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According to analyze the output characteristics of photovoltaic (PV) array and the advantages and disadvantages of the traditional maximum power point tracking (MPPT) method, an improved increment conductance (INC) method was proposed. Using MATLAB/Simulink modeling and simulating the boost DC/DC convert system, apply the improved method and the traditional INC to control the system respectively. The simulation results show that the improved method in the premise of ensuring stability of the system, make the PV array tracking the maximum power point more quickly and exactly under the change of circumstance.

In this document, a BP neural network based PID control is designed considering the characteristics of solar power generation based on profound research on BP neural network control technique. In addition, computerized control program has been developed for grid-connected photovoltaic inverters for power generation, which has been applied for current control of grid-connected inverters. Digital simulation tests indicate such controller can effectively improve waveform of grid-connection current and dynamic process of grid-connection control of photovoltaic system by dynamically changing the parameters of PID controller. In addition, it realizes same frequency and same phase for both output current of inverter and voltage of gird while improving immunity. In the end, smooth grid connection is realized for photovoltaic generation system.

The load forecast level in power system is a important symbol to measure operations and management of power system. This paper summarized the research conditions of the short-term load forecasting using artificial neural network method, and analyzed the characteristics of electrical load and factors of influencing power load forecasting accuracy. The paper used the particle swarm optimization neural network method in short-term load forecasting of power grid. Based on the analysis history loads in California power system, we established the load forecasting model considering the various affecting factors, and normalized the input load, meanwhile quantified date, atmosphere and other factors. The example showed that the model of neural network based on the particle swarm optimization algorithm can improve the prediction precision and speed, it's performance prediction is superior to the model based on BP neural network load forecasting.

Power load forecasting is one of the important parts in power system planning. This paper focused on the research of load forecasting and proposed an improved fuzzy AHP for mid-long term load forecasting. Firstly, Triangular fuzzy number is introduced to the model for indicating the judgments of experts, and then AHP is used for dealing with the judgments so as to obtain optimal weights of different forecasting schemes. The factors which can influence power load forecasting are taken into consideration in the proposed algorithm, meanwhile, the experts' experiences are combinated into the the processing of load forcasting to improve the accuracy. Finally, the experiment results shown that the presented algorithm can fit the medium and long term load forecasting better than other traditional approaches.

Reactive power optimization problem is typical non-linear problem with characteristics of multi-objective, uncertainty and multi-restriction. Classical mathematical techniques are inadequate and insufficient to the optimal operation of power systems due to the inherent complexity. This paper studied on PSO algorithm and reactive power optimization, and made some new ideas into PSO algorithm to improve its optimization ability. Adaptive particle swarm particle swarm algorithm was proposed for solving the problem of large-scale search failed, and presented a method of adjustment parameters for adaptive PSO algorithm according to the optimization speed. In meeting the conditions for convergence, the algorithm enabled the speed of particles in accordance with the ideal adjustment of the parameters of adaptive search. On this basis, to overcome the disadvantage that PSO maybe get in local optimization solution, an improved PSO was developed. The algorithm used statistical laws of particle fitness to classify particles, and took different evolutionary models for different kinds of particles. Finally, IEEE-6 node standard test system was computed for reactive power optimization, compared with standard PSO, the proposed PSO algorithm had more higher search efficiency and better ability of global optimization.