Fault Detection in Solar PV Systems Integrated with the Power Grid: Evaluating Logistic Regression through Confusion Matrix Analysis

Year : 2025 | Volume : 15 | Issue : 02 | Page : 45 52
    By

    Abhay Nema,

  • Sparsh Raj,

  1. Research Scholar, Department of Electrical &Eletronics Engineering, Vidhyapeeth Institute of Science and Technology Bhopal, Madhya Pradesh, India
  2. Research Scholar, Department of Electrical &Eletronics Engineering, Vidhyapeeth Institute of Science and Technology Bhopal, Madhya Pradesh, India

Abstract

This paper proposes a method for failure detection in grid-integrated solar photovoltaic (PV) systems using logistic regression and real-time sensor data. The approach effectively classifies and identifies seven distinct fault types. The developed model demonstrates a high fault identification accuracy, ranging from 93% to 96.5% across various fault types and operational conditions. By leveraging logistic regression, the system utilizes key independent variables that significantly influence the
classification process. Additionally, the paper includes a confusion matrix, which offers a visual representation of the differences and similarities between actual and predicted fault states, helping to evaluate the model’s performance. The matrix effectively illustrates the model’s accuracy in distinguishing between multiple fault categories. This clear classification performance reflects the strength of the proposed method in enhancing fault detection within photovoltaic (PV) systems. By accurately identifying different types of faults, the approach contributes significantly to improving the reliability and operational efficiency of PV installations. Overall, the method demonstrates strong potential in supporting predictive maintenance strategies and reducing downtime, thereby helping ensure consistent performance and longer system lifespan in real-world PV system applications. This method shows promise for real-world applications, where timely and accurate fault identification is essential to maintain optimal performance in solar power generation systems.

Keywords: Solar photovoltaic, PV, logistic regression analysis, real-time sensor data

[This article belongs to Journal of Power Electronics and Power Systems ]

How to cite this article:
Abhay Nema, Sparsh Raj. Fault Detection in Solar PV Systems Integrated with the Power Grid: Evaluating Logistic Regression through Confusion Matrix Analysis. Journal of Power Electronics and Power Systems. 2025; 15(02):45-52.
How to cite this URL:
Abhay Nema, Sparsh Raj. Fault Detection in Solar PV Systems Integrated with the Power Grid: Evaluating Logistic Regression through Confusion Matrix Analysis. Journal of Power Electronics and Power Systems. 2025; 15(02):45-52. Available from: https://journals.stmjournals.com/jopeps/article=2025/view=210743


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Regular Issue Subscription Original Research
Volume 15
Issue 02
Received 15/04/2025
Accepted 02/05/2025
Published 21/05/2025
Publication Time 36 Days
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