CN115718872A - Abnormal electricity utilization analysis method for transformer district users based on historical data - Google Patents
Abnormal electricity utilization analysis method for transformer district users based on historical data Download PDFInfo
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Abstract
The invention discloses a method for analyzing abnormal electricity consumption of a station area user based on historical data, which comprises the following steps of: s1, establishing a normal power utilization data set, an abnormal power utilization data set and a data set to be analyzed; s2, storing the electricity utilization data of users in the transformer area to a normal electricity utilization data set and an abnormal electricity utilization data set by taking the transformer area as a unit; s3, predicting normal electricity utilization data and abnormal electricity utilization data in the next detection period in the normal electricity utilization and abnormal electricity utilization data set; s4, acquiring a group of current power utilization data of users in the distribution room, and analyzing whether the group of current power utilization data of the users belong to the predicted normal power utilization data condition or not; s5, analyzing whether the group of power utilization data belongs to the predicted abnormal power utilization data condition or not; and S6, arranging special personnel to perform special analysis or field analysis. The invention can judge the abnormal condition of the power consumption of the user by analyzing the prior power consumption data of the user to obtain the conjecture of the subsequent power consumption data and comparing the current data, and has high accuracy.
Description
Technical Field
The invention belongs to the technical field of abnormal power utilization analysis, and particularly relates to a method for analyzing abnormal power utilization of a transformer area user based on historical data.
Background
In the existing electric quantity obtaining method, a user calculates the electric quantity of the user through a metering automation system, an electric energy platform or a marketing system, and the calculated electric quantity of the user is displayed to an interface for checking through a query statistic function; the error data is subjected to spot inspection and field detection, the spot inspection result is imported into a marketing system, and other systems such as a production scheduling platform obtain the data of the spot inspection result through an interface; and in the field detection, field operation and maintenance personnel carry an instrument to the field to carry out detection, and then a result is recorded into the system.
In an electric power system, adverse effects caused by abnormal values of user electricity consumption are huge, and the data of the abnormal values needs to be additionally regarded, so that the abnormal conditions of the user electricity consumption need to be counted in the existing data analysis.
The existing abnormal electricity consumption analysis of users adopts the electricity consumption analysis of a single electricity meter to obtain the state data of the electricity meter, however, the data has certain randomness, cannot reflect the whole electricity consumption level of users in the distribution area, and cannot make accurate abnormal electricity consumption judgment.
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art and provides a method for analyzing abnormal electricity consumption of a station area user based on historical data.
In order to achieve the purpose, the invention adopts the following technical scheme:
the abnormal electricity utilization analysis method for the transformer district users based on historical data comprises the following steps:
s1, establishing a normal electricity utilization data set, an abnormal electricity utilization data set and a data set to be analyzed;
s2, storing the electricity utilization data of users in the distribution area to a normal electricity utilization data set and an abnormal electricity utilization data set by taking the distribution area as a unit;
s3, predicting normal electricity utilization data and abnormal electricity utilization data in the next detection period in the normal electricity utilization data set and the abnormal electricity utilization data set;
s4, acquiring a group of current-period power utilization data of users in the distribution room, analyzing whether the group of current-period power utilization data of the users belongs to the predicted normal power utilization data condition, if so, adding the group of power utilization data of the users to a normal power utilization data set, and if not, entering the step S5;
s5, analyzing whether the group of power utilization data belongs to the predicted abnormal power utilization data condition, if so, adding the group of user power utilization data into an abnormal power utilization data set, if not, storing the group of user power utilization data into a data set to be analyzed, and entering the step S6;
and S6, arranging a special person to perform special analysis or field analysis, analyzing whether the power utilization condition is abnormal or not, adding the data to the normal power utilization data set or the abnormal power utilization data set according to the analysis result, completing the analysis of the power utilization condition of one group of users, and jumping to the step S3 to perform the analysis of the next group of users.
Further, the normal electricity consumption data set stores the condition of normal electricity consumption data of users in the platform area, including a storage period, a unit electricity consumption period and electricity consumption in the unit electricity consumption period;
the storage period of the electricity utilization data comprises a plurality of unit electricity utilization periods, and when the current-period electricity utilization data is stored in the subsequent detection process, the data of the most previous unit electricity utilization period are removed.
Further, the abnormal electricity utilization data set stores the conditions of the abnormal electricity utilization data of the users in the distribution room, including the time of the abnormal electricity utilization and the specific classification of abnormal events;
m abnormal electricity utilization events and corresponding abnormal electricity utilization events are stored in the abnormal electricity utilization data set, and when a new abnormal electricity utilization event is stored, the most previous abnormal electricity utilization event is rejected.
