Electric energy meter operation state monitoring system

Abstract

The invention relates to the technical field of electric energy metering and monitoring, in particular to an electric energy meter running state monitoring system which comprises an equipment layer, a transmission layer and an application layer, the equipment layer comprises an intelligent electric meter, a mutual inductor and a data acquisition terminal, the transmission layer is responsible for data transmission, and the application layer is composed of a server and a client. The server carries a fault diagnosis algorithm trained by historical fault data based on a machine learning technology, and the client provides a human-computer interaction interface; the server can comprise a data storage module, a remote control instruction generation unit and a data backup module, the data acquisition terminal can be provided with a storage unit, the mutual inductor comprises a voltage mutual inductor and a current mutual inductor, the client can be provided with an alarm module and comprises a computer client and a mobile client, and the equipment layer can further comprise a standby power supply module. Remote monitoring, fault diagnosis and remote control of the running state of the electric energy meter can be realized, the fault processing efficiency is improved, the operation and maintenance cost is reduced, and the metering reliability is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of electricity metering and monitoring technology, and is an electricity meter operation status monitoring system. Background Technology

[0002] With the rapid development of the power industry and the continuous advancement of smart grid construction, the accuracy and reliability of electricity metering have become increasingly important, directly affecting the operational efficiency of power companies and the vital interests of users. Against this backdrop, traditional electricity metering fault detection methods have gradually revealed many significant drawbacks and can no longer meet the operation and maintenance needs of modern power systems.

[0003] Traditional electricity metering fault detection primarily relies on manual on-site inspections, a method that requires a significant investment of human and material resources. Maintenance personnel must travel to different metering points in different areas, checking the operational status of each meter and related equipment one by one. This is not only labor-intensive but also extremely inefficient. Especially in areas with a wide distribution of users and complex geographical environments, the difficulty of manual inspections is further increased, often requiring a considerable amount of time to complete a comprehensive check. Furthermore, manual inspections have significant time interval limitations and long inspection cycles, making real-time monitoring of the meter's operational status impossible. This results in many faults going undetected until they have already caused some impact, only becoming apparent through user complaints or subsequent inspections.

[0004] Even though some regions have introduced preliminary remote monitoring systems, these systems still have many shortcomings in practical applications. Regarding data storage, many systems lack dedicated storage unit designs, making it difficult to effectively retain collected data, resulting in difficulties in tracing historical data and uncovering potential equipment hazards through data analysis. In terms of the intelligence level of fault diagnosis, existing systems mostly rely on simple threshold judgments, which are weak in identifying complex faults and prone to misjudgments or omissions, failing to provide maintenance personnel with accurate fault diagnosis basis. Furthermore, some systems have limited client-side functionality, only providing basic status display and lacking flexible alarm methods and multi-terminal adaptability, making it difficult for maintenance personnel to quickly obtain fault information and respond promptly.

[0005] The existing system also has shortcomings in ensuring continuous equipment operation and data security. Most systems lack backup power modules; once a mains power outage occurs, smart meters and data acquisition terminals will cease operation, leading to data acquisition interruption and even the loss of critical operational data. Furthermore, the entire data transmission and storage process lacks a robust backup mechanism, resulting in a high risk of data loss. Some systems also fail to effectively protect transmitted data, posing a risk of data leakage or tampering. These problems not only affect the accuracy and reliability of electricity metering but also impose additional operation and maintenance costs and management pressure on power companies. They can also cause unnecessary losses to users due to untimely fault handling. Summary of the Invention

[0006] This invention provides an energy meter operation status monitoring system that overcomes the shortcomings of the prior art. It can effectively solve the problems of low efficiency, untimely fault detection, insufficient system intelligence, and weak data security in traditional manual inspections.

