CN106685086B - Remote power utilization management system - Google Patents
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Abstract
The invention belongs to the field of power grid construction, and particularly relates to remote power management systems which comprise a data acquisition layer for acquiring information, a master station layer for processing and analyzing and a communication layer for communicating the data acquisition layer and the master station layer.
Description
Technical Field
The invention belongs to the field of power grid construction, and particularly relates to remote power utilization management systems.
Background
The power industry is a basic industry in national economy, is related to the national civilization, has the property of public service, and the power development is a basic guarantee for social progress and improvement of people's life. Ensuring that power grid enterprises timely recover the electric charge is a necessary condition for ensuring the power development, and is also a necessary choice for protecting national assets and maintaining the market order. However, for various reasons, electricity theft is still ubiquitous at present, and parts of the country are even rampant. The loss caused by electricity stealing to the power grid enterprise is huge.
The increasingly serious behavior of electricity stealing has great influence on the benefits of power supply enterprises and causes great loss to national economy. Electricity stealing not only damages the economic benefits of the country and the power enterprises, but also endangers the safe operation of the power grid and hinders the normal development of the power industry. Personal safety accidents are easily caused by the private connection and disconnection behaviors of individual users. In addition, part of industrial and commercial users, especially high energy consumption industries, have large electric charge in the cost, and the electricity stealing behavior destroys the environment of fair competition in the market.
At present, electricity stealing behavior is mainly achieved by the following means: 1. the power supply facilities of the power supply enterprises use electricity by unauthorized wiring; 2. power consumption of a power consumption metering device of a power supply enterprise is bypassed; 3. forging or starting a legal or authorized electricity metering device sealed by a metering and verifying mechanism to seal electricity consumption; 4. the power supply enterprise electricity metering device is damaged intentionally. 5. Deliberately causing the electricity metering device of the power supply enterprise to be inaccurate or invalid; 6. other methods are used to steal electricity.
However, for the above-mentioned behavior of electricity stealing, China still stays at the initial stage in the aspect of the construction of an electricity stealing prevention system for a long time, and mainly depends on manual investigation, and the prevention and control means is behind, so that electricity stealing molecules become rampant day by day, and the behavior of electricity stealing is increasingly serious.
Disclosure of Invention
Aiming at the technical problems of the traditional electricity stealing prevention method, the invention provides remote electricity management systems which are reasonable in design, simple in structure, convenient to operate and capable of accurately identifying electricity stealing users.
In order to achieve the purpose, the invention adopts the technical schemeThe invention provides remote power management systems, which comprise a data acquisition layer for acquiring information, a master station layer for processing and analyzing, and a communication layer for communication between the data acquisition layer and the master station layer, wherein the data acquisition layer comprises a high-voltage wireless acquisition device arranged on a high-voltage side of a transformer, a low-voltage special transformer acquisition device arranged on a low-voltage side of the transformer, and an intelligent electric meter arranged on a low-voltage user side, the master station layer comprises a performance module for providing a business application operation interface and an information display window of a system , an application module for realizing electricity stealing analysis operation, a component service module for providing general business service and safety service, and a database module for realizing mass information storage, access and arrangement, the application module comprises a single-phase power analysis unit for analyzing electricity stealing behavior according to detected power and actual power, a power utilization detection unit based on a particle algorithm, an abnormal user analysis unit based on horizontal migration detection, a trend analysis unit for suspicion degree analysis, a trend analysis unit for suspicion electric quantity analysis, and a steady electric quantity analysis unit for suspicion data migration, and a data analysis method for finding out a corresponding abnormal data, and a daily data migration processing method for obtaining a daily data, wherein the data, and a daily data processing method for refining daily consumption analysis includes a daily data processing method for obtaining a daily abnormal data processing method for obtaining a daily consumption data, and a daily consumption data processing method for obtaining a daily consumption data, and a daily consumption data processing method for obtaining1，X2，X3…Xn(ii) a To detect horizontal migration, the determination of the cumulative sum method is as follows, and the average of n data is first calculated according to the following formula:
wherein the content of the first and second substances,
time for daily electricity dataAverage value of daily electricity consumption of n days in span;
further, the cumulative sum is defined as the cumulative sum of the difference between each point value and the average value, that is:
wherein S isiIs the cumulative sum of the average values of the daily power consumption on the ith day in the time span of the numerical values of the points and the daily power consumption data, XiThe daily electricity consumption data of the ith day;
and drawing the calculated values according to the time sequence to obtain an accumulation sum value graph, and then calculating the slope of the accumulation sum value according to the condition of the accumulation sum value graph, wherein the calculation formula is as follows:
wherein k is the slope of the accumulated sum value of the daily electric quantity data in the time period from m to n, SmThe cumulative sum of the average value of the daily electricity consumption of the mth day in the time span of the numerical value of each point and the daily electricity consumption data is obtained; snThe cumulative sum of the average value of the daily electricity consumption of the nth day in the time span of the numerical value of each point and the daily electricity consumption data; xm+1Is the daily electricity data, X, at the m +1 th daynThe daily electricity consumption data of the nth day;
the method is used for analyzing the accumulated sum value graph of the user daily electricity consumption time series to know whether the user daily electricity consumption time series is in horizontal migration or not and further judge whether the electricity consumption user is suspected to be abnormal.
