Distributed resource monitoring method, device and equipment of power grid, medium and product

Abstract

The invention discloses a distributed resource monitoring method and device for a power grid, electronic equipment, a storage medium and a program product, and relates to the technical field of power grid monitoring, and the method comprises the steps: collecting the power grid state data of the power grid through a sensor according to a first sampling frequency; determining current load data of the power grid according to the power grid state data; determining a second sampling frequency of the sensor according to the current load data; and collecting power grid state data through the sensor according to the second sampling frequency. According to the embodiment of the invention, the sampling frequency of the sensor can be adjusted according to the power grid state data, the energy consumption of the sensor during power grid monitoring can be reduced, and the monitoring precision of the power grid is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and in particular to a distributed resource monitoring method, device, electronic equipment, storage medium and program product for a power grid. Background Art

[0002] As the scale of power grid expands, a series of problems may occur in the operation of power grid. Therefore, in the current power grid management, how to monitor the resources in the power grid has always been a more important issue.

[0003] In the current power grid management, the traditional method of power grid detection is mainly through manual inspection or fixed frequency sensor data collection and analysis. Manual inspection generally means that inspectors go to the places where inspection is required to collect data according to a fixed inspection cycle, and then bring the data back for summary and analysis. By analyzing the data, the monitoring of the power grid is completed.

[0004] The manual inspection process consumes a lot of human resources, and errors are prone to occur when aggregating data, resulting in reduced accuracy of power grid monitoring. Sensors collect data at a fixed sampling frequency, which will lead to excessive energy consumption of sensors, waste of resources, and low monitoring accuracy. Summary of the invention

[0005] The present invention provides a distributed resource monitoring method, device, electronic equipment, storage medium and program product for a power grid, which can reduce the energy consumption of sensors and improve the monitoring accuracy of the power grid.

[0006] According to one aspect of the present invention, a method for monitoring distributed resources of a power grid is provided, comprising: collecting power grid status data of the power grid according to a first sampling frequency through a sensor; determining current load data of the power grid according to the power grid status data; determining a second sampling frequency of the sensor according to the current load data; and collecting the power grid status data according to the second sampling frequency through the sensor.

[0007] According to another aspect of the present invention, a distributed resource monitoring device for a power grid is provided, comprising: a data acquisition module for acquiring power grid status data of the power grid through a sensor according to a first sampling frequency; a data processing module for determining current load data of the power grid based on the power grid status data; a sampling frequency determination module for determining a second sampling frequency of the sensor based on the current load data; and a sampling frequency adjustment module for acquiring the power grid status data through the sensor according to the second sampling frequency.

[0008] According to another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distributed resource monitoring method for a power grid as described in any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the distributed resource monitoring method for a power grid described in any embodiment of the present invention when executed.

[0010] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the distributed resource monitoring method for a power grid according to any embodiment of the present invention is implemented.

[0011] The technical solution of this embodiment collects the grid status data of the power grid according to the first sampling frequency through the sensor, and determines the current load data of the power grid according to the grid status data, and then determines the corresponding second sampling frequency according to the current load data of the power grid, and then collects the grid status data of the power grid according to the second sampling frequency. It can dynamically adjust the sampling frequency of the grid status data according to the load of the power grid, and then when the load of the power grid is small, the grid status data is collected at a lower sampling frequency, avoiding excessive data sampled by the sensor and resulting in a waste of sampling resources; when the load of the power grid is large, the grid status data is collected at a higher sampling frequency, avoiding insufficient data sampled by the sensor and inadequate monitoring of the grid status. The above technical solution can reduce the energy consumption of sensors during grid monitoring and improve the monitoring accuracy of the grid.

