Optimal Reactive Power Flow for Large-Scale Power Systems Using an Effective Metaheuristic Algorithm

Duong, Thanh Long; Duong, Minh Quan; Phan, Van-Duc; Nguyen, Thang Trung

doi:10.1155/2020/6382507

Cited by 48 publications

(27 citation statements)

References 41 publications

(72 reference statements)

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“…Real power loss reduction has been achieved with voltage stability enhancement with minimization of voltage deviation. [58] 0.1038 BPSO-TVAC [58] 0.2064 SPSO-TVAC [58] 0.1354 BPSO-CF [58] 0.1287 PG-PSO [58] 0.1202 SWT-PSO [58] 0.1614 PGSWT-PSO [58] 0.1539 MPG-PSO [58] 0.0892 QO-TLBO [55] 0.0856 TLBO [55] 0.0913 S-FS [57] 0.1220 ISFS [57] 0.0890 SFS [59] 0.0877 HTO 0.0869 SCCS 0.0867 [58] 0.1258 BPSO-TVAC [58] 0.1499 SPSO-TVAC [58] 0.1271 BPSO-CF [58] 0.1261 PG-PSO [58] 0.1264 SWT-PSO [58] 0.1488 PGSWT-PSO [58] 0.1394 MPG-PSO [58] 0.1241 QO-TLBO [55] 0.1191 TLBO [55] 0.1180 ALO [54] 0.1161 ABC [54] 0.1161 GWO [54] 0.1242 BA [54] 0.1252 S-FS [57] 0.1252 IS-FS [57] 0.1245 SFS [59] 0.1007 HTO 0.1004 SCCS 0.1003 HTO, SCCS algorithms have been evaluated in IEEE 30 bus system without considering L-index [40]. In Table 4, comparisons of results are presented.…”

Section: Resultsmentioning

confidence: 99%

Heat Transfer and Simulated Coronary Circulation System Optimization Algorithms for Real Power Loss Reduction

Lenin¹

2021

JES

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In this paper, the heat transfer optimization (HTO) algorithm and simulated coronary circulation system (SCCS) optimization algorithm has been designed for Real power loss reduction. In the projected HTO algorithm, every agent is measured as a cooling entity and surrounded by another agent, like where heat transfer will occur. Newton’s law of cooling temperature will be updated in the proposed HTO algorithm. Each value of the object is computed through the objective function. Then the objects are arranged in increasing order concerning the objective function value. This projected algorithm time “t” is linked with iteration number, and the value of “t” for every agent is computed. Then SCCS optimization algorithm is projected to solve the optimal reactive power dispatch problem. Actions of human heart veins or coronary artery development have been imitated to design the algorithm. In the projected algorithm candidate solution is made by considering the capillaries. Then the coronary development factor (CDF) will appraise the solution, and population space has been initiated arbitrarily. Then in the whole population, the most excellent solution will be taken as stem, and it will be the minimum value of the Coronary development factor. Then the stem crown production is called the divergence phase, and the other capillaries’ growth is known as the clip phase. Based on the arteries leader’s coronary development factor (CDF), the most excellent capillary leader’s (BCL) growth will be there. With and without L-index (voltage stability), HTO and SCCS algorithm’s validity are verified in IEEE 30 bus system. Power loss minimized, voltage deviation also reduced, and voltage stability index augmented.

