Equipment safety monitoring method, system and equipment of transformer substation, medium and product

Abstract

The invention relates to an equipment safety monitoring method and system for a transformer substation, equipment, a medium and a product. The method comprises the following steps: acquiring current environment data and current equipment operation data of a target substation in a current monitoring period; on the basis of the difference between the current environment data and a preset environment threshold value, the environment risk of the target substation is identified, and an environment risk result of the target substation is obtained; the preset environment threshold value is determined according to historical environment data of the target substation in a preset historical monitoring period; the preset historical monitoring period comprises a monitoring period in which the target substation is in an equipment normal state before the current monitoring period; and determining the equipment safety state of the target substation based on the environmental risk result and the current equipment operation data. By adopting the method, adaptation to different regional environments and equipment characteristics can be realized, and the monitoring precision of safety monitoring of the substation equipment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of substation monitoring, in particular to a substation equipment safety monitoring method, system, device, medium and product. BACKGROUND

[0002] With the rapid development of smart grids, the scale and complexity of substation equipment are continuously rising. As the core hub of the power system, substations are responsible for key functions such as voltage conversion, power distribution, and system protection. If substation equipment fails, it may trigger a chain reaction, leading to widespread power outages, and even secondary disasters such as fires and explosions. Therefore, substation equipment safety monitoring is directly related to the stable operation of the power network and the normal development of social and economic activities.

[0003] However, the wide distribution of substations in different regions results in significant differences in their environment, with climate, humidity, electromagnetic environment, and other factors varying greatly from place to place. These significant environmental differences can directly affect the dynamic characteristics of substation equipment operating parameters. Currently, most equipment safety monitoring methods are difficult to intelligently adapt to the environmental characteristics of different regions, which greatly reduces the accuracy of identifying and processing equipment operating parameter characteristics in complex and variable regional environments. This limitation has become a technical problem that needs to be solved in the field of substation equipment safety monitoring. SUMMARY

[0004] Therefore, it is necessary to provide a substation equipment safety monitoring method, system, device, medium and product that can improve monitoring accuracy to solve the above technical problems.

[0005] In a first aspect, the present application provides a substation equipment safety monitoring method, comprising:

[0006] obtaining current environment data and current equipment operating data of a target substation in a current monitoring period;

[0007] based on the difference between the current environment data and a preset environment threshold, identifying the environmental risk of the target substation to obtain an environmental risk result of the target substation; the preset environment threshold is determined according to historical environment data of the target substation in a preset historical monitoring period; the preset historical monitoring period includes a monitoring period in which the target substation is in a normal equipment state before the current monitoring period;

[0008] determining the equipment safety state of the target substation based on the environmental risk result and the current equipment operating data.

[0009] In one of the embodiments, the environment risk of the target transformer substation is identified based on the difference between the current environment data and the preset environment threshold, and an environment risk result of the target transformer substation is obtained, which comprises:

[0010] For each type of environment parameter, parameter risk data corresponding to the type of environment parameter is determined based on the difference between the current environment data corresponding to the type of environment parameter and the preset environment threshold corresponding to the type of environment parameter, so as to obtain a plurality of parameter risk data corresponding to a plurality of types of environment parameters.

[0011] The environment risk characterization value of the target transformer substation is obtained by combining a plurality of parameter risk data corresponding to a plurality of types of environment parameters respectively.

[0012] The environment risk characterization value and the preset environment risk threshold are compared, and the environment risk result of the target transformer substation is obtained according to the comparison result.

[0013] In one of the embodiments, the environment risk characterization value of the target transformer substation is obtained by combining a plurality of parameter risk data corresponding to a plurality of types of environment parameters respectively, which comprises:

[0014] The parameter fluctuation value of the parameter risk data collected in the current monitoring period is obtained; the parameter fluctuation value of the parameter risk data is obtained; the parameter fluctuation value is used to represent the fluctuation degree of the parameter risk data of a plurality of sampling points in the current monitoring period.

[0015] The contribution degree of the environment parameter type to the environment risk identification is determined based on the parameter fluctuation value; the parameter fluctuation value is directly proportional to the contribution degree.

[0016] The environment risk characterization value of the target transformer substation is obtained by combining a plurality of parameter risk data and a plurality of contribution degrees corresponding to a plurality of parameter risk data respectively.

[0017] In one of the embodiments, the device safety state of the target transformer substation is determined based on the environment risk result and the current device operation data, which comprises:

[0018] In the case that the difference between the current environment data and the preset environment threshold satisfies the preset risk condition, the device risk characterization value corresponding to the current device operation data is obtained; the device risk characterization value is used to represent the difference between the current device operation data and the preset device operation threshold.

[0019] In the case that the device risk characterization value is greater than or equal to the device risk threshold, it is determined that the target transformer substation is in the device abnormal state.

[0020] In one of the embodiments, the determining the equipment safety state of the target transformer substation based on the environment risk result and the current equipment operation data comprises:

[0021] In the case that the environment risk result indicates that the difference between the current environment data and the preset environment threshold does not satisfy the preset risk condition, an equipment risk fluctuation value corresponding to the current equipment operation data is obtained; the equipment risk fluctuation value is used to represent the fluctuation degree of the equipment risk representation value; the equipment risk representation value is used to represent the difference between the current equipment operation data and a preset equipment operation threshold;

[0022] In the case that the equipment risk fluctuation value is greater than or equal to an equipment fluctuation threshold, it is determined that the target transformer substation is in an equipment abnormal state.

[0023] In one of the embodiments, the preset equipment operation threshold is determined according to the historical equipment operation data of the target transformer substation in the preset historical monitoring period.

