MPPT method of SMESC wind power system based on IIWO optimization

Abstract

The invention relates to a MPPT method of a SMESC wind power system based on IIWO optimization. The method comprises the following steps of: initializing an intrusion weed algorithm parameter; substituting the randomly generated initial solutions Kp and rho s into a constructed Simulink model to calculate fitness values corresponding to each weed, outputting a fan control amount, and feed backingthe optimal speed in the current generation for the maximum power tracking of the fan by the SMESC; sorting the fitness values, and selecting the number of seeds allowed by the maximum number of populations for breeding and regeneration to produce offspring seeds; calculating the number of seeds produced by each weed according to the fitness values; substituting the position information of the offspring weed seeds into the SMESC parameter optimization model, outputting and generating a new control amount and fitness values of the offspring seed; judging whether a termination condition is satisfied; and outputting the optimal Kp and rho s if the termination condition is satisfied, wherein the corresponding power is the maximum power output by the fan.

Description

technical field [0001] The invention belongs to the technical field of new energy wind power generation, and relates to a maximum power tracking method. Background technique [0002] Due to the nonlinear characteristics of wind power generators, it is usually necessary to adopt certain control strategies to ensure the maximum power output of the wind turbines, so as to improve the power generation efficiency of the wind power system. Common control methods for wind turbine maximum power pointtracking (MPPT) include tip speed ratio control (Tip Speed ​​Ratio, TSR), extreme value search method (Extremum Seeking Control, ESC), hill-climbing search algorithm (Hill-climbing Search, HCS) and fuzzy inference optimal gradient method, etc. TSR control needs to measure the wind speed accurately in real time, and the addition of sensors will increase the complexity of the wind power system structure. The randomness and variability of wind speed make it difficult to implement in actual...

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Problems solved by technology

TSR control needs to measure the wind speed accurately in real time, and the addition of sensors will increase the complexity of the wind power system structure. The randomness and variability of wind speed make it difficult to implement in actual control; the HCS algorithm is simple and easy to implement, but when the wind speed is sharp In the case of changes, the control reference signal will mutate, which will lead to the deterioration of the algorithm control performance; ESC also has the advantages of simple principle and small amount of calculation, but its working principle determines that steady-state oscillation will inevitably occur in its application. phenomenon, which greatly limits the scope of application of the algorithm

The optimal gradient method of fuzzy reasoning does not need the maximum power curve and wind speed, and adopts the method of increasing and decreasing the step size in exponential form to adaptively track the maximum power point of the wind power system, and the control process is more complicated

The sliding mode extreme seeking control (Sliding Mode Extreme Seeking Control, SMESC) control method combines the sliding mode (Sliding Mode, SM) with ESC, without obtaining wind speed information and fan model parameters, etc., avoiding the differential link in traditional ESC control , has the advantages of simple structure and fast response, but in practical applications, it will cause power loss due to certain steady-state oscillation caused by improper parameter setting, and reduce the operating efficiency of the wind power system

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