CN117147960A - Electric energy meter electricity larceny identification method and device, intelligent terminal and electricity larceny identification system - Google Patents
Electric energy meter electricity larceny identification method and device, intelligent terminal and electricity larceny identification system Download PDFInfo
- Publication number
- CN117147960A CN117147960A CN202311112313.1A CN202311112313A CN117147960A CN 117147960 A CN117147960 A CN 117147960A CN 202311112313 A CN202311112313 A CN 202311112313A CN 117147960 A CN117147960 A CN 117147960A
- Authority
- CN
- China
- Prior art keywords
- identification
- signal
- identification result
- data
- electricity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000005611 electricity Effects 0.000 title claims abstract description 215
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000004891 communication Methods 0.000 claims abstract description 125
- 230000002159 abnormal effect Effects 0.000 claims abstract description 76
- 230000006399 behavior Effects 0.000 claims description 48
- 238000004458 analytical method Methods 0.000 claims description 7
- 230000000694 effects Effects 0.000 claims description 5
- 230000005856 abnormality Effects 0.000 abstract description 17
- 238000001514 detection method Methods 0.000 abstract description 12
- 230000008569 process Effects 0.000 abstract description 12
- 238000010586 diagram Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000002547 anomalous effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R22/00—Arrangements for measuring time integral of electric power or current, e.g. electricity meters
- G01R22/06—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods
- G01R22/061—Details of electronic electricity meters
- G01R22/066—Arrangements for avoiding or indicating fraudulent use
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R22/00—Arrangements for measuring time integral of electric power or current, e.g. electricity meters
- G01R22/06—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R22/00—Arrangements for measuring time integral of electric power or current, e.g. electricity meters
- G01R22/06—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods
- G01R22/061—Details of electronic electricity meters
- G01R22/063—Details of electronic electricity meters related to remote communication
Abstract
The invention discloses an electric energy meter electricity larceny identification method and device, an intelligent terminal and an electricity larceny identification system, wherein the method comprises the following steps: analyzing the received carrier communication signal corresponding to the electric energy meter to obtain electricity utilization data; carrying out abnormal recognition on the electricity consumption data to obtain a data recognition result; when the data identification result is data abnormality, carrying out abnormality identification on the carrier communication signal to obtain a signal identification result; and determining the probability of the electricity meter stealing behavior based on the signal identification result. The method can determine the probability of the electricity stealing behavior of the electric energy meter according to the electricity consumption data, is not easy to misjudge in the identification process, does not need complex detection equipment, and can improve the identification accuracy and reduce the cost.
Description
Technical Field
The invention relates to the technical field of power grid safety, in particular to an electric energy meter electricity larceny identification method, an intelligent terminal, an electric energy meter electricity larceny identification device and an electricity larceny identification system.
Background
Some enterprises take the electric energy of theft as a profit means, and adopt various methods for not counting or less metering so as to achieve the purpose of not paying or less paying electric fees. Resulting in a significant loss of electrical energy and a dramatic loss. The legal rights of power supply enterprises are seriously damaged, the normal power supply order is disturbed, the development of the electric power industry is seriously affected, and the safety power utilization is seriously threatened.
In the related art, in order to prevent the user from stealing electricity, methods such as a power analysis method, a temperature detection method, a wave recording analysis method, and the like are generally used to identify the electricity stealing behavior. Although the methods have certain effectiveness, the problems of low accuracy of misjudgment, high cost of detection equipment and the like in the identification process are also existed.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, a first object of the present invention is to provide an electric energy meter electricity larceny identification method, which can determine the probability of electricity larceny according to electricity consumption data, is not easy to misjudge during the identification process, and does not need complex detection equipment, so that the accuracy of identification can be improved, and the cost can be reduced.
The second object of the invention is to provide an intelligent terminal.
The third object of the invention is to provide an electric energy meter electricity larceny identification device.
A fourth object of the present invention is to propose an electricity theft identification system.
To achieve the above object, an embodiment of a first aspect of the present invention provides a method for identifying fraudulent use of electricity in an electric energy meter, the method comprising: analyzing the received carrier communication signals corresponding to the electric energy meter to obtain electricity utilization data; performing anomaly identification on the electricity consumption data to obtain a data identification result; when the data identification result is abnormal, carrying out abnormal identification on the carrier communication signal to obtain a signal identification result; and determining the probability of the electricity stealing behavior of the electric energy meter based on the signal identification result.
According to the electric energy meter electricity larceny identification method, firstly, received carrier communication signals corresponding to the electric energy meter are analyzed to obtain electricity utilization data, abnormal identification is conducted on the electricity utilization data to obtain a data identification result, when the data identification result is data abnormality, abnormal identification is conducted on the carrier communication signals to obtain a signal identification result, and the probability of electricity larceny behavior of the electric energy meter is determined based on the signal identification result. Therefore, the method can determine the probability of the electricity stealing behavior of the electric energy meter according to the electricity consumption data, is not easy to misjudge in the identification process, does not need complex detection equipment, and can improve the identification accuracy and reduce the cost.
In addition, the electric energy meter electricity larceny identification method according to the embodiment of the invention can also have the following additional technical characteristics:
according to an embodiment of the present invention, the electricity consumption data includes electricity consumption in a preset period, and the performing anomaly identification on the electricity consumption data to obtain a data identification result includes: determining a power consumption threshold based on the preset period; and when the electricity consumption is greater than or equal to the electricity consumption threshold, determining that the data identification result is abnormal.
According to one embodiment of the invention, the electricity usage threshold is determined based on historical electricity usage when no electricity meter theft activity is occurring.
According to another embodiment of the present invention, the electricity consumption data includes magnitudes of three-phase supply voltages/currents, and the performing anomaly recognition on the electricity consumption data to obtain a data recognition result includes: determining an imbalance of the three-phase supply voltage/current based on the magnitude of the three-phase supply voltage/current; and when the unbalance degree is larger than a preset unbalance degree threshold value, determining that the data identification result is abnormal.
