Auxiliary control subsystem of intelligent auxiliary equipment monitoring system for substation

Abstract

The present invention proposes an auxiliary control subsystem of a monitoring system for intelligent auxiliary equipment of a substation, wherein the system is provided with a data acquisition module, a data processing center, an intelligent control module, a remote control module, a display and alarm module and an authority management module; when the system is running, the data of the substation is collected by the data acquisition module, and then transmitted to the data processing center for analysis and processing, and a signal is sent to the intelligent control module and the display and alarm module when abnormal information is monitored, and the intelligent control module automatically issues a control instruction according to a preset control strategy after receiving the information; the display and alarm module displays the abnormal situation in a striking manner on the main interface of the monitoring system and issues an audible and visual alarm after receiving the information; after the management personnel receive the corresponding information, if manual intervention is required, the intelligent module is operated through the remote control module, and the authority issues involved in the operation process are authorized and authenticated by the authority management module; the system is efficient and convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer substation monitoring systems, and in particular to an auxiliary control subsystem of a transformer substation intelligent auxiliary equipment monitoring system. Background Art

[0002] With the development of intelligent power system, the monitoring equipment required for various equipment in existing substations is constantly increasing. The existing monitoring system of substation auxiliary equipment can no longer meet the growing demand. The existing monitoring is no longer limited to traditional partial equipment and simple parameters, but all relevant monitoring quantities such as environment, video, fire protection, heating and ventilation, lighting, SF6, security, access control, etc. are included in the integrated monitoring system. Through a variety of advanced sensors and monitoring technologies, comprehensive, real-time and accurate monitoring of the operating status of various auxiliary equipment in the substation is achieved without any omissions and blind spots; more monitoring technologies and monitoring equipment are applied to the substation monitoring system, making the substation monitoring system run slowly, prone to failures and inaccurate monitoring due to excessive load, and the control method has also undergone great changes, abandoning the backward mode of relying entirely on manual operation; more reliance on the use of intelligent algorithms and automatic control technologies to automatically adjust the operation of auxiliary equipment according to environmental monitoring data. For example, when the temperature exceeds the set threshold, the system will automatically start the ventilation equipment to cool down; when the light is insufficient, the lighting equipment will be automatically brightened; the demand for remote control convenience has also increased, allowing operation and maintenance personnel to access the network far away from the substation Real-time monitoring and operation of auxiliary equipment can improve the flexibility and efficiency of control. For this reason, the existing system cannot meet the monitoring needs of substations, and the development of new systems requires too high a construction cost and a long transformation time. Therefore, the applicant proposes a substation intelligent auxiliary equipment monitoring system auxiliary control subsystem based on the needs of substation monitoring. Through the system's powerful data analysis capabilities, it can deeply mine and analyze the massive monitoring data collected to extract valuable information. The system has good compatibility and scalability, and adopts an open architecture design. It supports the access of auxiliary equipment from different manufacturers and different types, breaking the information island. When new monitoring or control functions need to be added, the system can be quickly and conveniently upgraded and expanded without large-scale hardware modification and software rewriting.

[0003] Finally, in terms of security, strict permission management and encryption technology are used to ensure that only authorized personnel can operate and access, thus ensuring the security and stability of the system. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes an auxiliary control subsystem of a substation intelligent auxiliary equipment monitoring system, which is provided with a data acquisition module, a data processing center, an intelligent control module, a remote control module, a display and alarm module and a permission management module; when the system is running, the data of the substation is collected by the data acquisition module, and then transmitted to the data processing center for analysis and processing, and when abnormal information is detected, a signal is sent to the intelligent control module and the display and alarm module, and the intelligent control module automatically issues a control instruction according to a preset control strategy after receiving the information; after receiving the information, the display and alarm module displays the abnormal situation in a striking manner on the main interface of the monitoring system and issues an audible and visual alarm; after the management personnel receive the corresponding information, when human intervention is required, the intelligent module is operated through the remote control module, which is efficient and convenient.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] An auxiliary control subsystem of a substation intelligent auxiliary equipment monitoring system includes a data acquisition module, a data processing center, an intelligent control module, a display and alarm module, a remote control module and a permission management module, and is characterized in that: the data acquisition module of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system collects environmental data and operating status data of auxiliary equipment in the substation in real time, and transmits the collected data to the data processing center in a wired or wireless manner; the data processing center is installed on-site in the monitoring area, and uses advanced algorithms to quickly analyze and process, compares the received data with a preset normal range, and determines whether there is an abnormality. When the data is normal, it continues to receive and process subsequent data; when the data is abnormal, it transmits the abnormal information to the intelligent control module and the display and alarm module; the intelligent After receiving the abnormal information, the control module automatically issues control instructions according to the preset control strategy; after receiving the abnormal information, the display and alarm module will display the abnormal situation in a striking manner on the main interface of the substation monitoring system center, and issue an audible and visual alarm to remind the operation and maintenance personnel to pay attention and deal with it in time; the remote control module enables the operation and maintenance personnel to obtain monitoring data and send control instructions in real time through the network at a place far away from the substation; the operation and maintenance personnel can log in to the remote control system through a dedicated software application or web interface; after logging in, they can see the same monitoring interface as the substation site, and display various data and equipment status in real time; the authority management module always plays a role in the whole process, ensuring that only personnel with corresponding authority can operate and view specific data, so as to ensure the security and stability of the system.

