Method and system for monitoring running state of intelligent power grid equipment

Abstract

The invention relates to the technical field of power grid monitoring, in particular to an intelligent power grid equipment operation state monitoring method and system, and the method comprises the steps: obtaining load data and operation data of power grid equipment in each region in an operation process; determining load relative fluctuation conditions of the areas according to the load data of the areas; determining the load change condition of the region; predicting predicted load data in a future time period by using the load data in the region, and determining a predicted load relative fluctuation condition and a predicted load change condition in the future time period by using the predicted load data; determining the deviation degree of the load fluctuation in the future time period; and determining the strict condition of the operation data acquired by the power grid equipment at each moment, and monitoring the operation state of the power grid equipment according to the strict condition. Therefore, the monitoring accuracy and authenticity of the operation state of the power grid equipment are improved, and safe and stable operation of the power grid is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and in particular to a method and system for monitoring the operating status of smart grid equipment. Background Art

[0002] Smart grid is a power grid model that uses advanced communication, sensing and information technology to achieve comprehensive monitoring, control management and optimization of the power grid. With the introduction and development of the concept of smart grid, distributed sensors and intelligent instruments and equipment have been widely used in various nodes of the power grid network, realizing the comprehensive acquisition of power grid operation data. These data cover key information such as power supply quality of the power grid, operating status of power equipment, power consumption and load conditions, providing a rich data foundation for in-depth understanding of the operation status of the power grid.

[0003] In some scenarios, in the actual operation status monitoring process of smart grid equipment, load fluctuation is an extremely important interference factor, which seriously affects the working status of grid equipment, and then causes fluctuations and inaccuracies in analysis data. This greatly increases the difficulty and uncertainty of analysis when analyzing the operation status of grid equipment in the future. If the impact of load fluctuations cannot be effectively dealt with, it will be difficult to accurately assess the true operation status of grid equipment, which will pose a hidden danger to the safe and stable operation of the grid. In this way, due to the influence of load fluctuations, the monitoring accuracy and authenticity of the operation status of grid equipment are low, which affects the safe and stable operation of the grid. Summary of the invention

[0004] In order to solve the technical problem of low monitoring accuracy and authenticity of the operating status of power grid equipment, the purpose of the present invention is to provide a method and system for monitoring the operating status of smart grid equipment. The technical solution adopted is as follows: In a first aspect, an embodiment of the present invention provides a method for monitoring the operating status of smart grid equipment, comprising: acquiring load data and operating data of grid equipment in each area during operation; determining the relative load fluctuation of the area according to the load data of each area; determining the load change of the area using a first fluctuation curve of the relative load fluctuation of the area, the load data of the area and the relative load fluctuation; predicting the predicted load data of a future time period using the load data in the area, and determining the predicted load relative fluctuation and predicted load change of the future time period using the predicted load data; determining the degree of deviation of the load fluctuation of the future time period according to the relative load fluctuation of the area at each moment, the predicted load relative fluctuation, the predicted load change and the load change of the area; determining the strictness of the operating data obtained by the grid equipment at each moment according to the deviation degree, the extreme difference between the first fitting curve and the second fitting curve at the corresponding moment and the average value of each extreme difference, and monitoring the operating status of the grid equipment according to the strictness, wherein the first fitting curve is the upper limit of the impact of the load fluctuation on the grid equipment, and the second fitting curve is the lower limit of the impact of the load fluctuation on the grid equipment.

[0005] Optionally, determining the relative load fluctuation of the area according to the load data of each area includes: determining the differential curve of each area and the load mean of each area according to the load data of each area; determining the relative load fluctuation of the area according to the absolute value of the differential value on the differential curve of the area and the load mean of the area.

[0006] Optionally, determining the relative load fluctuation of the area based on the absolute value of the differential value on the differential curve of the area and the load mean of the area includes: determining a first ratio of the absolute value of the differential value on the differential curve to the load mean as the relative load fluctuation of the area.

[0007] Optionally, the use of the first fluctuation curve of the relative load fluctuation of the region, the load data of the region, and the relative load fluctuation to determine the load change of the region includes: determining the load change of the region based on the first number of load data in the differential curve of the load data of the region, the relative load fluctuation at each of the moments, and the average relative load fluctuation corresponding to the differential curve. Optionally, the use of the first number of load data in the differential curve of the load data of the region, the relative load fluctuation at each of the moments, and the average relative load fluctuation corresponding to the differential curve to determine the load change of the region includes: calculating the absolute value of the first difference between the relative load fluctuation at each of the moments and the average relative load fluctuation; determining the average of the absolute values ​​of the first differences as the load change of the region.