Preferably, the data acquisition layer is used for acquiring a total electric energy indication value, an electric energy peak value, an electric energy valley value, a current, a voltage, a power factor, a phase angle, an electric meter type, a transformer area line loss value, an electric energy same ratio value, a current three-phase imbalance rate and a voltage three-phase imbalance rate.
Preferably, the high-voltage wireless collector is uniformly arranged on the three-phase circuit in a busbar mode.
Preferably, the single-phase power analysis unit is based on the formula:
wherein Pr is the electricity consumption power measured by the electric energy meter, Pm is the actual electricity consumption power of the user,
is the phase angle.
Preferably, the trend suspected user analysis unit is based on the formula:
wherein X is the suspicion degree, eiFor the user i the amount of electricity used in this month,
the ring ratio of monthly electricity consumption in the distribution area increases, ei-1The power consumption of the last month of the user i,
average electricity consumption of the user in the month and the day.
Compared with the prior art, the invention has the advantages and positive effects that,
1. according to the invention, remote power utilization management systems are provided, mutual cooperation among the data acquisition layer, the master station layer and the communication layer is utilized, and the analysis method of the master station layer is utilized to effectively determine the power stealing users according to the data acquired by the acquisition layer, so that the power lines of the power stealing users are investigated, the power stealing and the power stealing behaviors are effectively avoided, and further the loss of a power supply company is reduced.
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In order to more clearly illustrate the technical solution of the embodiment of the present invention, is briefly introduced in the drawings required in the description of the embodiment, it is obvious that the drawings in the description below are embodiments of the present invention, and those skilled in the art can also obtain other drawings according to these drawings without inventive labor.
Fig. 1 is a schematic structural diagram of a remote power management system provided in embodiment 1;
fig. 2 is a flowchart of the remote electricity management system provided in embodiment 1;
fig. 3 is a schematic structural diagram of an application module provided in embodiment 1.
Detailed Description
In order to make the above objects, features and advantages of the present invention more clearly understandable, the present invention is further described below with reference to the accompanying drawings and examples.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and thus the present invention is not limited to the specific embodiments of the present disclosure.
Embodiment 1, as shown in fig. 1, fig. 2, and fig. 3, this embodiment provides special remote power management systems for electricity stealing behavior, including a data acquisition layer for acquiring information, in this embodiment, the data acquisition layer is mainly used to acquire a total electric energy indication value, an electric energy peak value, an electric energy valley value, a current, a voltage, a power factor, a phase angle, an electric meter type, a platform area line loss value, an electric energy same-ratio value, a current three-phase imbalance rate, and a voltage three-phase imbalance rate, wherein the electric energy same-ratio value, the current three-phase imbalance rate, and the voltage three-phase imbalance rate need to be obtained through simple calculations.
