A method and system for monitoring the operating status of smart grid devices

Abstract

The present invention relates to the technical field of power grid monitoring, and particularly relates to a method and system for monitoring the operating state of intelligent power grid equipment. The method includes: obtaining the load data and operating data of power grid equipment in each region during operation; determining the relative load fluctuation situation of the region according to the load data of each region; determining the load change situation of the region; predicting the predicted load data for a future time period by using the load data in the region, and determining the predicted relative load fluctuation situation and predicted load change situation for the future time period by using the predicted load data; determining the deviation degree of the load fluctuation for the future time period; determining the strictness of the operating data obtained by the power grid equipment at each moment, and monitoring the operating state of the power grid equipment according to the strictness. In this way, the present invention improves the monitoring accuracy and authenticity of the operating state of power grid equipment, and ensures the safe and stable operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and particularly to a method and system for monitoring the operating state of intelligent power grid equipment. Background Art

[0002] An intelligent power grid is a power grid mode that utilizes advanced communication, sensing, and information technologies to achieve comprehensive monitoring, control management, and optimization of the power grid. With the proposal and development of the concept of the intelligent power grid, distributed sensors and intelligent instrument devices have been widely applied to each node of the power grid network, enabling the comprehensive acquisition of power grid operation data. These data cover key information such as the power supply quality of the power grid, the operating state of power equipment, the consumption and load conditions of electric energy, providing a rich data basis for in-depth understanding of the power grid operating conditions.

[0003] In some scenarios, during the actual monitoring of the operating state of intelligent power grid equipment, load fluctuations, as a very important interference factor, seriously affect the working state of power grid equipment, thereby causing fluctuations and inaccuracies in the analysis data. This greatly increases the difficulty and uncertainty of analysis when subsequently analyzing the operating state of power grid equipment. If the impact of load fluctuations cannot be effectively addressed, it will be difficult to accurately evaluate the true operating state of power grid equipment, posing a potential hazard to the safe and stable operation of the power grid. Thus, due to the influence of load fluctuations, the monitoring accuracy and authenticity of the operating state of power grid equipment are relatively low, thereby affecting the safe and stable operation of the power grid. Summary of the Invention

[0004] In order to solve the technical problem of relatively low monitoring accuracy and authenticity of the operating state of power grid equipment, the purpose of the present invention is to provide a method and system for monitoring the operating state of intelligent power grid equipment, and the specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for monitoring the operating state of smart grid equipment, including: obtaining load data and operating data of grid equipment in each region during operation; determining the relative load fluctuation situation of the region according to the load data of each region; using the first fluctuation curve of the relative load fluctuation situation of the region, the load data of the region, and the relative load fluctuation situation to determine the load change situation of the region; using the load data in the region to predict the predicted load data in a future period, and using the predicted load data to determine the predicted relative load fluctuation situation and the predicted load change situation in the future period; determining the deviation degree of the load fluctuation in the future period according to the relative load fluctuation situation at each moment in the region, the predicted relative load fluctuation situation, the predicted load change situation, and the load change situation in the region; determining the strictness of the operating data obtained by the grid equipment at each moment according to the deviation degree, the range difference between the first fitting curve and the second fitting curve at the corresponding moment, and the average value of each range difference, and monitoring the operating state of the grid equipment according to the strictness, where the first fitting curve is the upper limit of the impact of load fluctuation on the grid equipment, and the second fitting curve is the lower limit of the impact of load fluctuation on the grid equipment.

[0006] Optionally, the determining the relative load fluctuation situation of the region according to the load data of each region includes: determining the difference curve of each region and the load mean value of each region according to the load data of each region; determining the relative load fluctuation situation of the region according to the absolute value of the difference value on the difference curve of the region and the load mean value of the region.

[0007] Optionally, the determining the relative load fluctuation situation of the region according to the absolute value of the difference value on the difference curve of the region and the load mean value of the region includes: determining that the first ratio of the absolute value of the difference value on the difference curve to the load mean value is the relative load fluctuation situation of the region.

