CN109752613B - Non-invasive load monitoring-based default electricity detection system and method - Google Patents
Non-invasive load monitoring-based default electricity detection system and method Download PDFInfo
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Abstract
The invention discloses a default electricity detection system based on non-invasive load monitoring, which is characterized by comprising the following components: the system comprises a data acquisition module, an information interaction module, a suspicious user screening module, an atypical civil electric appliance screening module, an default electricity utilization judgment index calculation module, a default electricity utilization decision module and a data information storage module. The detection method mainly comprises the following steps: the method can monitor suspicious users under the condition of not frightening the suspicious users, determine whether the suspicious users really store the behavior of 'civil electricity commercial' or not by comprehensively analyzing the atypical civil electrical appliances and the industrial and commercial indicating electrical appliances, and then collect the electricity charge for the total electricity consumption of the users according to the electricity charge standard of commercial users.
Description
Technical Field
The invention belongs to the field of advanced measurement systems, and particularly relates to a default electricity detection system and method based on non-invasive load monitoring.
Background
With the rapid development of society, a great amount of abnormal electricity utilization behaviors such as electricity stealing and default electricity utilization are accompanied. At present, the technical means of power supply enterprises in China are relatively backward in the aspect of automatic management of electric quantity, abnormal power utilization behaviors such as illegal power utilization and the like are frequent, huge economic losses are caused to power companies and power users, and the market economic order is seriously disturbed[1]。
The civil electricity business is a common default electricity utilization type in common residential districts. The method is an electricity utilization behavior of earning the electricity price difference by connecting the electricity equipment with high electricity price on the power supply line with low electricity price or changing the electricity utilization type privately. At present, the direct power supply price in partial areas of China can be mainly divided into 4 categories of agricultural power consumption, residential power consumption, large-scale industrial power consumption, general industrial and commercial power consumption and other power consumption. The residential electricity consumption comprises residential life electricity consumption, school electricity consumption and the like, and the general industrial and commercial electricity consumption and other electricity consumption comprise common industrial electricity consumption, commercial electricity consumption, non-residential lighting, non-industrial electricity consumption and the like. Among the 4-class electricity prices, agricultural electricity and residential electricity are low-price electricity compared with the other two types of electricity. According to the research on the selling electricity prices of Tianjin municipal power grids published in 2017, according to the classification, the electricity price of non-resident domestic electricity is generally 1.3 to 2.5 times that of resident domestic electricity, and the price difference can induce partial users to be 'civilian electricity for commercial use'.
At present, the detection aiming at 'civil and electric commerce' has the problems of lack of pertinence, consumption of a large amount of manpower and material resources, high dependence on the working attitude and the service skill of a worker and the like. With the continuous development of the power supply enterprise market, the default power utilization behaviors of users are more diversified and highly scientific, so that the concealment of the behaviors of 'civil and commercial' is stronger, and the detection difficulty is greatly increased; in addition, even if a user is detected to have an illegal electricity consumption behavior, the user cannot enter the home and cannot be effectively verified.
According to engineering practice experience, the performance of the "civil and electric commercial" user is two types: (1) the household electric appliance comprises industrial and commercial electric appliances basically unused by resident users, and (2) the use habit of a certain typical civil electric appliance is inconsistent with the normal civil habit, and the household electric appliance is particularly expressed in the aspects of the total daily electricity consumption, the accumulated running time, the starting and stopping times and the like of the electric appliance.
In the case of the (1) th case, some electric appliances are used only in industrial and commercial users and are not used substantially in residential users. Such as high-power electric ovens (pachyrhizus roasting machines, commercial pizza ovens), high-capacity dry cleaners, etc. In the present invention, such electric appliances are collectively called atypical consumer electric appliances.
In the case of the situation (2), some household electrical appliances have strong industrial and commercial behavior indicating performance, such as printers, hair dryers, electric cookers (and electrical appliances with similar functions), computers, washing machines, and the like. The indicative appliances are different for different types of commercial activities. A private print shop has a plurality of printers, the printers can be used uninterruptedly for a long time, a small hair salon uses a blower for many times, and a small restaurant uses an electric cooker for a long time.
Obviously, the analysis of the load electricity consumption data accurate to the electric appliance is more helpful to find the civil electricity commercial.
Reference to the literature
[1] In autumn bear, the research of a resident electricity consumption abnormality recognition system is realized [ D ], Beijing post and telecommunications university, 2018.
[2] Chenda, a basic type of illegal electricity consumption behavior with low price and high utilization [ J ], popular electricity consumption, 2016 (2).
[3] Mytilus edulis, Liubo, Koelreuteric; non-invasive resident power load monitoring and decomposition technology [ J ], southern power grid technology, 2013, 7(04): 1-5.
[4] Yaomaiwanzhi, linguisorban, etc.; research [ J ] of low-voltage user electricity stealing online monitoring analysis method based on electricity utilization behavior analysis, China New communication, 2015(2) 97-99.
Disclosure of Invention
Aiming at the prior art, the invention provides a default electricity detection system based on non-invasive load monitoring, which can conveniently and accurately judge whether a monitored electricity user is civil electricity commercial, thereby inhibiting the default behavior of a low-voltage user in time.