According to another embodiment of the present invention, the electricity consumption data includes three-phase power supply zero sequence current, and the performing anomaly identification on the electricity consumption data to obtain a data identification result includes: and when the three-phase power supply zero-sequence current is larger than a preset zero-sequence current threshold value, determining that the data identification result is abnormal.
According to an embodiment of the present invention, the performing anomaly recognition on the carrier communication signal to obtain a signal recognition result includes: determining a time domain feature and/or a frequency domain feature of the carrier communication signal; and carrying out anomaly identification based on the time domain features and/or the frequency domain features to obtain the signal identification result.
According to one embodiment of the present invention, the time domain feature includes a frequency of the carrier communication signal, and performing anomaly identification based on the time domain feature to obtain the signal identification result includes: acquiring a frequency difference value between the frequency of the carrier communication signal and a preset frequency threshold value; and when the frequency difference value is larger than a preset frequency difference value threshold value, determining that the signal identification result is abnormal.
According to another embodiment of the present invention, the time domain feature includes an amplitude of the carrier communication signal, and performing anomaly identification based on the time domain feature to obtain the signal identification result includes: acquiring an amplitude difference value between the amplitude of the carrier communication signal and a preset amplitude threshold value; and when the amplitude difference value is larger than a preset amplitude difference value threshold value, determining that the signal identification result is abnormal.
According to one embodiment of the present invention, the frequency domain feature includes a signal strength of the carrier communication signal, and performing anomaly identification based on the frequency domain feature to obtain the signal identification result includes: acquiring standard deviation of the signal intensity in preset time; and when the standard deviation is larger than a preset standard deviation threshold value, determining that the signal identification result is abnormal.
According to another embodiment of the present invention, the frequency domain feature includes a communication rate of the carrier communication signal, and performing anomaly identification based on the frequency domain feature to obtain the signal identification result includes: and when the communication rate is not in the preset rate range, determining that the signal identification result is abnormal.
According to one embodiment of the present invention, the determining, based on the signal recognition result, a probability that there is an electricity meter stealing behavior includes: when the signal identification result is that the signal is abnormal, determining that the probability of the electricity stealing behavior of the electric energy meter is a first probability; and when the signal identification result is that the signal is normal, determining that the probability of the electricity stealing behavior of the electric energy meter is a second probability, wherein the second probability is smaller than the first probability.
To achieve the above object, an embodiment of a second aspect of the present invention provides an intelligent terminal, including: the electric energy meter electricity larceny identification method is realized when the processor executes the program.
According to the intelligent terminal provided by the embodiment of the invention, through the electric energy meter electricity larceny identification method, the probability of the electric energy meter electricity larceny behavior can be determined according to the electricity consumption data, misjudgment is not easy to occur in the identification process, complex detection equipment is not needed, and therefore the identification accuracy can be improved, and the cost can be reduced.
To achieve the above object, an embodiment of a third aspect of the present invention provides an electric energy meter electricity theft identification device, the device including: the analysis module is used for analyzing the received carrier communication signals corresponding to the electric energy meter to obtain electricity utilization data; the first identification module is used for carrying out abnormal identification on the electricity consumption data to obtain a data identification result; the second identification module is used for carrying out abnormal identification on the carrier communication signal to obtain a signal identification result when the data identification result is abnormal; and the determining module is used for determining the probability of the electricity stealing behavior of the electric energy meter based on the signal identification result.
According to the electricity stealing identification device, the analysis module analyzes the received carrier communication signals corresponding to the electric energy meter to obtain electricity consumption data, the first identification module carries out abnormal identification on the electricity consumption data to obtain a data identification result, and the second identification module carries out abnormal identification on the carrier communication signals to obtain a signal identification result when the data identification result is data abnormality; the determining module determines the probability of the electric meter electricity larceny based on the signal identification result. Therefore, the device can determine the probability of the electricity stealing behavior of the electric energy meter according to the electricity consumption data, is not easy to misjudge in the identification process, does not need complex detection equipment, and can improve the identification accuracy and reduce the cost.
In order to achieve the above objective, a fourth aspect of the present invention provides an electricity theft identification system, where the system includes an electric energy meter, an LTU (Line Terminal Unit ) terminal and an intelligent terminal, where the LTU terminal performs carrier communication with the electric energy meter and the intelligent terminal through power lines, where the electric energy meter and the LTU terminal respectively send corresponding electricity utilization data to the intelligent terminal through carrier communication signals; the intelligent terminal is used for analyzing the received carrier communication signals corresponding to the electric energy meter to obtain electricity utilization data, carrying out anomaly identification on the electricity utilization data to obtain a data identification result, carrying out anomaly identification on the carrier communication signals to obtain a signal identification result when the data identification result is abnormal, and determining the probability of electric energy meter electricity stealing behavior based on the signal identification result.
According to the electricity stealing identification system provided by the embodiment of the invention, the electric energy meter and the LTU terminal respectively send corresponding electricity utilization data to the intelligent terminal through carrier communication signals; the intelligent terminal analyzes the received carrier communication signals corresponding to the electric energy meter to obtain electricity consumption data, performs abnormality recognition on the electricity consumption data to obtain a data recognition result, performs abnormality recognition on the carrier communication signals to obtain a signal recognition result when the data recognition result is data abnormality, and determines the probability of electricity stealing behavior of the electric energy meter based on the signal recognition result. Therefore, the system can determine the probability of the electricity stealing behavior of the electric energy meter according to the electricity consumption data, is not easy to misjudge in the identification process, does not need complex detection equipment, and can improve the identification accuracy and reduce the cost.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a method for identifying fraudulent use of electricity in an electric energy meter according to an embodiment of the present invention;
fig. 2 is a block schematic diagram of an intelligent terminal according to an embodiment of the present invention;
FIG. 3 is a block schematic diagram of an electric energy meter electricity theft identification device according to an embodiment of the invention;
fig. 4 is a block schematic diagram of an electrical theft identification system according to an embodiment of the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The electric energy meter electricity stealing identification method, the intelligent terminal, the electric energy meter electricity stealing identification device and the electricity stealing identification system provided by the embodiment of the invention are described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of an electric energy meter electricity theft identification method according to an embodiment of the invention.
