Multi-objective Operation Optimization Method of Steam Power System

Application of Improved Quasi-Affine Transformation Evolutionary Algorithm in Power System Stabilizer Optimization

“…. , X D } T ; (4) Assign each solution X i to the variable in the PSS and calculate the damping coefficient of each electromechanical oscillation mode under this operating state; (5) Evaluate the group according to the objective function based on characteristic value; (6) Use the improved quasi-affine transformation evolutionary algorithm to continuously update and search, generate offspring populations, and update next-generation candidate solutions; (7) The operation ends when reaching the maximum number of iterations, otherwise, it returns to the (4) step and enters the next cycle; (8) Finally, obtain the global optimal parameter combination of PSS and the fitness value of the objective function. The specific process is as follows:…”

“…Electronics 2022, 11, x FOR PEER REVIEW 8 of 18 (7) The operation ends when reaching the maximum number of iterations, otherwise, it returns to the (4) step and enters the next cycle; (8) Finally, obtain the global optimal parameter combination of PSS and the fitness value of the objective function. The specific process is as follows:…”

“…In control theory, the phase compensation approach can be used to design a power system stabilizer, but for multiple power system stabilizers in the system, the traditional phase compensation method usually carries out parameter setting sequentially, which will inevitably cause the interaction effect between each stabilizer [7]. To overcome this phenomenon, some studies have pointed out that the parameter tuning of PSS is equivalent to an optimal problem based on the eigenvalues of electromechanical modes, that is, to move the eigenvalues to the left half plane of the complex plane as much as possible.…”