[0012] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1is a flow chart of a distributed resource monitoring method for a power grid provided according to Embodiment 1 of the present invention;

[0015] Figure 2 is a flow chart of another distributed resource monitoring method for a power grid provided according to a second embodiment of the present invention;

[0016] Figure 3 is a schematic diagram of the structure of a distributed resource monitoring device for a power grid provided according to a third embodiment of the present invention;

[0017] Figure 4 It is a structural schematic diagram of an electronic device for implementing the distributed resource monitoring method of a power grid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] Embodiment 1

[0021] Figure 1 This is a flow chart of a distributed resource monitoring method for a power grid provided in the first embodiment of the present invention. This embodiment is applicable to the case where the sampling frequency of the sensor is adjusted as needed. The method can be executed by a distributed resource monitoring device for a power grid. The distributed resource monitoring device for a power grid can be implemented in the form of hardware and / or software. The distributed resource monitoring device for a power grid can be configured in an electronic device. Figure 1 As shown, the method may include:

[0022] S110. Collect grid status data of the power grid through a sensor according to a first sampling frequency.

[0023] The sensor may be a sensing device for collecting operating status data related to the power grid and status data of the environment in which the power grid is currently located. For example, the sensor may be a voltage sensor for collecting power grid voltage status data, a current sensor for collecting power grid current status data, or a sensor for collecting temperature and humidity in the current operating environment of the power grid.

[0024] The first sampling frequency may be a preset default sampling frequency of each sensor. When the sensor collects the grid status data of the grid, the first sampling frequency will be selected by default to collect the grid status data. The first sampling frequency may be a frequency set by the technician according to needs or other methods. The embodiment of the present invention does not limit the method for determining the first sampling frequency. The first sampling frequencies corresponding to different sensors may be the same or different.

[0025] The grid state data may be data acquired from the grid through sensors and used to monitor the grid. The grid state data may include grid operation state data and grid operation environment data. Exemplarily, the grid operation state data may include information such as voltage and current during grid operation, and the grid operation environment data may include information such as temperature and humidity in the grid operation environment.

[0026] Specifically, some important or fault-prone devices or lines in the power grid can be identified and these devices or lines can be determined as objects to be monitored. Then, sensors for collecting status data of these devices can be set around these objects. The sensors can collect real-time operating status data of the devices in the power grid during operation and external environmental data of the devices during operation according to a preset default sampling frequency.

[0027] S120: Determine current load data of the power grid according to the power grid status data.

[0028] The current load data may be data used to characterize the current load state of the power grid obtained by analyzing the current, voltage, power in the power grid state data and the temperature and humidity in the power grid operating environment through an algorithm. The algorithm used to calculate the current load data of the power grid may be a statistical analysis algorithm, a time series analysis algorithm, or a machine learning algorithm. The embodiment of the present invention does not limit the method for determining the current load of the power grid.

[0029] Specifically, after obtaining the status data of the power grid through sensors, the machine learning algorithm can be used to analyze the parameters such as current, voltage, power and temperature and humidity in the power grid operating environment in the collected power grid status data, thereby obtaining the current load data of the power grid and adjusting the sampling frequency of the sensor according to the current load data.

[0030] S130. Determine a second sampling frequency of the sensor according to current load data.

[0031] The second sampling frequency may be a sensor sampling frequency that is determined according to the current load data of the power grid after the current load data of the power grid is determined, and that conforms to the current load data of the power grid. It may be obtained by querying a mapping table of pre-set power grid load data and sensor sampling frequency, or it may be calculated by a pre-established model. The embodiment of the present invention does not limit the method for determining the second sampling frequency.

[0032] Exemplarily, after obtaining the current load data of the power grid, a pre-set mapping table of power grid load data and sensor sampling frequency can be queried according to the current load data of the power grid, and then the sensor sampling frequency corresponding to the current power grid load data can be determined according to the current load data of the power grid, and it can be determined as the second sampling frequency.

[0033] S140. Collect grid status data through sensors according to a second sampling frequency.

[0034] Specifically, after determining the second sampling frequency of the sensor, the sensor can be controlled to collect data on the monitored objects in the power grid according to the second sampling frequency, thereby reducing the waste of sampling resources when the power grid load is low and improving the sampling accuracy when the power grid load is high.

