIRBO, and the tensile behavior of this structure has been modeled at different temperatures. After simulation, the Young's modulus and yield stress of biphenylene at different temperatures have been obtained in the armchair direction and zig-zag direction. The Young's modulus in the zig-zag direction at all temperatures is about 14 to 29% higher than the other direction, which indicates the orthotropic behavior of this structure. In addition, with the increase in temperature, the failure strain and Young's modulus have decreased due to the increase in the distance between the atoms and the decrease in energy. It has also been shown that the failure of BPN is brittle. The results of this study show that BPN shares some of the exceptional properties of graphene. . In this study, the mechanical behavior of the newest allotrope of carbon called biphenylene network (BPN) has been investigated using molecular dynamics simulations. The structure of BPN consists of four, six, and eight-membered carbon rings hybridized with sp2. In this study, the interatomic potential is considered to be AIRBO, and the tensile behavior of this structure has been modeled at different temperatures. After simulation, the Young's modulus and yield stress of biphenylene at different temperatures have been obtained in the armchair direction and zig-zag direction. The Young's modulus in the zig-zag direction at all temperatures is about 14 to 29% higher than the other direction, which indicates the orthotropic behavior of this structure. In addition, with the increase in temperature, the failure strain and Young's modulus have decreased due to the increase in the distance between the atoms and the decrease in energy. It has also been shown that the failure of BPN is brittle. The results of this study show that BPN shares some of the exceptional properties of graphene. .

https://modelling.semnan.ac.ir/article_8372_23e7c97989dfd12fe580739dff4c9dc6.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 A Modified Grey Wolf Algorithm with Applications to Engineering A Modified Grey Wolf Algorithm with Applications to Engineering 189 195 8270 10.22075/jme.2023.29814.2401 FA Vahid Mahboub Assistant Professor, Department of Surveying Engineering, Faculty of Engineering, Golestan University, Aliabad Katoul, Iran Journal Article 2023 02 03 In this contribution, a modified gray wolf algorithm for use in engineering applications is presented. The grey wolf algorithm is one of the meta-heuristic optimization methods that has recently been widely used by researchers due to its good capabilities. The mechanism is free of derivation, simple in execution and implementation, and only needs target function as input of the problem, among other things that make the gray wolf algorithm popular and of interest. But the problem that can be mentioned about it is that the decreasing factor used in it is linear and in some non-linear problems, it may cause more error or late convergence to the original solution. This bottleneck is solved by presenting a modified grey wolf algorithm. Then the results are compared in the form of an applied numerical example in engineering sciences with the classic grey wolf algorithm and some similar proposed coefficients to determine the efficiency of the modified algorithm. In this contribution, a modified gray wolf algorithm for use in engineering applications is presented. The grey wolf algorithm is one of the meta-heuristic optimization methods that has recently been widely used by researchers due to its good capabilities. The mechanism is free of derivation, simple in execution and implementation, and only needs target function as input of the problem, among other things that make the gray wolf algorithm popular and of interest. But the problem that can be mentioned about it is that the decreasing factor used in it is linear and in some non-linear problems, it may cause more error or late convergence to the original solution. This bottleneck is solved by presenting a modified grey wolf algorithm. Then the results are compared in the form of an applied numerical example in engineering sciences with the classic grey wolf algorithm and some similar proposed coefficients to determine the efficiency of the modified algorithm.

