• Hassan Kubba
• Moneer Thamir

This paper presents a multi-objective real-coded Genetic Algorithm (RCGA) to solve the load flow problem (LFP). Since the load flow problem has multiple solutions, the local minima and the premature convergence are some of the drawbacks of the conventional methods. The GA is a search problem which uses a population of candidate solutions for solving the problem, thus reducing the possibility of ending at a local minima. A real-coded multi-objective GA is used since LFP involves a large number of variables, where all decision variables (unknowns) are expressed as real numbers. Explicit conversion to binary does not take place. A reduction of computational effort is an obvious advantage of a real-coded GA. Another advantage is that, an absolute precision is now attainable by making it possible to overcome the crucial decision of how many bits are needed to represent potential solutions. In LFP the cost function has two conflicting objectives, which are the mismatch active and reactive powers. The most straightforward approach to multi-objective optimization is the "Sum of Weighted Cost Functions". This approach is to weight each function and add them together. This approach is adopted in this research for its simplicity, easy of programming and gives the required accuracy. The application of GA for solving LFP of small-scale systems is on-line (real-time) solutions. But for large-scale systems, the CPU computational time for high accuracy convergence is large. In this paper, a sparsity technique and optimal ordering for the sparse matrices (which have too many elements of zero values) are implemented in order to reduce the storage requirement and simplify some arithmetic operations to reduce the total computing time for high accurate solution. The sparsity technique is achieved by entering only the non-zero elements; also two identification vectors are needed to identify the exact location of the elements in the original matrix. The proposed method was demonstrated on different test systems, such as 14-bus and 30-bus IEEE test systems, and a 362-bus with 599-branches IRAQI NATIONAL GRID. From the obtained results, it is concluded that the proposed method presents a highly accurate solution for the unknown variables, a large saving in storage

Genetic Algorithm Operators, Load Flow Problem, Multi-Objectives Minimization, Real-Coded Genetic Algorithm, Sparsity Technique

"A Multi-objective Real-Coded Genetic Algorithm for a Rapid and Efficient Load Flow Solution of Power Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.3, Issue 5, page no.29-35, May-2016, Available :http://www.jetir.org/papers/JETIR1605006.pdf

"A Multi-objective Real-Coded Genetic Algorithm for a Rapid and Efficient Load Flow Solution of Power Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.3, Issue 5, page no. pp29-35, May-2016, Available at : http://www.jetir.org/papers/JETIR1605006.pdf