Optimal design of fuzzy-PID controller for automatic generation control of multi-source interconnected power system

Abstract

This paper suggests a fuzzy logic controller (FLC) structure from seven membership functions (MFs) and its input–output relationship rules to design a secondary controller to reduce load frequency control (LFC) issues. The FLC is coupled to a proportional–integral–derivative (PID) controller as the proposed FPID controller, which is tuned by an optimized water cycle algorithm (WCA). The proposed WCA: FPID scheme was implemented with two models from the literature under the integral time absolute error cost function. Initially, a two-area non-reheat unit was implemented, and the gains of PID and FPID controllers were adjusted to verify the suitability of WCA in solving LFC issues. Then, in order to identify the robustness of the closed-loop system, sensitivity analysis is carried out. Additionally, a two-area non-reheat unit was tested under the governor dead band nonlinearity. To guarantee the suitability of the proposed FPID controller, a model with a mixture of power plants, such as reheat, hydro, and gas unit in each area was carried out with and without the HVDC link, which can increase practical issues with LFC. The proposed controller's robustness was studied for all models under numerous scenarios, step load perturbations (SLP), and different objective functions. Simulation results proved that the proposed FPID controller provided superior performance compared to recently reported techniques in terms of peaks and settling time.

A Low Power Analog Integrated Fractional Order Type-2 Fuzzy PID Controller

“…These inputs are subjected to fuzzy reasoning within the Type-2 Mamdani FIS which generates an output u(t) that adjusts the control signal. The altered u(t) is subsequently rescaled and integrated before being fed as input to the plant [13,63,64].…”

A Fuzzy-Based Proportional–Integral–Derivative with Space-Vector Control and Direct Thrust Control for a Linear Induction Motor

“…Figure 6 depicts the fuzzy-based technique for fine-tuning different PID coefficients in order to obtain a stable control signal. As shown in Figure 6, the input variables are represented in the fuzzed using seven overlapping triangular fuzzy memberships [46]. As seen in Figures 7 and 8, the fuzzification method is also used for output variables.…”

Synchronization and Optimal Operation of a 140 kVA Inverter in On-Grid Mode Using Mamdani Controllers in Cascade

“…Although the active power is negligible before minimum values of R, this must be considered at the moment of the design of the reactor and at the moment of quantifying the active power that will not be delivered to the reference bar, as shown in equation (16).…”

“…Sharma et al proposed a dual-structured fuzzy (Mamdanibased) to switch between proportional and integral actions to improve the frequency regulation in a microgrid, including a wind-diesel generator system combined with an ultra-capacitor storage unit [15]. Barakat presented a Mamdani-based fuzzy logic control to reduce load frequency control issues and step load perturbations in terms of peaks and settling time under diferent multisource interconnected power systems (reheat, hydro, and gas units with and without HVDC links) [16]. Fayez et al developed a fuzzy controller to command battery energy storage and a resistor brake to mitigate subsynchronous resonance oscillatory torque and speed response in steam turbines connected to power grids.…”