Stacked busbar and electric energy metering box depth fusion electricity metering management and control system
The electricity metering and control system, which deeply integrates stacked busbars and electricity metering boxes, enables dual data acquisition and cross-fit analysis. This solves the problems of space occupation and high failure rate of traditional metering boxes, improves the accuracy and security of metering data, and supports anti-theft analysis and load forecasting.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG CHAOJU INTELLIGENT TECHNOLOGY CO LTD
- Filing Date
- 2026-04-07
- Publication Date
- 2026-06-19
AI Technical Summary
Traditional metering box wiring methods occupy a large space, have poor heat dissipation, and a high failure rate. They also lack real-time cross-verification and early warning functions, making it difficult to detect and handle metering deviations in a timely manner, and failing to meet the high integration and security requirements of intelligent distribution networks.
The electricity metering and control system, which deeply integrates stacked busbars and electricity metering boxes, calculates local metering deviations in real time through dual data acquisition and cross-fit analysis. Combined with big data comparison and environmental monitoring, it generates early warnings and anomaly identification, enabling hierarchical identification and risk prediction of users' electricity consumption behavior.
It significantly improves the accuracy and consistency of metering data, enables timely detection of metering faults, reduces line loss statistical errors, and enhances the safety, stability, and anti-theft analysis capabilities of electricity metering boxes.
Smart Images
Figure CN122247008A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of metering and control, specifically to an electricity metering and control system that deeply integrates stacked busbars and electricity metering boxes. Background Technology
[0002] The new power system is driving the distribution network towards intelligence and digitalization, and the function of metering boxes is being upgraded accordingly. Metering boxes are transforming from single meter carriers into intelligent nodes that integrate energy flow, information flow and security flow, undertaking more data interaction and control functions, and becoming key units for end-point energy management. Their level of intelligence directly affects the overall efficiency of the system. However, traditional wiring methods have problems such as large space occupation, poor heat dissipation and high failure rate, which are difficult to meet the new requirements. Technological innovation is urgently needed to support the transformation, so as to achieve higher integration and performance in a limited space.
[0003] Therefore, some metering boxes use laminated busbars instead of cable connections to simplify the assembly process. The compact structure of the laminated busbars improves electrical performance, optimizes space utilization, reduces electromagnetic interference, and improves current transmission stability. This effectively addresses the challenge of complex wiring and provides a reliable connection solution for high-density integration. However, although the power metering and control system achieves branch metering, it still lacks real-time cross-verification with bus-side data, making it difficult to effectively detect metering deviations caused by metering device failures, communication anomalies, or human theft of electricity.
[0004] In addition, conventional systems mostly deal with metering deviations by alarming after the fact, and cannot predict or warn of the deterioration trend of the deviation. In terms of fault location and tracing, when metering data is abnormal or operating environment parameters exceed the threshold, maintenance personnel often need to rely on on-site inspections to troubleshoot the problem, which is not only inefficient, but also makes it difficult to locate the abnormal point in time, resulting in a lag in fault handling.
[0005] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention
[0006] This invention proposes a power metering and control system that deeply integrates stacked busbars and power metering boxes. This system achieves real-time calculation of local metering deviation by combining the overall power consumption of the busbar with the independent power consumption of each branch circuit through dual acquisition and cross-fit analysis. This significantly improves the consistency and accuracy of metering data. Simultaneously, by analyzing the trend of local deviation value changes, it can identify the deterioration trend of deviation values and issue early warnings of metering deterioration when they continue to rise, thus advancing the discovery of metering faults. Furthermore, it can combine the correlation analysis of box door opening and closing status with abnormal metering signals, and generate different levels of power fluctuation risk warnings based on the difference level by comparing the power consumption of different users with big data. This enables graded identification and risk prediction of abnormal fluctuations in user power consumption behavior, providing reliable data support for anti-theft analysis and load forecasting.
[0007] The objective of this invention can be achieved through the following technical solution: an electricity metering and control system that deeply integrates stacked busbars and electricity metering boxes, including an electricity metering module, a local data accounting module, an electricity traceability and verification module, a status control module, and an anomaly location and tracking module; The power metering module can independently collect power consumption data and collect data via a bus, and send the collected data to the local data calculation module; The local data accounting module performs a fitting analysis based on the power consumption data collected via the bus and the power consumption data collected independently to obtain the local metering completion rate. The power source traceability and verification module can independently store the power consumption of different users and compare the latest power statistics with big data to obtain the power fluctuation risk control results. The status control module can monitor the operating status of the power metering box and obtain the operating environment parameters and switch status parameters of the power metering box. The anomaly location and tracking module can perform anomaly assessment on the operating environment parameters and switch status parameters of the power metering box, and generate anomaly warnings based on the anomaly assessment results.
