KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Pinthurat, WatcharakornSubject: search.filters.subject.distribution network

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Simultaneous Voltage Regulation and Unbalance Compensation in Distribution Systems With an Information-Driven Learning Approach
    (2024-04-01)
    Pinthurat, Watcharakorn
    ;
    Surinkaew, Tossaporn
    ;
    Hredzak, Branislav
    High penetration and uneven allocation of single-phase and three-phase loads together with photovoltaic (PV) sources in low-voltage distribution systems can result in voltage unbalance and voltage regulation challenges. Previous approaches that treated the voltage unbalance and the voltage regulation as separate problems may have overlooked their interdependencies, thus potentially limiting the effectiveness of those methods. This article proposes a novel framework that can regulate the bus voltages and compensate for the voltage unbalance simultaneously, without requiring any information on phase connection of single-phase PV inverters. The proposed framework is designed based on a Markov game. The bus voltage deviation and the voltage unbalance factor (VUF) are used to define the reward function. Local observation states are obtained from smart meters, and the reactive current of the PV-based voltage source inverter (VSI) is defined as the action within the framework. The framework employs a multiagent deep deterministic policy gradient (known as multiagent deep deterministic policy gradient) algorithm that incorporates centralized training and decentralized execution. It is utilized to solve the framework. To ensure its effectiveness, the framework is trained and tested using real data acquired from smart meters. This data encompasses information on PV generation and load demand. The obtained results demonstrate that the bus voltages are regulated close to their nominal value and the VUF is maintained at less than 2% while the PV-VSIs operate within predefined limits.