Abstract: In response to the issues of frequent transitions between operating conditions in pumped storage units, the limited adaptability of conventional PID control to varying operating conditions, and the challenges of applying existing advanced control methods to real-time demanding PLC speed control systems, the potential introduction of high-frequency measurement noise associated with derivative components further complicates the situation. The innovative Adaptive Fuzzy PI controller (IAFPI) was proposed in this study, incorporating fuzzy control theory, with water head and power as fuzzy inference input variables. The refined model of the pumped storage regulation system, based on the elastic water hammer and logarithmic projection curve method, was initially established in this study in conjunction with the designed controller. Subsequently, the control parameters of this model were optimized using the Multiple Objective Particle Swarm Optimization (MOPSO) algorithm. Finally, by utilizing data from an actual pumped storage power plant in China, comparative simulation experiments are conducted under different loads and heads to compare the IAFPI controller with traditional PI control and conventional fuzzy PI control, respectively. The aim is to validate the improved adaptability of the unit to varying operating conditions achieved by the newly designed IAFPI controller.