In order to solve the above problems, the present invention provides a default electricity detection system based on non-intrusive load monitoring, comprising: the system comprises a data acquisition module, an information interaction module, a suspicious user screening module, an atypical civil electric appliance screening module, an default electricity utilization judgment index calculation module, a default electricity utilization decision module and a data information storage module;
the data acquisition module and the information interaction module acquire low-frequency load electricity consumption total data by using a data acquisition function, and acquire load electricity consumption detail data of each electric appliance in a detection range by analyzing the load electricity consumption total data by using a non-invasive power load monitoring technology, wherein the load electricity consumption detail data comprises the working state of the electric appliance, the electricity consumption power and the accumulated electric quantity; the information interaction function is also responsible for data interaction between the inside of the system and the outside, and at least comprises the display and output of monitoring and analysis results; the low-frequency load electricity consumption total data is metering data originated from an electric energy meter by an electric power company and is used for screening suspicious users, and the load electricity consumption detail data is used for judging whether the confirmed suspicious users really have default electricity consumption behaviors;
the suspicious user screening module is used for detecting suspicious users of default electricity consumption in low-voltage residential users, and judging whether the users are the suspicious users or not by calculating suspicious indexes of the default electricity consumption of the users and comparing the suspicious indexes with suspicious standard indexes of the default electricity consumption;
the screening module is used for identifying the types of electric appliances used by suspicious users at home, wherein the types of electric appliances at least comprise industry and business behavior indicative electric appliances and atypical civil electric appliances, and screening whether the types of electric appliances have the atypical civil electric appliances or not so as to directly judge whether the users use the electric appliances to implement default electricity utilization behaviors;
the default electricity utilization judgment index calculation module is used for calculating default electricity utilization judgment indexes;
the default electricity utilization decision module is used for comprehensively analyzing the default electricity utilization judgment indexes and determining whether the suspicious user really has default electricity utilization behaviors;
the data information storage module is used for storing various data information of the data acquisition module, the information interaction module, the suspicious user screening module, the atypical civil electric appliance screening module, the default electricity utilization judgment index calculation module and the default electricity utilization decision module.
The detection method for carrying out default electricity utilization by utilizing the default electricity utilization detection system based on non-intrusive load monitoring comprises the following steps:
step one, calculating a user default suspicious index of a low-voltage single-phase user:
whether the monitored user is abnormal or not is judged according to the abnormal index of the electricity consumption behavior of the low-voltage single-phase user, and the method comprises the following steps:
1) setting the average value of the specific value of the electric quantity of the user and the industry as W1When W is1If the number of the monitored users is more than or equal to 1.4, the monitored users are abnormal;
2) the electricity utilization ratio of the summer, the autumn, the winter and the spring is W2When W is2If the number of the monitored users is more than or equal to 1.35, the monitored users are abnormal;
3) setting the average electric quantity ratio of the user to the industry to be W in three months before and after october and one day3When the average electric quantity ratio W of the user to the industry is three months before and after october and one day3If the number is more than 1.5, the monitored user is abnormal;
4) setting the average electric quantity ratio of the user to the industry for three months before and after May and a day as W4When the average electric quantity ratio W of the user to the industry is around three months of May and one day4If the user is abnormal, the monitored user is more than 1.4；
5) Let the ratio of user to industry electric quantity be W5When W is5If the user number is more than 1.45, the monitored user is abnormal;
the suspicious index V of the power consumption default of the monitored user is calculated by the formula (1):
in the formula (1), KmIs an abnormality index WmWeight of (1), and Km∈[0.5,2]；
Step two, screening suspicious users:
if V > S0If the monitored user is a suspicious user, entering the step three, otherwise, if the monitored user does not have the default electricity utilization behavior, turning to the step seven; wherein S is0Index of suspicious standard for electricity consumption breach, S0The value is 7;
step three, identifying the atypical civil electric appliance:
identifying the electric appliance type of the suspicious user by analyzing the detail data of the electricity consumption of the suspicious user within one week, which is acquired by the non-invasive load monitoring technology, wherein the identification comprises the identification of the electric appliance type and the operation state, and entering the fourth step after the identification is finished;
step four, judging the atypical civil electric appliance:
screening whether the types of the electric appliances in the suspicious user include industrial and commercial electric appliances basically unused by the resident user, if so, turning to the step eight, otherwise, entering the step five;
step five, calculating default electricity utilization judgment indexes:
comprising determining the power P of the base load0And the indicative electrical appliance judges the running time length TjAnd determining the number of times of start and stop Nj；
The specific calculation method of each index is as follows:
1) obtaining a base load decision power P0. If the total power consumption of the user is in a certain power range in the time period exceeding 5.5 hours, the central power value of the power range is taken as the basisThe load power is the maximum value of the plurality of base load powers and is referred to as base load determination power P0(ii) a If the total power consumption of the user does not exist and the time length exceeding 5.5 hours is in a certain power range, the power value of the central point of the certain power range with the longest time length is taken as the basic load to judge the power P0；
2) And calculating the judgment operation time length of the indicative electrical appliance. Suspicious user electricity consumption detail data obtained by a non-invasive load monitoring technology are counted and recorded from the electricity consumption detail data to obtain the state vector of each indicative electrical appliance
Sum duration vector
Wherein,
indicating the status of the k indicative appliance of the suspected user during the monitoring period i on the j th day, and
0 indicates that the indicative appliance is in the off state, 1 indicates that the indicative appliance is in the on state,
the duration of the kth indicative appliance in monitoring period i on day j for the suspect user. Calculating the daily accumulated running time of each indicative electrical appliance according to the obtained state vector and time vector of each indicative electrical appliance:
in the formula (2), Tk,jThe total running time of an indicative electric appliance k in the jth day of a suspicious user is determined, wherein k is an equipment number;
will indicate the nature electric apparatus to be tired of dayMeasuring the maximum value in the operation time length as the indicative electric appliance judgment operation time length Tj；
3) And calculating the number of times of judging start and stop of the indicative electric appliance. Calculating the daily accumulated starting and stopping times according to the obtained indicating electric appliance state vectors:
in the formula (3), Nk,jSwitching times of a certain indicative electrical appliance k in the jth day for a suspicious user;
taking the maximum value of the daily accumulated starting and stopping times of each indicative electrical appliance as the judgment starting and stopping times N of the indicative electrical appliancej；
Step six, carrying out default electricity utilization decision:
comparing the basic load judgment power, the indicative electrical appliance judgment operation time length and the judgment start-stop times obtained by the calculation in the step five with corresponding threshold values respectively so as to judge whether the suspicious user has the default power utilization behavior, wherein the maximum experience value P of the basic load power of the normal user is usedmaxAs a threshold for the base load determination power; maximum experience value T of daily accumulated running time of electric appliance indicated by normal usermaxThe threshold value is used as the threshold value for judging the running time of the indicative electric appliance; maximum experience value N of daily accumulated switching times of electric appliances indicated by normal usermaxThe threshold value is used as the threshold value of the number of the starting and stopping of the indicating electric appliance.