[0007] Furthermore, the working steps of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system are specifically as follows:

[0008] Step 1: Data collection: The data collection module collects data from various sensors distributed in the substation, such as temperature, humidity, light and wind speed, etc. It also collects data on the operating status of the monitoring equipment, such as current, voltage and electromagnetic field strength, etc. The collected data is then transmitted to the data processing center by wireless or wired means.

[0009] Step 2: Data processing and analysis: The data processing center is responsible for receiving and processing a large amount of data from the data acquisition module. It uses high-performance servers and advanced data analysis algorithms to first clean and preprocess the collected massive monitoring data to remove noise and outliers, and then use data analysis algorithms, cluster analysis, regression analysis and association rule mining to conduct in-depth data mining; through long-term monitoring and analysis of equipment operation data, a health model for the equipment is constructed. When the operating parameters of the equipment deviate abnormally from the health model, the system can issue a fault warning in a timely manner; when an abnormality is found, the signal is sent to the intelligent control module and the display and alarm;

[0010] Step 3, abnormality handling and response; after receiving the abnormal signal, the intelligent control module automatically issues a control instruction according to the preset control strategy to control the linkage equipment to adjust and handle the abnormal situation. During the processing, the intelligent control module adopts the intelligent control technology algorithm to realize the adjustment and control of the linkage equipment;

[0011] Step 4: Display and alarm: After receiving the abnormal information, the display and alarm module displays the abnormal situation in a striking manner on the main interface of the substation monitoring system center, and issues an audible and visual alarm to remind the operation and maintenance personnel to pay attention and handle it in time;

[0012] Step 5: Operation control and emergency response. After receiving the alarm signal, the substation control center manager logs in to the remote control system through the remote control module using a dedicated software application or web interface. After logging in, you can see the same monitoring interface as the substation site, which displays various data and equipment status in real time. If the intelligent control module processing is found to be abnormal, the corresponding equipment can be controlled through human operation to achieve the purpose of quickly handling the abnormality. If the remote control processing requires corresponding permissions, the permission management module will perform authorization and verification to ensure that only personnel with corresponding permissions can operate and view specific data, thereby ensuring the security and stability of the system.

[0013] Furthermore, the analysis algorithm in the data processing analysis of step 2 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system is specifically:

[0014] Recursively divide the data based on the features, build a decision tree model, and use the information entropy calculation formula and information benefit calculation formula to select the optimal division features;

[0015] Information entropy calculation formula:

[0016]

[0017] Where: p(x i ) is the category x i Probability of occurrence;

[0018] Information gain calculation formula:

[0019]

[0020] Where: S is the data set;

[0021] A is a feature;

[0022] Sv is a subset divided based on the value v of feature A.

[0023] Furthermore, the cluster analysis in the data processing analysis of step 2 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system is specifically as follows:

[0024] Cluster analysis was processed using K-Means clustering and hierarchical clustering;

[0025] K-Means clustering: randomly select K points as the initial cluster centers, then assign the data points to the nearest clusters based on their distances from the cluster centers, recalculate the cluster centers, and repeat this process until the cluster centers no longer change;

[0026] Hierarchical clustering: by calculating the distance between data points, clusters are gradually merged or split to form a hierarchical structure;

[0027] The calculation formula uses Euclidean distance to calculate the distance between data points:

[0028]

[0029] Where: x i and i is the i-th eigenvalue of data points x and y.

