Journal Article10.24425/aee.2022.141676
Improved Differential Evolution Algorithm to solve multi-objective of optimal power flow problem
M. U. A. L-KAABI,J. A. A. L. Hasheme,L. A. A. L-BAHRANI +2 more
TL;DR: The IEEE 30-bus standard system has been used to validate the effectiveness and superiority of the approach proposed based on MATLAB software, and the results obtained by this method will be compared with other recent methods.
read more
Abstract: : This article presents a new efficient optimization technique namely the Multi-Objective Improved Differential Evolution Algorithm (MOIDEA) to solve the multi-objective optimal power flow problem in power systems. The main features of the Differential Evolution (DE) algorithm are simple, easy, and efficient, but sometimes, it is prone to stagnation in the local optima. This paper has proposed many improvements, in the exploration and exploitation processes, to enhance the performance of DE for solving optimal power flow (OPF) problems. The main contributions of the DE algorithm are i) the crossover rate will be changing randomly and continuously for each iteration, ii) all probabilities that have been ignored in the crossover process have been taken, and iii) in selection operation, the mathematical calculations of the mutation process have been taken. Four conflicting objective functions simultaneously have been applied to select the Pareto optimal front for the multi-objective OPF. Fuzzy set theory has been used to extract the best compromise solution. These objective functions that have been considered for setting control variables of the power system are total fuel cost (TFC), total emission (TE), real power losses (RPL), and voltage profile (VP) improvement. The IEEE 30-bus standard system has been used to validate the effectiveness and superiority of the approach proposed based on MATLAB software. Finally, to demonstrate the effectiveness and capability of the MOIDEA, the results obtained by this method will be compared with other recent methods
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Figures
Table 1. Comparison results with other recent optimization algorithms for Case 1 to Case 3 ![Fig. 1. Membership function [24]](/figures/figure1-1-3vax3zfbu2ws.png)
Fig. 1. Membership function [24] 
Fig. 4. The best Pareto set solutions obtained: Case 1 (a); Case 2 (b); Case 3 (c) 
Table 3. Reactive power of the generators obtained by MOIDEA for three Cases 
Table 2. Optimal control variables obtained by MOIDEA for three Cases 
Fig. 2. Structure of: control variable (a); state variable (b)
![Fig. 1. Membership function [24]](/figures/figure1-1-3vax3zfbu2ws.webp)
Citations
Investigation Study of Injecting Numerous DGs in IEEE 69 – bus Radial Networks Using Enhanced PSO and Ant Lion Optimization Algorithms
Ahmed Rahim Ali,A. A. R. Altahir,Shamam Alwash,Murtadha Al-Kaabi +3 more
- 20 Dec 2023
TL;DR: The numerical simulation results show that the results extracted by EPSO are more accurate than the corresponding obtained results by ALO from viewpoints of real loss reduction and allocation of DGs.
1
Multi Objective Optimal Power Flow Problems Using Hunger Games Search Algorithm
Murtadha Al-Kaabi,Virgil Dumbrava,Mircea Eremia,Lucian Toma,Cristian Lazaroiu,Haitham Aljanabi +5 more
- 26 Oct 2023
TL;DR: This paper deal with a multi-objective hunger game search (MOHGS) to solve the optimal power flow (OPF) problem and uses a fuzzy membership technique to determine the best compromise solution from all the produced Pareto optimal set.
1
The Performance of a Brushless DC Motor with Six Step Commutation-Based Speed Controller Optimized by PSO Algorithm
Fitriaty Pangerang,Faisal Arya Samman,Zahir Zainuddin,Rhiza S. Sadjad +3 more
- 21 Feb 2024
TL;DR: The proposed PSO-optimized PID controller significantly improves the performance of a BLDC motor with increased speed, torque, and reduced torque ripple.
1
A New Approach Meta-Heurestics Optimization Techniques to Solve Multi Objective Optimal Power Flow Problems
Murtadha Al-Kaabi,Virgil Dumbrava,Mircea Eremia,Lucian Toma,Cristian Lazaroiu,Al Igeb Bahaa Hussein +5 more
- 26 Oct 2023
TL;DR: A new approach meta-heuristic optimization technique has been proposed to solve multi objective optimal power flow (MOOPF) and it is demonstrated that the suggested methodology is efficient to produces well-distributed Pareto front solutions.
1
Parameter estimation of photovoltaic module relied on golden jackal optimization
T. H. T. Hanh,N. G. o +1 more
TL;DR: GJO is a reliable algorithm for estimating PV module parameters with high precision and efficiency compared to other optimization algorithms.
References
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems
TL;DR: The results of DA and BDA prove that the proposed algorithms are able to improve the initial random population for a given problem, converge towards the global optimum, and provide very competitive results compared to other well-known algorithms in the literature.
2.5K
Optimal power flow using particle swarm optimization
TL;DR: In this paper, an evolutionary-based approach to solve the optimal power flow (OPF) problem is presented. And the proposed approach has been examined and tested on the standard IEEE 30bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement.
1.4K
Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction
Kalyanmoy Deb
- 01 Jan 2011
TL;DR: This chapter provides a brief introduction to its operating principles and outline the current research and application studies of evolutionary multi-objective optmisation (EMO).
A Comprehensive Review of Swarm Optimization Algorithms
TL;DR: In this paper, the authors provide an in-depth survey of well-known swarm optimization algorithms and compare them with each other comprehensively through experiments conducted using thirty wellknown benchmark functions and a number of statistical tests are then carried out to determine the significant performances.