The specific process is as follows:
step 6-1, comparing the basic load to judge the power P0And Pmax(ii) a If P is0＞Pmax，PmaxIf the suspicious user has default power utilization behavior, turning to the step eight, otherwise, entering the step 6-2;
step 6-2, comparing indicative electric appliances to judge the running time TjAnd Tmax(ii) a If T isj＞Tmax，TmaxIf the suspicious user has the default electricity utilization behavior, turning to the step eight, otherwise, entering the step 6-3;
step 6-3. Comparing indicative electrical appliance to determine the number of times N of start and stopjAnd Nmax(ii) a If N is presentj＞Nmax，Nmax7-11, if the suspicious user has default electricity utilization behavior, turning to the step eight, and otherwise, entering the step seven;
seventhly, the default electricity utilization behavior does not exist, the suspected user is excluded from suspicion of default electricity utilization, and the default electricity utilization detection and analysis aiming at the user is completed;
step eight, the default electricity utilization behavior exists, the electricity fee is collected for the total electricity utilization amount of the user according to the electricity utilization charging standard of the commercial user, and the default electricity utilization detection analysis aiming at the user is completed;
step nine, repeating the steps one to eight until the default electricity utilization detection analysis of all target users in the designated area is completed;
tenthly, aiming at the default electricity utilization case generated by the default electricity utilization detection analysis, according to the matching degree of the abnormal user judgment result based on each single index and the detection analysis result, adjusting the weight value of each single index during the next detection, and taking the adjusted weight value as the weight value of the next default electricity utilization detection analysis;
wherein, the regulation rule of the weight scores of the single indexes is as follows: if the coincidence degree of the abnormal user judgment result based on a certain single index and the detection analysis result is higher, the corresponding weight score is increased, and if the coincidence degree of the abnormal user judgment result based on a certain single index and the detection analysis result is lower, the corresponding weight score is decreased.
Compared with the prior art, the invention has the beneficial effects that:
the invention discloses a system and a method for detecting default electricity consumption based on non-invasive load monitoring, which are used for monitoring a suspicious user under the condition of not frightening the suspicious user, determining whether the suspicious user really has 'civil electricity commercial' behavior by comprehensively analyzing atypical and civil electric appliances and industrial and commercial indicating electric appliances, and then collecting the electricity fee for the total electricity consumption of the user according to the electricity consumption charging standard of a commercial user.
Drawings
FIG. 1 is a system diagram of a non-intrusive load monitoring based default electricity detection system of the present invention;
FIG. 2 is a schematic flow chart of the method for detecting default electricity consumption based on non-intrusive load monitoring according to the present invention;
FIG. 3 is a graph of the total active power of the suspected user A for a certain day according to the detection method of the present invention;
FIG. 4 is a flow chart of a decision making process for power consumption default in the detection method of the present invention;
fig. 5 is a graph of the total active power of the suspicious user B for a certain day in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it should be understood that the preferred embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments, those skilled in the art can obtain other embodiments without creative efforts, and the embodiments are all within the protection scope of the present invention.
As shown in fig. 1, the invention relates to a default electricity detection system based on non-intrusive load monitoring, which comprises: the system comprises a data acquisition module, an information interaction module, a suspicious user screening module, an atypical civil electric appliance screening module, an default electricity utilization judgment index calculation module, a default electricity utilization decision module and a data information storage module.
(1) The data acquisition module and the information interaction module acquire low-frequency load electricity consumption total data and load electricity consumption detail data by using a data acquisition function; the information interaction function is also responsible for data interaction between the inside of the system and the outside, including but not limited to the display and output of monitoring and analysis results; the conventional low-frequency load electricity consumption total data is generally power company metering data (usually from a common electric energy meter) for screening suspicious users, and the load electricity consumption detail data is used for judging whether the confirmed suspicious users really have default electricity consumption behaviors; in addition, according to default useThe invention utilizes non-invasive power load monitoring technology to obtain the load electricity consumption detail data accurate to the electric appliance by analyzing the load electricity consumption total data[3]The method comprises the steps of working state of the electric appliance, power consumption, accumulated electric quantity and the like.
(2) The suspicious user screening module is used for detecting suspicious users of default electricity consumption in the low-voltage resident users, and judging whether the users are the suspicious users or not by calculating suspicious indexes of the default electricity consumption of the users and comparing the suspicious indexes with suspicious standard indexes of the default electricity consumption;
(3) the screening module of the atypical civil electric appliance is used for identifying the type of the electric appliance used by a suspicious user at home, wherein the type of the electric appliance at least comprises an industrial and commercial behavior indicative electric appliance, an atypical civil electric appliance and the like, and screening whether the atypical civil electric appliance exists in the type of the electric appliance, so that whether the user uses the electric appliance to implement default electricity utilization behaviors is directly judged.