As shown in fig. 1, the electric energy meter electricity larceny identification method according to the embodiment of the invention may include the following steps:
s1, analyzing the received carrier communication signals corresponding to the electric energy meter to obtain electricity utilization data.
S2, carrying out anomaly identification on the electricity consumption data to obtain a data identification result.
And S3, when the data identification result is abnormal, carrying out abnormal identification on the carrier communication signal to obtain a signal identification result.
S4, determining the probability of the electricity stealing behavior of the electric energy meter based on the signal recognition result.
Specifically, the LTU terminal is low-voltage intelligent monitoring equipment installed in the branch box and the user meter box, three-phase voltage and current data of a main loop and a branch loop of the low-voltage station distribution system can be collected, processed and stored, electric physical quantity data collected through internal calculation are obtained, complete electric energy data and electric energy quality data on the branch line and equipment running state information are obtained, and the LTU terminal, the upper-level station electric intelligent terminal and the master station are communicated with each other, so that basic functions such as automatic generation of station topology of the whole low-voltage station, station electric load monitoring and line loss calculation can be realized, and further advanced functions such as electricity stealing identification and the like can be realized by analyzing electricity consumption conditions of the electric energy meter. The LTU terminal performs carrier communication with the electric energy meter and the intelligent terminal through the power line, the electric energy meter can upload power consumption data (such as power consumption) of a user to the intelligent terminal through the power line inside the LTU terminal in a carrier communication signal mode, and the LTU terminal uploads the power consumption data (such as voltage, current and the like) of the branch line to the intelligent terminal through the power line in a carrier communication signal mode. After receiving the carrier communication signal, the intelligent terminal can analyze the carrier communication signal to obtain electricity data, analyze the analyzed electricity data to perform abnormality identification, and judge whether the electricity data is abnormal or not to obtain a data identification result. The intelligent terminal can perform abnormal recognition on one or more kinds of electricity consumption data, for example, the intelligent terminal can analyze the electricity consumption of a user and analyze whether the electricity consumption has larger change or not; the three-phase power supply current, voltage, power and the like of the branch circuit can be analyzed, and whether abnormal fluctuation occurs to the parameters or not is analyzed.
When the data identification result is abnormal, the intelligent terminal carries out abnormal identification on the carrier communication signal, and judges whether the carrier communication signal is interfered, tampered and the like in the transmission process so as to obtain a signal identification result. The intelligent terminal can determine the probability of the electricity stealing behavior of the electric energy meter according to the signal recognition result: when the signal identification result is normal, the carrier communication signal is not interfered and tampered, and the probability of the corresponding electric energy meter electricity stealing behavior is low; when the signal identification result is abnormal, the carrier communication signal is interfered and tampered, and illegal electricity stealing equipment possibly interferes or tampers the electric energy meter, so that the probability of electricity stealing behavior of the electric energy meter is high. The intelligent terminal can send the probability of the electricity stealing behavior of the electric energy meter to the upper computer in real time so as to be convenient for the staff to check.
According to one embodiment of the present invention, the electricity consumption data includes electricity consumption in a preset period, and the anomaly identification is performed on the electricity consumption data to obtain a data identification result, including: determining a power consumption threshold based on a preset period; and when the electricity consumption is greater than or equal to the electricity consumption threshold, determining that the data identification result is abnormal. The electricity consumption threshold is determined based on historical electricity consumption when no electricity meter stealing behavior occurs.
Specifically, the electric energy meter collects the electricity consumption of different electricity consumption periods of users, including peak Gu Zhifeng valley and the like, and uploads the data to the intelligent terminal through the power line carrier channel, the intelligent terminal can store the electricity consumption data and analyze the data, the electricity consumption of different periods and the electricity consumption of the electric energy meter when the electricity stealing behavior of the electric energy meter does not occur in different periods are divided, the electricity consumption threshold of the period is determined according to the electricity consumption of the electric energy meter when the electricity stealing behavior does not occur in one period, and the electricity consumption threshold of the period is larger than the electricity consumption of the electric energy meter when the electricity stealing behavior of the electric energy meter does not occur, for example, the electricity consumption of the electric energy meter when the electricity stealing behavior of the electric energy meter does not occur can be 2 times. The intelligent terminal determines a time period corresponding to the preset time period and an electric quantity threshold according to the preset time period, compares the electric quantity used in the preset time period with the electric quantity threshold, and determines that the data identification result is normal when the electric quantity used is smaller than the electric quantity threshold; and when the electricity consumption is greater than or equal to the electricity consumption threshold, determining that the data identification result is abnormal.
According to another embodiment of the present invention, the electricity consumption data includes the amplitude of three-phase power supply voltage/current, and the abnormality recognition is performed on the electricity consumption data to obtain a data recognition result, including: determining an imbalance of the three-phase supply voltage/current based on the magnitude of the three-phase supply voltage/current; and when the unbalance degree is larger than a preset unbalance degree threshold value, determining that the data identification result is abnormal. The preset unbalance degree threshold value can be calibrated according to practical situations, and is not limited herein.