[0008] Optionally, determining the load change condition of the area by using the first fluctuation curve of the relative load fluctuation condition of the area, the load data of the area, and the relative load fluctuation condition includes: determining the load change condition of the area according to the first quantity of the load data in the difference curve of the load data of the area, the relative load fluctuation condition at each moment, and the average relative load fluctuation condition corresponding to the difference curve. Optionally, determining the load change condition of the area according to the first quantity of the load data in the difference curve of the load data of the area, the relative load fluctuation condition at each moment, and the average relative load fluctuation condition corresponding to the difference curve includes: calculating the absolute value of the first difference between the relative load fluctuation condition at each moment and the average relative load fluctuation condition; determining the average value of the absolute values of the first differences as the load change condition of the area.

[0009] Optionally, determining the strictness of the operation data obtained by the power grid device at each moment according to the deviation degree, the range between the first fitting curve and the second fitting curve at the corresponding moment, and the average value of each range includes: using the DTW value between the first fluctuation curve of the relative load fluctuation condition of the area and the second fluctuation curve of the relative fluctuation condition of the operation data to determine the target relative fluctuation condition of the target operation data affected by the load fluctuation of the area on the operation data of the power grid device; determining the impact of the load fluctuation of the area on the power grid device according to the relative load fluctuation condition of the area and the target relative fluctuation condition; constructing a two-dimensional coordinate system with the impact of the power grid device at each moment as the ordinate and the relative load fluctuation condition as the abscissa; fitting the data points in the two-dimensional coordinate system to obtain a first fitting curve and a second fitting curve; calculating the third ratio between the range between the first fitting curve and the second fitting curve at the corresponding moment and the average value of the ranges; performing inverse proportional normalization processing on the third ratio to obtain a normalized value;

[0010] Determining the second product between the normalized value and the deviation degree as the strictness.

[0011] Optionally, determining the impact of the load fluctuation of the area on the power grid device according to the relative load fluctuation condition of the area and the target relative fluctuation condition includes: calculating the second ratio between the relative load fluctuation condition of the area and the target relative fluctuation condition; superimposing each second ratio to obtain the impact.

[0012] Optionally, fitting the data points in the two-dimensional coordinate system to obtain a first fitting curve and a second fitting curve includes: selecting the maximum value and the minimum value corresponding to the abscissa of each data point; fitting the data points corresponding to the maximum values to obtain a first fitting curve, and fitting the data points corresponding to the minimum values to obtain a second fitting curve.

[0013] In a second aspect, an embodiment of the present invention provides an intelligent power grid device operation status monitoring system, including: a processor and a memory; wherein, the memory is used to store a computer program that can run on the processor; the processor is used to execute the program stored on the memory to implement the steps of the intelligent power grid device operation status monitoring method mentioned in the first aspect.

[0014] The present invention has the following beneficial effects: First, obtain the load data and operation data of the power grid devices in each region during operation; then determine the relative load fluctuation situation of the region according to the load data of each region; and use the first fluctuation curve of the relative load fluctuation situation of the region, the load data of the region, and the relative load fluctuation situation to determine the load change situation of the region; secondly, use the load data in the region to predict the predicted load data in the future time period, and use the predicted load data to determine the predicted relative load fluctuation situation and predicted load change situation in the future time period; and determine the deviation degree of the load fluctuation in the future time period according to the relative load fluctuation situation, predicted relative load fluctuation situation, predicted load change situation, and load change situation of each moment in the region; finally, determine the strictness of the operation data obtained by the power grid devices at each moment according to the deviation degree, the range difference between the first fitting curve and the second fitting curve at the corresponding moment, and the average value of each range difference, and monitor the operation status of the power grid devices according to the strictness. The first fitting curve is the upper limit of the impact of load fluctuation on the power grid device, and the second fitting curve is the lower limit of the impact of load fluctuation on the power grid device.

