A Framework for Fault Detection and Diagnosis in Renewable Energy Power Systems

Thesis Abstract

The increasing integration of renewable energy sources into power systems introduces complex operational challenges, notably the timely detection and diagnosis of faults, which are critical for maintaining system reliability and optimizing performance. Faults in renewable energy power systems—such as photovoltaic (PV) arrays, wind turbines, and associated power electronic components—can lead to significant power losses, equipment damage, and system outages if not identified promptly. Despite advancements in monitoring technologies, existing fault detection approaches often suffer from high false alarm rates, delayed responses, and limited adaptability to dynamic grid conditions, underscoring the need for a comprehensive, systematic framework tailored to the unique characteristics of renewable energy systems. This study aims to develop an integrative Fault Detection and Diagnosis (FDD) framework optimized for renewable energy power systems, with specific objectives to (1) analyze the physiological and operational signatures of common faults, (2) design an intelligent multi-modal detection model combining signal processing, analytical algorithms, and machine learning techniques, and (3) validate the framework through empirical testing on real-world renewable energy datasets. The overarching goal is to enhance fault detection accuracy, reduce diagnosis latency, and facilitate proactive maintenance strategies. Employing a mixed-methods research design, the study combines qualitative analysis of fault signatures with quantitative model development and validation. The qualitative phase involves content analysis of failure reports, sensor data logs, and technical literature to identify fault patterns and influential factors. For the quantitative phase, a representative sample of 150 operational renewable energy installations—comprising solar farms and wind parks—located across different climatic zones, will be employed. Data will be collected through SCADA systems, embedded sensors, and fault logs over a 12-month period, ensuring a diverse dataset capturing normal and faulty operational states. Measurement instruments will include high-frequency voltage, current, temperature, and vibration sensors, with data logged at 1 kHz sampling rates to preserve signal fidelity. Validity and reliability of instrumentation will be established via calibration protocols and cross-verification with manual inspections. For data analysis, the study will utilize techniques such as wavelet transform and Empirical Mode Decomposition (EMD) for feature extraction, followed by supervised machine learning algorithms—specifically Random Forest, Support Vector Machine (SVM), and Convolutional Neural Networks (CNN)—to classify fault types. The analytical framework will draw on the Root Cause Analysis (RCA) and the Fault Tree Analysis (FTA) theories to underpin model design and interpretability. Model performance will be evaluated using metrics such as accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) curves, with cross-validation techniques applied to prevent overfitting. Expected findings include the identification of distinct fault signatures, the development of a high-precision fault classification model capable of real-time deployment, and insights into the correlation between environmental factors and fault occurrences. The proposed framework is anticipated to outperform existing detection methods in accuracy and speed, providing a scalable solution adaptable to various renewable energy configurations and operational conditions. This research advances knowledge by integrating signal processing, machine learning, and fault analysis into a cohesive detection framework specifically tailored for renewable energy applications, filling notable gaps in existing literature. It offers a practical, deployable system that can significantly improve fault management, reduce maintenance costs, and enhance system resilience. Conclusively, the study recommends the adoption of the developed FDD framework across renewable energy installations, emphasizes the importance of continuous monitoring, and advocates for further research into adaptive models incorporating evolving system dynamics and new sensor technologies for sustained fault detection efficacy.