DESIGN OF STATE ESTIMATOR AND BAD DATA DETECTION MODULE IN POWER SYSTEM BY USING PERCEPTRON ARTIFICIAL NEURAL NETWORK

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Abstract

State estimation is a key tool in energy management system for monitoring, control and static security analysis of power systems. Weighted least square, as a conventional method for solving state estimation problem, has deficiencies such as ill-conditioning of gain matrix and slow detection of bad data. Designing of state estimator by using artificial neural network can overcome the numerical results and converge to desirable state more rapidly with respect to weighted least square method. However, errors in measured data would result to bias in state estimation procedure. In this paper, with the aim of mitigation of bad data effect, a state estimator based on artificial neural network was presented that can improve the ability of proposed method. Efficiency of the proposed method has been investigated on two test systems with 9 and 14 buses. The results confirm abilities of the proposed method in solving state estimation problem.

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Journal of Modeling in Engineering
Volume 9, Issue 26
October 2011
Pages 13-22

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  • Receive Date: 28 January 2017
  • Revise Date:
  • Accept Date: 28 January 2017

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