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

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).