[0030] Furthermore, the regression analysis in the data processing analysis of step 2 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system is specifically as follows:

[0031] Regression analysis was processed using linear regression and polynomial regression;

[0032] Linear regression: Assuming a linear relationship between variables, a straight line is fitted using the least squares method;

[0033] Polynomial regression; consider the nonlinear relationship between variables and use polynomial functions for fitting;

[0034] Calculation formula:

[0035] y=β0+β 1x

[0036] Where: β0 is the intercept; the calculation formula of intercept β0 is:

[0037]

[0038] β1 is the slope;

[0039] The calculation formula of slope β1 is:

[0040]

[0041] and are the means of x and y respectively;

[0042] Furthermore, the association rule mining in the data processing analysis of step 2 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system is specifically as follows:

[0043] By scanning the data set multiple times, frequent item sets are found, and then association rules are generated based on the frequent item sets, using support calculation formulas and confidence calculation formulas;

[0044] Support calculation formula:

[0045]

[0046] Confidence calculation formula:

[0047]

[0048] Furthermore, the intelligent control technology and algorithm of step 3 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system are specifically as follows:

[0049] 1) Data collection: Select the appropriate sensor type according to the monitoring requirements, and determine the appropriate sampling frequency based on the system's requirements for response speed and data accuracy;

[0050] 2) Data preprocessing: using statistical methods, the 3σ principle and the boxplot method to detect outliers; and using the moving average method and the median filter method to smooth the data and reduce noise interference;

[0051] 3) Control model establishment;

[0052] Temperature Control Model:

[0053] Let the temperature threshold be Tth , the real-time collected temperature is T real , the cooling efficiency of the ventilation equipment is k (unit: ℃ / min), and the natural temperature rise rate of the environment is r (unit: ℃ / min); when T real >T th When the required ventilation time t is , it is calculated by the following formula:

[0054]

[0055] Lighting control model:

[0056] Assume the expected light intensity is L desired , the real-time light intensity is L real , the brightness adjustment coefficient of the lighting equipment is α, then the brightness ΔL that needs to be increased is: ΔL=L desired -L real , the corresponding adjustment amount x is:

[0057] 4) Decision making;

[0058] Use fuzzy logic or threshold judgment methods to make decisions; in temperature control, you can define fuzzy sets "high temperature", "moderate temperature", "low temperature", and decide the action of the ventilation equipment according to the fuzzy set to which the real-time temperature belongs;

[0059] 5) Execute control actions;

[0060] Use digital signal control or analog signal control to achieve precise adjustment of equipment;

[0061] 6) Feedback and Optimization

[0062] Use methods such as Kalman filtering or particle filtering to process and fuse feedback data to more accurately evaluate the control effect; use adaptive control algorithms, model reference adaptive control or self-correcting control to continuously adjust control parameters based on feedback;

[0063] 7) Dynamic adjustment and learning;

[0064] The reinforcement learning algorithm in machine learning, Q-learning or SARSA, is used to optimize future control strategies based on historical control decisions and feedback results.

[0065] The benefits of this application are:

[0066] 1. The auxiliary control subsystem of the intelligent auxiliary equipment monitoring system of the substation can comprehensively monitor and intelligently control the operation of the substation equipment, greatly improving the operation efficiency of the substation; it can accurately grasp the equipment operation status and environmental parameters in real time, and can promptly discover and solve potential problems, reducing power outages caused by equipment failures or environmental abnormalities, and ensuring a stable supply of electricity;

[0067] 2. The auxiliary control subsystem of the intelligent auxiliary equipment monitoring system of the substation can automatically adjust the operation of the auxiliary equipment according to the environmental monitoring data, avoiding unnecessary energy waste, reducing energy consumption, meeting the requirements of sustainable development, and saving energy and protecting the environment;

[0068] 3. The auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system can remotely control and intelligently control, which reduces the on-site workload of operation and maintenance personnel and reduces labor costs; at the same time, through fault prediction and advance maintenance, it reduces the cost of equipment maintenance and replacement, effectively reducing operation and maintenance costs;