[0015] In this way, the embodiment of the present invention can analyze the fluctuation situation of the operation data and the load fluctuation situation by obtaining the load and operation data during the operation of the intelligent power grid, determine the deviation degree of the load fluctuation in the future time period of the intelligent power grid device, determine the strictness of each operation data acquisition according to the deviation degree, and thus monitor the operation status of the power grid device according to the strictness. Therefore, by analyzing the impact of load fluctuation on the power grid device, the embodiment of the present invention can effectively cope with the impact of load fluctuation on the evaluation of the operation status of the power grid device, thereby accurately evaluating the true operation status of the power grid device, improving the monitoring accuracy and authenticity of the operation status of the power grid device, and ensuring the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 Flowchart of a method for monitoring the operating status of smart grid devices provided by an embodiment of the present invention;

[0018] Figure 2 Schematic diagram of the data distribution of the relative load fluctuation and impact conditions provided by an embodiment of the present invention;

[0019] Figure 3 Schematic diagram of the first fitting curve and the second fitting curve provided by an embodiment of the present invention;

[0020] Figure 4 Schematic diagram of the structure of a system for monitoring the operating status of smart grid devices provided by an embodiment of the present invention. Detailed implementation manners

[0021] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a method and system for monitoring the operating status of smart grid devices proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0023] The following specifically describes the specific solution of a method for monitoring the operating status of smart grid devices provided by the present invention in conjunction with the accompanying drawings.

[0024] Embodiment 1:

[0025] Please refer to Figure 1 , which shows the flowchart of the method for monitoring the operating status of smart grid devices provided by an embodiment of the present invention, including:

[0026] S101. Obtain the load data and operating data of the grid devices in each region during operation.

[0027] Specifically, in the embodiments of the present invention, sensors such as current sensors, voltage sensors, and vibration sensors can be installed in power grid devices such as transformers, fuses, and voltage regulating devices in the control system of the smart grid. It is used to collect the operation data of the power grid devices during operation, including but not limited to current data, voltage data, vibration data, load data, working mode, etc. The following embodiments of the present invention will be described by taking the above several types of operation data as examples. Among them, the load data includes but is not limited to power load, etc.

[0028] Furthermore, there is an inclusion relationship between the power grids when the data is acquired in the embodiments of the present invention. For example, a large area contains multiple small areas, and there are multiple users using electricity in a small area. Among them, the small area is the smallest area for monitoring the operation status of power grid devices, and there is one power grid device in a smallest area. Among them, the operation data in a large area and the operation data of the small areas that make up the large area are in an inclusion relationship.

[0029] Furthermore, in the embodiments of the present invention, after the sensors collect the load data and operation data of the power grid devices during operation, they are sent to the central controller and edge devices of the smart grid for processing.

[0030] S102, determine the relative load fluctuation situation of the area according to the load data of each area.

[0031] Specifically, when the embodiments of the present invention monitor the operation status of the power grid devices of the smart grid, the edge device can preliminarily analyze the acquired operation data and load data of the power grid devices locally, and then process the data after the preliminary analysis by the edge device in the cloud. By analyzing, it can be known that when the power grid device is operating, it will be affected by the load fluctuation. The area with a relatively large load fluctuation may cause a greater impact on the power grid device and affect the acquired operation status. However, when the impact of the acquired load fluctuation and the change of the operation status are within a certain range, the power grid device only has a certain deviation in the operation data caused by the load fluctuation, and it will not affect the normal working state of the power grid device.

[0032] Furthermore, the impact of the load fluctuation is different in different ranges. For example, for a large area, its own load is relatively large, and a small fluctuation of a load is not likely to cause a power supply fluctuation. For a small area, the same load fluctuation may cause an obvious change in its overall load and result in different impacts.

[0033] Furthermore, the fluctuations of the load have a certain impact on the operating state of grid equipment. When the load fluctuates greatly, it will cause an impact on the grid equipment. For example, when the grid equipment in a factory starts or stops, it will cause a large change in the load, and the current, voltage, and frequency of the grid equipment during operation will be affected. When the obtained impact is within the normal range, the resulting fluctuations can be set as the normal operating fluctuations of the grid equipment. However, when the relationship between the fluctuations in the grid equipment and the original load changes shows a more deviated situation, it may be an abnormality caused by the grid equipment itself.

