000			04032nam a22001577a 4500
082			_a629.8
100			_aIqbal, Saeed _929805
245			_aA Machine Learning-Driven Approach to Multi-Variable Solar Panel Health Monitoring / _cSaeed Iqbal
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a167p. _bSoft Copy _c30cm
500			_aThe health of solar PV systems is vital for maintaining efficiency, reliability, and safety. Although PV systems are inherently low-maintenance and lack moving parts, their performance depreciates over time due to environmental stressors such as dust accumulation, temperature fluctuations, microcracks, and panel misalignment. Effective monitoring systems enable the early detection of these issues, helping timely initiating the maintenance procedures, improving energy production, and extending system lifespan. This is of particular significance since improving PV efficiency through proactive management is often more cost-effective than expanding capacity through additional investments. This study presents an innovative approach for evaluating solar panel health using ML-driven analysis, as an alternate of traditional multi-sensor methodologies. The proposed technique extracts multiple attributes from a single data stream, employing ML algorithms to predict environmental impacts on PV performance. Sensor data was analysed to find key characteristics that accurately represent various aspects of panel health. Initially, temperature effects on a 100 kW grid-tied solar system deployed across various installation sites, including parking lots, rooftops, and ground-level arrays were examined. The results indicated that variations in operational temperature led to power losses of 27.95% in ground-mounted systems, 5.41% in rooftop systems, and 0.82% in parking-based systems. These outcomes were derived from temperature measurements at the three installation sites, further supported by weather station data. TMR sensors were also investigated for their ability to check multiple PV panel variables, particularly threeaxis orientation. Experimental findings revealed that the largest axis error values for Bx, By, and Bz, the magnetic field components along the X, Y, and Z axes, were below 0.1%, 0.3%, and 0.5%, respectively. However, TMR sensors proved insufficient for accurately capturing solar panel temperature fluctuations, prompting a shift toward EIS for a more comprehensive analysis. EIS experiments were conducted under controlled temperature conditions ranging from 10°C to 60°C, while maintaining all other environmental variables constant. The EIS response was recorded for 26 distinct parameters, providing a detailed dataset for analysis. The 26 distinct parameters are various electrical values obtained from the EIS experiments. ML classification of the EIS data using Caret-based techniques foundxvii the Light Gradient Boosting Machine and CatBoost Classifier as the more suitable models, achieving classification accuracies of 85.63% and 85.54%, respectively. Additionally, EIS was used to detect microcracks and dust accumulation under four real-world scenarios: (1) clean and healthy panels, (2) dusty but healthy panels, (3) clean panels with microcracks, and (4) dusty panels with microcracks. The Gradient Boost classifier achieved an impressive 97.5% accuracy in distinguishing these conditions. The experimental results indicated that combining EIS with machine learning provides a powerful approach for assessing solar panel temperature variations, detecting microcracks, and monitoring dust accumulation through DC power lines alone. This method demonstrates considerable potential for real-time PV system diagnostics, thereby enhancing predictive maintenance and operational monitoring.
650			_aPhD Robotics and Intelligent Machine Engineering _9123222
700			_aSupervisor : Dr. Muhammad Sajid _9119652
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/55812
942			_2ddc _cTHE
999			_c615201 _d615201