Optimized Neural Network Parameters Using Stochastic Fractal Technique to Compensate Kalman Filter for Power System-Tracking-State Estimation.

Abstract

Tracking-state estimation uses previous state vector and recent measurement data to give real-time update on the state of the power system noniteratively during the subsequent time sampling. This paper discusses Kalman filtering enhanced by optimized neural network parameters-based stochastic fractals search technique (KF-MLP-based SFS). Both KF gain (mismodeling error) and measurement noise were replaced by optimized multilayer perceptron (MLP-SFS). This optimized MLP-based SFS could suppress filter divergence and improve the accuracy. The proposed method was used to detect and identify anomalies exhibited in normal operation where loads fluctuate linearly, bad data condition, sudden loss of loads, generators, and transmission lines. The application of the proposed technique (KF-MLP-based SFS) is illustrated on the IEEE 57-bus system. Results of the presented approach are compared to the true state vector (load flow), KF standalone, and KF compensated by radial basis function.