Hydro governor parameter optimization method based on fuzzy self-adaptive DFPSO algorithm

Abstract

The invention discloses a hydro governor parameter optimization method based on the fuzzy self-adaptive DFPSO algorithm. The method comprises the steps of (1), establishing a hydroturbine regulating system mathematical model; (2), determining a fitness function of the fuzzy self-adaptive DFPSO algorithm; (3), implementing fuzziness setting on the speed inertia factor of the algorithm, wherein the linearly-decreasing inertia factor and the current particle optimal performance evaluation index are input fuzzily; (4), calculating the fitness values of particles, maintaining the individual optimal values and the global optimal values of the particles and updating the speeds and positions of the particles; (5), carrying out gene crossover between the particles when the crossover condition is met; (6), judging whether the end condition is met, stopping gene crossover and outputting the optimal value when the end condition is met, and implementing the steps (4) to (6) when the end condition is not met. According to the invention, a system can have good dynamic property on the conditions of frequency disturbance and loading disturbance, small overshoot, short stabilization time and short adjustment time.

Description

technical field [0001] The invention relates to the technical field of hydropower generation in electric power systems, to the technical field of water turbine governor parameter optimization schemes, and in particular to a water turbine governor parameter optimization method based on particle swarm algorithm of fuzzy self-adaptation and differential evolution. Background technique [0002] Hydropower stations convert water energy into electric energy and provide it to users through the power supply system. The safety of electricity use and the quality of electric energy are the most concerned issues for users. The regulating system of the turbine plays a decisive role in it, and the parameters of the governor of the hydro-generator set are extremely important to the stability of the regulating system of the turbine and the quality of electric energy, so the optimization of the governor parameters has always been a concern of researchers. [0003] PID adjustment is widely us...

AI Technical Summary

Problems solved by technology

As the iteration progresses, the selection of appropriate weight coefficients can improve the efficiency of the algorithm. However, in the speed update process of a typical particle swarm algorithm, the inertia weight is only linearly decreased by the number of iterations. In the early stage of the iteration, the larger weight coefficient It can make the particle have a strong global exploration ability, but if the particle is very close to the global optimal value at this time, then the large velocity inertia may cause the particle to deviate from the global optimal value and fly in the correct direction, thereby reducing the search accuracy

In the later stage of the iteration, all the particles have converged to the optimal value, and the smaller inertia factor can make the particles search finely, but the smaller the speed, the easier the particle group tends to be homogeneous, and the particles fall into the local extremum

It can be seen that in the typical PSO algorithm, the speed inertia only changes with the number of iterations, which is not conducive to the improvement of algorithm efficiency, and the particle diversity is seriously lost in the late iteration, and it is easy to fall into local optimum and stagnate.

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