Bus Voltage Control of Photovoltaic Grid Connected Inverter Based on Adaptive Linear Active Disturbance Rejection

Zhang, Miao; Zhuang, Keyu; Zhao, Tong; Chen, Xianli; Xue, Jingze; Qiao, Zheng; Cui, Shuai; Gao, Yunlong

doi:10.3390/en15155556

Cited by 7 publications

(2 citation statements)

References 34 publications

(44 reference statements)

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“…Aiming at the weak anti-interference ability of a photovoltaic grid connected inverter management scheme, an improved LADRC controller was proposed by Refs. [19,20], which optimizes the parameters by observing and estimating the total interference by linear extended state observer (LESO), compensating the interference by the PD controller and adding adaption. The results showed that the improved method has better rapidity and robustness.…”

Section: A Literature Reviewmentioning

confidence: 99%

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

2022

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This paper proposes a control scheme for the radar position servo system facing dead zone and friction nonlinearities. The controller consists of the linear active disturbance rejection controller (LADRC) and diagonal recurrent neural network (DRNN). The LADRC is designed to estimate in real time and compensate for the disturbance with vast matched and mismatched uncertainties, including the internal dead zone and friction nonlinearities and external noise disturbance. The DRNN is introduced to optimize the parameters in the linear state error feedback (LSEF) of the LADRC in real time and estimate the model information, namely Jacobian information, of the plant on-line. In addition, considering the Cauchy distribution, an adaptive tracking differentiator (ATD) is designed in order to manage the contradiction between filtering performance and tracking speed, which is introduced to the LADRC. Another novel idea is that the back propagation neuron network (BPNN) is also introduced to tune the parameters of the LADRC, just as in the DRNN, and the comparison results show that the DRNN is more suitable for high precision control due to its feedback structure compared with the static BPNN. Moreover, the regular controller performances and robust performance of the proposed control approach are verified based on the radar position servo system by MATLAB simulations.

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Section: A Literature Reviewmentioning

confidence: 99%

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

2022

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“…In [23], the LADRC controller was applied to the control scheme of a high-speed train, so that the train can achieve speed tracking control accurately and rapidly, and the impact caused by external interference is suppressed. In [24], in order to keep the DC bus voltage of the inverter stable during operation, LADRC is applied to the inverter control strategy to complete the control of the inverter bridge. This method enhances the response speed of the system and effectively responds to the interference caused by changes in external conditions.…”

Section: Introductionmentioning

confidence: 99%

MPPT Control Algorithm Based on Particle Swarm Optimization and Adaptive Linear Active Disturbance Rejection Control

et al. 2022

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Aiming at the problem of maximum power point tracking (MPPT) of photovoltaic arrays in photovoltaic power generation systems, a particle swarm optimization (PSO) MPPT method combined with adaptive linear active disturbance rejection control (A-LADRC) algorithm was proposed and designed. In this method, PSO is used to track the maximum power point (MPP), and then the A-LADRC controller was used to track the reference voltage. The controller enhances the anti-interference ability against various external disturbances in the MPPT process and accelerates the response speed of the system. Compared with the perturbation observation method (P&O), traditional PSO and LADRC, the proposed method has good tracking performance and an anti-interference ability under various external disturbances.

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Research on linear active disturbance rejection control strategy based on grid‐forming inverters

Hu,

Tang

2024

Circuit Theory & Apps

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Grid‐forming inverters play a vital role in connecting renewable energy sources to the grid, and maintaining stable output voltage is essential for system operation. However, traditional dual‐PI voltage–current loop control suffers from slow response and weak disturbance rejection, leading to suboptimal control performance of grid‐connected inverter output voltage. Moreover, employing traditional dual‐PI control in grid‐connected inverters results in low output impedance, making them prone to subsynchronous oscillations and instability under strong grid conditions. To address these challenges, this study introduces a novel dual‐loop control strategy based on linear active disturbance rejection control (LADRC), wherein voltage loop is regulated by LADRC while current loop employs PI control. Quantitative analysis and experimental findings demonstrate that compared with traditional dual‐PI voltage–current loop control, the LADRC‐based dual‐loop control strategy offers higher bandwidth and lower steady‐state error, thereby enhancing the tracking speed and precision of grid‐forming inverter output voltage. The LADRC‐based dual‐loop control strategy reduces output impedance in grid‐forming inverter systems, lowering THD of output voltage and improving harmonic suppression under nonlinear loads. Experimental results show its robustness against strong grid conditions compared with traditional dual‐PI control, ensuring stable output voltage.

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Bus Voltage Control of Photovoltaic Grid Connected Inverter Based on Adaptive Linear Active Disturbance Rejection

Cited by 7 publications

References 34 publications

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

MPPT Control Algorithm Based on Particle Swarm Optimization and Adaptive Linear Active Disturbance Rejection Control

Research on linear active disturbance rejection control strategy based on grid‐forming inverters

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