A multi-objective grey wolf optimizer (GWO)-based multi-layer perceptrons (MLPs) trainer for optimal PMUs placement

Abstract

Purpose This paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line. Design/methodology/approach An efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network (“GW-NN”) to place the PMUs in power grids optimally. Findings Following extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches. Practical implications The efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems. Originality/value This paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the cost and improve the observability of these grids in normal and faulty cases.

Multi‐objective precise phasor measurement locations to assess small‐signal stability using dingo optimizer

“…18 Additionally, the meta-heuristic algorithms include Binary Particle Swarm Optimization Technique, 19 Genetic Algorithm, 20 the Binary-Coded Ant Colony Optimization Technique, 21 Simulated Annealing 11 and other several bio-inspired algorithms. 22,23 In addition to these methods, various other critical methods are involved in the placement of PMUs, with some constraints like the inclusion of line outages, loss of PMUs, channel limits, and measurement limits in a basic network. 24 Various researchers have broadly explained the best positioning of multi-objective techniques with backup plans for common assessment tools.…”

“…The deterministic methods consist of programming methods such as Binary Integer Linear Programming, 13 Branch and Bound Method, 14 Recursive Quadratic Programming, 15 Mixed‐Integer Programming, 16 Mixed‐Integer Semidefinite Programming, 17 and Binary Search 18 . Additionally, the meta‐heuristic algorithms include Binary Particle Swarm Optimization Technique, 19 Genetic Algorithm, 20 the Binary‐Coded Ant Colony Optimization Technique, 21 Simulated Annealing 11 and other several bio‐inspired algorithms 22,23 . In addition to these methods, various other critical methods are involved in the placement of PMUs, with some constraints like the inclusion of line outages, loss of PMUs, channel limits, and measurement limits in a basic network 24 …”