Theoretical Energy Loss Research In Distribution Networks

Energy loss is an important economic and technical index of power grid, also a comprehensive reflection of network planning, technology management, network operation and management level. Facing our country's power shortages situation, the most effective way is to "save energy" and "find new energy". At the national advocate of optimizing structure, improving efficiency, energy saving and pollution reduction today, Energy-saving and consumption reduction is extremely important. The line losses' rate is a comprehensive reflection of the level of management, try to cut the line losses is a requirement for enterprise to improve its economic efficiency. Doing well the line losses calculation work, not only conducive to scientific planning of power grid construction, but also helpful to allocate reactive power compensation equipment reasonable, strengthen the operation management of transformer and line losses, reduce line losses, and has a very important significance for improving enterprise efficiency and energy-saving.In the beginning, this paper introduces the background and significance of theoretical energy loss research in distribution network. Then through comparative analysis the differences of the existing line losses calculation methods, explains the various factors on the calculation. Then, in order to achieve the goal of fast calculation and high calculation accuracy, this paper introduces ANN to the line losses calculation to consider the factors intelligently. Finally, combines with the new calculation method research of energy loss in distribution network, this paper selects neural network based on RBF, BP as a method of calculation.The method of ANN is different from the traditional method, it does not need the direct relationship or the regression equation of results and variables. RBF and BP model can map every complex nonlinear relationship in theory, so it is suitable to simulate the nonlinear relationship of line losses and power grid parameters, and because it is data-oriented, it can be applied to any distribution network. On account of the characteristics of neural network and the relationship between line losses and power grid parameters, in order to improve accuracy and efficiency of neural network learning, this paper presents a simple and efficient data processing method:training the neural network after normalized all the sample data, and then take calculation results compare with another group of test data. The research process:first, train the RBF network and calculate the line losses, according to the results, find the optimal parameters of the network. Then train BP network and calculate line losses, changes the training epochs and the modeling algorithm, find the optimal output. Finally select the optimal results to simulate a typical daily load curve, fine the error of electricity quantity, and confirm that neural network using in line losses calculation of distribution network is feasible.