[0035] Exemplarily, the grid state data of the power grid may be collected by the sensor according to the default sampling frequency, and the current load data of the power grid may be determined according to the grid state data, and then the mapping table of the pre-set grid load data and the sensor sampling frequency may be queried according to the current load data of the power grid, and then the sensor sampling frequency corresponding to the current grid load data may be determined according to the current load data of the power grid, and it may be determined as the new sensor sampling frequency, and the grid state data may continue to be collected according to the new sensor sampling frequency. If the load of the power grid is small and the new sensor sampling frequency is reduced, the grid state data may be collected according to the new sensor sampling frequency, thereby avoiding excessive data sampled by the sensor and resulting in a waste of sampling resources; if the load of the power grid is large and the new sensor sampling frequency is increased, the grid state data may be collected according to the new sensor sampling frequency, thereby increasing the amount of data collected and avoiding insufficient data sampled by the sensor and inadequate monitoring of the grid state.

[0036] The technical solution of this embodiment collects the grid status data of the power grid according to the first sampling frequency through the sensor, and determines the current load data of the power grid according to the grid status data, and then determines the corresponding second sampling frequency according to the current load data of the power grid, and then collects the grid status data of the power grid according to the second sampling frequency. It can dynamically adjust the sampling frequency of the grid status data according to the load of the power grid, and then when the load of the power grid is small, the grid status data is collected at a lower sampling frequency, avoiding excessive data sampled by the sensor and resulting in a waste of sampling resources; when the load of the power grid is large, the grid status data is collected at a higher sampling frequency, avoiding insufficient data sampled by the sensor and inadequate monitoring of the grid status. The above technical solution can reduce the energy consumption of sensors during grid monitoring and improve the monitoring accuracy of the grid.

[0037] Embodiment 2

[0038] Figure 2 This is a flow chart of a distributed resource monitoring method for a power grid provided in the second embodiment of the present invention. Based on the above embodiments, this embodiment refines the following steps: collecting power grid status data of the power grid by a sensor according to a first sampling frequency; determining the current load data of the power grid according to the power grid status data; determining the second sampling frequency of the sensor according to the current load data; and collecting power grid status data by a sensor according to the second sampling frequency. Figure 2 As shown, the method may include:

[0039] S210 . Collect grid status data of the power grid through a sensor according to a first sampling frequency.

[0040] Specifically, collecting grid status data of the power grid according to a first sampling frequency through a sensor may include: collecting vibration energy and temperature difference energy in the grid operating environment, converting the vibration energy and temperature difference energy into electrical energy to power the sensor; collecting grid operating status data and grid operating environment data of the power grid according to the first sampling frequency through the sensor; and using the grid operating status data and grid operating environment data as grid status data.

[0041] Among them, vibration energy can be the mechanical vibration energy generated by the equipment in the power grid during operation, such as transformers and transmission line supports. These locations usually have significant mechanical vibrations, which can be collected as vibration energy by vibration energy collectors; temperature difference energy can be obtained from the environment by thermoelectric generators based on the Seebeck effect. Among them, the Seebeck effect refers to the electromotive force generated between conductors or semiconductors of two different materials when there is a temperature difference at both ends. Among them, the sensor can be made of piezoelectric material.

[0042] The grid operation status data may be the operation status data of various electrical parameters and equipment inside the grid, and the grid operation environment data may be the external environment condition data of the grid. The grid operation status data may include information such as voltage and current, power data, frequency data and phase data during the operation of the grid; the grid operation environment data may include information such as temperature and humidity, wind speed and direction data, and interference data in the grid operation environment.

[0043] For example, the vibration energy generated by the transformer and transmission line support in the power grid during operation can be collected by the vibration energy collector, and the temperature difference energy generated by the temperature difference energy obtained from the environment by the thermoelectric generator based on the Seebeck effect can be converted into electrical energy, and then the AC-DC (referring to the conversion process of alternating current (AC) to direct current (DC)) rectifier circuit can be used to convert the AC power into direct current. At the same time, a low-power power management chip (such as LTC3588-1 chip) is used to achieve voltage conversion and rectification. In order to compensate for the instability and intermittency of environmental energy, a charging control chip (such as LTC4071 chip) and a lithium battery combination are used to achieve energy storage and management, which is used to monitor the battery charging status and prevent the battery from being overcharged. Then the sensor is powered by the stored electrical energy in the lithium battery.

