PID controller parameters optimization of hydro-turbine governing systems using deterministic-chaotic-mutation evolutionary programming (DCMEP)

This paper proposes an improved evolutionary programming (EP) method with deterministic mutation factor for on line PID parameters optimization of hydro-turbine governing systems. The mutation factors are usually generated with Gaussian or Cauchy random series in conventional evolutionary programming algorithms. Considering the difficulties of on line optimal parameters settings resulting from nonlinear time-variant hydro-turbine governing systems, this paper introduces deterministic chaos dynamics into the mutation operation of EP and provides a deterministic chaotic mutation evolutionary programming (DCMEP) method. The improved method develops the traditional concept that implements mutation operation with a fixed random distribution using a quasi-random deterministic way to generate the mutation factor. The test result of the two real hydro-turbine governing systems shows that the improved method can optimize the PID parameters efficiently, and the system has the characteristics of stability; low overshoot level and fast response.