[0069] 4. The auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system can timely detect and handle equipment failures and environmental abnormalities, effectively prevent the occurrence of safety accidents such as fire and equipment damage, ensure the safety of personnel and equipment, and improve safety;

[0070] 5. The auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system can adapt to the access of auxiliary equipment of different manufacturers and types and the addition of new functions, which prolongs the service life of the system, reduces the cost of system replacement, and enhances the compatibility and scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic diagram of the framework structure of the present invention;

[0072] Figure 2 It is a schematic diagram of the working process of the present invention;

[0073] Figure 3 It is a schematic diagram of the overall structure of the present invention;

[0074] Figure 4 It is a schematic diagram of the network topology structure of the present invention. DETAILED DESCRIPTION

[0075] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0076] like Figure 1-4 As shown, an auxiliary control subsystem of a substation intelligent auxiliary equipment monitoring system is shown, including a data acquisition module, a data processing center, an intelligent control module, a display and alarm module, a remote control module and a permission management module, such as Figure 1As shown, the data acquisition module of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system collects the environmental data in the substation and the operating status data of the auxiliary equipment in real time. The collected data include temperature, humidity, light, wind speed, etc., as well as the operating status data of the auxiliary equipment, such as current, voltage, etc., and transmits the collected data to the data processing center in a continuous manner through wired or wireless means; the data processing center is installed and monitored on-site in the area, and uses advanced algorithms to quickly analyze and process, compare the received data with the preset normal range to determine whether there is an abnormality. When the data is normal, it continues to receive and process subsequent data; when the data is abnormal, the abnormal information is transmitted to the intelligent control module and the display and alarm module; after receiving the abnormal information, the intelligent control module automatically issues control instructions according to the preset control strategy. For example, when the temperature is too high, the ventilation equipment will be started to cool down; when the current is unstable, the operating parameters of the relevant equipment may be adjusted; the display and alarm module After receiving abnormal information, the alarm module will display the abnormal situation in a striking manner on the main interface of the substation monitoring system center, and issue an audible and visual alarm to remind the operation and maintenance personnel to pay attention and deal with it in time; the remote control module shown in the figure enables the operation and maintenance personnel to obtain monitoring data and send control instructions in real time through the network at a place far away from the substation. These instructions are transmitted to the control module in the substation through an encrypted network channel to realize remote operation of the equipment; the operation and maintenance personnel can log in to the remote control system through a dedicated software application or a web page interface; after logging in, they can see the same monitoring interface as the substation site, display various data and equipment status in real time, and operate the equipment. For example, the operation and maintenance personnel can remotely start or turn off lighting equipment, adjust the wind speed of ventilation equipment, adjust the temperature of heating equipment, etc.; the permission management module shown in the figure always plays a role in the whole process, ensuring that only personnel with corresponding permissions can operate and view specific data, so as to ensure the security and stability of the system.

[0077] like Figure 2 As shown, the working steps of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system are as follows:

[0078] Step 1: Data collection: The data collection module collects data from various sensors distributed in the substation, such as temperature, humidity, light and wind speed, etc. It also collects data on the operating status of the monitoring equipment, such as current, voltage and electromagnetic field strength, etc. The collected data is then transmitted to the data processing center by wireless or wired means.

[0079] Step 2: Data processing and analysis: The data processing center is responsible for receiving and processing a large amount of data from the data acquisition module. It uses high-performance servers and advanced data analysis algorithms to first clean and preprocess the collected massive monitoring data to remove noise and outliers, and then use data analysis algorithms, cluster analysis, regression analysis and association rule mining to conduct in-depth data mining; through long-term monitoring and analysis of equipment operation data, a health model for the equipment is constructed. When the operating parameters of the equipment deviate abnormally from the health model, the system can issue a fault warning in a timely manner; when an abnormality is found, the signal is sent to the intelligent control module and the display and alarm;

[0080] As shown, the data is recursively divided based on the features, a decision tree model is constructed, and the information entropy calculation formula and the information benefit calculation formula are used to select the optimal division features;

[0081] Information entropy calculation formula:

[0082]

[0083] Where: p(x i ) is the category x i Probability of occurrence;

[0084] Information gain calculation formula:

[0085]

[0086] Where: S is the data set;

[0087] A is a feature;

[0088] Sv is a subset divided based on the value v of feature A;

[0089] The cluster analysis shown was processed using K-Means clustering and hierarchical clustering;

