Operation of Parallel Inverters in Microgrid Using New Adaptive PI Controllers Based on Least Mean Fourth Technique

Elnady, A.; Al-Shabi, Mohammad

doi:10.1155/2019/4854803

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…Aouchiche 14 adjusted the gain values of PI regulators in order to enhance the grid-connected system dynamic behavior, but it didn’t compare the performance of the FOPI and PI controllers. Elnady and Alshabi 15 suggested an adaptive PI controller, which has robust performance at different loading conditions, but is complex in its design procedure. Moeti and Asadi 16 proposed an adaptive PI current controller for a two-stage grid-connected photovoltaic inverter, which has the advantage of providing low grid current THD and desirable dynamic response, but it requires an accurate model of the system.…”

Section: Introductionmentioning

confidence: 99%

Research on the control strategy of LCL grid-connected inverters based on improved auto disturbance rejection

Han,

Xu,

Kang

et al. 2023

Science Progress

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The grid-connected inverter is the key to ensure stable, reliable, safe, and efficient operation of the power generation system; the quality of the grid-connected output current waveform directly affects the performance of the entire power generation system. To improve the anti-interference performance and reduce the output current harmonic content of the grid-connected inverter, an improved control strategy that combined repetitive control (RC) and auto disturbance rejection control (ADRC) is designed in this paper. Firstly, decoupled the ADRC to realize the individual adaptation between tracking performance parameters and anti-interference performance parameters of the controller, through which the difficulty of adjusting parameters is reduced. Secondly, the control approach is devised by adding RC to ADRC. To demonstrate the effectiveness of the proposed method in this paper, detailed experimental studies are conducted using proportional integral control, traditional ADRC, and the proposed method under normal power grids, weak power grids, and periodic disturbances. And dynamic performance simulation experiment is done to verify the dynamic performance of the self-disturbance rejection controller before and after the addition of RC links. The results indicated the effectiveness and feasibility of the proposed method. Finally, after simulation, the steady state and dynamic performance are conducted on a hardware testing platform. The impacts of the obtained results indicate the effectiveness and feasibility of the control algorithm proposed, the ability to suppress intermediate frequency disturbances is improved, the bandwidth of the auto disturbance rejection controller is expanded, and the harmonic content of the output current is depressed.

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Section: Introductionmentioning

confidence: 99%

Research on the control strategy of LCL grid-connected inverters based on improved auto disturbance rejection

Han,

Xu,

Kang

et al. 2023

Science Progress

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“…With the help of proposed controller, damping resonance, proper transient performance and enough stability margin are obtained. In [18], a novel adaptive PI current controller is employed in a microgrid to stabilize the magnitude and frequency of the voltage across the loads. Also, the parameters of PI controllers are adapted using least mean fourth (LMF) algorithm to improve system stability at different loading conditions.…”

Section: Introductionmentioning

confidence: 99%

Robust model reference adaptive PI controller based sliding mode control for three-phase grid connected photovoltaic inverter

Moeti¹,

Asadi²

2021

Turk J Elec Eng & Comp Sci

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In this paper, the design of a new robust model reference adaptive PI (MRAC-PI) current controller is proposed for a two stage grid connected photovoltaic (PV) inverter. Perturb & observe (P&O) algorithm is implemented in the boost part in order to extract the maximum power from the PV array. Firstly, the current dynamics with considering the system uncertainties are written in dq frame and are simplified by employing a decoupling system. Then, the MRAC-PI controller is designed based on a sliding mode control (SMC) to improve the robustness of the controller under system uncertainties. The parameters of PI controller are tuned based on SMC laws to tracks the desired reference model and provides fast and desirable dynamic response. The stability of the robust MRAC-PI is proven by using Lyapunov's theory and Barbalet's lemma. Finally, the performance of the proposed current controller and the conventional PI based voltage oriented control (VOC) during variations in filter inductance and solar irradiance are compared through simulation results. These results show the effectiveness of the proposed controller.

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“…Among the neural network-based controllers, the single adaptive neuron system retains the advantages of the neural network and can satisfy the requirements of the real-time control of fast processes [29,30]. Therefore, a single-neuron adaptive hysteresis compensation method is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Single-Neuron Adaptive Hysteresis Compensation of Piezoelectric Actuator Based on Hebb Learning Rules

Qin

Duan

2020

Micromachines

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This paper presents an adaptive hysteresis compensation approach for a piezoelectric actuator (PEA) using single-neuron adaptive control. For a given desired trajectory, the control input to the PEA is dynamically adjusted by the error between the actual and desired trajectories using Hebb learning rules. A single neuron with self-learning and self-adaptive capabilities is a non-linear processing unit, which is ideal for time-variant systems. Based on the single-neuron control, the compensation of the PEA’s hysteresis can be regarded as a process of transmitting biological neuron information. Through the error information between the actual and desired trajectories, the control input is adjusted via the weight adjustment method of neuron learning. In addition, this paper also integrates the combination of Hebb learning rules and supervised learning as teacher signals, which can quickly respond to control signals. The weights of the single-neuron controller can be constantly adjusted online to improve the control performance of the system. Experimental results show that the proposed single-neuron adaptive hysteresis compensation method can track continuous and discontinuous trajectories well. The single-neuron adaptive controller has better adaptive and self-learning performance against the rate-dependence of the PEA’s hysteresis.

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Operation of Parallel Inverters in Microgrid Using New Adaptive PI Controllers Based on Least Mean Fourth Technique

Cited by 9 publications

References 27 publications

Research on the control strategy of LCL grid-connected inverters based on improved auto disturbance rejection

Research on the control strategy of LCL grid-connected inverters based on improved auto disturbance rejection

Robust model reference adaptive PI controller based sliding mode control for three-phase grid connected photovoltaic inverter

Single-Neuron Adaptive Hysteresis Compensation of Piezoelectric Actuator Based on Hebb Learning Rules

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