Power distribution network electricity utilization power abnormity detection method based on objective correlation factors
An anomaly detection and distribution network technology, applied in data processing applications, electrical digital data processing, special data processing applications, etc., can solve the problems of less data and unusable data, and achieve strong pertinence, high matching degree, and high detection results accurate effect
Pending Publication Date: 2019-10-01
SHANGHAI MUNICIPAL ELECTRIC POWER CO +2
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[0003] In view of the fact that there are few sample data of electricity theft and leakage in China, it is impossible to use supervised learning to learn and build abnormal electricity consumption behavior identification models. A method is used to identify the degree and type of abnormal electricity consumption of users through comprehensive abnormal indicators and sub-item abnormal indicators, and assist users The management unit conducts electricity consumption inspection, user management and other aspects, and the abnormal detection method of the distribution network to improve the work efficiency of the relevant departments of the power company needs to be developed urgently
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[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.
[0041] The density-based local outlier factor detection (Local Outlier Factor, LOF) algorithm is a relatively representative algorithm in the density-based outlier detection method. This algorithm will calculate an outlier factor LOF for each point in the data set, and determine whether it is an outlier factor by judging whether the LOF is close to 1. If the LOF is much greater than 1, it is considered an outlier factor, and if it is close to 1, it is a no...
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The invention relates to a power distribution network electricity utilization power abnormity detection method based on objective correlation factors. The method comprises the following steps: 1, establishing a theoretical electricity consumption model combined with comprehensive electricity consumption characteristic information for electricity consumption users; 2, collecting the actual power consumption of the power consumption user in real time; 3, obtaining a deviation between the actual power consumption and the theoretical power consumption model; and 4, carrying out outlier detection on the deviation by using an LOF algorithm and obtaining an abnormal suspected user detection result. Compared with the prior art, the method has the advantages of high detection accuracy, strong pertinence and the like.
Description
technical field [0001] The invention relates to the technical field of abnormal detection of distribution network power consumption, in particular to a method for detecting abnormal power consumption of distribution network based on objective correlation factors. Background technique [0002] Abnormal electricity consumption behaviors of distribution network users include electricity theft, electricity leakage, electricity theft, changes in the nature of electricity consumption, and user changes. Such behaviors will reduce the efficiency of demand-side management and the effectiveness of policy formulation. one of the research hotspots. [0003] In view of the fact that there are few sample data of electricity theft and leakage in China, it is impossible to use supervised learning to learn and build abnormal electricity consumption behavior identification models. A method is used to identify the degree and type of abnormal electricity consumption of users through comprehensi...
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IPC IPC(8): G06Q50/06G06F17/50
CPCG06Q50/06G06F30/20
Inventor 田英杰吴力波周阳马戎施政昱陈伟苏运郭乃网瞿海妮张琪祁时志雄宋岩庞天宇沈泉江
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO