earticle

영어

There are a large amount of advantages to make efficient load phase balancing, such as loss minimization, energy restoration, security, reliability and voltage balance. Optimal load phase balance is obtained by solving the load re-distribution problem as a combinatorial optimization problem. This enables the best switching option that gives a balanced load arrangement among the phases and minimizes power loss to be arrived at. In this paper, adding decaying self-feedback continuous neural network (ADSCHNN) is applied to realize phase swapping for load re-arrangement in the low voltage circuit of the distribution network. The network energy function of the ADSCHNN is constructed for objective function that defines the load phase balancing problem. The ADSCHNN is applied to solve the problem when load is represented in terms of current flow at the connection points, and when load is defined in terms of the real power. The results obtained using ADSCHNN are compared with those from a heuristic algorithm, and from fuzzy logic expert system. Simulations results on real practical data show that the ADSCHNN is very effective and outperforms other known algorithms in terms of the maximum difference of the phase currents or powers.