[0024] In a second aspect, the present application further provides an equipment safety monitoring system of a transformer substation, comprising:

[0025] an acquisition module, configured to acquire current environment data and current equipment operation data of a target transformer substation in a current monitoring period;

[0026] an environment risk module, configured to identify the environment risk of the target transformer substation based on the difference between the current environment data and a preset environment threshold, to obtain an environment risk result of the target transformer substation; the preset environment threshold is determined according to historical environment data of the target transformer substation in a preset historical monitoring period; the preset historical monitoring period comprises a monitoring period in which the target transformer substation is in an equipment normal state before the current monitoring period;

[0027] a safety state module, configured to determine the equipment safety state of the target transformer substation based on the environment risk result and the current equipment operation data.

[0028] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor realizes the following steps when executing the computer program:

[0029] acquiring current environment data and current equipment operation data of a target transformer substation in a current monitoring period;

[0030] identify an environment risk of the target transformer substation based on a difference between the current environment data and a preset environment threshold, to obtain an environment risk result of the target transformer substation; the preset environment threshold is determined according to historical environment data of the target transformer substation in a preset historical monitoring period; the preset historical monitoring period includes a monitoring period in which the target transformer substation is in a device normal state before the current monitoring period;

[0031] determine a device safety state of the target transformer substation based on the environment risk result and the current device operation data.

[0032] In a fourth aspect, the present application further provides a computer readable storage medium, which has a computer program stored thereon, and the computer program is executed by a processor to implement the following steps:

[0033] obtain current environment data and current device operation data of a target transformer substation in a current monitoring period;

[0034] identify an environment risk of the target transformer substation based on a difference between the current environment data and a preset environment threshold, to obtain an environment risk result of the target transformer substation; the preset environment threshold is determined according to historical environment data of the target transformer substation in a preset historical monitoring period; the preset historical monitoring period includes a monitoring period in which the target transformer substation is in a device normal state before the current monitoring period;

[0035] determine a device safety state of the target transformer substation based on the environment risk result and the current device operation data.

[0036] In a fifth aspect, the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the following steps:

[0037] obtain current environment data and current device operation data of a target transformer substation in a current monitoring period;

[0038] identify an environment risk of the target transformer substation based on a difference between the current environment data and a preset environment threshold, to obtain an environment risk result of the target transformer substation; the preset environment threshold is determined according to historical environment data of the target transformer substation in a preset historical monitoring period; the preset historical monitoring period includes a monitoring period in which the target transformer substation is in a device normal state before the current monitoring period;

[0039] determine a device safety state of the target transformer substation based on the environment risk result and the current device operation data.

[0040] The device safety monitoring method, system, device, medium and product of the substation, by acquiring the current environment data and the current device operation data of the target substation in the current monitoring period, identifying the environmental risk of the target substation based on the difference between the current environment data and the preset environment threshold, obtaining the environmental risk result of the target substation, determining the device safety state of the target substation based on the environmental risk result and the current device operation data, wherein the preset environment threshold is determined according to the historical environment data of the target substation in the preset historical monitoring period, and the preset historical monitoring period includes the monitoring period in which the target substation is in a device normal state before the current monitoring period. The application can learn and determine the preset environment threshold corresponding to the target substation in the current monitoring period through the historical environment data of the target substation in the past safe operation, so as to obtain the localized environment baseline specially for the target substation in the current monitoring period. By taking the preset environment threshold as a reference, the deviation degree of the current environment data deviating from the preset environment threshold is monitored, so that the risk of the current environmental state of the target substation is more accurately and reasonably identified, the unified standard is avoided to measure the substations in different regions, the real state of the device of the target substation can be more truly reflected, the adaptation to the environmental and device characteristics in different regions is realized, and the monitoring accuracy of the device safety monitoring of the substation is improved. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0042] Figure 1 An application environment diagram of the device safety monitoring method of the substation in an embodiment;

[0043] Figure 2 A flowchart of the device safety monitoring method of the substation in an embodiment;

[0044] Figure 3 A flowchart of obtaining the environmental risk result in an embodiment;

[0045] Figure 4 A flowchart of obtaining the environmental risk result in an embodiment;

[0046] Figure 5 A flowchart of determining the device safety state in an embodiment;

[0047] Figure 6A flowchart for determining the equipment safety state in another embodiment;

[0048] Figure 7 A structural block diagram of the equipment safety monitoring system of the substation in an embodiment;

[0049] Figure 8 An internal structural diagram of the computer equipment in an embodiment. DETAILED DESCRIPTION

[0050] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0051] It should be noted that the terms "first", "second", and the like used in the present application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "include" and "have" and any variations thereof used in the present application are intended to cover non-exclusive inclusion. The term "a plurality of" used in the present application refers to two or more. The term "and / or" used in the present application refers to one of the options or any combination of multiple options.

[0052] Since the substation equipment usually operates in a high-voltage, strong electromagnetic field, and the like, it is prone to failure due to external interference. The current substation equipment safety management system usually needs to be installed in different regional substations. Due to the large difference in environment in different regions, the system parameter characteristics change, and there is a lack of adaptive operation and maintenance strategies for different regional substations. In the complex and changeable regional environment, the recognition and processing accuracy of the equipment operating parameter characteristics in the substation are greatly reduced, the temperature and humidity compensation device of the transformer in different regions has low starting accuracy, thereby resulting in high energy consumption, and the transformer equipment may be damaged due to high temperature and humidity.

