# A Heuristic Algorithm for Locating Line-to-Line Faults in Photovoltaic Systems
## 1 Introduction  
Electric power is an essential element in modern human society, indispensable across both civilian and industrial sectors. Over the past few decades, to secure adequate electrical energy, humans have built numerous thermal power plants (including coal-, oil-, and gas-fired power plants) and nuclear power plants for electricity generation. However, in the past decade, as time and technology have progressed, environmental issues such as air pollution and global warming have gained increasing attention. Consequently, in pursuit of more environmentally friendly and clean energy sources, different renewable energy industries have emerged, including hydropower, wind power, and solar power generation. Solar photovoltaic (PV) technology, known for being clean and pollution-free as well as being endlessly renewable has become one of the ideal renewable energy solutions. Statistics released by the International Renewable Energy Agency (IRENA) show that the global installed capacity for solar PV systems reached 716 GW by the end of 2020 [1]. Many large solar power plants are unstaffed and cover vast areas, making the effective management of tens of thousands of solar panels and their operational status a crucial issue that needs in-depth consideration. Equipment failures, if minor, may lead to reduced solar power output and economic losses; in more severe cases, they could cause power outages and even fires [2, 3, 4], potentially resulting in casualties. For those reasons, operation and maintenance (O&M) strategies [5, 6] and fault diagnosis techniques [7, 8, 9, 10, 11, 12, 13] are critical for early fault detection and timely troubleshooting. Recent advances in PV fault detection have increasingly leveraged artificial intelligence and machine learning techniques. [PERSON] and [PERSON] [14] comprehensively reviewed fault detection methods, categorizing them into electrical signal-based and visual/thermal imaging-based approaches. For line-to-line fault detection specifically, [PERSON] et al. [15] proposed an incipient fault identification method for DC integration systems that can rapidly isolate affected branches. Machine learning approaches have shown particular promise, with [PERSON] et al. [16] achieving over 99.8% detection accuracy for line-to-line faults even under high fault resistance conditions. [PERSON] et al. [17] further demonstrated that optimized SVM and XGBoost classifiers could distinguish line-to-line faults from other fault types using minimal training samples. Recent studies also include [PERSON] et al. and [PERSON] et al., who proposed advanced wavelet transform and voltage-based algorithms for accurate fault detection and location in PV systems [18; 19]. However, of the commonly used fault diagnosis techniques, most can only identify the type of potential fault within a PV array, rather than precisely point to the fault's location. The main limitations of existing line-to-line fault localization methods include:  
1. The computational complexity of AI-based approaches requiring extensive training datasets and high processing power [16; 20]
2. The inability to provide precise fault location within module groups, only identifying affected strings [15]
3. The requirement for additional sensors or complex measurement systems [17]
4. The lack of intuitive, interpretable fault localization processes that maintenance personnel can understand [21]  
This study, therefore, focuses on investigating line-to-line short-circuit faults that may occur within PV arrays [2; 22]. By utilizing an optimal sensor configuration combined with observational insights, this study aims to propose a heuristic line-to-line fault location algorithm that can narrow down the fault area from an entire array to a specific module group, thereby reducing troubleshooting time and saving human resource costs. Moreover, temperature rise caused by line-to-line short circuits is a significant concern, as highlighted by [PERSON] et al.