Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis

A novel application of the spherical prune differential evolution algorithm (SpDEA) to solve optimal power flow (OPF) problems in electric power systems is presented. The SpDEA has several merits, such as its high convergence speed, low number of parameters to be designed, and low computational procedures. Four objectives, complete with their relevant operating constraints, are adopted to be optimized simultaneously. Various case studies of multiple objective scenarios are demonstrated under MATLAB environment. Static voltage stability index of lowest/weak bus using modal analysis is incorporated. The results generated by the SpDEA are investigated and compared to standard multi-objective differential evolution (MODE) to prove their viability. The best answer is chosen carefully among trade-off Pareto points by using the technique of fuzzy Pareto solution. Two power system networks such as IEEE 30-bus and 118-bus systems as large-scale optimization problems with 129 design control variables are utilized to point out the effectiveness of the SpDEA. The realized results among many independent runs indicate the robustness of the SpDEA-based approach on OPF methodology in optimizing the defined objectives simultaneously.

Download Full-text

Improved differential evolution algorithm based on chaotic theory and a novel Hill-Valley method for large-scale multimodal optimization problems

2015 International Congress on Technology, Communication and Knowledge (ICTCK) ◽

10.1109/ictck.2015.7582681 ◽

2015 ◽

Cited By ~ 1

Author(s):

Parisa Molavi Damanahi ◽

Gelareh Veisi ◽

Seyyed Javad Seyyed Mahdavi Chabok

Keyword(s):

Differential Evolution ◽

Large Scale ◽

Optimization Problems ◽

Differential Evolution Algorithm ◽

Multimodal Optimization ◽

Evolution Algorithm ◽

Improved Differential Evolution Algorithm ◽

Chaotic Theory

Download Full-text

Optimal Power Flow Using an Improved Hybrid Differential Evolution Algorithm

The Open Electrical & Electronic Engineering Journal ◽

10.2174/1874129001711010177 ◽

2017 ◽

Vol 11 (1) ◽

pp. 177-192

Author(s):

Gonggui Chen ◽

Zhengmei Lu ◽

Zhizhong Zhang ◽

Zhi Sun

Keyword(s):

Differential Evolution ◽

Power Flow ◽

Optimal Power Flow ◽

Large Scale ◽

Differential Evolution Algorithm ◽

Local Optimum ◽

Power Losses ◽

De Algorithm ◽

Optimal Power ◽

Evolution Algorithm

Objective: In this paper, an improved hybrid differential evolution (IHDE) algorithm based on differential evolution (DE) algorithm and particle swarm optimization (PSO) has been proposed to solve the optimal power flow (OPF) problem of power system which is a multi-constrained, large-scale and nonlinear optimization problem. Method: In IHDE algorithm, the DE is employed as the main optimizer; and the three factors of PSO, which are inertia, cognition, and society, are used to improve the mutation of DE. Then the learning mechanism and the adaptive control of the parameters are added to the crossover, and the greedy selection considering the value of penalty function is proposed. Furthermore, the replacement mechanism is added to the IHDE for reducing the probability of falling into the local optimum. The performance of this method is tested on the IEEE30-bus and IEEE57-bus systems, and the generator quadratic cost and the transmission real power losses are considered as objective functions. Results: The simulation results demonstrate that IHDE algorithm can solve the OPF problem successfully and obtain the better solution compared with other methods reported in the recent literatures.

Download Full-text

Differential Evolution Algorithm with Space Reduction for Solving Large-Scale Global Optimization Problems

Handbook of Research on Machine Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2229-4.ch029 ◽

2017 ◽

pp. 671-694

Author(s):

Ahmed Fouad Ali ◽

Nashwa Nageh Ahmed

Keyword(s):

Global Optimization ◽

Differential Evolution ◽

Large Scale ◽

Optimization Problems ◽

Differential Evolution Algorithm ◽

Search Space ◽

Evolution Algorithm ◽

Scale Spaces ◽

Heuristics Algorithm ◽

Scale Optimization

Differential evolution algorithm (DE) is one of the most applied meta-heuristics algorithm for solving global optimization problems. However, the contributions of applying DE for large-scale global optimization problems are still limited compared with those problems for low dimensions. In this chapter, a new differential evolution algorithm is proposed in order to solve large-scale optimization problems. The proposed algorithm is called differential evolution with space partitioning (DESP). In DESP algorithm, the search variables are divided into small groups of partitions. Each partition contains a certain number of variables and this partition is manipulated as a subspace in the search process. Searching a limited number of variables in each partition prevents the DESP algorithm from wandering in the search space especially in large-scale spaces. The proposed algorithm is investigated on 15 benchmark functions and compared against three variants DE algorithms. The results show that the proposed algorithm is a cheap algorithm and obtains good results in a reasonable time.

Download Full-text