(4) And the default electricity utilization judgment index calculation module is used for calculating default electricity utilization judgment indexes.
(5) And the default electricity utilization decision module is used for comprehensively analyzing the default electricity utilization judgment indexes and determining whether the suspicious user really has default electricity utilization behaviors.
(6) The data information storage module is used for storing various data information of the data acquisition module, the information interaction module, the suspicious user screening module, the atypical civil electric appliance screening module, the default electricity utilization judgment index calculation module and the default electricity utilization decision module, such as suspicious user electricity consumption data, electricity utilization detail data, operation result data of all modules including default electricity utilization decision results and the like.
The detection method of the default electricity utilization by using the default electricity utilization detection system based on the non-intrusive load monitoring is shown in fig. 2. The specific implementation steps are described as follows: the method comprises the following steps:
step one, calculating a user default suspicious index of a low-voltage single-phase user:
the user default suspicion index calculation method is various, in consideration of the feasibility and universality of the method, electric power companies in different regions are known to record the daily power consumption data of each user, and the user default suspicion index calculation method for low-voltage single-phase users is established in the reference [4] of the invention. And (5) after the calculation is finished, entering the next step.
Specifically, according to the obtained electricity utilization historical data of normal users and default electricity utilization users, the proportional relation between the number of normal users and the number of default users is obtained through statistics, and then the electricity utilization behavior index range is divided into a normal value range and an abnormal value range according to the proportional relation; the proportion of normal users and default users obtained through statistical analysis may have some deviations from the true value, and the proportion is only used for preliminary screening of default power utilization detection during division, so that a small number of normal users are allowed to be included in the range of abnormal users in order to ensure that default power utilization users are not omitted.
Whether the monitored user is abnormal or not is judged according to the abnormal index of the electricity consumption behavior of the low-voltage single-phase user, and the method comprises the following steps:
1) setting the average value of the specific value of the electric quantity of the user and the industry as W1When W is1If the number of the monitored users is more than or equal to 1.4, the monitored users are abnormal;
2) the electricity utilization ratio of the summer, the autumn, the winter and the spring is W2When W is2If the number of the monitored users is more than or equal to 1.35, the monitored users are abnormal;
3) setting the average electric quantity ratio of the user to the industry to be W in three months before and after october and one day3When the average electric quantity ratio W of the user to the industry is three months before and after october and one day3If the number is more than 1.5, the monitored user is abnormal;
4) setting the average electric quantity ratio of the user to the industry for three months before and after May and a day as W4When the average electric quantity ratio W of the user to the industry is around three months of May and one day4If the user number is more than 1.4, the monitored user is abnormal;
5) let the ratio of user to industry electric quantity be W5When W is5If the user number is more than 1.45, the monitored user is abnormal;
the suspicious index V of the power consumption default of the monitored user is calculated by the formula (1):
in the formula (1), KmIs an abnormality index WmWeight of (1), and Km∈[0.5,2]；
Taken together, this is shown in table 1.
TABLE 1 user behavior index of electricity consumption violation and suspicious index table
Step two, screening suspicious users: if V > S0If the monitored user is a suspicious user, entering the step three, otherwise, if the monitored user does not have the default electricity utilization behavior, turning to the step seven; wherein S is0Index of suspicious standard for electricity consumption breach, S0The value is 7.
The suspicious number V of the power consumption default of the user 3, which is 7.39 and is greater than S, can be obtained from table 10And marking as suspicious user B, and entering the next step.
Step three, identifying the atypical civil electric appliance: the detail data of the electricity consumption of the suspicious user within a week obtained by analyzing the non-invasive load monitoring technology, for example, fig. 5 is a graph of the total active power of the suspicious user B on a certain day (one day of the week), the type of the electric appliance of the suspicious user B is identified, and the identification result indicates that the electric appliances commonly used by the suspicious user are electric appliances such as a refrigerator, an electric cooker, a water heater and an electric lamp, and the next step is performed.
Step four, judging the atypical civil electric appliance: screening whether the types of the electric appliances in the suspicious user include industrial and commercial electric appliances which are basically not used by the resident user or not according to the identification result, if so, turning to the step eight, otherwise, entering the step five; the suspicious user B has no atypical civil electric appliance and enters the next step. Similarly, if the suspicious user B has the atypical civil electric appliance, go to step eight.
And step five, calculating the default electricity utilization judgment index.
For the case (2) mentioned in the background of the invention section of "civil electricity commercial", it is necessary to find a suitable judgment index. And calculating the default electricity utilization judgment indexes, including the basic load judgment power, the indicative electrical appliance judgment operation time length and the judgment start-stop times, and entering the next step after the calculation is finished.
First, among the residential users, the normal daily cumulative operating time period of the indicative appliance is short or the daily cumulative number of times of start and stop is small. If the user has some business behavior, the electricity utilization behavior of some indicative appliance will be obviously different from that of a normal user. For example, a small hair salon may switch a hair dryer on and off multiple times during the course of a day. Considering that multiple indicative electrical appliances may exist in a user, in order to reliably judge, the maximum value of daily accumulated operation time of all the indicative electrical appliances in the user is used as the judgment operation time, and the maximum value of daily accumulated start-stop times is used as the judgment start-stop times.