[0034] Furthermore, as an optional embodiment of the present invention, determining the relative load fluctuation situation of each region based on the load data of each region includes: determining the difference curve of each region and the load mean value of each region according to the load data of each region; determining the relative load fluctuation situation of the region according to the absolute value of the difference value on the difference curve of the region and the load mean value of the region.

[0035] Specifically, in the embodiment of the present invention, the load data of each obtained region is subjected to a first-order difference to obtain the difference curve of the load data within each region, and then the load mean value of the load data of each region is calculated. In the embodiment of the present invention, the absolute value of the difference value at each time point in the obtained difference curve is taken. Then, the absolute value of the difference value in the obtained difference curve is adjusted by its own order of magnitude through the above-obtained load mean value to obtain the relative load fluctuation situation of the region. As an optional embodiment of the present invention, determining the relative load fluctuation situation of the region according to the absolute value of the difference value on the difference curve of the region and the load mean value of the region includes: determining that the first ratio of the absolute value of the difference value on the difference curve to the load mean value is the relative load 0 fluctuation situation of the region.

[0036] Among them, the embodiment of the present invention specifically uses the following formula to calculate the relative load fluctuation situation:

[0037]

[0038] In the above formula, represents the relative load fluctuation situation at the z-th moment in the region. represents the absolute value of the difference value at the z-th moment in the difference curve of the region. represents the load mean value of the i-th region.

[0039] S103. Using the first fluctuation curve of the relative load fluctuation situation of the region, the load data of the region, and the relative load fluctuation situation, determine the load change situation of the region.

[0040] Specifically, in the embodiments of the present invention, when calculating the impact of the obtained load change on the operation of power grid equipment, when the load in the power grid changes, the response of the power grid equipment to this change is not completed instantaneously, but there is a certain delay, which may cause the change in the operation state of the power grid equipment to lag behind the actual change of the load. Therefore, the embodiments of the present invention determine the load change situation of the region based on the matching relationship between the load fluctuation situation and the operation data fluctuation situation.

[0041] Further, as an optional embodiment of the present invention, determining the load change situation of the region by using the first fluctuation curve of the relative load fluctuation situation of the region, the load data of the region, and the relative load fluctuation situation includes: determining the load change situation of the region according to the first quantity of the load data in the difference curve of the load data of the region, the relative load fluctuation situation at each moment, and the average relative load fluctuation situation corresponding to the difference curve.

[0042] Further, as an optional embodiment of the embodiments of the present invention, determining the load change situation of the region according to the first quantity of the load data in the difference curve of the load data of the region, the relative load fluctuation situation at each moment, and the average relative load fluctuation situation corresponding to the difference curve includes: calculating the absolute value of the first difference between the relative load fluctuation situation at each moment and the average relative load fluctuation situation; determining the average value of the absolute values of each first difference as the load change situation of the region.

[0043] Specifically, the embodiments of the present invention specifically calculate the load change situation of the region by using the following formula:

[0044]

[0045] In the above formula, represents the load change situation of the i-th region. represents the first quantity of the data points in the obtained difference curve. represents the obtained th moment of the relative load fluctuation situation. represents the average relative load fluctuation situation of the obtained difference curve.

[0046] Further, in the embodiments of the present invention, by calculating the non-disorder situation of each load change in the difference curve of the obtained load data, when the obtained is larger, it indicates that the change of its load is more disordered. At the same time, when calculating the impact of load fluctuation, the disordered load may have a greater probability of interfering with the operation of power grid equipment. The disordered load change is not easy to be compensated by the load. Therefore, when performing subsequent monitoring, it is necessary to improve the monitoring level of the edge equipment at the current position.

[0047] S104. Predict the predicted load data for a future period using the load data within the region, and determine the relative predicted load fluctuation and the predicted load change for the future period using the predicted load data.

[0048] Specifically, in the embodiment of the present invention, the load data obtained by the edge device is transmitted to the cloud device. The cloud device predicts the predicted load data for a future period for the load data obtained at each moment through an AutoRegressive Integrated Moving Average (ARIMA) model, and then calculates the relative predicted load fluctuation and the predicted load change for the future period by combining the predicted load data with the above-mentioned implementation methods of the relative load fluctuation and the load change.

