Evolutionary computation based reactive power optimization

Durairaj, S.; Fox, B.

doi:10.1049/ic:20070597

Cited by 10 publications

(9 citation statements)

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“…The objective function given in [4], [5], [7] for the reactive power optimization is shown in the Equation (9). The active power loss of the power system is minimized which is represented as the objective function.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The increasing demand in power systems affects power losses, power quality and the economic operation of the systems. In power systems, the reactive power optimization (RPO) can efficiently decrease the total active power losses of the energy systems and improve the voltage level, which has an impress on economical management of power system [1][2][3][4][5][6][7]. RPO denotes that all these reactive setting methods, which can be found through the optimization of some specific variables when structure parameters and load situation of system are given, and under the premise that when all specified constraint conditions are satisfied, which can fix one or more performance indexes of system to approach the optimization [8].…”

Section: Introductionmentioning

confidence: 99%

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Reactive Power Optimization with Chaotic Firefly Algorithm and Particle Swarm Optimization in A Distribution Subsystem Network

Yap¹,

Nurettin²,

etinkaya³

2016

IJEEE

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In this paper the minimization of power losses in a real distribution network have been described by solving reactive power optimization problem. The optimization has been performed and tested on Konya Eregli Distribution Network in Turkey, a section of Turkish electric distribution network managed by MEDAŞ (Meram Electricity Distribution Corporation). The network contains about 9 feeders, 1323 buses (including 0.4 kV, 15.8 kV and 31.5 kV buses) and 1311 transformers. This paper prefers a new Chaotic Firefly Algorithm (CFA) and Particle Swarm Optimization (PSO) for the power loss minimization in a real distribution network. The reactive power optimization problem is concluded with minimum active power losses by the optimal value of reactive power. The formulation contains detailed constraints including voltage limits and capacitor boundary. The simulation has been carried out with real data and results have been compared with Simulated Annealing (SA), standard Genetic Algorithm (SGA) and standard Firefly Algorithm (FA). The proposed method has been found the better results than the other algorithms.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization with Chaotic Firefly Algorithm and Particle Swarm Optimization in A Distribution Subsystem Network

Yap¹,

Nurettin²,

etinkaya³

2016

IJEEE

View full text Add to dashboard Cite

show abstract

“…In [16], reactive power optimization has been solved by adjusting generator voltages, transformer taps, and capacitors/reactors and three GA/SA/TS hybrid algorithms have been used. In the other study [17], the reactive power optimization problem has been solved by using evolutionary computation techniques such as genetic and particle swarm optimization algorithms, and voltage bus magnitude, transformer tap setting, and the reactive power injected by capacitor banks were selected as control variables. Authors applied the proposed algorithms to IEEE 30-bus and IEEE 118-bus systems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization

Yapıcı

Çetinkaya

2017

Mathematical Problems in Engineering

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The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.

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“…RPO is realized by Lu and Ma neuro-dynamic programming [1]. Durairaj and Fox have applied evolutionary computation based RPO to 30 and 118 bus systems of IEEE [2]. Hazrai and Sinha have done active and reactive power optimization via particle swarm algorithm [3].…”

Section: Introductionmentioning

confidence: 99%

Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm

et al. 2016

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Original scientific paper Reactive power optimization (RPO) is a major field of study to ensure power systems for operating in a secure and economical manner. RPO can be used for decreasing of active power losses, voltage control, and for the optimization of the power coefficients in power systems. The non-linear power loss function is used as an object function that will be minimized. In this study Chaotic Artificial Bee Colony (CABC) algorithm is used to minimize the active power loss of power systems. Chaotic maps such as logistic map and Henon map are used against the random number generator. CABC is applied on the IEEE6-bus and IEEE 30-bus test systems and the results are shown. Accordingly, the results have been evaluated and observed that the stability critical values found by CABC can be used to produce good potential solutions. Simulation results are promising and show the effectiveness of the applied approach. Keywords: chaos; Chaotic Artificial Bee Colony; optimization; power systemsSmanjenje gubitka aktivne snage u elektro-energetskim sustavima primjenom algoritma kaotične umjetne kolonije pčela Izvorni znanstveni članak Optimizacija reaktivne snage -Reactive power optimization (RPO) -osnovno je područje istraživanja u svrhu sigurnog i ekonomičnog rada energetskih sustava. RPO se može primijeniti za smanjenje gubitaka aktivne snage, reguliranje napona te za optimizaciju energetskih koeficijenata u energetskim sustavima. Funkcija ne-linearnog gubitka snage koristi se kao funkcija cilja koju treba smanjiti. U ovom se radu algoritam Kaotične umjetne kolonije pčela -Chaotic Artificial Bee Colony (CABC) -primjenjuje za smanjenje gubitka aktivne snage u energetskim sustavima. Rabe se kaotične mape kao što su logistička mapa i Henon mapa. CABS se primjenjuje na provjeravanim sustavima IEEE 6-sabirnice i IEEE 30-sabirnice i daju se rezultati. Provjerom rezultata ustanovilo se da primjena kritičnih vrijednosti stabilnosti dobivenih pomoću CABS može rezultirati dobrim potencijalnim rješenjima. Rezultati simulacije su obećavajući i pokazuju učinkovitost primijenjenog pristupa.

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Evolutionary computation based reactive power optimization

Cited by 10 publications

References 0 publications

Reactive Power Optimization with Chaotic Firefly Algorithm and Particle Swarm Optimization in A Distribution Subsystem Network

Reactive Power Optimization with Chaotic Firefly Algorithm and Particle Swarm Optimization in A Distribution Subsystem Network

An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization

Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm

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