The data management system comprises a data management system and a data management system, wherein the data management system comprises a data management layer and a data access layer, the data management system comprises a data management layer and a main station layer, the main station layer is used for processing analysis, the main station layer comprises a software part and a hardware part, the hardware part of the main station layer mainly comprises a data mining library server, a disk array, an application server, a front-end server, an interface server, a workstation, a clock, a firewall device and related network devices, in the embodiment, the database server forms a database module which is used for storing, accessing and sorting mass information, in the embodiment, the data management system adopts an SQLServer2000 database, and in the aspect of data. In the embodiment, the data stored in the database has the following aspects that (1) the enterprise file information comprises a unit, a responsible person, a postcode, an address and the like, and a code, remark information and the like of a business area to which the enterprise file information belongs; (2) the subscriber profile information relates to data. The system comprises a user name, a gender, the year and month of birth, an address, a zip code, the time of living, a code of a business area and remark information, and the like; (3) the data acquisition layer acquires the data. The system comprises current data, voltage data, active power data and reactive power data collected by a field load control device; (4) the data were analyzed and compared. The method mainly aims at the preset data range of enterprises and users and is used for detecting whether the power consumption of the enterprises and the users is standard or not; (5) electricity price related data. The method comprises the steps of including enterprise electricity price, household electricity price, peak hour electricity rate and corresponding time period, valley hour electricity rate and corresponding time period and the like; (6) data about system accounts. Including the account name, password, and permissions possessed by the system account.
The master station layer also includes a service application operation interface and an information display window of a providing system , the performance module is a service application operation interface and an information display window of a providing system , and is a part of the system directly facing an operation user, in this embodiment, the performance module mainly includes a user management unit, a basic service unit, a browsing query unit, an electricity consumption parameter setting unit, a data management unit, an alarm management unit and a quit system unit, wherein the user management unit is used for managing various types of operators in the whole software system, only a system administrator with administrator identity can use the performance module, the system administrator can add or delete corresponding operators, the identity of the operators can be changed, the password of a certain operator can be changed, the basic service unit is used for performing the same basic work of a power supply part , including the operation of users, electricity sales, electricity consumption data acquisition, remote control, electricity consumption data output and the like, the system administrator and ordinary electricity sales personnel can use the performance module, the browsing query unit mainly includes the following information of users, the information of the users, the electricity purchase information, the information of the electricity purchase information, the information of the enterprise, the information, the enterprise information, the information.
The main station layer also comprises a service module which mainly provides component service support such as general service and safety service of the whole system, realizes special service logic service of the system and provides general technical support for the application module.
The key point in this embodiment is an application module, which implements specific business logic and is a core layer of a system master station, and in order to accurately determine an electricity stealing user, in this embodiment, the application module is divided into a single-phase power analysis unit that analyzes an electricity stealing behavior according to a detected power consumption and an actual power consumption, an electricity detection unit based on a particle algorithm group algorithm, an abnormal user analysis unit based on horizontal migration detection, a trend suspected user analysis unit for suspicion analysis, and an integrated data analysis unit that analyzes data and finds the most suspicious electricity stealing data.
The single-phase electric energy analysis unit measures the single-phase electric quantity through the intelligent electric meter, and deduces the power consumption of a user. The active load measured by the electric energy meter can be calculated by the formula:
in the formula PmActive power, U, measured for electric energy metersABIs AB phase voltage, IAFor the phase of the a-phase current,
is the phase angle. According to the three-phase power calculation formula, the user active load theoretical value can be calculated by the following formula:
in the formulaPr is the actual active load power of the user, U is the voltage, I is the current,
is the phase angle.
Therefore, the following relation exists between the active load measured by the electric energy meter and the theoretical value of the active load of the user
Wherein Pr is the electricity consumption power measured by the electric energy meter, Pm is the actual electricity consumption power of the user,
is the phase angle.
The actual power consumption of the user can be calculated through the formula, so that the electric energy obtained through the actual power consumption function of the user is compared with the electric energy collected by the electric energy meter, and whether the user changes the electric energy meter or not can be determined, and the electricity stealing behavior is achieved.
