Abstract
This paper presents an automatic generation control algorithm applicable for a multi-area power system. A more practical models is used to represent the main components of the power system. The frequency bias factor (B) is set optimally by using an artificial intelligence technique. The generation rate constraints (GRC) are taken into consideration with dead band characteristics of the governor. For the first time, a sequential optimization of the fractional order (PID) control parameters, the governor speed regulation (R) and the frequency bias factor (B) is proposed. The objective function to be minimized is the updated integral time absolute error (UITAE). The tuning of the control parameters is dependent on minimizing the objective function by using the Grey wolf optimization (GWO) algorithm. The proposed algorithm is tested on an interconnected three power pools different system (Reheat, Gas and Hydro) with varying degrees of load step change. The simulation results of the presented process are compared to those achieved by using the particle swarm optimization method. The results obtained reveal the robustness of the proposed algorithm in terms of settling time and peak overshoot.
Highlights
Automatic generation control (AGC) or load frequency control is vital in power system operation
The minimized performance to be in the presented algorithm is the updated integral time absolute errors (UITAE), that are given as follow: UITAE= ∫ts t {∑3 { |Δfi| + |ΔPtie−i| +
The nonlinear constraints like generation rate constraints (GRC), boiler dynamics and Governor Dead Band (GDB) of the different plants implemented in the simulation
Summary
Automatic generation control (AGC) or load frequency control is vital in power system operation. A sequential optimization of the major load frequency regulation with the parameters of the PID controllers has interested many researchers [ 4647]. It was discovered that setting the droop regulation optimally by sequential tuning of the controller parameters and the speed regulation improved in the AGC dynamic efficiency. The goal of this study is to optimize the frequency bias factor(B). This is achieved through a FOPID controller with AGC system. Sequential tuning of frequency droop and frequency bias factor for the controller parameters must be carried out by minimizing grey wolf optimization (GWO) algorithm [18] based on the output index.
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