Multi Objective for PMU Placement in Compressed Distribution Network Considering Cost and Accuracy of State Estimation

Kong Kong,Yu Yu,Yuan Yuan,Wang Wang

doi:10.3390/app9071515

Abstract

A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state estimation error as the objective functions and taking full observability of distribution network as the constraint, a multi objective model of optimal PMU placement (OPP) is proposed. A hybrid state estimator based on supervisory control and data acquisition (SCADA) and PMU measurements is proposed. To reduce the number of PMUs required for full observability, SCADA measurement data are also considered into the constraint by update and equivalent. In addition, a non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve the model to get the Pareto set. Finally, the optimal solution is selected from the Pareto set by the technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of the proposed method is verified by IEEE standard bus systems.

Highlights

High penetration of distributed generator and controllable load has changed the fundamental way of the operation in the distribution network from a passive to active network [1,2,3]
Taking the cost and the state estimation accuracy into consideration, a multi objective method of optimal phasor measurement unit (PMU) placement is proposed in this paper
The network compression based on the pre-configuration and existing measurements can reduce the number of PMUs of the most economical solution and the compromised solution, and reduce the state estimation error under the same number of PMUs

Summary

Introduction

High penetration of distributed generator and controllable load has changed the fundamental way of the operation in the distribution network from a passive to active network [1,2,3]. The distribution network is developing in an intelligent direction [4]. The uncertainty of distributed generator and load brings great challenges to the stable operation of the distribution network, such as bidirectional power flow and increased voltage fluctuation. The distribution network topology changes dynamically to reduce power loss and voltage deviation [5]. Real-time measurement devices need to be added to the distribution network. Improving the observability of the distribution network is an important way to realize the intellectualization of the distribution network

Methods

Discussion

Conclusion