[0044] Exemplarily, after the sensor is powered by the electric energy stored in the lithium battery, the grid operation status data and grid operation environment data of the power grid can be collected through the sensor according to a preset default sampling frequency, and then the grid operation status data and grid operation environment data can be used as grid status data.

[0045] Optionally, after collecting the operating status data of these devices, these data can be transmitted to the processing module or control module through a transmission line or wireless transmission or other devices with data transmission function through low-power transmission to further process the data, thereby completing the monitoring of the power grid and ensuring the safe and stable operation of the power grid. Transmitting the acquired data through low-power transmission can reduce energy consumption during the communication process.

[0046] For example, the collected power grid status data can be processed through LoRa (LoRa communication technology is a low-power wide area network communication technology) communication technology or NB-IoT (Narrowband Internet of Things) combined with data compression and processing algorithms, thereby reducing the amount of transmitted data and further reducing communication energy consumption; using intermittent wake-up mode, the sensor enters a sleep state during inactive periods and activates when needed, and using data compression and processing algorithms to reduce the amount and frequency of data transmission.

[0047] S220: Determine current load data of the power grid according to the power grid status data.

[0048] Specifically, determining the current load data of the power grid based on the power grid status data may include: parsing the power grid status data to obtain power grid operation status data and power grid operation environment data; and determining the current load data of the power grid based on the power grid operation status data and the power grid operation environment data.

[0049] Exemplarily, the grid state data can be collected by sensors, and then transmitted to the processor through a transmission device for parsing, to obtain grid operation state data including current, voltage, active power, reactive power, power factor and equipment status, as well as external environmental condition data of the grid including temperature, humidity, wind speed, rainfall, atmospheric pollutant concentration and geological data. Then, key parameters such as voltage and current, temperature and humidity are selected from the grid operation state data and the external environmental condition data, and the current load data of the grid is calculated by a preset algorithm. Among them, the algorithm used to calculate the current load data of the grid can be an algorithm such as a demand coefficient method, a utilization coefficient method, a unit area power method, a unit index method or a unit product power consumption method. The embodiment of the present invention does not limit the method for determining the current load data of the grid.

[0050] S230: Input the current load data into a pre-trained frequency adjustment model to obtain a target sampling frequency that matches the current load data.

[0051] The frequency adjustment model may be a model for obtaining the optimal sensor sampling frequency corresponding to the current load data according to the current load data. The target sampling frequency may be the optimal sensor sampling frequency corresponding to the current load data obtained by the frequency adjustment model according to the current load data after the current load data is input into the frequency adjustment model.

[0052] Specifically, after obtaining the current load data of the power grid, the current load data can be input into a pre-trained frequency adjustment model. The frequency adjustment model can match the sensor sampling frequency corresponding to the current load data based on the current load data, and determine the sensor sampling frequency corresponding to the current load data as the target sampling frequency.

[0053] Optionally, the frequency adjustment model is trained in the following manner: obtaining historical grid status data at different sampling frequencies, preprocessing the historical grid status data, and obtaining training samples, wherein the training samples may include grid status data at different sampling frequencies and its corresponding current load data; training a pre-constructed frequency adjustment model with the training samples to obtain a trained frequency adjustment model.

[0054] Among them, the historical power grid state data may be various parameters and state information of the power grid operation in the past period of time. The historical power grid state data may include but are not limited to the power grid voltage, current, frequency, active power, reactive power, load demand, generator output and line flow and other power grid operation state data and operation environment data. These data can be collected and stored by various sensors set in the power grid, and can be used for subsequent adjustment of the sampling frequency of sensors in the power grid and analysis of the state of the power grid. The training sample can be a sample data obtained after processing by cleaning, time alignment, feature selection and standardization, and used for training the frequency adjustment model.

