Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System

Guia, Justin D. de; Concepcion, Ronnie; Calinao, Hilario A.; Lauguico, Sandy; Dadios, Elmer P.; Vicerra, Ryan Rhay P.

doi:10.1109/icitee49829.2020.9271699

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…To create the best prediction model, the ensemble learning technique combines a range of basic learner algorithm pattern schemes. The resulting perfect prediction model outperforms the fundamental learning algorithms by a wide margin [9,27].…”

Section: Ensemble Learning Algorithmsmentioning

confidence: 99%

Photovoltaic system fault detection techniques: a review

El-Banby,

Moawad,

Abouzalm

et al. 2023

Neural Comput & Applic

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Solar energy has received great interest in recent years, for electric power generation. Furthermore, photovoltaic (PV) systems have been widely spread over the world because of the technological advances in this field. However, these PV systems need accurate monitoring and periodic follow-up in order to achieve and optimize their performance. The PV systems are influenced by various types of faults, ranging from temporary to permanent failures. A PV system failure poses a significant challenge in determining the type and location of faults to quickly and cost-effectively maintain the required performance of the system without disturbing its normal operation. Therefore, a suitable fault detection system should be enabled to minimize the damage caused by the faulty PV module and protect the PV system from various losses. In this work, different classifications of PV faults and fault detection techniques are presented. Specifically, thermography methods and their benefits in classifying and localizing different types of faults are addressed. In addition, an overview of recent techniques using different artificial intelligence tools with thermography methods is also presented.

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Section: Ensemble Learning Algorithmsmentioning

confidence: 99%

Photovoltaic system fault detection techniques: a review

El-Banby,

Moawad,

Abouzalm

et al. 2023

Neural Comput & Applic

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“…This baseline was used to detect abnormal power consumption. De Guia et al [59] used bagging for anomaly detection in photovoltaic systems. Liang et al [60] introduced a novel anomaly detection framework for power grids.…”

Section: ) Ensemble Methodsmentioning

confidence: 99%

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

et al. 2023

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The smart grid integrates Information and Communication Technologies (ICT) into the traditional power grid to manage the generation, distribution, and consumption of electrical energy. Despite its many advantages, it faces significant challenges, such as detecting abnormal behaviours in the grid. Identifying anomalous behaviours helps to discover unusual user power consumption, faulty infrastructure, power outages, equipment failures, energy thefts, or cyberattacks. Machine learning (ML)-based techniques on smart meter data has shown remarkable results in anomaly detection. However, traditional ML-based anomaly detection requires smart meters to share local data with a central server, which raises concerns regarding data security and user privacy. Server-based model training faces additional challenges, such as the requirement of centralised computing power, reliable network communication, large bandwidth capacity, and latency issues, all of which affect the real-time anomaly detection performance. Motivated by these concerns, we propose a Federated Learning (FL)-based smart grid anomaly detection scheme where ML models are trained locally in smart meters without sharing data with a central server, thus ensuring user privacy. In the proposed approach, a global model is downloaded from the server to smart meters for ondevice training. After local training, local model parameters are sent to the server to improve the global model. We secure the model parameter updates from adversaries using the SSL/TLS protocol. Using standard datasets, we investigate the anomaly detection performance of federated learning and observe that FL models achieve anomaly detection performance comparable to centralised ML models while ensuring user privacy. Further, our study shows that the proposed FL-based models perform efficiently in terms of memory, CPU usage, bandwidth and power consumption at edge devices and are suitable for implementation in resourceconstrained environments, such as smart meters, for anomaly detection.

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“…In [53], the Meanshift clustering method utilizes grid-tied inverters and solar-irradiance to perform pre-classification and anomaly detection on time series data pertaining to electrical parameters. The authors in [5] focus on both realtime anomaly detection and classify the type of anomaly.…”

Section: Unsupervised Anomaly Detectionmentioning

confidence: 99%

Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

Hossein Rahimighazvini,

Zeyad Khashroum,

Maryam Bahrami

et al. 2024

Int. J. Sci. Res. Arch.

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Power electronics pertains to the conception, regulation, and utilization of electronic power circuits to proficiently administer and transform electrical energy. Power electronics play a crucial role in maintaining the reliability, efficiency, and security of complex production systems. Also, increasingly important in various applications such as renewable energy systems, electric vehicles, and industrial automation. However, modern power electronics systems are vulnerable to both cyber and physical anomalies due to the integration of information and communication technologies. So far, different methods have been used to detect abnormalities. This survey provides an overview of the state-of-the-art in anomaly detection in power electronics using machine learning and deep learning methods. It highlights the potential of these techniques in addressing the growing complexity and vulnerability of power electronics systems.

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Application of Ensemble Learning with Mean Shift Clustering for Output Profile Classification and Anomaly Detection in Energy Production of Grid-Tied Photovoltaic System

Cited by 11 publications

References 14 publications

Photovoltaic system fault detection techniques: a review

Photovoltaic system fault detection techniques: a review

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

Power electronics anomaly detection and diagnosis with machine learning and deep learning methods: A survey

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