Real-time detection of wildfire risk caused by powerline vegetation faults using advanced machine learning techniques

Wildfires, also known as bushfires, happened more and more frequently in the last decades. Especially in countries like Australia, the dry and warm climate there make bushfire become one of the most frequent local hazards. Among different kinds of causes of bushfires, overhead powerline vegetation fault is one of the most common causes that relate to human activities. Reducing the bushfire risk from this perspective has attracted many scholars to study efficient strategies and systems. However, most of them started their research from the angle of powerline faults, while limited literature has explored the characteristics of the vegetations and their ignition features. The objective of this study is to explore and discover the numerical patterns from the contact to the ignition process between different upper story vegetations and the powerlines. Those patterns can not only help provide real-time warnings of bushfire caused by powerline vegetation faults but also avoid false alarm. To achieve this, we collected the voltage and current records of 188 ignition field tests that simulated the powerline vegetation faults. To explore the numerical patterns behind and develop a real-time alarming system, this study proposed a machine learning-based model, namely Hybrid Step XGBoost. According to the tests, the model could identify the safe contacts or the danger contacts between the powerlines and the upper story vegetation with an accuracy of 98.17%. Its performance also surpassed some advanced deep learning networks in our experiments.