Intelligent power grid power utilization on-line monitoring and management system

Abstract

The invention provides an intelligent power grid electricity utilization on-line monitoring and management system, and relates to the technical field of intelligent power grids, the system structure is composed of a data sensing layer, a network communication layer, a monitoring and early warning layer and an application control layer, the system structure of Internet + intelligent power grid is adopted, and a large database is arranged in the system; the system is used for monitoring and early warning the electrical load and the power grid environment, so that the whole system can early warn the power grid environment abnormity in time on the basis of early warning of the electrical load, the mode of covering early warning of the electrical load and the environment at the same time is adopted, the early warning range coverage is wider, early warning of abnormal phenomena is more timely, and the early warning efficiency is improved. According to the method and the system, the power grid environment is trained, so that safety accidents in the power grid environment are effectively avoided, hierarchical arrangement is adopted, the calculation pressure of the system during training can be effectively reduced, resource waste caused by repeated calculation of an application control layer is reduced, the training calculation pressure is reduced through regional hierarchical training, and then the intrusion prevention and electricity larceny prevention intensity can be improved by improving the training frequency.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to an online monitoring and management system for electricity consumption in a smart grid. Background Art

[0002] With the rapid development of smart grids, the topological characteristics of power grids and the corresponding operating modes are becoming more and more complex, and the number of devices connected to the grid is also increasing significantly. The traditional manual-based monitoring and operation methods of power grid equipment can no longer meet the development of smart grids. Building a smart grid equipment monitoring and operation big data platform can collect, store, manage, analyze and process power grid equipment data, and then obtain the real-time operating status of the equipment and other business application functions, effectively supporting the safe and stable operation of smart grid equipment.

[0003] According to the demand, a smart grid operation monitoring system is disclosed with Chinese patent number "CN202110626271.8", which belongs to the field of smart grid technology. The present invention solves the problem that existing electricity users cannot regulate electricity consumption, resulting in energy waste. It includes a user electricity consumption collection module and a user optimal electricity consumption decision module; the user electricity consumption collection module collects and obtains the electricity consumption of each user in each time period, and sends the electricity consumption information to the user optimal electricity consumption decision module; the user optimal electricity consumption decision module establishes an electricity consumption evaluation model according to the electricity consumption information, obtains the user's electricity consumption evaluation value according to the electricity consumption evaluation model, and then uses the clustering algorithm to screen out the users to be optimized, and then obtains the optimal electricity consumption plan for the users to be optimized according to the AHP tomography analysis method.

[0004] The above patent documents and prior art have the following technical problems when used:

[0005] Problem 1: When monitoring power load data in the above patent document, only the power consumption in each period is monitored, and the load of each device cannot be checked, resulting in the inability to judge the damage of the device based on the power load;

[0006] Problem 2: In the above documents and existing technologies, a single grid load monitoring structure is generally used, which cannot monitor the grid environment at the same time, resulting in a small coverage area, insufficient data support, and low security during monitoring and early warning management;

[0007] Question three: In the above patent documents and prior art, when controlling the system to prevent intrusion, in order to increase security, the model is generally used to train all devices in the entire system at the same time, resulting in large calculation data and high system calculation pressure, which is not conducive to actual use. Summary of the invention

[0008] Technical issues solved

[0009] In view of the shortcomings of the prior art, the present invention provides an online monitoring and management system for smart grid electricity consumption, which solves the following problems:

[0010] 1. The power grid data collection is not targeted enough, and it is impossible to know the power load of each device through the power grid data, so as to realize the problem of equipment self-checking;

[0011] 2. The power grid monitoring data is single and the environmental data cannot be monitored simultaneously, resulting in low coverage and low installability;

[0012] 3. When the system conducts anti-intrusion training, the computing pressure is high, resulting in a reduction in the number of training sessions, thus affecting the anti-intrusion strength.

