Method for determining stability of power mode based on gray relational analysis

Abstract

The invention relates to a method for determining the stability of a power mode based on gray relational analysis and belongs to the field of power supply and distribution management. Based on a two-stage decision algorithm of mode recognition, the method defines a power mode time sequence by a 12-month coefficient for users with no historical electricity stealing records and having meter data more than two years, determines the power mode stability by a gray relational analysis method, predicts a reasonable range of current electricity consumption with reference to a historical power model so as to determine whether the user's current electricity consumption is abnormal. The reasonable range can improve the accuracy of the abnormal judgment without an increase in the workload of manual review, and can narrow the scope of the review, improve the efficiency of the review and the reliability of the meter data, promotes rapid improvement in the quality of a meter reading system by finding a weak link, promotes the development and operation of corresponding computer analysis software and, improve the automation and informatization level of service related to meter reading, can be widely used in the field of centralized meter reading management system design, operation and management.

Description

technical field [0001] The invention belongs to the field of power generation, power transformation or power distribution, and in particular relates to a method for discriminating power consumption patterns of users in power supply and distribution systems. Background technique [0002] Since 2010, under the unified deployment of the State Grid Corporation of China, power supply companies around the world have begun to comprehensively promote the application of low-voltage centralized meter reading systems, and adopt centralized meter reading management systems (referred to as centralized meter reading systems) to centrally collect users' electricity consumption data (referred to as centralized meter reading systems) data), and has basically achieved full coverage. The purpose of implementing this work is to reduce the workload of meter reading and improve the quality of meter reading, and to improve the informationization and automation level of electricity management. [...

AI Technical Summary

Problems solved by technology

At present, the work is only to find out the list of users to be eliminated, and the reason for the missing data depends entirely on manual on-site investigation, which is inefficient and not conducive to the timely completion of the elimination task

[0006] (3) Whether the collection data is abnormal does not completely correspond to the size of the increase / decrease from the previous month - seasonal changes and other factors will also cause a large change in monthly electricity consumption, causing normal misjudgment as abnormal; on the contrary, the monthly electricity consumption is running at a low level But users who do not fluctuate very much will be misjudged as normal

The former in vain increases the workload of manual supplementary copying every month, while the latter causes the loss of electricity and electricity bills and fails to recover in time

[0007] (4) After arranging check-copying for abnormal users, once the check-copy result is found to be inconsistent with the set-copy data, the check-copy data is often taken as the standard, and there is a lack of necessary supervision and control over the quality of manual check-copy

[0008] (5) Collected copy data contains a lot of useful information, which can guide many aspects of work such as defect elimination, anti-stealing electricity, defect elimination / quality evaluation of verification and copying, but it is currently only used as one of the basis for accounting; at the same time, the defect elimination work There is also a lack of necessary information communication among the inspection team, inspection team, and verification team, which is not conducive to the timely discovery of problems and the orderly advancement of work

[0016] (1) Limit the deletion of user objects to the case of missing collection data. In fact, there are some users whose data is not all missing but has defects. Data defects include partial date data missing, frozen value mutations, etc. If these phenomena are not correct After combing, it is still impossible to fully judge the correctness of the accumulated power

[0017] (2) The principle of "month-on-month fluctuation rate ≥ 50%" of monthly electricity consumption in the collection of abnormal judgments does not take into account the impact of seasonal climate change

Temperature-sensitive users have large power consumption fluctuations when the seasons alternate. According to this rule, they will be misjudged as users who need to check and copy, which will increase the workload of checking and copying in vain.

[0018] (3) The review principle of "month-on-month volatility ≥ 50%" of monthly electricity consumption does not take into account the influence of power theft factors

Electricity theft may be at a low level of electricity consumption, and there is not much fluctuation between months. According to the principle of month-on-month volatility, this abnormal situation may be misjudged as normal, which delays the recovery of electricity bills

[0019] (4) The review principle does not take into account the impact of user-specific power consumption patterns

If the abnormality is judged simply based on the monthly electricity consumption level and the chain-to-month volatility without the user's own electricity consumption pattern, it will also cause misjudgment and increase the review workload in vain

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