[0049] S105. Determine the deviation degree of the load fluctuation for a future period based on the relative load fluctuation at each moment in the region, the relative predicted load fluctuation, the predicted load change, and the load change in the region.

[0050] Specifically, the deviation degree refers to the deviation between the relative fluctuation of the load data at a future moment and the relative fluctuation of the load data that has been obtained at the current moment. Among them, as an optional embodiment of the present invention, determining the deviation degree of the load fluctuation for a future period based on the relative load fluctuation at each moment in the region, the relative predicted load fluctuation, the predicted load change, and the load change in the region includes: calculating the absolute value of the second difference between the relative load fluctuation at each moment and the relative predicted load fluctuation, and calculating the absolute value of the third difference between the load change in the region and the predicted load change; determining the first product between the absolute value of the second difference and the absolute value of the third difference as the deviation degree.

[0051] Specifically, the embodiment of the present invention specifically calculates the deviation degree using the following formula:

[0052]

[0053] In the above formula, represents the deviation between the relative load fluctuation of the load data at the th moment obtained and the relative load fluctuation of the predicted load data for the predicted future period. represents the relative load fluctuation at the th moment actually collected, represents the relative predicted load fluctuation calculated based on the predicted load data predicted according to the load data at the th moment. represents the overall load change obtained from the historical data to the It represents the predicted load change in the future period calculated from the predicted load data at the i-th moment.

[0054] S106. Determine the strictness of the operation data obtained by the power grid equipment at each moment according to the deviation degree, the range between the first fitting curve and the second fitting curve at the corresponding moment, and the average value of each range, and monitor the operation state of the power grid equipment according to the strictness.

[0055] Specifically, the embodiment of the present invention quantifies the strictness when the edge device performs multi-level monitoring according to the deviation degree of the obtained load fluctuation. Among them, as an optional embodiment of the present invention, determining the strictness of the operation data obtained by the power grid equipment at each moment according to the deviation degree, the range between the first fitting curve and the second fitting curve at the corresponding moment, and the average value of each range includes: using the DTW value between the first fluctuation curve of the relative load fluctuation of the area and the second fluctuation curve of the relative fluctuation of the operation data of the power grid equipment to determine the target relative fluctuation of the target operation data affected by the load fluctuation of the area on the operation data of the power grid equipment; determining the impact of the load fluctuation of the area on the power grid equipment according to the relative load fluctuation of the area and the target relative fluctuation; constructing a two-dimensional coordinate system with the impact of the power grid equipment at each moment as the ordinate and the relative load fluctuation as the abscissa; fitting the data points in the two-dimensional coordinate system to obtain the first fitting curve and the second fitting curve; calculating the third ratio between the range between the first fitting curve and the second fitting curve at the corresponding moment and the average value of the range; performing inverse proportional normalization processing on the third ratio to obtain a normalized value; determining that the second product between the normalized value and the deviation degree is the strictness.

[0056] Specifically, the embodiment of the present invention matches the first fluctuation curve of the relative load fluctuation of the obtained area and the second fluctuation curve of the relative fluctuation of the operation data of the power grid equipment through the Dynamic Time Warping (DTW) algorithm. When matching, the DTW distance between the obtained load fluctuation and the multi-dimensional data during the operation of the power grid equipment is calculated as the above matching reference. Since there will be a one-to-many phenomenon in the DTW distance, the embodiment of the present invention selects the matching result with the smallest DTW value as the target relative fluctuation of the target operation data affected by the load fluctuation of the area on the operation data of the power grid equipment.

[0057] Further, as an optional embodiment of the present invention, determining the impact of the load fluctuation in the area on the grid equipment according to the relative load fluctuation situation in the area and the target relative fluctuation situation includes: calculating a second ratio between the relative load fluctuation situation in the area and the target relative fluctuation situation; superimposing the second ratios to obtain the impact situation.

