Gong Qingwu

Reconfiguration in Distribution Systems Based on Refined Genetic Algorithm for Improving Voltage Quality

In this paper, a new network reconfiguration approach is presented in which the voltage balance index is taken for the objective function and the optimal structure of distributed network can be found through the refined genetic algorithm (RGA). In the meantime, by simplifying the distribution network, the length of chromosome is reduced and the efficiency of the algorithm is increased. The calculations show that with this method not only the voltage profile can be improved, but also the aim of reducing the network loss can be obtained. At last, a practical calculation example is given to validate the feasibility of the method.

An accurate fault location and its data processing by least square method

When a fault occurs on a transmission line, it is required to find out the fault point, clear the fault and resume power supply as soon as possible. This requires a highly accurate fault location. Besides the principle of the algorithm, the precision of fault location is also influenced by the way the sampled data is processed. In this paper, the differential equation is taken as the algorithm of transmission line fault location. The estimation, by the least square method, has optimum statistical character under certain conditions. After the parameter is estimated by the least square method, considering the model of a noisy system cannot be easily obtained, the auxiliary variable method is introduced to rectify it. The EMTP simulation and RTDS simulation confirm the validity of the data processing method and show that the result is steady. This indicates that the method works well in reducing the influence of noise. The accuracy can satisfy the requirement in engineering.

Distribution network reconfiguration based on the minimum comprehensive cost with distributed generations

A new network reconfiguration approach with distributed generations (DG) is presented for minimum power loss and switches operation times in which the comprehensive cost is taken for objective function and the optimal structure of distributed network can be found through the improved adaptive GA. At the same time the coding method based on the loop is adapted which greatly reduces the infeasible solutions produced during genes algorithm operations in the application to distribution system reconfiguration. The test conducted on IEEE33-bus system has showed that the total profits were increased greatly with this method, and the power loss can be reduced, the voltage profile also increased.

A New Multi-method Combination Forecasting Model for ESDD Predicting

Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. To cope with the problems existing in the ESDD predicting by multivariate linear regression (MLR), back propagation (BP) neural network and least squares support vector machines (LSSVM), a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is proposed. The model is a WNN with three layers, whose input layer has three neurons and output layer has one neuron, namely, regarding the ESDD forecasting results of MLR, BP and LSSVM as the inputs of the model and the observed value as the output. In the interest of better reflection of the influence of each single forecasting model on ESDD and increase of the accuracy of ESDD prediction, the paper uses Morlet wavelet to construct WNN, error backpropagation algorithm to train the network and genetic algorithm to determine the initials of the parameters. Simulation results show that the accuracy of the proposed combination ESDD forecasting model is higher than that of any single model and also higher than that of traditional linear combination forecasting (LCF) model. The model provides a new feasible way to increase the accuracy of pollution distribution map of power network.