Electricity larceny evidence obtaining method and device based on storage type mutual inductor

Abstract

The invention provides an electricity larceny evidence obtaining method and device based on a storage type mutual inductor, and relates to the technical field of electricity larceny prevention of an electric power system. The method comprises the following steps: acquiring a first path of data and a second path of data of a target voltage level line at a target moment; taking a difference value between the second accumulated electric quantity and the first accumulated electric quantity as a real-time electric quantity deviation; calculating an electric quantity deviation threshold by using the first path of data and the second path of data; and according to the real-time electric quantity deviation and an electric quantity deviation threshold value, judging whether an electricity stealing behavior occurs in the target voltage level line at the target moment, and obtaining evidence of the electricity stealing behavior when the electricity stealing behavior occurs in the target voltage level line at the target moment. According to the invention, the accuracy of electricity larceny detection can be improved, and the possibility of false alarm and missing alarm is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of preventing electricity theft in power systems, and in particular to a method and device for collecting evidence of electricity theft based on a storage-type transformer. Background Art

[0002] Electricity theft has long been rampant in the power system, severely damaging the economic interests of power companies and threatening the safe and stable operation of the power grid. As electricity demand grows, theft becomes increasingly covert and diverse, making traditional methods of collecting evidence difficult to combat. Traditional methods cannot accurately determine the time, amount, and method of theft. Traditional transformer data is unreliable and susceptible to physical tampering and malicious adjustments, making evidence collection challenging. Existing methods primarily rely on video surveillance or environmental sensors. These methods are not only vulnerable to corruption but also unable to accurately quantify the amount of stolen electricity. Furthermore, judicial evidence is flawed, lacking detailed reports that meet legal requirements, making it difficult to support legal proceedings and hindering the protection of the legitimate rights and interests of power companies. Therefore, the development of accurate and reliable methods for collecting evidence on electricity theft is urgent. Summary of the Invention

[0003] The present application provides a method and device for collecting evidence of electricity theft based on a storage-type transformer, so as to solve the problem of inaccurate results of electricity theft evidence collection in the prior art.

[0004] In a first aspect, the present application provides a method for collecting evidence of electricity theft based on a storage transformer, comprising:

[0005] Obtain first and second data of the target voltage level circuit at the target time, where the first data includes the tamperable current, voltage, power, and first accumulated power on the target voltage level circuit; and the second data includes the non-tamperable current, voltage, second accumulated power, voltage harmonic component, current phase difference, instantaneous power fluctuation, ambient temperature, and humidity on the target voltage level circuit;

[0006] using the difference between the second accumulated power and the first accumulated power as a real-time power deviation;

[0007] Calculating a power deviation threshold using the first data channel and the second data channel;

[0008] Based on the real-time power deviation and the power deviation threshold, it is determined whether power theft occurs in the target voltage level circuit at the target time, and power theft evidence is collected when power theft occurs in the target voltage level circuit at the target time.

[0009] In a second aspect, the present application provides an electricity theft evidence collection device based on a storage transformer, comprising:

[0010] A data acquisition module is configured to acquire first and second data of a target voltage level circuit at a target time, wherein the first data includes tamperable current, voltage, power, and first accumulated power on the target voltage level circuit; and the second data includes non-tamperable current, voltage, second accumulated power, voltage harmonic components, current phase difference, instantaneous power fluctuation, ambient temperature, and humidity on the target voltage level circuit;

[0011] a deviation calculation module, configured to use the difference between the second accumulated power and the first accumulated power as a real-time power deviation;

[0012] A threshold calculation module, configured to calculate a power deviation threshold using the first data channel and the second data channel;

[0013] The electricity theft judgment module is used to judge whether electricity theft occurs in the target voltage level circuit at the target time based on the real-time electricity quantity deviation and the electricity quantity deviation threshold, and to collect electricity theft evidence when electricity theft occurs in the target voltage level circuit at the target time.

[0014] The present application provides a method and device for obtaining evidence of electricity theft based on a storage-type mutual inductor, by obtaining the first data and the second data of the target voltage level circuit at the target time, the first data including the tamperable current, voltage, power and first accumulated electricity on the target voltage level circuit, and the second data including the non-tamperable current, voltage, second accumulated electricity, voltage harmonic components, current phase difference, instantaneous power fluctuation, ambient temperature and humidity on the target voltage level circuit; the difference between the second accumulated electricity and the first accumulated electricity is used as the real-time electricity deviation; the electricity deviation threshold is calculated using the first data and the second data; based on the real-time electricity deviation and the electricity deviation threshold, it is determined whether electricity theft occurs in the target voltage level circuit at the target time, and electricity theft evidence is obtained when electricity theft occurs in the target voltage level circuit at the target time. The present application can more comprehensively understand the operating status of the circuit by simultaneously collecting the first data (tamperable data) and the second data (non-tamperable data) on the target voltage level circuit. By comparing these two data sources, especially using the difference between the second accumulated power and the first accumulated power as the real-time power deviation, potential electricity theft can be accurately identified. This dual data source comparison method significantly improves the accuracy of electricity theft detection and reduces the possibility of false alarms and missed alarms. Since the current, voltage, second accumulated power and other information in the second data source cannot be tampered with, they provide strong evidentiary support for electricity theft evidence. When the real-time power deviation exceeds the pre-calculated power deviation threshold, the system can immediately determine it as electricity theft and collect evidence based on this data. This evidence collection method based on tamper-proof data greatly enhances the credibility and legal effectiveness of electricity theft evidence. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0016] Figure 1 This is a schematic diagram of the evidence collection process of the electricity theft evidence collection system based on the storage type transformer provided in an embodiment of the present application;

[0017] Figure 2 This is a flowchart of an implementation method for collecting evidence of electricity theft based on a storage transformer provided in an embodiment of the present application;