https://modelling.semnan.ac.ir/article_8270_188432c5f0d199e25a6308b1ebc5c1b7.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System Modeling and Reduction of Nox Production in Submerged Combustion Vaporizer Using Fuzzy Inference System 197 211 8267 10.22075/jme.2023.30609.2451 FA Hanieh Fani Maleki Master's student in Chemical Engineering, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, Iran Amir Ehsan Pheili Monfared Assistant Professor, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, Iran Mahmoud Rahmati Assistant Professor, Department of Chemical Engineering, Graduate University of Advanced Technology, Kerman, Iran Journal Article 2023 05 10 Submerged combustion vaporizers are one of the industrial equipments that produce a large amount of nitrogen oxides (NOx). These equipments are actually heat exchangers that are used in liquefied natural gas (LNG) terminals to evaporate liquefied natural gas and convert it into gas. Since previous studies have shown that the operating conditions of this equipment are effective on the amount of NOx production in it, artificial intelligence tools were used in this research to model and then optimize NOx emission in this equipment. For this purpose, 63 laboratory data were extracted from the researchers' previous researches, and then a combination of adaptive neural fuzzy inference system and genetic algorithm was used to model the data. In the developed system, oxygen concentration, temperature, water-oxygen concentration and solution pH were considered as input parameters to the model and NOx reduction percentage as output. The statistical analysis of the built model showed that this model with correlation coefficient of 0.9714, mean square error of 1.0938, average absolute error percentage of 4.9713 and maximum absolute error percentage of 13.2144 has a good accuracy in estimating the amount of NOx reduction. In the next step after the development of the model, the genetic algorithm and the built model were used to optimize the operating conditions with the lowest NOx emission rate. The results of this part of the research also showed that if the operating conditions are optimized, it is possible to reduce the amount of NOx released up to 37.24% Submerged combustion vaporizers are one of the industrial equipments that produce a large amount of nitrogen oxides (NOx). These equipments are actually heat exchangers that are used in liquefied natural gas (LNG) terminals to evaporate liquefied natural gas and convert it into gas. Since previous studies have shown that the operating conditions of this equipment are effective on the amount of NOx production in it, artificial intelligence tools were used in this research to model and then optimize NOx emission in this equipment. For this purpose, 63 laboratory data were extracted from the researchers' previous researches, and then a combination of adaptive neural fuzzy inference system and genetic algorithm was used to model the data. In the developed system, oxygen concentration, temperature, water-oxygen concentration and solution pH were considered as input parameters to the model and NOx reduction percentage as output. The statistical analysis of the built model showed that this model with correlation coefficient of 0.9714, mean square error of 1.0938, average absolute error percentage of 4.9713 and maximum absolute error percentage of 13.2144 has a good accuracy in estimating the amount of NOx reduction. In the next step after the development of the model, the genetic algorithm and the built model were used to optimize the operating conditions with the lowest NOx emission rate. The results of this part of the research also showed that if the operating conditions are optimized, it is possible to reduce the amount of NOx released up to 37.24%

https://modelling.semnan.ac.ir/article_8267_5bb758dc1231520e6f2cf24aa00aecbf.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 A Transformer-Based Model for Abnormal Activity Recognition A Transformer-Based Model for Abnormal Activity Recognition 213 221 8569 10.22075/jme.2024.32914.2604 FA Amir Mohammad Ahmadi Master's student, Faculty of Electrical and Computer Science, Semnan University, Semnan, Iran Kourosh Kiani Associate Professor, Faculty of Electrical and Computer Science, Semnan University, Semnan, Iran Razieh Rastgoo Assistant Professor, Electrical and Computer Faculty, Semnan University, Semnan, Iran Journal Article 2024 01 07 Given the increasing daily volume of videos generated by security cameras in personal and public spaces, monitoring the activities present in videos has become crucial. Many video surveillance systems are designed to verify performance accuracy and provide alerts during the occurrence of abnormal activities. In this regard, various intelligent models have been proposed for detecting activities in videos. Considering recent advances in artificial intelligence, particularly deep learning, this paper introduces a model based on the Transformer network. To reduce computational complexity, keypoints of the human body are utilized in this approach. Fifteen key body points are input into the Transformer model, leveraging parallel processing during training and a self-attention mechanism. This enhances the speed and accuracy of the model. Experimental results on the JHMDB public database indicate an improvement in the accuracy of detecting abnormal activities compared to baseline models. Given the increasing daily volume of videos generated by security cameras in personal and public spaces, monitoring the activities present in videos has become crucial. Many video surveillance systems are designed to verify performance accuracy and provide alerts during the occurrence of abnormal activities. In this regard, various intelligent models have been proposed for detecting activities in videos. Considering recent advances in artificial intelligence, particularly deep learning, this paper introduces a model based on the Transformer network. To reduce computational complexity, keypoints of the human body are utilized in this approach. Fifteen key body points are input into the Transformer model, leveraging parallel processing during training and a self-attention mechanism. This enhances the speed and accuracy of the model. Experimental results on the JHMDB public database indicate an improvement in the accuracy of detecting abnormal activities compared to baseline models.

