AGC Method of fossil-fuel power station Based on Hybrid Genetic Algorithm

In the process of automatic generation control (AGC), it is difficult to achieve the optimal setting of generator parameters, resulting in a certain deviation between the control effect and the ideal state. Therefore, this paper proposes a hybrid genetic algorithm based AGC control method for fossil-fuel power station. Combined with the relationship between the generator operation mechanism and the overall output of fossil-fuel power station, a generator power system model is constructed to quantify the load change caused by 1% frequency change. In the process of implementing AGC control, hybrid genetic algorithm is introduced, and single step learning and multi-step learning actions are set respectively. When the difference between the two learning results is constant, the corresponding parameter values are taken as the setting results of the generator, so as to achieve AGC control in fossil-fuel power station. According to the test results, the upper and lower limits of the regulating capacity of the power plant under the designed control method change linearly with the change of the regulating capacity efficiency coefficient, which is consistent with the ideal control effect.