[0008] In a preferred embodiment of the present invention, when the power metering module independently collects power consumption data, it performs power statistics through different branches of the stacked busbar, wherein the stacked busbar is divided into a grid input side and a user output side. The power metering module independently collects data from different branches on the user's output side, obtains the power usage of each branch, and records it as independent power consumption. The power metering module collects data from the power grid input side via a bus to obtain the overall power usage of the power metering box and records it as the total power consumption. When collecting data on electricity usage, the electricity metering module divides electricity usage into multiple cycles, specifically long cycles and short cycles. The long cycle is the daily electricity consumption, and the short cycle is the hourly electricity consumption.
[0009] In a preferred embodiment of the present invention, the local data accounting module calculates the sum of all independent electricity consumption and the overall electricity consumption using a formula to obtain a local deviation value, and compares the local deviation value with a set threshold. If the local deviation value is greater than the set threshold, a metering anomaly signal is generated. If the local deviation value is less than or equal to the set threshold, the local deviation value is statistically analyzed, and the time information of obtaining the local deviation value is recorded at the same time.
[0010] In a preferred embodiment of the present invention, the local data accounting module organizes the data according to the acquisition time of the local deviation value, generates a local deviation value change sequence in chronological order, and analyzes the change trend of the local deviation value change sequence. If the analysis results show that the local deviation value continues to rise, a measurement deterioration warning is generated.
[0011] In a preferred embodiment of the present invention, the process by which the local data accounting module performs trend analysis on the local deviation value variation sequence is as follows: The local data calculation module calculates the difference between adjacent local deviation values to obtain the local deviation value fluctuation. If the local deviation value fluctuation is positive, it indicates that the local deviation value is decreasing; if the local deviation value fluctuation is negative, it indicates that the local deviation value is increasing. The local data calculation module records all points where the local deviation value increases and obtains a preset detection period. Within any detection period, if the proportion of samples with increasing local deviation values exceeds a set threshold, it is determined that the local deviation value is continuously increasing.
[0012] As a preferred embodiment of the present invention, the method for the power source traceability verification module to perform big data comparison of the power consumption of different users includes: S1: Store the independent power consumption data obtained from the same branch into an independent data set; S2: Compare the latest acquired independent power consumption of the same branch with the data in the corresponding independent data set; S3: Compare the latest acquired independent power consumption data for the same branch within the long and short periods with the corresponding data within the same period in the independent data set. Based on the difference obtained from the comparison, determine the threshold level and generate different levels of power fluctuation risk warnings.
[0013] In a preferred embodiment of the present invention, the operating environment parameters acquired by the state control module include the stacked busbar temperature and the hot spot temperature, and the switch status parameters acquired by the state control module are the metering box door switch status. After acquiring the operating environment parameters, the anomaly location and tracking module performs instantaneous and dynamic comparisons of the operating environment parameters. The specific instantaneous comparison process is as follows: The temperature of the stacked busbar is compared with the set threshold, and the temperature of the hot spot is compared with the set threshold. Based on the comparison results, it is determined whether the temperature of the stacked busbar and the temperature of the hot spot exceed the set temperature threshold, and a corresponding alarm signal is generated when any temperature exceeds the set threshold. The dynamic comparison process is as follows: the heating rate of the stacked busbar is compared with the set heating threshold, the thermoelectric temperature is compared with the set heating threshold, and the heating rate of the stacked busbar and the hot spot is determined according to the comparison result to see if the heating rate exceeds the set heating threshold. When any heating rate exceeds the set threshold, a corresponding alarm signal is generated.
[0014] In a preferred embodiment of the present invention, when the abnormal location tracking module obtains that the switch status parameter is in the open state, if a metering deterioration warning is subsequently generated, a metering abnormality warning is generated; if no metering deterioration warning is subsequently generated, the switch reminder warning is maintained.
[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves dual acquisition and cross-fit analysis of the overall power consumption of the bus and the independent power consumption of each branch through power branching, bus synchronous metering and local data accounting. It can calculate the local metering deviation in real time and generate an alarm reminder in time when the deviation exceeds the threshold, which significantly improves the consistency and accuracy of metering data. At the same time, by analyzing the trend of the local deviation value change sequence, it can identify the deterioration trend of the deviation value and issue a metering deterioration warning in advance when it continues to rise, thus advancing the detection point of metering faults and effectively reducing the statistical error of line loss caused by inaccurate metering.