https://modelling.semnan.ac.ir/article_8569_b9c1cc7be479cdeacdf4875ef84fdf17.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Investigating the Factors Affecting the Transient Overvoltages Caused by Lightning in the Low-Voltage Network and the Role of the SPD Installed in the Substation in Protecting the Network Investigating the Factors Affecting the Transient Overvoltages Caused by Lightning in the Low-Voltage Network and the Role of the SPD Installed in the Substation in Protecting the Network 223 239 8532 10.22075/jme.2024.32554.2576 FA Ahmad Moradi MSc Student, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran Asghar Akbari Foroud Professor, Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran Journal Article 2023 12 04 Surges caused by direct or indirect lightning strikes on distribution networks threaten network equipment and consumers. Several parameters affect the intensity of transient over-voltages caused by lightning (surge), and significant studies have been done in this field. However, the effect of some parameters on the surges in the low-voltage network has not been investigated. Among these cases, we can mention using self-supporting cable instead of open wire and the effect of neutrals distributed along the low-voltage feeder. Therefore, in this article, the role of the two mentioned factors on the intensity of transient over-voltages in the low-voltage distribution network is investigated. The presence of a surge protective device (SPD) in the secondary of the distribution transformer has a significant effect on the protection of the distribution transformer against direct and indirect lightning strikes to the low-voltage distribution network. But so far, some of the effects of this SPD on surges at the input of low-voltage consumers have not been investigated. Therefore, in this article, the positive and negative role of the SPD installed in the distribution substation on the consumers is investigated. Based on the investigations, it is determined that the SPD installed at the substation has a positive or negative effect on which subscribers and under what conditions. Based on the studies, it was concluded that the presence of SPD at the distribution substation reduces the intensity of the surge for some subscribers and increases the intensity of the surge for others. Surges caused by direct or indirect lightning strikes on distribution networks threaten network equipment and consumers. Several parameters affect the intensity of transient over-voltages caused by lightning (surge), and significant studies have been done in this field. However, the effect of some parameters on the surges in the low-voltage network has not been investigated. Among these cases, we can mention using self-supporting cable instead of open wire and the effect of neutrals distributed along the low-voltage feeder. Therefore, in this article, the role of the two mentioned factors on the intensity of transient over-voltages in the low-voltage distribution network is investigated. The presence of a surge protective device (SPD) in the secondary of the distribution transformer has a significant effect on the protection of the distribution transformer against direct and indirect lightning strikes to the low-voltage distribution network. But so far, some of the effects of this SPD on surges at the input of low-voltage consumers have not been investigated. Therefore, in this article, the positive and negative role of the SPD installed in the distribution substation on the consumers is investigated. Based on the investigations, it is determined that the SPD installed at the substation has a positive or negative effect on which subscribers and under what conditions. Based on the studies, it was concluded that the presence of SPD at the distribution substation reduces the intensity of the surge for some subscribers and increases the intensity of the surge for others.

https://modelling.semnan.ac.ir/article_8532_b8d34b2640faba455af578cecd0181e3.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Comparison of IGDT, TOAT and STC Methods in Robust Planning of Transmission Network Expansion in the Presence of Uncertainties Comparison of IGDT, TOAT and STC Methods in Robust Planning of Transmission Network Expansion in the Presence of Uncertainties 241 255 8373 10.22075/jme.2023.31185.2489 FA Shahriar Abbasi Assistant Professor, Department of Electrical and Computer Engineering, Faculty of Boys 1, Kermanshah Branch, Technical and Vocational Universtiy (TVU), Kermanshah, Iran 0009-0004-7680-1944 Journal Article 2023 07 08 Transmission network expansion planning (TNEP) is a classic issue in power system studies that has been studied many times. Aim of TNEP is providing enough capacity to transfer power from generation section to load centers in a reliable and economically efficient manner. The mission of this problem is identifying where, when and what type of new transmission lines should be installed in transmission network. Purpose of this article is comparing the IGDT, TOAT and STC methods in robust TNEP (RTNEP) in the presence of load and wind power generation uncertainties. Using these methods, robust expansion plans for the modified 6-bus Garver test system are determined and compared. The simulations results confirm validity of these methods in RTNEP. Therefore, these methods can be easily implemented on any large and real scale power system. Moreover, different types of uncertainties can be easily considered in this planning. Simulation results show, the IGDT method has more computational burden, which considering that the RTNEP problem is a long-term problem, the CPU running time and computational burden are not important. Therefore, the IGDT method is preferable to the TOAT and STC methods due to the ability to find the optimal expansion plans with less investment cost. . Transmission network expansion planning (TNEP) is a classic issue in power system studies that has been studied many times. Aim of TNEP is providing enough capacity to transfer power from generation section to load centers in a reliable and economically efficient manner. The mission of this problem is identifying where, when and what type of new transmission lines should be installed in transmission network. Purpose of this article is comparing the IGDT, TOAT and STC methods in robust TNEP (RTNEP) in the presence of load and wind power generation uncertainties. Using these methods, robust expansion plans for the modified 6-bus Garver test system are determined and compared. The simulations results confirm validity of these methods in RTNEP. Therefore, these methods can be easily implemented on any large and real scale power system. Moreover, different types of uncertainties can be easily considered in this planning. Simulation results show, the IGDT method has more computational burden, which considering that the RTNEP problem is a long-term problem, the CPU running time and computational burden are not important. Therefore, the IGDT method is preferable to the TOAT and STC methods due to the ability to find the optimal expansion plans with less investment cost. .

