A newly constrained multi-objective differential evolution optimization technique (CMODE) for security constrained economic/environmental dispatch (EED) was proposed. The proposed CMODE evolved a constrained multi-objective version of differential evolution (DE) by employing the traditional multi-objective differential evolution (DEMO) and constrain handle technique to balance the search between feasible region and infeasible region. The proposed CMODE method had been applied to solve the security constrained EED problem. Experiments had been carried on a standard test system. The results demonstrate the high efficiency of the proposed method to solve security constrained EED problem, and the necessary of taking security constrains into consideration.

CA6140 is the most widely used kind of ordinary horizontal lathes in a factory workshop. This paper, with the aid of Pro / E software and its related functions, visually displays the structure of Lathe CA6140’s main components, dynamically simulates assembly and disassembly processes of its typical mechanism, and thereby realizes its non-damage dynamic simulation assembly and disassembly, creates the mode of combining actual and virtual dynamic assembly and disassembly and realizes the optimization of its assembly and disassembling actions.

This paper focused on power system security problems and proposed risk assessment and preventive control method of electric power system based on voltage risk. The method built up a system voltage risk indicators, and have system voltage risk identified through risk assessment, on this basis, reduced the risk of the system voltage by optimizing the control variables of the system, which of course can increase system security. Finally, Refers to a 5-node tests as examples to verify availability of this method.

This paper has discussed the treatment of uncertainty in the market environment, using chance-constrained programming to describe the risk and Monte Carlo simulation method to calculate the risk factor, the risk factor is bound to establish transmission network flexible planning model, the result is a transmission planning of minimum comprehensive cost under uncertain environment for the future program.

This paper has proposed a wind farm generation output forecasting model based on projection pursuit (PP) and back propagation neural network (BPNN), in order to eliminate the influence of the bad points and mutations on and enhance robustness of the forecasting model. A median absolute deviation is used as projection index function, effectively avoiding the influence of the outlier. Firstly, Extract the principal components of each factor by PP. Then, input the principal components to the BPNN for training the network. Finally, forecast the wind farm generation output via the trained network. The simulation shows that the proposed approach is of higher accuracy.