Hardware implementation of an active learning self-organizing neural network to predict the power fluctuation events of a photovoltaic grid-tied system

Goh, S.; Kow, Ken Weng; Tan, Michelle T.T.; Rajkumar, R.; Wong, Yee Wan

doi:10.1016/j.micpro.2022.104448

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…The identified features then provide the necessary distribution parameters to detect the actual disturbance range in the power system. An automatic target adjustment technique is used in AWN that tests the level of power quality systems involved, based on a hierarchy of priorities [4]. The adjustment technique minimizes the latency and improves the accuracy of the fluctuation detection process.…”

Section: Introductionmentioning

confidence: 99%

Automatic Electrical System Fault Diagnosis Using a Fuzzy Inference System and Wavelet Transform

Zhang

2023

Processes

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Electrical systems consist of varied components that are used for power distribution, supply, and transfer. During transmission, component failures occur as a result of signal interruptions and peak utilization. Therefore, fault diagnosis should be performed to prevent fluctuations in the power distribution. This article proposes a fluctuation-reducing fault diagnosis method (FRFDM) for use in power distribution networks. The designed method employs fuzzy linear inferences to identify fluctuations in electrical signals that occur due to peak load demand and signal interruptions. The fuzzy process identifies the fluctuations in electrical signals that occur during distribution intervals. The linear relationship between two peak wavelets throughout the intervals are verified across successive distribution phases. In this paper, non-recurrent validation for these fluctuations is considered based on the limits found between the power drop and failure. This modification is used for preventing surge-based faults due to external signals. The inference process hinders the distribution of new devices and re-assigns them based on availability and the peak load experienced. Therefore, the device from which the inference outputs are taken is non-linear, and the frequently employed wavelet transforms are recommended for replacement or diagnosis. This method improves the fault detection process and ensures minimal distribution failures.

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Section: Introductionmentioning

confidence: 99%

Automatic Electrical System Fault Diagnosis Using a Fuzzy Inference System and Wavelet Transform

Zhang

2023

Processes

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Uncertainty analysis of photovoltaic cells to determine probability of functional failure

Zhang

Wang

et al. 2023

Applied Energy

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Recent Trends in Real-Time Photovoltaic Prediction Systems

Gallardo,

Amor,

Gutiérrez

2023

Energies

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Photovoltaic power forecasting is an important problem for renewable energy integration in the grid. The purpose of this review is to analyze current methods to predict photovoltaic power or solar irradiance, with the aim of summarizing them, identifying gaps and trends, and providing an overview of what has been achieved in recent years. A search on Web of Science was performed, obtaining 60 articles published from 2020 onwards. These articles were analyzed, gathering information about the forecasting methods used, the horizon, time step, and parameters. The most used forecasting methods are machine learning and deep learning based, especially artificial neural networks. Most of the articles make predictions for one hour or less ahead and predict power instead of irradiance, although both parameters are strongly correlated, and output power depends on received irradiance. Finally, they use weather variables as inputs, consisting mainly of irradiance, temperature, wind speed and humidity. Overall, there is a lack of hardware implementations for real-time predictions, being an important line of development in future decades with the use of embedded prediction systems at the photovoltaic installations.

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Hardware implementation of an active learning self-organizing neural network to predict the power fluctuation events of a photovoltaic grid-tied system

Cited by 3 publications

References 23 publications

Automatic Electrical System Fault Diagnosis Using a Fuzzy Inference System and Wavelet Transform

Automatic Electrical System Fault Diagnosis Using a Fuzzy Inference System and Wavelet Transform

Uncertainty analysis of photovoltaic cells to determine probability of functional failure

Recent Trends in Real-Time Photovoltaic Prediction Systems

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