[0053] Based on this, in order to solve the above technical problems, the present application provides a substation equipment safety monitoring method, which can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 or the server 104 obtains the current environmental data and current equipment operation data of the target substation in the target area in the current monitoring period, and learns the preset environmental threshold from the historical environmental data of the target substation in the preset historical monitoring period. The preset historical monitoring period includes the monitoring period before the current monitoring period when the target substation is in a normal equipment state. Then, through the difference between the current environmental data and the preset environmental threshold, the environmental risk of the target substation is identified, and the environmental risk result of the target substation is obtained. The equipment safety status of the target substation is determined in combination with the environmental risk result and the current equipment operation data.

[0054] Terminal 102 may include, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Head-mounted devices may include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, and the like. Server 104 may be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0055] In an exemplary embodiment, Figure 2 As shown, a method for monitoring equipment safety in a substation is provided. Figure 1 The server in FIG. 1 is used as an example to illustrate the method, including the following steps 202 to 206. Among them:

[0056] Step 202: Acquire current environmental data and current equipment operation data of the target substation in the current monitoring period.

[0057] Among them, current environmental data refers to real-time data related to the environment of the target substation during the current monitoring period. The current environmental data may include environmental parameter data such as temperature, humidity, air pressure, wind speed, and electromagnetic field strength. Environmental data can reflect the real-time status of the external and internal environment of the substation.

[0058] The current device operation data refers to real-time operation state data of various devices in the target substation, such as a transformer, a circuit breaker, a gas insulated switchgear (GIS) device and the like, in a current monitoring period, which can include voltage, current, power, internal temperature of the device, vibration, partial discharge and other key operation parameters to reflect the health status and performance of the device.

[0059] Exemplarily, the server collects the current environment temperature data and the current environment humidity data at a plurality of predetermined sampling time points in the current monitoring period, and collects the current transformer temperature data and the current circuit breaker temperature data at a plurality of predetermined sampling time points in the current monitoring period.

[0060] In step 204, an environment risk of the target substation is identified based on a difference between the current environment data and a preset environment threshold, and an environment risk result of the target substation is obtained.

[0061] The preset environment threshold is determined according to historical environment data of the target substation in a preset historical monitoring period, and the preset historical monitoring period includes a monitoring period in which the target substation is in a device normal state before the current monitoring period.

[0062] The preset environment threshold of the embodiment of the present application can represent a normal fluctuation range or an acceptable ideal value of the environment parameter (such as temperature, humidity, electromagnetic field intensity and the like) of the target substation in the current monitoring period. The embodiment of the present application uses the historical monitoring period in which the device of the target substation is in a normal operation state before the current monitoring period, selects the historical environment data in the device normal state to determine the preset environment threshold, so that the preset environment threshold can truly reflect the adaptability of the device of the target substation to the environment in the healthy state.

[0063] Exemplarily, the server selects a historical monitoring period in which the device is in a normal operation state in a preset historical period, collects historical environment data, determines a preset environment threshold according to the historical environment data, compares the current environment data with the preset environment threshold, and quantifies the difference between the two through the difference between the two to represent the deviation degree of the current environment state from the normal safe state, so as to further identify the environment risk of the target substation in the current monitoring period. The server can quantify the difference through the absolute difference between the current environment data and the preset environment threshold, or the preset environment threshold can include a mean value calculated from the historical environment data and a standard deviation representing the dispersion degree, and the server can quantify the difference through the degree of deviation of the current environment data from the standard deviation.

[0064] In step 206, a device safety state of the target substation is determined based on the environment risk result and the current device operation data.

[0065] Exemplarily, after obtaining the environment risk result, the server comprehensively judges in combination with the current device running data to determine the device safety state of the target transformer substation. For example, the server can perform correlation analysis on the environment risk result and the current device running data through a preset logical rule, a decision tree model or a machine learning algorithm. For example, it can be set that when the environment temperature reaches a certain specific level and the internal temperature of a certain key device also exceeds its own threshold value, it is determined that the device safety state of the target transformer substation is abnormal.

[0066] The device safety monitoring method of the transformer substation described above, by obtaining the current environment data and the current device running data of the target transformer substation in the current monitoring period, identifying the environment risk of the target transformer substation based on the difference between the current environment data and the preset environment threshold value, obtaining the environment risk result of the target transformer substation, and determining the device safety state of the target transformer substation based on the environment risk result and the current device running data, wherein the preset environment threshold value is determined according to the historical environment data of the target transformer substation in a preset historical monitoring period, and the preset historical monitoring period includes the monitoring period in which the target transformer substation is in a device normal state before the current monitoring period. The embodiments of the present application can learn and determine the preset environment threshold value corresponding to the current monitoring period of the target transformer substation through the historical environment data of the target transformer substation in the past safe operation for different target transformer substations in different regions, so as to obtain the localized environment baseline specially for the target transformer substation in the current monitoring period. With the preset environment threshold value as a reference, the deviation degree of the current environment data deviating from the preset environment threshold value is monitored, so that the risk of the current environment state of the target transformer substation is more accurately and reasonably identified, avoiding the use of a unified standard to measure transformer substations in different regions, which can more truly reflect the real state of the device of the target transformer substation, realizes the adaptation to different regional environments and device characteristics, and improves the monitoring accuracy of the device safety monitoring of the transformer substation.

[0067] In actual application, the current environment data obtained by the server can include environment data corresponding to different environment parameter types respectively, and correspondingly, the preset environment threshold value can include environment threshold values corresponding to different environment parameter types respectively. When identifying the environment risk, the server can compare the current environment data with the preset environment threshold value corresponding thereto for different environment parameter types respectively, so as to obtain environment risk identification results corresponding to different environment parameter types respectively. Alternatively, the server can also fuse the differences between the environment data and the environment threshold values for different environment parameter types, so as to obtain a comprehensive environment risk result of the target transformer substation.