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Section: Resultsmentioning

confidence: 99%

Heat Transfer and Simulated Coronary Circulation System Optimization Algorithms for Real Power Loss Reduction

Lenin¹

2021

JES

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“…Power loss (MW) BPSO-TS [10] 4.5213 TS [10] 4.6862 BPSO [10] 4.6862 ALO [11] 4.5900 QO-TLBO [12] 4.5594 TLBO [12] 4.5629 SGA [13] 4.9408 BPSO [13] 4.9239 HAS [13] 4.9059 S-FS [14] 4.5777 IS-FS [14] 4.5142 SFS [16] 4…”

Section: Methodsmentioning

confidence: 99%

“…Yet various factors are influenced in the poor performance of the techniques. In conventional methods inequality constraints are unable to be included successfully and in evolutionary based algorithms balancing the exploration and exploitation are major task to reach the most excellent solution [11][12][13][14][15][16][17][18]. There should be proper trade-off between exploration and exploitation because when trade-off failed then it not at all possible to reach a better solution [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Real Power Loss Reduction by Blue Noddy and European Night Crawler Optimization Algorithms

Lenin¹

2021

IJCAI

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In this paper Blue noddy optimization (BNO) algorithm and European Night crawler optimization (ENO) algorithm are applied to solve the power loss reduction problem. Key objective is to reduce the power loss with voltage stability enhancement and minimization of voltage deviation. Exodus and Preying behaviour of the Blue noddy has been imitated to formulate the algorithm. In the mathematical formulation of Exodus deed -collusion between the Blue noddy has been avoided and blue noddy will converge in the direction of most excellent companion. Position update of the Blue noddy is based on the most excellent explore agent. Preying behaviour is based on the line and angle of preying. Logically the angle, velocity will be transformed by the Blue noddy and it will do spiral act in the air to seizure the prey. Exploration and Exploitation is augmented through the Exodus and preying behaviour. In ENO algorithm reproduction nature of the European Night crawler is imitated to design the algorithm. European Night crawler population is created through the off-springs with two different kinds of reproduction. The dimension of the adolescent European Night crawler is alike to the parent. In the method Cross over operation has been implemented by considering the parent European Night crawler and Cauchy mutation has been included in order to elude the solution to be trapped under local optima. With and without voltage stability (L -index) proposed BNO and ENO algorithms are verified in IEEE 30 Bus system. Active power loss reduction has been achieved with L-index improvement and voltage deviation minimized.Povzetek: V tem prispevku sta za reševanje problema zmanjšanja izgube energije uporabljena algoritem BNO za optimizacijo in algoritem ENO (European Night crawler optimization).

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“…A fractional evolutionary based method that achieves the objectives of ORPD problem by including the FACTS controllers is presented in [13]. The author in [14] proposes a stochastic fractal search methodology for the solution of ORPD problem considering the voltage stability index (VSI), voltage deviation and power losses objectives. A Levy Interior Search technique is proposed in [15] for the solution of multiobjective (MO) based ORPD problem.…”

Section: A Literature Reviewmentioning

confidence: 99%

Solving Reactive Power Scheduling Problem using Multi-objective Crow Search Algorithm

Salkuti¹

2021

IJACSA

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This paper presents the solution of multi-objective based optimal reactive power dispatch (MO-ORPD) problem by optimizing the system power losses and voltage stability enhancement index (VSEI)/L-index objectives. ORPD problem is considered as an important issue from system security and operational point of view for optimal steady-state operation of power system. Here, single-objective based ORPD problem is solved using Crow Search Algorithm (CSA) and multi-objective based ORPD problem is solved using multi-objective CSA (MO-CSA). The CSA is considered as an efficient and robust algorithm which determines the global optimal solution for solving the non-linear and discontinuous objective functions. Two standard test systems, i.e., IEEE 30 and 57 bus systems are considered to show the effectiveness, suitability and robustness of CSA and MO-CSA for solving the ORPD problem.

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Optimal Reactive Power Flow for Large-Scale Power Systems Using an Effective Metaheuristic Algorithm

Cited by 48 publications

References 41 publications

Heat Transfer and Simulated Coronary Circulation System Optimization Algorithms for Real Power Loss Reduction

Heat Transfer and Simulated Coronary Circulation System Optimization Algorithms for Real Power Loss Reduction

Real Power Loss Reduction by Blue Noddy and European Night Crawler Optimization Algorithms

Solving Reactive Power Scheduling Problem using Multi-objective Crow Search Algorithm

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