Specifically, the unbalance degree is an important index for judging the electricity stealing behavior of the electric energy meter, and by calculating the unbalance degree between the three-phase power supply currents or voltages, the abnormal load distribution condition possibly caused by electricity stealing can be detected. The method for calculating the unbalance coefficient will be described below using three-phase power supply voltages as an example. For three-phase voltages, the magnitudes of the three phases are measured separately. Let the magnitudes of the A, B, C phases be Va, vb, vc, respectively. The magnitudes of the three phases are added and then divided by 3 to give a positive sequence magnitude Vavg (i.e., average magnitude), the formula vavg= (va+vb+vc)/3. The Unbalance degree is expressed by using an Unbalance coefficient (Unbalance Factor), and the calculation formula of the Unbalance coefficient is as follows: unbalance factor= (Max-Vavg)/Vavg x 100%, where Max is the largest of the three phase magnitudes, and the imbalance Factor is in percent, the greater the percent representing the imbalance in current or voltage in the system.
The intelligent terminal can acquire the amplitude of the three-phase power supply voltage/current through the data uploaded by the LTU terminal, then determine the unbalance degree of the three-phase power supply voltage/current through the calculation method, and compare the unbalance degree with a preset unbalance degree threshold value. When the unbalance degree is smaller than or equal to a preset unbalance degree threshold value, determining that the data identification result is normal; and when the unbalance degree is larger than a preset unbalance degree threshold value, determining that the data identification result is abnormal.
According to another embodiment of the present invention, the electricity consumption data includes three-phase power supply zero sequence current, and the abnormality identification is performed on the electricity consumption data to obtain a data identification result, including: and when the three-phase power supply zero-sequence current is greater than a preset zero-sequence current threshold value, determining that the data identification result is abnormal. The preset zero sequence current threshold value can be calibrated according to actual conditions, and is not limited here.
In particular, in an ideal case, the three phase currents in a three phase power supply system should be perfectly balanced, i.e. equal in both magnitude and phase. In actual operation, current imbalance is caused by load imbalance, ground faults, nonlinear load equipment and the like, wherein one of the current imbalance is zero sequence current, and the asymmetric current appears on a zero line. Therefore, when the three-phase power supply system has the electricity stealing equipment, larger zero sequence current for three-phase power supply exists in the data uploaded by the LTU terminal.
The intelligent terminal can acquire three-phase power supply zero sequence current through data uploaded by the LTU terminal, and compares the three-phase power supply zero sequence current with a preset zero sequence current threshold. When the three-phase power supply zero-sequence current is smaller than or equal to a preset zero-sequence current threshold value, determining that the data identification result is abnormal; and when the three-phase power supply zero-sequence current is greater than a preset zero-sequence current threshold value, determining that the data identification result is abnormal.
According to one embodiment of the present invention, performing anomaly recognition on a carrier communication signal to obtain a signal recognition result includes: determining a time domain feature and/or a frequency domain feature of the carrier communication signal; and carrying out anomaly identification based on the time domain features and/or the frequency domain features to obtain a signal identification result.
Specifically, the electric energy meter can upload the electricity consumption data (such as electricity consumption) of the user to the intelligent terminal in a carrier communication signal mode through an internal power line of the LTU terminal, and the LTU terminal uploads the electricity consumption data (such as voltage, current and the like) of the branch line to the intelligent terminal in a carrier communication signal mode through the power line. After receiving the carrier communication signal, the intelligent terminal processes the acquired carrier communication signal through technical means such as demodulation and filtering to remove noise and extract useful signal characteristics, wherein the signal characteristics comprise time domain characteristics (frequency, amplitude and the like) and frequency domain characteristics (signal strength, communication rate and the like). The intelligent terminal can conduct anomaly identification according to the time domain features and/or the frequency domain features, match the corresponding anomalies, and obtain a signal identification result.
According to one embodiment of the present invention, the time domain feature includes a frequency of a carrier communication signal, and the signal recognition result is obtained by performing anomaly recognition based on the time domain feature, including: acquiring a frequency difference value between the frequency of the carrier communication signal and a preset frequency threshold value; and when the frequency difference value is larger than a preset frequency difference value threshold value, determining that the signal identification result is abnormal. The preset frequency threshold value and the preset frequency difference value threshold value can be calibrated according to practical situations, and are not limited herein.
Specifically, the frequency of the carrier communication signal should be stable under normal conditions, and if the frequency is abnormally shifted, i.e., out of a predetermined range or fluctuates greatly, it means that the signal may be disturbed or tampered with. After acquiring the frequency of the carrier communication signal, the intelligent terminal can perform difference between the frequency of the carrier communication signal and a preset frequency threshold value to obtain a frequency difference value, compare the frequency difference value with the preset frequency difference value threshold value, and determine that the signal identification result is normal when the frequency difference value is smaller than or equal to the preset frequency difference value threshold value; and when the frequency difference value is larger than a preset frequency difference value threshold value, determining that the signal identification result is abnormal. The frequency difference value is an absolute value of a difference between the frequency of the carrier communication signal and a preset frequency threshold value.
According to another embodiment of the present invention, the time domain feature includes an amplitude of a carrier communication signal, and the signal recognition result is obtained by performing anomaly recognition based on the time domain feature, including: acquiring an amplitude difference value between the amplitude of the carrier communication signal and a preset amplitude threshold value; and when the amplitude difference value is larger than a preset amplitude difference value threshold value, determining that the signal identification result is abnormal. The preset amplitude threshold value and the preset amplitude difference threshold value can be calibrated according to actual conditions, and the calibration is not limited herein.
In particular, the amplitude of the carrier communication signal should be relatively stable under normal conditions, and if the amplitude changes abnormally, such as suddenly increases or decreases, it indicates that the signal may be disturbed or tampered with. After the intelligent terminal obtains the amplitude of the wave communication signal, the amplitude of the carrier communication signal and a preset amplitude threshold value can be subjected to difference to obtain an amplitude difference value, the amplitude difference value is compared with the preset amplitude difference value threshold value, and when the amplitude difference value is smaller than or equal to the preset amplitude difference value threshold value, the signal identification result is determined to be normal; and when the amplitude difference value is larger than a preset amplitude difference value threshold value, determining that the signal identification result is abnormal. The amplitude difference value is the absolute value of the difference between the amplitude of the carrier communication signal and a preset amplitude threshold value.