[0068] In one exemplary embodiment, as shown in Figure 3 Step 204 includes steps 302 to 306. Among them:

[0069] At step 302, for each type of environmental parameter, the parameter risk data corresponding to the type of environmental parameter is determined based on the difference between the current environmental data corresponding to the type of environmental parameter and the preset environmental threshold corresponding to the type of environmental parameter.

[0070] For example, the server quantifies the risk degree of a specific parameter by comparing the current environmental data of each type of environmental parameter with the preset environmental threshold corresponding thereto, thereby obtaining the parameter risk data corresponding to each type of environmental parameter.

[0071] For example, the preset environmental temperature threshold can include the mean of historical environmental temperature data, and the preset environmental humidity threshold can include the mean of historical environmental humidity data. The server calculates the absolute difference between the current environmental temperature data of the current monitoring period and the preset environmental temperature threshold, thereby obtaining the first parameter risk data corresponding to the environmental temperature parameter. The server calculates the absolute difference between the current humidity data of the current monitoring period and the preset environmental humidity threshold, thereby obtaining the second parameter risk data corresponding to the environmental humidity parameter.

[0072] For another example, the preset environmental temperature threshold can include the mean and the standard deviation of historical environmental temperature data, and the preset environmental humidity threshold can include the mean and the standard deviation of historical environmental humidity data. In order to standardize the difference, the server can calculate the absolute difference between the current environmental temperature data of the current monitoring period and the mean of historical environmental temperature data, and then divide the absolute difference by the standard deviation of historical environmental temperature data, thereby obtaining the first parameter risk data. The server can calculate the absolute difference between the current environmental humidity data of the current monitoring period and the mean of historical environmental humidity data, and then divide the absolute difference by the standard deviation of historical environmental humidity data, thereby obtaining the second parameter risk data.

[0073] In other embodiments, the server can use a normalization method to standardize the difference between the current environmental data and the preset environmental threshold corresponding to each type of environmental parameter.

[0074] At step 304, the environmental risk representation value of the target substation is determined by combining the plurality of parameter risk data.

[0075] For example, after obtaining the parameter risk data corresponding to each type of environmental parameter, the server fuses the plurality of parameter risk data, thereby determining a single numerical value or a comprehensive result that can reflect the overall environmental risk of the target substation, i.e., as the environmental risk representation value.

[0076] At step 306, the environmental risk representation value is compared with the preset environmental risk threshold, and the environmental risk result of the target substation is obtained according to the comparison result.

[0077] Exemplarily, if the environmental risk representation value is greater than the preset environmental risk threshold value, it is determined that the target transformer substation is in an environmental high risk. If the environmental risk representation value is less than or equal to the preset environmental risk threshold value, it is determined that the target transformer substation is in an environmental low risk.

[0078] In this embodiment, by calculating a plurality of parameter risk data respectively corresponding to a plurality of environmental parameter types, and combining the plurality of parameter risk data to determine the environmental risk representation value of the target transformer substation, the potential influence of the plurality of environmental parameters on the transformer substation equipment can be quantified from multiple dimensions, and a more accurate and comprehensive environmental risk representation is provided.

[0079] It should be noted that the implementation of step 304 is not unique. Exemplarily, the server can set different weights for different environmental parameter types according to the importance of the influence of each environmental parameter type on the safety of the transformer substation equipment, perform weighted operation on the parameter risk data and the corresponding weight to obtain the environmental risk representation value.

[0080] Alternatively, the server can also use a machine learning model, such as a model based on fuzzy logic or neural network, to input all the parameter risk data as the input of the model, and output the environmental risk representation value through a preset logical rule or a trained model.

[0081] In one possible implementation, as shown in Figure 4 Step 304 can include steps 402 to 406. Among them:

[0082] Step 402, obtaining the parameter fluctuation value of the parameter risk data collected in the current monitoring period.

[0083] The parameter fluctuation value can refer to the change amplitude, frequency or stability of the parameter risk data in the current monitoring period.

[0084] Exemplarily, the server can set a plurality of time sampling points in the current monitoring period to obtain the current environmental data at the plurality of time sampling points and the corresponding parameter risk data. The server determines the fluctuation of the parameter risk data corresponding to each environmental parameter type to obtain a plurality of parameter fluctuation values of a plurality of parameter risk data corresponding to a plurality of environmental parameter types.

[0085] Step 404, determining the contribution degree of the environmental parameter type to the environmental risk identification based on the parameter fluctuation value.

[0086] The parameter fluctuation value can be proportional to the contribution degree.

[0087] Step 406, combining the plurality of parameter risk data and the contribution degree of the plurality of parameter risk data respectively corresponding to the plurality of parameter risk data to obtain the environmental risk representation value of the target transformer substation.

[0088] Exemplarily, the server performs a weighted fusion operation on the plurality of parameter risk data and the contribution degrees respectively corresponding to the plurality of parameter risk data to obtain the environment risk representation value.

[0089] In this implementation, the contribution degrees of different environment parameter types in the calculation of the environment risk representation value are determined by the parameter fluctuation values of the parameter risk data, which can more flexibly cope with the dynamic changes of the environment parameters, thereby improving the accuracy and real-time performance of the environment risk representation value.

[0090] In another possible implementation, the server can also assign a weight of each environment parameter type in the calculation of the environment risk representation value according to the deviation degree of the parameter risk data corresponding to each environment parameter type from the preset environment threshold.