[0007] The technical solution of the present invention is achieved through the following measures: an electricity meter operation status monitoring system, which adopts a layered distributed architecture, including a device layer, a transmission layer and an application layer, and each layer works together to realize remote monitoring, fault diagnosis and remote control of the electricity meter operation status; The device layer includes smart meters, current transformers, and data acquisition terminals. The smart meters are used to collect electricity consumption data, the current transformers are used to convert and adapt the measurement signals, and the data acquisition terminals are used to receive the electricity consumption data output by the smart meters and transmit it to the transmission layer. The transport layer is used to transmit data output from the device layer to the application layer; The application layer includes a server and a client. The server is equipped with a fault diagnosis algorithm, which is trained on historical fault data based on machine learning technology and used to achieve automatic fault diagnosis. The client provides a human-computer interaction interface to display status information and fault alarms.

[0008] The following are further optimizations and / or improvements to the above-mentioned technical solution: The aforementioned server may include a data storage module for storing received power consumption data and fault diagnosis results.

[0009] The aforementioned machine learning techniques may include neural network algorithms, support vector machine algorithms, or decision tree algorithms.

[0010] The aforementioned client may be equipped with an alarm module, which will issue an alarm via sound, light, or message push when a fault is detected.

[0011] The aforementioned data acquisition terminal may be equipped with a storage unit for temporary storage of the collected electricity consumption data.

[0012] The aforementioned server may be equipped with a remote control command generation unit, which is used to generate remote control commands based on fault diagnosis results and send them to the device layer through the transport layer.

[0013] The aforementioned instrument transformers may include voltage transformers and current transformers, which are used to convert high voltage and large current into low voltage and small current signals, respectively.

[0014] The aforementioned clients may include computer clients and mobile clients, and the human-computer interaction interface may include a data chart display area, a fault information prompt area, and an operation control area.

[0015] The aforementioned equipment layer may also include a backup power module for providing continuous power to smart meters and data acquisition terminals during mains power outages.

[0016] The aforementioned server may be equipped with a data backup module for regularly backing up and restoring the data in the storage unit.

[0017] This invention, employing a layered distributed architecture, organically combines the device layer, transmission layer, and application layer to achieve full-process monitoring and management of the electricity meter's operating status. Compared to traditional manual inspections and basic remote monitoring systems, it possesses significant technical advantages and practical value. In the data acquisition and transmission phase, the smart meters in the device layer comprehensively collect electricity consumption data, while the current transformers ensure measurement accuracy through signal conversion. The data acquisition terminal not only handles data reception and transmission but also features a storage unit that enables temporary data storage, preventing data loss due to transmission interruptions. The transmission layer, acting as a bridge for data flow, ensures smooth data transmission between the device layer and the application layer, laying the foundation for subsequent data analysis and fault diagnosis. The application layer server is equipped with a fault diagnosis algorithm based on machine learning technology. Through training on historical fault data, it can accurately identify various faults, significantly improving the accuracy and intelligence of fault diagnosis compared to traditional threshold-based methods, effectively reducing misjudgments and missed diagnoses. The server's data storage module can store power consumption data and fault diagnosis results long-term. Combined with the data backup module's regular backup and recovery management functions, this significantly reduces the risk of data loss, ensuring data security and integrity. It also provides reliable data support for subsequent data analysis, power consumption trend prediction, and equipment operation and maintenance optimization. Furthermore, the server's remote control command generation unit can quickly generate control commands based on fault diagnosis results and send them to the device layer via the transport layer, enabling timely fault handling and improving operation and maintenance response speed. The client design fully considers the actual needs of operation and maintenance personnel. The alarm module issues alarms through multiple methods such as sound, light, and message push, ensuring that operation and maintenance personnel can detect faults immediately. Dual adaptation between computer and mobile clients, along with a human-machine interface that includes a data chart display area, a fault information prompt area, and an operation control area, allows operation and maintenance personnel to conveniently view equipment operating status, obtain fault information, and perform related operations regardless of their location, further improving the flexibility and efficiency of operation and maintenance work. The backup power module added to the equipment layer can continuously supply power to the smart meter and data acquisition terminal when the mains power is interrupted, ensuring the continuity of data acquisition and avoiding monitoring interruption caused by power failure. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall system architecture according to an embodiment of the present invention. Detailed Implementation