[0016] 2. In this invention, by independently storing and comparing the electricity consumption of different users with big data, the electricity statistics are compared and analyzed with historical data over multiple periods. Based on the difference level, different levels of electricity change risk warnings are generated, which changes the limitation of relying solely on single-point threshold alarms. This enables hierarchical identification and risk prediction of abnormal fluctuations in user electricity consumption behavior, providing reliable data support for anti-electricity theft analysis and load forecasting.
[0017] 3. This invention also comprehensively monitors the operating environment parameters and switch status of the electricity metering box, and performs instantaneous static and long-term dynamic comparisons based on the acquired operating environment parameters to quickly identify temperature anomalies and their deterioration trends. Furthermore, by combining the correlation analysis between the box door switch status and metering anomaly signals, the accuracy of the alarm of the linkage risk control is further improved, the anomaly type is accurately located, and the safe and stable operation of the electricity metering box is effectively guaranteed. Attached Figure Description
[0018] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.
[0019] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a system flowchart of the present invention. Detailed Implementation
[0020] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.
[0021] Example 1: Please refer to Figure 1 - Figure 2 As shown, the electricity metering and control system, which deeply integrates the stacked busbar and the electricity metering box, includes an electricity metering module, a local data accounting module, an electricity traceability and verification module, a status control module, and an anomaly location and tracking module.
[0022] Among them, the power metering module serves as the data acquisition front end, relying on the electricity meter or other power metering devices installed on the stacked busbar structure to collect data; The local data accounting module and the power traceability verification module serve as the core of data processing, responsible for the fitting analysis of metering data and the longitudinal comparison of user electricity consumption behavior; The status control module and the anomaly location and tracking module serve as operation support units, performing real-time monitoring and anomaly diagnosis of the metering box's operating environment and equipment status. The stacked busbar includes at least a grid input side busbar and a user output side busbar. The grid input side busbar is connected to the main incoming terminal of the power supply line, while the user output side busbar has multiple branch output terminals, which are connected to each user's load. When collecting electricity consumption data, the energy metering module adopts a dual-channel acquisition mode. Independent data acquisition channel: An independent metering chip is set on each user output branch to collect the current, voltage and power data of the branch in real time. The independent power consumption of each branch is calculated by accumulation. The independent power consumption is stored according to different time granularities of long period and short period. Specifically, it includes the daily power consumption automatically calculated at midnight every day in the long period and the hourly power consumption recorded on the hour in the short period. Bus acquisition channel: A total metering unit is set at the incoming end of the power grid input side to collect the total amount of electricity entering the power metering box in real time, so as to obtain the overall electricity consumption. The overall electricity consumption is also stored according to the time granularity of daily and hourly. The power metering module packages the independently collected data and the bus-collected data together and sends them to the local data calculation module through the internal communication bus.
[0023] After receiving the data sent by the electricity metering module, the local data accounting module executes the following processing flow: Local Deviation Calculation: The local data accounting module first sums up the independent power consumption of all branches to obtain the total independent power consumption. Then, it substitutes this sum with the overall power consumption within the same time period into the formula to calculate the local deviation value. The calculation formula is as follows: Where Q is the total power consumption and q is the sum of independent power consumption, this indicator reflects the degree of consistency between branch metering and bus metering; Threshold Comparison and Anomaly Generation: The local data accounting module has a preset measurement deviation threshold. If the calculated local deviation value is greater than the measurement deviation threshold, a measurement anomaly signal is immediately generated. This signal includes the time point of the anomaly and the specific deviation value. If the local deviation value is less than or equal to the measurement deviation threshold, the deviation value and its corresponding timestamp are stored in the local deviation history record. Deviation Trend Analysis: The local data accounting module serializes the stored local deviation history records to form a sequence of local deviation value changes arranged chronologically. During trend analysis, the module calculates the difference between two adjacent deviation values in turn. By subtracting the next local deviation value from the previous one, the local deviation value fluctuation is obtained. When the local deviation value fluctuation is positive, it indicates that the current deviation value has decreased compared to the previous period; when the local deviation value fluctuation is negative, it indicates that the deviation value has increased.
[0024] The local data accounting module marks all records where deviation values increase and sets a detection cycle. Within any detection cycle, if the percentage of instances where deviation values increase exceeds a set proportion, it is determined that the local deviation value is showing a continuous upward trend, and a metering degradation warning is generated. This warning is used to indicate potential problems such as performance degradation of the metering device or poor line contact, facilitating early intervention and troubleshooting by maintenance personnel.