[0055] Exemplarily, historical grid status data containing multiple parameters (such as voltage, current, power, etc.) and different sampling frequencies can be collected from the power system, and the load data at the time point corresponding to these status data can be obtained, and then these data can be cleaned, time-aligned, feature selected, and standardized, etc., to ensure the quality and consistency of the data. The preprocessed data is organized into training samples, each of which contains the grid status data at a specific time point and the corresponding load data and the corresponding sampling frequency. The pre-designed frequency adjustment model is trained through these training samples. The model optimizes its internal parameters by learning the relationship between the grid load and the sampling frequency, and finally obtains the optimal corresponding relationship between the grid load and the sampling frequency.

[0056] S240: Determine the target sampling frequency as the second sampling frequency of the sensor.

[0057] Specifically, after the target sampling frequency is obtained through the frequency adjustment model, the target sampling frequency may be determined as the second sampling frequency of the sensor, so that the sensor continues to collect the state data of the power grid according to the optimized and adjusted sampling frequency.

[0058] S250: Collect grid status data through sensors according to a second sampling frequency.

[0059] Specifically, after determining the second sampling frequency of the sensor, the sensor can be controlled to collect status data of each monitored object in the power grid according to the second sampling frequency, and then when the power grid load is low, the status data of the power grid is collected through a lower sampling frequency, thereby reducing the waste of sampling resources when the power grid load is low; when the power grid load is high, the status data of the power grid is collected through a higher sampling frequency, thereby improving the sampling accuracy when the power grid load is high, and avoiding the situation where the sensor sampling data is insufficient due to the low sampling frequency, resulting in the inability to accurately monitor the status of the power grid.

[0060] Exemplarily, if the power grid has different load changes in different time periods, the sampling frequency can be adjusted accordingly according to the different loads. When the load on the power grid is low, the power grid operation is relatively stable due to the small power demand. At this time, although it is necessary to monitor the state of the power grid to ensure its safe operation, too high a sampling frequency may lead to unnecessary waste of resources. Therefore, the sampling frequency can be reduced, energy consumption can be reduced, and the service life of sensors and energy storage units can be extended. When the load increase is detected, the sampling frequency is automatically increased by the controller to ensure the accuracy and timeliness of data collection, thereby more accurately monitoring the state of the power grid. Through this solution of dynamically adjusting the sampling frequency of the sensor, it is possible to avoid waste of resources during low-load periods and ensure that sufficient data can be collected during high-load periods to support the safe and stable operation of the power grid.

[0061] Optionally, after collecting grid status data through sensors according to a first sampling frequency, it may also include: predicting a grid status of the grid based on the grid status data; if the grid status is abnormal, generating an adjustment strategy based on the grid status, wherein the adjustment strategy may include a configuration information adjustment strategy and a parameter information adjustment strategy; and adjusting the configuration information and parameter information of the grid through the adjustment strategy.

[0062] Among them, the power grid situation can be a comprehensive reflection of the overall operation status and security situation of the power grid at a certain moment or in a certain period of time. The power grid situation can include multiple aspects such as the load level, power quality, equipment health, network topology and security threats of the power grid. Power grid situation awareness can monitor, analyze and evaluate the real-time operation status, security threats and risks of the power grid to form a judgment and early warning of the security situation of the power grid.

[0063] The adjustment strategy can be a series of measures and action plans formulated to deal with problems or risks that may arise in the power grid during the operation of the power grid, based on the prediction results and actual situation of the power grid situation. The adjustment strategy can be used to automatically generate and / or manually set strategies for adjusting the configuration information and parameter information of the power grid according to the current power grid situation, and can be used to optimize the operation status of the power grid and improve the safety and stability of the power grid.

[0064] Among them, the configuration information adjustment strategy can be a strategy for adjusting the configuration parameters of the equipment in the power grid, the network topology and the system parameters. By adjusting the configuration information, it is possible to adapt to the needs of power grid operation and respond to changes in the power grid situation. The configuration information adjustment strategy can include a strategy for adjusting configuration information such as the operation mode, the setting value of the protection device and the system parameters.

[0065] Among them, the parameter information adjustment strategy can be a strategy for adjusting parameters such as operating parameters and control parameters of equipment in the power grid. By adjusting the parameter information, the operating performance of the equipment and the overall operating efficiency of the power grid can be improved. The parameter information adjustment strategy can include strategies for adjusting current, voltage and power factor, as well as strategies for adjusting parameters such as control parameter adjustment and filter parameter adjustment in the control algorithm.

