Supervised Learning based Method for Condition Monitoring of Overhead Line Insulators using Leakage Current Measurement
As a new practical and economical solution to the aging problem of overhead line (OHL) assets, the technical policies of most power grid companies in the world experienced a gradual transition from scheduled preventive maintenance to a risk-based approach in asset management. Even though the accumulation of contamination is predictable within a certain degree, there are currently no effective ways to identify the risk of the insulator flashover in order to plan its replacement. This paper presents a novel machine learning (ML) based method for estimating the flashover probability of the cup-and-pin glass insulator string. The proposed method is based on the Extreme Gradient Boosting (XGBoost) supervised ML model, in which the leakage current (LC) features and applied voltage are used as the inputs. The established model can estimate the critical flashover voltage (U50%) for various designs of OHL insulators with different voltage levels. The proposed method is also able to accurately determine the condition of the insulator strings and instruct asset management engineers to take appropriate actions.
Code (0)
등록된 구현이 없습니다.
Tasks
Asset ManagementManagementSimilar Papers 제목 키워드 기반
AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines
Ceramic insulators are important to electronic systems, designed and installed to protect humans from the danger of high voltage electric current. However, insulators are not immortal, and natural deterioration can gradu…
Anomaly DetectionTransfer LearningHigh Voltage Insulator Surface Evaluation Using Image Processing
High voltage insulators are widely deployed in power systems to isolate the live- and dead-part of overhead lines as well as to support the power line conductors mechanically. Permanent, secure and safe operation of powe…
Vocal Bursts Intensity PredictionTime series forecasting based on optimized LLM for fault prediction in distribution power grid insulators
Surface contamination on electrical grid insulators leads to an increase in leakage current until an electrical discharge occurs, which can result in a power system shutdown. To mitigate the possibility of disruptive fau…
Language ModelingLanguage ModellingLarge Language ModelTime Series+1An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators
Inspection of insulators is important to ensure reliable operation of the power system. Deep learning is being increasingly exploited to automate the inspection process by leveraging object detection models to analyse ae…
Anomaly DetectionFault DetectionObjectobject-detection+2Hopf insulators and their topologically protected surface states
Three-dimensional (3D) topological insulators in general need to be protected by certain kinds of symmetries other than the presumed U(1) charge conservation. A peculiar exception is the Hopf insulators which are 3D topo…