Abstract
Grey wolf optimizer (GWO) is a new technique, which can be applied successfully for solving optimized problems. The GWO indeed simulates the leadership hierarchy and hunting mechanism of grey wolves. There are four types of grey wolves which are alpha, beta, delta and omega. Those four types can be used for simulating the leadership hierarchy. In order to complete the process of GWO a three main steps of hunting, searching for prey, encircling prey and attacking prey are implemented. This work describes a novel meta-heuristic based on grey wolf optimization for optimum allocation of STATCOM devices on power system grid to minimized load buses voltage deviations and system power losses. Bus voltages have been solved by controlling the reactive power of shunt compensator. The Contingency management problem (such as system over-loading and a single line outages) by optimum installation of STATCOM devices, has been presented. Simulations are performed on IEEE 30-bus power system indicate that the proposed approach is a powerful search and optimization technique that may yield better solutions to engineering problems than those obtained using traditional algorithms.
Highlights
Optimization techniques have become more popular in the last two decades, and extended to cover different areas of study
Grey wolf optimizer (GWO) technique is applied to multi-input multi-output electric power systems, to determine the optimum location and sizing of installing (STATCOM) devices in power systems for improving the system voltage stability
The optimal power flow consists of multiple objective functions is formulated to be minimize, power losses, voltage deviations and allocation of STATCOM devices.Generally the problem can be formulated as a nonlinear and constrained optimization problem [21,22,23]: Minimize: f(x,u)
Summary
Optimization techniques have become more popular in the last two decades, and extended to cover different areas of study. Genetic algorithms (GA), Ant colony optimization (ACO), and particle swarms optimization (PSO) are the most well-known techniques from meta-heuristic optimization techniques[1,2,3] Those techniques are the most used optimization techniques due to their simplicity, flexibility, derivation free mechanism, and local optima avoidance. The meta-heuristic optimization techniques have different advantages makes them the first choice for solving optimization problems Their simplicity comes from being reveal of natural phenomena, animals behaviors, or evolutionary concepts[4]. A distribution vector, and 'crossing over' are combining together to form landscape particle swarm optimizer, LSPSO This technique provides updating the velocity and escape from local minima. Grey wolf optimizer (GWO) technique is applied to multi-input multi-output electric power systems, to determine the optimum location and sizing of installing (STATCOM) devices in power systems for improving the system voltage stability. The obtained results indicate the accuracy of the proposed GWO technique for the optimum allocation of STATCOM for system over-loading and a single line outages
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