[0066] For example, the grid status data is collected by sensors according to the set sampling frequency, and then the grid status data is analyzed, and the grid situation is predicted based on the analysis results, and then the analysis results and prediction results are displayed on the user interface to assist users in monitoring the situation of the grid. After obtaining these data, the user can analyze these data, formulate corresponding adjustment strategies, and adjust the parameters of the grid through the adjustment strategies, so as to make the grid operate more stably and safely. A feedback mechanism can also be established to collect user feedback and usage, and then further optimize the adjustment strategies of configuration information such as operation mode, setting values ​​of protection devices and system parameters, and parameter information such as current, voltage and power factor according to user feedback and usage.

[0067] The technical solution of this embodiment collects the grid state data of the power grid according to the first sampling frequency through the sensor, and determines the current load data of the power grid according to the grid state data, and then determines the second sampling frequency corresponding to the current load data according to the current load data of the power grid and the pre-trained frequency adjustment model, and then collects the grid state data of the power grid according to the second sampling frequency, which can dynamically adjust the sampling frequency of the grid state data according to the load of the power grid, and then when the load of the power grid is small, the grid state data is collected through a sampling frequency with a lower frequency, avoiding excessive data sampled by the sensor, resulting in a waste of sampling resources; when the load of the power grid is large, the grid state data is collected through a sampling frequency with a higher frequency, avoiding insufficient data sampled by the sensor, and the situation where the grid state is not monitored in place. The above technical solution can reduce the energy consumption of the sensor during grid monitoring and improve the monitoring accuracy of the power grid. By predicting the grid state of the power grid according to the grid state data; if the grid state is abnormal, the configuration information strategy and parameter information strategy of the power grid are generated according to the grid state, and the configuration information and parameter information of the power grid are adjusted through the configuration information strategy and parameter information strategy, further ensuring the safe and stable operation of the power grid.

[0068] Embodiment 3

[0069] Figure 3 This is a schematic diagram of the structure of a distributed resource monitoring device for a power grid provided in Embodiment 3 of the present invention. Figure 3As shown, the device may include: a data acquisition module 310 , a data processing module 320 , a sampling frequency determination module 330 and a sampling frequency adjustment module 340 .

[0070] The data acquisition module 310 is used to collect the grid status data of the power grid through the sensor according to the first sampling frequency; the data processing module 320 is used to determine the current load data of the power grid according to the grid status data; the sampling frequency determination module 330 is used to determine the second sampling frequency of the sensor according to the current load data; the sampling frequency adjustment module 340 is used to collect the grid status data through the sensor according to the second sampling frequency.

[0071] In the technical solution of this embodiment, the data acquisition module collects the grid status data of the power grid through the sensor according to the first sampling frequency; the data processing module determines the current load data of the power grid according to the grid status data; the sampling frequency determination module determines the second sampling frequency of the sensor according to the current load data; the sampling frequency adjustment module collects the grid status data through the sensor according to the second sampling frequency. It avoids excessive data sampled by the sensor, resulting in a waste of sampling resources; when the load of the power grid is large, the grid status data is collected at a higher frequency, avoiding insufficient data sampled by the sensor and inadequate monitoring of the grid status. The above technical solution can reduce the energy consumption of the sensor during grid monitoring and improve the monitoring accuracy of the grid.

[0072] Furthermore, the data acquisition module 310 is specifically used to collect vibration energy and temperature difference energy in the power grid operating environment, convert the vibration energy and temperature difference energy into electrical energy to power the sensor; collect the power grid operating status data and power grid operating environment data of the power grid through the sensor according to the first sampling frequency; and use the power grid operating status data and power grid operating environment data as power grid status data.

[0073] Furthermore, the data processing module 320 is specifically used to parse the power grid state data to obtain the power grid operation state data and the power grid operation environment data; and determine the current load data of the power grid according to the power grid operation state data and the power grid operation environment data.

[0074] Furthermore, the sampling frequency determination module 330 is specifically used to input the current load data into a pre-trained frequency adjustment model to obtain a target sampling frequency that matches the current load data; and determine the target sampling frequency as the second sampling frequency of the sensor.