[0008] Optionally, the method of determining the strict conditions of the operating data acquired by the power grid device at each moment according to the degree of deviation, the extreme differences between the first fitting curve and the second fitting curve at corresponding moments, and the average value of each extreme difference includes: determining the target relative fluctuation of the target operating data affected by the load fluctuation in 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 in the region and the second fluctuation curve of the relative fluctuation of the operating data; determining the impact of the load fluctuation in the region on the power grid device according to the relative fluctuation of the load in the region and the target relative fluctuation; constructing a two-dimensional coordinate system with the impact of the power grid device at each moment as the ordinate and the relative fluctuation of the load 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 extreme differences between the first fitting curve and the second fitting curve at corresponding moments and the average value of the extreme differences; performing inverse proportional normalization 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.

[0009] Optionally, determining the impact of the load fluctuation in the area on the power grid equipment based on the relative load fluctuation in the area and the target relative fluctuation includes: calculating a second ratio between the relative load fluctuation in the area and the target relative fluctuation; and superimposing each of the second ratios to obtain the impact.

[0010] Optionally, fitting the data points in the two-dimensional coordinate system to obtain the first fitting curve and the second fitting curve includes: selecting the maximum value and the minimum value corresponding to the horizontal coordinate of each data point; fitting the data points corresponding to each of the maximum values ​​to obtain the first fitting curve, and fitting the data points corresponding to each of the minimum values ​​to obtain the second fitting curve.

[0011] In a second aspect, an embodiment of the present invention provides a smart grid equipment operating status monitoring system, comprising: a processor and a memory; wherein the memory is used to store computer programs that can be run on the processor; the processor is used to execute the program stored in the memory to implement the steps of the smart grid equipment operating status monitoring method mentioned in the first aspect.

[0012] The present invention has the following beneficial effects: firstly, the load data and operation data of the power grid equipment in each area are obtained during operation; then, the relative load fluctuation of the area is determined according to the load data of each area; and the load change of the area is determined 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; secondly, the predicted load data of the future period is predicted by using the load data in the area, and the predicted load relative fluctuation and predicted load change of the future period are determined by using the predicted load data; and the deviation degree of the load fluctuation of the future period is determined according to the relative load fluctuation of the area at each moment, the predicted load relative fluctuation, the predicted load change and the load change of the area; finally, the strictness of the operation data obtained by the power grid equipment at each moment is determined according to the deviation degree, the extreme difference of the first fitting curve and the second fitting curve at the corresponding moment and the average value of each extreme difference, and the operation state of the power grid equipment is monitored according to the strictness. The first fitting curve is the upper limit of the impact of the load fluctuation on the power grid equipment, and the second fitting curve is the lower limit of the impact of the load fluctuation on the power grid equipment.

[0013] In this way, the embodiment of the present invention can determine the degree of deviation of the load fluctuation of the smart grid device in the future period by analyzing the load and operation data acquired during the operation of the smart grid, the fluctuation of the operation data and the fluctuation of the load, and determine the strictness of the acquisition of each operation data according to the deviation degree, so as to 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 deal with the impact of load fluctuation on the evaluation of the operation status of the power grid device, thereby accurately evaluating the real 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

[0014] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 paying creative work.

[0015] Figure 1 A flow chart of a method for monitoring the operating status of smart grid equipment provided by one embodiment of the present invention; Figure 2 A schematic diagram of data distribution of load relative fluctuation and impact conditions provided by an embodiment of the present invention; Figure 3 A schematic diagram of a first fitting curve and a second fitting curve provided for an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a smart grid equipment operating status monitoring system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a smart grid equipment operation status monitoring method and system proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] The specific scheme of the method for monitoring the operating status of smart grid equipment provided by the present invention is described in detail below with reference to the accompanying drawings.

[0019] Embodiment 1: See also Figure 1 , which shows a flow chart of a method for monitoring the operating status of a smart grid device provided by an embodiment of the present invention, including: S101, acquiring load data and operation data of power grid equipment in each area during operation.