[0018] Figure 3 Schematic diagram of the installation position of the storage transformer provided in an embodiment of the present application;

[0019] Figure 4 This is a schematic diagram of the structure of an electricity theft evidence collection device based on a storage transformer provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0022] In order to solve the problems in the existing technology such as the strong concealment of electricity theft, easy tampering of data, and insufficient judicial compliance of evidence reports, the present application provides an electricity theft evidence collection method based on a storage-type transformer. The electricity theft evidence collection method is applied to an electricity theft evidence collection system based on a storage-type transformer. On the basis of the first-channel data collection of the traditional electricity meter, a secret storage transformer electricity metering device is added to synchronously detect the first-channel data, and the data is transmitted to the data center via wireless communication. Dynamic multi-dimensional analysis and encrypted storage evidence collection technology are used to achieve accurate evidence collection of electricity theft through dynamic analysis and comparison of the two-channel data, effectively solving the problem of difficulty in existing electricity theft evidence collection.

[0023] Among them, reference Figure 1The electricity theft evidence collection system based on a storage-type transformer includes a storage-type transformer, a storage communication module, an independent energy acquisition unit, a data comparison and analysis unit, an electricity theft evidence report generation unit, and an anti-electricity theft monitoring unit. The storage communication module is respectively connected to the storage-type transformer, the independent energy acquisition unit, and the data comparison and analysis unit for communication. The data comparison and analysis unit is connected to the electricity theft evidence report generation unit for communication. The electricity theft evidence report generation unit is connected to the anti-electricity theft monitoring unit for communication.

[0024] The storage transformer includes a traditional metering coil and a hidden monitoring coil, which collect two channels of power data. The storage transformer unit in this embodiment collects data in real time. The storage communication module has a built-in independent energy acquisition unit, enabling maintenance-free operation and collecting data every 15 minutes. This dual-channel data transmission ensures timely and complete data recording.

[0025] The storage and communication module is used to obtain metering data from traditional metering coils and monitoring data from monitoring coils, and simultaneously store the original timestamps and environmental parameters of both data streams. The storage and communication module requires parameter configuration, including VPN tunnel connection information, remote server address, and local storage path. The independent energy acquisition unit's operating status must also be checked to ensure stable power supply to the module, ensuring the proper functioning of data collection, processing, and transmission functions.

[0026] An independent energy acquisition unit is used to inductively acquire energy from the power line to power the storage and communication modules. It does not directly obtain data and only provides power support.

[0027] The data comparison and analysis unit includes a storage module and an electricity theft judgment module, which are used to receive and compare the data collected by the two channels of storage transformers. It uses multi-dimensional data comparison and dynamic threshold adjustment technology, combined with multiple indicators such as line loss and voltage harmonics. When it is found that the power deviation of the two channels of data exceeds the dynamic alarm threshold, an electricity theft alarm signal is issued and the electricity theft behavior is recorded in detail after confirmation by the company's background and manual verification. After confirmation by the company's background and manual verification, the accuracy of electricity theft detection is improved and the false alarm rate is reduced.

[0028] The electricity theft evidence report generation unit generates a legally binding electricity theft evidence report based on stored and analyzed data. This comprehensive and accurate report includes the start and end dates of the theft, the basis for calculating the amount of electricity stolen, relevant user or substation information, and analysis and judgment of the theft, providing comprehensive support for legal proceedings.

[0029] The anti-electricity theft monitoring unit is used to review the differences in two-way data and abnormal environmental parameter values ​​within 24 hours after confirmation by the company's backend and manual verification. It combines the data from the storage transformer unit for comprehensive analysis, and classifies and compiles statistics on electricity theft behaviors, providing data support and decision-making basis for subsequent anti-electricity theft work.

[0030] Furthermore, the communication module connects to the storage transformer and the independent energy acquisition unit, enabling maintenance-free operation and real-time data collection. This data is uploaded to a remote server via a VPN channel as an energy black box, while simultaneously storing large amounts of energy data locally to ensure real-time and complete data. Both data streams are synchronously collected, timestamped, encrypted, and transmitted via the storage communication module, forming the basis for dual-channel data comparison.

[0031] This embodiment of the application adds a hidden storage transformer energy metering device to the traditional energy meter, using the energy black box data as a benchmark for comparison with the data from the electricity consumption information collection system. It can accurately determine the start and end time of the electricity theft and the amount of stolen electricity. By integrating and calculating the difference, a detailed evidence report is generated, providing strong evidence for power companies to recover electricity bills and for legal proceedings, effectively solving the problem of difficulty in obtaining evidence of electricity theft.

[0032] Figure 2 The implementation flow chart of the electricity theft evidence collection method based on a storage transformer provided in the embodiment of the present application is detailed as follows:

[0033] In step 201, the first data and the second data of the target voltage level line at the target time are obtained, the first data includes the tamperable current, voltage, power and first accumulated power on the target voltage level line, and the second data includes the non-tamperable current, voltage, second accumulated power, voltage harmonic components, current phase difference, instantaneous power fluctuation, ambient temperature and humidity on the target voltage level line.

[0034] In an embodiment of the present application, two data channels, namely, a first channel of data and a second channel of data, are obtained at different times for a target voltage level circuit. The first channel of data includes the tamperable current, voltage, power, and first accumulated power of the target voltage level circuit. The second channel of data includes the non-tamperable current, voltage, second accumulated power, voltage harmonic components, current phase difference, instantaneous power fluctuation, ambient temperature, and humidity of the target voltage level circuit.

[0035] In a possible implementation, obtaining the first data and the second data of the target voltage level circuit at the target time may include:

[0036] A storage transformer is installed on the target voltage level line, and the storage transformer includes a metering coil and a hidden monitoring coil;

[0037] Obtain the first data of the target voltage level line collected by the metering coil at the target time;

[0038] Obtain the second data of the target voltage level circuit collected by the secret monitoring coil at the target time.