[0075] The frequency adjustment model is trained in the following manner: historical grid status data at different sampling frequencies are obtained, and the historical grid status data are preprocessed to obtain training samples, wherein the training samples may include grid status data at different sampling frequencies and their corresponding current load data; a pre-constructed frequency adjustment model is trained through the training samples to obtain a trained frequency adjustment model.

[0076] Furthermore, the device may also include: an information adjustment module, which is used to predict the power grid status of the power grid based on the power grid status data after collecting the power grid status data through sensors according to the first sampling frequency; if there is an abnormality in the power grid status, an adjustment strategy is generated according to the power grid status, wherein the adjustment strategy includes configuration information adjustment and parameter information adjustment; and the configuration information and parameter information of the power grid are adjusted through the adjustment strategy.

[0077] The distributed resource monitoring device for a power grid provided in the embodiment of the present invention can execute the distributed resource monitoring method for a power grid provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0078] Embodiment 4

[0079] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0080] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0081] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0082] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a distributed resource monitoring method for a power grid.

[0083] In some embodiments, the distributed resource monitoring method of the power grid can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the distributed resource monitoring method of the power grid described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the distributed resource monitoring method of the power grid in any other appropriate manner (for example, by means of firmware).

[0084] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0085] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0086] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0087] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0088] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0089] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0090] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0091] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed resource monitoring method for a power grid, characterized in that: include: Collecting grid state data of the grid according to a first sampling frequency by a sensor; Determining current load data of the power grid according to the power grid state data; determining a second sampling frequency of the sensor according to the current load data; The power grid status data is collected by the sensor according to the second sampling frequency.

2. The method according to claim 1, characterized in that The collecting of grid state data of the grid according to the first sampling frequency by the sensor includes: Collecting vibration energy and temperature difference energy in the power grid operating environment, converting the vibration energy and temperature difference energy into electrical energy to power the sensor; Collecting grid operation status data and grid operation environment data of the grid by the sensor according to a first sampling frequency; The power grid operation status data and the power grid operation environment data are used as the power grid status data.

3. The method according to claim 2, characterized in that Determining the current load data of the power grid according to the power grid state data includes: Parsing the power grid state data to obtain the power grid operation state data and the power grid operation environment data; The current load data of the power grid is determined according to the power grid operation status data and the power grid operation environment data.

4. The method according to claim 1, characterized in that: The determining the second sampling frequency of the sensor according to the current load data comprises: Inputting the current load data into a pre-trained frequency adjustment model to obtain a target sampling frequency matching the current load data; The target sampling frequency is determined as a second sampling frequency of the sensor.

5. The method according to claim 4, characterized in that The frequency adjustment model is trained in the following way: Acquire historical grid state data at different sampling frequencies, preprocess the historical grid state data, and obtain training samples, wherein the training samples include grid state data at different sampling frequencies and corresponding current load data; The pre-built frequency adjustment model is trained using the training samples to obtain a trained frequency adjustment model.

6. The method according to claim 1, characterized in that After collecting the grid state data by the sensor according to the first sampling frequency, the method further includes: Predicting a power grid situation of the power grid according to the power grid status data; If the power grid situation is abnormal, an adjustment strategy is generated according to the power grid situation, wherein the adjustment strategy includes a configuration information adjustment strategy and a parameter information adjustment strategy; The configuration information and parameter information of the power grid are adjusted through the adjustment strategy.

7. A distributed resource monitoring device for a power grid, characterized in that: include: A data acquisition module, used for acquiring grid state data of the grid through a sensor according to a first sampling frequency; A data processing module, used to determine the current load data of the power grid according to the power grid state data; A sampling frequency determination module, used to determine a second sampling frequency of the sensor according to the current load data; A sampling frequency adjustment module is used to collect the power grid status data through the sensor according to the second sampling frequency.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the distributed resource monitoring method for a power grid according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the distributed resource monitoring method for a power grid according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the method for monitoring distributed resources in a power grid according to any one of claims 1 to 6.

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