Optimal location of UPQC for power quality improvement: novel hybrid approach

Abstract

Purpose In general, the optimal reactive power compensation could drastically enhance the performance of distributed network by the reduction of power loss and by enhancement of line loadability and voltage profile. Till now, there exist various reactive power compensation models including capacitor placement, joined process of on-load tap changer and capacitor banks and integration of DG. Further, one of the current method is the allocation of distribution FACTS (DFACTS) device. Even though, the DFACTS devices are usually used in the enhancement of power quality, they could be used in the optimal reactive power compensation with more effectiveness. Design/methodology/approach This paper introduces a power quality enhancement model that is based on a new hybrid optimization algorithm for selecting the precise unified power quality conditioner (UPQC) location and sizing. A new algorithm rider optimization algorithm (ROA)-modified particle swarm optimization (PSO) in fitness basis (RMPF) is introduced for this optimal selections. Findings Through the performance analysis, it is observed that as the iteration increases, there is a gradual minimization of cost function. At the 40th iteration, the proposed method is 1.99 per cent better than ROA and genetic algorithm (GA); 0.09 per cent better than GMDA and WOA; and 0.14, 0.57 and 1.94 per cent better than Dragonfly algorithm (DA), worst solution linked whale optimization (WS-WU) and PSO, respectively. At the 60th iteration, the proposed method attains less cost function, which is 2.07, 0.08, 0.06, 0.09, 0.07 and 1.90 per cent superior to ROA, GMDA, DA, GA, WS-WU and PSO, respectively. Thus, the proposed model proves that it is better than other models. Originality/value This paper presents a technique for optimal placing and sizing of UPQC. To the best of the authors’ knowledge, this is the first work that introduces RMPF algorithm to solve the optimization problems.

A Deep Neural Network as a Strategy for Optimal Sizing and Location of Reactive Compensation Considering Power Consumption Uncertainties

“…In [11], the research proposes a heuristic that considers the modified artificial bee colony algorithm for optimal reactive compensation placement through UPQC to improve the power quality of the IEEE 30-node network and compares the results with other methods such as PSO and the genetic algorithm.…”

Recent Trends in Power Quality Improvement: Review of the Unified Power Quality Conditioner

“…Some hybrid algorithms have been used to determine the optimal location of the UPQC by focusing on efficiency, system cost, and voltage stability index such as a hybrid algorithm of the genetic algorithm and dragonfly algorithm (DA) known as the genetically modified DA algorithm [152] and crow search mating-based lion algorithm [153]. In [154], a novel rider optimization algorithm (ROA)-modified PSO on a fitness basis is presented for the improvement of PQ by choosing a particular location and size for the UPQC. Accordingly, a steady-state model of UPQC is derived to set the forward/backward sweep load flow with the objective of enhancing voltage and current profiles, while minimizing power loss and the investment costs.…”

Design and analysis of electric spring based on battery-less current-source inverter

“…However, the fluctuations and intermittency of renewable energy pose challenges to the power system, leading to degradation of power quality and even instability of the power system (Çelik et al , 2022). Reactive power compensation technology (Chen et al , 2022; Gaddala and Raju, 2020) is one of the measures to solve the above problems, but they are incapable of regulating active power. Energy storage systems, such as pumped storage power stations (Asgari et al , 2022), batteries and supercapacitors (Ravada et al , 2021), can regulate the active power in response to the intermittency of renewable energy sources.…”