[0039] The storage transformer in the embodiment of the present application adopts a dual-channel design. One channel is in the form of a traditional electric energy meter, which transmits the power consumption data to the power information collection system through the original channel; the other channel is independently embedded in the transformer to form an electric energy black box, which uses electromagnetic shielding technology to avoid external interference, collects high-precision current signals in real time, and then calculates the voltage and electricity as well as the corresponding voltage harmonics and line losses through the internal metering device. The power consumption data is transmitted to the remote server in real time through the VPN channel in a built-in secret manner, and large-capacity data is stored locally. The storage transformer includes a traditional metering coil and a secret monitoring coil. For example, refer to Figure 3 , install the storage transformer on the target voltage level line to collect data.

[0040] The two-channel data collected by the storage transformer has the following characteristics:

[0041] Data homology: Both coils sample the same primary current. Ideally, the deviation of high-precision current data approaches zero.

[0042] Anti-interference difference: The covert monitoring coil is electromagnetically shielded and encrypted, making it less susceptible to external tampering (such as strong magnetism and high-frequency interference).

[0043] Synchronicity: The timestamps of the two-way data are strictly aligned, supporting millisecond-level difference analysis.

[0044] Different voltage levels require different storage transformers. In this embodiment, storage transformers are installed appropriately on different voltage levels based on the grid architecture and user distribution. For example, high-voltage lines utilize specialized high-voltage storage transformers, with specialized insulation and fixtures ensuring stable installation. Alternatively, low-voltage users utilize adapted low-voltage storage transformers to ensure compatibility with existing lines and equipment.

[0045] The conventional metering coil of the storage transformer is connected to the energy metering circuit in the conventional manner, working in conjunction with the traditional energy meter to ensure normal metering functions are not affected. The hidden monitoring coil is installed inside the transformer and electromagnetically shielded with high-permeability materials to prevent external interference and ensure a reliable connection with the primary current for accurate monitoring data collection.

[0046] Specifically, the stored-type transformer synchronously drives the metering coil and the hidden monitoring coil according to a preset collection cycle. The metering coil performs the metering function of a traditional energy meter, collecting the first-channel data of the target voltage level line at the target time. The hidden monitoring coil is responsible for collecting accurate monitoring data, collecting the second-channel data of the target voltage level line at the target time.

[0047] When collecting data from two coils, a high-precision A / D conversion chip is used to accurately convert the analog current and voltage signals into digital signals to obtain high-resolution first-channel data and second-channel data.

[0048] Among them, the preset collection period can be set to 15 minutes, 10 minutes, 5 minutes, or according to actual needs.

[0049] In addition, when acquiring two channels of data, the embodiment of the present application needs to use high-precision time synchronization technology to calibrate the timestamps to ensure that the time accuracy of the two channels of data is within the millisecond level, thereby ensuring the timeliness and accuracy of data comparison.

[0050] In an embodiment of the present application, after acquiring two channels of data, multi-dimensional data comparison and dynamic threshold adjustment technology are used to determine electricity theft behavior. Specifically, according to the multi-dimensional data comparison rules, the line loss rate is calculated, and parameters such as the voltage harmonic content and the current phase difference are analyzed. At the same time, timestamp synchronization is performed to accurately align the timestamps of the two channels of data to ensure the accuracy of the comparison. These parameters are compared with the preset thresholds, and a composite threshold model is used to calculate the dynamic threshold. For example, the deviation range is automatically optimized based on the data of the same period in the past seven days to dynamically adjust the threshold. During the calculation process, the current dynamic threshold is accurately calculated based on the real-time collected data such as the line loss rate and the voltage total harmonic distortion rate, combined with the dynamic weight coefficient obtained by the sliding window regression analysis.

[0051] In a possible implementation, after obtaining the first path of data and the second path of data of the target voltage level circuit at the target time, the method may further include:

[0052] After the first and second data are collected, the communication modules store the data. One channel transmits the data to the electricity consumption information collection system through the traditional electricity meter communication interface in accordance with standard protocols such as DL / T 645. The other channel uses VPN encryption technology to upload the data to the remote server through a wireless communication network (such as 4G / 5G), and at the same time, backs it up in a local large-capacity storage device (such as a solid-state drive) to ensure that the data is not lost.

[0053] In step 202, the difference between the second accumulated power and the first accumulated power is used as the real-time power deviation.

[0054] In this embodiment of the present application, the real-time power deviation is calculated in the data comparison and analysis unit. The real-time power deviation is determined by subtracting the first accumulated power in the first data path from the second accumulated power in the second data path at the same moment. That is, the real-time power deviation = the second accumulated power - the first accumulated power.

[0055] In step 203, the power deviation threshold is calculated using the first data channel and the second data channel.

[0056] In the embodiment of the present application, the power deviation threshold is calculated in the data comparison and analysis unit, and the power deviation threshold is calculated using the first data and the second data in step 201.

[0057] In one possible implementation, calculating the power deviation threshold using the first data path and the second data path may include:

[0058] Calculate the real-time line loss rate using the tamper-proof instantaneous power fluctuations on the target voltage level line;

[0059] Calculate the dynamic weight coefficient using real-time power deviation, real-time line loss rate and voltage total harmonic distortion rate;

[0060] Calculate the dynamic adjustment coefficient using the standard deviation of the first data channel and the standard deviation of the second data channel;

[0061] The power deviation threshold is calculated using the real-time line loss rate, dynamic weight coefficient and dynamic adjustment coefficient.

[0062] Alternatively, the traditional dynamic threshold value relies only on the statistical characteristics of single-channel data, while this embodiment combines the difference characteristics of dual-channel data with environmental parameters to calculate the dynamic threshold value.