According to one embodiment of the present invention, the frequency domain feature includes a signal strength of a carrier communication signal, and the signal recognition result is obtained by performing anomaly recognition based on the frequency domain feature, including: acquiring standard deviation of signal intensity in preset time; and when the standard deviation is larger than a preset standard deviation threshold value, determining that the signal recognition result is abnormal. The preset time and the preset standard deviation threshold value can be calibrated according to practical situations, and the method is not limited.
In particular, the presence of an illegal power theft device may cause the intensity of the carrier communication signal to fluctuate or be unstable, the fluctuation in signal intensity may appear as abrupt enhancement or attenuation, or the regularity of the signal intensity variation may not be in line with the normal condition. After acquiring the signal intensity of the carrier communication signal in the preset time, the intelligent terminal can calculate the standard deviation of the signal intensity in the preset time, compare the standard deviation with a preset standard deviation threshold value, and determine that the signal identification result is normal when the standard deviation is smaller than or equal to the preset standard deviation threshold value; and when the standard deviation is larger than a preset standard deviation threshold value, determining that the signal recognition result is abnormal.
According to another embodiment of the present invention, the frequency domain feature includes a communication rate of a carrier communication signal, and the signal recognition result is obtained by performing anomaly recognition based on the frequency domain feature, including: and when the communication rate is not in the preset rate range, determining that the signal recognition result is abnormal. The preset rate range can be calibrated according to practical situations, and is not limited herein.
Specifically, under normal conditions, the carrier communication signal should have a continuous, stable transmission characteristic, the communication rate should be fixed, and if a signal interruption or discontinuity occurs, it may indicate that there is an abnormality, and the presence of an illegal power-stealing device may interfere with or block the transmission of the carrier communication signal, resulting in loss or interruption of the signal. After the intelligent terminal acquires the communication rate of the carrier communication signal, the communication rate can be compared with a preset rate range, and when the communication rate is in the preset rate range, the signal identification result is determined to be normal; when the communication rate is not in the preset rate range, determining that the signal recognition result is abnormal.
According to one embodiment of the invention, determining the probability of the existence of electricity meter theft behavior based on the signal recognition result comprises: when the signal identification result is that the signal is abnormal, determining that the probability of the electricity stealing behavior of the electric energy meter exists as a first probability; when the signal identification result is that the signal is normal, determining that the probability of the electricity stealing behavior of the electric energy meter is a second probability, wherein the second probability is smaller than the first probability.
Specifically, the intelligent terminal can determine the probability of the electricity stealing behavior of the electric energy meter according to the signal recognition result: when the signal identification result is normal, the carrier communication signal is not interfered and tampered, and the corresponding probability of the electricity stealing behavior of the electric energy meter is the second probability; when the signal identification result is abnormal, the carrier communication signal is interfered and tampered, and the illegal electricity stealing equipment possibly interferes or tampers the electric energy meter, so that the probability of electricity stealing behavior of the electric energy meter is the first probability. The intelligent terminal can send the probability of the electricity stealing behavior of the electric energy meter to the upper computer in real time so as to be convenient for the staff to check.
In summary, according to the method for identifying the electricity theft of the electric energy meter, firstly, the received carrier communication signal corresponding to the electric energy meter is analyzed to obtain electricity utilization data, the electricity utilization data is subjected to anomaly identification to obtain a data identification result, when the data identification result is that the data is anomalous, the carrier communication signal is subjected to anomaly identification to obtain a signal identification result, and the probability of the electricity theft behavior of the electric energy meter is determined based on the signal identification result. Therefore, the method can determine the probability of the electricity stealing behavior of the electric energy meter according to the electricity consumption data, is not easy to misjudge in the identification process, does not need complex detection equipment, and can improve the identification accuracy and reduce the cost.
Corresponding to the embodiment, the invention further provides an intelligent terminal.
Fig. 2 is a block schematic diagram of an intelligent terminal according to an embodiment of the present invention.
As shown in fig. 2, an intelligent terminal 200 according to an embodiment of the present invention includes: the electric energy meter electricity larceny identification method is realized by the memory 210, the processor 220 and a program stored in the memory 210 and capable of running on the processor 220, wherein the processor 220 executes the program.
According to the intelligent terminal provided by the embodiment of the invention, through the electric energy meter electricity larceny identification method, the probability of the electric energy meter electricity larceny behavior can be determined according to the electricity consumption data, misjudgment is not easy to occur in the identification process, complex detection equipment is not needed, and therefore the identification accuracy can be improved, and the cost can be reduced.
Corresponding to the embodiment, the invention also provides an electricity stealing identification device.
Fig. 3 is a schematic block diagram of an electric energy meter electricity larceny identification device according to an embodiment of the invention.
As shown in fig. 3, an electric energy meter electricity larceny identification device 100 according to an embodiment of the present invention includes: the device comprises a parsing module 110, a first identification module 120, a second identification module 130 and a determination module 140.
The analysis module 110 is configured to analyze the received carrier communication signal corresponding to the electric energy meter to obtain electricity consumption data. The first identification module 120 is configured to perform anomaly identification on the electricity consumption data to obtain a data identification result. The second identifying module 130 is configured to identify an abnormality of the carrier communication signal to obtain a signal identifying result when the data identifying result is that the data is abnormal. The determining module 140 is configured to determine a probability that there is an electric energy meter fraudulent activity based on the signal identification result.
According to one embodiment of the present invention, the electricity consumption data includes electricity consumption in a preset period, and the first identification module 120 performs anomaly identification on the electricity consumption data to obtain a data identification result, and is specifically configured to determine an electricity consumption threshold based on the preset period; and when the electricity consumption is greater than or equal to the electricity consumption threshold, determining that the data identification result is abnormal.
According to one embodiment of the invention, the power usage threshold is determined based on historical power usage when no power meter theft activity is occurring.
