Abstract
The mathematical model of load frequency control is established in the interconnected power system of hydro, thermal, and wind for solving the problem of frequency instability in this paper. Besides, the improved grey wolf optimization algorithm (GWO) is presented based on the offspring grey wolf optimizer (OGWO) search strategy to handle local convergence for the GWO algorithm in the later stage. The experimental results show that the improved grey wolf algorithm has a superior optimization ability for the standard test function. The traditional proportional integral derivative (PID) controller cannot track the random disturbance of wind power in the hydro, thermal, and wind interconnected power grid. However, the proposed OGWO dynamically adjusts the PID controller control parameters to follow the wind power random disturbance, regional frequency deviation, and tie-line power deviation.
Highlights
As the energy crisis becomes more and more serious, a large number of new energy sources, such as wind power, has an impact on the grid after being connected to it [1]
Load frequency control has been actively researched in different strategies, including the traditional proportional integral derivative (PID) control [3], auto-interference control [4], [5], optimal control [6], sliding mode control [7], robust control [8], [9], and adaptive control [10]
The control parameters of PID controller KP1-KP2, Ki1-Ki2, and Kd1-Kd2 are selected by three different algorithms: offspring grey wolf optimizer (OGWO), grey wolf optimization algorithm (GWO), and particle swarm optimization (PSO), respectively
Summary
As the energy crisis becomes more and more serious, a large number of new energy sources, such as wind power, has an impact on the grid after being connected to it [1]. In [13], the firefly optimization algorithm was employed to revise the PID parameters to control the load frequency control of the multi-region interconnected power grid. In [16], [17], the grey wolf algorithm was used to control and optimize multi-area load frequency control. In [19], the grey wolf algorithm with fixed weight showed a superior result in the optimal frequency control. The method led to slow convergence and low accuracy Those intelligent optimization strategies showed superior optimized control parameters than the traditional PID control strategy. The simulation results show that the improved grey wolf algorithm has a superior controlled performance response
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