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Start date: 2023Subject: search.filters.subject.photovoltaic

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Now showing 1 - 3 of 3
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    Enhancing PV system modeling accuracy with the irradiance intensity selection technique
    (2026-06-01)
    Songtrai, Sasiwimon
    ;
    Chinnavornrungsee, Perawut
    ;
    Sriprapha, Kobsak
    ;
    Kobayashi, Tomonao
    ;
    Niemcharoen, Surasak
    This study is a method to improve the accuracy of photovoltaic (PV) power modeling by the irradiance intensity segmentation. The developed model was compared with 2 years of data from PV system in Thailand and the original 1D5P and weight function models to determine accuracy. The results show that by applying the irradiance intensity selection technique with a 1D5P equivalent circuit model improved the accuracy of the proposed model. The proposed method achieved a significantly lower %root mean square error (%RMSE) compared with other models yielding %RMSE values of 0.26% under clear-sky and 2.80% under cloudy conditions. Seasonal evaluation further demonstrated improved prediction accuracy, with the lowest deviation observed during the rainy period, attributed to reduced dust accumulation. A 2 years comparison confirmed the proposed model exhibited the lowest deviation of 0.69%, outperforming conventional PV simulation programs. These findings indicate that irradiance segmentation enhances forecasting performance and provides accurate PV output estimation under real environmental conditions.
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    An Intelligent Voltage Control With Power Loss Model Integration in Active Distribution Network
    (2025-01-01)
    Pinthurat, Watcharakorn
    ;
    Deanseekeaw, Anurak
    ;
    Surinkaew, Tossaporn
    ;
    Boonraksa, Terapong
    ;
    Boonraksa, Promphak
    The increasing integration of renewable energy sources (RESs), particularly distributed PV systems, poses significant challenges to voltage stability in modern distribution systems. Existing studies use reactive power control to address voltage deviations but incur high losses, with no systematic solution achieving both voltage regulation and loss minimization. This paper proposes a novel voltage control strategy based on multi-agent deep reinforcement learning (MADRL), leveraging decentralized agent coordination to maintain voltage levels while minimizing PV inverter and system losses. Also, a new framework is formulated based on a Markov game, wherein each PV inverter operates as an autonomous agent that adjusts its reactive power output via a centralized training process. The agents, defined as PV inverters, employ the multi-agent twin-delayed deep deterministic policy gradient algorithm to collaboratively minimize voltage deviations. Through the use of local observations and shared global information during training, agents learn robust control policies that generalize to varying conditions and enable decentralized execution without ongoing coordination. Performance of the proposed control strategy is validated on a modified IEEE 33-node distribution system under high variability in PV generation and load demand. Results show that the proposed control strategy significantly improves voltage regulation and reduces power losses compared to state-of-the-art MADRL techniques.
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    Potential of Photovoltaic on Small Stratospheric Satellite in Real-World Experiment
    (2023-01-01)
    Techavijit, Pirada
    ;
    Wongphuangfuthaworn, Natthapong
    ;
    Sukchalerm, Polkit
    ;
    Plodpai, Aniwat
    This paper investigates the potential of monocrystalline photovoltaic (PV) panels to generate power when installed on stratospheric satellites through near-space flight experiments conducted at altitudes of 0-23 kilometers above sea level. It also compares the results with a mathematical model developed by ASHRAE in previous research, which reveals the differences caused by various factors such as atmospheric attenuation and high-level clouds. The main objective of this study is to identify the optimal altitude for deploying stratospheric satellites with appropriately sized PV panels that can generate sufficient power to meet the communication system's requirements for long-term operation.