Method for on-line identification of electricity stealing behavior of high-voltage power supply and high-voltage metering special transformer user

Abstract

The invention discloses a method for on-line identification of the electricity stealing behavior of a high-voltage power supply and high-voltage metering special transformer user, and the method comprises the following steps: (1) collecting the real-time data of the primary side current of a power supply incoming line through power distribution automation equipment, collecting the secondary side current data according to a metering collection device, and determining the change trend of the real-time data according to the two data; (2) judging whether the current is abnormal or not according to the real-time data change, representing the primary side current by i1, representing the secondary side current by i2 and representing the transformation ratio data of the current transformer by k, and formulating an error coefficient; (3) analyzing according to real-time hourly data every day, if delta is greater than 20% in the same period, indicating that electricity stealing suspicion exists, and if delta is greater than 20%, indicating that the electricity stealing suspicion exists. The information is automatically sent to related workers for evidence obtaining and investigation on site; and (4) taking the next group of real-time data for analysis and calculation. According to the invention, the electricity stealing behavior of the high-voltage power supply and high-voltage metering special transformer user is identified online for the first time, and the problem that the electricity stealing investigation of the special transformer user is passive for a long time is solved.

Description

Technical field:

[0001] The present invention relates to a technology for preventing electric power theft, and in particular to a method for online identifying electric power theft behavior of users of high-voltage power supply and high-metering special transformers. Background technology:

[0002] This year, affected by various internal and external factors such as the economic downturn, the operating pressure of power grid companies has increased. In order to ensure the economic benefits of power grid companies, line loss management has become a top priority for power grid companies. Special transformer users have large electricity consumption, which is particularly critical in medium-voltage line loss management. However, the methods of stealing electricity by special transformer users are becoming more and more technological, and the current investigation and punishment of electricity theft is relatively passive, and there are often situations of inadequate monitoring, untimely detection, and inaccurate judgment. Therefore, there is an urgent need for an online automatic identification method for electricity theft that can detect electricity theft in a timely manner, deal with it in a timely manner, and maintain normal power supply and use order. Summary of the invention:

[0003] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a method for online identification of electricity theft behavior of high-supply and high-meter dedicated transformer users with reasonable design and good anti-electricity theft effect and timely and accurate.

[0004] The technical solution of the present invention is:

[0005] A method for online identification of electricity theft behavior of high-supply and high-meter dedicated transformer users, comprising the following steps:

[0006] (1) Collect the real-time data of the primary current of the power supply line through the distribution automation equipment, and collect the secondary current data through the metering and collection device, and determine the real-time data change trend based on these two data;

[0007] (2) According to the above real-time data changes, determine whether the current is abnormal. The primary current is represented by i1, the secondary current is represented by i2, and the transformation ratio data of the current transformer is represented by k. The error coefficient is formulated:

[0008]

[0009] (3) Analyze the real-time hourly data every day. Three consecutive hourly data constitute a cycle. If Δ is greater than 20% in the same cycle, it means there is a suspicion of electricity theft. The relevant staff will be automatically assigned to collect evidence and investigate on site.

[0010] (4) Take a set of real-time data for analysis and calculation to complete a work cycle.

[0011] The beneficial effects of the present invention are:

[0012] 1. The present invention realizes the online identification of electricity theft by users of high-supply and high-meter dedicated transformers for the first time, solving the problem that the investigation and punishment of electricity theft by users of dedicated transformers has been relatively passive for a long time.

[0013] 2. The present invention uses the primary and secondary currents of the dedicated high-supply and high-meter dedicated transformer users as the criterion for judging electricity theft for the first time, and combines the current transformer ratio data of the high-supply and high-meter dedicated transformer users to identify electricity theft with extremely high accuracy.