The system also comprises a power utilization detection unit based on a particle algorithm group algorithm, wherein the power utilization detection unit is mainly based on the historical load data of users, the change trends of the same type of power utilization load in the same region have similarity, but different types and quantities of equipment cause different load base numbers, and the load change of the users per se in the same months in adjacent years is not large.
In the formula, XiminAnd XimaxThe minimum value and the maximum value of the ith user in the load data.
After the user industrial electricity load data is subjected to the grouping processing, a historical electricity load pattern curve and a user inspection day standard electricity load curve are extracted by using a PSO algorithm, and then the matching of the user inspection day electricity load and the self historical load pattern is matched based on the time sequence of correlation coefficients, so that not only is the result showing the change trend reflected, but also the load base number is relatively close, therefore, the matching degree of HLD and CLD is calculated by adopting the measurement based on the average relative distance, and the definition is as follows:
the method comprises the steps of obtaining the matching degree of a user inspection daily load curve and a similar user load mode curve in the same region, determining the preference degree of the two matching degrees according to actual conditions after the user inspection daily load curve is matched with a user historical load mode curve, finally weighting and summing to obtain the power utilization normality of a user, setting an alarm threshold value of the power utilization normality by a power supply enterprise according to credit condition evaluation of the user, and determining the user as a normal user when the power utilization normality of the user is greater than the alarm threshold value; on the contrary, the number of times of abnormal electricity utilization of the user is increased by 1, after Q is more than a plurality of times within the specified time length, the user is classified as a suspected user of abnormal electricity utilization, inspection personnel needs to be sent to the user for field detection, and whether electricity stealing behaviors exist in the user is judged according to the detection result. The number Q of abnormal electricity utilization times can also be set according to the credit condition evaluation of the user, and whether the user is suspected to steal electricity can be judged through a particle algorithm group algorithm.
The application module further comprises an abnormal user analysis unit based on horizontal migration detection, and the abnormal user analysis unit is applied to detection of abnormal power consumption in the daily power consumption sequence in a fixed time span based on the horizontal migration judgment method of the accumulated sum value, and corresponding methods are refined. For a simple illustration, the following: according to the fact that the time span of the known daily electricity consumption data contains n data points, namely the daily electricity consumption data corresponding to n days, the time sequence obtained after the daily electricity consumption is subjected to stabilization processing is recorded as X1，X2，X3…Xn. To detect horizontal migration, the determination of the cumulative sum method is as follows, and the average of n data is first calculated according to the following formula:
further, the cumulative sum is defined as the cumulative sum of the difference between each point value and the average value, that is:
and drawing the calculated values according to the time sequence to obtain an accumulation sum value graph, and then calculating the slope of the accumulation sum value according to the condition of the accumulation sum value graph, wherein the calculation formula is as follows:
the relatively straight line segment in the accumulation sum value graph means that data points in the period of time are not suddenly changed, and the inflection point in the accumulation sum value graph means that the data points are suddenly changed horizontally.
The application module further comprises a trend suspected user analysis unit, the trend suspected user analysis unit is mainly used for analyzing a built-in trend suspected analysis algorithm, the trend suspected analysis algorithm is used for analyzing the general change trend of the power consumption in a certain period of a certain platform area (compared with the power consumption in the last period) and comparing and analyzing the change trend of the power consumption in each equal period of each user in the platform area by taking the general change trend as a trend reference, if the trend of the users is not to a large extent, the users are judged to be suspected, the electricity stealing suspicion degree is measured according to the condition that the trend is not , the essential content of the trend suspected analysis algorithm is provided, and the period is in months in .
The algorithm is mainly used for naming users in a cell under a cell, for example, M users exist in a certain cell. The electricity consumption of the ith user in the current month is eiThe power consumption of the user in the last month is ei-1The average electricity consumption of the user in the month and the day is
The number of days in this month is N, the electricity consumption in this month and the electricity consumption in the previous month in the platform area are E, E respectively-1，
Therefore, the ring ratio of the monthly electricity consumption in the platform area increases as follows:
meanwhile, the increase trend of the monthly electricity consumption of each user can be obtained:
suspected user determination rule that the trend of the power consumption of the user in the current month
The user(s) is a normal user, and if the user(s) is smaller than the normal user, the user(s) is a suspected user. In the suspected user, the greater the deviation degree of the incremental trend, the greater the suspicion degree. The suspicion degree is calculated as:
wherein X is the suspicion degree, eiFor the user i the amount of electricity used in this month,
the ring ratio of monthly electricity consumption in the distribution area increases, ei-1The power consumption of the last month of the user i,average electricity consumption of the user in the month and the day. If the users with large power consumption need to be screened out according to the size of the power consumption, part of suspects can be removed according to the screening threshold value, and the rest suspects are all the users with large power consumption. And finally, obtaining the users needing to be checked.