According to another embodiment of the present invention, the power consumption data includes the amplitude of the three-phase power supply voltage/current, and the first recognition module 120 performs anomaly recognition on the power consumption data to obtain a data recognition result, and is specifically configured to determine the unbalance degree of the three-phase power supply voltage/current based on the amplitude of the three-phase power supply voltage/current; and when the unbalance degree is larger than a preset unbalance degree threshold value, determining that the data identification result is abnormal.
According to another embodiment of the present invention, the power consumption data includes three-phase power supply zero-sequence current, and the first identification module 120 performs abnormality identification on the power consumption data to obtain a data identification result, and is specifically configured to determine that the data identification result is abnormal when the three-phase power supply zero-sequence current is greater than a preset zero-sequence current threshold.
According to an embodiment of the present invention, the second identifying module 130 performs anomaly identification on the carrier communication signal to obtain a signal identification result, which is specifically used for determining a time domain feature and/or a frequency domain feature of the carrier communication signal; and carrying out anomaly identification based on the time domain features and/or the frequency domain features to obtain a signal identification result.
According to one embodiment of the present invention, the time domain feature includes a frequency of the carrier communication signal, and the second identifying module 130 performs anomaly identification based on the time domain feature to obtain a signal identification result, and is specifically configured to obtain a frequency difference between the frequency of the carrier communication signal and a preset frequency threshold; and when the frequency difference value is larger than a preset frequency difference value threshold value, determining that the signal identification result is abnormal.
According to another embodiment of the present invention, the time domain feature includes an amplitude of the carrier communication signal, and the second identifying module 130 performs anomaly identification based on the time domain feature to obtain a signal identification result, and is specifically configured to obtain an amplitude difference between the amplitude of the carrier communication signal and a preset amplitude threshold; and when the amplitude difference value is larger than a preset amplitude difference value threshold value, determining that the signal identification result is abnormal.
According to an embodiment of the present invention, the frequency domain features include signal strength of the carrier communication signal, and the second identifying module 130 performs anomaly identification based on the frequency domain features to obtain a signal identification result, and is specifically configured to obtain a standard deviation of the signal strength in a preset time; and when the standard deviation is larger than a preset standard deviation threshold value, determining that the signal recognition result is abnormal.
According to another embodiment of the present invention, the frequency domain feature includes a communication rate of the carrier communication signal, and the second identifying module 130 performs anomaly identification based on the frequency domain feature to obtain a signal identification result, and is specifically configured to determine that the signal identification result is signal anomaly when the communication rate is not within a preset rate range.
According to one embodiment of the present invention, the determining module 140 determines, based on the signal identification result, a probability of existence of an electric energy meter electricity larceny, and specifically is configured to determine, when the signal identification result is that the signal is abnormal, the probability of existence of the electric energy meter electricity larceny as a first probability; when the signal identification result is that the signal is normal, determining that the probability of the electricity stealing behavior of the electric energy meter is a second probability, wherein the second probability is smaller than the first probability.
It should be noted that, for details not disclosed in the electric energy meter electricity larceny identification device in the embodiment of the present invention, please refer to details disclosed in the electric energy meter electricity larceny identification method in the embodiment of the present invention, and details are not described here again.
According to the electric energy meter electricity larceny identification device, the analysis module analyzes the received carrier communication signals corresponding to the electric energy meter to obtain electricity consumption data, the first identification module carries out abnormal identification on the electricity consumption data to obtain a data identification result, and the second identification module carries out abnormal identification on the carrier communication signals to obtain a signal identification result when the data identification result is data abnormality; the determining module determines the probability of the electric meter electricity larceny based on the signal identification result. Therefore, the device can determine the probability of the electricity stealing behavior of the electric energy meter according to the electricity consumption data, is not easy to misjudge in the identification process, does not need complex detection equipment, and can improve the identification accuracy and reduce the cost.
Corresponding to the embodiment, the invention also provides an electricity stealing identification system.
FIG. 4 is a block diagram of an embodiment of the present invention of an electrical theft identification system.
As shown in fig. 4, the electricity larceny identification system 300 in the embodiment of the invention includes an electric energy meter 310, an LTU terminal 320 and an intelligent terminal 330, wherein the LTU terminal 320 performs carrier communication with the electric energy meter 310 and the intelligent terminal 330 through power lines, and the electric energy meter 310 and the LTU terminal 320 respectively send corresponding electricity utilization data to the intelligent terminal 330 through carrier communication signals; the intelligent terminal 330 is configured to analyze the received carrier communication signal corresponding to the electric energy meter to obtain electricity consumption data, perform anomaly identification on the electricity consumption data to obtain a data identification result, perform anomaly identification on the carrier communication signal to obtain a signal identification result when the data identification result is data anomaly, and determine the probability of electric energy meter electricity stealing behavior based on the signal identification result.
According to one embodiment of the present invention, the electricity consumption data includes electricity consumption in a preset period, and the intelligent terminal 330 performs anomaly identification on the electricity consumption data to obtain a data identification result, and is specifically configured to determine an electricity consumption threshold based on the preset period; and when the electricity consumption is greater than or equal to the electricity consumption threshold, determining that the data identification result is abnormal.
According to one embodiment of the invention, the power usage threshold is determined based on historical power usage when no power meter theft activity is occurring.
According to another embodiment of the present invention, the power consumption data includes the amplitude of the three-phase power supply voltage/current, and the intelligent terminal 330 performs anomaly recognition on the power consumption data to obtain a data recognition result, and is specifically configured to determine the unbalance degree of the three-phase power supply voltage/current based on the amplitude of the three-phase power supply voltage/current; and when the unbalance degree is larger than a preset unbalance degree threshold value, determining that the data identification result is abnormal.
