Design and Parameter Tuning of Nonlinear Active Disturbance Rejection Controller for Permanent Magnet Direct Drive Wind Power Converter System

Abstract

When the permanent magnet direct-drive wind power converter system is disturbed by the grid side and the motor side, the output voltage of the converter and the DC bus voltage will change suddenly, and the power quality of the grid will deteriorate. The nonlinear active disturbance rejection controller (NLADRC) is used in the grid-connected inverter control system. Because of its ability to estimate and compensate disturbances, it can effectively improve the system's anti-disturbance ability and tracking accuracy. However, NLADRC still has problems such as many parameters to be adjusted and unclear physical meaning. This paper explores the influence of various parameters on the dynamic performance of the entire system, and summarizes the direction and law of parameter tuning. At the same time, the frequency domain analysis method is used to express the gain parameter in the expanded state observer in the form of bandwidth, which provides a basis for parameter adjustment in actual engineering. The control algorithm proposed in this paper is applied to the 3MW permanent magnet direct-drive wind power experimental platform. The experimental results under various working conditions show that NLADRC can effectively improve the anti-interference performance of the grid-connected inverter.

The Improved Particle Swarm Optimization Method: An Efficient Parameter Tuning Method with the Tuning Parameters of a Dual-Motor Active Disturbance Rejection Controller

“…The ADRC system parameters optimized by the BM, PSO algorithm [14], IMA optimization [17], and IPM algorithm optimization are compared and validated during motor starting, speed variation, and load addition processes to confirm the efficacy of the IPM for ADRC parameter optimization. Initially, a step signal is transmitted with an initial value of 1000 that shifts to 1200 after 0.1 s. The speed waveforms for the four methods are depicted in Figure 7a-d.…”

“…The performance of the IPM, PSO algorithm [14], and IMA [17] a dual-motor coupling system, as depicted in Figure 6. Here, the range of p c is [0.7, 1.0], and the range of p m is [0.01, 0.06].…”

“…Zhang et al built upon the foundation established by Gao [15] in summarizing the effects of various parameters on system dynamics and adjusted gain parameters of ESO using a BM. The experimental results under different operating conditions demonstrated that the anti-interference performance of grid-connected inverters can be effectively enhanced by nonlinear ADRCs (NLADRCs) with adjusted parameters [17]. Nevertheless, the BM is highly sensitive to small changes in control system parameters, which may cause instability in some cases.…”

Tuning of Auto Disturbance Rejection Controller Parameters Based on Improved Grey Wolf Optimizer

“…In the Equation, Xi, α(t), Xi, β(t), and Xi, γ(t) respectively are the positions of Xi(t) under the leadership of α wolf, β wolf, and γ wolf under the current iteration number, and can be obtained from Equation (7). k1 and k2 represent the acceleration constant in the particle swarm algorithm.…”

“…Zhang et al [7] studied the impact of various parameters in ADRC on the dynamic performance of the system, summarized the rules for adjusting ADRC parameters, and used frequency domain analysis to adjust the parameters of the extended state observer in the form of bandwidth, providing a basis for parameter adjustment in practical engineering.…”