[0019] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0020] The present invention will be further described below with reference to embodiments: Example 1: As Figure 1As shown, this embodiment provides an electricity meter operation status monitoring system, which adopts a layered distributed architecture, including a device layer, a transmission layer and an application layer. Each layer works together to realize remote monitoring, fault diagnosis and remote control of the electricity meter operation status. The device layer includes smart meters, instrument transformers, and data acquisition terminals. Smart meters collect electricity consumption data, instrument transformers convert and adapt the signals for measurement, and data acquisition terminals receive the electricity consumption data output by the smart meters and transmit it to the transmission layer. The device layer mainly includes various energy metering devices, such as smart meters and instrument transformers, as well as data acquisition terminals. Smart meters are responsible for collecting users' electricity consumption data in real time, including information such as voltage, current, power, and energy consumption. Instrument transformers convert high voltage and high current into signals suitable for smart meter measurement. Data acquisition terminals are responsible for collecting data from smart meters, performing preliminary processing and storage, and also have the function of communicating with the transmission layer.

[0021] The transport layer is used to transmit data output from the device layer to the application layer; the transport layer is also responsible for transmitting data collected by the device layer to the application layer. Multiple communication methods are combined, including wired communication (such as RS-485 bus, Ethernet) and wireless communication (such as GPRS, 4G, NB-IoT). For scenarios with short distances and large data transmission volumes, RS-485 bus or Ethernet is preferred to ensure data transmission stability and high speed. For widely distributed, remote areas or small users with difficult wiring, wireless communication methods such as GPRS, 4G, and NB-IoT are used to achieve remote data transmission.

[0022] The application layer comprises a server and a client. The server carries a fault diagnosis algorithm trained on historical fault data using machine learning techniques, enabling automatic fault diagnosis. The client provides a human-machine interface to display status information and fault alarms. The application layer is the core of the system, primarily consisting of the server and client. The server receives, stores, and manages data from the transport layer, runs data analysis and fault diagnosis algorithms, and enables remote monitoring and control of the electricity metering device. The client provides an interface for power company managers and maintenance personnel, allowing them to view the real-time operating status of the electricity metering device, receive fault alarm information, and perform corresponding processing. Smart meters can be selected from three-phase or single-phase smart meters that comply with national power industry standards, capable of collecting key power consumption parameters such as voltage, current, power, and energy consumption in real time. Current transformers, as signal conversion components, can convert high voltage and high current in the power system into low voltage and low current signals that the smart meter can measure, ensuring the accuracy of data acquisition. The data acquisition terminal, based on a low-power microcontroller, connects to the smart meter via a communication interface to receive and transmit data. The transmission layer can select an appropriate communication method according to the actual scenario to ensure stable data transmission. The server can be an industrial-grade server with powerful data processing and storage capabilities, while the client provides a convenient operating interface for maintenance personnel. This allows for remote monitoring of the electricity meter's operating status, eliminating the need for manual on-site inspections and significantly improving monitoring efficiency. Simultaneously, machine learning-based fault diagnosis algorithms can quickly and accurately identify faults, supporting timely fault handling.

[0023] In this embodiment, the server includes a data storage module for storing received electricity consumption data and fault diagnosis results. The data storage module can employ a large-capacity hard disk array, supporting long-term storage of massive amounts of data and possessing high-speed read / write capabilities. It can promptly store real-time electricity consumption data collected by the smart meter and fault diagnosis results generated by the server. This enables effective data retention, providing a data foundation for subsequent data analysis, fault tracing, and electricity consumption trend prediction, thus solving the problem of insufficient data storage in traditional systems.

[0024] In this embodiment, machine learning techniques include neural network algorithms, support vector machine algorithms, or decision tree algorithms. Neural network algorithms possess powerful nonlinear fitting capabilities and can handle complex power consumption data; support vector machine algorithms have high classification accuracy with small sample data and are suitable for fault type identification; decision tree algorithms are highly interpretable, making it easier for maintenance personnel to understand the fault diagnosis logic. By selecting the appropriate algorithm based on the actual fault data characteristics, the accuracy and reliability of fault diagnosis can be improved, effectively reducing false positives and false negatives compared to traditional threshold-based methods.

