Early Detection of Vegetation Ignition Due to Powerline Faults

Kandanaarachchi, Sevvandi; Anantharama, Nandini; Muñoz, Mario A.

doi:10.1109/tpwrd.2020.3006553

Cited by 25 publications

(5 citation statements)

References 20 publications

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“…An optimization framework to balance the wildfire risk and power shut-off within impacted areas is presented in [28], but it does not quantify the operation risk of each line. The fire ignition score due to high impedance faults through contact with vegetation is calculated in [29], while it requires the ignition event to occur first. The probability of wildfire ignition is computed in [30], but it only considered the wind speed.…”

Section: B Literature Reviewmentioning

confidence: 99%

Quantifying the Risk of Wildfire Ignition by Power Lines under Extreme Weather Conditions

Bayani¹,

Waseem²,

Manshadi³

et al. 2021

Preprint

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This paper presents a surrogate model to quantify the risk of wildfire ignition by individual power lines under extreme weather conditions. Wind speed and wind gust can lead to conductor clashing, which is a cause of igniting disastrous wildfires. The 3D non-linear vibration equations of power lines are employed to generate a dataset that considers physical, structural, and meteorological parameters, including the span of the power line, conductor diameter, wind speed, wind gust, phase clearance, and wind direction. A set of machine learning models is assembled based on these features to generate a score representing the risk of conductor clashing for each power line within a network, quantifying the risk of wildfire ignition. The rendered score represents the chance of the conductor clashing in place of simulating a Runge-Kutta method. A discussion on the impact of various meteorological parameters on power lines under the energization risk is presented. Besides, it is shown how the presented risk measure can be utilized to weigh in the fire safety and service continuity trade-off.

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Section: B Literature Reviewmentioning

confidence: 99%

Quantifying the Risk of Wildfire Ignition by Power Lines under Extreme Weather Conditions

Bayani¹,

Waseem²,

Manshadi³

et al. 2021

Preprint

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“…The sparks from the combustion caused a fire inside the surrounding residential courtyard walls; in the winter of 2018, a cable grounding occurred in a certain city in the western region, causing a grounding arc to cause a fire in the cable trench, resulting in power outages for tens of thousands of users [34]; in addition, according to the Western Power Coordination Commission in the United States, fires caused by power failures account for approximately 7.8%-9.6% of forest VOLUME XX, 2017 fires, of which 54% are related to medium voltage distribution network lines coming into contact with vegetation [35]. At the beginning of 2009, several serious ignition points of the "Black Saturday" wildfire in Victoria, Australia were caused by a tree collision on the line, resulting in 173 deaths and nearly $10 billion in damage [36].…”

Section: A High Resistance Faultmentioning

confidence: 99%

Transmission Line Fault Diagnosis Method Based on Improved Multiple SVM Model

Sun,

Liu,

Lin

et al. 2023

IEEE Access

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The accurate diagnosis of transmission line fault types is a prerequisite for quickly removing faults and restoring power supply, as well as the key to effectively reducing user economic losses, ensuring stable operation of the power system. The rapid development of artificial intelligence technology has been a promising way for fault diagnosis. However, the existing methods are still limited by small simples and accuracy of generalization. To overcome these problems, a transmission line fault diagnosis method based on an improved multiple SVM (MSVM) model is proposed in this paper. Firstly, the transmission line was selected as the research object, and its fault types and causes were analyzed in detail. Then, typical fault information are selected and corresponding MSVM model is established. Meanwhile, genetic algorithm (GA) is adopted to optimize model parameters to improve the accuracy of generalization. Finally, an improved IEEE-30 node test system and a real-world testing data are adopted to verify the accuracy and feasibility of the proposed method. Through analysis, fault diagnosis accuracy of the proposed method can be improved by up to 11% with better fitness value. INDEX TERMS Transmission line; fault diagnosis; improved multiple SVM model

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“…Some examples of human errors which can lead to wildfires include improperly discarded cigarettes, unattended campfires and the burning of debris [18]. Power distribution system faults and the failure of power distribution infrastructure, especially during elevated fire danger conditions, can result in the initiation of wildfires [19]. There is a wide variety of issues which might cause power distribution system faults.…”

Section: Introductionmentioning

confidence: 99%

Power Distribution System Faults and Wildfires: Mechanisms and Prevention

Bandara

Rajeev

Gad

2023

Forests

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Wildfires are one of the most hazardous natural disasters in Australia in terms of fatalities, property damage and financial losses. Events of catastrophic wildfires are recorded across the world including in the United States and Canada. Failures along power distribution infrastructure and network faults have been identified as some of the causes for the initiation of wildfires. Thus, it is critical to better understand the mechanisms and the potential prevention strategies for wildfires caused by power distribution system faults. In this light, this paper presents how the power distribution network faults cause wildfires highlighting the main mechanisms. Further, this paper reviewed studies on recent advancements for the prediction, detection and prevention of wildfires. Condition assessment of power distribution infrastructure including poles, crossarms, overhead cables and other attachments are paramount to detect potential defects and to carry out timely replacements which can subsequently mitigate the possibility of wildfire initiation. Therefore, this paper summarized the studies on condition monitoring and surveillance techniques for power distribution infrastructure. Altogether, this paper aimed to enhance the awareness about the prevention strategies for wildfires caused by power distribution system faults.

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Early Detection of Vegetation Ignition Due to Powerline Faults

Cited by 25 publications

References 20 publications

Quantifying the Risk of Wildfire Ignition by Power Lines under Extreme Weather Conditions

Quantifying the Risk of Wildfire Ignition by Power Lines under Extreme Weather Conditions

Transmission Line Fault Diagnosis Method Based on Improved Multiple SVM Model

Power Distribution System Faults and Wildfires: Mechanisms and Prevention

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