Further, the data set to be analyzed is used for temporarily storing uncertain user electricity consumption data, namely when the current electricity consumption data is judged to belong to neither the normal electricity consumption data set nor the abnormal electricity consumption data set, the current data is temporarily stored in the data set to be analyzed, and after subsequent human intervention and judgment are carried out to obtain a final analysis result, the final analysis result is stored in the corresponding data set and deleted in the data set to be analyzed.
Further, step S3 specifically includes:
according to the normal electricity data of the user, which are stored in the normal electricity data set, establishing an analysis model of the normal electricity data of the user, and predicting the normal electricity data of the user in the next detection period through integral calculation according to the historical data of the user:
E＝∫P(t)·S(t)·dt
wherein, E is the total power consumption of the station user in a detection period, P (t) is the power consumption of the station user at a certain moment, and S (t) is the power consumption period;
and predicting the probability of the occurrence of different abnormal electricity utilization types in the next detection period according to the abnormal electricity utilization data of the users stored in the abnormal electricity utilization data set and according to the occurrence frequency and the events of different abnormal events, and when the probability of the occurrence of the abnormal electricity utilization events is more than 75%, estimating the occurrence of the abnormal electricity utilization time.
Further, in step S4, the acquiring a group of current power consumption data of the user specifically includes:
acquiring the number or name of a distribution area with an error to be analyzed;
grouping user data in the transformer area to form a plurality of user groups, and analyzing comprehensive electricity utilization information of the user groups;
and acquiring user information and electric meter information of the user group from the metering automation system or the electric energy platform through the interface.
Further, acquiring a station area number or a station area name of the error to be analyzed specifically includes:
monitoring the power distribution area as required to determine the power distribution area to be analyzed, acquiring power distribution area information from the electric energy platform through an interface, and storing the power distribution area information into a data table;
grouping user data in the same distribution area to form a plurality of user groups, and analyzing comprehensive power utilization information of the user groups, specifically:
user groups are customized in the same distribution area, and are specified by unit, distribution area, manufacturer, batch and geographic position factors; and after the definition of the user group is completed, classifying the acquired archive information and the electric meter information, wherein the archive information comprises electric meter reading and position information.
Further, user information and electric meter information of the user group are acquired from the metering automation system or the electric energy platform through the interface, and the method specifically comprises the following steps:
accessing a metering automation system or an electric energy platform according to the district number or the district name, and inquiring archive information, district general table information and electric meter information of the district;
after the user information and the ammeter information are obtained, the electric quantity in a group of users is counted, and the electricity consumption time intervals of the users are marked in detail, so that the electricity consumption of each time interval of each user every day is subdivided;
and in each period, the electricity consumption of the users and the proportion in the daily electricity consumption are counted, so that the electricity consumption states of different users are obtained.
Further, in step S4, analyzing whether the current-term electricity consumption data of the group of users belongs to the predicted normal electricity consumption data specifically includes:
judging whether the current-period power consumption data belongs to the range of the normal power consumption data in the next detection period obtained in the step S3:
if the current electricity utilization data belongs to the normal electricity utilization data, the current electricity utilization data is judged to be the normal electricity utilization data, and the current electricity utilization data is updated and added to the normal electricity utilization data set;
if not, judging whether the current-period electricity utilization data is abnormal electricity utilization data or not, and then entering step S5.
Further, step S5 specifically includes:
analyzing whether the electricity utilization data belongs to the predicted abnormal electricity utilization data condition:
if the electricity utilization data belongs to the abnormal electricity utilization data set, updating the electricity utilization data to the abnormal electricity utilization data set;
if not, the electricity utilization data is updated to the data set to be analyzed, and the step S6 is entered.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the method of the invention analyzes the past power consumption data of the user to obtain the conjecture of the subsequent power consumption data, compares the current date data, can judge the abnormal condition of the power consumption of the user, and has strong pertinence and high accuracy; the historical electricity utilization data guides the abnormity analysis of the subsequent electricity utilization data, so that the influence of random factors in the later analysis process can be avoided, the judged result is closer to the real result, and the electricity utilization level of the whole distribution room can be accurately reflected.
Drawings
FIG. 1 is a general flow diagram of the process of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the embodiments of the present invention are not limited thereto.