[0020] Specifically, the embodiments of the present invention can install sensors, such as current sensors, voltage sensors, and vibration sensors, in power grid equipment such as transformers, fuses, and voltage regulators in the control system of the smart grid. They are used to collect operating data of power grid equipment 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 are described using the above operating data as examples. Among them, load data includes but is not limited to power load, etc.

[0021] Furthermore, in the embodiment of the present invention, there is an inclusion relationship between power grids when data is acquired. For example, a large area contains multiple small areas, and multiple users use electricity in a small area, where the small area is the smallest area for monitoring the operating status of power grid equipment, and there is one power grid equipment in a smallest area. The operating data in a large area and the operating data of the corresponding small areas that constitute the large area are in an inclusion relationship.

[0022] Furthermore, in the embodiment of the present invention, after each sensor collects the load data and operation data of the power grid equipment during operation, it sends the data to the central controller and edge device of the smart grid for processing.

[0023] S102, determining the relative load fluctuation of each area according to the load data of each area.

[0024] Specifically, when the embodiment of the present invention monitors the operating status of the power grid equipment of the smart grid, the edge device can perform a preliminary analysis of the operating data and load data of the power grid equipment locally, and the data after the preliminary analysis by the edge device is processed in the cloud. By analyzing the power grid equipment during operation, it will be affected by load fluctuations. Areas with relatively large load fluctuations may cause its power grid equipment to be subjected to greater impacts and affect its acquired operating status. However, when the impact of the acquired load fluctuations and the changes in the operating status are within a certain range, the power grid equipment only has a certain deviation in the operating data caused by the load fluctuations, which will not affect the normal working state of the power grid equipment.

[0025] Furthermore, the impact of load fluctuations in different ranges is different. For example, for a large area, the load itself is relatively large, and a small fluctuation in load is not likely to cause power supply fluctuations. For a small area, the same load fluctuation may cause a significant change in the overall load, resulting in different impacts.

[0026] Furthermore, load fluctuations have a certain impact on the operating state of power grid equipment. When the load fluctuates greatly, it will cause an impact on the power grid equipment. For example, when the power grid equipment in a factory is started and stopped, it will cause a large change in load. The current, voltage and frequency of the power grid equipment will be impacted during operation. When the acquired impact is within the normal range, the fluctuation caused by it can be set as the normal operating fluctuation of the power grid equipment. However, when the relationship between the acquired fluctuation in the power grid equipment and the original load change shows a more deviated situation, it may be an abnormality caused by the power grid equipment itself.

[0027] Further, as an optional embodiment of the present invention, determining the relative load fluctuation of a region based on the load data of each region includes: determining the differential curve of each region and the average load of each region based on the load data of each region; determining the relative load fluctuation of the region based on the absolute value of the differential value on the differential curve of the region and the average load of the region.

[0028] Specifically, the embodiment of the present invention performs a first-order difference on the load data of each region, obtains a differential curve of the load data in each region, and then calculates the load mean of the load data in each region. The embodiment of the present invention takes the absolute value of the differential value at each time point in the obtained differential curve. Then, the absolute value of the differential value in the obtained differential curve is adjusted by its own order of magnitude using the load mean obtained above, so as to obtain the relative load fluctuation of the region. As an optional embodiment of the present invention, determining the relative load fluctuation of the region based on the absolute value of the differential value on the differential curve of the region and the load mean of the region includes: determining the first ratio of the absolute value of the differential value on the differential curve to the load mean as the relative load fluctuation of the region.

[0029] The embodiment of the present invention specifically uses the following formula to calculate the relative load fluctuation: In the above formula, Indicates the relative load fluctuation in the area at the zth moment. Represents the absolute value of the difference value at the zth moment in the difference curve of the area. represents the load mean of the ith area.

[0030] S103, determining a load change in the region by using a first fluctuation curve of a relative load fluctuation in the region, the load data of the region, and the relative load fluctuation.

[0031] Specifically, when calculating the influence of load changes on the operation of power grid equipment in the embodiment of the present invention, when the load in the power grid changes, the response of the power grid equipment to such changes is not instantaneous, but there is a certain delay, which may cause the change of the operation state of the power grid equipment to lag behind the actual change of the load. Therefore, the embodiment of the present invention determines the load change of the region based on the matching relationship between the fluctuation of the load and the fluctuation of the operation data.