[0090] K-Means clustering: randomly select K points as the initial cluster centers, then assign the data points to the nearest clusters based on their distances from the cluster centers, recalculate the cluster centers, and repeat this process until the cluster centers no longer change;

[0091] Hierarchical clustering: by calculating the distance between data points, clusters are gradually merged or split to form a hierarchical structure;

[0092] The calculation formula uses Euclidean distance to calculate the distance between data points:

[0093]

[0094] Where: x i and iis the i-th eigenvalue of data points x and y;

[0095] The regression analyses shown were processed using linear and polynomial regression;

[0096] Linear regression: Assuming a linear relationship between variables, a straight line is fitted using the least squares method;

[0097] Polynomial regression; consider the nonlinear relationship between variables and use polynomial functions for fitting;

[0098] Calculation formula:

[0099] y=β0+β 1x

[0100] Where: β0 is the intercept; the calculation formula of intercept β0 is:

[0101]

[0102] β1 is the slope;

[0103] The calculation formula of slope β1 is:

[0104]

[0105] and are the means of x and y respectively;

[0106] The specific association rule mining shown is as follows:

[0107] By scanning the data set multiple times, frequent item sets are found, and then association rules are generated based on the frequent item sets, using support calculation formulas and confidence calculation formulas;

[0108] Support calculation formula:

[0109]

[0110] Confidence calculation formula:

[0111]

[0112] Step 3, abnormality handling and response; after receiving the abnormal signal, the intelligent control module automatically issues a control instruction according to the preset control strategy to control the linkage equipment to adjust and handle the abnormal situation. During the processing, the intelligent control module adopts the intelligent control technology algorithm to realize the adjustment and control of the linkage equipment;

[0113] The intelligent control technology and algorithm shown are:

[0114] 1) Data collection: Select the appropriate sensor type according to the monitoring requirements, and determine the appropriate sampling frequency based on the system's requirements for response speed and data accuracy;

[0115] 2) Data preprocessing: using statistical methods, the 3σ principle and the boxplot method to detect outliers; and using the moving average method and the median filter method to smooth the data and reduce noise interference;

[0116] 3) Control model establishment;

[0117] Temperature Control Model:

[0118] Let the temperature threshold be T th , the real-time collected temperature is T real , the cooling efficiency of the ventilation equipment is k (unit: ℃ / min), and the natural temperature rise rate of the environment is r (unit: ℃ / min); when T real >T th When the required ventilation time t is , it is calculated by the following formula:

[0119]

[0120] Lighting control model:

[0121] Assume the expected light intensity is L desired , the real-time light intensity is L real , the brightness adjustment coefficient of the lighting equipment is α, then the brightness ΔL that needs to be increased is: ΔL=L desired -L real , the corresponding adjustment amount x is:

[0122] 4) Decision making;

[0123] Use fuzzy logic or threshold judgment methods to make decisions; in temperature control, you can define fuzzy sets "high temperature", "moderate temperature", "low temperature", and decide the action of the ventilation equipment according to the fuzzy set to which the real-time temperature belongs;

[0124] 5) Execute control actions;

[0125] Use digital signal control or analog signal control to achieve precise adjustment of equipment;

[0126] 6) Feedback and Optimization

[0127] Use methods such as Kalman filtering or particle filtering to process and fuse feedback data to more accurately evaluate the control effect; use adaptive control algorithms, model reference adaptive control or self-correcting control to continuously adjust control parameters based on feedback;

[0128] 7) Dynamic adjustment and learning;

[0129] Use reinforcement learning algorithms in machine learning, such as Q-learning or SARSA, to optimize future control strategies based on historical control decisions and feedback results

[0130] Step 4: Display and alarm: After receiving the abnormal information, the display and alarm module displays the abnormal situation in a striking manner on the main interface of the substation monitoring system center, and issues an audible and visual alarm to remind the operation and maintenance personnel to pay attention and handle it in time;

[0131] Step 5: Operation control and emergency response. After receiving the alarm signal, the substation control center manager logs in to the remote control system through the remote control module using a dedicated software application or web interface. After logging in, you can see the same monitoring interface as the substation site, which displays various data and equipment status in real time. If the intelligent control module processing is found to be abnormal, the corresponding equipment can be controlled through human operation to achieve the purpose of quickly handling the abnormality. If the remote control processing requires corresponding permissions, the permission management module will perform authorization and verification to ensure that only personnel with corresponding permissions can operate and view specific data, thereby ensuring the security and stability of the system.