[0058] Specifically, the embodiment of the present invention specifically calculates the impact situation using the following formula:

[0059]

[0060] In the above formula, represents the impact of the load fluctuation at the th moment in the i-th area on the grid equipment. represents the target relative fluctuation situation of the dimension of the operation data of the th grid equipment caused by the load fluctuation at the th moment obtained by matching through the DTW algorithm. represents the relative load fluctuation situation at the th moment. represents the number of grid equipment.

[0061] Further, the embodiment of the present invention constructs a two-dimensional coordinate space according to the obtained relative load fluctuation situation and impact situation. Exemplarily, as Figure 2 shown, Figure 2 is a schematic diagram of the data distribution of the relative load fluctuation situation and the impact situation provided by the embodiment of the present invention. Figure 2 In it, the abscissa represents the relative load fluctuation situations at each obtained moment, and the ordinate represents the impact of the load fluctuation on the grid equipment under the relative load fluctuation situation at the current moment. Figure 2 The two-dimensional coordinate space in it includes multiple data points.

[0062] Further, when performing curve fitting in the embodiment of the present invention, as an optional embodiment of the present invention, fitting the data points in the two-dimensional coordinate system to obtain a first fitting curve and a second fitting curve includes: selecting the maximum value and the minimum value corresponding to the abscissa of each data point; fitting the data points corresponding to the maximum values at each moment to obtain a first fitting curve, and fitting the data points corresponding to the minimum values at each moment to obtain a second fitting curve.

[0063] Specifically, when performing curve fitting in the embodiment of the present invention, the maximum value and the minimum value of the impact situation corresponding to each abscissa are selected, and at the same time, the obtained maximum values and minimum values are curve-fitted by means of spline interpolation to obtain a first fitting curve and a second fitting curve. Exemplarily, as Figure 3 shown, Figure 3Schematic diagram of the first fitting curve and the second fitting curve provided by an embodiment of the present invention Figure 3 Among them, the data points in the first fitting curve are the maximum impact conditions corresponding to each abscissa, and the data points in the second fitting curve are the minimum impact conditions corresponding to each abscissa.

[0064] Furthermore, the edge device can collect load fluctuation data and detect the change trend of the load. If the load changes greatly, the edge device can adjust the accuracy of the analysis algorithm, increase the multiple monitoring of the operating state of the grid equipment, so as to reduce the impact of load fluctuations on the analysis results, and it meets the multi-level monitoring of the equipment state by adjusting the strict situation of the edge device. When analyzing the impact of the load fluctuation shock obtained, since the grid equipment that makes up the load is different, it will cause different impact situations when the load fluctuations are the same. The main reason is that the composition of the active power and reactive power required by the load during operation is different. Therefore, the embodiment of the present invention can determine the deviation degree of the load fluctuation in the future period through the load data in the power grid.

[0065] Furthermore, the embodiment of the present invention specifically calculates the strict situation by the following formula:

[0066]

[0067] In the above formula, represents the strict situation of the grid equipment in the operating state collected at the th moment. represents the deviation situation between the relative load fluctuation of the load data obtained at the th moment and the relative load fluctuation of the predicted load data in the predicted future period. represents the difference situation, that is, the range, between the upper limit and the lower limit of the impact corresponding to the relative load fluctuation situation at the th moment on the first fitting curve and the second fitting curve in the th area. represents the average value of all ranges obtained in the th area. (-) represents the inverse normalization function, which is used to perform inverse normalization processing on

[0068] Further, in the embodiments of the present invention, the deviation between the actual load change and the predicted load change is judged. If the predicted load change deviates, it indicates that the load change law is not particularly obvious. At the same time, when the load change in the current area differs greatly from the predicted load change, the monitoring level needs to be improved. At the same time, the affected range between the current obtained impact upper limit and impact lower limit also needs to be considered. The larger the affected range, the more complex the corresponding current load composition may be, and the corresponding monitoring situation needs to be improved.

