POWER SYSTEM DYNAMIC VARIABLE ESTIMATION WITH PMU

Abstract

PMU’s have a significant importance in present day power systems. They have a wide applications in the management, protection of power as well as in state estimation. It has been seen that full observability of power system for the available data is required for state estimation. Although, this can be achieved by conformist estimators using bus voltage, real and reactive power flows as well as by power injection data derived from SCADA but with the increasing use of PMU’s time harmonized voltage phasors measurement of the bus to which all the branches are incident to that bus can be easily obtained. This characteristic feature of PMU makes the measurements of PMU better than SCADA measurements. However, the high cost associated with PMU’s makes their positioning at all the buses unviable. In the present study, a two stage novel algorithm is projected for optimal placement of PMU for wide ranging observability. The ever-increasing size and intricacy of the power system, monitoring as well as control has become a significant issue for which EMS at the control centre is responsible. State Estimator provides the finest approximation of the current operating state of the system based on the collected set of measurements. The data consisting of power flows, power injections and voltage magnitudes are generally processed by SCADA systems. However with the introduction of PMUs, both voltage magnitude and angle can be calculated that are elements of the state vector as these are time synchronized with the GPS. Consequently, the dynamic view of the power system can be captured more accurately. Hence, the emergence of new technologies in the area of monitoring is imperative. Therefore, the generator rotor speed 𝜔 and rotor angle 𝛿 are simulated in this dissertation. The dynamic variables are also estimated by EKF method for transient condition on the test system under consideration in a definite time interval. The transient condition is considered and the simulations are performed in MATLAB. The estimated dynamic variables under transient conditions obtained by applying the EKF algorithm and the dynamic variables calculated with the Fourth order Runge Kutta method are compared to depict the efficacy of the algorithm. The performance indices (𝜀𝜔,𝜀𝛿) are also calculated which indicate the accuracy of the EKF method employed to the test system.