Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring sy...Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid.In contradiction to conventional methods,transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit(PMU)at one of the generator buses.This minimal requirement makes the proposed methodology ideal for providing backup protection.Spectral analysis of equivalent power factor angle(EPFA)variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid.Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence.The proposed system can considerably assist system protection center(SPC)in fault localization and to restore the line at the earliest.Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.展开更多
To solve the problems of ladle slag detection system （SDS）, such as high cost, short service life, and inconvenient maintenance, a new SDS realization method based on hidden Markov model （HMM） was put forward. The...To solve the problems of ladle slag detection system （SDS）, such as high cost, short service life, and inconvenient maintenance, a new SDS realization method based on hidden Markov model （HMM） was put forward. The physical process of continuous casting was analyzed, and vibration signal was considered as the main detecting signal according to the difference in shock vibration generated by molten steel and slag because of their difference in density. Automatic control experiment platform oriented to SDS was established, and vibration sensor was installed far away from molten steel, which could solve the problem of easy power consumption by the sensor. The combina- tion of vector quantization technology with learning process parameters of HMM was optimized, and its revaluation formula was revised to enhance its recognition effectiveness. Industrial field experiments proved that this system requires low cost and little rebuilding for current devices, and its slag detection rate can exceed 95%.展开更多
文摘Remote monitoring of transmission lines of a power system is significant for improved reliability and stability during fault conditions and protection system breakdowns.This paper proposes a smart backup monitoring system for detecting and classifying the type of transmission line fault occurred in a power grid.In contradiction to conventional methods,transmission line fault occurred at any locality within power grid can be identified and classified using measurements from phasor measurement unit(PMU)at one of the generator buses.This minimal requirement makes the proposed methodology ideal for providing backup protection.Spectral analysis of equivalent power factor angle(EPFA)variation has been adopted for detecting the occurrence of fault that occurred anywhere in the grid.Classification of the type of fault occurred is achieved from the spectral coefficients with the aid of artificial intelligence.The proposed system can considerably assist system protection center(SPC)in fault localization and to restore the line at the earliest.Effectiveness of proposed system has been validated using case studies conducted on standard power system networks.
基金Item Sponsored by National Natural Science Foundation of China (50374061)863 National High Technology Program of China(2004AA-1Z2060)
文摘To solve the problems of ladle slag detection system （SDS）, such as high cost, short service life, and inconvenient maintenance, a new SDS realization method based on hidden Markov model （HMM） was put forward. The physical process of continuous casting was analyzed, and vibration signal was considered as the main detecting signal according to the difference in shock vibration generated by molten steel and slag because of their difference in density. Automatic control experiment platform oriented to SDS was established, and vibration sensor was installed far away from molten steel, which could solve the problem of easy power consumption by the sensor. The combina- tion of vector quantization technology with learning process parameters of HMM was optimized, and its revaluation formula was revised to enhance its recognition effectiveness. Industrial field experiments proved that this system requires low cost and little rebuilding for current devices, and its slag detection rate can exceed 95%.
