Simulation of Wind Generating Set Based on PID

The pitch control strategy of the wind generator set in the paper applies power feedback closed loop control to implement that variable-speed constant-frequency wind turbine regulates constant power removal by adjusting pitch angle under the condition above the rated speed. The present output power of the generator is measured by power acquisition, and the power deviation is calculated out by comparing with the given power (the rated power of the generator). And the power variance is the input of the power controller. The controller sends the order of blade referring to pitch angle according to power variance, and the pitch-regulated mechanism adjusts the blade of the fan according to the pitch angle. The actual wind power generation system is a multivariable, nonlinear, strong coupling and time-varying complicated system, and it is difficult to establish the accurate mathematical model. So traditional PID algorithm is difficult to be qualified for the power controller of large high-performance wind generator set. The research indicates that parameter self-tuning PID controller based on fuzzy logic not only can solve the problem of linear control, but also adapts to nonlinear system [31]. Based on the basic rues of pitch control, the paper proposes a design scheme of fuzzy PID power controller, and simulates the wind generator set of fuzzy PID power controller. Introduction PID control. PID control is a traditional matured control mode. It is easy and effective, which makes it applied widely. And there has been integrated theoretical and technical level. However, PID parameters must be adjusted according to the controlled objects before applying PID control, and the accurate mathematical model of the controlled system should be known. When the controlled objects can’t be accurately determined, or there is delay links, or the controlled objects change because of the temperature and component parameters, PID control can’t achieve the expected control quality [32]. The conventional PID controller is a linear controller. It computes the deviation according to the given value and the actual output value. The proportion (P), integral (I) and differential (D) of the deviation is selected as the output of the controller and acts on the controlled objects. The control rule in the time domain is 0 1 ( ) ( ) ( ) ( ) t P D I de t u t K e t e t dt T T dt   = ⋅ + ⋅ ⋅ + ⋅     ∫ (1) The transfer function form is ( ) 1 ( ) 1 ( ) P D I U s G s K T s E s T s   = = ⋅ + + ⋅   ⋅   = (2) In the formula, P K is proportional coefficient, I T is integral time constant, and ( ) e t is system deviation. ( ) ( ) ( ) e t r t y t = − (3) In computer control system, digital PID controller is used, and the expression of control rule is [ ] 0 ( ) ( ) ( ) ( ) ( 1) k P I D i u k K e k K e i K e k e k = = ⋅ + ⋅ + ⋅ − − ∑ (4) 3rd International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2015) © 2015. The authors Published by Atlantis Press 54 In the formula, ( ) e i is the deviation of the i sample, I K is integral coefficient and D K is differential coefficient. The functions of the components of PID controller are as follows. Proportional component: reflecting the deviation ( ) e t of the system according to a certain proportion. Once there is deviation, the controller has the function of control at once to reduce the deviation. The greater the proportional coefficient P K , the shorter the adjustment time of the system, the less the stable error. However, the greater P K , the greater the overshoot, the more unstable the system. Integral component: eliminating the steady state error of the system and improving the indifference degree of the system. The greater the integral coefficient I K is, the stronger the function of the integrals is, the smaller the steady state error, the shorter the adjustment time is. But the greater I K is, the worse the stability is. Differentiation component: reflecting the variation tendency and rate of change of the deviation timely, and effectively improving dynamic performance of the system. It is general that the system overshoot reduces with the increase of integral coefficient D K . However, if D K is greater, the system stability reduces. Fuzzy control In 1965, L.A.Zadch, the professor in University of California, firstly proposed the concept of membership function to describe the fuzzy set theory of fuzziness, which lays the foundation of fuzzy mathematics. The introduction of fuzzy set can directly express the judgment and the thinking process of the people with simple mathematical form, which not only makes the process meeting the fact and the thinking mode of the people possible, but also meets the urgent requirement of self-adapting to scientific development. Under the background, the fuzzy control theory emerges as an important application branch of fuzzy mathematics [33]. Automatic control field is widely applied by fuzzy mathematics. Fuzzy control has the incomparable advantages compared with other traditional control methods, so it becomes an important branch in control field. The advantages of applying fuzzy algorithm in domain field are as follows [34]. 1) The applied language methods have not need to master accurate process mathematical model. The complicated production process is difficult to acquire the accurate mathematical model of operation process of the system, but language method can achieve the similarity. 2) The written fuzzy condition sentences are very easy to be added to the control link of the process. 3) For applying fuzzy control method, the dynamic quality of the process is evidently better than the conventional PID control. And it has greater adaptability to the change of process parameters.