A multi-objective optimization-based model for the deployment of reclosers and remote-controlled switches using NSGA2 and entropy weighted TOPSIS method

Abstract

<p>Since they are fast, remote-controlled, automated and intelligent, reclosers<br />and switches are an inevitable solution for improving the reliability of<br />intelligent electrical distribution networks at optimal cost. However, their<br />location and coordination have great effects on the protection and automation<br />strategies of complex electrical distribution networks against multiple<br />unpredictable faults. Which requires a flexible and multi-criteria optimization<br />method. In this article, we propose a multi-objective method based on an<br />analytical model by considering the fault rate, restoration times, outage cost<br />and coordination between devices. The non-dominated genetic sorting<br />algorithm II was proposed to obtain the optimal Pareto solutions, and a<br />technique of performance control by similarity with the ideal solution was<br />used to classify them. The objective criteria weights are based on the entropy<br />method which allows solutions to be obtained and better classified with the<br />minimum of subjectivity. The IEEE33 and IEEE13 bus test networks were<br />used to verify the method. The results obtained are compared to a binary<br />multi-objective particle swarm optimization method and the results show that<br />the proposed method reduces the overall costs, reduces the undelivered<br />energy of the system and improves the reliability of the service.</p>

Renewable Energy Real-Time Carrying Capacity Assessment Method and Response Strategy under Typhoon Weather

“…where R U i and R d i are the upward and downward climb rates of unit i, respectively. (4) Wind turbine constraints 0 ≤ P i,t ≤ P i,t,max (30) where P i,t,max is the maximum output of wind farm i at moment t.…”

“…The following is a comparison of the convergence of Mucilage Algorithm and Particle Swarm Algorithm in ZDT1 and ZDT2 functions [30], which shows that the Mucilage Algorithm is better than the Particle Swarm Algorithm in dealing with the nonlinear problems in terms of convergence [31].…”

An Integrated Node Selection Model Using FAHP and FTOPSIS for Data Retrieval in Ubiquitous Computing

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