[0091] For example, the server divides the absolute difference between the current environment temperature data and the historical temperature mean by the standard deviation to obtain a deviation degree quantization value between the current environment temperature data and the preset environment temperature threshold. The server determines the weight corresponding to each environment parameter type according to the deviation degree quantization value corresponding to the different environment parameter types, where the deviation degree quantization value and the assigned weight can be proportional. Then, the server performs a weighted summation on the plurality of parameter risk data and the weights respectively corresponding thereto to obtain the environment risk representation value.

[0092] In this implementation, the weights are dynamically assigned according to the deviation degree represented by the parameter risk data, which can more accurately capture and quantify the environment factors that actually threaten the safety of the substation equipment, more sensitively respond to and highlight the most significant current environmental abnormalities, and ensure that the parameters with greater deviation between the current environment data and the preset environment threshold have higher influence in the final calculation of the environment risk representation value.

[0093] In another possible implementation, the server can also combine the parameter fluctuation value and the deviation degree in a linear combination manner to calculate the weight corresponding to each environment parameter type by using a preset constant weight for balancing the influence of the two factors, the parameter fluctuation value, and the deviation degree quantization value. Alternatively, the server can also fuse the two factors by using a nonlinear function, fuzzy logic, or a machine learning model, for example, when the deviation degree reaches a certain critical value, the weight of the fluctuation value will sharply increase to highlight the parameters that are unstable and highly abnormal.

[0094] In this implementation, since the parameter risk data can be currently not deviated much but fluctuate sharply, which indicates a potential out-of-control risk, or can be deviated much but relatively stable, which indicates a persistent abnormality, combining the two can more realistically evaluate the risk.

[0095] The following will illustrate the process of determining the equipment safety state of the target substation by combining the environment risk result and the current equipment operation data.

[0096] In some embodiments, the server can perform differentiated processing for different environmental risk results. When the environmental risk result indicates that the target substation is at high environmental risk, the risk characterization value of the equipment in the monitoring substation is monitored to see if there are sudden abnormalities; when the environmental risk result indicates that the target substation is at low environmental risk, the risk characterization value of the equipment in the monitoring substation is monitored to see if there are abnormal fluctuations.

[0097] In an exemplary embodiment, Figure 5 As shown, step 206 includes steps 502 to 504. Among them:

[0098] Step 502 : When the environmental risk result indicates that the difference between the current environmental data and the preset environmental threshold satisfies the preset risk condition, obtain the equipment risk characterization value corresponding to the current equipment operation data.

[0099] Among them, the preset risk condition can be used to indicate that the quantitative value of the difference between the current environmental data and the preset environmental threshold is greater than the preset environmental risk threshold, that is, if the preset risk condition is met, it can be determined that the target substation is at a high environmental risk; if the preset risk condition is not met, it can be determined that the target substation is at a low environmental risk.

[0100] The equipment risk characterization value is used to characterize the difference between the current equipment operation data and a preset equipment operation threshold. The preset equipment operation threshold can be determined based on the historical equipment operation data of the target substation within a preset historical monitoring period.

[0101] Exemplarily, the server determines device parameter risk data corresponding to each device operating parameter type by utilizing the difference between the current device operating data corresponding to the device operating parameter type and a preset device operating threshold value corresponding to the device operating parameter type. The server then combines the multiple device parameter risk data corresponding to the multiple device operating parameter types to determine an equipment risk representation value for the target substation.

[0102] Step 504: When the equipment risk characterization value is greater than or equal to the equipment risk threshold, it is determined that the target substation is in an equipment abnormality state.

[0103] Wherein, when the equipment risk characterization value is less than the equipment risk threshold, the server determines that the target substation is in a normal equipment state.

[0104] In this embodiment, the safety status of the equipment can be accurately observed by analyzing the equipment safety characterization value through the equipment operating parameters. If the environment is at a higher risk, the system focuses on the degree of deviation between the equipment risk characterization value and the preset ideal value to distinguish whether there is an abnormality, thereby accurately judging whether the temperature and humidity compensation component needs to be enabled.

[0105] In one exemplary embodiment, as shown in Figure 6 Step 206 further includes steps 602-604. Among them:

[0106] Step 602, in the case where the environmental risk result indicates that the difference between the current environmental data and the preset environmental threshold does not meet the preset risk condition, the device risk fluctuation value corresponding to the current device operation data is obtained.

[0107] Among them, the device risk fluctuation value is used to represent the fluctuation degree of the device risk representation value, and the device risk representation value is used to represent the difference between the current device operation data and the preset device operation threshold. The preset device operation threshold can be determined according to the historical device operation data of the target transformer substation in a preset historical monitoring period.

[0108] Exemplarily, the server can set multiple time sampling points in the current monitoring period to obtain the current device operation data of the multiple time sampling points and the corresponding device parameter risk data. The server determines the device risk representation value of the target transformer substation at the multiple time sampling points by combining the multiple device parameter risk data corresponding to the multiple device operation parameter types respectively at the multiple time sampling points. Then, the device risk fluctuation value corresponding to the current device operation data is obtained by the variance of the device risk representation value at the multiple time sampling points.

[0109] Step 604, in the case where the device risk fluctuation value is greater than or equal to the device fluctuation threshold, it is determined that the target transformer substation is in an abnormal device state.

[0110] Among them, the server determines that the target transformer substation is in a normal device state in the case where the device risk fluctuation value is less than the device fluctuation threshold.

[0111] In this embodiment, the device safety representation value is analyzed by the device operation parameter, which can accurately observe the safety condition of the device. If the environmental risk is low, the variance of the device safety representation value is focused on, and the volatility of the device safety representation value is controlled. Once the abnormal fluctuation is triggered, the temperature and humidity compensation component can be started immediately.