https://modelling.semnan.ac.ir/article_8373_dc7598d633dc850b4d65bb474952dc64.pdf

Semnan University Press Journal of Modeling in Engineering 2008-4854 22 76 2024 04 20 Planning the Production Power of Thermal, Wind and Solar Units Using the Sine Cosine Algorithm Planning the Production Power of Thermal, Wind and Solar Units Using the Sine Cosine Algorithm 257 271 8362 10.22075/jme.2023.29942.2408 FA Majid Khalili PhD Student, Department of Electrical Engineering, Islamic Azad University, Saveh branch, Saveh, Iran Javad Nikoukar Assistant Professor, Department of Electrical Engineering, Islamic Azad University, Saveh branch, Saveh, Journal Article 2023 02 16 Dynamic production power planning to meet hourly load demand is one of the important issues in production management and operation of power systems. In this article, the problem of optimal load dispatch considering transmission network losses, considerations and practical limitations of thermal power plants such as increasing and decreasing ramp rates, prohibited production areas, steam valve effect with the combination of renewable resources including wind farms and solar units has been raised. Renewable energy sources have reduced environmental pollution due to the non-use of fossil fuels, but these sources have uncertainty and random nature in production. On the other hand, wind and solar sources are considered to be part of fast start-up sources and thermal sources are considered to be part of slow start-up thermal sources. Considering the mentioned cases together complicates the problem of optimal load distribution, in this article, a new method based on the sine-cosine algorithm is used to determine the contribution of different production sources in the load supply. To solve this problem, which has non-convex cost functions, a new method based on the sine-cosine algorithm has been used. In order to evaluation the effectiveness of the proposed method, simulation results and numerical studies on a sample system including 6 thermal units, 5 wind units and 13 solar units have been implemented and compared with other metaheuristic algorithms. The results of numerical studies show the superiority of the proposed method over other methods while having the appropriate speed and accuracy. . Dynamic production power planning to meet hourly load demand is one of the important issues in production management and operation of power systems. In this article, the problem of optimal load dispatch considering transmission network losses, considerations and practical limitations of thermal power plants such as increasing and decreasing ramp rates, prohibited production areas, steam valve effect with the combination of renewable resources including wind farms and solar units has been raised. Renewable energy sources have reduced environmental pollution due to the non-use of fossil fuels, but these sources have uncertainty and random nature in production. On the other hand, wind and solar sources are considered to be part of fast start-up sources and thermal sources are considered to be part of slow start-up thermal sources. Considering the mentioned cases together complicates the problem of optimal load distribution, in this article, a new method based on the sine-cosine algorithm is used to determine the contribution of different production sources in the load supply. To solve this problem, which has non-convex cost functions, a new method based on the sine-cosine algorithm has been used. In order to evaluation the effectiveness of the proposed method, simulation results and numerical studies on a sample system including 6 thermal units, 5 wind units and 13 solar units have been implemented and compared with other metaheuristic algorithms. The results of numerical studies show the superiority of the proposed method over other methods while having the appropriate speed and accuracy. .

https://modelling.semnan.ac.ir/article_8362_b9ff2a610df8daa66e48b68b6bb6fe7d.pdf