MPPT hysteresis control algorithm based on predictions of improved FIR filter

Abstract

Provided is an MPPT hysteresis control algorithm based on predictions of an improved FIR filter. The algorithm is characterized in that on the basis of a conventional perturbation observation method, hysteresis control and a self-adaptive prediction mechanism are added, that is to say, output power of the current working voltage can be combined with output power of a photovoltaic array of working voltage before and after predictions in order to form three-point hysteresis control; by determination of a disturbance rule, a distribution direction is determined; while tacking speed is increased, precision of a control system is taken into consideration; step factors of a self-adaptive predictor can be updated in an on-line manner so that convergence performance of the system is improved and steady-state performance of the system is enhanced and loss is reduced; the novel algorithm can compensate for deficiencies of a conventional disturbance observation method in tracking speed and steady-state performance; MPPT control can be rapidly and stably applied so that oscillation and misjudgment of greatest power points can be greatly reduced; dynamic response and steady-state performance of the system are improved; and in addition, the algorithm is easy for realization of software programming.

Description

technical field [0001] The invention relates to the technical field of MPPT algorithms, in particular to an MPPT hysteresis control algorithm based on improved FIR filter prediction. Background technique [0002] At present, the commonly used MPPT algorithms include disturbance and observation method and fuzzy control method. The perturbation and observation method is also called hill-climbing method. Its basic idea is: first perturb the output voltage of the photovoltaic cell, then observe the change of the output power of the photovoltaic cell, and continuously change the direction of the perturbation voltage according to the trend of the power change. Due to the limitation of the actual detection and control accuracy, if the step size of the voltage disturbance is constant, there will be oscillation problems, and because the external environment changes from time to time, the P-U characteristic curve of photovoltaic cells changes from time to time, and misjudgment may occ...

AI Technical Summary

Problems solved by technology

The fuzzy control process needs to sample the data, then calculate, then fuzzify, fuzzy reasoning operation, clarify, and finally get the result. This method is more complicated and requires a CPU with a higher computing speed. The choice of fuzzy language variables should be appropriate. , the clear calculation is more complicated, so it is difficult to realize in practical applications

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