Through above-mentioned 4 units, utilize different algorithms, and then confirm certain suspect, rethread comprehensive data analysis unit finds out the user that appears simultaneously in 4 units or the user that appears twice at least, confirms the biggest suspect user, and then sends out the police dispatch newspaper, arranges that power supply company personnel go to the investigation before.
The communication layer is a link between the master station layer and the data acquisition layer, provides various available wired and wireless communication channels, and provides a link foundation for information interaction between the master station and the acquisition terminal. The communication channels mainly used are: a private optical fiber network, a GPRS/CDMA wireless public network, and a private wireless network, and in this embodiment, a GPRS wireless public network is used.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention in other forms, and any person skilled in the art may apply the above modifications or changes to the equivalent embodiments with equivalent changes, without departing from the technical spirit of the present invention, and any simple modification, equivalent change and change made to the above embodiments according to the technical spirit of the present invention still belong to the protection scope of the technical spirit of the present invention.
Claims (5)
1, remote power management system, comprising a power management system for managing power consumptionThe intelligent electric energy monitoring system comprises a data acquisition layer for acquiring information, a master station layer for processing and analyzing and a communication layer for communication between the data acquisition layer and the master station layer, wherein the data acquisition layer comprises a high-voltage wireless acquisition device arranged on a high-voltage side of a transformer, a low-voltage special transformer acquisition device arranged on a low-voltage side of the transformer and an intelligent electric meter arranged on a low-voltage user side, the master station layer comprises a performance module for providing a service application operation interface and an information display window of , an application module for realizing electricity stealing analysis operation, a component service module for providing general service and safety service and a database module for realizing mass information storage, access and arrangement, the application module comprises a single-phase electric energy analysis unit for analyzing electricity stealing behavior according to detected electric power and actual electric power, an electricity utilization detection unit based on a particle algorithm group algorithm, an abnormal user analysis unit based on horizontal migration detection, a trend suspicion analysis unit for suspicion degree analysis and a trend analysis unit for summarizing single-phase electric energy analysis unit, an electricity utilization detection unit, an abnormal user analysis unit and a trend analysis unit for finding out the most suspicion integrated analysis of electric quantity, and a stable analysis data analysis and a steady analysis unit for finding out abnormal electric quantity, and a corresponding daily data processing method for refining daily data, wherein the daily migration of the abnormal data acquisition sequence comprises a daily migration time span of the corresponding n, and a daily data acquisition time series, and a daily data acquisition method for refining step of the integrated analysis unit, and a daily data acquisition1，X2，X3…Xn(ii) a To detect horizontal migration, the determination of the cumulative sum method is as follows, and the average of n data is first calculated according to the following formula:
wherein the content of the first and second substances,
the average value of daily electricity consumption of n days in the time span of the daily electricity consumption data is obtained;
further, the cumulative sum is defined as the cumulative sum of the difference between each point value and the average value, that is:
wherein S isiIs the cumulative sum of the average values of the daily power consumption on the ith day in the time span of the numerical values of the points and the daily power consumption data, XiThe daily electricity consumption data of the ith day;
and drawing the calculated values according to the time sequence to obtain an accumulation sum value graph, and then calculating the slope of the accumulation sum value according to the condition of the accumulation sum value graph, wherein the calculation formula is as follows:
wherein k is the slope of the accumulated sum value of the daily electric quantity data in the time period from m to n, SmThe cumulative sum of the average value of the daily electricity consumption of the mth day in the time span of the numerical value of each point and the daily electricity consumption data is obtained; snThe cumulative sum of the average value of the daily electricity consumption of the nth day in the time span of the numerical value of each point and the daily electricity consumption data; xm+1Is the daily electricity data, X, at the m +1 th daynThe daily electricity consumption data of the nth day;
the method is used for analyzing the accumulated sum value graph of the user daily electricity consumption time series to know whether the user daily electricity consumption time series is in horizontal migration or not and further judge whether the electricity consumption user is suspected to be abnormal.
2. The remote power management system of claim 1, wherein the data acquisition layer is configured to acquire a total power indication, a power peak, a power valley, a current, a voltage, a power factor, a phase angle, a meter type, a platform line loss, a power unity ratio, a current three-phase imbalance rate, and a voltage three-phase imbalance rate.
3. The remote power management system according to claim 1, wherein the high voltage wireless collector is uniformly arranged on the three-phase circuit in a busbar type.
4. The remote power management system of claim 1, wherein the single-phase power analysis unit is based on the formula:
wherein Pr is the electricity consumption power measured by the electric energy meter, Pm is the actual electricity consumption power of the user,
is the phase angle.
5. The remote power management system according to claim 1, wherein the trending suspected user analysis unit is based on a formula:
wherein X is the suspicion degree, eiFor the user i the amount of electricity used in this month,
annual monthly power consumption ratio for platform areaA tendency to grow, ei-1The power consumption of the last month of the user i,
average electricity consumption of the user in the month and the day.
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| CN107861389B (en) * | 2017-10-09 | 2020-11-10 | 珠海格力电器股份有限公司 | Electric equipment display processing method |
| CN107907759A (en) * | 2017-10-26 | 2018-04-13 | 国网黑龙江省电力有限公司信息通信公司 | The electricity monitoring system and method for power equipment |
| CN109243162A (en) * | 2018-11-21 | 2019-01-18 | 杭州中自沣泰环境技术有限公司 | Meter register method, apparatus and system based on intelligence instrument |
| CN112816775A (en) * | 2020-12-15 | 2021-05-18 | 国网宁夏电力有限公司营销服务中心(国网宁夏电力有限公司计量中心) | Hidden line electricity stealing troubleshooting and detecting method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102403794A (en) * | 2011-09-26 | 2012-04-04 | 国网信息通信有限公司 | Integrated management system of intelligent power grid |
| CN102570610A (en) * | 2012-01-19 | 2012-07-11 | 四川富谊智能电力技术有限公司 | Intelligent electricity-stealing preventing system and method |
| CN203455946U (en) * | 2013-09-30 | 2014-02-26 | 国家电网公司 | Automatic collection system of power consumption |
| CN105203924A (en) * | 2015-10-10 | 2015-12-30 | 上海博英信息科技有限公司 | Electricity usage trend abnormity suspicion analysis method and anti-electric-larceny monitoring system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140210460A1 (en) * | 2013-01-30 | 2014-07-31 | Hampden Kuhns | Contactless electric meter reading devices |
-
2017
- 2017-01-19 CN CN201710043100.6A patent/CN106685086B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102403794A (en) * | 2011-09-26 | 2012-04-04 | 国网信息通信有限公司 | Integrated management system of intelligent power grid |
| CN102570610A (en) * | 2012-01-19 | 2012-07-11 | 四川富谊智能电力技术有限公司 | Intelligent electricity-stealing preventing system and method |
| CN203455946U (en) * | 2013-09-30 | 2014-02-26 | 国家电网公司 | Automatic collection system of power consumption |
| CN105203924A (en) * | 2015-10-10 | 2015-12-30 | 上海博英信息科技有限公司 | Electricity usage trend abnormity suspicion analysis method and anti-electric-larceny monitoring system |
Non-Patent Citations (2)
| Title |
|---|
| 低压台区用户的防窃电的趋势嫌疑度分析方法研究;杨小铭等;《电器与能效管理技术》;20160530(第10期);第28-30及36页 * |
| 基于水平迁移侦测的异常用电在线分析方法;王新娜等;《电器自动化》;20161130;第38卷(第6期);第108-111页 * |
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