[0112] In one exemplary embodiment, a device safety monitoring method of a transformer substation is provided, which includes:

[0113] Step A1, the server obtains a preset environmental temperature threshold and a preset humidity threshold corresponding to a target transformer substation in a current monitoring period.

[0114] Among them, the period of the current monitoring period can be 2 hours.

[0115] For example, the server can collect historical environmental data in different regional substations in a historical period through a plurality of sensor components in the regional substations, the sensor components including a plurality of temperature sensors for collecting environmental temperature in the regional substations and a plurality of humidity sensors for collecting environmental humidity in the regional substations. The substations in different regions can include northern regional substations, central regional substations, and southern regional substations divided according to geographical regions. For a target substation in a certain region, the server collects historical environmental temperature data and historical environmental humidity data in a historical monitoring period in which the substation equipment is in safe operation in the past month, and a preset environmental temperature threshold includes the mean of the historical environmental temperature data, and a preset environmental humidity threshold includes the mean of the historical environmental humidity data.

[0116] Step A2, the server obtains current environmental temperature data and current environmental humidity data of the target substation in the current monitoring period, and determines an environmental risk representation value.

[0117] For example, the server calculates the absolute difference between the environmental temperature data of the current 2h and the mean of the historical environmental temperature data, and after standardization, determines the first parameter risk data. The absolute difference between the environmental humidity data of the current 2h and the mean of the historical environmental humidity data is calculated, and after standardization, the second parameter risk data is determined. The first parameter risk data and the second parameter risk data are weighted and summed to determine the environmental risk representation value.

[0118] Wherein, the weight can be calculated by the standard deviation of the parameter risk data, the weight is equal to the standard deviation divided by the sum of all parameter standard deviations, and the final representation value is generated by weighted summation based on the weight of the parameter risk data, and the representation value is equal to the sum of all feature parameter values multiplied by the corresponding weight. The environmental risk representation value is analyzed by the environmental feature parameters, and the weight proportion of temperature and humidity in risk assessment is automatically allocated according to the climate characteristics of the substation location. This dynamic adjustment ensures that the risk assessment of different regions is more in line with the actual needs, and avoids one-size-fits-all judgment.

[0119] Step A3, the server identifies the environmental risk of the target substation in the current monitoring period according to the environmental risk representation value.

[0120] For example, in the case where the environmental risk representation value is greater than the preset environmental risk threshold, it is determined that the target substation is in an environmental high risk in the current monitoring period, and in the case where the environmental risk representation value is less than or equal to the preset environmental risk threshold, it is determined that the target substation is in an environmental low risk in the current monitoring period.

[0121] For example, the absolute difference between the server computing environment risk characterization value and the preset environment risk threshold value is obtained as an environment risk characterization difference value; if the environment risk characterization difference value is less than or equal to a predetermined environment risk characterization difference threshold value, it is determined as an environment lower risk tendency label; if the environment risk characterization difference value is greater than the predetermined environment risk characterization difference threshold value, it is determined as an environment higher risk tendency label.

[0122] The preset environment risk threshold value can be the weighted sum of the mean of the parameter risk data of the substation in the safe operation state in the past 1 month, and the weight ratio can be 1:1.

[0123] The embodiment of the present application can accurately divide the risk tendency level by dividing the environment risk characterization value and the absolute value of the difference between the preset environment risk threshold value, reduce the risk analysis misjudgment, reduce the excessive alarm under the risk label, improve the rapid response under the risk label, effectively reduce the energy consumption of the temperature and humidity compensation component, improve the accuracy of the operation of the temperature and humidity compensation component, and enhance the equipment safety.

[0124] Step A4, the server obtains the preset transformer temperature threshold value and the preset circuit breaker temperature threshold value corresponding to the target substation in the current monitoring period.

[0125] For example, the server can collect historical equipment operation data in different regional substations in the historical period through a plurality of contact temperature measurement components in the regional substations, and the contact temperature measurement components include a plurality of thermometer sleeves for collecting transformer temperatures and circuit breaker temperatures in the regional substations. For the target substation, the server collects historical transformer temperature data and historical circuit breaker temperature data in the historical monitoring period in which the substation equipment is in a safe operation state in the past month, the preset transformer temperature threshold value includes the mean of the historical transformer temperature data, and the preset circuit breaker temperature threshold value includes the mean of the historical circuit breaker temperature data.

[0126] Step A5, the server obtains the current transformer temperature data and the current circuit breaker temperature data of the target substation in the current monitoring period, and determines the equipment risk characterization value.

[0127] For example, the server calculates the absolute difference between the transformer temperature data in the current 2h and the mean of the historical transformer temperature data, and after standardization, determines the first equipment parameter risk data. The absolute difference between the circuit breaker temperature data in the current 2h and the mean of the historical circuit breaker temperature data is calculated, and after standardization, the second equipment parameter risk data is determined. The first equipment parameter risk data and the second equipment parameter risk data are summed to determine the equipment risk characterization value.

[0128] Step A6, if the target substation is in a high-risk environment in the current monitoring period, determine that the target substation is in an equipment abnormal state if the equipment risk characterization value is greater than or equal to the equipment risk threshold value.

[0129] For example, if the environment is a high-risk tendency label, the server can calculate the difference between the equipment safety characterization value and the predetermined equipment safety characterization value threshold to determine the equipment safety difference value. If the equipment safety difference value is greater than or equal to the predetermined equipment safety difference threshold, it is determined that there is an anomaly, to determine whether to enable the temperature and humidity compensation component. The equipment safety characterization value threshold is obtained in advance and is set between [0, 20].