Second, some indicative appliances, whether normal residential or business users, may be in use throughout the day, such as computers, freezers, and the like. At the moment, the judgment operation time length and the judgment starting and stopping times of the indicative electric appliance are only used, so that whether the user has the 'civil and commercial' behavior or not cannot be reliably judged. In contrast, the present invention proposes a base load determination power index. If the total power of the user electricity is in a certain power range for a long time, taking the central power value of the power range as the basic load power, and taking the maximum value of the basic load power as the basic load judgment power; and if the total power consumption of the user does not exist and the time length of the total power consumption of the user exceeds 5.5 hours is within a certain power range, the power value of the center point of the certain power range with the longest time length is taken as the basic load to judge the power. Wherein, the electricity consumption detail data of a large number of normal users are statistically analyzed, and more than 5.5 hours are regarded as 'long time'; the central power value is related to the selection of the clustering method described later. For the suspicious user a, the active total power curve of a certain day is shown in fig. 3, and after analysis, three base load power values, namely 85W, 3326W and 323W, can be marked, and the final base load decision power is 3326W.
The specific calculation method of each index is as follows:
(1) and acquiring the base load judgment power. The basic load judgment power index is that if the total power of the electricity used by the user is in a certain power within the time period of more than 5.5 hoursThe central power value of the power range is used as the base load power, and the maximum value of the base load powers is called the base load judgment power P0(ii) a If the total power consumption of the user does not exist and the time length exceeding 5.5 hours is in a certain power range, the power value of the central point of the certain power range with the longest time length is taken as the basic load to judge the power P0. Load power clustering is carried out on the daily electricity consumption data of the suspicious users to obtain a plurality of load power clusters and the time lengths thereof; taking the load power cluster center point power value with the duration exceeding 5.5 hours in all the load power clusters as the basic load power PjSorting according to the running power and selecting the maximum load from multiple basic loads
Determining power P as base load0(ii) a If no load power cluster with the duration exceeding 5.5 hours exists in all the load power clusters, the power value of the center point of the load power cluster with the longest duration is taken as the basic load to judge the power P0；
Load power clustering is performed on the suspicious user B daily electricity consumption data (as shown in FIG. 5), so as to obtain 3 load power clusters, wherein the power value and the duration of the central point of each load power cluster are respectively as follows: 85W, 14.4 hours; 1310W, 7.8 hours; 3326W, 0.5 hour. Taking the power value of the center point of the load power cluster with the duration exceeding 5.5 hours in the load power cluster as the basic load power Pj. So the base load power PjComprises the following steps: 85W, 1310W. Then the largest of the multiple 'base' load powers is selected
Determining power P as "base" load 1310W0。
(2) And calculating the judgment operation time length of the indicative electrical appliance. The detail data of the electricity consumption of the suspicious user obtained by the non-intrusive load monitoring technology, such as the graph of the active total power of the suspicious user B in a certain day in fig. 5, then the daily accumulated operation duration and the daily accumulated start-stop times of each indicative electrical appliance are counted and recorded from the detail data of the electricity consumption, and the state vector of each indicative electrical appliance is obtained
Sum duration vector
Wherein,
indicating the status of the kth indicative appliance of the suspicious user B in the monitoring period i on the jth day, and
0 indicates that the indicative appliance is in the off state, 1 indicates that the indicative appliance is in the on state,
the duration of the kth indicative appliance in monitoring period i on day j for suspect user B. Calculating the daily accumulated running time of each indicative electric appliance according to the obtained state vector and time vector of each commercial behavior indicative electric appliance:
in the formula (2), Tk,jThe total running time of a certain indicative electric appliance k (equipment number) in the j day for the suspicious user.
Taking the maximum value of daily accumulated operation time of each commercial behavior indicative electrical appliance as the judgment operation time Tj。
The maximum daily accumulated time of the electric rice cooker with the indicative electric appliance is 5 hours, so the judgment time of the suspicious user B is Tj＝18000s。
(3) And calculating the number of times of judging start and stop of the indicative electric appliance. Calculating the daily accumulated starting and stopping times according to the obtained indicating electric appliance state vectors:
in the formula (3), Nk,jAnd (4) switching times of some indicative electric appliance k (equipment number) in the j day for the suspicious user B.
The maximum switch times in the daily accumulated switch times of each commercial behavior indicative electrical appliance are taken as the judgment times NjThe maximum daily accumulated switching frequency of the indicative electric rice cooker is 13, so the number of times of the suspicious user is Nj＝13。
And step six, carrying out default electricity utilization decision. And respectively comparing the basic load judgment power, the indicative electrical appliance judgment operation time length and the judgment start-stop times with corresponding threshold values, and judging whether the suspicious user has the default power utilization behavior.
In the invention, the maximum empirical value P of the base load power of a normal user is usedmaxAs the threshold value of the basic load judgment power, the maximum empirical value T of the daily accumulated running time of the electric appliance indicated by a normal user is usedmaxThe threshold value is used as the threshold value for judging the running time of the indicative electric appliance; maximum experience value N of daily accumulated switching times of electric appliances indicated by normal usermaxThe threshold value is used as the threshold value of the number of the starting and stopping of the indicating electric appliance.
The decision process of the present invention is shown in the decision tree of fig. 4. In the invention, when the basic load judgment power, the indicative electrical appliance judgment operation time length and the judgment start-stop times threshold are selected, different scenes need to be adjusted by about 20% according to the strict degree of screening by screening personnel. If the screening is strict, selecting a smaller value for each judgment index threshold; if the screening is loose, each judgment index threshold value is selected to be a large value. Taking the exemplary threshold of the basic load determination power as 1000W, considering that after the adjustment of the upper and lower 20%, the threshold range is 800-1200W, similarly, the exemplary threshold of the indicating electrical appliance determination operation time duration is 10 hours, the threshold range is 8-12 hours, and the exemplary threshold of the indicating electrical appliance determination start-stop times is 9, and the threshold range is 7-11. Since the general screening scenario is considered in this embodiment, that is, the screening severity of the screener is normal, the relevant threshold values are all selected as the middle values of the range.