[0032] Further, as an optional embodiment of the present invention, utilizing a first fluctuation curve of the relative load fluctuation of a region, the load data of the region, and the relative load fluctuation to determine the load change of the region includes: determining the load change of the region based on a first quantity of load data in a differential curve of the load data of the region, the relative load fluctuations at each moment, and the average relative load fluctuation corresponding to the differential curve.

[0033] Further, as an optional embodiment of the present invention, determining the load change of the region based on the first number 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 the absolute value of the first difference between the relative load fluctuation at each moment and the average relative load fluctuation; determining the average value of the absolute values ​​of each first difference as the load change of the region.

[0034] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the load change of the area: In the above formula, Indicates the load change of the i-th area. Represents the first number of data points in the acquired difference curve. Indicates the first The relative load fluctuation at a certain moment. Indicates the relative fluctuation of the average load of the acquired differential curve.

[0035] Furthermore, the embodiment of the present invention calculates the non-chaotic situation of each load change in the differential curve of the acquired load data, and when the acquired The larger it is, the more disordered the load changes are. At the same time, when calculating the impact of load fluctuations, the disordered load may interfere with the operation of the power grid equipment. Disordered load changes are not easily compensated by the load, so it is necessary to improve the monitoring level of the edge device at the current location during subsequent monitoring.

[0036] S104, using the load data in the area to predict the predicted load data for the future period, and using the predicted load data to determine the predicted load relative fluctuation and predicted load change for the future period.

[0037] Specifically, the embodiment of the present invention transmits the load data obtained by the edge device to the cloud device. The cloud device predicts the predicted load data for the future time period through the AutoRegressive Integrated Moving Average (ARIMA) model for the load data obtained at each moment, and then uses the predicted load data in combination with the above-mentioned implementation methods of the relative load fluctuation and load change to calculate the predicted load relative fluctuation and predicted load change for the future time period.

[0038] S105, determining the degree of deviation of 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.

[0039] Specifically, the degree of deviation refers to the deviation between the relative fluctuation of the load data at the future moment and the relative fluctuation of the load data already acquired at the current moment. Wherein, as an optional embodiment of the present invention, determining the degree of deviation of the load fluctuation in the future time period according to the relative load fluctuation at each moment of the region, 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; determining the first product between the absolute value of the second difference and the absolute value of the third difference as the degree of deviation.

[0040] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the degree of deviation: In the above formula, Indicates the first The deviation between the relative load fluctuation of the load data at a certain moment and the relative load fluctuation of the predicted load data in the predicted future period. Indicates the actual collection The relative load fluctuation at each moment, Indicates that according to The relative fluctuation of the predicted load calculated from the predicted load data predicted by the load data at each moment. Indicates the overall load change from the historical data obtained to the i-th moment. It represents the predicted load change in the future period calculated based on the predicted load data at the i-th moment.

[0041] S106, determining the strictness of the operation data acquired by the power grid device at each moment according to the degree of deviation, the extreme differences of the first fitting curve and the second fitting curve at corresponding moments, and the average value of each extreme difference, and monitoring the operation status of the power grid device according to the strictness.

[0042] Specifically, the embodiment of the present invention quantifies the strictness of the edge device when performing multi-level monitoring according to the deviation degree of the acquired load fluctuation. Among them, as an optional embodiment of the present invention, determining the strict situation of the operating data acquired by the power grid equipment at each moment according to the degree of deviation, the extreme difference between the first fitting curve and the second fitting curve at the corresponding moment, and the average value of each extreme difference includes: using the DTW value between the first fluctuation curve of the relative fluctuation of the load in the area and the second fluctuation curve of the relative fluctuation of the operating data to determine the target relative fluctuation of the target operating data of the load fluctuation in the area that affects the operating data of the power grid equipment; determining the impact of the load fluctuation in the area on the power grid equipment according to the relative fluctuation of the load in 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 vertical coordinate and the relative fluctuation of the load as the horizontal coordinate; 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 extreme difference of the first fitting curve and the second fitting curve at the corresponding moment and the average value of the extreme difference; performing inverse proportional normalization processing on the third ratio to obtain a normalized value; determining the second product between the normalized value and the degree of deviation as the strict situation.