[0130] The temperature control in the temperature and humidity compensation component can use a cabinet-type explosion-proof air conditioner, model BFKG-280R or BFKG-16, and the humidity control can use a condensation dehumidification device, model YNEN-CS3-60T.

[0131] Step A7, if the target substation is in a low-risk environment in the current monitoring period, determine that the target substation is in an equipment abnormal state if the fluctuation degree quantization value of the equipment risk characterization value is greater than or equal to the equipment fluctuation threshold value.

[0132] For example, if the environment is a low-risk tendency label, the server can calculate the variance of the equipment safety characterization value, and if the variance of the equipment safety characterization value is greater than or equal to the variance threshold, it is determined that there is an anomaly. The equipment safety characterization value variance threshold is obtained in advance, and specifically, it can be the mean of the equipment safety characterization value variance within 1 month of the equipment safety operation period.

[0133] The embodiment has the following beneficial effects:

[0134] (1) For the problems of low precision, high energy consumption, and high risk of equipment damage of the temperature and humidity compensation component caused by differences in regional environment in substation equipment safety management, through the collaborative work of multiple modules, accurate environmental risk identification, dynamic compensation strategy adjustment, and equipment abnormality early warning are achieved, significantly improving the safety and economy of substation operation.

[0135] (2) Collecting environmental characteristic parameters and equipment operation parameters in different regional substations in historical periods to obtain real-time environmental temperature and humidity data of different regional substations. Collecting historical data records long-term environmental change trends and builds a dedicated environment characteristic for each substation to provide regional characteristic labels for subsequent analysis.

[0136] (3) Analyzing the environmental risk characterization value based on the environmental characteristic parameters, and automatically assigning the weight proportion of temperature and humidity in risk assessment according to the climate characteristics of the substation location. This dynamic adjustment ensures that risk assessment in different regions is more in line with actual needs and avoids one-size-fits-all judgments.

[0137] (4) The environmental risk tendency label is divided based on the absolute value of the difference between the environmental risk characterization value and the predetermined environmental risk characterization value threshold. The risk tendency level can be accurately divided, the risk analysis misjudgment situation can be reduced, the over-alarm under the risk label can be reduced, the rapid response under the high-risk label can be achieved, the energy consumption of the temperature and humidity compensation component can be effectively reduced, the accuracy of the temperature and humidity compensation component operation can be improved, and the equipment safety can be enhanced.

[0138] In particular, by analyzing the equipment safety characterization value through the device operation parameter, the safety condition of the device can be accurately observed. If the environmental risk tendency label is high, the system focuses on the difference of the equipment safety characterization value to distinguish whether there is an abnormality, so as to accurately judge whether the temperature and humidity compensation component needs to be enabled. If the environmental risk tendency label is low, the variance of the equipment safety characterization value is observed, and the volatility of the equipment safety characterization value is controlled. Once the abnormal fluctuation is triggered, the temperature and humidity compensation component can be started immediately. When the real-time data breaks through any threshold, the analysis is triggered immediately, which prevents sudden abnormalities and identifies progressive degradation.

[0139] It should be understood that, although each step in the flowchart involved in each of the above-described embodiments is shown in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each of the above-described embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or stages or steps or stages in other steps.

[0140] It can be understood that the term "based on" used in the present application is used to describe one or more factors that affect the determination, and does not exclude other factors that can affect the determination. For example, the phrase "determining A based on B" means that the determination of A can be based entirely or at least partially on factor B, that is, B is one factor that affects the determination of A, but does not exclude that the determination of A is also based on C.

[0141] Based on the same inventive concept, the embodiments of the present application also provide a substation equipment safety monitoring system for implementing the above-mentioned substation equipment safety monitoring method. The problem-solving implementation scheme provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more substation equipment safety monitoring system embodiments provided below can be referred to the limitations of the substation equipment safety monitoring method described above, which will not be repeated here.

[0142] In one example embodiment, as shown in FIG. 7, there is provided a device safety monitoring system for a substation, comprising: an acquisition data module 702, an environment risk module 704, and a safety state module 706, wherein: Figure 7

[0143] The acquisition data module 702 is configured to acquire current environment data and current device operation data of the target substation in a current monitoring period.

[0144] The environment risk module 704 is configured to identify an environment risk of the target substation based on a difference between the current environment data and a preset environment threshold, to obtain an environment risk result of the target substation.

[0145] The safety state module 706 is configured to determine a device safety state of the target substation based on the environment risk result and the current device operation data.

[0146] In one embodiment, the environment risk module 704 comprises a parameter risk unit, a risk characterization unit, and a risk result unit.

[0147] The parameter risk unit is configured to, for each type of environment parameter, determine parameter risk data corresponding to the type of environment parameter based on a difference between current environment data corresponding to the type of environment parameter and a preset environment threshold corresponding to the type of environment parameter.

[0148] The risk characterization unit is configured to combine a plurality of parameter risk data to determine an environment risk characterization value of the target substation.

[0149] The risk result unit is configured to compare the environment risk characterization value with a preset environment risk threshold, and obtain an environment risk result of the target substation according to a comparison result.

[0150] In one embodiment, the risk characterization unit, when performing the operation of combining a plurality of parameter risk data to determine an environment risk characterization value of the target substation, is configured to: acquire a parameter fluctuation value of the parameter risk data collected in the current monitoring period; determine a contribution degree of the type of environment parameter to the identification of the environment risk based on the parameter fluctuation value; and combine the plurality of parameter risk data and the contribution degree corresponding to each of the plurality of parameter risk data to obtain the environment risk characterization value of the target substation.

