Application of Fuzzy Control in a Photovoltaic Grid-Connected Inverter

Zheng, Zhaohong; Zhang, Tianxia; Xue, Jiaxiang

doi:10.1155/2018/3806372

Cited by 10 publications

(9 citation statements)

References 12 publications

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“…Reference [17] adopts an adaptive fuzzy approximation strategy to control grid-connected inverter, but its learning algorithm relies on data information. Reference [18] introduces fuzzy control based on deadbeat grid-connected control, but the determination of fuzzy rules requires experience. In reference [19], particle swarm optimization (PSO) is used to optimize the grid connection design, but the local optimization performance is poor.…”

Section: Introductionmentioning

confidence: 99%

Research on Photovoltaic Grid‐Connected Control Strategy Based on Active Disturbance Rejection of Adaptive Extended State Observer

Liu

Xue

et al. 2021

Advances in Civil Engineering

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In the photovoltaic inverter grid-connected power generation system, the output power of photovoltaic panels is affected by illumination and temperature. The change of output power of photovoltaic panels will lead to the fluctuation of DC bus voltage. If the control is improper, it will directly affect the regular operation of the system. In order to improve the performance of the grid-connected inverter system, an active disturbance rejection control method based on adaptive extended state observer (ESO) is proposed. Firstly, a feedforward PI current inner loop controller is designed, which simplifies the structure of the control system and improves the tracking performance of the current. Then, the DC bus voltage outer loop ADRC is designed, and a conversion method that ignores the essential difference between nonlinear/time-varying and time-varying/linear is proposed. Through the conversion of time-invariant nonlinear system and time-varying linear system, the stability of the extended state observer is proved by the Routh criterion. Secondly, to solve the problem of mutual restriction between the stability and observation accuracy of the extended state observer, an adaptive function online automatic tuning ESO parameter method is proposed. Finally, the simulation results show that the proposed method has better dynamic and static performance, and the grid-connected voltage and current harmonics are small, which proves the correctness and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Research on Photovoltaic Grid‐Connected Control Strategy Based on Active Disturbance Rejection of Adaptive Extended State Observer

Liu

Xue

et al. 2021

Advances in Civil Engineering

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“…The connection of the photovoltaic panels through a single-phase inverter to an AC grid was presented in work [22]. In work [23], a photovoltaic system connected through a single-phase inverter to an AC grid, controlled through a fuzzy logic controller, was presented. A comparison between the fuzzy logic controller (FLC) and the conventional PI controller used for interfacing a photovoltaic system, with a grid through a three-phase inverter, was presented in [24,25].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of a Photovoltaic System Connected to a Three-Phase Grid by Using PI or Fuzzy Logic Controllers

Livinti

2021

Sustainability

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This work presents a comparative study for a photovoltaic system connected to a three-phase grid through a three-phase inverter controlled by a fuzzy logic controller or through a proportional-integral (PI) controller. The innovative aspect of this article consists of the elaboration of two models in MATLAB–Simulink for the photovoltaic system consisting of 40 photovoltaic panels and of the control algorithms of the three-phase inverter for each type of controller. The comparative analysis of the results obtained through simulation has established that the use of the fuzzy logic controller (for controlling the three-phase inverter) leads to transferring to the alternative current (AC) grid an electric power of 6.5 kW, whilst the use of the PI type controller leads to transferring to the AC grid an electric power of 4.6 kW under the same value of the intensity of solar radiation. The total harmonic distortion (THD) of the voltage resulting after the filtering circuit has a lower value in case of using the fuzzy logic controller than in case of using the PI controller. Through the usage of the fuzzy logic controller for controlling the inverter, an increase in quantity and quality of the electric energy introduced to an AC grid from a photovoltaic system was noticed, compared to the usage of a PI controller.

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“…Inductive loads in the post-grid-connected stage cause grid-connected current distortion [19]. e deadbeat control uses the discrete-time model of a system to predict the amplitude of the controlled variable one or more sampling times in advance [20].…”

Section: Introductionmentioning

confidence: 99%

Research on Improved Deadbeat Control Strategy Based on Interpolation Prediction and Online Inductance Identification

Xie

Xue

et al. 2020

Journal of Electrical and Computer Engineering

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Aiming at the problem of control delay and inductance deviation, which exist in the traditional deadbeat control of the full-bridge circuit, an improved deadbeat control strategy was proposed. An improved Newton interpolation prediction algorithm was proposed to compensate the delay problem of deadbeat control, and an on-line inductance identification algorithm based on double frequency sampling was proposed to correct the inductance deviation. A mathematical model of deadbeat control for full-bridge inverter was established; besides, the performance of different interpolation prediction algorithms was analyzed. An online inductor identification model is established, on the basis of which the online inductance identification compensation formula is derived. It is indicated that an output constant current of 10 A is available with the deadbeat control relative error of only 0.2%, the grid-connected power factor up to 0.999, and the output current’s total harmonic distortion of only 2.37%. The prototype experiment shows that the output current’s total harmonic distortion is as low as 2.403% and the power factor is as high as 0.998. The results show that the improved deadbeat control strategy can effectively improve the control accuracy and the quality of grid-connected power.

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Application of Fuzzy Control in a Photovoltaic Grid-Connected Inverter

Cited by 10 publications

References 12 publications

Research on Photovoltaic Grid‐Connected Control Strategy Based on Active Disturbance Rejection of Adaptive Extended State Observer

Research on Photovoltaic Grid‐Connected Control Strategy Based on Active Disturbance Rejection of Adaptive Extended State Observer

Comparative Study of a Photovoltaic System Connected to a Three-Phase Grid by Using PI or Fuzzy Logic Controllers

Research on Improved Deadbeat Control Strategy Based on Interpolation Prediction and Online Inductance Identification

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