Anomaly detection and predictive maintenance for photovoltaic systems

Benedetti, Massimiliano De; Leonardi, Fabio Salvatore; Messina, Fabrizio; Santoro, Corrado; Vasilakos, Athanasios V.

doi:10.1016/j.neucom.2018.05.017

Cited by 129 publications

(70 citation statements)

References 23 publications

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“…Virtual sensors such as the ones used for the experiments of this paper [2] are interesting solutions in this context. Similarly, [3] proposes a virtual sensor to anticipate failures on photo-voltaic systems. Additionally, [11] presents a failure anticipation approach for aircraft systems.…”

Section: Related Workmentioning

confidence: 99%

Contextualisation of Datasets for Better Classification Models: Application to Airbus Helicopters Flight Data

Guilly

Daouayry

Maisonneuve

et al. 2020

Communications in Computer and Information Science

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For helicopters, anticipating failures is crucial. To this end, the analysis of flight data allows to develop predictive maintenance approaches, for which Airbus Helicopters (AH) has proposed several solutions, some based on machine learning using predictive models. One recurrent problem in this setting is the contextualization of the data, that is to identify the data better fitting the phenomenon being modeled. Indeed, helicopters are complex systems going through different flight phases. Experts therefore have to identify the adequate ones, in which the selected flight parameters are stable and consistent with the studied problem. In this paper, we propose a generic solution to contextualize classification data, and present an experimental study on AH flight data: the results are encouraging and allow to keep domain experts involved the process.

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Section: Related Workmentioning

confidence: 99%

Contextualisation of Datasets for Better Classification Models: Application to Airbus Helicopters Flight Data

Guilly

Daouayry

Maisonneuve

et al. 2020

Communications in Computer and Information Science

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show abstract

“…A similar approach is also applied for controlling the exhaust gas temperature of gas turbine engine and detecting wearing problems: integrating data recorded through different sensors of the turbine, allows the monitoring of the turbine's performance and fault detection [44]. Another application belonging to the energy field regards the analysis of temperature and irradiance values to predict the performance and failures of photovoltaic plants [45]; interestingly, as implemented in a power grid company, ANNs may also be integrated in ontological models to optimize the faults diagnostics and repairing processes [46].…”

Section: State Of the Art: Anns Applications In Ammentioning

confidence: 99%

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

2019

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In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.

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“…Recent research on the anomaly and fault detection of PV power plants has proposed Outlier Mining Techniques to detect the decreased output power value [25,26]. An anomaly detection algorithm is reported in the literature that applies the auto threshold level to classify the decreased output power of PV plants.…”

Section: Introductionmentioning

confidence: 99%

On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

Shin¹,

Kim²

2020

Energies

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This paper presents an on-line diagnosis method for large photovoltaic (PV) power plants by using a machine learning algorithm. Most renewable energy output power is decreased due to the lack of management tools and the skills of maintenance engineers. Additionally, many photovoltaic power plants have a long down-time due to the absence of a monitoring system and their distance from the city. The IEC 61724-1 standard is a Performance Ratio (PR) index that evaluates the PV power plant performance and reliability. However, the PR index has a low recognition rate of the fault state in conditions of low irradiation and bad weather. This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants.

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Anomaly detection and predictive maintenance for photovoltaic systems

Cited by 129 publications

References 23 publications

Contextualisation of Datasets for Better Classification Models: Application to Airbus Helicopters Flight Data

Contextualisation of Datasets for Better Classification Models: Application to Airbus Helicopters Flight Data

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

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