According to another embodiment of the present invention, the power consumption data includes three-phase power supply zero-sequence current, and the intelligent terminal 330 performs anomaly identification on the power consumption data to obtain a data identification result, and is specifically configured to determine that the data identification result is abnormal when the three-phase power supply zero-sequence current is greater than a preset zero-sequence current threshold.
According to an embodiment of the present invention, the intelligent terminal 330 performs anomaly recognition on the carrier communication signal to obtain a signal recognition result, which is specifically used for determining a time domain feature and/or a frequency domain feature of the carrier communication signal; and carrying out anomaly identification based on the time domain features and/or the frequency domain features to obtain a signal identification result.
According to one embodiment of the present invention, the time domain feature includes a frequency of the carrier communication signal, and the intelligent terminal 330 performs anomaly identification based on the time domain feature to obtain a signal identification result, and is specifically configured to obtain a frequency difference between the frequency of the carrier communication signal and a preset frequency threshold; and when the frequency difference value is larger than a preset frequency difference value threshold value, determining that the signal identification result is abnormal.
According to another embodiment of the present invention, the time domain feature includes an amplitude of the carrier communication signal, and the intelligent terminal 330 performs anomaly identification based on the time domain feature to obtain a signal identification result, and is specifically configured to obtain an amplitude difference between the amplitude of the carrier communication signal and a preset amplitude threshold; and when the amplitude difference value is larger than a preset amplitude difference value threshold value, determining that the signal identification result is abnormal.
According to one embodiment of the present invention, the frequency domain features include signal strength of the carrier communication signal, and the intelligent terminal 330 performs anomaly identification based on the frequency domain features to obtain a signal identification result, and is specifically configured to obtain a standard deviation of the signal strength in a preset time; and when the standard deviation is larger than a preset standard deviation threshold value, determining that the signal recognition result is abnormal.
According to another embodiment of the present invention, the frequency domain feature includes a communication rate of the carrier communication signal, and the intelligent terminal 330 performs anomaly identification based on the frequency domain feature to obtain a signal identification result, and is specifically configured to determine that the signal identification result is signal anomaly when the communication rate is not within a preset rate range.
According to one embodiment of the present invention, the intelligent terminal 330 determines, based on the signal recognition result, a probability of existence of the electric energy meter electricity larceny behavior, and is specifically configured to determine, when the signal recognition result is that the signal is abnormal, the probability of existence of the electric energy meter electricity larceny behavior as a first probability; when the signal identification result is that the signal is normal, determining that the probability of the electricity stealing behavior of the electric energy meter is a second probability, wherein the second probability is smaller than the first probability.
It should be noted that, for details not disclosed in the electricity larceny identification system of the embodiment of the present invention, please refer to details disclosed in the electricity larceny identification method of the electric energy meter of the embodiment of the present invention, and details are not described here again.
According to the electricity stealing identification system provided by the embodiment of the invention, the electric energy meter and the LTU terminal respectively send corresponding electricity utilization data to the intelligent terminal through carrier communication signals; the intelligent terminal analyzes the received carrier communication signals corresponding to the electric energy meter to obtain electricity consumption data, performs abnormality recognition on the electricity consumption data to obtain a data recognition result, performs abnormality recognition on the carrier communication signals to obtain a signal recognition result when the data recognition result is data abnormality, and determines the probability of electricity stealing behavior based on the signal recognition result. Therefore, the system can determine the probability of the electricity stealing behavior of the electric energy meter according to the electricity consumption data, is not easy to misjudge in the identification process, does not need complex detection equipment, and can improve the identification accuracy and reduce the cost.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered as a ordered listing of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (14)
1. An electric energy meter electricity theft identification method, characterized in that the method comprises the following steps:
analyzing the received carrier communication signals corresponding to the electric energy meter to obtain electricity utilization data;
performing anomaly identification on the electricity consumption data to obtain a data identification result;
when the data identification result is abnormal, carrying out abnormal identification on the carrier communication signal to obtain a signal identification result;
and determining the probability of the electricity stealing behavior of the electric energy meter based on the signal identification result.
2. The method of claim 1, wherein the electricity consumption data includes electricity consumption in a preset period, and the performing anomaly identification on the electricity consumption data to obtain a data identification result includes:
determining a power consumption threshold based on the preset period;
and when the electricity consumption is greater than or equal to the electricity consumption threshold, determining that the data identification result is abnormal.
3. The method of claim 2, wherein the power usage threshold is determined based on historical power usage when no power meter theft activity is occurring.
4. The method of claim 1, wherein the electricity consumption data includes magnitudes of three-phase supply voltages/currents, and the performing anomaly identification on the electricity consumption data to obtain a data identification result includes:
Determining an imbalance of the three-phase supply voltage/current based on the magnitude of the three-phase supply voltage/current;
and when the unbalance degree is larger than a preset unbalance degree threshold value, determining that the data identification result is abnormal.
5. The method according to claim 1, wherein the electricity consumption data comprises three-phase power supply zero sequence current, and the performing anomaly identification on the electricity consumption data to obtain a data identification result comprises:
and when the three-phase power supply zero-sequence current is larger than a preset zero-sequence current threshold value, determining that the data identification result is abnormal.
6. The method according to any one of claims 1-5, wherein the performing anomaly identification on the carrier communication signal to obtain a signal identification result includes:
determining a time domain feature and/or a frequency domain feature of the carrier communication signal;
and carrying out anomaly identification based on the time domain features and/or the frequency domain features to obtain the signal identification result.
7. The method of claim 6, wherein the time domain signature comprises a frequency of the carrier communication signal, wherein anomaly identification based on the time domain signature results in the signal identification result, comprising:
Acquiring a frequency difference value between the frequency of the carrier communication signal and a preset frequency threshold value;
and when the frequency difference value is larger than a preset frequency difference value threshold value, determining that the signal identification result is abnormal.