[0063] The first step is to calculate the real-time line loss rate.

[0064] The instantaneous power fluctuations on the target voltage level line that cannot be tampered with can include input power and output power. Inputting the input power and output power into the line loss rate calculation formula can calculate the real-time line loss rate. That is, the line loss rate calculation formula is:

[0065]

[0066] Where ΔP 线损 is the real-time line loss rate, P 输入 is the input power, P 输出 is the output power.

[0067] The second step is to calculate the dynamic weight coefficients, which include the line loss rate dynamic weight coefficient and the voltage harmonic distortion rate coefficient. This embodiment optimizes the dynamic weight coefficients through sliding window regression analysis.

[0068] In this embodiment, the acquired data is first divided into data windows, for example, with a period of 30 days and a time slice of 5 minutes each, to form a historical data set.

[0069] Then, the least squares fitting formula is used to fit the influence of line loss rate and harmonic distortion rate on power deviation, and the dynamic threshold is optimized. That is:

[0070] Input the real-time power deviation, real-time line loss rate, and voltage harmonic distortion rate calculated using the second channel data into the least squares fitting formula to calculate the dynamic weight coefficient. The least squares fitting formula is:

[0071] ΔQ=k1·ΔP 线损 +k2·THD V +∈

[0072] Among them, ΔQ is the real-time power deviation, ΔP 线损 is the real-time line loss rate, THD V is the voltage total harmonic distortion rate, k1 is the first fitting coefficient, k2 is the second fitting coefficient, and ∈ is the residual.

[0073] Among them, the voltage harmonic distortion rate is used to measure the intensity of power grid harmonic interference.

[0074] Then, the first fitting coefficient and the second fitting coefficient are input into the first formula to calculate the line loss rate dynamic weight coefficient and the voltage harmonic distortion rate coefficient. The first formula is:

[0075]

[0076] Among them, α(t) is the dynamic weight coefficient of line loss rate, β(t) is the voltage harmonic distortion rate coefficient, and max() is the maximum value function.

[0077] The third step is to calculate the dynamic adjustment coefficient. The dynamic adjustment coefficient in this embodiment is used to reflect the anti-interference advantage of the covert monitoring coil. That is, the anti-interference characteristics of the covert monitoring coil are used to define the credibility weight. When the data collected by the covert monitoring coil is more stable (i.e., σ 监测 <σ 计量 ), increase the dynamic adjustment coefficient, and improve the sensitivity of the power deviation threshold to abnormal differences.

[0078] The standard deviation of the first data channel and the standard deviation of the second data channel are input into the second formula to calculate the dynamic adjustment coefficient. The second formula is:

[0079]

[0080] Among them, γ(t) is the dynamic adjustment coefficient, σ 计量 is the standard deviation of the first channel data, σ 监测 is the standard deviation of the second channel data.

[0081] In addition, the standard deviation σ of the first channel data 计量 This is the standard deviation of the data collected by the covert monitoring coil, reflecting the intensity of the coil's instantaneous fluctuations. Because the covert monitoring coil utilizes electromagnetic shielding and encrypted storage, its data is less susceptible to external interference, resulting in a typically low standard deviation, indicating data stability.

[0082] The standard deviation σ of the second channel data 监测 This is the standard deviation of the data collected by the metering coil, which reflects the intensity of instantaneous fluctuations in the data collected by the metering coil. Metering coils are susceptible to external tampering or interference, so the standard deviation may be high.

[0083] The fourth step is to input the real-time line loss rate, dynamic weight coefficient and dynamic adjustment coefficient calculated in the above three steps into the threshold calculation formula to calculate the power deviation threshold. The threshold calculation formula is:

[0084]

[0085] Among them, Q 计算 (t) is the power deviation threshold, Q 基准 is the baseline threshold, γ(t) is the dynamic adjustment coefficient, σ 双路 is the standard deviation of the first and second data at the target time, σ 历史 is the standard deviation mean of the first channel data and the second channel data under historical normal working conditions at the same time as the target time, α(t) is the dynamic weight coefficient of the line loss rate, ΔP 线损 is the real-time line loss rate, β(t) is the voltage harmonic distortion coefficient, THD V is the total harmonic distortion of voltage.

[0086] It should be noted that the reference threshold Q 基准 It is set based on the maximum deviation of historical normal operating conditions. For example, it can be 3% or 5%, depending on the actual situation.

[0087] The standard deviation σ of the first and second data at the target time 双路 Used to reflect the instantaneous fluctuation intensity.

[0088] The standard deviation mean σ of the first and second channel data under historical normal working conditions at the same time as the target time 历史 Normalization is required before calculation.

[0089] In addition, this embodiment balances environmental interference and power theft signals by dynamically adjusting the threshold weight, reducing missed reports and false reports. In the absence of power theft, the difference between the two-way data is minimal (i.e., σ 双路 ≈0), the power deviation threshold is dominated by the real-time line loss rate and voltage harmonic distortion coefficient. 双路 >2σ 历史 ), triggering the threshold to dynamically amplify and avoid missing reports.

[0090] In the embodiments of this application, multi-dimensional data comparison and dynamic threshold adjustment technology are used to comprehensively analyze and determine electricity theft, combining multiple indicators such as line loss, voltage, and current, thereby improving detection accuracy and reducing false alarm rates. Simultaneously, during the data collection process, real-time monitoring and preliminary analysis are performed to remove noise and outliers. Dynamic analysis is then conducted in combination with historical data and power system operating patterns to enhance the ability to identify electricity theft.

[0091] Furthermore, through differential analysis and credibility weighting, this embodiment of the application allows dynamic thresholds to better align with actual grid conditions, amplifying abnormal fluctuation thresholds and reducing missed reports. Furthermore, the covert monitoring coil provides stable, "clean" data, protecting threshold calculations from external tampering, ensuring data reliability and detection accuracy.

[0092] In step 204, based on the real-time power deviation and the power deviation threshold, it is determined whether power theft occurs in the target voltage level line at the target time, and power theft evidence is collected when power theft occurs in the target voltage level line at the target time.

[0093] In this embodiment of the present application, the real-time power deviation calculated in step 202 and the power deviation threshold calculated in step 203 are used to determine whether power theft occurs on the target voltage level circuit at the target time. When power theft occurs on the target voltage level circuit at the target time, power theft evidence is collected.

[0094] In one possible implementation, determining whether electricity theft occurs on a target voltage level line at a target time based on the real-time electricity quantity deviation and the electricity quantity deviation threshold may include:

[0095] Determine whether the real-time power deviation is less than or equal to the power deviation threshold;

[0096] If the real-time power deviation is less than or equal to the power deviation threshold, it is determined that no power theft occurs on the target voltage level line at the target time;

[0097] If the real-time power deviation is greater than the power deviation threshold, it is determined that power theft occurs on the target voltage level line at the target time.

[0098] Optionally, in the electricity theft judgment module in the data comparison and analysis unit, it is judged whether the real-time electricity deviation is less than or equal to the electricity deviation threshold. If it is less than or equal to, it is determined that no electricity theft occurred in the target voltage level circuit at the target time; if it is greater than, it is determined that electricity theft occurred in the target voltage level circuit at the target time. Accordingly, an electricity theft alarm signal is issued, and the electricity theft is recorded in detail after confirmation by the company's background and manual verification, that is, evidence of electricity theft is collected.

[0099] In one possible implementation, collecting evidence of electricity theft when electricity theft occurs on a target voltage level line at a target time may include:

[0100] When the real-time power deviation exceeds the power deviation threshold, electricity theft is detected. The data comparison and analysis unit immediately issues an electricity theft alarm signal and immediately stores the two-way data collected by the storage transformer in the data center, including acquisition time, current, voltage, and other related data. This data is encrypted and stored using an advanced encryption algorithm, namely the AES-256 encryption algorithm and SHA-256+HMAC completeness check, to prevent data tampering. At the same time, relevant environmental information related to the electricity theft, such as temperature and humidity, is recorded to provide more basis for subsequent analysis and judgment.

[0101] Specifically, upon receiving an electricity theft alarm, the system quickly initiates the data storage process. Using the AES-256 encryption algorithm and SHA-256+HMAC, it encrypts the two-channel data collected by the storage transformer, environmental parameters, and data integrity check codes. The encrypted data is then stored in a local secure storage area and simultaneously uploaded to the encrypted storage space of a remote server to prevent unauthorized data tampering and theft.

[0102] After encryption, the judicial compliance evidence collection unit calculates the amount of electricity stolen based on the encrypted stored data through the dual-channel data difference integration method. Specifically, the difference between the two channels of data within the electricity theft period is integrated to accurately obtain the value of the stolen electricity. Combined with the timestamp information in the data, the start and end time of the electricity theft behavior is accurately determined. It also covers environmental parameters (temperature, humidity) and data integrity check codes to prevent tampering (SHA-256+HMAC algorithm). At the same time, the unit stores key data on the blockchain to ensure the non-tamperability of judicial evidence; the difference between the two channels of data within the electricity theft period is integrated through a specific algorithm to accurately determine the start and end time of the electricity theft behavior and the amount of electricity stolen, and generate an electricity theft evidence report containing detailed information on the electricity theft behavior to ensure the legality and validity of the evidence, and provide strong support for the power supply company to recover electricity bills and judicial proceedings.

[0103] After determining that electricity theft occurs, the embodiment of the present application uses an advanced encryption algorithm to encrypt and store relevant data to ensure the authenticity and integrity of the data and prevent tampering.

[0104] The amount of electricity stolen is calculated by integrating the difference between the two data sources during the electricity theft period using a specific algorithm, accurately determining the start and end time of the electricity theft and the amount of electricity stolen. The specific formula for the cumulative electricity difference integration method can be defined as:

[0105]

[0106] Among them, ΔQ1 is the stolen power, P1(t) is the instantaneous power collected by the metering coil, P2(t) is the instantaneous power collected by the secret monitoring coil, and tstart is the starting time of electricity theft, t end The time when the electricity theft behavior ends.

[0107] Since the data in the actual system is discretely sampled, the above formula can be converted into a summation form in this embodiment, that is:

[0108]

[0109] Among them, ΔQ1 is the amount of electricity stolen, Δt i is the time interval of the i-th sampling point, N is the total number of sampling points in the electricity theft period, and i is the sequence number of the sampling point.

[0110] According to the above calculation and analysis results, the electricity theft evidence report generation unit automatically generates an electricity theft evidence report that meets judicial requirements. The report lists in detail the start and end time of the electricity theft behavior, the calculation process of the stolen electricity, the relevant user or station area information, the analysis and judgment basis of the electricity theft behavior (such as the difference between two-way data, threshold adjustment), and is accompanied by a data integrity check code. In addition, the key data is stored through blockchain evidence technology (such as the Ethereum alliance chain), and the tamper-proof nature of the blockchain is used to enhance the credibility and judicial effectiveness of the evidence. The embodiment of the present application clearly marks the two-way difference and the basis for threshold adjustment in the quantitative report, thereby improving the credibility of the evidence and providing stronger support for judicial proceedings.

[0111] After generating a theft evidence report, the anti-theft monitoring unit initiates anti-theft measures if the theft is confirmed by the company's backend and manually verified within 24 hours. The anti-theft monitoring unit takes appropriate measures based on the pre-set theft type and severity classification standards.

[0112] For minor acts of electricity theft (such as occasional small differences in electricity consumption), warning messages are sent to users through remote communication methods (such as power carrier communication) to remind them to use electricity in a standardized manner.

