Network loss reduction and voltage improvement by optimal placement and sizing of distributed generators with active and reactive power injection using fine-tuned PSO

Abstract

<span>Minimization of real power loss and improvement of voltage authenticity of the network are amongst the key issues confronting power systems owing to the heavy demand development problem, contingency of transmission and distribution lines and the financial costs. The distributed generators (DG) has become one of the strongest mitigating strategies for the network power loss and to optimize voltage reliability over integration of capacitor banks and network reconfiguration. This paper introduces an approach for the optimizing the placement and sizes of different types of DGs in radial distribution systems using a fine-tuned particle swarm optimization (PSO). The suggested approach is evaluated on IEEE 33, IEEE 69 and a real network in Malaysian Context. Simulation results demonstrate the productiveness of active and reactive power injection into the electric power system and the comparison depicts that the suggested fine-tuned PSO methodology could accomplish a significant reduction in network power loss than the other research works.</span>

The Optimal Placement and Sizing of Distributed Generation in an Active Distribution Network with Several Soft Open Points

“…The authors of Ref. [23] proposed the PSO method to determine the optimal allocation and capacity of DGs for the power loss minimization of the system. For the same radial system, comprehensive learning PSO was utilized for the sizing and placement in Ref.…”

“…The main reason behind this was to alleviate the computational time than that of a random selection of initial positions. Therefore, the positions of the particles were initialized according to Equation (23).…”

Studies enhancement of transient stability by single machine infinite bus system and setting purpose genetic algorithm

“…In order to obtain high accuracy and mend stability margins, optimized positioned phasor measuring unit (PMU) were combined in a wide area measurement system (WAMS) and increased the conventional L index on 9-bus and 14-bus networks [7]. Education (EDN) has been restored with finite-time particle swarm optimization (FPSO) algorithm for advanced production and loading (APL) [8], [9]. The neural network was used to detect plant echo using the derivation of the root mean square system [10].…”

Optimal integration of photovoltaic distributed generation in electrical distribution network using hybrid modified PSO algorithms

“…In 2020, used virus colony search (VCS) algorithm for reduced the not supplied energy (NSE) [19], comprehensive learning PSO (CLPSO) algorithm with an objective of minimizing the APL [20], applied various adaptive acceleration coefficients PSO algorithms on maximizing the APL level [21], various adaptive PSO algorithms for minimizing the three technical parameters [22], and hybrid chaotic maps and adaptive acceleration coefficients PSO algorithm to multi-objective functions [23]. Recently, applied finetuned particle swarm optimization (FPSO) algorithm for APL with EDN reconfiguration [24], chaotic grey wolf optimizer (CGWO) to minimize a multi-objective function considering overcurrent relays indices [25], and adaptive quantum inspired evolutionary algorithm (AQiEA) to minimization of APL in addition to voltage dependent load models [26]. The authors in this paper have proposed various hybrid PSO algorithms based on chaotic maps and adaptive acceleration coefficients for the optimal location and sizing of PV-DG sources in IEEE 33-bus and 69-bus EDNs to minimize simultaniousely three technical parameters represented by the multi-objective function (MOF).…”