[0025] The status control module is responsible for sensing the physical operating status of the electricity metering box, including a temperature sensor array and a door magnetic switch sensor. The temperature sensor array is attached to key nodes of the stacked busbar, specifically including an incoming temperature sensor on the grid input side, temperature sensors at the output ends of each branch circuit, and an infrared thermometer for detecting the overall hot spot temperature, with the thermoelectric point being the hottest point inside the metering box. The door magnetic switch sensor is installed at the door of the metering box to detect the door's open or closed status in real time. The status control module sends the collected stacked busbar temperature, hot spot temperature, and door open / closed status to the anomaly location and tracking module in real time.
[0026] After receiving the operating environment parameters and switch status parameters, the anomaly location and tracking module executes the following anomaly assessment process: Instantaneous comparison: The anomaly location and tracking module compares the real-time temperature value of each temperature measuring point with the preset temperature safety threshold. If the real-time temperature of any measuring point exceeds the corresponding temperature threshold, the module immediately generates an over-temperature alarm signal corresponding to the location of that measuring point.
[0027] Dynamic comparison: The anomaly location and tracking module calculates the temperature change at each temperature measuring point per unit time to obtain the heating rate. It then compares the calculated heating rate with a preset heating rate threshold. If the heating rate at any measuring point exceeds this threshold, the anomaly location and tracking module generates an alarm signal for excessively rapid heating corresponding to that measuring point. Dynamic comparison effectively identifies rapid heating phenomena caused by overload or poor contact, overcoming the limitation of instantaneous comparison which only focuses on absolute temperature. Switch Status Correlation Analysis: The anomaly location and tracking module monitors the switch status of the control box door in real time. When the door status changes from closed to open, the module initiates a correlation analysis process: the open status is recorded. If the local data calculation module subsequently generates a metering degradation warning, it is determined that the previous door opening behavior may be related to metering anomalies, and the metering degradation warning is upgraded to a metering anomaly warning. If no metering degradation warning subsequently occurs, it is determined to be a normal door opening event, and a switch reminder warning is generated. This mechanism helps to distinguish between normal maintenance door opening and suspected electricity theft door opening behavior.
[0028] Example 2: Please refer to Figure 1 - Figure 2 As shown, the power traceability and verification module is responsible for establishing historical records of power consumption behavior for each branch and performing longitudinal comparative analysis. The specific implementation steps are as follows: S1: For each user branch, the power source verification module stores its daily independent power consumption and hourly independent power consumption separately, forming an independent data set corresponding to that branch. This set is organized in chronological order. S2: When the electricity metering module uploads new independent electricity consumption data, the electricity traceability and verification module automatically triggers the comparison process to extract the latest acquired daily electricity consumption data and hourly electricity consumption data; S3: The electricity traceability verification module compares the latest data with historical data of the same period type in an independent dataset. Specifically, for daily electricity consumption, the module compares it with the daily electricity consumption of the same period last year, the same day last month, and the same day last week; for hourly electricity consumption, the module compares it with the electricity consumption of the same period yesterday. By calculating the percentage difference between the current data and the historical data for the same period, the module matches this difference with multiple preset threshold levels. When the difference is less than the first threshold, no power fluctuation risk warning is generated; when the difference is between the first and second thresholds, a level two power fluctuation risk warning is generated; when the difference exceeds the second threshold, a level one power fluctuation risk warning is generated, where the second threshold is greater than the first threshold.
[0029] Different levels of early warning can be used to help determine whether there are abnormal fluctuations in users' electricity consumption behavior, providing a basis for anti-electricity theft analysis.
[0030] Thresholds, preset values, preset ranges, etc. are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or common-sense influences. Furthermore, the settings for weighting ratios, influence factors, etc., are based on the magnitude of each parameter's influence on the results. The specific values are allocated to ultimately reflect the impact on the results. The settings for input and storage are also determined by a combination of large-scale model analysis of sample data and human experience. Appropriate adjustments can also be made based on seasonal or rational influence conditions.
[0031] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims
1. A power metering management and control system with the deep integration of laminated busbar and electric energy metering box, characterized in that, It includes an energy metering module, a local data calculation module, an energy traceability and verification module, a status control module, and an anomaly location and tracking module; The power metering module can independently collect power consumption data and collect data via a bus, and send the collected data to the local data calculation module; The local data accounting module performs a fitting analysis based on the power consumption data collected via the bus and the power consumption data collected independently to obtain the local metering completion rate. The power source traceability and verification module can independently store the power consumption of different users and compare the latest power statistics with big data to obtain the power fluctuation risk control results. The status control module can monitor the operating status of the power metering box and obtain the operating environment parameters and switch status parameters of the power metering box. The anomaly location and tracking module can perform anomaly assessment on the operating environment parameters and switch status parameters of the power metering box, and generate anomaly warnings based on the anomaly assessment results.