[0013] Technical Solution

[0014] To achieve the above objectives, the present invention is implemented through the following technical solutions: an online monitoring and management system for smart grid electricity consumption, wherein the system structure consists of a data perception layer, a network communication layer, a monitoring and early warning layer, and an application control layer;

[0015] The data perception layer is used to sense and read the power consumption data and external environment data of the connected devices, and transmit the data to the monitoring and early warning layer for analysis and early warning. The data perception layer uses non-intrusive load monitoring technology to monitor the power load of the connected smart meters, and the data perception layer uses sensor technology to monitor regional environmental parameters;

[0016] The network communication layer is used for data information transmission between the entire system, and the network communication layer has 4G / 5G communication mode, Wi F i communication mode, GPRS communication mode and RS485 communication mode;

[0017] The monitoring and early warning layer is connected to each level in the system and is used for monitoring and early warning of power grid data in the system. A large database is provided inside the monitoring and early warning layer, and the collected power grid power equipment data and power grid environment data are compared and analyzed based on this database for early warning processing.

[0018] The application control layer is used for regulating and controlling the entire system, and issues instructions to the monitoring and early warning layer to train the regional intrusion identification model, and divides the data perception layer into regions, and sets a management layer in each region, so that the monitoring and early warning layer trains each management layer during training, and the management layer then sends the adjustments of each training to the data perception unit in each region, and the data perception unit adjusts its own monitoring parameters according to the instructions.

[0019] Preferably, the data perception layer is divided into multiple sub-modules according to regions, each module is provided with a group of management layers, each management layer is connected to multiple data perception units, each data perception unit is connected to multiple electrical equipment for power load monitoring with the assistance of non-invasive load monitoring technology, and the surface of the data perception unit is connected to at least one group of data collection modules for collecting, organizing and decomposing the load data of the equipment connected to the surface.

[0020] Preferably, the data perception layer at least collects data on the grid environment temperature, grid connection topology, current, voltage and conductor temperature when monitoring the regional environment, and the data perception layer reads each parameter through corresponding sensors or collectors.

[0021] Preferably, a controller is provided inside the network communication layer, and the controller is electrically connected to a plurality of communication modes, and the controller performs self-adjustment and adaptation of the corresponding communication mode through data transmission.

[0022] Preferably, the application control layer manages and monitors the anti-intrusion and anti-electricity theft behaviors of the entire system, and the application control layer is the first-level management layer, the monitoring and early warning layer and the network communication layer are the second-level following layer, the various management layers in the data perception layer are the third-level following layer, and the various data perception units connected to the management layer are the fourth-level following layer.

[0023] Preferably, the power-consuming equipment data collected by each data sensing unit in the data sensing layer has an independent proprietary database, and the proprietary database is composed of the information of the power-consuming equipment and the power load collected each time.

[0024] Preferably, the large database within the monitoring and early warning layer is divided into a power load database and a power environment database, and the power load database communicates with the power consumption data of the data perception layer, and the power environment database communicates with the environmental parameter data of the data perception layer.

[0025] Preferably, the warning scope of the monitoring and early warning layer includes at least early warning reminders of current and voltage anomalies, data power load anomalies, power grid environment anomalies, communication anomalies, network topology anomalies, network intrusion and power grid power theft.

[0026] Beneficial Effects

[0027] The present invention provides an online monitoring and management system for smart grid electricity consumption. It has the following beneficial effects:

[0028] 1. The present invention adopts a data perception layer, a network communication layer, a monitoring and early warning layer and an application control layer to form a system structure, and performs non-invasive monitoring of the power load of the power grid through the data perception layer, realizes the decomposition and viewing of the power load of the equipment, is convenient for timely discovery of power failures, power theft and other failures, and monitors the power load of individual equipment in real time, so as to facilitate the monitoring of power consumption anomalies when statistics are being collected, and achieves the effect of saving electricity through timely early warning of abnormal power consumption, with high safety.

