Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21720%2F24%3A00373636" target="_blank" >RIV/68407700:21720/24:00373636 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.enconman.2024.118076" target="_blank" >https://doi.org/10.1016/j.enconman.2024.118076</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.enconman.2024.118076" target="_blank" >10.1016/j.enconman.2024.118076</a>
Alternative languages
Result language
angličtina
Original language name
Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier
Original language description
Accurate and reliable fault detection procedures are crucial for optimizing photovoltaic (PV) system performance. Establishing a trustworthy PV array model is the primary step and a vital tool for monitoring and diagnosing PV systems. This paper outlines a two-step approach for creating a reliable PV array model and implementing a fault detection procedure using Random Forest Classifiers (RFCs). Firstly, we extracted the five unknown parameters of the one-diode model (ODM) by combining the current–voltage translation method to predict the reference curve and employing the modified grey wolf optimization (MGWO) algorithm. In the second step, we simulated the PV array to obtain maximum power point (MPP) coordinates and construct operational databases through co-simulations in PSIM/MATLAB. We developed two RFCs: one for fault detection (a binary classifier) and another for fault diagnosis (a multiclass classifier). Our results confirmed the accuracy of the PV array modeling approach. We achieved a root mean square error (RMSE) value of 0.0122 for the ODM parameter extraction and RMSEs lower than 0.3 in dynamic PV array output current simulations under cloudy conditions. Regarding the fault detection procedure, our results demonstrate exceptional classification accuracy rates of 99.4% for both fault detection and diagnosis, surpassing other tested models like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Neural Networks (MLP Classifier), Decision Trees (DT), and Stochastic Gradient Descent (SGDC).
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20704 - Energy and fuels
Result continuities
Project
<a href="/en/project/TK70020002" target="_blank" >TK70020002: DEPLOYMENT OF SMART RENEWABLE ENERGY COMMUNITIES</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy Conversion and Management
ISSN
0196-8904
e-ISSN
1879-2227
Volume of the periodical
2024
Issue of the periodical within the volume
301
Country of publishing house
GB - UNITED KINGDOM
Number of pages
15
Pages from-to
—
UT code for WoS article
001164623100001
EID of the result in the Scopus database
2-s2.0-85182280217