[0043] Specifically, the embodiment of the present invention matches the first fluctuation curve of the relative fluctuation of the load in the above-obtained area with the second fluctuation curve of the relative fluctuation of the operating data of the power grid equipment through the Dynamic Time Warping (DTW) algorithm, and when matching, the DTW distance between the obtained load fluctuation and the multi-dimensional data of the power grid equipment during operation is calculated as the above matching reference. Among them, the DTW distance will have a one-to-many phenomenon, so the embodiment of the present invention selects the matching result with the smallest DTW value as the target relative fluctuation of the target operating data that the load fluctuation of the area affects the operating data of the power grid equipment.

[0044] Further, as an optional embodiment of the present invention, determining the impact of regional load fluctuations on power grid equipment based on the regional load relative fluctuations and target relative fluctuations includes: calculating a second ratio between the regional load relative fluctuations and the target relative fluctuations; and superimposing each second ratio to obtain the impact.

[0045] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the impact situation: In the above formula, Indicates the first The impact of load fluctuations at a certain moment on power grid equipment. Indicates the first The load fluctuation at the moment causes the The relative fluctuation of the target in the dimension of the operating data of each power grid equipment. Indicates The relative load fluctuation at a certain moment. Indicates the number of power grid devices.

[0046] Furthermore, the embodiment of the present invention constructs a two-dimensional coordinate space based on the acquired relative load fluctuation and impact conditions. Figure 2 As shown, Figure 2 A schematic diagram of data distribution of load relative fluctuation and impact conditions provided by an embodiment of the present invention, Figure 2 In the figure, the horizontal axis represents the relative load fluctuation at each moment, and the vertical axis represents the impact of load fluctuation on power grid equipment under the relative load fluctuation at the current moment. Figure 2 The two-dimensional coordinate space includes multiple data points.

[0047] Furthermore, when performing curve fitting in an 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 the first fitting curve and the second fitting curve includes: selecting the maximum value and the minimum value corresponding to the horizontal coordinate of each data point; fitting the data points corresponding to the maximum value at each moment to obtain the first fitting curve, and fitting the data points corresponding to the minimum value at each moment to obtain the second fitting curve.

[0048] Specifically, the embodiment of the present invention selects the maximum and minimum values ​​of the impact conditions corresponding to each horizontal coordinate when performing curve fitting, and simultaneously performs curve fitting on each obtained maximum and minimum value by spline interpolation to obtain a first fitting curve and a second fitting curve. For example, Figure 3 As shown, Figure 3A schematic diagram of a first fitting curve and a second fitting curve provided by an embodiment of the present invention, Figure 3 In the figure, the data points in the first fitting curve are the maximum impact conditions corresponding to each horizontal coordinate, and the data points in the second fitting curve are the minimum impact conditions corresponding to each horizontal coordinate.

[0049] Furthermore, the edge device can collect load fluctuation data and detect the changing trend of the load. If the load changes greatly, the edge device can adjust the accuracy of the analysis algorithm and increase the multiple monitoring of the operating status of the power grid equipment to reduce the impact of load fluctuations on the analysis results. It adjusts the strict conditions of the edge device to meet the multi-level monitoring of the equipment status. When the impact of the fluctuation shock of the acquired load is obtained, since the power grid equipment that makes up the load is different, it will cause the impact conditions to be different when the load fluctuation is the same. The main reason is that the composition of the active power and reactive power required by the load when working is different. Therefore, the embodiment of the present invention can determine the degree of deviation of the load fluctuation in the future time period through the load data in the power grid.

[0050] Furthermore, the embodiment of the present invention specifically uses the following formula to calculate the strict situation: In the above formula, Indicates The strict conditions of the power grid equipment in operation collected at each moment. Indicates the first The deviation between the relative load fluctuation of the load data at a certain moment and the relative load fluctuation of the predicted load data in the predicted future period. Indicates the first fitting curve and the second fitting curve in the i-th region. The relative load fluctuation at a certain moment corresponds to the difference between the upper and lower limits of the impact, that is, the range. Indicates the first The average of all the ranges in the region. (-) indicates the inverse normalization function, which is used to Perform inverse proportional normalization.

[0051] Furthermore, in the embodiment of the present invention, the deviation between the actual load change and the predicted load change is determined. The deviation in the predicted load change indicates that the load change pattern is not particularly obvious. At the same time, when the load change in the current area is significantly different from the predicted load change, it is necessary to increase the monitoring level. At the same time, it is also necessary to consider the affected range between the currently obtained upper and lower impact limits. The larger the affected range, the more complex the corresponding current load composition may be, and the corresponding monitoring situation needs to be improved.

