Reinforcement learning based PID controller design for LFC in a microgrid

Esmaeili, Mehran; Shayeghi, Hossein; Nejad, Hamid Mohammad; Younesi, Abdollah

doi:10.1108/compel-09-2016-0408

Cited by 25 publications

(13 citation statements)

References 22 publications

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“…The control laws of the fuzzy FI (FPI) are represented by a set of chosen IF … THEN rules. The controller parameters are tuned by using nondominated sorting genetic algorithm II (NSGA‐II) algorithm to minimize cost function …”

Section: Control Strategymentioning

confidence: 99%

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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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Section: Control Strategymentioning

confidence: 99%

“…The controller parameters are tuned by using nondominated sorting genetic algorithm II (NSGA-II) algorithm to minimize cost function. 19…”

Section: The Fuzzy Pi Controlmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, Xue et al used the reinforcement learning-based fuzzy PID to control load frequency of an island micro-grid [22]. Esmaeili et al proposed an immune learning algorithm for the PID controller design [23]. Rout and Wang et al adopted an adaptive PID controller, which was developed using the derived parameters, to accomplish the path following task for an autonomous underwater vehicle [24].…”

Section: Introductionmentioning

confidence: 99%

A robust controlling methodology for a grouting process

Zhang

Guo

2019

Automatika

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The grouting technology is an effective and economic method in the grouting industry field. In this paper, a nonlinear model for the grouting dynamic process was established, and the controlling parameters were further modified through a robust method. Moreover, the grouting pressure system for the neural network was also modelled based on a sensitivity analysis algorithm, and in particular, the iterative learning algorithm and Lyapunov asymptotical theory. The results showed that such a robust controlling methodology was better than the normal manual operation method. The subsequent numerical simulations demonstrated that the tuning methodology could meet all the requirements for the grouting control with the maximum pressure variable in the range of 8.1%. The present study and the proposed method could be applied to various engineering projects and especially, to implement in the real control of damming grouting.

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“…Meanwhile, Q-learning algorithm [19] is also widely used in tuning parameters for controllers. Researches [8,20] used this algorithm to tune fuzzy controllers. Carlucho et al [ 2] tuned the PID controller using an incremental Q-PID algorithm by dynamically dividing the actions into more specific areas to obtain higher controlling accuracy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive PID controller based on Q ‐learning algorithm

Shi

Lam

Xiao

et al. 2018

CAAI Transactions on Intelligence Technology

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An adaptive proportional-integral-derivative (PID) controller based on Q-learning algorithm is proposed to balance the cart-pole system in simulation environment. This controller was trained using Q-learning algorithm and implemented the learned Q-tables to change the gains of linear PID controllers according to the state of the system during the control process. The adaptive PID controller based on Q-learning algorithm was trained from a set of fixed initial positions and was able to balance the system starting from a series of initial positions that are different from the ones used in the training session, which achieved equivalent or even better performances in comparison with the conventional PID controller and the controller only uses Q-learning algorithm. This indicates the advantage of t h ea d a p t i v eP I Dc o n t r o l l e rb a s e do nQ-learning algorithm both in the generality of balancing the cart-pole system from a relatively wide range of initial positions and in the stabilisability of achieving smaller steady-state error.

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Reinforcement learning based PID controller design for LFC in a microgrid

Cited by 25 publications

References 22 publications

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

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

A robust controlling methodology for a grouting process

Adaptive PID controller based on Q ‐learning algorithm

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