A new hybrid evolutionary strategy for reactive power dispatch

Das, D. Bhagwan; Patvardhan, C.

doi:10.1016/s0378-7796(02)00209-2

Cited by 50 publications

(11 citation statements)

References 19 publications

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“…Evolutionary programming [7] 5.0159 Evolutionary strategy [8] 4.610 Genetic algorithm [9] 4.665 Proposed method 4.568 …”

Section: Methods Minimum Lossmentioning

confidence: 99%

“…A great saving of active power has been reported by the authors for the IEEE 30-bus system. A hybrid evolutionary strategy approach for reactive power dispatch in IEEE 30-bus system is reported in Reference [8]. The test results show that the proposed algorithm gives better results with less computational burden and is fairly consistent in reaching the near optimum solutions.…”

Section: Introductionmentioning

confidence: 99%

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Improved genetic algorithm for multi‐objective reactive power dispatch problem

Devaraj¹

2007

Int Trans Elec Energy Syst

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SUMMARYThis paper presents an improved genetic algorithm (GA) approach for solving the multi-objective reactive power dispatch problem. Loss minimization and maximization of voltage stability margin are taken as the objectives. Maximum L-index of the system is used to specify the voltage stability level. Generator terminal voltages, reactive power generation of capacitor banks and tap changing transformer setting are taken as the optimization variables. In the proposed GA, voltage magnitudes are represented as floating point numbers and transformer tap-setting and reactive power generation of capacitor bank are represented as integers. This alleviates the problems associated with conventional binary-coded GAs to deal with real variables and integer variables with total number of permissible choices not equal to 2 5 . Crossover and mutation operators which can deal with mixed variables are proposed. The proposed method has been tested on IEEE 30-bus system and is compared with conventional methods and binary-coded GA. The proposed method has produced the loss which is less than the value reported earlier and is well suitable for solving the mixed integer optimization problem.

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“…Evolutionary programming [7] 5.0159 Evolutionary strategy [8] 4.610 Genetic algorithm [9] 4.665 Proposed method 4.568 …”

Section: Methods Minimum Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved genetic algorithm for multi‐objective reactive power dispatch problem

Devaraj¹

2007

Int Trans Elec Energy Syst

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“…For generator, one random value change is added to the generator voltage. 4) Swap Random LS [18]: Two same type controllers are chosen randomly and their control value are swapped. 5) Max-Min Random LS: One controller is selected randomly and its output is promoted or reduced to maximum or minimum.…”

Section: ) Voltage Correction Lsmentioning

confidence: 99%

Multiobjective Optimal Reactive Power Flow Using Elitist Nondominated Sorting Genetic Algorithm: Comparison and Improvement

Li¹,

Li²,

Duan³

2010

Journal of Electrical Engineering and Technology

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-Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

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“…To eliminate the RPD problems, various optimization algorithms have been implemented over a period of time to achieve the optimal results. Some of the known stochastic methods implemented for solution of RPD problem includes the linear programming (LP) [ 7 ], interior point method (IPM) [ 8 ], quadratic programming (QP) [ 9 ], genetic algorithm (GA) [ 10 ], particle swarm optimization (PSO) [ 11 , 12 ], multi-objective optimization particle swarm optimization (MOPSO) algorithm [ 13 ], fractional Order PSO (FO-PSO) [ 6 ], harmony search algorithm (HSA) [ 14 ], gaussian bare-bones water cycle algorithm (NGBWCA) [ 15 ], tabu search (TS) [ 16 ], comprehensive learning particle swarm optimization [ 17 ], teaching learning based optimization (TLBO) [ 18 ], adaptive GA (AGA) [ 19 ], seeker optimization algorithm (SOA) [ 20 ], jaya algorithm [ 21 ], differential evolution (DE) [ 2 , 3 , 5 , 22 , 23 , 24 , 25 ], Artificial Bee Colony Algorithm [ 26 ], Hybrid Artificial Physics PSO [ 27 ], improved antlion optimization algorithm [ 28 ], Chaotic Bat Algorithm [ 29 ], classification-based Multi-objective evolutionary algorithm [ 30 ], evolution strategies (ES) [ 31 ], evolutionary programming (EP) [ 32 ], firefly algorithm (FA) [ 33 ], gravitation search optimization algorithm (GSA) [ 34 , 35 ], bacteria foraging optimization (BFO) [ 36 ], bio-geography-based optimization algorithm (BBO) [ 37 ] and grey wolf based optimizer algorithm (GWO) [ 38 ]. In 2017, another advanced optimizer has also been applied to the problems RPD known as gradient-based WCA (GWCA) [ 39 , 40 ] and results demonstrate the relevance and pr...…”

Section: Introductionmentioning

confidence: 99%

A New Fractional Particle Swarm Optimization with Entropy Diversity Based Velocity for Reactive Power Planning

Waleed

Yasir

Raja

et al. 2020

Entropy

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Optimal Reactive Power Dispatch (ORPD) is the vital concern of network operators in the planning and management of electrical systems to reduce the real and reactive losses of the transmission and distribution system in order to augment the overall efficiency of the electrical network. The principle objective of the ORPD problem is to explore the best setting of decision variables such as rating of the shunt capacitors, output voltage of the generators and tap setting of the transformers in order to diminish the line loss, and improve the voltage profile index (VPI) and operating cost minimization of standard electrical systems while keeping the variables within the allowable limits. This research study demonstrates a compelling transformative approach for resolving ORPD problems faced by the operators through exploiting the strength of the meta-heuristic optimization model based on a new fractional swarming strategy, namely fractional order (FO)–particle swarm optimization (PSO), with consideration of the entropy metric in the velocity update mechanism. To perceive ORPD for standard 30 and 57-bus networks, the complex nonlinear objective functions, including minimization of the system, VPI improvement and operating cost minimization, are constructed with emphasis on efficacy enhancement of the overall electrical system. Assessment of the results show that the proposed FO-PSO with entropy metric performs better than the other state of the art algorithms by means of improvement in VPI, operating cost and line loss minimization. The statistical outcomes in terms of quantile–quantile illustrations, probability plots, cumulative distribution function, box plots, histograms and minimum fitness evaluation in a set of autonomous trials validate the capability of the proposed optimization scheme and exhibit sufficiency and also vigor in resolving ORPD problems.

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A new hybrid evolutionary strategy for reactive power dispatch

Cited by 50 publications

References 19 publications

Improved genetic algorithm for multi‐objective reactive power dispatch problem

Improved genetic algorithm for multi‐objective reactive power dispatch problem

Multiobjective Optimal Reactive Power Flow Using Elitist Nondominated Sorting Genetic Algorithm: Comparison and Improvement

A New Fractional Particle Swarm Optimization with Entropy Diversity Based Velocity for Reactive Power Planning

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