A Novel Approach to Control of Autonomous Microgrid Systems

Abouheaf, Mohammed I.; Mahmoud, Magdi S.; Hussain, Syed A.

doi:10.5963/ijee0505001

Cited by 7 publications

(3 citation statements)

References 34 publications

(43 reference statements)

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“…The optimal control solution finds the optimal policies and the optimality conditions which relate the optimal value function to the optimal strategy [28]. An online VI‐based control system is developed to control an autonomous smartgrid in [29]. This technique is implemented in real time using partial knowledge about the smartgrid's dynamics.…”

Section: Introductionmentioning

confidence: 99%

Model‐free adaptive learning control scheme for wind turbines with doubly fed induction generators

Abouheaf

Gueaieb

Sharaf³

2018

IET Renewable Power Generation

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The classical control mechanisms of the wind turbines are generally based on precise modeling approaches to ensure robust and effective interplay between the wind turbines and the main power grids in both autonomous and grid-connected modes. The paper presents an innovative intelligent control system for the doubly fed induction generator wind turbines. The proposed system uses model-free control polices. The online controller is based on a policy iteration reinforcement learning paradigm along with an adaptive actor-critic technique. It is shown to be robust against the turbine's high nonlinearities and stochastic variations in the input-output conditions. These are associated with single and double rotor doubly fed large scale induction generators driven by wind turbines in the range of 5-7 MW. The performance of the controller is validated against challenging scenarios of coexisting undesired situations like severe wind changes with load excursions and abrupt shifts in the loads. Nomenclature J Inertia constant of the wind turbine. s Rotor slip. f b , ω b Base frequency and base angular frequency. ωr, ωs Rotor and stator angular frequencies. T E , T M Electromagnetic torque and mechanical torque. d, q Subscripts indicate the direct and quadratic components. r, s Subscripts indicate the rotor and stator values. Rr, Rs Self rotor and stator resistances. Lrr, LssRotor and stator self inductance. Lm Magnetizing inductance. Lrm Mutual inductance between two rotor coils. L dd Double-Cage self inductance. i dr , i ds , i dd , iqr, iqs, i qd Rotor, stator, and double-cage currents in the dq frame. vr, vsr Rotor and stator internal voltages.

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Section: Introductionmentioning

confidence: 99%

Model‐free adaptive learning control scheme for wind turbines with doubly fed induction generators

Abouheaf

Gueaieb

Sharaf³

2018

IET Renewable Power Generation

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“…[101] focuses an overview of RL methods with emphasis on demand response applications. RL methods have been used for the control of standalone MGs [102][103][104][105]. A novel actor-critic based implementation for the regulation of autonomous MGs based on a heuristic dynamic programming algorithm with partial knowledge of the MG's dynamics was first presented in [102].…”

Section: B Reinforcement Learningmentioning

confidence: 99%

“…RL methods have been used for the control of standalone MGs [102][103][104][105]. A novel actor-critic based implementation for the regulation of autonomous MGs based on a heuristic dynamic programming algorithm with partial knowledge of the MG's dynamics was first presented in [102]. In [103], a RL fuzzy controller is proposed for the frequency regulation of an islanded MG is proposed.…”

Section: B Reinforcement Learningmentioning

confidence: 99%

The Evolution of Research in Microgrids Control

Vasilakis

Zafeiratou

Lagos

et al. 2020

IEEE Open J. Power Energy

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Microgrids (MGs), as novel paradigms of active Distribution Networks, have been gaining increasing interest by the research community in the last 20 years. Currently, they are considered as key components in power system decentralization, providing viable solutions for rural electrification, enhancing resilience and supporting local energy communities. Their main characteristic is the coordinated control of the interconnected distributed energy resources (DER), which can be realized by various methods, ranging from decentralized communicationfree approaches to centralized ones, where decisions are taken at a central point. This paper provides an overview of this development focusing on the technical control solutions proposed by reseachers for the various levels of MG organization hierarchy. A critical assessment of selected, popular technologies is provided and open research questions regarding the trend to more decentralized power systems are discussed.

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