[0113] If the theft is moderate (e.g., a significant power discrepancy but not a serious threat to grid security), the theft circuit is remotely disconnected. By controlling the intelligent switching device, the user's power supply is quickly cut off, preventing further theft. Simultaneously, relevant power system parameters, such as voltage amplitude or power factor, are adjusted to prevent the theft device from functioning properly, making the theft more difficult.

[0114] In the event of serious electricity theft (such as the use of strong magnetic interference to severely distort grid data and threaten the safe operation of the grid), law enforcement agencies will be notified immediately and detailed evidence of the theft (such as theft evidence report and encrypted data storage location) will be provided. Law enforcement will then be cooperated with on-site handling. During the handling process, drones or remote monitoring equipment will be used to monitor the theft area in real time to prevent recurrence and ensure the safe and stable operation of the grid.

[0115] This embodiment also regularly inspects hardware devices such as the stored-type transformer, data comparison and analysis unit, and anti-electricity theft monitoring unit. The inspection checks the device for damage and loose connections. Professional testing instruments are used to test device performance indicators (such as transformer accuracy and communication module signal strength). Aging or damaged components are promptly replaced to ensure normal operation of the equipment.

[0116] Regularly update system software to fix known vulnerabilities and optimize data processing algorithms (such as improving the dynamic threshold calculation model and enhancing data encryption and decryption efficiency) to enhance system security and stability. Furthermore, based on actual operating data and user feedback, adjust system parameters (such as data collection cycles and threshold settings) to adapt to different grid operating environments and user electricity usage habits.

[0117] Establish a comprehensive system operation log to record key events such as system startup, data collection, electricity theft alarms, and the implementation of anti-theft measures. By analyzing the operation log, we can summarize the occurrence patterns of electricity theft and the system's weak links, provide data support for system optimization and upgrades, and continuously improve the system's anti-theft capabilities and overall performance.

[0118] The present application provides a method for obtaining evidence of electricity theft based on a storage-type mutual inductor, by obtaining the first data and the second data of the target voltage level circuit at the target time, the first data including the tamperable current, voltage, power and first accumulated electricity on the target voltage level circuit, and the second data including the non-tamperable current, voltage, second accumulated electricity, voltage harmonic components, current phase difference, instantaneous power fluctuation, ambient temperature and humidity on the target voltage level circuit; the difference between the second accumulated electricity and the first accumulated electricity is used as the real-time electricity deviation; the electricity deviation threshold is calculated using the first data and the second data; based on the real-time electricity deviation and the electricity deviation threshold, it is determined whether electricity theft occurs in the target voltage level circuit at the target time, and electricity theft evidence is obtained when electricity theft occurs in the target voltage level circuit at the target time. The present application can more comprehensively understand the operating status of the circuit by simultaneously collecting the first data (tamperable data) and the second data (non-tamperable data) on the target voltage level circuit. By comparing these two data sources, especially using the difference between the second accumulated power and the first accumulated power as the real-time power deviation, potential electricity theft can be accurately identified. This dual data source comparison method significantly improves the accuracy of electricity theft detection and reduces the possibility of false alarms and missed alarms. Since the current, voltage, second accumulated power and other information in the second data source cannot be tampered with, they provide strong evidentiary support for electricity theft evidence. When the real-time power deviation exceeds the pre-calculated power deviation threshold, the system can immediately determine it as electricity theft and collect evidence based on this data. This evidence collection method based on tamper-proof data greatly enhances the credibility and legal effectiveness of electricity theft evidence.

[0119] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0120] The following are device embodiments of the present application. For details not fully described therein, please refer to the corresponding method embodiments described above.

[0121] Figure 4 The following is a schematic diagram of the structure of a power theft evidence collection device based on a storage transformer provided in an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown, which are detailed as follows:

[0122] like Figure 4 As shown, the electricity theft evidence collection device 4 based on the storage type mutual inductor includes:

[0123] A data acquisition module 41 is configured to acquire first and second data of a target voltage level circuit at a target time, wherein the first data includes the tamperable current, voltage, power, and first accumulated power of the target voltage level circuit; and the second data includes the non-tamperable current, voltage, second accumulated power, voltage harmonic components, current phase difference, instantaneous power fluctuation, ambient temperature, and humidity of the target voltage level circuit;

[0124] a deviation calculation module 42, configured to use the difference between the second accumulated power and the first accumulated power as a real-time power deviation;

[0125] A threshold calculation module 43 is used to calculate a power deviation threshold using the first data path and the second data path;

[0126] The electricity theft judgment module 44 is used to judge whether electricity theft occurs in the target voltage level line at the target time based on the real-time electricity deviation and the electricity deviation threshold, and to collect electricity theft evidence when electricity theft occurs in the target voltage level line at the target time.

[0127] The present application provides an electricity theft evidence collection device based on a storage-type mutual inductor, which obtains the first data and the second data of the target voltage level circuit at the target time, wherein the first data includes the tamperable current, voltage, power and first accumulated power on the target voltage level circuit, and the second data includes the non-tamperable current, voltage, second accumulated power, voltage harmonic components, current phase difference, instantaneous power fluctuation, ambient temperature and humidity on the target voltage level circuit; the difference between the second accumulated power and the first accumulated power is used as the real-time power deviation; the power deviation threshold is calculated using the first data and the second data; based on the real-time power deviation and the power deviation threshold, it is determined whether the target voltage level circuit has committed electricity theft at the target time, and electricity theft evidence is collected when the target voltage level circuit has committed electricity theft at the target time. The present application can more comprehensively understand the operating status of the circuit by simultaneously collecting the first data (tamperable data) and the second data (non-tamperable data) on the target voltage level circuit. By comparing these two data sources, especially using the difference between the second accumulated power and the first accumulated power as the real-time power deviation, potential electricity theft can be accurately identified. This dual data source comparison method significantly improves the accuracy of electricity theft detection and reduces the possibility of false alarms and missed alarms. Since the current, voltage, second accumulated power and other information in the second data source cannot be tampered with, they provide strong evidentiary support for electricity theft evidence. When the real-time power deviation exceeds the pre-calculated power deviation threshold, the system can immediately determine it as electricity theft and collect evidence based on this data. This evidence collection method based on tamper-proof data greatly enhances the credibility and legal effectiveness of electricity theft evidence.