2. The power metering and management system with laminated busbars and depth fusion of electric energy metering box according to claim 1, characterized in that, When the power metering module independently collects power consumption data, it performs power statistics through different branches of the stacked busbar, wherein the stacked busbar is divided into the grid input side and the user output side. The power metering module independently collects data from different branches on the user's output side, obtains the power usage of each branch, and records it as independent power consumption. The power metering module collects data from the power grid input side via a bus to obtain the overall power usage of the power metering box and records it as the total power consumption. When collecting data on electricity usage, the electricity metering module divides electricity usage into multiple cycles, specifically long cycles and short cycles. The long cycle is the daily electricity consumption, and the short cycle is the hourly electricity consumption.
3. The power metering and management system with laminated busbars and depth fusion of electric energy metering box according to claim 1, characterized in that, The local data accounting module calculates the sum of all independent electricity consumption and the overall electricity consumption using a formula to obtain a local deviation value. It then compares the local deviation value with a set threshold. If the local deviation value is greater than the set threshold, a metering anomaly signal is generated. If the local deviation value is less than or equal to the set threshold, the local deviation value is statistically analyzed, and the time information of obtaining the local deviation value is recorded at the same time.
4. The power metering and management system with laminated busbars and depth fusion of electric energy metering box according to claim 3, characterized in that, The local data accounting module organizes the data according to the acquisition time of the local deviation value, generates a local deviation value change sequence in chronological order, and analyzes the trend of the local deviation value change sequence. If the analysis results show that the local deviation value continues to rise, a measurement deterioration warning is generated.
5. The power metering and management system with laminated busbars and depth fusion of electric energy metering box according to claim 4, characterized in that, The process by which the local data accounting module performs trend analysis on the local deviation value variation sequence is as follows: The local data calculation module calculates the difference between adjacent local deviation values to obtain the local deviation value fluctuation. If the local deviation value fluctuation is positive, it indicates that the local deviation value is decreasing; if the local deviation value fluctuation is negative, it indicates that the local deviation value is increasing. The local data calculation module records all points where the local deviation value increases and obtains a preset detection period. Within any detection period, if the proportion of samples with increasing local deviation values exceeds a set threshold, it is determined that the local deviation value is continuously increasing.
6. The power metering and management system with laminated busbars and depth fusion of electric energy metering box according to claim 1, characterized in that, The method used by the power source traceability and verification module to compare the power consumption of different users using big data includes: S1: Store the independent power consumption data obtained from the same branch into an independent data set; S2: Compare the latest acquired independent power consumption of the same branch with the data in the corresponding independent data set; S3: Compare the latest acquired independent power consumption data for the same branch within the long and short periods with the corresponding data within the same period in the independent data set. Based on the difference obtained from the comparison, determine the threshold level and generate different levels of power fluctuation risk warnings.
7. The power metering and management system with laminated busbars and depth fusion of electric energy metering box according to claim 1, characterized in that, The operating environment parameters acquired by the status control module include the temperature of the stacked busbar and the hot spot temperature, and the switch status parameters acquired by the status control module are the metering box door open / closed status. After acquiring the operating environment parameters, the anomaly location and tracking module performs instantaneous and dynamic comparisons of the operating environment parameters. The specific instantaneous comparison process is as follows: The temperature of the stacked busbar is compared with the set threshold, and the temperature of the hot spot is compared with the set threshold. Based on the comparison results, it is determined whether the temperature of the stacked busbar and the temperature of the hot spot exceed the set temperature threshold, and a corresponding alarm signal is generated when any temperature exceeds the set threshold. The dynamic comparison process is as follows: the heating rate of the stacked busbar is compared with the set heating threshold, the thermoelectric temperature is compared with the set heating threshold, and the heating rate of the stacked busbar and the hot spot is determined according to the comparison result to see if the heating rate exceeds the set heating threshold. When any heating rate exceeds the set threshold, a corresponding alarm signal is generated.
8. The power metering and management system with laminated busbars and depth fusion of electric energy metering box according to claim 1, characterized in that, When the abnormal location tracking module obtains the switch status parameter as "on", if a metering deterioration warning is subsequently generated, it will generate a metering abnormality warning; if no metering deterioration warning is subsequently generated, it will maintain the switch reminder warning.