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Author: search.filters.author.Boonraksa, Terapong

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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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    Comparison of power output forecasting on the photovoltaic system using adaptive neuro-fuzzy inference systems and particle swarm optimization-artificial neural network model
    (2020-01-01)
    Dawan, Promphak
    ;
    Sriprapha, Kobsak
    ;
    Kittisontirak, Songkiate
    ;
    Boonraksa, Terapong
    ;
    Junhuathon, Nitikorn
    The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.