[0052] Furthermore, the embodiment of the present invention obtains the strict conditions of the corresponding operating data at each moment mentioned above, and adjusts the affected range between the impact upper limit and the impact lower limit of the impacted condition according to the obtained strict conditions. Specifically, the reduction multiple of the affected range is determined according to the strict conditions, and the affected range is reduced according to the reduction multiple. Among them, the higher the strict conditions, the greater the reduction multiple. For example, the original affected range affected by the impact is in the range of 0-10, and after being reduced by 0.8, it becomes 1-9. It is determined 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 but within the original affected range, it is necessary to increase the monitoring level to avoid abnormalities. When it is outside the original affected range, a status warning is required.

[0053] The embodiment of the present invention can determine the degree of deviation of the load fluctuation of the smart grid device in the future period by analyzing the load and operation data acquired during the operation of the smart grid, the fluctuation of the operation data and the fluctuation of the load, and determine the strictness of the acquisition of each operation data according to the deviation degree, so as to monitor the operation status of the power grid device according to the strictness. Therefore, the embodiment of the present invention can effectively deal with the impact of load fluctuation on the evaluation of the operation status of the power grid device by analyzing the impact of load fluctuation on the power grid device, so as to accurately evaluate the real operation status of the power grid device, improve the monitoring accuracy and authenticity of the operation status of the power grid device, and ensure the safe and stable operation of the power grid.

[0054] Embodiment 2: Corresponding to the smart grid device operating status monitoring method provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides a smart grid device operating status monitoring system, which is used to execute the above smart grid device operating status monitoring method. Figure 4 A schematic diagram of a smart grid equipment operation status monitoring system provided by an embodiment of the present invention is shown in FIG. Figure 4 The smart grid equipment operation status monitoring system may have relatively large differences due to different configurations or performances, and may include one or more processors 401 and memory 402, the memory 402 is used to store computer programs that can be run on the processor 401, and the processor 401 is used to execute the program stored in the memory 402 to achieve the above Figure 1 The various steps in the method embodiment. Among them, the memory 402 can be a temporary storage or a permanent storage. The application stored in the memory 402 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the smart grid device operation status monitoring system.

[0055] Furthermore, the processor 401 can be configured to communicate with the memory 402, and execute a series of computer executable instructions in the memory 402 on the smart grid device operation status monitoring system. The smart grid device operation status monitoring system can also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input and output interfaces 405, and one or more keyboards 406.

[0056] Specifically in this embodiment, the smart grid equipment operation status monitoring system includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to achieve the above Figure 1 The various steps in the method embodiment are similar to those in the method embodiment, and have the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0057] It should be noted that the smart grid equipment operation status monitoring system provided by the embodiment of the present invention and the smart grid equipment operation status monitoring method provided by the embodiment 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 smart grid equipment operation status monitoring method, and has the same or similar beneficial effects, and the repeated parts will not be repeated.

[0058] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying 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.

[0059] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on 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 fluctuation of the area 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; The strictness of the operating data acquired by the power grid equipment at each moment is determined according to the degree of deviation, the extreme differences of the first fitting curve and the second fitting curve at corresponding moments, and the average value of each extreme difference, and the operating state of the power grid equipment is monitored according to the strictness, wherein the first fitting curve is the upper limit of the impact of the load fluctuation on the power grid equipment, and the second fitting curve is the lower limit of the impact of the load fluctuation on the power grid equipment.

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 each 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; The relative load fluctuation of the area is determined according to the absolute value of the difference value on the difference curve of the area and the load mean of the area.

3. The method for monitoring the operating status of smart grid equipment according to claim 2, 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.

4. 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 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.

5. The method for monitoring the operating status of smart grid equipment according to claim 4, 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.

6. The method for monitoring the operating status of smart grid equipment according to any one of claims 1 to 5, 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.

7. The method for monitoring the operating status of smart grid equipment according to any one of claims 1 to 5, 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.

8. The method for monitoring the operating status of smart grid equipment according to claim 7, 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.

9. The method for monitoring the operating status of smart grid equipment according to claim 7, 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.

10. 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 9.

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