[0128] In one possible implementation, the data acquisition module may be used to:

[0129] A storage transformer is installed on the target voltage level line, and the storage transformer includes a metering coil and a hidden monitoring coil;

[0130] Obtain the first data of the target voltage level line collected by the metering coil at the target time;

[0131] Obtain the second data of the target voltage level circuit collected by the secret monitoring coil at the target time.

[0132] In one possible implementation, the threshold calculation module may be used to:

[0133] Calculate the real-time line loss rate using the tamper-proof instantaneous power fluctuations on the target voltage level line;

[0134] Calculate the dynamic weight coefficient using real-time power deviation, real-time line loss rate and voltage total harmonic distortion rate;

[0135] Calculate the dynamic adjustment coefficient using the standard deviation of the first data channel and the standard deviation of the second data channel;

[0136] The power deviation threshold is calculated using the real-time line loss rate, dynamic weight coefficient and dynamic adjustment coefficient.

[0137] In a possible implementation, the threshold calculation module may also be used to:

[0138] Input the input power and output power into the line loss rate calculation formula to calculate the real-time line loss rate. The line loss rate calculation formula is:

[0139]

[0140] Where ΔP 线损 is the real-time line loss rate, P 输入 is the input power, P 输出 is the output power.

[0141] In a possible implementation, the threshold calculation module may also be used to:

[0142] The real-time power deviation, the real-time line loss rate and the voltage total harmonic distortion rate are input into the least square fitting formula to obtain the first fitting coefficient and the second fitting coefficient;

[0143] Obtaining a dynamic weight coefficient according to the first fitting coefficient and the second fitting coefficient;

[0144] Among them, the least squares fitting formula is:

[0145] ΔQ=k1·ΔP 线损 +k2·THD V +∈

[0146] Among them, ΔQ is the real-time power deviation, ΔO 线损 is the real-time line loss rate, THD V is the voltage total harmonic distortion rate, k1 is the first fitting coefficient, k2 is the second fitting coefficient, and ∈ is the residual.

[0147] In a possible implementation, the dynamic weight coefficient may include a line loss rate dynamic weight coefficient and a voltage harmonic distortion rate coefficient, and the threshold calculation module may further be used to:

[0148] The first fitting coefficient and the second fitting coefficient are input into the first formula to calculate the line loss rate dynamic weight coefficient and the voltage harmonic distortion rate coefficient. The first formula is:

[0149]

[0150] Among them, α(t) is the dynamic weight coefficient of line loss rate, β(t) is the voltage harmonic distortion rate coefficient, and max() is the maximum value function.

[0151] In a possible implementation, the threshold calculation module may also be used to:

[0152] The standard deviation of the first data channel and the standard deviation of the second data channel are input into the second formula to calculate the dynamic adjustment coefficient. The second formula is:

[0153]

[0154] Among them, γ(t) is the dynamic adjustment coefficient, σ 计量 is the standard deviation of the first channel data, σ 监测 is the standard deviation of the second channel data.

[0155] In a possible implementation, the threshold calculation module may also be used to:

[0156] Input the real-time line loss rate, dynamic weight coefficient, and dynamic adjustment coefficient into the threshold calculation formula to calculate the power deviation threshold. The threshold calculation formula is:

[0157]

[0158] Among them, Q 计算 (t) is the power deviation threshold, Q 基准 is the baseline threshold, γ(t) is the dynamic adjustment coefficient, σ 双路 is the standard deviation of the first and second data at the target time, σ 历史 is the standard deviation mean of the first channel data and the second channel data under historical normal working conditions at the same time as the target time, α(t) is the dynamic weight coefficient of the line loss rate, ΔP 线损is the real-time line loss rate, β(t) is the voltage harmonic distortion coefficient, THD V is the total harmonic distortion of voltage.

[0159] In one possible implementation, the electricity theft determination module may be used to:

[0160] Determine whether the real-time power deviation is less than or equal to the power deviation threshold;

[0161] If the real-time power deviation is less than or equal to the power deviation threshold, it is determined that no power theft occurs on the target voltage level line at the target time;

[0162] If the real-time power deviation is greater than the power deviation threshold, it is determined that power theft occurs on the target voltage level line at the target time.

[0163] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0164] Those skilled in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0165] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned embodiments of the electricity theft evidence collection method based on the storage transformer. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc.

[0166] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for collecting evidence of electricity theft based on a storage transformer, characterized in that: include: Obtain first and second data of the target voltage level circuit at the target time, where the first data includes the tamperable current, voltage, power, and first accumulated power on the target voltage level circuit; and the second data includes the non-tamperable current, voltage, second accumulated power, voltage harmonic component, current phase difference, instantaneous power fluctuation, ambient temperature, and humidity on the target voltage level circuit; using the difference between the second accumulated power and the first accumulated power as a real-time power deviation; Calculating a power deviation threshold using the first data channel and the second data channel; Based on the real-time power deviation and the power deviation threshold, it is determined whether power theft occurs in the target voltage level circuit at the target time, and power theft evidence is collected when power theft occurs in the target voltage level circuit at the target time.

