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dc.contributor.author Latani, T
dc.contributor.author Parameswaran, G
dc.contributor.author Priyanthan, G
dc.contributor.author Hemapala, KTMU
dc.contributor.editor Abeysooriya, R
dc.contributor.editor Adikariwattage, V
dc.contributor.editor Hemachandra, K
dc.date.accessioned 2024-03-22T05:18:05Z
dc.date.available 2024-03-22T05:18:05Z
dc.date.issued 2023-12-09
dc.identifier.citation T. Latani, G. Parameswaran, G. Priyanthan and K. T. M. U. Hemapala, "Coordination of PV Smart Inverters for Grid Voltage Regulation," 2023 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2023, pp. 84-89, doi: 10.1109/MERCon60487.2023.10355465. en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22372
dc.description.abstract In the contemporary energy market, the utilization of photovoltaic (PV) is increasing considerably. This change brings new challenges to the power grid because of its variable and intermittent nature. One of the main issues is voltage violations and PV curtailment. A smart inverter (SI) provides a fast response method to regulate the voltage by varying real or reactive power at the point of common coupling (PCC). When multiple SIs operate under an autonomous control scheme, the reactive power level exceeds the threshold level. This creates an undesirable situation in the system. This paper mainly considers the coordination of the SI using a deep reinforcement learning algorithm (DRL). The DRL agent learns the policy through interaction with the IEEE-37 test feeder in the OpenDSS simulation to find out the optimal action. By defining the rewards scheme of the action carefully, the reactive power of SI can be utilized optimally, and the PV voltage will be maintained within the normal operating zone. Validation of the DRL agent’s performance is done with the local autonomous control scheme. The results assure that a well-trained DRL agent can coordinate multiple SIs for voltage regulation and PV curtailment reduction. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.uri https://ieeexplore.ieee.org/document/10355465 en_US
dc.subject Deep Reinforcement learning en_US
dc.subject Smart inverters en_US
dc.subject Deep deterministic policy gradient en_US
dc.subject Photovoltaic en_US
dc.subject Voltage regulation and PV curtailment en_US
dc.title Coordination of pv smart inverters for grid voltage regulation en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Engineering Research Unit, University of Moratuwa en_US
dc.identifier.year 2023 en_US
dc.identifier.conference Moratuwa Engineering Research Conference 2023 en_US
dc.identifier.place Katubedda en_US
dc.identifier.pgnos pp. 84-89 en_US
dc.identifier.proceeding Proceedings of Moratuwa Engineering Research Conference 2023 en_US
dc.identifier.email tlatani18@gmail.com en_US
dc.identifier.email gayani.parameswaran@gmail.com en_US
dc.identifier.email govindarajpriyanthan@gmail.com en_US
dc.identifier.email ktmudayanga@gmail.com en_US


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