Voltage stability of solar dish-Stirling based autonomous DC microgrid using grey wolf optimised FOPID-controller

Kumar, Ramesh; Sinha, Nidul

doi:10.1080/14786451.2020.1806843

Cited by 7 publications

(9 citation statements)

References 31 publications

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“…Since the proposed algorithm is an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm, two points of view for the comparisons are conducted. In the first one, the proposed FOPID-FOAC algorithm is compared to two approaches for tuning the FOPID parameters, which are FOPID [66] and FOPID-GWO [30]. From the second point of view, the proposed FOPID-FOAC algorithm is compared with FOPID-IAC, and FOPID-DDPG [16] controllers to reflect the effect of the fractional-order actorcritic reinforcement learning algorithms to optimize the FOPID parameters compared to the IAC algorithm and DDPG algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…It is worth mentioning that the classical PID controller is actually a special case of the FOPID controller with n ¼ 1 and k ¼ 1 [30]. Accordingly, the performance of the FOPID controller can be greatly promoted via the tuning of the two extra fractional orders n and k of the generalized FOPID controller.…”

Section: Fractional-order Pidmentioning

confidence: 99%

“…Among model-free tuning methods, machine learning approaches can be a proper solution in tuning the FOPID controller parameters without prior system dynamics information [23,41,45]. Tuning of FOPID parameters based on the metaheuristic optimization algorithms was investigated in several papers [30,38,43,44,53,79]. In fact, these tuning methods are off-line schemes.…”

Section: Introductionmentioning

confidence: 99%

“…Verifying the effectiveness of the developed FOPID-FOAC controller via applying the FOPID-FOAC controller to two uncertain nonlinear systems; the first one is the 2-DOF helicopter system for tracking and regularization issues and the second one is the IP system for the stabilizing issues. Moreover, the performance of the proposed FOPID-FOAC controller scheme is compared with four controller schemes; they are the FOPID controller [66], the FOPID-GWO [30], the FOPID based on the regular IAC (FOPID-IAC), and the FOPID-DDPG [16].…”

Section: Introductionmentioning

confidence: 99%

“…30.65% IAE, 32.83% ISE, 6.65% rising time, 10.15% Max.OS and 8.245% settling time compared to the other control methods. For the IP system, the improvements reached a reduction of about 43.83% IAE, 47.58% ISE, 11.16% rising time, 48.08% Max.OS and 10.72% settling time compared to the other control methods.…”

mentioning

confidence: 99%

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Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

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In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic neural network with adaptive learning rates. Moreover, a FO rectified linear unit (RLU) is introduced to enable the AC neural network to define and optimize its own activation function. By the means of the Lyapunov theorem, the convergence and the stability analysis of the proposed algorithm are investigated. The FO operators for the FOAC learning algorithm are obtained using the gray wolf optimization (GWO) algorithm. The effectiveness of the proposed approach is proven by extensive simulations based on the tracking problem of the two degrees of freedom (2-DOF) helicopter system and the stabilization issue of the inverted pendulum (IP) system. Moreover, the performance of the proposed algorithm is compared against optimized FOPID control approaches in different system conditions, namely when the system is subjected to parameter uncertainties and external disturbances. The performance comparison is conducted in terms of two types of performance indices, the error performance indices, and the time response performance indices. The first one includes the integral absolute error (IAE), and the integral squared error (ISE), whereas the second type involves the rising time, the maximum overshoot (Max. OS), and the settling time. The simulation results explicitly indicate the high effectiveness of the proposed FOPID-FOAC controller in terms of the two types of performance measurements under different scenarios compared with the other control algorithms.

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Section: Resultsmentioning

confidence: 99%

Section: Fractional-order Pidmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Shalaby

El-Hossainy²,

Abozalam

et al. 2022

Neural Comput & Applic

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Modeling and control of a solar-thermal dish-stirling coupled PMDC generator and battery based DC microgrid in the framework of the ENERGY NEXUS

Dhaked

Birla

2022

Energy Nexus

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An Optimal Adaptive Control Strategy for Energy Balancing in Smart Microgrid Using Dynamic Pricing

et al. 2022

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Energy balancing in smart microgrid plays a vital role to improve the reliability and resolves the load shedding problem to ensure consistent energy supply. However, energy balancing is challenging due to uncertain and intermittent nature of renewable energy integrated in smart microgrid. To solve such problems, dynamic energy pricing mechanism is developed that maintain energy balance for overcoming the gap between demand and supply. Thus, the particle swarm optimization based super twisting sliding mode controller (PSO-STSMC) is developed which uses dynamic energy pricing to control renewable energy resources' generation according to the consumers' demand for real time closed loop energy balancing in an energy market. The proposed PSO-STSMC based model is compared with existing models like proportional integral derivative (PID) controller, proportional integral (PI) controller, proportional derivative (PD) controller, and fractional order proportional derivative (FO-PD) controller and the optimized models of the particle swarm optimization based proportional integral (PSO-PI) controller and particle swarm optimization based proportional integral derivative (PSO-PID) controller. Simulations results demonstrate that energy price regulation by PSO-STSMC consistently controls the elastic demand for real time energy balancing.INDEX TERMS Smart grid, dynamic price server, elastic demand, renewable energy sources, dynamic energy price, elastic demand, demand side load management, energy balance, super twisting sliding mode controller.

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Voltage stability of solar dish-Stirling based autonomous DC microgrid using grey wolf optimised FOPID-controller

Cited by 7 publications

References 31 publications

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Optimal fractional-order PID controller based on fractional-order actor-critic algorithm

Modeling and control of a solar-thermal dish-stirling coupled PMDC generator and battery based DC microgrid in the framework of the ENERGY NEXUS

An Optimal Adaptive Control Strategy for Energy Balancing in Smart Microgrid Using Dynamic Pricing

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