[0025] In this embodiment, the client is equipped with an alarm module. When a fault is detected, the alarm module issues an alarm via sound, light, or push notification. Sound alarms can use a buzzer, light alarms can use LED indicator lights, and push notifications can be sent via SMS, app notifications, etc. This ensures that maintenance personnel can promptly detect fault information in different scenarios, preventing problems from escalating due to delayed fault detection and improving fault response speed.

[0026] In this embodiment, the data acquisition terminal is equipped with a storage unit for temporarily storing the collected power consumption data. The storage unit can be a Flash memory, which has a power-off data retention function. When a communication interruption occurs at the transmission layer, the power consumption data collected by the data acquisition terminal can be temporarily stored in the storage unit and uploaded to the server after communication is restored. This avoids data loss caused by communication interruption, ensures the integrity of data acquisition, and solves the problem of data loss in traditional systems.

[0027] In this embodiment, the server is equipped with a remote control command generation unit, which generates remote control commands based on fault diagnosis results and sends them to the device layer via the transport layer. When the server diagnoses a fault in the energy meter or related equipment, the remote control command generation unit can generate control commands such as remote circuit breaker operation and parameter adjustment, which are then transmitted to the data acquisition terminal via the transport layer, and the data acquisition terminal executes the corresponding operations. This enables remote fault handling, eliminating the need for maintenance personnel to travel to the site, significantly shortening fault handling time and reducing maintenance costs.

[0028] In this embodiment, the instrument transformer includes a voltage transformer and a current transformer, which are used to convert high voltage and large current into low voltage and small current signals, respectively. The voltage transformer can convert a high voltage in the kV range into a standard low voltage of 100V, and the current transformer can convert a large current of hundreds or even thousands of amperes into a standard small current of 5A or 1A. This allows smart meters to safely and accurately measure voltage and current parameters in the power system, providing reliable basic data for energy metering and condition monitoring.

[0029] In this embodiment, the client includes a computer client and a mobile client. The human-computer interaction interface includes a data chart display area, a fault information prompt area, and an operation control area. The computer client can be installed in the power company's monitoring center, supporting multi-window display and large data volume processing. The mobile client is compatible with mobile devices such as smartphones and tablets, making it convenient for maintenance personnel to view relevant information when they are out in the field. The data chart display area can display electricity consumption data trends in the form of line charts, bar charts, etc. The fault information prompt area can list the fault type, location, and occurrence time. The operation control area provides function buttons such as remote control and data query. This improves the client's adaptability and ease of operation, meets the usage needs of maintenance personnel in different scenarios, and further improves work efficiency.

[0030] In this embodiment, the device layer also includes a backup power module to provide continuous power to the smart meter and data acquisition terminal during mains power outages. The backup power module can be a lithium battery pack with charge / discharge protection. It is normally charged by mains power and automatically switches to power supply mode when mains power is interrupted, ensuring continuous operation of the smart meter and data acquisition terminal for several hours or even longer. This avoids monitoring interruptions and data loss due to mains power outages, ensuring the continuity and stability of system operation.

[0031] In this embodiment, the server is equipped with a data backup module for regularly backing up and restoring the data in the storage unit. The data backup module can be set to automatically back up data daily, migrating the data in the storage unit to a remote server or cloud storage platform. It also has data recovery capabilities, allowing for rapid recovery of data lost or damaged in the storage unit. This significantly reduces the risk of data loss, ensures data security and integrity, and provides data assurance for the reliable operation of the system.

[0032] In this invention, the system can acquire real-time operating data from the electricity metering device through the transmission layer, including parameters such as voltage, current, power, electricity consumption, and power factor. The system displays this data in an intuitive interface, allowing maintenance personnel to monitor the operating status of the electricity metering device at any time. If abnormal data fluctuations are detected, timely analysis and processing can be performed. The data analysis function can perform in-depth analysis of the large amount of historical data collected, statistically analyze user electricity consumption, analyze electricity load curves, and predict electricity consumption trends. By comparing data from different time periods, electricity consumption patterns and potential problems can be identified, providing decision-making basis for power companies' load management, power dispatch, and marketing.