[0069] Further, in the embodiments of the present invention, by strictly obtaining the corresponding operation data at each moment above, the affected range between the impact upper limit and impact lower limit of the impact situation is adjusted according to the obtained strict situation. Specifically, the reduction multiple of the affected range is determined according to the strict situation, and the affected range is reduced according to this reduction multiple. Among them, the higher the strict situation, the larger the reduction multiple. For example, the original affected range affected by the impact is in the range of 0-10. After being reduced by 0.8, it becomes 1-9. According to whether the currently calculated impact situation is within the reduced affected range affected by the impact, if it is within the range, it is normal. If it is outside the reduced affected range and within the original affected range, the monitoring level needs to be improved to avoid anomalies. When it is outside the original affected range, a status warning is required.

[0070] The embodiments of the present invention can analyze the fluctuations of the operation data and the load during the operation of the obtained smart grid, determine the deviation degree of the load fluctuation of the smart grid equipment in the future period, determine the strict situation when each operation data is obtained according to this deviation degree, and thus monitor the operation state of the grid equipment according to this strict situation. Therefore, by analyzing the impact of load fluctuations on grid equipment, the embodiments of the present invention can effectively cope with the impact of load fluctuations on the evaluation of the operation state of grid equipment, thereby accurately evaluating the true operation state of grid equipment, improving the monitoring accuracy and authenticity of the operation state of grid equipment, and ensuring the safe and stable operation of the grid.

[0071] Embodiment 2:

[0072] Corresponding to the smart grid equipment operation state monitoring method provided in the above embodiment, based on the same technical concept, the embodiments of the present invention also provide a smart grid equipment operation state monitoring system. This smart grid equipment operation state monitoring system is used to execute the above smart grid equipment operation state monitoring method. Figure 4 The structural schematic diagram of a smart grid equipment operation state monitoring system provided by an embodiment of the present invention is as Figure 4As shown. The operation state monitoring system of smart grid devices can vary significantly due to different configurations or performances, and may include one or more processors 401 and a memory 402. The memory 402 is used to store computer programs that can run on the processor 401. The processor 401 is used to execute the programs stored in the memory 402 to implement the above Figure 1 each step in the method embodiments. Among them, the memory 402 can be transient storage or persistent storage. The application programs stored in the memory 402 can include one or more modules (not shown in the figure), and each module can include a series of computer-executable instructions in the operation state monitoring system of smart grid devices.

[0073] Furthermore, the processor 401 can be set to communicate with the memory 402 and execute a series of computer-executable instructions in the memory 402 on the operation state monitoring system of smart grid devices. The operation state monitoring system of smart grid devices can also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input / output interfaces 405, and one or more keyboards 406.

[0074] Specifically in this embodiment, the operation state monitoring system of smart grid devices includes a processor, a communication interface, a memory, and a communication bus; among them, the processor, the communication interface, and the memory complete mutual communication through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to implement the above Figure 1 each step in the method embodiments, and has the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0075] It should be noted that the operation state monitoring system of smart grid devices provided in the embodiments of the present invention and the operation state monitoring method of smart grid devices provided in the embodiments of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned operation state monitoring method of smart grid devices and has the same or similar beneficial effects. The repeated parts will not be described again.

[0076] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0077] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A method for monitoring the operating status of smart grid equipment, characterized in that: The smart grid equipment operating status monitoring method comprises: Obtain load data and operation data of power grid equipment in each area during operation; Determining relative load fluctuations of the areas according to the load data of each area; Determine the load change of the area by using the first fluctuation curve of the relative load fluctuation of the area, the load data of the area and the relative load fluctuation; Using the load data in the area to predict the predicted load data for a future period, and using the predicted load data to determine the predicted load relative fluctuation and predicted load change for the future period; Determine the degree of deviation of the load fluctuation in the future period according to the relative load fluctuation of the area at each moment, the predicted relative load fluctuation, the predicted load change and the load change of the area; Determine the strictness of the operation data acquired by the power grid device at each moment according to the degree of deviation, the range of the first fitting curve and the second fitting curve at the corresponding moment, and the average value of each range, and monitor the operation state of the power grid device according to the strictness, wherein the first fitting curve is the upper limit of the impact of the load fluctuation on the power grid device, and the second fitting curve is the lower limit of the impact of the load fluctuation on the power grid device; Wherein, determining the relative load fluctuation of the area according to the load data of each area includes: Determine a differential curve of each region and a load mean value of each region according to the load data of each region; Determine a relative load fluctuation of the area according to an absolute value of a differential value on a differential curve of the area and a load mean value of the area; Wherein, determining the load change of the region by using the first fluctuation curve of the relative load fluctuation of the region, the load data of the region and the relative load fluctuation includes: The load change of the area is determined according to the first amount of load data in the differential curve of the load data of the area, the relative load fluctuation at each of the moments and the average relative load fluctuation corresponding to the differential curve.

