Publication:
Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition

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dc.citedby0
dc.contributor.authorYew W.H.en_US
dc.contributor.authorFat Chau C.en_US
dc.contributor.authorMahmood Zuhdi A.W.en_US
dc.contributor.authorSyakirah Wan Abdullah W.en_US
dc.contributor.authorYew W.K.en_US
dc.contributor.authorAmin N.en_US
dc.contributor.authorid58765606400en_US
dc.contributor.authorid25824209000en_US
dc.contributor.authorid56589966300en_US
dc.contributor.authorid57209655076en_US
dc.contributor.authorid57361611300en_US
dc.contributor.authorid7102424614en_US
dc.date.accessioned2024-10-14T03:20:18Z
dc.date.available2024-10-14T03:20:18Z
dc.date.issued2023
dc.description.abstractFor renewable energy systems to operate as efficiently and as effectively as possible, maximum power point tracking (MPPT) controllers are essential. They make it possible to precisely and dynamically track the peak output of solar panels or wind turbines, ensuring that the system will be stable and reliable even in the face of changing environmental factors. Recently, more robust algorithms based on deep reinforcement learning (DRL) have been proposed. These DRL-based algorithms optimize the local and global maximum power point (MPP) using deep Q-learning and deep deterministic policy gradient (DDPG). In this study, MATLAB models of a DRL-based MPPT algorithm were developed, tested, and compared to simulation based on two established MPPT algorithms-the Particle Swarm Optimization (PSO), and the Perturb and Observe (P&O). The simulations were conducted under various conditions, including standard test conditions (STC), and partial shading conditions (PSC). Simulation results demonstrate that at STC, both the DRL-based MPPT and PSO algorithm tracks the steady-state power at 0.02 seconds, outperforming the traditional P&O technique of 0.08 seconds. However, the PSO algorithm manages to track 1.18% more power than DRL MPPT at PSC. Despite the limitations of training the DRL, it shows a promising method for addressing MPPT issues under PSC. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/RSM59033.2023.10326748
dc.identifier.epage12
dc.identifier.scopus2-s2.0-85179849009
dc.identifier.spage9
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85179849009&doi=10.1109%2fRSM59033.2023.10326748&partnerID=40&md5=01d82e586999d026817ffb2883a41b49
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34511
dc.pagecount3
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - 2023 IEEE Regional Symposium on Micro and Nanoelectronics, RSM 2023
dc.subjectdeep reinforcement learning
dc.subjectenergy
dc.subjectmaximum power point tracking (MPPT)
dc.subjectoff-grid PV
dc.subjectpartial shading conditions (PSC)
dc.subjectparticle swarm optimization (PSO)
dc.subjectperturb and observe (P&O)
dc.subjectDeep learning
dc.subjectGlobal optimization
dc.subjectLearning algorithms
dc.subjectMATLAB
dc.subjectMaximum power point trackers
dc.subjectParticle swarm optimization (PSO)
dc.subjectRenewable energy resources
dc.subjectCondition
dc.subjectDeep reinforcement learning
dc.subjectEnergy
dc.subjectMaximum Power Point Tracking
dc.subjectOff-grid PV
dc.subjectOff-grids
dc.subjectPartial shading
dc.subjectPartial shading condition
dc.subjectParticle swarm
dc.subjectParticle swarm optimization
dc.subjectPerturb and observe
dc.subjectReinforcement learnings
dc.subjectSwarm optimization
dc.subjectReinforcement learning
dc.titleInvestigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Conditionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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