Improved Harmony Algorithm and Power Loss Index for optimal locations and sizing of capacitors in radial distribution systems

Ali, E. S.; Elazim, S.M. Abd; Abdelaziz, Almoataz Y.

doi:10.1016/j.ijepes.2015.11.085

Cited by 117 publications

(86 citation statements)

References 26 publications

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“…For the load balancing in the distribution system, an appropriate branch is identified. 18 This is defined by the LB index for the jth branch is obtained by using Equation (7).…”

Section: Power Flow Methodsmentioning

confidence: 99%

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Selvaraj

Kanimozhi

2019

Int Trans Electr Energ Syst

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Summary The distribution flexible alternating current system (D‐FACT) devices are being increasingly placed in the distribution system due to their several practical and environmental benefits. In this work, a novel grey wolf optimizer (GWO) algorithm as a meta‐heuristic technique is applied to solve the reconfiguration problem, the installation of distribution static compensator (D‐STATCOM) problem, and the combination of these two problem. The aim of this research is to diminish the total power loss and the load‐balancing (LB) index of the radial distribution system. First, the index vector (IV) method is employed to suggest the candidate busses for D‐STATCOM allocation. Then, the GWO algorithm is applied to establish the locations and sizes of the D‐STATCOM from the candidate busses. An overall accurateness and consistency of the proposed GWO algorithm has been validated and experienced on a 33‐bus, a 69‐bus, and a real‐time 31‐bus radial distribution system, India with three kinds of load levels. In addition, the proposed method results are ideal as compared with other intellectual methods like genetic algorithm (GA) and fuzzy GA.

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“…For the load balancing in the distribution system, an appropriate branch is identified. 18 This is defined by the LB index for the jth branch is obtained by using Equation (7).…”

Section: Power Flow Methodsmentioning

confidence: 99%

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Selvaraj

Kanimozhi

2019

Int Trans Electr Energ Syst

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“…R ij and X ij , respectively, are the resistance and reactance of the line connected to the buses i and j, P j and Q j are the real and reactive powers of the load at bus j, and V j is the voltage magnitude at bus j [16]. Since we only consider real power loss minimization in the objective function, we can define the power loss index as follows [17].…”

Section: Power Loss Indexmentioning

confidence: 99%

Optimal Placement and Sizing of PV-STATCOM in Power Systems Using Empirical Data and Adaptive Particle Swarm Optimization

Sirjani¹

2018

Sustainability

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Solar energy is a source of free, clean energy which avoids the destructive effects on the environment that have long been caused by power generation. Solar energy technology rivals fossil fuels, and its development has increased recently. Photovoltaic (PV) solar farms can only produce active power during the day, while at night, they are completely idle. At the same time, though, active power should be supported by reactive power. Reactive power compensation in power systems improves power quality and stability. The use during the night of a PV solar farm inverter as a static synchronous compensator (or PV-STATCOM device) has recently been proposed which can improve system performance and increase the utility of a PV solar farm. In this paper, a method for optimal PV-STATCOM placement and sizing is proposed using empirical data. Considering the objectives of power loss and cost minimization as well as voltage improvement, two sub-problems of placement and sizing, respectively, are solved by a power loss index and adaptive particle swarm optimization (APSO). Test results show that APSO not only performs better in finding optimal solutions but also converges faster compared with bee colony optimization (BCO) and lightening search algorithm (LSA). Installation of a PV solar farm, STATCOM, and PV-STATCOM in a system are each evaluated in terms of efficiency and cost.

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“…The flower pollination algorithm (FPA) presented by Abdelaziz et al and the improved harmony search algorithm (IHA) introduced by Ali et al are used to find the locations of capacitors and their sizing. In both cases, the candidate buses for installing capacitors are suggested by LSF.…”

Section: Introductionmentioning

confidence: 99%

“…Mostly of the previous contributions use the LSF to rank the buses and to obtain a reduced set of candidate buses to locate the capacitors. However, this locations set cannot be too small to avoid the loosing of solutions of interest.…”

Section: Introductionmentioning

confidence: 99%

Capacitor placement by variables' inclusion and interchange improved algorithm

Abril

2020

Int Trans Electr Energ Syst

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Summary Very diverse optimization methods have been used to determining the optimal capacitor placement in distribution systems. Most of these methods analyze the use of fixed capacitor banks on circuits with only one load state. Other contributions consider the compensation with capacitors switched at different load levels. Almost all previous works represent the load as constant power with respect to voltage variations. This work improves the variables' inclusion and interchange algorithm with a new voltage‐dependent model that considers the variation of active and reactive load with the increase of voltage due to the capacitor placement. The load model used in this work can represent the load by a generalized function of the voltage, in which the constant power load, the constant current load, or the constant impedance load are particular cases. This voltage‐dependent model is the fundamental novelty of the method. The presented algorithm maximizes the annual cost saving in a circuit with several load levels by determining the locations, sizes, and control in time of the needed fixed and switched capacitors. The optimization is constrained with the bounds of minimum and maximum voltages. The capacitor placement solutions proposed by the developed method for various examples of four known circuits are compared with several of the most recently published solutions for these examples. The comparison shows the effectiveness of this method. The solutions obtained for different models of loads show the importance of considering, in the optimization, the load dependence with voltage.

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Improved Harmony Algorithm and Power Loss Index for optimal locations and sizing of capacitors in radial distribution systems

Cited by 117 publications

References 26 publications

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Optimal Placement and Sizing of PV-STATCOM in Power Systems Using Empirical Data and Adaptive Particle Swarm Optimization

Capacitor placement by variables' inclusion and interchange improved algorithm

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