The specific process is as follows:
step 6-1,Comparing the base load to determine power P0Maximum empirical value P of base load power of normal usermax(ii) a If P is0＞Pmax，PmaxIf the suspicious user has default power utilization behavior, the suspicious user goes to the step eight, otherwise, the suspicious user goes to the step 6-2;
in this embodiment, the power P is determined based on the base load of the suspicious user B01310W maximum empirical value P of base load power with normal usersmaxCompare 1000W; p0＞PmaxAnd if so, the suspicious user has illegal electricity utilization behavior and then the step eight is carried out.
Step 6-2, comparing indicative electric appliances to judge the running time TjMaximum experience value T of daily accumulated running time of electric appliance instructed by normal usermax(ii) a If T isj＞Tmax，TmaxIf the suspicious user has the default electricity utilization behavior, the suspicious user goes to the step eight, otherwise, the suspicious user goes to the step 6-3;
in this embodiment, the indicative appliance of the suspicious user B determines the operation duration TjMaximum empirical value T of 18000s (5 hours) and normal user indicative appliance daily accumulated operation timemax(10 hours) comparison. T isj＜TmaxEntering the step 6-3;
step 6-3, comparing the indicative electric appliances to judge the number N of times of starting and stoppingjMaximum experience value N of daily accumulated switching times of electric appliances instructed by normal usersmax(ii) a If N is presentj＞Nmax，NmaxIf the suspicious user has the default electricity utilization behavior, turning to the step eight, otherwise, entering the step seven;
in this embodiment, the electric appliance indicated by the suspicious user B determines the number of times of start and stop Nj13 and the maximum experience value N of the daily accumulated switching times of the electric appliance instructed by the normal usermax＝9；Nj＞NmaxAnd if so, the suspicious user has illegal electricity utilization behavior and then the step eight is carried out.
Seventhly, the default electricity utilization behavior does not exist, the suspected user is excluded from suspicion of default electricity utilization, and the default electricity utilization detection and analysis aiming at the user is completed;
step eight, the default electricity utilization behavior exists, the electricity fee is collected for the total electricity utilization amount of the user according to the electricity utilization charging standard of the commercial user, and the default electricity utilization detection analysis aiming at the user is completed;
step nine, repeating the steps one to eight until the default electricity utilization detection analysis of all target users in the designated area is completed;
tenthly, aiming at the default electricity utilization case generated by the default electricity utilization detection analysis, according to the matching degree of the abnormal user judgment result based on each single index and the detection analysis result, during the next detection, the weight value of each single index is adjusted in a limited range, and the adjusted weight value is used as the weight value of the next default electricity utilization detection analysis;
wherein, the regulation rule of the weight scores of the single indexes is as follows: if the matching degree of the abnormal user judgment result based on a certain single index and the detection analysis result is higher, the corresponding weight score can be properly adjusted to be larger, and if the matching degree of the abnormal user judgment result based on a certain single index and the detection analysis result is lower, the corresponding weight score can be properly adjusted to be smaller. Therefore, with the continuous and deep development of the detection work of the default electricity utilization, the weight values of all the indexes are more reasonable along with the adjustment and gradually tend to be optimal, and finally the accuracy of the detection of the default electricity utilization is improved.
While the present invention has been described above in connection with the accompanying drawings, it is not intended to be limited to the specific embodiments described above, which are intended to be illustrative only and not limiting. Those skilled in the art, having the benefit of this disclosure, will appreciate that many modifications are possible in the exemplary embodiments without departing from the scope and spirit of the present invention, as described herein.
Claims (2)
1. A method for detecting electricity consumption default based on non-intrusive load monitoring, the method using an electricity consumption default detection system, the electricity consumption default detection system comprising: the system comprises a data acquisition module, an information interaction module, a suspicious user screening module, an atypical civil electric appliance screening module, an default electricity utilization judgment index calculation module, a default electricity utilization decision module and a data information storage module;
the data acquisition module and the information interaction module acquire low-frequency load electricity consumption total data by using a data acquisition function, and acquire load electricity consumption detail data of each electric appliance in a detection range by analyzing the low-frequency load electricity consumption total data by using a non-invasive power load monitoring technology, wherein the load electricity consumption detail data comprises the working state of the electric appliance, the electricity consumption power and the accumulated electric quantity; the information interaction function is also responsible for data interaction between the inside of the system and the outside, including monitoring and displaying and outputting analysis results; the low-frequency load electricity consumption total data is metering data originated from an electric energy meter by an electric power company and is used for screening suspicious users, and the load electricity consumption detail data is used for judging whether the confirmed suspicious users really have default electricity consumption behaviors;
the suspicious user screening module is used for detecting suspicious users of default electricity consumption in low-voltage residential users, and judging whether the users are the suspicious users or not by calculating suspicious indexes of the default electricity consumption of the users and comparing the suspicious indexes with suspicious standard indexes of the default electricity consumption;
the screening module of the atypical civil electric appliance is used for identifying the type of the electric appliance used by a suspicious user at home, wherein the type of the electric appliance comprises an industrial and commercial behavior indicative electric appliance and an atypical civil electric appliance, and screening whether the atypical civil electric appliance exists in the type of the electric appliance, so that whether the user uses the atypical civil electric appliance to implement default electricity utilization behavior is directly judged;
the default electricity utilization judgment index calculation module is used for calculating default electricity utilization judgment indexes;
the default electricity utilization decision module is used for comprehensively analyzing the default electricity utilization judgment indexes and determining whether the suspicious user really has default electricity utilization behaviors;
the data information storage module is used for storing various data information of the data acquisition module and the information interaction module, the suspicious user screening module, the atypical civil electric appliance screening module, the default electricity utilization judgment index calculation module and the default electricity utilization decision module; the default electricity utilization detection method is characterized by comprising the following steps:
step one, calculating a user default suspicious index of a low-voltage single-phase user:
whether the monitored user is abnormal or not is judged according to the abnormal index of the electricity consumption behavior of the low-voltage single-phase user, and the method comprises the following steps:
1) setting the average value of the specific value of the electric quantity of the user and the industry as W1When W is1If the number of the monitored users is more than or equal to 1.4, the monitored users are abnormal;