[0014] 3. The present invention is reasonably designed and can detect electricity theft in a timely manner and deal with it in a timely manner, thereby maintaining the normal order of electricity supply and use, and has good economic benefits after promotion. Description of the drawings:

[0015] Figure 1 A flow chart of a method for online identification of electricity theft behavior by high-supply and high-meter dedicated transformer users;

[0016] Figure 2 It is a practical application diagram of the present invention;

[0017] Figure 3 This is the primary side A and C phase current data diagram;

[0018] Figure 4 This is the secondary side A and C phase current data diagram measured on the high voltage side;

[0019] Figure 5 This is the current transformer ratio data diagram;

[0020] Figure 6 Schematic diagram of undercounting of current due to strong magnetism;

[0021] Figure 7 Schematic diagram of short-circuiting the current transformer resulting in under-measurement of current. Specific implementation method:

[0022] Example: See Figure 1-Figure 7 , in the figure:

[0023] The method for online identification of electricity theft behavior of high-supply and high-meter dedicated transformer users is as follows:

[0024] (1) Collect the real-time data of the primary current of the power supply line through the distribution automation equipment, and collect the secondary current data through the metering and collection device, and determine the real-time data change trend based on these two data;

[0025] (2) Determine whether the current is abnormal based on the current values ​​on the primary and secondary sides and the transformation ratio data of the current transformer. The primary current is represented by i1, the secondary current is represented by i2, and the transformation ratio data of the current transformer is represented by k. The error coefficient is formulated;

[0026]

[0027] (3) Analyze the real-time hourly data every day. Three consecutive hourly data constitute a cycle. If Δ is greater than 20% in the same cycle, it means there is a suspicion of electricity theft. The relevant staff will be automatically assigned to collect evidence and investigate on site.

[0028] (4) Take a set of real-time data for analysis and calculation.

[0029] Practical Application:

[0030] The practical application of online identification of electricity theft behavior of high-power and high-meter dedicated transformer users is as follows: Figure 2 As shown. The distribution automation equipment can collect real-time data of the primary current of the power supply line. According to the information of the user's metering device, it can be compared with the secondary current collected by the power consumption collection system in real time to determine whether the current is normal. If the data is abnormal for three consecutive hours, the electricity theft user can be automatically identified through analysis and calculation, and on-site evidence collection and investigation can be carried out.

[0031] The primary current collected by the distribution system, the secondary current collected by the power collection system, and the current transformer ratio data are as follows: Figure 3-5 shown.

[0032] Example analysis:

[0033] Taking the data collected in real time by the distribution automation system and the power collection system as an example for analysis, within the same cycle (taking the data of the three hours at 1 o'clock, 2 o'clock, and 3 o'clock), 100 high-supply high-metering special transformer users are selected as monitoring objects. Due to limited space, only 3 user data are listed. The data of distribution automation equipment and power consumption information collection system are shown in Table 1, where i 1U Indicates the primary current of phase A, i 1W Indicates the primary current of phase C, i 2U Indicates the secondary current of phase A, i 2W represents the secondary current of phase C, and K represents the transformation ratio of the high-voltage current transformer.

[0034] Table 1 Real-time data collected by the remote system

[0035]

[0036]

[0037] After the real-time data collected enters the computer's analysis and calculation program, the electricity theft users and the types of electricity theft can be quickly determined. The online identification of electricity theft behavior can identify the electricity theft users among the 100 high-supply and high-metering special transformer users in real time. According to the analysis and calculation results, the users with serial numbers 1 and 2 in Table 1 are suspected electricity theft users, and the types of electricity theft can be automatically determined. Both of them have obvious under-counting of current in the metering device. After the suspected electricity theft users are identified, they are sent to the relevant staff for on-site evidence collection and investigation. The on-site electricity theft situations of serial numbers 1 and 2 are as follows: Figure 6 and Figure 7 After collecting evidence and investigating on the spot, the electricity theft users were charged electricity fees and breach of contract fees in accordance with laws and regulations, thus maintaining the normal order of electricity supply and use.

[0038] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification made according to the technical essence of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A method for online identification of electricity theft behavior of high-power supply and high-meter dedicated transformer users, characterized by: : Includes the following steps: (1) Collect the real-time data of the primary current of the power supply line through the distribution automation equipment, and collect the secondary current data through the metering and collection device, and determine the real-time data change trend based on these two data; (2) According to the above real-time data changes, determine whether the current is abnormal. The primary current is represented by i1, the secondary current is represented by i2, and the transformation ratio data of the current transformer is represented by k. The error coefficient is formulated: (3) Analyze the real-time hourly data every day. Three consecutive hourly data constitute a cycle. If Δ is greater than 20% in the same cycle, it means there is a suspicion of electricity theft. The relevant staff will be automatically assigned to collect evidence and investigate on site. (4) Take a set of real-time data for analysis and calculation to complete a work cycle.

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