8. The method of claim 6, wherein the time domain feature comprises an amplitude of the carrier communication signal, wherein performing anomaly identification based on the time domain feature results in the signal identification result, comprising:
acquiring an amplitude difference value between the amplitude of the carrier communication signal and a preset amplitude threshold value;
and when the amplitude difference value is larger than a preset amplitude difference value threshold value, determining that the signal identification result is abnormal.
9. The method of claim 6, wherein the frequency domain features include signal strengths of the carrier communication signals, and wherein performing anomaly identification based on the frequency domain features results in the signal identification results comprises:
acquiring standard deviation of the signal intensity in preset time;
and when the standard deviation is larger than a preset standard deviation threshold value, determining that the signal identification result is abnormal.
10. The method of claim 6, wherein the frequency domain features include a communication rate of the carrier communication signal, and wherein performing anomaly identification based on the frequency domain features results in the signal identification result comprises:
And when the communication rate is not in the preset rate range, determining that the signal identification result is abnormal.
11. The method of claim 1, wherein determining the probability of electrical energy meter theft behavior based on the signal recognition result comprises:
when the signal identification result is that the signal is abnormal, determining that the probability of the electricity stealing behavior of the electric energy meter is a first probability;
and when the signal identification result is that the signal is normal, determining that the probability of the electricity stealing behavior of the electric energy meter is a second probability, wherein the second probability is smaller than the first probability.
12. An intelligent terminal, characterized by comprising: a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the electric energy meter theft identification method according to any one of claims 1 to 11 when executing the program.
13. An electrical energy meter theft identification device, the device comprising:
the analysis module is used for analyzing the received carrier communication signals corresponding to the electric energy meter to obtain electricity utilization data;
the first identification module is used for carrying out abnormal identification on the electricity consumption data to obtain a data identification result;
The second identification module is used for carrying out abnormal identification on the carrier communication signal to obtain a signal identification result when the data identification result is abnormal;
and the determining module is used for determining the probability of the electricity stealing behavior of the electric energy meter based on the signal identification result.
14. A power theft identification system is characterized by comprising an electric energy meter, an LTU terminal and an intelligent terminal, wherein the LTU terminal is in carrier communication with the electric energy meter and the intelligent terminal through a power line, and the power theft identification system comprises a power meter, an LTU terminal and an intelligent terminal,
the electric energy meter and the LTU terminal respectively send corresponding power utilization data to the intelligent terminal through carrier communication signals;
the intelligent terminal is used for analyzing the received carrier communication signals corresponding to the electric energy meter to obtain electricity utilization data, carrying out anomaly identification on the electricity utilization data to obtain a data identification result, carrying out anomaly identification on the carrier communication signals to obtain a signal identification result when the data identification result is abnormal, and determining the probability of electric energy meter electricity stealing behavior based on the signal identification result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311112313.1A CN117147960A (en) | 2023-08-30 | 2023-08-30 | Electric energy meter electricity larceny identification method and device, intelligent terminal and electricity larceny identification system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311112313.1A CN117147960A (en) | 2023-08-30 | 2023-08-30 | Electric energy meter electricity larceny identification method and device, intelligent terminal and electricity larceny identification system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117147960A true CN117147960A (en) | 2023-12-01 |
Family
ID=88905582
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311112313.1A Pending CN117147960A (en) | 2023-08-30 | 2023-08-30 | Electric energy meter electricity larceny identification method and device, intelligent terminal and electricity larceny identification system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117147960A (en) |
-
2023
- 2023-08-30 CN CN202311112313.1A patent/CN117147960A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11215675B2 (en) | Method to estimate battery health for mobile devices based on relaxing voltages | |
| US10459013B2 (en) | Apparatus for and method of providing measurements of uncertainty in respect of a transfer function | |
| CN108828406A (en) | The fault recognition method and its system of non-intrusion type user power utilization | |
| KR101352204B1 (en) | Apparatus and method for classification of power quality disturbances at power grids | |
| US7315790B2 (en) | System, and method for quantifying voltage anomalies | |
| CN113533910A (en) | Method and system suitable for converter transformer partial discharge early warning | |
| KR20130015382A (en) | Characterics of the harmonic waveforms by damaged insulation in power equipment | |
| CN115955161A (en) | Method, apparatus, device and medium for estimating slip of adaptive asynchronous induction motor | |
| CN110618314A (en) | Harmonic wave responsibility division method for resisting short-circuit fault interference of power distribution system | |
| KR101322656B1 (en) | System and method for assessing harmonic contribution, and a medium having computer readable program for executing the method | |
| CN112100945A (en) | Ground short circuit detection method, motor controller and computer readable storage medium | |
| CN108896805B (en) | Chopped wave signal verification method and system and electric energy detection device | |
| CN103999316A (en) | Method for generating a signal indicating an oscillation in an electrical energy supply network | |
| CN112162172B (en) | Series fault arc detection method and system based on limited sampling data | |
| CN117147960A (en) | Electric energy meter electricity larceny identification method and device, intelligent terminal and electricity larceny identification system | |
| EP2681572B1 (en) | Method for adaptation of ground fault detection | |
| CN116699459A (en) | Method and system for detecting grounding state of intelligent end screen grounding device of high-voltage sleeve | |
| CN114002551B (en) | CVT fault determining method and device based on secondary voltage characteristics | |
| CN115327445A (en) | Method and system for judging abnormity of grounding currents of iron core and clamping piece of converter transformer | |
| CN113238172B (en) | Current transformer neutral wire abnormity judgment method based on neutral wire resistance | |
| CN115343579B (en) | Power grid fault analysis method and device and electronic equipment | |
| CN113671239B (en) | Intelligent overvoltage identification method, device and system for high-voltage switch PT cabinet | |
| CN115598462A (en) | Power distribution user power consumption abnormity detection method based on injection signal | |
| CN116859166A (en) | Lightning arrester running state detection method and system | |
| CN117554731A (en) | Low-voltage distribution line channel monitoring and intelligent diagnosis method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |