CN114722722A - Power consumption data anomaly detection method and system based on big data analysis - Google Patents
Power consumption data anomaly detection method and system based on big data analysis Download PDFInfo
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Abstract
The invention discloses a big data analysis-based power consumption data anomaly detection method and system, and belongs to the field of power safety detection. The invention comprises the following steps: the method comprises the steps of integrating historical electricity stealing behaviors, obtaining historical electricity stealing data, and constructing an accurate electric electricity stealing identification and electricity stealing feedback data analysis model; measuring the accuracy of the accurate electric power stealing identification and electric power stealing feedback data analysis model through the covariance of standardized processing, and correcting the accurate identification and electric power stealing feedback data analysis model by using a loss function; and acquiring real-time power utilization data, and inputting the real-time power utilization data into an accurate identification and electricity stealing feedback data analysis model to obtain whether electricity stealing phenomena and line loss data exist. The user electricity stealing analysis method based on the big data is beneficial to efficient investigation and treatment of electricity stealing behaviors, and meanwhile, the analysis can be carried out on the electricity stealing behaviors, so that the line loss management of a power supply company is facilitated.
Description
Technical Field
The invention relates to the field of electric power safety detection, in particular to a power utilization data abnormity detection method and system based on big data analysis.
Background
In recent years, along with the continuous development of economy in China, the scale of a power grid, the power supply range and the number of users are continuously enlarged, the power consumption of the users is also increased day by day, and illegal electricity stealing behaviors are difficult to avoid. The electricity stealing breaks the normal electricity utilization order, seriously affects the operation safety of a power grid, threatens the personal and property safety, and causes great economic loss to power supply enterprises and society due to the damage of power equipment such as a transformer and the like caused by electricity stealing and personal electric shock accidents.
The traditional investigation of electricity stealing users is mainly through carrying out manual person-by-person inspection on the transformer area with higher loss, thereby wasting time and labor, having huge workload, requiring more personnel for field inspection and having higher labor cost. Moreover, the checking work is lack of pertinence, the checking time of a single household is long, the report omission easily occurs, the workers are often required to go to the site for checking for many times, and the efficiency is low. Therefore, how to solve the above problems needs to be studied by those skilled in the art.
Disclosure of Invention
In view of this, the present invention provides a method and a system for detecting abnormal electricity consumption data based on big data analysis, so as to solve the problems existing in the background art.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power utilization data anomaly detection method based on big data analysis comprises the following steps:
the method comprises the steps of integrating historical electricity stealing behaviors, obtaining historical electricity stealing data, and constructing an accurate electric electricity stealing identification and electricity stealing feedback data analysis model;
measuring the accuracy of the accurate electric power stealing identification and electric power stealing feedback data analysis model through the covariance of standardized processing, and correcting the accurate identification and electric power stealing feedback data analysis model by using a loss function;
and collecting real-time power utilization data, and inputting the real-time power utilization data into an accurate identification and electricity stealing feedback data analysis model to obtain whether an electricity stealing phenomenon exists and line loss data.
Optionally, the power stealing position is positioned by using a Bayesian algorithm according to the line loss data.
Optionally, according to the line loss data, statistics is carried out on whether the user i belongs to the line loss set C within M dayssusNumber of times MiDefining the expression of the suspicion degree of the user as;
λi＝Mi/M；
finally according to suspicion degree lambdaiThe sizes are arranged in descending order to obtain the suspicion degree ranking p of the user to be detectedi(ii) a And finally, selecting a plurality of users with the top ranking for field inspection.
Optionally, collecting real-time power consumption, and using an A/D collecting circuit to collect power consumption data of a suspicious user by setting sampling frequency and input voltage; the acquisition and conversion of real-time electricity utilization data are completed through the A/D conversion chip.
Optionally, the method further comprises the step of eliminating line loss data caused by circuit reasons:
reading all line loss data, sequencing the line loss data from large to small, and extracting the first 5 percent;
judging the service life of the line, wherein the longer the service life of the line is, the higher the line loss is possibly;
judging power supply radius, and rejecting if the power supply radius exceeds the power supply radius
And judging whether the wire diameter is reasonable or not, and rejecting if the wire diameter is unreasonable.
Optionally, the power stealing position is located by using a bayesian algorithm, which specifically comprises the following steps:
wherein P (B)i) For subscriber B in lineiProbability of occurrence of power stealing, P (A) is probability of high line loss, P (A | B)i) For user BiThe probability of power theft and line loss is high.
Optionally, the historical electricity stealing data includes sudden change of electricity, abnormal current and strong magnetic interference events.
Optionally, the real-time power consumption data comprise zero line current, live line current, strong magnetic interference time, power consumption, voltage and transformer area line loss.
An electricity consumption data abnormity detection system based on big data analysis comprises
A model building module: the method is used for integrating historical electricity stealing behaviors, obtaining historical electricity stealing data and constructing an electric power electricity stealing accurate identification and electricity stealing feedback data analysis model;
a model correction module: the method is used for measuring the accuracy of the electric power stealing accurate identification and electric power stealing feedback data analysis model through the covariance of standardized processing, and correcting the accurate identification and electric power stealing feedback data analysis model by using a loss function;
the electricity stealing analysis module: the real-time power utilization data are input into the accurate identification and electricity stealing feedback data analysis model, and whether electricity stealing phenomena and line loss data exist or not are obtained.
Optionally, the system further comprises an electricity stealing positioning module: and the method is used for positioning the electricity stealing position by utilizing a Bayesian algorithm according to the line loss data.
Compared with the prior art, the invention discloses and provides the electricity consumption data anomaly detection method and system based on big data analysis, and the method and system have the following beneficial effects:
1. the user electricity stealing analysis method based on the big data is beneficial to efficient investigation and treatment of electricity stealing behaviors, and meanwhile, the analysis can be carried out on the electricity stealing behaviors, so that the line loss management of a power supply company is facilitated.
2. Through the real power consumption data of the analysis actual power consumer, adopt big data mining technology to realize anti-electricity-stealing identification analysis, discover electricity-stealing suspected user, reduce electricity-stealing suspected user's quantity and electricity-stealing investigation scope, improve anti-electricity-stealing work efficiency to based on the accurate discernment of establishing and electricity-stealing feedback data analysis model, can confirm electricity-stealing suspected user crowd, make things convenient for follow-up investigation to confirm actual electricity-stealing user from suspected user crowd.
3. The Bayesian algorithm is utilized to realize the electricity stealing positioning of the user, the troubleshooting difficulty is reduced, and the working efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a schematic structural diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The actions of electricity stealing, fraudulent use of electricity and the like cause great economic loss to electric power enterprises, although the electric power enterprises invest a large amount of manpower and material resources to carry out detection and positioning, the electric power enterprises are positioned and detected to be difficult due to various electricity stealing means and continuous updating of the used technical means, so the embodiment of the invention discloses an electricity consumption data anomaly detection method based on big data analysis, which comprises the following steps as shown in figure 1:
s1: the method comprises the steps of integrating historical electricity stealing behaviors, obtaining historical electricity stealing data, and constructing an accurate electric electricity stealing identification and electricity stealing feedback data analysis model;
s2: measuring the accuracy of the accurate electric power stealing identification and electric power stealing feedback data analysis model through the covariance of standardized processing, and correcting the accurate identification and electric power stealing feedback data analysis model by using a loss function;
s3: and acquiring real-time power utilization data, and inputting the real-time power utilization data into an accurate identification and electricity stealing feedback data analysis model to obtain whether electricity stealing phenomena and line loss data exist.
In S3, it is counted that user i belongs to the line loss set C within M days based on the line loss datasusNumber of times MiDefining the expression of the suspicion degree of the user as;
λi＝Mi/M；
finally according to suspicion degree lambdaiThe sizes are arranged in descending order to obtain the suspicion degree ranking p of the user to be detectedi(ii) a And finally, selecting a plurality of users with the top ranking for field inspection.
Collecting real-time power consumption, and realizing power consumption data collection of a suspicious user by setting sampling frequency and input voltage by using an A/D (analog/digital) collection circuit; the acquisition and conversion of real-time electricity utilization data are completed through the A/D conversion chip.
Due to accidental factors such as weather and meter errors, parameters such as a strong magnetic interference event, a zero live current value and meter voltage in a certain day have certain randomness, and therefore the accuracy of judging electricity stealing only through data of one day is not high. In order to prevent the abnormality such as strong magnetic interference events caused by accidental factors such as weather and meter errors, the result accuracy can be obviously improved by adopting continuous multi-day data comprehensive analysis.
The electric energy information of non-technical loss is contained in the line loss, but the factor that influences the line loss condition is various, for net rack factors such as power supply radius, line footpath, steal the electric incident for effectively utilizing line loss data location:
(1) reading all data from a line loss system, sorting the data from large to small, extracting the top 5%, and considering that the higher the line loss, the higher the possibility of electricity stealing exists.
(2) And judging the service life of the line, wherein the longer the service life of the line is, the higher the line loss is possibly.
(3) And judging the power supply radius, and rejecting the part of data if the power supply radius is exceeded.
(4) And judging whether the wire diameter is reasonable or not, and rejecting if the wire diameter is unreasonable.
Through the steps, the line loss rate caused by the system can be effectively eliminated.
S4: according to the line loss data, the power stealing position is positioned by using a Bayesian algorithm, and the method specifically comprises the following steps:
wherein P (B)i) For subscriber B in lineiProbability of occurrence of power stealing, P (A) is probability of high line loss, P (A | B)i) For user BiThe probability of power theft and line loss is high.
The historical electricity stealing data comprise electric quantity mutation, current abnormity and strong magnetic interference events. The real-time electricity consumption data comprise zero line current, live wire current, strong magnetic interference time, electricity consumption, voltage and transformer area line loss.
In the embodiment, a power consumption data anomaly detection system based on big data analysis is also disclosed, as shown in fig. 2, including
A model building module: the method is used for integrating historical electricity stealing behaviors, acquiring historical electricity stealing data and constructing an electric power electricity stealing accurate identification and electricity stealing feedback data analysis model;
a model correction module: the method is used for measuring the accuracy of the electric power electricity stealing accurate identification and electricity stealing feedback data analysis model through the covariance of standardized processing, and correcting the accurate identification and electricity stealing feedback data analysis model by using a loss function;
the electricity stealing analysis module: the real-time power utilization data are input into the accurate identification and electricity stealing feedback data analysis model, and whether electricity stealing phenomena and line loss data exist or not are obtained.
In addition, still include the electricity stealing orientation module: and the method is used for positioning the electricity stealing position by utilizing a Bayesian algorithm according to the line loss data.
In another embodiment, the method further comprises monitoring the sensitive transformer area, and determining the electricity stealing behavior judgment index from the four aspects of current, voltage, power factor and electric quantity in the electricity stealing prevention monitoring process of the sensitive transformer area. When a user steals electricity by changing current, abnormity such as current imbalance or current loss can be found through analysis of current metering data; when a user steals electricity by changing voltage, the recorded value of the voltage exceeds a normal range, so that the problems of voltage phase loss, voltage unbalance and the like are caused; if the user realizes the electricity stealing by the phase shifting method, the power factor is abnormal, because under the normal condition, the power factor only changes within a certain range, and when the electricity stealing action occurs, the power factor exceeds the normal range; because the change of the electric quantity is related to the current, the voltage and the power factor, when the three parameters are abnormal, the electric quantity also has certain abnormality, and if the electric quantity data has the condition of sudden rise, sudden fall or continuous electricity consumption of 0, electricity stealing behavior can exist.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A power utilization data anomaly detection method based on big data analysis is characterized by comprising the following steps:
the method comprises the steps of integrating historical electricity stealing behaviors, obtaining historical electricity stealing data, and constructing an electric power electricity stealing accurate identification and electricity stealing feedback data analysis model;
measuring the accuracy of the accurate electric power stealing identification and electric power stealing feedback data analysis model through the covariance of standardized processing, and correcting the accurate identification and electric power stealing feedback data analysis model by using a loss function;
and acquiring real-time power utilization data, and inputting the real-time power utilization data into an accurate identification and electricity stealing feedback data analysis model to obtain whether electricity stealing phenomena and line loss data exist.
2. The electricity data anomaly detection method based on big data analysis according to claim 1, characterized in that according to line loss data, a Bayesian algorithm is used to locate electricity stealing positions.
3. The electricity consumption data anomaly detection method based on big data analysis according to claim 1, wherein according to line loss data, statistics is carried out on whether a user i belongs to a line loss set C within M dayssusNumber of times MiDefining the expression of the suspicion of the user as follows;
λi＝Mi/M；
finally according to suspicion degree lambdaiThe sizes are arranged in descending order to obtain the suspicion degree ranking p of the user to be detectedi(ii) a And finally, selecting a plurality of users with the top ranking for field inspection.
4. The power consumption data anomaly detection method based on big data analysis according to claim 1, characterized in that real-time power consumption is collected, and an A/D collection circuit is used to realize power consumption data collection of a suspicious user by setting sampling frequency and input voltage; the acquisition and conversion of real-time electricity utilization data are completed through the A/D conversion chip.
5. The electricity consumption data anomaly detection method based on big data analysis according to claim 1, characterized by further comprising the step of rejecting line loss data due to circuit reasons:
reading all line loss data, sorting the line loss data from large to small, and extracting the first 5 percent;
judging the service life of the line, wherein the longer the service life of the line is, the higher the line loss is possibly;
judging power supply radius, and rejecting if the power supply radius exceeds the power supply radius
And judging whether the wire diameter is reasonable or not, and rejecting if the wire diameter is unreasonable.
6. The electricity consumption data anomaly detection method based on big data analysis according to claim 2, characterized in that a Bayesian algorithm is used to locate electricity stealing positions, specifically as follows:
wherein P (B)i) For subscriber B in lineiProbability of occurrence of power stealing, P (A) is the probability of high line loss of the line, P (A | B)i) For user BiThe probability of power theft and line loss is high.
7. The big data analysis-based power consumption data anomaly detection method according to claim 1, wherein the historical power stealing data comprises sudden changes in power, current anomalies, and strong magnetic interference events.
8. The electricity data anomaly detection method based on big data analysis according to claim 1, wherein the real-time electricity data comprises zero line current, live line current, strong magnetic interference time, electricity consumption, voltage and transformer area line loss.
9. An electricity consumption data abnormity detection system based on big data analysis is characterized by comprising
A model building module: the method is used for integrating historical electricity stealing behaviors, acquiring historical electricity stealing data and constructing an electric power electricity stealing accurate identification and electricity stealing feedback data analysis model;
a model correction module: the method is used for measuring the accuracy of the electric power electricity stealing accurate identification and electricity stealing feedback data analysis model through the covariance of standardized processing, and correcting the accurate identification and electricity stealing feedback data analysis model by using a loss function;
the electricity stealing analysis module: the real-time power utilization data acquisition module is used for acquiring real-time power utilization data and inputting the real-time power utilization data into the accurate identification and power stealing feedback data analysis model to obtain whether a power stealing phenomenon exists and line loss data.
10. The electricity data anomaly detection system based on big data analysis according to claim 9, further comprising an electricity stealing positioning module: and the method is used for positioning the electricity stealing position by utilizing a Bayesian algorithm according to the line loss data.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115826909A (en) * | 2023-02-24 | 2023-03-21 | 国网山东省电力公司枣庄供电公司 | Electricity stealing detection system based on big data analysis |
| CN116562653A (en) * | 2023-06-28 | 2023-08-08 | 广东电网有限责任公司 | Distributed energy station area line loss monitoring method and system |
| CN116701947A (en) * | 2023-08-02 | 2023-09-05 | 成都汉度科技有限公司 | Method and system for detecting electricity stealing behavior |
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2022
- 2022-04-24 CN CN202210451077.5A patent/CN114722722A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115826909A (en) * | 2023-02-24 | 2023-03-21 | 国网山东省电力公司枣庄供电公司 | Electricity stealing detection system based on big data analysis |
| CN115826909B (en) * | 2023-02-24 | 2023-05-12 | 国网山东省电力公司枣庄供电公司 | Big data analysis-based electricity larceny detection system |
| CN116562653A (en) * | 2023-06-28 | 2023-08-08 | 广东电网有限责任公司 | Distributed energy station area line loss monitoring method and system |
| CN116562653B (en) * | 2023-06-28 | 2023-11-28 | 广东电网有限责任公司 | Distributed energy station area line loss monitoring method and system |
| CN116701947A (en) * | 2023-08-02 | 2023-09-05 | 成都汉度科技有限公司 | Method and system for detecting electricity stealing behavior |
| CN116701947B (en) * | 2023-08-02 | 2023-11-03 | 成都汉度科技有限公司 | Method and system for detecting electricity stealing behavior |
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