Session 26: Early Warning Technology of New Power System Equipment Condition in Extreme Weather

Early Warning Technology of New Power System Equipment Condition in Extreme Weather
“极端天气下新型电力系统电力设备状态早期预警技术”

The evolution towards new power systems, dominated by renewables and power electronics, necessitates a paradigm shift from traditional condition monitoring to proactive early warning and predictive health management for critical electrical equipment. Existing methods, often reliant on offline measurements or threshold alarms for specific parameters, fall short in detecting incipient faults and latent defects under complex, dynamic grid conditions. This gap hinders effective preventive maintenance and jeopardizes system resilience. This special session is dedicated to pioneering research and innovative technologies that enable the early detection and proactive warning of potential failures in transformers, circuit breakers, cables, and power electronic converters. We focus on moving beyond simple state reporting towards intelligent systems capable of identifying subtle, evolving anomalies that precede functional failures.

Topics (Including but not limited to)  

• Early Fault Feature Extraction: Sensing and signal processing techniques for weak signatures of incipient faults (e.g., partial discharge, contact degradation, early thermal anomalies, subtle mechanical changes).
  早期故障特征提取：面向初期故障（如局部放电、接触退化、早期热异常、微弱机械变化）微弱特征的传感与信号处理技术。
• AI-driven Predictive Analytics & Early Warning Models: Machine learning and deep learning models for trend analysis, degradation trajectory prediction, and early fault classification.
  智能预警与预测分析：用于趋势分析、退化轨迹预测与早期故障分类的机器学习与深度学习模型。
• Multi-source Data Fusion for Latent Risk Identification: Integrating electrical, thermal, vibrational, acoustic, and environmental data to uncover hidden interdependencies and latent risks.
  潜伏风险多源信息感知：融合电、热、振、声、环境等多源数据，揭示隐藏关联与潜在风险。
• Resilience-oriented Early Warning: Frameworks that incorporate stress from grid dynamics (e.g., harmonics, ramping) and extreme weather into early warning systems.
  韧性导向的早期预警：将电网动态（如谐波、功率波动）与极端天气应力纳入预警体系的构建方法。
• Benchmarking and Validation of Early Warning Technologies: Methodologies and case studies for evaluating the sensitivity, reliability, and lead time of early warning approaches.
  预警技术评估与验证：评估早期预警方法灵敏度、可靠性及预警前置时间的基准方法与案例研究。