2. The method for collecting evidence of electricity theft based on a storage transformer according to claim 1 is characterized in that: The step of obtaining the first data and the second data of the target voltage level circuit at the target time includes: A storage transformer is arranged on the target voltage level line, wherein the storage transformer includes a metering coil and a hidden monitoring coil; Acquire a first channel of data of the target voltage level circuit collected by the metering coil at the target time; Obtain the second data of the target voltage level circuit at the target time collected by the secret monitoring coil.

3. The method for collecting evidence of electricity theft based on a storage transformer according to claim 1, characterized in that: The calculating the power deviation threshold by using the first data and the second data includes: Calculate the real-time line loss rate by using the tamper-proof instantaneous power fluctuation on the target voltage level line; Calculating a dynamic weight coefficient using the real-time power deviation, the real-time line loss rate, and the voltage total harmonic distortion rate; Calculating a dynamic adjustment coefficient using a standard deviation of the first data path and a standard deviation of the second data path; The power deviation threshold is calculated using the real-time line loss rate, the dynamic weight coefficient, and the dynamic adjustment coefficient.

4. The method for collecting evidence of electricity theft based on a storage transformer according to claim 3 is characterized in that: The instantaneous power fluctuation includes input power and output power. The calculation of the real-time line loss rate using the tamper-proof instantaneous power fluctuation on the target voltage level line includes: The input power and the output power are input into the line loss rate calculation formula to calculate the real-time line loss rate. The line loss rate calculation formula is: Where ΔP 线损 is the real-time line loss rate, P 输入 is the input power, P 输出 is the output power.

5. The method for collecting evidence of electricity theft based on a storage transformer according to claim 3 is characterized in that: The calculating of the dynamic weight coefficient by using the real-time power deviation, the real-time line loss rate and the voltage total harmonic distortion rate includes: Inputting the real-time power deviation, the real-time line loss rate, and the voltage total harmonic distortion rate into a least squares fitting formula to obtain a first fitting coefficient and a second fitting coefficient; Obtaining the dynamic weight coefficient according to the first fitting coefficient and the second fitting coefficient; Wherein, the least squares fitting formula is: ΔQ=k1·ΔP 线损 +k2·THD V +∈ Wherein, ΔQ is the real-time power deviation, ΔP 线损 is the real-time line loss rate, THD V is the voltage total harmonic distortion rate, k1 is the first fitting coefficient, k2 is the second fitting coefficient, and ∈ is the residual.

6. The method for collecting evidence of electricity theft based on a storage transformer according to claim 5, characterized in that: The dynamic weight coefficient includes a line loss rate dynamic weight coefficient and a voltage harmonic distortion rate coefficient. The dynamic weight coefficient is obtained according to the first fitting coefficient and the second fitting coefficient, including: The first fitting coefficient and the second fitting coefficient are input into a first formula to calculate the line loss rate dynamic weight coefficient and the voltage harmonic distortion rate coefficient. The first formula is: Wherein, α(t) is the dynamic weight coefficient of the line loss rate, β(t) is the voltage harmonic distortion rate coefficient, and max() is the maximum value function.

7. The method for collecting evidence of electricity theft based on a storage transformer according to claim 3, characterized in that: The calculating the dynamic adjustment coefficient by using the standard deviation of the first data path and the standard deviation of the second data path includes: The standard deviation of the first data path and the standard deviation of the second data path are input into a second formula to calculate the dynamic adjustment coefficient. The second formula is: Among them, γ(t) is the dynamic adjustment coefficient, σ 计量 is the standard deviation of the first channel data, σ 监测 is the standard deviation of the second channel data.

8. The method for collecting evidence of electricity theft based on a storage transformer according to claim 3 is characterized in that: The calculating the power deviation threshold by using the real-time line loss rate, the dynamic weight coefficient, and the dynamic adjustment coefficient includes: The real-time line loss rate, the dynamic weight coefficient, and the dynamic adjustment coefficient are input into a threshold calculation formula to calculate the power deviation threshold. The threshold calculation formula is: Among them, Q 计算 (t) is the power deviation threshold, Q 基准 is the reference threshold, γ(t) is the dynamic adjustment coefficient, σ 双路 is the standard deviation of the first data path and the second data path within the target time, σ 历史 is the standard deviation mean of the first channel data and the second channel data under historical normal working conditions at the same time as the target time, α(t) is the dynamic weight coefficient of the line loss rate, ΔP 线损 is the real-time line loss rate, β(t) is the voltage harmonic distortion coefficient, THD V is the total harmonic distortion rate of the voltage.

9. The method for collecting evidence of electricity theft based on a storage transformer according to claim 1, characterized in that: The determining, based on the real-time power deviation and the power deviation threshold, whether power theft occurs in the target voltage level circuit at the target time includes: Determine whether the real-time power deviation is less than or equal to the power deviation threshold; If the real-time power deviation is less than or equal to the power deviation threshold, it is determined that no power theft occurs on the target voltage level circuit at the target time; If the real-time power deviation is greater than the power deviation threshold, it is determined that power theft occurs in the target voltage level circuit at the target time.

10. A device for collecting evidence of electricity theft based on a storage transformer, characterized in that: include: A data acquisition module is configured to acquire first and second data of a target voltage level circuit at a target time, wherein the first data includes tamperable current, voltage, power, and first accumulated power on the target voltage level circuit; and the second data includes non-tamperable current, voltage, second accumulated power, voltage harmonic components, current phase difference, instantaneous power fluctuation, ambient temperature, and humidity on the target voltage level circuit; a deviation calculation module, configured to use the difference between the second accumulated power and the first accumulated power as a real-time power deviation; A threshold calculation module, configured to calculate a power deviation threshold using the first data channel and the second data channel; The electricity theft judgment module is used to judge whether electricity theft occurs in the target voltage level circuit at the target time based on the real-time electricity quantity deviation and the electricity quantity deviation threshold, and to collect electricity theft evidence when electricity theft occurs in the target voltage level circuit at the target time.

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