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Chair: Prof. Ming Dong, State Key Laboratory of Power Transmission Equipment System Security and New Technology, Chongqing University, China
Vice-chair: Prof. Qingxin Shi, North China Electric Power University, Beijing, China
Vice-chair: Dr. Jinjin Din, Chief engineer of System and New Energy Technology Center of the AHEPRI
This session will cover state-of-the-art machine learning and data analytics methods with a special focus on asset management and equipment operation & maintenance (O&M) work for power systems. Power system is equipment intensive. Under the current regulations, asset management and equipment O&M has become one of the most critical responsibilities for electric utility companies. The industry is strongly encouraged to make the most cost-effective decisions for equipment diagnostics, status prediction and planning of equipment maintenance, inspection, replacement and upgrade in order to provide “affordable electricity” to the public. In China, this requirement is further strengthened by the ongoing Power Transmission and Distribution Tariff Reformation (输配电价改革)
On the other hand, modern power system is also data intensive - a large amount of data is continuously generated from power system equipment via various inspection, testing and monitoring devices and activities.
As a result, almost all utility companies worldwide are trying to leverage the use of advanced analytics and machine learning for asset management and equipment O&M work, with the goal to balance system reliability, performance and cost. This panel will present research developments in this important field. Diverse domestic and foreign experts from both academia and industry will be invited.