Research On System Analysis And State Estimation Model Based On Phasor Measurement Unit (PMU)

With the rapid development of power system, modern power systems are becoming larger and more complex with large generation units installed, extra high voltage AC/DC hybrid transmission network, other new technologies and power markets applied. All these bring a new challenge to the security of power system. Therefore,power system stability control becomes more and more important. The voltage phasor real-time measurement is the key for power system stability control.Synchronized phasor measurement units(PMUs) based on GPS have evolved into a practical method for measurement of power system voltage and current phasors at present age. PMUs with optimal location in a power system reduce the computational time and errors level of state estimation. Taking the minimum number of installed PMUs as objective function, full network observability of power system operation state and maximum state measurement redundancy as restrict condition, an optimal PMU placement(OPP) problem is proposed in this thesis. Furthermore,considering that installing all the required PMU at once is difficult due to the high price of PMU, the phased optimal allocation model is proposed. That is, installing equal number of PMU at each stage and these PMU have to improve the system to maximize redundancy and after the completion of the final installation of the system to meet the observable.These two conditions are constraints.And we can solve the model to obtain the optimal solution by the adoption of improved adaptive genetic algorithm. The optimal location of PMU has applied to Zhejiang 32-Bus system.The data measured by PMU can attach to the state estimation, which can reduce the interference of the random errors and improve the precision of the state estimation. This thesis still compared traditional model and a new model with PMU measurements and discussed. The model of a hybrid measurements system composed of traditional SCADA and PMUs differs from the linear state estimation model. Simultaneity analyzed the precision improvement of state estimation. At last, conclusion is verified by the numerical calculation results of three examples.