State estimation using hybrid kalman filter and optimal placement of phasor measurement unit for an indian utility grid

Abstract

Electricity becomes the most inevitable basic need in today s life. The increase in the demand for it increases the size and complexity of the power system network. The monitoring of the power system parameters is a challenging task due to the dynamic nature of the load and the complexity in the network. To have an uninterrupted power supply, Wide Area Monitoring Protection and Control (WAMPAC) of the power system network is the need of the hour. The early Supervisory Control and Data Acquisition (SCADA) system does not support the present day grid for monitoring and control of power system parameters. The conventional way of monitoring the parameters of the system is less accurate due to the presence of noise in the measuring devices. Hence a new way of estimating the parameters has been addressed in the present research.The primary state variables that define the state of the system arevoltage magnitude (V) and phase angle (and#948;). In static state estimation, the states are assumed to be time-varying and are estimated using the Weighted Least Squares (WLS) method. Static state estimation is made for IEEE 14 bus and 30 bus system, but this method is numerically unstable for some implied conditions and it does not converge to an acceptable solution. It also has some uncertainties due to random errors in measurements. To reduce these uncertainties, Regularized Least Square (RLS) state estimation is utilized. Estimation error gets reduced in RLS compared to WLS. Though the RLS minimizes the estimation error, the results can still be improved by employing filtering techniques.A Kalman Filter (KF) is an optimal estimator that has the capability to estimate parameters from uncertain, noisy and indirect newline