[0132] like Figure 3 , Figure 4 As shown, shown is the overall architecture diagram of the auxiliary control subsystem, which is divided into an application layer, a support layer and a physical layer. The application layer is arranged in the auxiliary control host and is connected to the auxiliary equipment monitoring system of the centralized control station, including equipment operating parameters, alarm information, report functions, control strategies, control status, operation and maintenance class client and operation and maintenance class WEB terminal, etc. The support layer includes data display, real-time monitoring, data analysis, data acquisition, external interface and control channel, etc.; the physical layer includes intelligent access control, air conditioning, lighting, fans, dehumidifiers, intelligent lighting, water pumps, cameras, micro-meteorology and other intelligent equipment, which are controlled by the auxiliary control subsystem. Through comprehensive monitoring, intelligent control, efficient data processing, good compatibility, support for remote operation and high security, it can significantly improve the operating efficiency and safety of the substation and reduce the operation and maintenance costs.

[0133] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. An auxiliary control subsystem of a substation intelligent auxiliary equipment monitoring system, comprising a data acquisition module, a data processing center, an intelligent control module, a display and alarm module, a remote control module and a permission management module, characterized in that: The data acquisition module of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system collects the environmental data and the operating status data of the auxiliary equipment in the substation in real time, and transmits the collected data to the data processing center in a continuous manner through wired or wireless means; the data processing center is installed on-site in the monitoring area, and uses advanced algorithms to quickly analyze and process, compare the received data with the preset normal range, and determine whether there is an abnormality. When the data is normal, it continues to receive and process subsequent data; when the data is abnormal, the abnormal information is transmitted to the intelligent control module and the display and alarm module; after receiving the abnormal information, the intelligent control module automatically issues a control instruction according to the preset control strategy; the display and alarm module After receiving the abnormal information, the module will display the abnormal situation in a striking manner on the main interface of the substation monitoring system center, and send out sound and light alarms to remind the operation and maintenance personnel to pay attention and deal with it in time; the remote control module enables the operation and maintenance personnel to obtain monitoring data and send control instructions in real time through the network at a place far away from the substation; the operation and maintenance personnel can log in to the remote control system through a dedicated software application or web interface; after logging in, they can see the same monitoring interface as the substation site, which displays various data and equipment status in real time; the authority management module always plays a role in the whole process, ensuring that only personnel with corresponding authority can operate and view specific data, so as to ensure the security and stability of the system.

2. According to claim 1, the auxiliary control subsystem of the intelligent auxiliary equipment monitoring system of a substation is characterized by: The working steps of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system are specifically as follows: Step 1: Data collection: The data collection module collects data from various sensors distributed in the substation, such as temperature, humidity, light and wind speed, etc. It also collects data on the operating status of the monitoring equipment, such as current, voltage and electromagnetic field strength, etc. The collected data is then transmitted to the data processing center by wireless or wired means. Step 2: Data processing and analysis: The data processing center is responsible for receiving and processing a large amount of data from the data acquisition module. It uses high-performance servers and advanced data analysis algorithms to first clean and preprocess the collected massive monitoring data to remove noise and outliers, and then uses data analysis algorithms, cluster analysis, regression analysis and association rule mining to conduct in-depth data mining. Through long-term monitoring and analysis of equipment operation data, a health model of the equipment is constructed. When the equipment operation parameters deviate abnormally from the health model, the system can issue a fault warning in time; when an abnormality is found, the signal is sent to the intelligent control module and the display and alarm; Step 3, abnormality handling and response; after receiving the abnormal signal, the intelligent control module automatically issues a control instruction according to the preset control strategy to control the linkage equipment to adjust and handle the abnormal situation. During the processing, the intelligent control module adopts the intelligent control technology algorithm to realize the adjustment and control of the linkage equipment; Step 4: Display and alarm: After receiving the abnormal information, the display and alarm module displays the abnormal situation in a striking manner on the main interface of the substation monitoring system center, and issues an audible and visual alarm to remind the operation and maintenance personnel to pay attention and handle it in time; Step 5: Operation control and emergency response. After receiving the alarm signal, the substation control center manager logs in to the remote control system through the remote control module using a dedicated software application or web interface. After logging in, you can see the same monitoring interface as the substation site, which displays various data and equipment status in real time. If the intelligent control module processing is found to be abnormal, the corresponding equipment can be controlled through human operation to achieve the purpose of quickly handling the abnormality. If the remote control processing requires corresponding permissions, the permission management module will perform authorization and verification to ensure that only personnel with corresponding permissions can operate and view specific data, thereby ensuring the security and stability of the system.

