Name
Title | ||
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A modified MOEA/D approach to the solution of multi-objective optimal power flow problem. |
Abstract | ||
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Display Omitted The paper solved the multi-objective optimal power flow (MOOPF) problems.A modified multi-objective evolutionary algorithm based decomposition (MOEA/D) approach is applied to solve MOOPF problems.A modified decomposition method, a mixed constraint handling mechanism and a fuzzy compromise solution method are integrated.Performances of the modified MOEA/D has been compared with those of multi-objective PSO, NSGA-II, etc.The comparisons show the modified MOEA/D approach performs effectively and yields competitive solutions. This study presents a modified multi-objective evolutionary algorithm based decomposition (MOEA/D) approach to solve the optimal power flow (OPF) problem with multiple and competing objectives. The multi-objective OPF considers the total fuel cost, the emissions, the power losses and the voltage magnitude deviations as the objective functions. In the proposed MOEA/D, a modified Tchebycheff decomposition method is introduced as the decomposition approach in order to obtain uniformly distributed Pareto-Optimal solutions on each objective space. In addition, an efficiency mixed constraint handling mechanism is introduced to enhance the feasibility of the final Pareto solutions obtained. The mechanism employs both repair strategy and penalty function to handle the various complex constraints of the MOOPF problem. Furthermore, a fuzzy membership approach to select the best compromise solution from the obtained Pareto-Optimal solutions is also integrated. The standard IEEE 30-bus test system with seven different cases is considered to verify the performance of the proposed approach. The obtained results are compared with those in the literatures and the comparisons confirm the effectiveness and the performance of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.asoc.2016.06.022 | Appl. Soft Comput. |
Keywords | Field | DocType |
Multi-objective optimization,Optimal power flow,MOEA/D,MOPSO,NSGA-II | Mathematical optimization,Evolutionary algorithm,Power flow,Fuzzy logic,Voltage,Decomposition method (constraint satisfaction),Multi-objective optimization,Mathematics,Pareto principle,Penalty method | Journal |
Volume | Issue | ISSN |
47 | C | 1568-4946 |
Citations | PageRank | References |
14 | 0.56 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingrui Zhang | 1 | 15 | 0.91 |
| Qinghui Tang | 2 | 14 | 0.56 |
| Po Li | 3 | 14 | 1.57 |
| Daxiang Deng | 4 | 15 | 1.04 |
| Yalin Chen | 5 | 14 | 0.56 |