随着电力系统的数字化和智能化发展,配变重过载预测成为了实现智能状态检修的关键技术之一。配变过载时空因子在现实场景中通常呈偏置分布。其中,部分高风险罕见(high risk and rare,HRR)因子一旦出现,将对变压器造成无法逆转的伤害。为...随着电力系统的数字化和智能化发展,配变重过载预测成为了实现智能状态检修的关键技术之一。配变过载时空因子在现实场景中通常呈偏置分布。其中,部分高风险罕见(high risk and rare,HRR)因子一旦出现,将对变压器造成无法逆转的伤害。为此,该文提出一种基于提高关联规则关键重要性(improved association rules‐criticality importance,IAR‐CI)模型的配变过载预测方法。首先,考虑内部与外部因素,收集多个数据源并建立配变运行状态数据库,且通过ICA识别与配变重过载强关联的罕见高危时段与HRR;其次,基于关键性重要度(criticality importance,CI)度量计算,设计一种因子权重计算方法,准确衡量因子的风险权重;最后,应用TBFP‐Growth算法,增强模型的运行效率。采用中国南方某地区电网数据进行算例仿真。研究表明,该方法能够提升配变重过载的预测性能,有助于后续巡检、检测策略的合理统筹和科学规划,可在降低电力设备运维检修成本的同时提高供电的可靠性。展开更多
Critical links are defined as easily damaged links with massive transport in highway networks, which also need intensive improvement. The total travel time increment caused by a link＇s failure reflects its importance...Critical links are defined as easily damaged links with massive transport in highway networks, which also need intensive improvement. The total travel time increment caused by a link＇s failure reflects its importance and is taken as the measure of importance. Links are subdivided into segments according to their structure features and environments. Each segment＇s unreliability is the probability of its function failure that cannot be recovered within an expected time. The measure of criticality is defined as the expected total travel time increment and can be obtained from the product of importance and reliability. It reflects a links＇ importance and ability to provide continuous service for evacuation and rescues under earthquake situation. Critical links can then be identified from the sequence of their criticality. These measures are calculated in the highway network of earthquake-hit areas in Wenchuan. Results collected in geographic information system （GIS） visualization are consistent with the situation revealed in this earthquake, which indicates that the presented method can be used to identify critical links in advance and give guidance regarding refugee evacuation and facility protection from earthquakes.展开更多
文摘随着电力系统的数字化和智能化发展,配变重过载预测成为了实现智能状态检修的关键技术之一。配变过载时空因子在现实场景中通常呈偏置分布。其中,部分高风险罕见(high risk and rare,HRR)因子一旦出现,将对变压器造成无法逆转的伤害。为此,该文提出一种基于提高关联规则关键重要性(improved association rules‐criticality importance,IAR‐CI)模型的配变过载预测方法。首先,考虑内部与外部因素,收集多个数据源并建立配变运行状态数据库,且通过ICA识别与配变重过载强关联的罕见高危时段与HRR;其次,基于关键性重要度(criticality importance,CI)度量计算,设计一种因子权重计算方法,准确衡量因子的风险权重;最后,应用TBFP‐Growth算法,增强模型的运行效率。采用中国南方某地区电网数据进行算例仿真。研究表明,该方法能够提升配变重过载的预测性能,有助于后续巡检、检测策略的合理统筹和科学规划,可在降低电力设备运维检修成本的同时提高供电的可靠性。
基金The National High Technology Research and Development Program of China(863 Program)(No2007AA11Z205)the Jiangsu Graduate Innovation Program
文摘Critical links are defined as easily damaged links with massive transport in highway networks, which also need intensive improvement. The total travel time increment caused by a link＇s failure reflects its importance and is taken as the measure of importance. Links are subdivided into segments according to their structure features and environments. Each segment＇s unreliability is the probability of its function failure that cannot be recovered within an expected time. The measure of criticality is defined as the expected total travel time increment and can be obtained from the product of importance and reliability. It reflects a links＇ importance and ability to provide continuous service for evacuation and rescues under earthquake situation. Critical links can then be identified from the sequence of their criticality. These measures are calculated in the highway network of earthquake-hit areas in Wenchuan. Results collected in geographic information system （GIS） visualization are consistent with the situation revealed in this earthquake, which indicates that the presented method can be used to identify critical links in advance and give guidance regarding refugee evacuation and facility protection from earthquakes.
