DYNAMIC STATE ESTIMATION AND OPTIMAL CONTROL OF MULTI-AREA POWER SYSTEM USING PMU

Abstract

Power system is always associated with various levels of monitoring and control strategies which establish a secure and stable operation of the system. The expansion and increase in complexity of the system into multiple areas bring about the need for Wide Area Monitoring and Control strategies rather than local systems and controllers. The advancements in Wide Area Measurement Systems using Phasor Measurement Units provided the opportunity for these real-time controllers. Dynamic State Estimation (DSE) is an area of power system monitoring in which the dynamic states of the various power system components are estimated. Estimating the values of internal states representing the synchronous generators or other components are very much essential in various control and protection strategies which are based on state space model. DSE of synchronous generators is generally done using Extended Kalman Filter based techniques locally using the input and output measurements of the generator. The Kalman filter is an optimal design of estimator model, designed based on the known covariance of system model and measurement uncertainty so that the expectation of estimation error is minimum. The measurement update gain in the estimator model is optimised to achieve the minimisation. As the synchronous generator model is nonlinear in nature, various other nonlinear estimators can also be used for the state estimation. Wide Area Monitoring and Control technique requires the states to be estimated remotely. Although Phasor Measurement Units (PMU) can be utilised for remotely obtaining the measurement signals required for the estimator model, these measurements are limited to the output measurements like voltage, current and frequency, and input measurements like field input and torque input are not available. This hinders the utilisation of the basic Kalman filter based techniques, as they require both input and output measurements for the estimation. The issue challenges the requirement for an output only estimator model which is able to estimate the system states without the input measurements.