Abstract
In this paper, we presents a novel approach for solving optimal power flow (OPF) problems using a hybrid differential evolution and harmony search (DEHS). The DEHS method is an improved differential evolution method based on the harmony search scheme. Harmony Search has strong and easy to combine with other methods in optimization and the Differential Evolution algorithm has a very great ability to search solutions with a fast speed to converge, contrary to the most meta-heuristic algorithms. The DEHS method has the flexible adjustment of the parameters to get a better optimal solution. Moreover, an effective constraint handling framework in the method is employed for properly handling equality and inequality constraints of the problem. The proposed DEHS has been tested on three systems including IEEE-30 bus system with quadratic fuel cost function, IEEE-30 bus system with valve point effects fuel cost function and IEEE-57 bus system with quadratic fuel cost function. The obtained results from DEHS algorithm have been compared to those from other methods in the literature. The result comparison has indicated that the proposed DEHS method is more effective than many other methods for obtaining the optimal solution for the test systems. Therefore, the proposed DEHS is a very favorable method for solving the optimal power flow problems.
Highlights
Optimal power flow (OPF) problem is the important fundamental issues in power system operation
The proposed differential evolution and harmony search (DEHS) algorithm has been applied to OPF problems in three different power systems including IEEE-30 bus system with quadratic fuel cost function, IEEE-30 bus system with valve point effects fuel cost function and IEEE-57 bus system with quadratic fuel cost function
It is observed that DEHS algorithm gives better total cost than other methods in a clearly manner
Summary
Optimal power flow (OPF) problem is the important fundamental issues in power system operation. There have been many methods developed to solve OPF problem from classical methods such as Newton’s method, gradient search, linear programming (LP), nonlinear programming, quadratic programming (QP), etc to methods based on artificial intelligence and evolutionary based methods such as ant colony optimization (ACO), genetic algorithm (GA), improved evolutionary programming (IEP), tabu search (TS), simulated annealing (SA), etc. These methods have been effectively for solving the problem. The numerical results from the proposed method are compared to those from many other methods in the literature (Figure 1)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