[0151] ​In an embodiment, the security state module 706, when determining the equipment security state of the target transformer substation based on the environment risk result and the current equipment operation data, is configured to: in a case where the environment risk result indicates that the difference between the current environment data and the preset environment threshold satisfies a preset risk condition, obtain an equipment risk representation value corresponding to the current equipment operation data; the equipment risk representation value is used to represent the difference between the current equipment operation data and a preset equipment operation threshold; and in a case where the equipment risk representation value is greater than or equal to an equipment risk threshold, determine that the target transformer substation is in an equipment abnormal state.

[0152] In an embodiment, the security state module 706, when determining the equipment security state of the target transformer substation based on the environment risk result and the current equipment operation data, is further configured to: in a case where the environment risk result indicates that the difference between the current environment data and the preset environment threshold does not satisfy the preset risk condition, obtain an equipment risk fluctuation value corresponding to the current equipment operation data; the equipment risk fluctuation value is used to represent a fluctuation degree of the equipment risk representation value; the equipment risk representation value is used to represent the difference between the current equipment operation data and a preset equipment operation threshold; and in a case where the equipment risk fluctuation value is greater than or equal to an equipment fluctuation threshold, determine that the target transformer substation is in an equipment abnormal state.

[0153] In an embodiment, the preset equipment operation threshold is determined according to historical equipment operation data of the target transformer substation in a preset historical monitoring period.

[0154] The above-mentioned various modules in the equipment security monitoring system of the transformer substation can be realized by software, hardware and combinations thereof in whole or in part. The above-mentioned various modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to the above-mentioned various modules.

[0155] In an exemplary embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in FIG. 8. Figure 8As shown in the figure. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the environmental data and the equipment operation data of the target substation. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with the terminal outside through network connection. The computer program is executed by the processor to realize a substation equipment safety monitoring method.

[0156] Those skilled in the art can understand that, Figure 8 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0157] In one exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the substation equipment safety monitoring method provided by the embodiments of the present application.

[0158] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by the processor to realize the substation equipment safety monitoring method provided by the embodiments of the present application.

[0159] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by the processor to realize the substation equipment safety monitoring method provided by the embodiments of the present application.

[0160] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0161] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0162] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0163] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method of equipment safety monitoring of a substation, characterized by, The method comprises: obtaining current environment data and current equipment operation data of a target transformer substation in a current monitoring period; based on the difference between the current environment data and the preset environment threshold, identifying the environmental risk of the target transformer substation, obtaining the environmental risk result of the target transformer substation; the preset environment threshold is determined according to the historical environment data of the target transformer substation in a preset historical monitoring period; the preset historical monitoring period includes the monitoring period of the target transformer substation in the equipment normal state before the current monitoring period; based on the environmental risk result and the current equipment operation data, determining the equipment safety state of the target transformer substation.

2. The method of claim 1, wherein, The method comprises: for each type of environmental parameter, based on the difference between the current environment data corresponding to the environmental parameter type and the preset environment threshold corresponding to the environmental parameter type, determine the parameter risk data corresponding to the environmental parameter type, to obtain a plurality of parameter risk data corresponding to a plurality of environmental parameter types; combining a plurality of parameter risk data, determining the environmental risk representation value of the target transformer substation; comparing the environmental risk representation value with the preset environmental risk threshold, and obtaining the environmental risk result of the target transformer substation according to the comparison result.

3. The method of claim 2, wherein, The method comprises: obtaining the parameter fluctuation value of the parameter risk data collected in the current monitoring period; based on the parameter fluctuation value, determining the contribution degree of the environmental parameter type to environmental risk identification; the parameter fluctuation value is proportional to the contribution degree; combining a plurality of parameter risk data and a plurality of contribution degrees corresponding to a plurality of parameter risk data, obtaining the environmental risk representation value of the target transformer substation.

4. The method of claim 1, wherein, The method comprises: in the case that the difference between the current environment data and the preset environment threshold satisfies the preset risk condition, obtaining the equipment risk representation value corresponding to the current equipment operation data; the equipment risk representation value is used to represent the difference between the current equipment operation data and the preset equipment operation threshold; in the case that the equipment risk representation value is greater than or equal to the equipment risk threshold, determining that the target transformer substation is in an equipment abnormal state.

5. The method of claim 1, wherein, The method comprises: In a case where the environmental risk result indicates that the difference between the current environmental data and the preset environmental threshold does not satisfy a preset risk condition, an equipment risk fluctuation value corresponding to the current equipment operation data is obtained; the equipment risk fluctuation value is used to represent a fluctuation degree of an equipment risk representation value; and the equipment risk representation value is used to represent a difference between the current equipment operation data and a preset equipment operation threshold. In a case where the equipment risk fluctuation value is greater than or equal to an equipment fluctuation threshold, it is determined that the target transformer substation is in an equipment abnormal state.

6. The method of claim 4 or claim 5, wherein, The preset equipment operation threshold is determined according to historical equipment operation data of the target transformer substation in a preset historical monitoring period.

7. An equipment safety monitoring system for a substation, characterized by The system comprises: an acquisition data module configured to acquire current environmental data and current equipment operation data of a target transformer substation in a current monitoring period; an environmental risk module configured to identify an environmental risk of the target transformer substation based on a difference between the current environmental data and a preset environmental threshold, and obtain an environmental risk result of the target transformer substation; the preset environmental threshold is determined according to historical environmental data of the target transformer substation in a preset historical monitoring period; and the preset historical monitoring period comprises monitoring periods in which the target transformer substation is in an equipment normal state before the current monitoring period; a safety state module configured to determine an equipment safety state of the target transformer substation based on the environmental risk result and the current equipment operation data.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 6.

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