Photovoltaic system monitoring and fault detection using peer systems

Alcañiz, Alba; Nikam, Maitheli M.; Snow, Yitzi; Isabella, Olindo; Ziar, Hesan

doi:10.1002/pip.3558

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Intelligent fault diagnostic methods are currently drawing more attention from experts and academics both domestically and internationally as a crucial tool for data-driven control and application methods. Alcaiz et al [7] proposed a collaborative approach for fault detection of residential PV systems that relies on PV systems. The study showed that the method has a strong ability to distinguish between system and actual fault problems.…”

Section: Related Workmentioning

confidence: 99%

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

Liu

2023

J. meas. eng.

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The reliability of modern building electrical systems are receiving increasing attention as they become more intelligent and complex. As the majority of building electrical systems use neutral point grounding, earth faults or short circuits can get worse over time and damage both the distribution system and the electrical equipment. To this end, the corresponding three phases and four categories, namely three-phase voltage, three-phase current after fault, three-phase voltage distortion rate, three-phase current distortion rate, a total of 12 dimensional fault feature vectors and 10 fault simulation types, were summarised and extracted in conjunction with the actual operating conditions of the system. Using traditional fault identification ideas and neural network algorithm as reference, a 12-dimensional fault feature vector is used as the model input to construct a building electrical fault diagnosis and detection model based on ELM algorithm. Results showed that the ELM-based model’s classification accuracy for this experimental sample was 97.56 %, its AUC was 0.92, and its RMSE was 0.3521. These figures were higher than the classification accuracy and performance of the BP algorithm and GA-BP algorithm fault diagnosis models, and they also demonstrate better robustness and generalizability. The model also has a 97.27 % correct rate in fault discrimination, while the computation time is only 0.201 s, and its fault identification and diagnosis speed is faster than other algorithmic models. At the same time, this research model has a good fault monitoring accuracy of up to 98.6 % for building electrical systems. The research can provide a more sensitive, accurate and rapid fault monitoring method for the current building electrical system. It also improves the reliability of the building electrical system in a complex environment and achieves better protection of the system. This has a certain significance for the development of the building electrical industry.

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Section: Related Workmentioning

confidence: 99%

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

Liu

2023

J. meas. eng.

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Federated learning for solar energy applications: A case study on real-time fault detection

Abdelmoula,

Oufettoul,

Lamrini

et al. 2024

Solar Energy

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Benchmarking photovoltaic plant performance: a machine learning model using multi-dimensional neighbouring plants

Anamiati,

Landberg,

Guerra

et al. 2024

EPJ Photovolt.

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The goal of this study is to monitor the performance of a photovoltaic plant by comparing its power output against others with similar characteristics, referred to as neighbours. The purpose of the 15 neighbours is to have the best reference for the performance of the plant in question, in other words, how the plant under analysis performs compared to the 15 neighbours. A machine learning model based on a feed forward Neural Network was employed to model power production as a function of environmental signals and the sun's position. Here, the data from the neighbours are used to train the model and the data from the plant under analysis are used to evaluate the model and predict the power output. Once the power is predicted, the performance ratio of the plant is calculated. The procedure has been tested and validated at several plants for three different cases and the numerical results highlight how the model is able to identify under/over performing plants. Therefore the developed strategy provides industries a valid tool on the correct functioning of a plant.

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Photovoltaic system monitoring and fault detection using peer systems

Cited by 3 publications

References 27 publications

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

Federated learning for solar energy applications: A case study on real-time fault detection

Benchmarking photovoltaic plant performance: a machine learning model using multi-dimensional neighbouring plants

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