Anomaly Detection Mechanism for Solar Generation using Semi-supervision Learning Model

Tsai, Chia-Wei; Yang, Chung-Tu; Hsu, Feng-Ling; Tang, Hsih-Min; Fan, Nien-Chin; Lin, Cheng-Yang

doi:10.1109/indo-taiwanican48429.2020.9181310

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Authors in [42] introduce SolarClique, a data-driven method for identifying anomalies in the electricity generation of solar power facilities, which does not require sensor equipment for fault/anomaly detection and relies only on the array's output and the output of adjacent arrays for operational anomaly detection. An anomaly detection method using a semi-supervised learning model was suggested in [43] to predict solar panel settings to avoid situations where the solar panel cannot produce electricity due to equipment degradation. This method uses a clustering model to filter normal behaviors and an Autoencoder model for neural network classification.…”

Section: Related Workmentioning

confidence: 99%

Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies

Ahmed

Attar

Amer

et al. 2023

Systems

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Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault).

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Section: Related Workmentioning

confidence: 99%

Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies

Ahmed

Attar

Amer

et al. 2023

Systems

View full text Add to dashboard Cite

show abstract

“…The semi-supervised algorithms were used in literature to predict the failure of the solar sensors. (Tsai et al, 2020) proposed an AD technique based on a semi-supervised learning model to predict equipment conditions to assure continuous operation and precise solar panel power production. This method built the classifier using the AE neuron network model and the clustering technique for normal incident filtration.…”

Section: Semi-supervised Algorithmsmentioning

confidence: 99%

Anomaly Detection in Renewable Energy Big Data Using Deep Learning

Katamoura,

Aksoy

2023

International Journal of Intelligent Information Technologies

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This work aims to review the literature on anomaly detection (AD) in renewable energy. Due to the significance of the RE data quality and sensor performance, it is crucial to ensure that the measurement device works correctly and maintains data accuracy. The review identifies the relevant studies on big data anomaly detection in the energy field and synthesizes the related techniques. Also, the study shows a need for segmentation annotations for solar system electroluminescence imagery complicating the domain development of anomaly segmentation approaches. Consequently, most processes create machine learning (ML) models using semi-supervised techniques. Still, these approaches need more generalization regarding variation in environmental or systematic conditions. Furthermore, the studies discussed here focus on existing algorithms that used big data and AD to propose an improved analysis framework. Finally, the work presents a framework to solve the problem of identifying sensors' issues that will appear in data anomalies.

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“…Instead, it exclusively needs the assembly outcome of the array and those of close arrays for operating anomaly detection. An anomaly detection technique utilizing a semi-supervision learning model is suggested by [27] to predetermine solar panel conditions for bypassing the circumstance that the solar panel cannot produce power precisely as a result of equipment deterioration. This method utilizes the clustering model for regular actions filtration and the neuron network model, Autoencoder, to establish the classification.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Schemes for Anomaly Detection in Solar Power Plants

et al. 2022

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The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize the latest updates in machine learning technology to accurately and timely disclose different system anomalies. This paper addresses this issue by evaluating the performance of different machine learning schemes and applying them to detect anomalies on photovoltaic components. The following schemes are evaluated: AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest. These models can identify the PV system’s healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such a complex solution space.

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Anomaly Detection Mechanism for Solar Generation using Semi-supervision Learning Model

Cited by 5 publications

References 5 publications

Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies

Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies

Anomaly Detection in Renewable Energy Big Data Using Deep Learning

Machine Learning Schemes for Anomaly Detection in Solar Power Plants

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