Most Valuable Player Algorithm Based State Estimation for Energy Systems

Subramaniyan, Shanmugapriya; Maharajan, D.

doi:10.13052/dgaej2156-3306.3543

Cited by 2 publications

(2 citation statements)

References 13 publications

(18 reference statements)

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“…Although MVPA is a new evolutionary algorithm (published in 2020), this algorithm has been used several times to solve other special problems. For example, problems such as Partially Shaded PV Generation System [19], Energy Control Center for Energy System Security [20], and Optimal Antenna Network Positioning [21]. This is one of our bases in deciding to use MVPA in this study.…”

Section: Most Valuable Player Algorithm (Mvpa)mentioning

confidence: 99%

MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem

Armanto

Putra

Pickerling

2022

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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Tetris is one of those games that looks simple and easy to play. Although it seems simple, this game requires strategy and continuous practice to get the best score. This is also what makes Tetris often used as research material, especially research in artificial intelligence. These various studies have been carried out. Starting from applying state-space to reinforcement learning, one of the biggest obstacles of these studies is time. It takes a long to train artificial intelligence to play like a Tetris game expert. Seeing this, in this study, apply the Genetic Algorithms (GA) and the most valuable player (MVPA) algorithm to optimize state-space training so that artificial intelligence (agents) can play like an expert. The optimization means in this research is to find the best weight in the state space with the minimum possible training time to play Tetris with the highest possible value. The experiment results show that GAs and MVPA are very effective in optimizing the state space in the Tetris game. The MVPA algorithm is also faster in finding solutions. The resulting state space weight can also get a higher value than the GA (MVPA value is 249 million, while the GA value is 68 million).

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Section: Most Valuable Player Algorithm (Mvpa)mentioning

confidence: 99%

MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem

Armanto

Putra

Pickerling

2022

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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“…The second is the more popular deep learning method in recent years [9][10][11][12]. The data driven deep neural network can automatically extract the abstract feature expression of voltage sag disturbance from massive data, so as to achieve accurate identification of voltage sag causes, and has strong generalization ability [13].…”

Section: Introductionmentioning

confidence: 99%

Real-time Monitoring Technology of Voltage Sag Disturbance in Distribution Network Based on TCN-Attention Neural Network and Flink Flow Computing

Chen

Tian

et al. 2023

DGAEJ

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In the face of the challenges brought by the complexity of power grid, diversification of disturbance factors, isolation of monitoring points and other issues to the cause identification of voltage sag disturbance, this paper proposes a real-time monitoring technology for voltage sag disturbance in distribution network based on TCN-Attention neural network and Flink flow calculation, which has important practical significance for controlling voltage sag and reducing economic losses. This method uses Temporal Convolutional Network (TCN) to extract the cross time nonlinear characteristics of voltage sag time series data, which effectively solves the problems of long-term dependence on time series and low training output efficiency of existing time series models. In order to further improve the recognition accuracy of the model, Attention mechanism is introduced to mine the duration relationship in voltage sag data. At the same time, the method also constructs a parallel real-time monitoring platform based on Flink streaming computing framework, embeds the TCN-Attention voltage sag cause identification model generated by training, so as to realize real-time identification and monitoring analysis of voltage sag disturbances at each monitoring point of the distribution network. In this paper, various voltage sags are simulated on IEEE 14 bus system using PSCAD software, and the proposed method is verified and tested. The deep learning fusion model has high recognition accuracy for the cause of voltage sag, and the flow computing platform has excellent performance in time delay and throughput indicators, and can realize the parallel real-time monitoring and analysis of voltage sag causes in distribution network.

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Most Valuable Player Algorithm Based State Estimation for Energy Systems

Cited by 2 publications

References 13 publications

MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem

MVPA and GA Comparison for State Space Optimization at Classic Tetris Game Agent Problem

Real-time Monitoring Technology of Voltage Sag Disturbance in Distribution Network Based on TCN-Attention Neural Network and Flink Flow Computing

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