[0033] In this invention, the fault diagnosis function, based on a preset fault diagnosis algorithm, performs real-time analysis of the data from the electricity metering device to automatically determine whether a fault exists. Common fault types include meter faults, transformer faults, line faults, and communication faults. Once a fault is detected, the system immediately issues an alarm message, detailing the fault type, location, and possible causes, helping maintenance personnel quickly locate and resolve the problem. The remote control function allows maintenance personnel to remotely operate the electricity metering device when necessary, such as remote meter reading, remote setting of meter parameters, and remote circuit breaker operation. This plays a crucial role in handling overdue payments and emergency fault handling, effectively improving work efficiency and response speed. Regarding data storage and management, the system possesses powerful data storage capabilities, storing collected electricity metering data and fault information in a database. Simultaneously, data backup, data recovery, and data cleanup operations enable effective data management, ensuring data security and integrity, and facilitating subsequent querying and analysis.

[0034] In this invention, the data acquisition terminal is based on a low-power, high-performance microcontroller and equipped with rich interface circuits. It communicates with smart meters via an RS-485 interface to collect meter data, and integrates a wireless communication module (such as a GPRS module, 4G module, or NB-IoT module) to achieve remote data transmission. To ensure the accuracy and stability of data acquisition, the terminal is also designed with high-precision power supply circuits and anti-interference circuits. The selection of communication modules should be based on different application scenarios and communication requirements: for short-distance, high-speed data transmission, Ethernet modules are preferred due to their high transmission rate and good stability; in long-distance wireless communication scenarios, GPRS modules are suitable for situations where the transmission rate requirement is not high and the coverage is wide, 4G modules can provide a higher transmission rate to adapt to large data volume transmission, and NB-IoT modules, due to their low power consumption and wide coverage, are suitable for low-speed, long-connection power data acquisition scenarios such as small, dispersed users.

[0035] In this invention, the system develops efficient data processing algorithms to perform preprocessing operations such as denoising, filtering, and normalization on the collected raw data to improve data quality. Fault diagnosis algorithms, such as Support Vector Machine (SVM) and neural network algorithms, are designed based on machine learning and data mining techniques. Through learning and training on a large amount of historical fault data, the accuracy and reliability of fault diagnosis are improved. The database design selects databases such as MySQL or Oracle, which are suitable for storing and managing massive amounts of data. A reasonable data table structure is designed, including user information tables, electricity metering data tables, and fault information tables. Relationships between tables are established to ensure data consistency and integrity, and database query statements are optimized to improve data query and retrieval efficiency. The human-computer interaction interface adopts a user-friendly graphical user interface (GUI) design with a simple and clear layout. It can intuitively display the operating status of the electricity metering device, data statistics charts, fault alarm information, etc., while providing rich operation menus and shortcut buttons, supporting functions such as data query, report generation, and remote control. In actual power field verification, the system was piloted in different regions and among different types of users. The results showed that it is stable and reliable, effectively improving the efficiency and accuracy of power metering fault detection, reducing operation and maintenance costs, and gaining user approval. Test data indicates that all system functions can be implemented normally, with a fault diagnosis accuracy rate of over 95%, a data transmission error rate of less than 1%, and the ability to quickly process large amounts of data with a response time of less than one second. The throughput meets actual needs, and it exhibits good compatibility with mainstream power metering devices and communication modules.

[0036] During operation, the smart meter collects electricity consumption data in real time. The current transformer converts high voltage and high current into signals suitable for the smart meter's measurement. The data acquisition terminal receives the electricity consumption data output by the smart meter. If the transmission layer communication is normal, the data is directly transmitted to the application layer server through the transmission layer. If communication is interrupted, the data is temporarily stored in the data acquisition terminal's storage unit and uploaded after communication is restored. After receiving the data, the server stores it using the data storage module. At the same time, the fault diagnosis algorithm based on machine learning technology analyzes the data and automatically diagnoses whether there is a fault. If a fault is detected, the server's remote control command generation unit can generate corresponding control commands and send them to the data acquisition terminal for execution through the transmission layer. Meanwhile, the client's alarm module issues alarms through sound, light, or message push. Maintenance personnel can view fault information and equipment operating status through the human-machine interface of a computer client or mobile client. The server's data backup module backs up the data in the storage unit periodically, and the backup power module at the device layer supplies power to the smart meter and data acquisition terminal when the mains power is interrupted. This process enables full monitoring of the electricity meter's operating status, accurate fault diagnosis and rapid handling, ensuring data security and continuous system operation, improving the overall level of power operation and maintenance management, and reducing operation and maintenance costs.

