A Multi-objective Placement of Phasor Measurement Units Considering Observability and Measurement Redundancy using Firefly Algorithm

Abstract

-This paper proposes a multi-objective optimal placement method of Phasor Measurement Units (PMUs) in large electric transmission systems. It is proposed for minimizing the number of PMUs for complete system observability and maximizing measurement redundancy of the buses, simultaneously. The measurement redundancy of the bus indicates that number of times a bus is able to monitor more than once by PMUs set. A high level of measurement redundancy can maximize the system observability and it is required for a reliable power system state estimation. Therefore, simultaneous optimizations of the two conflicting objectives are performed using a binary coded firefly algorithm. The complete observability of the power system is first prepared and then, single line loss contingency condition is added to the main model. The practical measurement limitation of PMUs is also considered. The efficiency of the proposed method is validated on IEEE 14, 30, 57 and 118 bus test systems and a real and large-scale Polish 2383 bus system. The valuable approach of firefly algorithm is demonstrated in finding the optimal number of PMUs and their locations by comparing its performance with earlier works.

A New Power System Restoration Technique based on WAMS Partitioning

“…Many heuristic methods like Genetic Algorithms [8], Particle Swarm Optimization [9], Hybrid Genetic Particle Swarm Optimization [10] and many others have been applied since. Meta-heuristic methods such as Binary Imperialistic Competition Algorithm (BICA) [11], Artificial Bee Colony (ABC) Algorithm [12] and Firefly Algorithm [13] have also been employed. An Integer Linear Programming (ILP) is developed in [14] for system complete observability.…”

Development of electrical power transmission system linear hybrid state estimator based on circuit analysis techniques

“…The test case systems are assumed to be partially observable through PMUs. From the existing studies, for complete system observability based on PMUs only, an optimal number of 4, 10, and 17 PMUs are required for IEEE 14 bus, IEEE 30 bus, and IEEE 57 bus, respectively [39] , [40] , [41] , [42] . Therefore, each test case considers a random number of PMUs for partial observability (less than the optimal number).…”

Integrated Mutation Strategy With Modified Binary PSO Algorithm for Optimal PMUs Placement

“…The integer linear programming (ILP) method is widely used for solving the OPP problem [5]- [8] since it is capable of solving the OPP problem in a very short time. The exhaustive search (ES) method [9]- [11] and heuristic algorithms such as simulated annealing (SA) [12], genetic algorithm (GA) [13], [14], firefly algorithm (FA) [15], tabu search [16], differential evolution (DE) [17], [18], and particle swarm optimization (PSO) through a binary variant called binary PSO (BPSO) [19]- [25] have shown that they are also capable of finding the optimal placement of PMUs. In these existing studies, many constrained factors such as the effect of the zero-injection bus (ZIB), conventional measurement, a single PMU loss, line outage and PMU's channel limits are considered while solving the OPP problem.…”

“…Among these factors, the PMU's channel limits is rarely considered while using the heuristic algorithms. The GA [13], [14] and FA [15] are the only heuristic algorithms considering the PMU's channel limit when solving the OPP problem.…”