Academic literature on the topic 'Vegetation High-Impedance Faults'

Abstract. Vegetation management is important to the power transmission and distribution networks. The encompassed towering tree is always the key factor of the high impedance faults(HIFs).LiDAR is an efficient way to detect trees with 3D point cloud. The classical tree detection algorithm can handle the tree with high and distinct trunk,but limited to the tree with messy trunks. While the deeplearning based detection algorithms are also suffered from the terrain noise points. In this paper, we propose an efficient LiDAR reconstruction system which can efficiently reconstruct the point cloud of surrounding vegetation without the ground plane noise. We also use different weight strategies to improve the localization accuracy. We have conducted our system on the real power network environment and the height detection result shows that our algorithm has a better accuracy and robustness compared with the classical methods.

The transmission lines of an electricity system are susceptible to a wide range of unusual fault conditions. The transmission line, the longest part of the electricity grid, sometimes passes through wooded areas. Storms, cyclones, and poor vegetation management (including tree cutting) increase the risk of cross-country faults (CCFs) and high-impedance fault (HIF) syndrome in these regions. Recognizing and classifying CCFs associated with HIF syndrome is the most challenging part of the project. This study extracted signal characteristics associated with CCF and HIF syndrome using the Tunable Q Wavelet Transform (TQWT). An adaptive tunable Q-factor wavelet transform (TQWT) based feature-extraction approach for CCHIF fault signals with high impact, short response period, and broad resonance frequency bandwidth was presented. In the first part, the time–frequency distribution of the vibration signal is used to determine the distinctive frequency range. Adaptive optimal matching of the impact characteristic components in the vibration signal was achieved by optimizing the number of decomposition layers, quality factor, and redundancy of TQWT based on the characteristic frequency band. In the last, the TQWT inverse transform was utilized to recreate the best sub-band to boost its weak impact characteristics. The effectiveness of the approach is confirmed by simulation and experimental findings in signal processing. The best decomposition level for signature features that can be extracted has been decided by Minimum Description length (MDL). The IEEE 39-bus system is used to test the suggested approach with reactor switching and the Ferranti effect.

Increasing modernization of the world has brought about a human component to natural disasters, which are exacerbated by the growing threat of climate change. The Western United States and Australia have witnessed some of the deadliest, costliest, and destructive wildfires in the recent past with downed electric power lines being a significant factor amongst the causes. The relationship between wildfires and powerlines is not a newly discovered phenomenon, however, utilities across the globe are struggling to find an optimal solution to this problem. While existing regulations allow utilities to schedule power shutdowns, they are often accompanied by massive financial losses and discomfort to consumers. Utilities also need to factor in the climatic conditions in the region of their service and the flammability of the vegetation surrounding their lines while making decisions pertaining to system planning, load shedding, and protection. This multi-faceted problem can be dealt with in multiple ways – one such technique involves detection of a falling line into sensitive vegetation before it encounters the earth. This approach essentially boils down the problem into detecting a single line open circuit fault. The open circuit is momentary and hence, speed is of the essence in such a protection scheme. In this thesis, detection of an open circuit is carried out in two different ways, viz., with and without communication support between the various elements of the system, with the latter technique being a novel proposal with the aim of achieving a secure protection scheme with minimal additional infrastructural requirements.
Master of Science
The contact of a live wire with the earth is a fault. While most faults can be cleared using traditional protection techniques, there is a higher risk associated with power lines that come in contact with dry surfaces, flammable plants, and bushes, which cannot be detected that easily. These surfaces offer very high resistance to the flow of current and are hence termed high impedance faults. These high impedance faults have the potential to spark and cause a fire, which can snowball into a wildfire depending on the geography and climatic conditions of the area. For years, this has been a major problem in places like Australia and California leading to loss of lives, power, and money, but the optimal solution is evasive. While several techniques to combat this problem exist, the focus of this thesis is essentially what is known as the Open Circuit Fault. The technique revolves around the detection of the fault while the falling conductor is midair. Given the short time frame, high-speed detection is of the essence. This thesis will focus on achieving open circuit detection without the need for any communication support and is a novel contribution to this field.

Vegetation High-Impedance Faults (VHIFs) are relevant and under-addressed power dis- tribution system disturbances. They are low-energy events, represented by the contact between power lines and nearby vegetation, that are not detected by traditional protection devices. Despite not harmful to power equipment, they can ignite fires in vegetation with great potential to life and property damage. After devastating HIF-related fires in 2009, the Victorian Government found the lack of technical solutions to prevent similar disasters and funded a vegetation ignition testing program to foster further research. It staged hundreds of VHIFs that generated the data pertained to this thesis. In the related literature, High-Impedance Faults (HIFs) comprise an extensive research field, but few works are solely dedicated to studying VHIFs. Although generally treated as a single problem, different high-impedance conducting surfaces introduce significant variance in faults’ characteristics and behaviours. For these reasons, the staged VHIFs recordings represent a niche type of faults having specific behaviours with significant potential for insights regarding phenomenon characterization. The main contributions from this thesis result from using the staged VHIF data to address the knowledge gaps related to its characterization and detection method. Initial investigations presented the likely presence of discriminative features in the signals’ high- frequency (HF) spectrum. The results gave confidence for the production of a machine learning-based VHIF classifier, conceptualized and discussed as part of a potential detection method. Subsequently, the existence of discriminative information and invariance in the HF signals was proved with the application of renowned signal representation techniques and machine learning algorithms. A study regarding the importance of using HF signals was also performed to support the chosen approach when conceptualizing the classifier. It led to the finding that although the accessibility of such signals might be not optimal, they may be imperative for an effective VHIF detection method. To deflate some of the potential implementation concerns, a low-cost, proof-of-concept prototype was produced, attesting the capabilities of real-time classification. Lastly, an unsupervised learning technique was used to capture some of the convoluted and complex fault signatures in the time domain. The found patterns led to insights about VHIF behaviour and signatures signals that resulted in more detailed phenomena characterization.