A reinforcement learning approach for MPPT control method of photovoltaic sources

Kofinas, Panagiotis; Doltsinis, Stefanos; Dounis, Anastasios I.; Vouros, George A.

doi:10.1016/j.renene.2017.03.008

Cited by 89 publications

(60 citation statements)

References 24 publications

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“…The Q-learning controllers have been trained offline for different desired targets, such temperature and irradiation, and then we transferred the training models to the P&O controller to increase the efficiency of energy conversion. In contrast, the approach in reference [18] adopted Q-learning as an on-policy algorithm. Due to the different approaches between two studies, the comparison with the method in reference [18] was not carried out.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, the approach in reference [18] adopted Q-learning as an on-policy algorithm. Due to the different approaches between two studies, the comparison with the method in reference [18] was not carried out. However, based on the simulation results, the proposed h-POQL has clearly shown faster response based on the change of weather conditions, with less than a second compared to more than two seconds [18], meaning h-POQL could be more efficient.…”

Section: Discussionmentioning

confidence: 99%

“…In references [16,17], the authors present the good simulation results of reinforcement learning. In addition, reference [18] has presented considerable results towards a universal MPPT control method. It has also mentioned the potential future research on this topic including state-space reduction, RL algorithm optimization, a comparison between different RL algorithms, a more efficient optimal procedure, and the practical experiments.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, some researchers from National Chiayi University in Taiwan have proposed a RL-based MPPT method for the PV system [16]. One of the latest research examples in this area can be found in reference [18] where the authors proposed a new Q-learning based MPPT method for the PV system with larger state spaces, compared to only four states in reference [17] and reference [16]. The simulated results with good system performance from these papers show that the application of RL in the field of MPPT control is emerging and promising, which can help to improve the efficiency in renewable energy conversion, especially for solar and wind energy systems.…”

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confidence: 99%

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Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

Phan¹,

Lai²

2019

Applied Sciences

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Due to the rising cost of fossil fuels and environmental pollution, renewable energy (RE) resources are currently being used as alternatives. To reduce the high dependence of RE resources on the change of weather conditions, a hybrid renewable energy system (HRES) is introduced in this research, especially for an isolated microgrid. In HRES, solar and wind energies are the primary energy resources while the battery and fuel cells (FCs) are considered as the storage systems that supply energy in case of insufficiency. Moreover, a diesel generator is adopted as a back-up system to fulfill the load demand in the event of a power shortage. This study focuses on the development of HRES with the combination of battery and hydrogen FCs. Three major parts were considered including optimal sizing, maximum power point tracking (MPPT) control, and the energy management system (EMS). Recent developments and achievements in the fields of machine learning (ML) and reinforcement learning (RL) have led to new challenges and opportunities for HRES development. Firstly, the optimal sizing of the hybrid renewable hydrogen energy system was defined based on the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software for the case study in an island in the Philippines. According to the assessment of EMS and MPPT control of HRES, it can be concluded that RL is one of the most emerging optimal control solutions. Finally, a hybrid perturbation and observation (P&O) and Q-learning (h-POQL) MPPT was proposed for a photovoltaic (PV) system. It was conducted and validated through the simulation in MATLAB/Simulink. The results show that it showed better performance in comparison to the P&O method.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

Phan¹,

Lai²

2019

Applied Sciences

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“…In practical operating scenarios, the performance of the PV cells are non-uniform due to the partial shading problems that are related to the different weather conditions [12]. The partial shading conditions cause an extreme reduction in the maximum extracted power of the PV system [13]. In partial shading situations, the output PV power of the partially shaded array is less than the algebraic sum of the total output powers of the connected PV modules in the array.…”

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confidence: 99%

Multifunctional Distributed MPPT Controller for 3P4W Grid-Connected PV Systems in Distribution Network with Unbalanced Loads

et al. 2019

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The integration of photovoltaic (PV) systems with three-phase four-wire (3P4W) distribution networks has imposed several challenges related to existing unbalanced loads, reactive power generation and harmonics content. In this paper, a multifunctional distributed maximum power point (MPPT) controller for grid integration of PV systems is proposed. The proposed distributed MPPT controller is developed based on employing a four-leg three-level T-type multilevel inverter. The proposed inverter performs multifunctionalities, including distributed MPPT, neutral current compensation for the unbalanced loads, supplying reactive power into the grid and the grid integration. Moreover, the proposed inverter overcomes the stochastic behavior of both the PV generation with partial shading problems and its operation with unbalanced loads as well. Furthermore, the new proposed controller injects sinusoidal output currents with decreased levels of total harmonic distortion (THD) into the grid. The tested case study is investigated for the various operating scenarios of PV generation and load demands. The results and tabulated performance comparisons have proven the superior performance of the proposed multifunctional PV generation system. The results show the ability of the proposed controller to efficiently extract distributed MPPT for all PV modules at all the tested scenarios. Additional improvement of the energy efficiency is achieved through the elimination of the neutral current due to existing unbalanced loads.

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Low-Cost Photovoltaic Maximum Power Point Tracking Project for Autonomous Electric Vehicle Prototype

Hubert¹,

Valdiero

Goergen³

et al. 2020

Proceedings of IDEAS 2019

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A reinforcement learning approach for MPPT control method of photovoltaic sources

Cited by 89 publications

References 24 publications

Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

Control Strategy of a Hybrid Renewable Energy System Based on Reinforcement Learning Approach for an Isolated Microgrid

Multifunctional Distributed MPPT Controller for 3P4W Grid-Connected PV Systems in Distribution Network with Unbalanced Loads

Low-Cost Photovoltaic Maximum Power Point Tracking Project for Autonomous Electric Vehicle Prototype

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