Comprehensive learning particle swarm optimization for reactive power dispatch

Mahadevan, K.; Kannan, P.

doi:10.1016/j.asoc.2009.08.038

Cited by 266 publications

(135 citation statements)

References 27 publications

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“…The statistical comparison results of 50 trial runs have been list in Table 5. Here the results of PSO and CLPSO from [5] are also listed. Under the initial operating condition, system loss is 141.84MW (Megawatt), after operating with the set of approaches, it can be reduced to a better level.…”

Section: Ieee 118-bus Test System and Simulation Resultsmentioning

confidence: 99%

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An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow

Cao¹,

Wang²,

Li³

2014

IJCA

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Section: Ieee 118-bus Test System and Simulation Resultsmentioning

confidence: 99%

“…Others like Simulated annealing approach (SA) [4]. Bio-inspired computing algorithms like Particle swarm optimization (PSO) [5,6], Ant Colony Search algorithm(ACS) [7], Artificial Neural Network(ANN) [8] and Differential evolution(DE) [9,10].…”

Section: Introductionmentioning

confidence: 99%

An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow

Cao¹,

Wang²,

Li³

2014

IJCA

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“…The results are compared with the other heuristic methods reported in the literature and RPA demonstrated its effectiveness and robustness in reducing the real power loss. [23] 4.98 PSO (Zhao et al, 2005) [24] 4.9262 LP (Mahadevan et al, 2010) [25] 5.988 EP (Mahadevan et al, 2010) [25] 4.963 CGA (Mahadevan et al, 2010) [25] 4.980 AGA (Mahadevan et al, 2010) [25] 4.926 CLPSO (Mahadevan et al, 2010) [25] 4.7208 HSA (Khazali et al, 2011) [26] 4.7624 BB-BC (Sakthivel et al, 2013) [27] 4.690 MCS(Tejaswini sharma et al,2016) [28] 4.87231 Proposed RPA method 4.2876…”

Section: Discussionmentioning

confidence: 99%

Minimization of Real Power Loss by River Progression Algorithm

Lenin¹

2017

IJARCSSE

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Abstract-In this paper, a newmethodology, calledRiverprogression algorithm (RPA) used for solving reactive power problem. The strategy of the RPA as an optimization algorithm was an derivative from nature & after examining the whole riverprogression process which involves the flow of streams and rivers into the sea in the natural world.The proposed River progression algorithm (RPA) has been tested in standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed algorithm in dropping the real power loss.Key words -Nature-inspired algorithm, Reactive Power, Transmission loss, voltage stability,RiverProgression Algorithm I. INTRODUCTIONThe main objective of reactive power dispatchproblem is to minimize the real power loss and bus voltage deviation by fulfilling a set of physical and working constraints imposed by apparatus limitations and security needs. Various mathematical techniqueslike the gradient method [1-2], Newton method [3] and linear programming [4][5][6][7] have been adopted to solve the optimal reactive power dispatch problem. Both the gradient and Newton methods has the difficulty in overseeing inequality constraints. If linear programming is applied then the input-output function has to be articulated as a set of linear functions which mostly lead to loss of correctness. The problem of voltage stability and collapse play a major role in power system planning and operation [8]. Global optimization has received extensive research alertness, and a great number of methods have been applied to solve this problem. Evolutionary algorithms such as PSO &genetic algorithm have been already designed to solve the reactive power flow problem [9,10].Evolutionary algorithm is a heuristic approach used for minimization problems by utilizing nonlinear and non-differentiable continuous space functions. In [11], Genetic algorithm has been used to solve optimal reactive power flow problem. In [12], Hybrid differential evolution algorithm is proposed to improve the voltage stability index. In [13] Biogeography Based algorithm is considered to solve the reactive power dispatch problem. In [14], afuzzy based method is used to solve the optimal reactive power scheduling method .In [15],an improved evolutionary programming is used to solvethe optimal reactive power dispatch problem. In [16], the optimal reactive power flow problem is solved by integrating a genetic algorithm with a nonlinearinterior point method. In [17], apattern algorithm is used to solve ac-dc optimal reactive powerflow model with the generator capability limits. In [18], proposes a two-step approach to evaluate Reactive power reserves with respect to operating constraints and voltage stability. In [19], a programming based proposed approach used to solve the optimal reactive power dispatch problem. In [20], presents aprobabilistic algorithm for optimal reactive power provisionin hybrid electricity markets with uncertain loads.In this work, a new optimization algorithm, recognized as the riverprogress...

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“…A fuzzy based hybrid PSO approach for solving OPF problem considering the forecasting uncertainties of wind speed and load demand in power systems was proposed in [37]. A comprehensive learning PSO (CLPSO) was developed to reactive power dispatch to reduce grid congestions [38]. A new multi-objective PSO (MOPSO) technique for solving OPF problem was proposed in [39].…”

Section: Particle Swarm Optimization Based Approachmentioning

confidence: 99%

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

Ming

Wan

2014

J. Mod. Power Syst. Clean Energy

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Optimal power flow (OPF) is one of the key tools for optimal operation and planning of modern power systems. Due to the high complexity with continuous and discrete control variables, modern heuristic optimization algorithms (HOAs) have been widely employed for the solution of OPF. This paper provides an overview of the latest applications of advanced HOAs in OPF problems. The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced, including genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and evolutionary programming (EP), etc.

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Comprehensive learning particle swarm optimization for reactive power dispatch

Cited by 266 publications

References 27 publications

An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow

An Improved Biogeography-based Optimization Algorithm for Optimal Reactive Power Flow

Minimization of Real Power Loss by River Progression Algorithm

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

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