6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,faul...6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.展开更多
the power network fault detection system can only analyze all kinds of fault signal in stationary sequence： the internal grid and external disturbance presents degeneration, tiny, signal intensity changes randomly fl...the power network fault detection system can only analyze all kinds of fault signal in stationary sequence： the internal grid and external disturbance presents degeneration, tiny, signal intensity changes randomly fluctuate, that will cause the system to detect the fault isolation ability of confusion and small fault signal is not strong; The article propose a method of power network fault detection for based on GSM, Through the underlying sensing equipment acquisition abnormal information of current, voltage power in the network and GSM networking scheme can filter the interference factors in extraction of fault information from and attribute value. The embedded gateway take STM32 chip as the core to monitoring data processing, to achieve a unified data management and user remote access, realizing method of system software are given, to construct the monitor management information platform. The actual test system show that, identification diagnosis ability of fault signal separation ability and small signal increases 17%, also meet the requirements.展开更多
基金supported in part by the National Key Research and Development Project under Grant 2020YFB1806805partially funded through a grant from Qualcomm。
文摘6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.
文摘the power network fault detection system can only analyze all kinds of fault signal in stationary sequence： the internal grid and external disturbance presents degeneration, tiny, signal intensity changes randomly fluctuate, that will cause the system to detect the fault isolation ability of confusion and small fault signal is not strong; The article propose a method of power network fault detection for based on GSM, Through the underlying sensing equipment acquisition abnormal information of current, voltage power in the network and GSM networking scheme can filter the interference factors in extraction of fault information from and attribute value. The embedded gateway take STM32 chip as the core to monitoring data processing, to achieve a unified data management and user remote access, realizing method of system software are given, to construct the monitor management information platform. The actual test system show that, identification diagnosis ability of fault signal separation ability and small signal increases 17%, also meet the requirements.
