| 引用本文: | 刘 宣,唐 悦,卢继哲,等.基于概率预测的用电采集终端电量异常在线实时识别方法[J].电力系统保护与控制,2021,49(19):99-106. |
| LIU Xuan,TANG Yue,LU Jizhe,et al.Online real time anomaly recognition method for power consumption of electric energy data acquisition terminal based on probability prediction[J].Power System Protection and Control,2021,49(19):99-106 |
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| 摘要: |
| 电力市场环境下用电信息采集系统采集的用电量成为市场结算的重要依据。实时识别用电采集终端上送的异常电量，不但可以提升数据质量，也可以为发现采集终端的故障、识别异常用电行为提供参考。针对现有异常数据识别方法识别性能和实时性不高的问题，提出基于概率预测的电量异常在线实时识别方法。首先，在分析电量异常类型和特点的基础上，提出离线训练概率预测模型、在线实时识别异常数据的检测方法。其次，提出了基于状态空间模型的结构化用电量模型对用户用电规律进行建模，并采用变分贝叶斯推断训练模型，以实现用电量的概率分布预测。最后，利用预测标准分数衡量电量实测数据与电量概率预测结果之间的差异，从而实现异常数据的在线识别。采用实际电量数据进行验证，并与其他方法进行对比，验证了该方法的实用性和有效性。 |
| 关键词: 用电信息采集终端 异常识别 概率预测 结构化电量模型 变分贝叶斯推断 |
| DOI：DOI: 10.19783/j.cnki.pspc.201534 |
| 分类号: |
| 基金项目:国家电网公司科技项目资助(1100-201919158A- 0-0-00) |
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| Online real time anomaly recognition method for power consumption of electric energy data acquisition terminal based on probability prediction |
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LIU Xuan, TANG Yue, LU Jizhe, YE Aliaosha, HOU Shuai, YE Fangbin
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(1. China Electric Power Research Institute, Beijing 100192, China; 2. Marketing Service Center of
State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311121, China)
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| Abstract: |
| The electricity consumption collected by a power user electric energy data acquisition system in the electricity market environment becomes an important basis for market settlement. Real-time identification of abnormal power data can not only improve the quality of power data, but also provide a reference for detection of faults in the collection terminal and identification of abnormal power consumption behavior. In order to solve the problems of existing anomaly recognition methods, a method based on probability prediction is proposed. First, on the basis of analyzing the types and characteristics of electric power anomalies, a detection method for offline training probability prediction models and online identification of abnormal data is proposed. Then, a structured power consumption model based on the state space model is proposed to model the user power consumption rules. The Variational Bayesian Inference is used to train the model in order to realize the probability prediction of power consumption. Finally, it uses the prediction standard score to measure the difference between the measured electricity data and the electricity probability prediction result, so as to realize the online identification of abnormal data. Real electricity data are used for verification and comparison with other methods to verify the practicability and effectiveness of this method.
This work is supported by the Science and Technology Project of State Grid Corporation of China (No. 1100-201919158A-0-0-00). |
| Key words: electric energy data acquire terminal abnormal recognition probabilistic prediction structured electricity model variational Bayesian inference |
