A Real-Time Optimization of Reactive Power for An Intelligent System Using Genetic Algorithm

Abstract

Power factor (PF) is a measure of how effectively electricity is used. The low power factor causes considerable power losses along the power supply chain. In particular, it overloads the distribution system and increases the power plant's burden to compensate the expected power losses. Most of the existing PF correction techniques are developed based on placing centralized capacitors, assuming that power systems are static. However, the power systems are dynamic systems such that their states change over time, necessitating dynamic correction systems. In the emerging smart grid systems, real-time measurements can easily be taken for voltage, current and harmonics. Then, the measured data can be transmitted to a PF controller to reach the desired PF value. However, the problem that will arise in real-time applications is how to determine and adjust the optimal capacitor size that can balance the power factor. In this regard, we propose a real-time correction system based on multi-step capacitor banks to improve PF in cooperation with de-tuned filters to mitigate the harmonics. First, a mathematical model has been formulated for the proposed power factor correction system. The mathematical model can be employed to determine the optimal operational settings of the multi-step capacitor and the reactor value that optimize the reactive power while considering the desired PF value and restricting the harmonics. Second, a genetic optimization approach is applied to solve the proposed mathematical model as it can provide accurate solution in a short computational time. A Monte Carlo simulation approach is considered for validating the proposed PF correction system. The simulation results show that the average PF of the randomly generated test instances has improved from 0.7 to 0.95 (35% increase). Furthermore, we conducted real experiments using a PF testbed for experimental validation. The results are found to be consistent with the simulation results, which validate the effectiveness and applicability of the proposed correction system. Furthermore, the saved kVA in one day is estimated to be 26% of total kVA.

Power Loss Minimization for Distribution Networks with Load Tap Changing Using Genetic Algorithm and Environmental Impact Analysis

“…Nature-inspired metaheuristics are the most commonly used optimization techniques to overcome power factor problem by reactive power and voltage control. Abdelhady et al [33] estimate the optimum capacitor and the reactor values to control reactive power and power factor with Genetic Algorithm since the technique is compatible with real-time applications. Pires et al [34] propose Particle Swarm Optimization for reactive power compensation and cost minimization of energy storage systems in distribution networks.…”

Multi-Objective Optimal Siting and Sizing of Distributed Generators and Shunt Capacitors Considering the Effect of Voltage-Dependent Nonlinear Load Models

“…These variants referred to as adaptive and exponential PSO were compared in terms of how well they improved distribution system. The GA was implemented in [24] to manage various capacitors and shunt reactors and maximized reactive power in real-time applications. The improved decomposition-based EA (I-DBEA) in [21] and Manta Ray foraging optimization (MRFO) were implemented in [23] and are employed to solve Multi-objective DG integration into distribution problems to minimize VD and losses and maximize VSI.…”

Pareto Front-Based Multiobjective Optimization of Distributed Generation Considering the Effect of Voltage-Dependent Nonlinear Load Models

“…Most research work based on a single objective heuristic, metaheuristic, or hybrid evolutionary algorithm is available in the literature to solve single objective DG and DG-SC allocation problems. That includes, particle swarm optimization (PSO) [3], intersect mutation differential evolution (IMDE) [4], grey wolf optimization (GWO) in [13], an improved artificial eco-system based optimization (EAEO) [14], genetic algorithm (GA) in [15], enhanced GA (EGA) [16], gravitational search algorithm (GSA) in [17], Mixed integer nonlinear programming (MINLP) in the platform of general algebraic modeling system (GAMS) [18], improved binary PSO (IBPSO) [19], a spotted hyena optimizer (SHO) [20], and basic open source MINLP (BOMINLP) [21].…”