3. The auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system according to claim 2 is characterized by: The analysis algorithm in the data processing analysis of step 2 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system is specifically: Recursively divide the data based on the features, build a decision tree model, and use the information entropy calculation formula and information benefit calculation formula to select the optimal division features; Information entropy calculation formula: Where: p(x i ) is the category x i Probability of occurrence; Information gain calculation formula: Where: S is the data set; A is a feature; Sv is a subset divided based on the value v of feature A.

4. The auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system according to claim 2 is characterized by: The cluster analysis in the data processing analysis of step 2 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system is specifically as follows: Cluster analysis was processed using K-Means clustering and hierarchical clustering; K-Means clustering: randomly select K points as the initial cluster centers, then assign the data points to the nearest clusters based on their distances from the cluster centers, recalculate the cluster centers, and repeat this process until the cluster centers no longer change; Hierarchical clustering: by calculating the distance between data points, clusters are gradually merged or split to form a hierarchical structure; The calculation formula uses Euclidean distance to calculate the distance between data points: Where: x i and i is the i-th eigenvalue of data points x and y.

5. The auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system according to claim 2 is characterized by: The regression analysis in the data processing analysis of step 2 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system is specifically as follows: Regression analysis was processed using linear regression and polynomial regression; Linear regression: Assuming a linear relationship between variables, a straight line is fitted using the least squares method; Polynomial regression; consider the nonlinear relationship between variables and use polynomial functions for fitting; Calculation formula: y＝β0+β 1x Where: β0 is the intercept; the calculation formula of intercept β0 is: β1 is the slope; The calculation formula of slope β1 is: and are the means of x and y respectively.

6. The auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system according to claim 2 is characterized by: The association rule mining in the data processing analysis of step 2 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system is specifically as follows: By scanning the data set multiple times, frequent item sets are found, and then association rules are generated based on the frequent item sets, using support calculation formulas and confidence calculation formulas; Support calculation formula: Confidence calculation formula:

7. The auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system according to claim 2 is characterized by: The intelligent control technology and algorithm of step 3 of the auxiliary control subsystem of the substation intelligent auxiliary equipment monitoring system are specifically as follows: 1) Data collection: Select the appropriate sensor type according to the monitoring requirements, and determine the appropriate sampling frequency based on the system's requirements for response speed and data accuracy; 2) Data preprocessing: using statistical methods, the 3σ principle and the boxplot method to detect outliers; and using the moving average method and the median filter method to smooth the data and reduce noise interference; 3) Control model establishment; Temperature Control Model: Let the temperature threshold be T th , the real-time collected temperature is T real , the cooling efficiency of the ventilation equipment is k (unit: ℃ / min), and the natural temperature rise rate of the environment is r (unit: ℃ / min); when T real >T th When the required ventilation time t is , it is calculated by the following formula: Lighting control model: Assume the expected light intensity is L desired , the real-time light intensity is L real , the brightness adjustment coefficient of the lighting equipment is α, then the brightness ΔL that needs to be increased is: ΔL=L desired -L real , the corresponding adjustment amount x is: 4) Decision making; Use fuzzy logic or threshold judgment methods to make decisions; in temperature control, you can define fuzzy sets "high temperature", "moderate temperature", and "low temperature", and decide the action of the ventilation equipment based on the fuzzy set to which the real-time temperature belongs; 5) Execute control actions; Use digital signal control or analog signal control to achieve precise adjustment of equipment; 6) Feedback and Optimization Use methods such as Kalman filtering or particle filtering to process and fuse feedback data to more accurately evaluate the control effect; use adaptive control algorithms, model reference adaptive control or self-correcting control to continuously adjust control parameters based on feedback; 7) Dynamic adjustment and learning; The reinforcement learning algorithm in machine learning, Q-learning or SARSA, is used to optimize future control strategies based on historical control decisions and feedback results.

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