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Failure probability and location identification of damaged insulators using normal function and monitored leakage
Moein Monemi1, Seyed Mohammad Shahrtash1, Mohsen Kalantar1
1Center of Excellence for Power System Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
This study proposes using insulator leakage current data and normal distribution to predict electrical insulator failure rates. This method aims to optimize maintenance scheduling and prevent power grid faults by identifying damaged insulators proactively.
Area of Science:
- Electrical Engineering
- Materials Science
- Reliability Engineering
Background:
- Insulators are critical for high-voltage transmission lines, preventing current leakage to towers.
- Degradation of insulators over time leads to failures, causing service disruptions and costly maintenance.
- Current methods for detecting defective insulators are time-consuming and necessitate prolonged power outages.
Purpose of the Study:
- To examine insulator problems and failures across various voltage levels.
- To propose a methodology for determining insulator failure rates for maintenance planning.
- To enable proactive identification and maintenance of damaged electrical insulators.
Main Methods:
- Analysis of insulator leakage current (LC) data.
- Application of normal distribution to model insulator failure rates.
- Calculation of failure probability based on LC measurements.
Main Results:
- A method to calculate the probability of insulator failure using LC data.
- Quantification of insulator failure rates for maintenance prioritization.
- Identification of specific insulators requiring maintenance or replacement.
Conclusions:
- The proposed methodology enables data-driven decision-making for insulator maintenance.
- Predictive maintenance based on LC data can prevent power grid faults.
- Optimized maintenance strategies reduce downtime and improve grid reliability.
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