Multi‐objective robust fuzzy fractional order proportional–integral–derivative controller design for nonlinear hydraulic turbine governing system using evolutionary computation techniques

Abstract

The present paper proposes a novel multi‐objective robust fuzzy fractional order proportional–integral–derivative (PID) controller design for nonlinear hydraulic turbine governing system (HTGS) by using evolutionary computation techniques. The fuzzy fractional order PID (FOPID) controller takes closed loop error and its fractional derivative as inputs and performs fuzzy logic operations. Then, it produces the output through the fractional order integrator. The predominant advantages of the proposed controller are its capability to handle complex nonlinear processes like HTGS in heuristic manner, due to fuzzy incorporation and extending an additional flexibility in tuning the order of fractional derivative/integral terms to enhance the closed loop performance. The present work formulates the optimal tuning problem of fuzzy FOPID controller for HTGS as a multi‐objective one instead of a traditional single‐objective one towards satisfying the conflicting criteria such as less settling time and minimum damped oscillations simultaneously to ensure the improved dynamic performance of HTGS. The multi‐objective evolutionary computation techniques such as non‐dominated sorting genetic algorithm‐II (NSGA‐II) and modified NSGA‐II have been utilized to find the optimal input/output scaling factors of the proposed controller along with the order of fractional derivative/integral terms for HTGS system under no load and load turbulence conditions. The performance of the proposed fuzzy FOPID controller is compared with PID and FOPID controllers. The simulations have been conducted to test the tracking capability and robust performance of HTGS during dynamic set point changes for a wide range of operating conditions and model parameter variations, respectively. The proposed robust fuzzy FOPID controller has ensured better fitness value and better time domain specifications than the PID and FOPID controllers, during optimization towards satisfying the conflicting objectives such as less settling time and minimum damped oscillations simultaneously, due to its special inheritance of fuzzy and FOPID properties.

State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations

“…Various designs have been proposed to enhance the hydro-turbine efficiency; for instance, Piraisoodi et al [197] designed a multi-objective robust fuzzy fractional-order proportional integral derivative controller plan for non-linear HTGS by implementing evolutionary computation; Guo and Guo [198] designed a non-linear HTGS by applying feedback linearization by accomplishing sliding mode control type design; and Ouadday et al [199] designed a dual-core composite sandwich panel for hydraulic turbines, tested the panel via numerical simulation, and applied a synergistic governing controller for non-linear HTGS.…”

“…Various designs have been proposed to enhance the hydro-turbine efficiency, which have been tested via numerical simulations [146,163,168,177,178,[195][196][197][201][202][203]. Vibration due to powerhouse operation has been analyzed by several researchers [151,176,[201][202][203]205,206,208,209,226]; however, more comprehensive studies should be completed to connect the effect of vibration from turbines operation with design on the dam body, to assure dam safety.…”

Nonlinear Modeling of Dynamic Characteristics of Pump-Turbine

“…The complete characteristic curve of a pump-turbine in China was obtained according to the measured data, and the transfer coefficient of the first-order partial derivative in the mathematical model for the pump-turbine was obtained by using the external characteristic method. Based on the established nonlinear mathematical model for the pump-turbine and the complete speed regulation system model of a pumped storage unit [16][17][18], the established speed regulation system was simulated [19].…”

Multiobjective Optimal Control of FOPID Controller for Hydraulic Turbine Governing Systems Based on Reinforced Multiobjective Harris Hawks Optimization Coupling with Hybrid Strategies

“…For example, Chen et al [35] proposed chaotic nondominated sorting genetic algorithm II (NSGAII) to optimize a multiobjective optimization problem of FOPID controller's parameter tuning, whose objective functions are composed of ISE and ITSE. Piraisoodi et al [36] proposed a multiobjective robust fuzzy FOPID controller designed for nonlinear HTGS by using NSGAII.…”