1,832 research outputs found
Spring search algorithm for simultaneous placement of distributed generation and capacitors
Purpose. In this paper, for simultaneous placement of distributed generation (DG) and capacitors, a new approach based on Spring Search Algorithm (SSA), is presented. This method is contained two stages using two sensitive index Sv and Ss. Sv and Ss are calculated according to nominal voltageand network losses. In the first stage, candidate buses are determined for installation DG and capacitors according to Sv and Ss. Then in the second stage, placement and sizing of distributed generation and capacitors are specified using SSA. The spring search algorithm is among the optimization algorithms developed by the idea of laws of nature and the search factors are a set of objects. The proposed algorithm is tested on 33-bus and 69-bus radial distribution networks. The test results indicate good performance of the proposed methodΠ¦Π΅Π»Ρ. Π ΡΡΠ°ΡΡΠ΅ Π΄Π»Ρ ΠΎΠ΄Π½ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠΉ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΠΈ ΠΊΠΎΠ½Π΄Π΅Π½ΡΠ°ΡΠΎΡΠΎΠ² ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ Π½ΠΎΠ²ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠΉ Π½Π° "ΠΏΡΡΠΆΠΈΠ½Π½ΠΎΠΌ" Π°Π»Π³ΠΎΡΠΈΡΠΌΠ΅ ΠΏΠΎΠΈΡΠΊΠ° (Spring Search Algorithm, SSA). ΠΠ°Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ ΡΠΎΡΡΠΎΠΈΡ ΠΈΠ· Π΄Π²ΡΡ
ΡΡΠ°ΠΏΠΎΠ² Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π΄Π²ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Sv ΠΈ Ss. ΠΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Sv ΠΈ Ss ΡΠ°ΡΡΡΠΈΡΡΠ²Π°ΡΡΡΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ Π½ΠΎΠΌΠΈΠ½Π°Π»ΡΠ½ΡΠΌ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΠ΅ΠΌ ΠΈ ΠΏΠΎΡΠ΅ΡΡΠΌΠΈ Π² ΡΠ΅ΡΠΈ. ΠΠ° ΠΏΠ΅ΡΠ²ΠΎΠΌ ΡΡΠ°ΠΏΠ΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡΡΡ ΡΠΈΠ½Ρ-ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΡ Π΄Π»Ρ ΡΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠΉ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΠΈ ΠΊΠΎΠ½Π΄Π΅Π½ΡΠ°ΡΠΎΡΠΎΠ² ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ Sv ΠΈ Ss. ΠΠ°ΡΠ΅ΠΌ, Π½Π° Π²ΡΠΎΡΠΎΠΌ ΡΡΠ°ΠΏΠ΅ ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΈ ΠΊΠ°Π»ΠΈΠ±ΡΠΎΠ²ΠΊΠ° ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠΉ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΠΈ ΠΊΠΎΠ½Π΄Π΅Π½ΡΠ°ΡΠΎΡΠΎΠ² Π²ΡΠΏΠΎΠ»Π½ΡΡΡΡΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° SSA. "ΠΡΡΠΆΠΈΠ½Π½ΡΠΉ" Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΏΠΎΠΈΡΠΊΠ° Π²Ρ
ΠΎΠ΄ΠΈΡ Π² ΡΠΈΡΠ»ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ² ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΡ
Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠ΄Π΅ΠΉ Π·Π°ΠΊΠΎΠ½ΠΎΠ² ΠΏΡΠΈΡΠΎΠ΄Ρ, Π° ΡΠ°ΠΊΡΠΎΡΡ ΠΏΠΎΠΈΡΠΊΠ° ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡ ΡΠΎΠ±ΠΎΠΉ Π½Π°Π±ΠΎΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ². ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΡΠ΅ΡΡΠΈΡΡΠ΅ΡΡΡ Π½Π° ΡΠ°Π΄ΠΈΠ°Π»ΡΠ½ΡΡ
ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠ΅ΡΡΡ
Ρ 33 ΠΈ 69 ΡΠΈΠ½Π°ΠΌΠΈ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ Ρ
ΠΎΡΠΎΡΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π°
Recent trends of the most used metaheuristic techniques for distribution network reconfiguration
Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution
power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for
large distribution networks, which requires large computational times. For solving this type of problem, some researchers
prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some
literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover
most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to
identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints
used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm
optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune
algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This
paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics
applied to DNR in order to continue developing new best algorithms and improving solutions for the topi
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Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review
YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000
Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems
This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units
OPTIMAL POWER MANAGEMENT OF DGS AND DSTATCOM USING IMPROVED ALI BABA AND THE FORTY THIEVES OPTIMIZER
In this study an improved Ali Baba and the forty thieves Optimizer (IAFT) is proposed and successfully adapted and applied to enhance the technical performances of radial distribution network (RDN). The standard AFT governed by two sensible parameters to balance the exploration and the exploitation stages. In the proposed variant a modification is introduced using sine and cosine functions to create flexible balance between Intensification and diversification during search process. The proposed variant namely IAFT applied to solve various single and combined objective functions such as the improvement of total power losses (TPL), the minimization of total voltage deviation and the maximization of the loading capacity (LC) under fixed load and considering the random aspect of loads. The exchange of active powers is elaborated by integration of multi distribution generation based photovoltaic systems (PV), otherwise the optimal management of reactive power is achieved by the installation of multi DSTATCOM. The efficiency and robustness of the proposed variant validated on two RDN, the 33-Bus and the 69-Bus. The qualities of objective functions achieved and the statistical analysis elaborated compared to results achieved using several recent metaheuristic methods demonstrate the competitive aspect of the proposed IAFT in solving with accuracy various practical problems related to optimal power management of RDN
Optimal Capacitor Placement - A Bibliometric Survey
In this paper, Bibliometric survey has been carried out on Optimal Capacitor Placement from 1981 to 2021. Scopus database has been used for the analysis. There were total 909 documents found on the topic of Optimal Capacitor placement. The statistical analysis is carried out source-wise, year-wise, area-wise, Country-wise, University wise, author-wise, and based on funding agency. Network analysis is also carried out based on Co-authorship, Co-occurrence, Citation Analysis and Bibliographic coupling. Results are presented. During 2016, there were 77 documents published which is the highest. International Journal of Electrical Power and Energy Systems of Elsevier has published 37 documents during the period of study which is highest under the category of sources. VOSviewer 1.6.16 is the software that is used for statistical analysis and network analysis on the database. It provides a very effective way to analyze the co-authorship, co-occurrences, citations and bibliometric couplings etc. The source for all Tables and figures is www.scopus.com, The data is assessed on 6th June, 2021
Simultaneous allocation of multiple distributed generation and capacitors in radial network using genetic-salp swarm algorithm
In recent years, the problem of allocation of distributed generation and capacitors banks has received special attention from many utilities and researchers. The present paper deals with single and simultaneous placement of dispersed generation and capacitors banks in radial distribution network with different load levels: light, medium and peak using genetic-salp swarm
algorithm. The developed genetic-salp swarm algorithm (GA-SSA) hybrid optimization takes the system input variables of radial distribution network to find the optimal solutions to maximize the benefits of their installation with minimum cost to minimize the active and reactive power losses and improve the voltage profile. The validation of the proposed hybrid genetic-salp swarm algorithm was carried out on IEEE 34-bus test systems and real Algerian distributed network of Djanet (far south of Algeria) with 112-bus. The numerical results endorse the ability of the proposed algorithm to achieve a better results with higher accuracy compared to the result obtained by salp swarm algorithm, genetic algorithm, particle swarm optimization and the hybrid particle swarm optimization algorithms.Π ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ Π³ΠΎΠ΄Ρ Π·Π°Π΄Π°ΡΠ° ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠΉ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΠΈ Π±Π°ΡΠ°ΡΠ΅ΠΉ ΠΊΠΎΠ½Π΄Π΅Π½ΡΠ°ΡΠΎΡΠΎΠ² ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°Π΅Ρ ΠΎΡΠΎΠ±ΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΠΌΠ½ΠΎΠ³ΠΈΡ
ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ ΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΎΡΠ΄Π΅Π»ΡΠ½ΠΎΠ΅ ΠΈ ΡΠΎΠ²ΠΌΠ΅ΡΡΠ½ΠΎΠ΅ ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ΠΈΠ΅ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠΉ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΠΈ Π±Π°ΡΠ°ΡΠ΅ΠΉ ΠΊΠΎΠ½Π΄Π΅Π½ΡΠ°ΡΠΎΡΠΎΠ² Π² ΡΠ°Π΄ΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ ΠΏΡΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΡΠΎΠ²Π½ΡΡ
Π½Π°Π³ΡΡΠ·ΠΊΠΈ: ΡΠ»Π°Π±ΠΎΠΌ, ΡΡΠ΅Π΄Π½Π΅ΠΌ ΠΈ ΠΏΠΈΠΊΠΎΠ²ΠΎΠΌ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡ ΡΠ°Π»ΡΠΏΠΎΠ² (genetic-salp swarm algorithm). Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΠΎΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡ ΡΠ°Π»ΡΠΏΠΎΠ² (GA-SSA) ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅Ρ ΡΠΈΡΡΠ΅ΠΌΠ½ΡΠ΅ Π²Ρ
ΠΎΠ΄Π½ΡΠ΅ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΡΠ°Π΄ΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ Π΄Π»Ρ ΠΏΠΎΠΈΡΠΊΠ° ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Ρ ΡΠ΅Π»ΡΡ ΠΌΠ°ΠΊΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ² ΠΈΡ
ΡΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ Ρ ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡΠ½ΡΠΌΠΈ Π·Π°ΡΡΠ°ΡΠ°ΠΌΠΈ Π΄Π»Ρ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΎΡΠ΅ΡΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΈ ΡΠ΅Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ ΠΈ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΡΠΈΠ»Ρ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ. Π’Π΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π³ΠΈΠ±ΡΠΈΠ΄Π½ΠΎΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡ ΡΠ°Π»ΡΠΏΠΎΠ² Π±ΡΠ»ΠΎ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ Π½Π° ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΡ
34-ΡΠΈΠ½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
IEEE ΠΈ ΡΠ΅Π°Π»ΡΠ½ΠΎΠΉ 112-ΡΠΈΠ½ΠΎΠΉ Π°Π»ΠΆΠΈΡΡΠΊΠΎΠΉ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ ΠΠΆΠ°Π½Π΅ΡΠ° (ΠΊΡΠ°ΠΉΠ½ΠΈΠΉ ΡΠ³ ΠΠ»ΠΆΠΈΡΠ°). Π§ΠΈΡΠ»Π΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡ ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π΄ΠΎΡΡΠΈΠ³Π°ΡΡ Π»ΡΡΡΠΈΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Ρ Π±ΠΎΠ»ΡΡΠ΅ΠΉ ΡΠΎΡΠ½ΠΎΡΡΡΡ ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠΌ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΡΠΎΡ ΡΠ°Π»ΡΠΏΠΎΠ², Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠΌ, ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠ΅ΠΉ ΡΠΎΡ ΡΠ°ΡΡΠΈΡ ΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°ΠΌΠΈ Π³ΠΈΠ±ΡΠΈΠ΄Π½ΠΎΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠΎΡ ΡΠ°ΡΡΠΈΡ
MSA for Optimal Reconfiguration and Capacitor Allocation in Radial/Ring Distribution Networks
This work presents a hybrid heuristic search algorithm called Moth Swarm Algorithm (MSA) in the context of power loss minimization of radial distribution networks (RDN) through optimal allocation and rating of shunt capacitors for enhancing the performance of distribution networks. With MSA, different optimization operators are used to mimic a set of behavioral patterns of moths in nature, which allows for flexible and powerful optimizer. Hence, a new dynamic selection strategy of crossover points is proposed based on population diversity to handle the difference vectors LΓ©vy-mutation to force MSA jump out of stagnation and enhance its exploration ability. In addition, a spiral motion, adaptive Gaussian walks, and a novel associative learning mechanism with immediate memory are implemented to exploit the promising areas in the search space. In this article, the MSA is tested to adapt the objective function to reduce the system power losses, reduce total system cost and consequently increase the annual net saving with inequity constrains on capacitor size and voltage limits. The validation of the proposed algorithm has been tested and verified through small, medium and large scales of standard RDN of IEEE (33, 69, 85-bus) systems and also on ring main systems of 33 and 69-bus. In addition, the obtained results are compared with other algorithms to highlight the advantages of the proposed approach. Numerical results stated that the MSA can achieve optimal solutions for losses reduction and capacitor locations with finest performance compared with many existing algorithms
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