In recent years, with the development of new energy technology, a hybrid water system of wind power and conventional energy supplement was rapidly developed in China. However, wind resources are affected by seasonal and geographical patterns, water system, wind energy, and conventional energy cooperation. The two are mutual coupling, so the control effect is difficult to guarantee. Therefore studies of one kind of establishment of auxiliary control system based on dynamic decoupling in the wind, the stability, water effect, and promotion of wind water pumping technology to solve the power shortage in agricultural and pastoral areas, have important practical significance for improving the ecological environment in China. The traditional decoupling algorithm with a feed-forward compensation method based on expert rules and the state equation decoupling method based on requirements, moths model of controlled object were described, because the wind is certain nonlinear water system, and mathematical models are difficult to access, so these methods are difficult to use. An intelligent decoupling method based on an inverse model does not need to rely on the mathematical model, or the nonlinear control system in the field, and achieved good results. Based on the in-depth analysis of the water system on the wind, the wind water pumping system, physical and chemical complex, the wind, the auxiliary power coupling problem, the paper put forward a fuzzy control method based on a neural network inverse decoupling variable. By using the neural network method to identify the inverse system of the original system, and being connected in series with the original system, a pseudo linear system, the wind, the auxiliary power is decoupling. At the same time, the fuzzy controller to control the wind, auxiliary power independently, using the variable universe theory to improve the fuzzy control theory domain, enhance the system's ability to adapt to the environment. The control system consisted of the inverse system decoupling compensation based on a neural network, and based on the variable universe fuzzy algorithm in wind, and the auxiliary power independent control of the two parts. A neural network decoupling compensator, utilizing the self-learning characteristic of the neural network, the reverse identification of the original system, thus approaching wind inverse model of water system, the parallel inverse system with the original system, a pseudo linear system, so that the wind power transmission and auxiliary power transmission and conversion into subsystems are independent of each other, then a linear system control method was used to control the variable universe fuzzy algorithm based on a fuzzy controller. First, according to the wind, the auxiliary power was set the detection feedback value and system, the wind, the auxiliary power closed the loop control; and then the variable universe fuzzy algorithm was used to solve the parameter sensor parameter drift and system device of fixed rules of fuzzy algorithm, the algorithm is adaptation to environment improvement. In order to verify the feasibility of this method, the inverse modeling method of a model simulation test; at the same time, in order to validate the proposed algorithm, the test of comparison, carried on the analysis to the control system from the point of system performance. The inverse model in the simulation experiment of variable pump and diesel engine control showed average errors of 6.3% and 4.5%, and a centrifugal pump speed error in the actual operation of the control within 5%, proved the effectiveness of this method. This fully illustrated the influence of the method in this paper that can effectively restrain the velocity changes, and has extremely vital significance to the promotion of the water system of the wind.