[0029] 2. The present invention adopts the system structure of "Internet + Smart Grid" and sets up a large database inside the system to monitor and warn of power load and power grid environment, so that the entire system can give timely warning of power grid environment abnormalities on the basis of power load warning, with a wide coverage. Compared with the traditional single warning structure, the method of simultaneously covering the power load and the environment for warning is adopted, the warning range is wider, and the warning for abnormal phenomena is more timely, thereby effectively avoiding the occurrence of safety accidents in the power grid environment.

[0030] 3. The present invention adopts regional and hierarchical control of the data perception layer, and covers all levels in the entire system with the monitoring and early warning layer. Anti-intrusion and anti-electricity theft training instructions are issued through the application control layer, and transmission refrigeration is issued through the monitoring and early warning layer. Anti-intrusion training is carried out at a fixed time with the cooperation of the network communication layer and the internal management layer of the data perception layer. After the training, the management layer sends instructions to the data perception unit to adjust the corresponding parameters. Compared with the traditional method in which each unit in the entire system participates at the same time, the hierarchical setting is adopted, which can effectively reduce the computing pressure of the system during training and reduce the waste of resources caused by repeated calculations in the application control layer. Therefore, the training computing pressure can be reduced through regional and hierarchical training, and then the anti-intrusion and anti-electricity theft strength can be improved by increasing the number of training times. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a system structure diagram of the present invention;

[0032] Figure 2 It is the hierarchical management diagram of the application control layer of the present invention;

[0033] Figure 3 A flow chart for recording the data perception layer device of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0036] like Figure 1-3 As shown in the figure, the smart grid electricity online monitoring and management system consists of a data perception layer, a network communication layer, a monitoring and early warning layer, and an application control layer.

[0037] The data perception layer is used to sense and read the power consumption data and external environment data of the connected devices, and transmit the data to the monitoring and early warning layer for analysis and early warning. The data perception layer uses non-intrusive load monitoring technology to monitor the power load of the connected smart meters, and the data perception layer uses sensor technology to monitor regional environmental parameters;

[0038] The network communication layer is used for data information transmission between the entire system, and the network communication layer has 4G / 5G communication mode, Wi F i communication mode, GPRS communication mode and RS485 communication mode;

[0039] The monitoring and early warning layer is connected to each level in the system and is used for monitoring and early warning of power grid data in the system. A large database is set up inside the monitoring and early warning layer, and it is used as a comparison basis to conduct comparative analysis and early warning processing on the collected power grid power equipment data and power grid environment data;

[0040] The application control layer is used for the regulation and control of the entire system, and issues instructions to the monitoring and early warning layer to train the regional intrusion identification model, and divides the data perception layer into regions, and sets a management layer in each region, so that the monitoring and early warning layer trains each management layer during training, and the management layer then sends the adjustments of each training to the data perception unit in each region. The data perception unit adjusts its own monitoring parameters according to the instructions. Compared with the traditional method in which each unit in the entire system participates at the same time, the hierarchical setting can effectively reduce the computing pressure of the system during training and reduce the waste of resources caused by repeated calculations in the application control layer, thereby reducing the computing pressure of training through regional hierarchical training, and then increasing the number of training times to improve the anti-intrusion and anti-electricity theft strength. Specific embodiment 2:

[0042] like Figure 1-3 As shown, the data perception layer is divided into multiple sub-modules according to the region, each module is provided with a group of management layers, each management layer is connected to multiple data perception units, each data perception unit is connected to multiple electrical equipment for power load monitoring with the assistance of non-invasive load monitoring technology, and the surface of the data perception unit is connected to at least one group of data collection modules for collecting, collating and decomposing the load data of the equipment connected to the surface, so as to realize the decomposition and viewing of the power load of the equipment, facilitate timely discovery of power failures, power theft and other faults, and real-time monitoring of the power load of each device, so as to facilitate the monitoring of power abnormalities when statistics are being made, and achieve the effect of saving electricity through timely early warning of abnormal power consumption, with high safety.