2. The method for monitoring the operating status of smart grid equipment according to claim 1, characterized in that: Determining the relative load fluctuation of the region according to the absolute value of the difference value on the difference curve of the region and the load mean of the region includes: A first ratio of an absolute value of a differential value on the differential curve to the load average is determined as a relative load fluctuation condition of the region.

3. The method for monitoring the operating status of smart grid equipment according to claim 1, characterized in that: Determining the load change of the region according to the first amount of load data in the differential curve of the load data of the region, the relative load fluctuation at each moment, and the average relative load fluctuation corresponding to the differential curve includes: Calculating an absolute value of a first difference between the relative load fluctuation at each of the moments and the average relative load fluctuation; An average value of the absolute values ​​of the first differences is determined as the load change condition of the area.

4. The method for monitoring the operating status of smart grid equipment according to any one of claims 1 to 3, characterized in that: The step of determining the degree of deviation of the load fluctuation in the future period according to the relative load fluctuation of the region at each moment, the predicted relative load fluctuation, the predicted load change and the load change of the region includes: Calculating the absolute value of the second difference between the relative load fluctuation at each moment and the predicted relative load fluctuation, and calculating the absolute value of the third difference between the load change of the region and the predicted load change; A first product between an absolute value of the second difference and an absolute value of the third difference is determined as the degree of deviation.

5. The method for monitoring the operating status of smart grid equipment according to any one of claims 1 to 3, characterized in that: The step of determining the strictness of the operation data acquired by the power grid device at each moment according to the degree of deviation, the ranges of the first fitting curve and the second fitting curve at corresponding moments, and the average values ​​of the ranges includes: Determine the target relative fluctuation of target operating data affected by the load fluctuation of the region on the operating data of the power grid device by using the DTW value between the first fluctuation curve of the relative fluctuation of the load of the region and the second fluctuation curve of the relative fluctuation of the operating data; Determine the impact of the load fluctuation in the area on the power grid equipment according to the relative load fluctuation in the area and the relative target fluctuation; A two-dimensional coordinate system is constructed with the impact of the power grid equipment at each moment as the vertical coordinate and the relative load fluctuation as the horizontal coordinate; Fitting the data points in the two-dimensional coordinate system to obtain a first fitting curve and a second fitting curve; Calculating a third ratio between the ranges of the first fitting curve and the second fitting curve at corresponding moments and an average value of the ranges; Performing inverse proportional normalization processing on the third ratio to obtain a normalized value; A second product between the normalized value and the degree of deviation is determined as the strict case.

6. The method for monitoring the operating status of smart grid equipment according to claim 5, characterized in that: Determining the impact of the load fluctuation in the area on the power grid equipment according to the relative load fluctuation in the area and the relative target fluctuation includes: calculating a second ratio between the relative load fluctuation of the area and the target relative load fluctuation; The second ratios are superimposed to obtain the impact condition.

7. The method for monitoring the operating status of smart grid equipment according to claim 5, characterized in that: The fitting of the data points in the two-dimensional coordinate system to obtain a first fitting curve and a second fitting curve comprises: Select the maximum and minimum values ​​corresponding to the horizontal coordinate of each data point; The data points corresponding to the maximum values ​​are fitted to obtain a first fitting curve, and the data points corresponding to the minimum values ​​are fitted to obtain a second fitting curve.

8. A smart grid equipment operation status monitoring system, characterized in that: include: A processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; A processor is used to execute the program stored in the memory to implement the steps of the smart grid equipment operating status monitoring method as described in any one of claims 1 to 7.

Images