Examples
As shown in fig. 1, the method for analyzing abnormal electricity consumption of users in a distribution room based on historical data of the present invention includes the following steps:
s1, establishing a normal electricity utilization data set, an abnormal electricity utilization data set and a data set to be analyzed; wherein, the normal electricity data set specifically is as follows:
and storing the normal electricity consumption data of the users in the distribution area, wherein the normal electricity consumption data comprises a storage period, a unit electricity consumption period and the electricity consumption in the unit electricity consumption period. The storage period of the data comprises a plurality of unit power utilization periods (for example, N unit power utilization periods), and when the current power utilization data is stored in the subsequent detection process, the data of the most previous unit power utilization period is deleted, and the whole method follows the principle of first-in first-out, namely the normal power utilization data set is ensured to store the data of the N unit power utilization periods all the time.
The abnormal electricity utilization data set specifically comprises the following steps:
the condition of the abnormal electricity consumption data of the users in the distribution area is stored, and the condition comprises the time of the abnormal electricity consumption and the specific classification of the abnormal events (different numbers or definitions can be respectively assigned to different abnormal events). M abnormal electricity utilization events and corresponding abnormal electricity utilization events are stored in the abnormal electricity utilization data set, and after the latter abnormal electricity utilization event is stored, the former abnormal electricity utilization event is deleted, namely the first-in first-out principle is followed.
The data set to be analyzed is specifically:
the method is used for temporarily storing uncertain user electricity utilization data (namely, when the current electricity utilization data is judged to belong to neither a normal electricity utilization data set nor an abnormal electricity utilization data set, the current electricity utilization data is temporarily stored in a data set to be analyzed), and when the current electricity utilization data is considered to be intervention and judged to obtain a final result, the final result is stored in a corresponding data set and is deleted in the data set to be analyzed.
S2, storing the electricity utilization data of users in the transformer area to a normal electricity utilization data set and an abnormal electricity utilization data set by taking the transformer area as a unit; in this embodiment, the unit is a cell, and in actual implementation, the unit may also be a group of users or a single user.
S3, analyzing and predicting normal electricity utilization data and abnormal electricity utilization data in the next detection period in the normal electricity utilization data set and the abnormal electricity utilization data set; the method specifically comprises the following steps:
s31, establishing an analysis model of the normal power utilization data of the user according to the normal power utilization data of the user, which are stored in the normal power utilization data set, and predicting the normal power utilization data in the next detection period of the user through integral calculation according to the historical data of the user:
E＝∫P(t)·S(t)·dt
wherein, E is the total power consumption of the station user in a detection period, P (t) is the power consumption of the station user at a certain moment, and S (t) is the power consumption period.
After the normal electricity utilization data of the users in the distribution area in the next detection period is predicted, an offset value needs to be added/subtracted on the basis of the electricity utilization data, and the offset value can be obtained from the standard deviation of the electricity utilization data stored in the normal electricity utilization data set by the users in the distribution area.
And S32, predicting the probability of the occurrence of different abnormal electricity utilization types in the next detection period according to the abnormal electricity utilization data of the users stored in the abnormal electricity utilization data set and the occurrence frequency and events of different abnormal events, and when the probability of the occurrence of the abnormal electricity utilization events is more than 75%, estimating the occurrence of the abnormal electricity utilization time.
S4, acquiring a group of current power utilization data of the user, analyzing whether the current power utilization data of the user meets the predicted normal power utilization data condition, if so, adding the group of power utilization data of the user to a normal power utilization data set, and if not, entering the step S5;
in this embodiment, the current power consumption data of a group of users is used as a unit, and in actual implementation, the unit may be a distribution area or a single user.
Step S4 specifically includes:
s41, acquiring a station area number or a station area name of an error to be analyzed; the method specifically comprises the following steps:
the area to be analyzed is determined by monitoring the area as required; and the station area information is acquired from the electric energy platform through an interface and is stored in a data table.
S42, grouping the user data in the same area to form a plurality of user groups, and analyzing the comprehensive electricity utilization information of the user groups; the method specifically comprises the following steps:
the user group is customized in the same distribution area, and can be specified by factors such as units, distribution areas, manufacturers, batches, geographic positions and the like. And after the definition of the user group is completed, classifying the acquired archive information and the electric meter information, wherein the archive information comprises electric meter reading and position information.
S43, acquiring user information, electric meter information and the like of a user group from a metering automation system or an electric energy platform through an interface; the method specifically comprises the following steps:
and accessing the metering automation system or the electric energy platform according to the district number or the district name, and inquiring the archive information, the district summary table information and the electric meter information of the district. After the user information and the electric meter information are obtained, the electric quantity in a group of users is counted, and the electricity consumption time intervals of the users are marked in detail, so that the electricity consumption of each time interval of each user every day is subdivided.
