Distribution grid reconfiguration based on extreme learning machine

To minimize the active power loss of distribution grid reconfiguration,a neural network reconfiguration model based on the extreme learning machine is proposed,which reflects the nonlinear relationship between the load pattern and the switch state of distribution grid.Having simple network structure and fast training speed,the model takes the load pattern as its input and outputs the switch states to reconfigure the distribution grid with minimum active power loss.The structural risk minimization rule of the statistical learning theory is introduced into the extreme learning machine based on the empirical risk minimization to minimize the empirical risk and confidence interval.The actual risk is thus minimized and the expectation error is decreased.Simulative research is carried out for two typical cases of distribution network reconfiguration with different reconfiguration models:support vector machine,BP neural network and extreme learning machine.Results show that the proposed model has both better generalization performance and faster training speed.