access icon free Data-driven approach for real-time distribution network reconfiguration

Finding a global optimal solution to the distribution network reconfiguration (DNR) problem in a short time is a challenging task. This study proposes a real-time online data-driven DNR (3DNR) method. Power loss minimisation, lowest bus voltage maximisation and reliability maximisation are taken as objectives. First, in this study, a methodology combining heuristic algorithm and metaheuristic algorithm to solve DNR is proposed. Then a set of data that satisfies the data drive model requirements is obtained. Next, the improved convolution neural network is used to train the data set of DNR. Unlike the state-of-art methods, the proposed 3DNR can realise the real-time online reconfiguration without power flow calculation. The feasibility and effectiveness of the proposed method are demonstrated on IEEE-34, IEEE-123, and a practical distribution system in Taiwan.

Inspec keywords: distribution networks; load flow; learning (artificial intelligence); minimisation; power distribution economics; neural nets; search problems; optimisation

Other keywords: real-time online data-driven DNR; real-time online reconfiguration; 3DNR; heuristic algorithm algorithm; global optimal solution; data-driven approach; power loss minimisation; practical distribution system; distribution network reconfiguration problem; improved convolution neural network; data drive model requirements; reliability maximisation; metaheuristic algorithm; lowest bus voltage maximisation; real-time distribution network reconfiguration

Subjects: Optimisation techniques; Neural computing techniques; Optimisation techniques; Distribution networks; Power system management, operation and economics; Knowledge engineering techniques

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