Counting the power consumption of the users and the ratio of the power consumption per day in each time period, thereby obtaining the power consumption states of different users; when sampling the user of different periods, alright select daily user that generally carries out the power consumption in this period and carry out the data selective call, avoid taking place the state that the data is empty, can count out the state that the power consumption generally can not appear in certain period simultaneously, when the selective call sample, avoid extracting the user data of this period, when data generally is zero, can't carry out the contrast of data.
And S44, analyzing whether the current electricity utilization data of the group of users belongs to the predicted normal electricity utilization data condition.
Judging whether the current-period electricity consumption data belongs to the range of the normal electricity consumption data (including a floating value) in the next detection period obtained in the step S3:
if the current electricity utilization data belong to the normal electricity utilization data, the current electricity utilization data are judged to be the normal electricity utilization data, and the electricity utilization data are updated and added to the normal electricity utilization data set. Through the cyclic update to data, can adjust according to user's power consumption custom, data authenticity is higher.
If not, judging whether the current-period electricity utilization data belong to the abnormal electricity utilization data condition or not, and entering the step S5.
S5, analyzing whether the group of electricity utilization data belongs to the predicted abnormal electricity utilization data condition, if so, adding the group of user electricity utilization data to an abnormal electricity utilization data set, and if not, entering the step S6; the method comprises the following specific steps:
analyzing whether the group of power utilization data belongs to the predicted abnormal power utilization data condition:
and if so, updating the electricity utilization data to an abnormal electricity utilization data set. Through the cyclic update to data, can adjust according to user's power consumption custom, it is more reasonable that user's abnormal data gets statistical analysis, and data authenticity is higher.
If not, the group of user electricity utilization data is stored in a data set to be analyzed, and the step S6 is carried out.
And S6, arranging a special person to perform special analysis or field analysis, analyzing whether the power utilization condition is abnormal or not, adding the data to the normal power utilization data set or the abnormal power utilization data set according to the analysis result, completing the analysis of the power utilization condition of one group of users, and jumping to the step S3 to perform the analysis of the next group of users.
Since there is a certain contingency in the user electricity consumption, it is not abnormal data, but system identification may not be directly determined as normal electricity consumption data. For the data, the data needs to be temporarily stored, and after the data is analyzed by a professional, the abnormal data or the normal data is determined and then added to the corresponding normal power utilization data set or the abnormal power utilization data set.
It should also be noted that in the present specification, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
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. The abnormal electricity utilization analysis method for the transformer district users based on historical data is characterized by comprising the following steps of:
s1, establishing a normal electricity utilization data set, an abnormal electricity utilization data set and a data set to be analyzed;
s2, storing the electricity utilization data of users in the transformer area to a normal electricity utilization data set and an abnormal electricity utilization data set by taking the transformer area as a unit;
s3, predicting normal electricity utilization data and abnormal electricity utilization data in the next detection period in the normal electricity utilization data set and the abnormal electricity utilization data set;
s4, acquiring a group of current-period power utilization data of users in the distribution room, analyzing whether the group of current-period power utilization data of the users belongs to the predicted normal power utilization data condition, if so, adding the group of power utilization data of the users to a normal power utilization data set, and if not, entering the step S5;
s5, analyzing whether the group of power utilization data belongs to the predicted abnormal power utilization data condition, if so, adding the group of user power utilization data into an abnormal power utilization data set, if not, storing the group of user power utilization data into a data set to be analyzed, and entering the step S6;
and S6, arranging a special person to perform special analysis or field analysis, analyzing whether the power utilization condition is abnormal or not, adding the data to the normal power utilization data set or the abnormal power utilization data set according to the analysis result, completing the analysis of the power utilization condition of one group of users, and jumping to the step S3 to perform the analysis of the next group of users.
2. The abnormal power consumption analysis method for the users in the distribution room based on the historical data as claimed in claim 1, wherein the normal power consumption data set stores the normal power consumption data of the users in the distribution room, including the storage period, the unit power consumption period and the power consumption in the unit power consumption period;
the storage period of the electricity utilization data comprises a plurality of unit electricity utilization periods, and when the current-period electricity utilization data is stored in the subsequent detection process, the data of the most previous unit electricity utilization period are removed.
3. The abnormal power utilization analysis method for the users in the distribution room based on the historical data as claimed in claim 1, wherein the abnormal power utilization data set stores the abnormal power utilization data of the users in the distribution room, including the time of the abnormal power utilization and the specific classification of the abnormal events;
m abnormal electricity utilization events and corresponding abnormal electricity utilization events are stored in the abnormal electricity utilization data set, and when a new abnormal electricity utilization event is stored, the most previous abnormal electricity utilization event is rejected.
