| 摘要: |
| 为解决配电网调度员故障仿真培训自动评价问题，提出了一种基于隔离信息相似度的配电网故障仿真培训评价方法。该方法通过主动隔离范围集、被动隔离范围集、失电负荷量及负荷数、误操作步骤数等构建隔离信息阵以描述调度员及教练员故障仿真培训处理的信息模型。基于人工智能推理思想，通过相似度理论描述实时和标准隔离信息阵之间的相似度以实现调度员故障仿真培训的评价。通过主动隔离范围纵向相似度、被动隔离范围纵向相似度、重要指标纵向相似度、防误操作纵向相似度实现实时和标准隔离信息阵单点元素的相似度分析。通过单点元素的相似度及欧几里德距离实现故障馈线的整体横向相似度，并结合多馈线故障及海明距离实现隔离信息阵综合相似度计算。实例分析表明该方法能较为全面的反映调度员故障隔离及转供操作对配电网运行的影响，能有效的区分不同隔离操作之间的评价档次，方法简便，具有较好的实用价值。 |
| 关键词: 配电网 人工智能 隔离信息阵 相似度 培训评价 |
| DOI：10.7667/PSPC171319 |
| 投稿时间：2017-09-04修订日期：2017-11-12 |
| 基金项目:国家自然科学基金项目(61403321) |
|
| A distribution network fault training evaluation method based on isolation information matrix similarity |
| ZHANG Wei |
| (Jicheng Electronics Corporation, Jinan 250100, China) |
| Abstract: |
| In order to solve the problem of automatic evaluation of dispatcher training, a distribution network fault training evaluation method based on isolation information matrix similarity is proposed. The isolation information matrix is constructed through active isolation range set, passive isolation range set, power loss load and load number, which is the information model of dispatcher and trainer fault simulation training. Based on the artificial intelligence reasoning theory, the similarity between the real-time and standard isolation information matrixes is described by similarity theory to realize the evaluation of dispatcher fault simulation training. Through the active isolation range longitudinal similarity, passive isolation range longitudinal similarity and important index longitudinal similarity, the similarity analysis of the single element of real-time and standard isolation information matrix is realized. Through the similarity of single element and Euclidean distance, the fault feeder horizontal similarity is realized, and the isolation information matrix overall similarity is realized by combining multi feeder fault and Hamming distance. The analysis shows that the method can better reflect the influence of dispatcher fault isolation and transfer operation on the distribution network operation, which can effectively distinguish the evaluation grade between different isolation operations and has good practical value. This work is supported by National Natural Science Foundation of China (No. 61403321). |
| Key words: distribution network artificial intelligence isolation information matrix similarity training evaluation |
