This paper discusses the primary causes from the point of synergistic effects to improve power system vulnerability in the power system planning and safety operation. Based on the vulnerability conception in the compl...This paper discusses the primary causes from the point of synergistic effects to improve power system vulnerability in the power system planning and safety operation. Based on the vulnerability conception in the complex network theory the vulnerability of the power system can be evaluated by the minimum load loss rate when considering power supply ability.Consequently according to the synergistic effect theory the critical line of the power system is defined by its influence on failure set vulnerability in N-k contingencies.The cascading failure modes are proposed based on the criterion whether the acceptable load curtailment level is below a preset value.Significant conclusions are revealed by results of IEEE 39 case analysis weak points of power networks and heavy load condition are the main causes of large-scale cascading failures damaging synergistic effects can result in partial failure developed into large-scale cascading failures vulnerable lines of power systems can directly lead the partial failure to deteriorate into a large blackout while less vulnerable lines can cause a large-scale cascading failure.展开更多
In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the tradit...In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.展开更多
基金The National Natural Science Foundation of China(No.51277028)
文摘This paper discusses the primary causes from the point of synergistic effects to improve power system vulnerability in the power system planning and safety operation. Based on the vulnerability conception in the complex network theory the vulnerability of the power system can be evaluated by the minimum load loss rate when considering power supply ability.Consequently according to the synergistic effect theory the critical line of the power system is defined by its influence on failure set vulnerability in N-k contingencies.The cascading failure modes are proposed based on the criterion whether the acceptable load curtailment level is below a preset value.Significant conclusions are revealed by results of IEEE 39 case analysis weak points of power networks and heavy load condition are the main causes of large-scale cascading failures damaging synergistic effects can result in partial failure developed into large-scale cascading failures vulnerable lines of power systems can directly lead the partial failure to deteriorate into a large blackout while less vulnerable lines can cause a large-scale cascading failure.
基金Supported by the National High Technology Research and Development Programme of China （ No. 2007AA01Z401 ） and the National Natural Science Foundation of China （No. 90718003, 60973027）.
文摘In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.