4. The method for analyzing the abnormal electricity consumption of the users in the distribution room based on the historical data as claimed in claim 1, wherein the data set to be analyzed is used for temporarily storing uncertain electricity consumption data of the users, namely when the current electricity consumption data is judged to belong to neither the normal electricity consumption data set nor the abnormal electricity consumption data set, the current data is temporarily stored in the data set to be analyzed, and when the subsequent human intervention and judgment are performed, the final analysis result is obtained, the current data is stored in the corresponding data set and deleted in the data set to be analyzed.
5. The abnormal power consumption analysis method for the users in the distribution room based on the historical data as claimed in claim 1, wherein the step S3 is specifically as follows:
according to the normal electricity data of the user, which is stored in the normal electricity data set, establishing an analysis model of the normal electricity data of the user, and predicting the normal electricity data in the next detection period of the user through integral calculation according to the historical data of the user:
E＝∫P(t)·S(t)·dt
wherein, E is the total power consumption of the station user in a detection period, P (t) is the power consumption of the station user at a certain moment, and S (t) is the power consumption period;
and predicting the probability of the occurrence of different abnormal electricity utilization types in the next detection period according to the abnormal electricity utilization data of the users stored in the abnormal electricity utilization data set and according to the occurrence frequency and the events of different abnormal events, and when the probability of the occurrence of the abnormal electricity utilization events is more than 75%, estimating the occurrence of the abnormal electricity utilization time.
6. The method for analyzing abnormal power consumption of users in a distribution room based on historical data as claimed in claim 1, wherein in step S4, the step of obtaining a group of current power consumption data of users specifically comprises:
acquiring the number or name of a distribution area with an error to be analyzed;
grouping user data in the transformer area to form a plurality of user groups, and analyzing comprehensive power utilization information of the user groups;
and acquiring user information and electric meter information of the user group from the metering automation system or the electric energy platform through the interface.
7. The abnormal power consumption analysis method for the power consumption users in the distribution room based on the historical data as claimed in claim 6, wherein the power consumption analysis method for the power consumption users in the power consumption room comprises the following specific steps:
the method comprises the steps of determining a to-be-analyzed transformer area by monitoring the transformer area as required, acquiring transformer area information from an electric energy platform through an interface, and storing the transformer area information into a data table;
grouping user data in the same distribution area to form a plurality of user groups, and analyzing comprehensive power utilization information of the user groups, specifically:
user groups are customized in the same distribution area, and are specified by unit, distribution area, manufacturer, batch and geographic position factors; and after the definition of the user group is completed, classifying the acquired archive information and the electric meter information, wherein the archive information comprises electric meter reading and position information.
8. The method for analyzing abnormal electricity consumption of users in distribution areas based on historical data of claim 7, wherein the user information and the meter information of the user group are obtained from the metering automation system or the electric energy platform through an interface, and the method comprises the following specific steps:
accessing a metering automation system or an electric energy platform according to the number or name of the transformer area, and inquiring file information, transformer area general table information and electric meter information of the transformer area;
after the user information and the ammeter information are obtained, the electric quantity in a group of users is counted, and the electricity consumption time intervals of the users are marked in detail, so that the electricity consumption of each time interval of each user every day is subdivided;
and counting the power consumption of the users and the ratio of the daily power consumption in each time period so as to obtain the power consumption states of different users.
9. The method for analyzing the abnormal electricity consumption of the users in the distribution room based on the historical data as claimed in claim 1, wherein the step S4 of analyzing whether the current electricity consumption data of the group of users belongs to the predicted normal electricity consumption data specifically comprises the following steps:
judging whether the current-period power consumption data belongs to the range of the normal power consumption data in the next detection period obtained in the step S3:
if the current electricity utilization data belongs to the normal electricity utilization data, the current electricity utilization data is judged to be the normal electricity utilization data, and the current electricity utilization data is updated and added to the normal electricity utilization data set;
if not, judging whether the current-period electricity utilization data is abnormal electricity utilization data or not, and then entering step S5.
10. The abnormal power consumption analysis method for the users in the distribution room based on the historical data as claimed in claim 1, wherein the step S5 is specifically as follows:
analyzing whether the electricity utilization data belongs to the predicted abnormal electricity utilization data condition:
if the electricity utilization data belongs to the abnormal electricity utilization data set, updating the electricity utilization data to the abnormal electricity utilization data set;
if not, the power utilization data is updated to the data set to be analyzed, and the step S6 is carried out.
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