[0043] When the data perception layer uses non-intrusive load monitoring technology to monitor power load, taking residential electricity consumption as an example, in actual use, the data perception unit corresponds to the residential electricity consumption equipment, which is generally a smart meter. Each household uses at least one smart meter, and the smart meter is connected to the household socket through the power grid line. A data collection module is connected inside the smart meter to collect statistics, preprocess, extract features and decompose the power load. In order to reduce the user's equipment cost loss, the following two methods are used when reading the load:

[0044] First: When decorating a new house, users can directly install a smart socket on the wall and connect it to a smart meter. The smart socket can read the power consumption of the appliances connected to the surface, and then transmit the collected data to the data collection module through the smart meter for power load decomposition.

[0045] Second: For the house equipment that has been renovated and uses ordinary sockets, the user identity authentication can be carried out by logging into the system structure. After the authentication is successful, the user selects his own proprietary database to select and set the brand, type, model, etc. of the equipment. For example, if a Midea rice cooker is selected, the model is MB-FB40E108, and the power is 780W. When the equipment is connected to the socket, the power consumption is monitored by the electric meter. The monitored power load and the corresponding internal information of the proprietary database are simultaneously transmitted to the data collection module for power load decomposition;

[0046] Users can view the power load by logging into the system. After the load is decomposed, the total load used and the load of each electrical appliance can be viewed. By comparison, this helps users to spontaneously adjust their power usage habits, avoid peak hours of power usage as much as possible, or reduce unnecessary power consumption. Secondly, users can find abnormalities in indoor electrical equipment as early as possible and replace them in time, which not only avoids power accidents to some extent, but also reduces power loss. For example, some electrical appliances have been used for a period of time, although there is still no obvious fault and can be used normally, but their power consumption is several times the normal state before. Since there is no fault that affects the use, users may not be able to find out in time that the appliance has reached the end of its service life or there are safety hazards. However, under this system, users can know the individual energy consumption information of each electrical appliance in the house. When it is abnormal, users will be able to get early warning in time to avoid safety accidents as much as possible.

[0047] The data perception layer monitors the regional environment, including at least the data collection of the grid environment temperature, grid connection topology, current, voltage and conductor temperature, and the data perception layer reads various parameters through corresponding sensors or collectors. For example, when reading the current and voltage, current transformers, voltage arc rods, etc. can be used to monitor and read the data. By reading the conductor temperature, an early warning can be issued when the conductor temperature exceeds the set threshold to avoid excessive load, which may cause the conductor temperature to rise, resulting in fire and other pigtail situations. For example, when conducting a risk assessment on one of the lines, the line operation parameters, maintenance plans, geographic information, site information and other information can be collected first. According to the risk factors, the collected data can be comprehensively analyzed to screen out valuable information, and the correlation between this information and the safe operation of the power grid system can be determined, providing a reference for the early warning work of the power network.

[0048] Each data sensing unit in the data sensing layer has an independent proprietary database for the power equipment data collected, and the proprietary database is composed of the information of the power equipment and the power load collected each time. Users can modify the power equipment by entering the proprietary database, and can also view and compare the power load, so as to timely discover power problems and understand the power usage situation. The proprietary database can be internally connected to the big database of the monitoring and early warning layer for comparison and early warning of power loads. Specific embodiment three:

[0050] like Figure 1-3 As shown, a controller is arranged inside the network communication layer, and the controller is electrically connected to a plurality of communication modes, and the controller performs self-adjustment and adaptation of the corresponding communication mode by means of data transmission. The controller inside the network communication layer is used for data communication between various levels in the system, and adopts HPLC technology. According to the principle of frequency shift and frequency division, the HPLC technology moves the original signal from carrier modulation to different transmission frequency bands, and couples it to the power line for transmission through a coupling device with a wave trap. When the transmitting end sends data, the data to be sent is first modulated onto a high-frequency carrier of a specific frequency, and then the signal power is amplified and coupled to the power line. At the receiving end, the useful signal is extracted by the coupling device with a wave trap, and the high-frequency signal is separated by the receiver, thereby filtering out interference, and modulating the signal collected by the collection end into a voice or data signal. The collection terminal is responsible for monitoring and correcting load fluctuations, completing local power data collection through the smart energy meter, and monitoring the power load status of each area of ​​the smart grid through the load monitoring circuit, and storing it in the large database of the monitoring and early warning layer. Specific embodiment four:

[0052] like Figure 1-3As shown, the application control layer manages and monitors the anti-intrusion and anti-electricity theft behaviors of the entire system, and the application control layer is the first-level management layer, the monitoring and early warning layer and the network communication layer are the second-level following layer, and each management layer in the data perception layer is the third-level following layer. Each data perception unit connected to the management layer is the fourth-level following layer. When actually used for anti-intrusion training, the data perception unit only participates in the identification of anomalies and does not participate in daily simulation training, thereby sharing the computing pressure of the data center and reducing the waste of computing resources caused by repeated calculations of most terminals.

[0053] The simulation training model between the entire application control layer and the monitoring and early warning layer can be set according to any existing anti-grid intrusion training model. By setting the application control layer and the monitoring and early warning layer models and conducting daily simulation training, it can be applied to any anti-electricity theft and anti-intrusion power grid model, such as the monitoring model given in 4.1 of the article "Research on Electricity Theft Detection in Smart Grid Neighborhood Networks_Lin Jian". According to the actual monitoring and early warning needs, the existing monitoring and early warning model is selected for implantation and setting. Specific embodiment five:

[0055] like Figure 1-3 As shown, the large database inside the monitoring and early warning layer is divided into a power load database and a power environment database, and the power load database is interoperable with the power consumption data of the data perception layer, and the power environment database is interoperable with the environmental parameter data of the data perception layer. The warning scope of the monitoring and early warning layer at least includes warning reminders of current and voltage anomalies, data power load anomalies, power grid environment anomalies, communication anomalies, network topology anomalies, network intrusions and power grid power theft.

[0056] The electric load database is used to compare the power, power consumption time and power load of conventional power-consuming equipment, so as to facilitate timely warning based on the user's power load. If there is a significant increase in power load and there is a significant deviation between the power load and the equipment power load in the proprietary database, the user may face problems such as theft or intrusion of the power grid, and timely warning can be issued.

[0057] The power environment database is used to monitor and warn the data of power grid lines and end connection topology structures. The line access on the power grid surface needs to be issued by the application control layer to the monitoring and warning layer before the power grid can be increased or decreased. When the power grid surface adds lines or branches without the instructions of the application control layer, it indicates that the power grid surface has been invaded to a large extent. In this case, a warning will be issued through the monitoring and warning layer, and the basic data of the power environment data will be added to the power environment database. When the data perception layer perceives that the power environment exceeds the set threshold, the monitoring and warning layer will issue an environmental abnormality warning, thereby realizing the coverage of the power grid environment warning processing.

[0058] When monitoring and warning, in order to protect the user's information, the user can choose whether to use the system structure to monitor the power load. The monitoring and early warning layer covers the power grid laying structure of the entire system structure, and conducts comprehensive monitoring of the system power grid environment. For the user's power equipment, such as the monitoring and early warning function of various parameters of the user's home appliances, the user can choose to connect or not. When connected, the system will issue an early warning reminder through the data perception unit, that is, the smart meter, when it detects that the power load of the user is significantly different from the power load of the equipment with existing data in the actual proprietary database. If you choose not to connect, the user will log in to the system independently and enter the proprietary database for manual analysis of the power load. The user makes a choice based on the actual usage scenario. Specific embodiment six:

[0060] like Figure 1-3 As shown, the process of load decomposition and its corresponding model in the non-invasive load monitoring technology used in this application can be set with reference to existing publicly available documents, such as the file titled "Research on Key Technologies of Non-invasive Load Decomposition for Smart Electricity Consumption_Wu Zhao", Section 2.1 Non-invasive Load Decomposition Principles and Processes, which clearly introduces the process of load decomposition, etc. Therefore, by combining the load decomposition process in the existing non-invasive load detection technology, the corresponding load decomposition structural content of this application can be improved.