[0037] It should be noted that, in this invention, the layered distributed architecture refers to a system that is functionally divided into a device layer, a transmission layer, and an application layer that work independently yet collaboratively. Each layer performs its own function and achieves the overall functionality through data interaction. Machine learning technology refers to a technology that enables computers to learn patterns from historical data through algorithms, thereby enabling the analysis and judgment of new data. The data storage module refers to a hardware or software module used to store various types of data generated during system operation. The backup power module refers to a power supply device that automatically intervenes to supply power to the equipment when the main power supply is interrupted. The remote control command generation unit refers to a functional module that can generate control commands based on preset logic or diagnostic results. The alarm module refers to a module that issues warning signals in a specific way when an abnormal situation is detected.

[0038] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. An electric energy meter operating state monitoring system, characterized by, The layered distributed architecture is adopted, including a device layer, a transmission layer and an application layer, and the remote monitoring of the running state of the electric energy meter, fault diagnosis and remote control are realized by the cooperation of the layers. The device layer includes a smart meter, a mutual inductor and a data acquisition terminal; the smart meter is used for collecting power consumption data, the mutual inductor is used for converting and adapting the measured signals, and the data acquisition terminal is used for receiving the power consumption data output by the smart meter and transmitting it to the transmission layer. The transmission layer is used for transmitting the data output by the device layer to the application layer. The application layer includes a server and a client; the server carries a fault diagnosis algorithm, which is obtained based on machine learning technology through historical fault data training, and is used for realizing automatic fault diagnosis; the client provides a man-machine interface, which is used for displaying state information and fault alarm.

2. The electric energy meter operating state monitoring system according to claim 1, characterized in that, The server includes a data storage module for storing the received power consumption data and fault diagnosis results.

3. The system for monitoring the operating state of an electric energy meter according to claim 1, characterized in that, The machine learning technology includes a neural network algorithm, a support vector machine algorithm or a decision tree algorithm.

4. The system for monitoring the operating state of an electric energy meter according to claim 1 or 2 or 3, characterized in that, The client is provided with an alarm module which sends an alarm through sound, light or message push mode when detecting a fault.

5. The system for monitoring the operating state of an electric energy meter according to claim 1 or 2 or 3, characterized in that, The data acquisition terminal is provided with a storage unit for temporarily storing the collected power consumption data.

6. The electric energy meter operating state monitoring system according to claim 1 or 2 or 3, characterized by, The server is provided with a remote control instruction generation unit for generating a remote control instruction according to the fault diagnosis result and sending it to the device layer through the transmission layer.

7. The system for monitoring the operational status of an electric energy meter according to claim 1 or 2 or 3, characterized in that, The mutual inductor includes a voltage mutual inductor and a current mutual inductor, which are used for converting high voltage and large current into low voltage and small current signals, respectively.

8. The system for monitoring the operational status of an electric energy meter according to claim 1 or 2 or 3, characterized in that, The client includes a computer client and a mobile client, and the man-machine interface is provided with a data chart display area, a fault information prompt area and an operation control area.

9. The system for monitoring the operational status of an electric energy meter according to claim 1 or 2 or 3, characterized in that, The device layer further includes a backup power module for providing continuous power supply for the smart meter and the data acquisition terminal when the mains power is interrupted.

10. The system for monitoring the operational status of an electric energy meter according to claim 1 or 2 or 3, characterized in that, The server is provided with a data backup module for regularly backing up and recovering the data in the storage unit.

Images