2) the electricity utilization ratio of the summer, the autumn, the winter and the spring is W2When W is2If the number of the monitored users is more than or equal to 1.35, the monitored users are abnormal;
3) setting the average electric quantity ratio of the user to the industry to be W in three months before and after october and one day3When the average electric quantity ratio W of the user to the industry is three months before and after october and one day3If the number is more than 1.5, the monitored user is abnormal;
4) setting the average electric quantity ratio of the user to the industry for three months before and after May and a day as W4When the average electric quantity ratio W of the user to the industry is around three months of May and one day4If the user number is more than 1.4, the monitored user is abnormal;
5) let the ratio of user to industry electric quantity be W5When W is5If the user number is more than 1.45, the monitored user is abnormal;
the suspicious index V of the power consumption default of the monitored user is calculated by the formula (1):
in the formula (1), KmIs an abnormality index WmWeight of (1), and Km∈[0.5,2]；
Step two, screening suspicious users:
if V > S0If the monitored user is a suspicious user, entering the step three, otherwise, if the monitored user does not have the default electricity utilization behavior, turning to the step seven; wherein S is0Index of suspicious standard for electricity consumption breach, S0The value is 7;
step three, identifying the atypical civil electric appliance:
identifying the electric appliance type of the suspicious user by analyzing the detail data of the electricity consumption of the suspicious user within one week, which is acquired by the non-invasive load monitoring technology, wherein the identification comprises the identification of the electric appliance type and the operation state, and entering the fourth step after the identification is finished;
step four, judging the atypical civil electric appliance:
screening whether the types of the electric appliances in the suspicious user comprise industrial and commercial electric appliances which are not used by the resident user, if so, turning to the step eight, otherwise, entering the step five;
step five, calculating default electricity utilization judgment indexes:
comprising determining the power P of the base load0And the indicative electrical appliance judges the running time length TjAnd determining the number of times of start and stop Nj，
The specific calculation method of each index is as follows:
1) obtaining a base load decision power P0(ii) a If the total power consumption of the user is in a certain power range in the time period exceeding 5.5 hours, the central power value of the power range is taken as the basic load power, and the maximum value of the basic load power is called the basic load judgment power P0(ii) a If the total power consumption of the user does not exist and the time length exceeding 5.5 hours is in a certain power range, the power value of the central point of the certain power range with the longest time length is taken as the basic load to judge the power P0；
2) Calculating the indicative electric appliance judgment operation time length Tj(ii) a Suspicious user electricity consumption detail data obtained by a non-invasive load monitoring technology are counted and recorded from the electricity consumption detail data to obtain the state vector of each indicative electrical appliance
Sum duration vector
Wherein,
indicating the status of the k indicative appliance of the suspected user during the monitoring period i on the j th day, and
0 indicates that the indicative appliance is in the off state, 1 indicates that the indicative appliance is in the on state,
the duration of the kth indicative appliance in the monitoring period i in the jth day of the suspicious user; calculating the daily accumulated running time of each indicative electrical appliance according to the obtained state vector and time vector of each indicative electrical appliance:
in the formula (2), Tk,jThe total running time of an indicative electric appliance k in the jth day of a suspicious user is determined, wherein k is an equipment number;
taking the maximum value of daily accumulated operation time of each indicative electrical appliance as the judgment operation time T of the indicative electrical appliancej；
3) Calculating the number N of the judgment start-stop times of the indicative electric appliancej(ii) a Calculating the daily accumulated starting and stopping times according to the obtained indicating electric appliance state vectors:
in the formula (3), Nk,jSwitching times of a certain indicative electrical appliance k in the jth day for a suspicious user;
taking the maximum value of the daily accumulated starting and stopping times of each indicative electrical appliance as the judgment starting and stopping times N of the indicative electrical appliancej；
Step six, carrying out default electricity utilization decision:
respectively judging the basic load obtained by the calculation in the step five into power and indicating electrical appliance judgment operationComparing the line duration and the judgment starting and stopping times with corresponding threshold values thereof so as to judge whether the suspicious user has the default power utilization behavior, wherein the maximum empirical value P of the basic load power of the normal user is usedmaxAs a threshold for the base load determination power; maximum experience value N of daily accumulated running time of electric appliance indicated by normal usermaxThe threshold value is used as the threshold value for judging the running time of the indicative electric appliance; maximum experience value N of daily accumulated switching times of electric appliances indicated by normal usermaxThe threshold value is used as the threshold value of the number of times of judging the start and stop of the indicative electric appliance;
the specific process is as follows:
step 6-1, comparing the basic load to judge the power P0And Pmax(ii) a If P0＞Pmax，PmaxIf the suspicious user has default power utilization behavior, turning to the step eight, otherwise, entering the step 6-2;
step 6-2, comparing indicative electric appliances to judge the running time TjAnd Tmax(ii) a If T isj＞Tmax，TmaxIf the suspicious user has the default electricity utilization behavior, turning to the step eight, otherwise, entering the step 6-3;
step 6-3, comparing the indicative electric appliances to judge the number N of times of starting and stoppingjAnd Nmax(ii) a If N is presentj＞Nmax，Nmax7-11, if the suspicious user has default electricity utilization behavior, turning to the step eight, and otherwise, entering the step seven;
seventhly, the default electricity utilization behavior does not exist, the suspected user is excluded from suspicion of default electricity utilization, and the default electricity utilization detection and analysis aiming at the user is completed;
step eight, the default electricity utilization behavior exists, the electricity fee is collected for the total electricity utilization amount of the user according to the electricity utilization charging standard of the commercial user, and the default electricity utilization detection analysis aiming at the user is completed;
step nine, repeating the steps one to eight until the default electricity utilization detection analysis of all target users in the designated area is completed;
tenthly, aiming at the default electricity utilization case generated by the default electricity utilization detection analysis, according to the matching degree of the abnormal user judgment result based on each single index and the detection analysis result, adjusting the weight value of each single index during the next detection, and taking the adjusted weight value as the weight value of the next default electricity utilization detection analysis;
wherein, the regulation rule of the weight scores of the single indexes is as follows: if the coincidence degree of the abnormal user judgment result based on a certain single index and the detection analysis result is higher, the corresponding weight score is increased, and if the coincidence degree of the abnormal user judgment result based on a certain single index and the detection analysis result is lower, the corresponding weight score is decreased.
2. The method for detecting default electricity consumption based on non-invasive load monitoring as claimed in claim 1, wherein in step six, Pmax＝1000W，Tmax10 h, Nmax＝9。
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Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110412347B (en) * | 2019-08-12 | 2021-04-20 | 贵州电网有限责任公司 | Electricity stealing behavior identification method and device based on non-invasive load monitoring |
| CN110988420A (en) * | 2019-12-02 | 2020-04-10 | 深圳供电局有限公司 | Anti-electricity-theft system and anti-electricity-theft control method |
| CN111242161B (en) * | 2019-12-27 | 2023-08-25 | 国网山西省电力公司大同供电公司 | Non-invasive non-resident user load identification method based on intelligent learning |
| CN112765826B (en) * | 2021-01-27 | 2022-05-06 | 长沙理工大学 | Indoor hemp planting identification method based on power frequency distribution relative entropy |
| CN113341249A (en) * | 2021-06-01 | 2021-09-03 | 江西凯天电力科技发展有限公司 | Intelligent electricity consumption monitoring and alarming system and method |
| CN113705710A (en) * | 2021-09-02 | 2021-11-26 | 国网安徽省电力有限公司营销服务中心 | Method and device for identifying default electricity consumption of electric vehicle charging facility |
| CN114236208A (en) * | 2021-11-17 | 2022-03-25 | 国网天津市电力公司 | Commercial default power consumption behavior detection device of civilian electricity |
| CN116660621B (en) * | 2023-07-27 | 2023-09-26 | 江西琰圭技术服务有限公司 | Electricity larceny prevention intelligent management system for local sampling analysis |
| CN117787572B (en) * | 2024-02-27 | 2024-05-17 | 国网山西省电力公司临汾供电公司 | Abnormal electricity utilization user identification method and device, storage medium and electronic equipment |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140132523A (en) * | 2013-05-08 | 2014-11-18 | 삼성전자주식회사 | Non-intrusive load monitoring apparatus and method |
| CN103995161B (en) * | 2014-06-03 | 2016-06-22 | 深圳市康拓普信息技术有限公司 | A kind of method and system for screening tax evasion electricity user |
| CN105938610A (en) * | 2016-07-06 | 2016-09-14 | 国网天津宝坻供电有限公司 | Illegal electricity utilization judgment and report automatic generation method applied to electricity utilization collection system |
| CN106707099B (en) * | 2016-11-30 | 2019-04-12 | 国网上海市电力公司 | Monitoring and positioning method based on abnormal electricity consumption detection model |
| CN106815677A (en) * | 2016-12-09 | 2017-06-09 | 国网北京市电力公司 | The recognition methods of non-intrusion type load and device |
| CN107247737B (en) * | 2017-05-10 | 2018-12-11 | 国家电网公司 | The analysis of platform area default electricity use and method for digging based on electricity consumption |
| CN107561354B (en) * | 2017-08-17 | 2019-09-10 | 江苏智臻能源科技有限公司 | More electric appliance non-intruding discrimination methods based on power duration characteristic |
| CN108345906B (en) * | 2018-02-09 | 2022-02-22 | 无锡英臻科技有限公司 | Non-invasive electrical appliance identification method based on Boost model |
| CN108693499B (en) * | 2018-08-02 | 2020-10-16 | 国网江苏省电力有限公司电力科学研究院 | User power load identification intelligent electric energy meter detection method and device |
-
2018
- 2018-12-31 CN CN201811651387.1A patent/CN109752613B/en active Active
Non-Patent Citations (5)
| Title |
|---|
| "Fault Diagnosis of SEPIC Converters Based on PSO-DBN and Wavelet Packet Energy Spectrum";Quan Sun et al.;《2017 Prognostics and System Health Management Conference (PHM-Harbin)》;20171231 * |
| "Wavelet Entropy-Based Traction Inverter Open Switch Fault Diagnosis in High-Speed Railways";Keting Hu et al.;《entropy》;20161231 * |
| "基于小波包变换-主元分析-神经网络算法的多电平逆变器故障诊断";宋保业 等;《山东科技大学学报（自然科学版）》;20190228;第38卷(第1期) * |
| "基于小波包能量谱和 ELM 的光伏逆变器多故障在线诊断";姜媛媛 等;《仪 器 仪 表 学 报》;20150930;第 36 卷(第 9 期) * |
| "小波包–能量谱在高压断路器机械故障诊断中的应用";赵海龙 等;《电 网 技 术》;20040330;第 28 卷(第 6 期) * |
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