Enhancing Power Quality in Microgrids With a New Online Control Strategy for DSTATCOM Using Reinforcement Learning Algorithm

Bagheri, Mehdi; Nurmanova, Venera; Abedinia, Oveis; Naderi, Mohammad Salay

doi:10.1109/access.2018.2852941

Cited by 58 publications

(37 citation statements)

References 15 publications

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“…For the concerned complex processes, automatic control technology exists in all aspects of process control, such as production control and [15][16][17][18][19] power control. [20][21][22][23][24][25] For the control of a production process, generally, it is difficult or impossible to build its accurate model. Therefore, data-driven-based methods were used for modeling or control.…”

Section: Introductionmentioning

confidence: 99%

Stability and stabilization for a class of complex production processes via LMIs

Wang

Guo

2019

Optim Control Appl Methods

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Summary This paper deals with the problem of stability and stabilization for a non‐Newton mechanical system, and the system is described by the so‐called pattern class variable rather than a state or output variable. At the beginning of this paper, the method of pattern‐moving–based dynamics description is introduced, and it describes the dynamic properties of a concerned production process at a larger granularity. Then, a pattern‐moving–based nonlinear state space model is put forward. Moreover, a feature of output‐class partition is defined and extracted, and two constraint conditions are given for the system based on the feature. Through dealing with difference of a Lyapunov function in two cases, new methods of stability and stabilization are proposed by using the LMI and S‐procedure approaches. They guarantee the stability and robust stabilization of the system and present the relationship between the feature of output‐class partition and system stability. A sintering process and numerical examples are used to demonstrate the effectiveness and practicability of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Stability and stabilization for a class of complex production processes via LMIs

Wang

Guo

2019

Optim Control Appl Methods

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“…A new online reference control method using the reinforcement learning algorithm for DSTATCOM is presented in Bagheri et al for reactive power control. The main target was to compensate the reactive power, unbalanced load current, and harmonics in a microgrid, utilizing voltage and current parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed filter with its improved controller can compensate unstable currents, maintain DC-link voltage at steady state, and help in the correction of the power factor of the supply side adjacent to unity. However, the proposed complex control could not fully compensate voltage and current problems A new online reference control method using the reinforcement learning algorithm for DSTATCOM is presented in Bagheri et al 11 for reactive power control. The main target was to compensate the reactive power, unbalanced load current, and harmonics in a microgrid, utilizing voltage and current parameters.…”

Section: Introductionmentioning

confidence: 99%

Power quality improvement of multimicrogrid using improved custom power device called as distributed power condition controller

Esmaeili

Shayeghi

Valipour

et al. 2019

Int Trans Electr Energ Syst

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Summary The aim of this paper is to improve power quality in a multimicrogrid (MMG) with high penetration of various distributed generators in island and interconnected mode using an improved custom power device called distributed power condition controller (DPCC). The DPCC is an improved, low‐cost, reliable combined custom power device to compensate simultaneously the voltage drop, harmonic distortions, and feeder losses in the MMG, which includes the verity of nonlinear source. It has great market potential and has been selected as the solution for more specific applications. The capability of the DPCC is increased by discrete fuzzy‐based PI controller. In addition, the power injection model of DPCC is presented. Two different case studies corresponding to the island mode and high penetration of renewable generation and nonlinear load in connected mode are considered. According to theoretical studies and simulation results, DPCC successfully improves power quality of the MMGs.

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“…Many controllers have been designed based on the optimization techniques including model‐matching method, performance index minimization, state‐feedback control strategy, reinforcement learning algorithm, genetic algorithm, and honey‐bee mating optimization…”

Section: Introductionmentioning

confidence: 99%

“…It can be achieved by optimizing both the gain and phase indices of control system. Many controllers have been designed based on the optimization techniques including model-matching method, 2-7 performance index minimization, 8 state-feedback control strategy, 9,10 reinforcement learning algorithm, 11 genetic algorithm, 12 and honey-bee mating optimization. 13,14 Especially many researchers have used the model-matching method in the design of feedforward compensator to improve reference tracking.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of robust feedforward compensator with gain and phase specifications

Jon¹,

Pak²

2019

Optim Control Appl Methods

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Summary This paper deals with the design of the robust feedforward compensator for improving the tracking performance of control systems with gain and phase specifications. The feedforward compensator is designed for single‐input–single‐output continuous‐time linear systems with constrained input and model uncertainty. The design objective is to make the transfer characteristic from command to output be one in the presence of the input saturation and the model uncertainty. To do that, the design of the feedforward compensator is formulated as a new gain‐phase optimization problem with both the gain and phase specifications. Then, the design procedures are proposed by using frequency‐matching method based on the closed‐loop frequency response. The proposed method provides a good trade‐off between system performance and control input to overcome performance reduction due to input saturation, and guarantees the robustness of the overall system in the presence of the model uncertainty. Two design examples illustrate that the frequency characteristics and the transient response of the control system are improved by the proposed method.

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Enhancing Power Quality in Microgrids With a New Online Control Strategy for DSTATCOM Using Reinforcement Learning Algorithm

Cited by 58 publications

References 15 publications

Stability and stabilization for a class of complex production processes via LMIs

Stability and stabilization for a class of complex production processes via LMIs

Power quality improvement of multimicrogrid using improved custom power device called as distributed power condition controller

Optimal design of robust feedforward compensator with gain and phase specifications

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