[0061] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0062] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Smart grid electricity online monitoring and management system, characterized by: The system structure consists of a data perception layer, a network communication layer, a monitoring and early warning layer, and an application control layer; The data perception layer is used to sense and read the power consumption data and external environment data of the connected devices, and transmit the data to the monitoring and early warning layer for analysis and early warning. The data perception layer uses non-intrusive load monitoring technology to monitor the power load of the connected smart meters, and the data perception layer uses sensor technology to monitor regional environmental parameters; The network communication layer is used for data information transmission between the entire system, and the network communication layer has 4G / 5G communication mode, WiFi communication mode, GPRS communication mode and RS485 communication mode; The monitoring and early warning layer is connected to each level in the system and is used for monitoring and early warning of power grid data in the system. A large database is provided inside the monitoring and early warning layer, and the collected power grid power equipment data and power grid environment data are compared and analyzed based on this database for early warning processing. The application control layer is used for regulating and controlling the entire system, and issues instructions to the monitoring and early warning layer to train the regional intrusion identification model, and divides the data perception layer into regions, and sets a management layer in each region, so that the monitoring and early warning layer trains each management layer during training, and the management layer then sends the adjustments of each training to the data perception unit in each region, and the data perception unit adjusts its own monitoring parameters according to the instructions.

2. The smart grid electricity online monitoring and management system according to claim 1 is characterized by: The data perception layer is divided into multiple sub-modules according to regions. Each module is provided with a group of management layers. Each management layer is connected to multiple data perception units. Each data perception unit is connected to multiple electrical devices for power load monitoring with the assistance of non-invasive load monitoring technology, and at least one group of data collection modules is connected to the surface of the data perception unit for collecting, organizing and decomposing the load data of the devices connected to the surface.

3. The smart grid electricity online monitoring and management system according to claim 1 is characterized by: The data perception layer monitors the regional environment by at least collecting data on the grid environment temperature, grid connection topology, current, voltage and wire temperature, and the data perception layer reads each parameter through corresponding sensors or collectors.

4. The smart grid electricity online monitoring and management system according to claim 1 is characterized by: A controller is disposed inside the network communication layer, and the controller is electrically connected to a plurality of communication modes, and the controller performs self-adjustment and adaptation of the corresponding communication mode through data transmission.

5. The smart grid electricity online monitoring and management system according to claim 1 is characterized by: The application control layer manages and monitors the anti-intrusion and anti-electricity theft behaviors of the entire system, and the application control layer is the first-level management layer, the monitoring and early warning layer and the network communication layer are the second-level following layer, the various management layers in the data perception layer are the third-level following layer, and the various data perception units connected to the management layer are the fourth-level following layer.

6. The smart grid electricity online monitoring and management system according to claim 1 is characterized by: The power-consuming equipment data collected by each data sensing unit in the data sensing layer has an independent proprietary database, and the proprietary database is composed of the information of the power-consuming equipment and the power load collected each time.

7. The smart grid electricity online monitoring and management system according to claim 1 is characterized by: The large database inside the monitoring and early warning layer is divided into a power load database and a power environment database, and the power load database communicates with the power consumption data of the data perception layer, and the power environment database communicates with the environmental parameter data of the data perception layer.

8. The smart grid electricity online monitoring and management system according to claim 1 is characterized by: The warning scope of the monitoring and early warning layer includes at least early warning reminders of current and voltage anomalies, data power load anomalies, power grid environment anomalies, communication anomalies, network topology anomalies, network intrusion and power grid theft.

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