High temperature alarm system and method for power distribution station
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ELIDA (FUJIAN) TECH CO LTD
- Filing Date
- 2026-05-07
- Publication Date
- 2026-06-02
Smart Images
Figure CN122135507A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of temperature alarm processing technology, specifically to a high-temperature alarm system and alarm method for power distribution substations. Background Technology
[0002] Substations are critical nodes in power transmission and distribution networks. Their internal electrical equipment (such as transformers, switchgear, and busbars) are prone to abnormally high temperatures when operating under high loads for extended periods or when there are potential faults. If these temperatures are not detected and addressed in a timely manner, they may lead to major safety accidents such as electrical fires, equipment breakdowns, or even station shutdowns. Therefore, establishing a real-time, reliable, and efficient high-temperature alarm and response system is of paramount importance for ensuring the safe and stable operation of substations.
[0003] Currently, the temperature monitoring and alarm solutions commonly used in the industry for power distribution stations mainly rely on deploying temperature sensors on key equipment. Temperature information is typically obtained through wireless temperature probes, and alarms are triggered when a fixed temperature threshold is set. These setups achieve online temperature detection to a certain extent. However, the environment inside power distribution stations is complex, and equipment operating conditions are variable. Localized momentary interference (such as changes in sunlight or sudden ventilation interruptions) may cause the temperature to briefly exceed the threshold, triggering false alarms. In addition, most systems only record the data and time when the threshold is exceeded and use this simple timing method as the basis for judgment, which fails to quantify the cumulative effect of abnormal risks in a timely manner. Summary of the Invention
[0004] The purpose of this invention is to provide a high-temperature alarm system and method for substations, solving the following technical problems: (1) How to effectively handle and reduce the false alarm rate of temperature alarm information, quantify alarm risk information, and trigger linkage processing automatically in a timely manner; (2) How to comprehensively improve the safety assurance capabilities and intelligent operation and maintenance level of power distribution stations.
[0005] The objective of this invention can be achieved through the following technical solutions: For high temperature alarm systems in substations, including: The temperature monitoring module is used to acquire temperature monitoring data at each monitoring point of each monitored equipment in the substation based on a preset sampling period. The alarm detection module is used to determine whether the temperature monitoring data contains a definite alarm message based on preset parsing rules, and to obtain the alarm duration of the monitored device from the definite alarm message as the alarm detection result. The alarm determination module is used to determine whether the monitored device is experiencing a false alarm based on the alarm duration and alarm temperature value. If it is not a false alarm, proceed to the joint response module; If it is a false alarm, proceed to the initialization module; The linkage response module is used to obtain high temperature distribution characteristic parameters based on the alarm information and the high temperature distribution of the monitored equipment when the alarm is determined to be non-false alarm. It then performs a comprehensive risk value calculation by fusing these parameters with the alarm information and the high temperature distribution of the monitored equipment. Based on the comprehensive risk value, it dynamically determines the alarm level and executes the linkage response plan corresponding to the alarm level. The initialization module is used to initialize the alarm information when the alarm is determined to be a false alarm.
[0006] Preferably, it further includes: The sensor deployment module is used to construct a 3D model of the substation through laser scanning and photogrammetry. Based on the operating principles of electrical equipment and the theory of heat transfer, the key heat-generating parts of each device are analyzed and marked in a three-dimensional model; Based on the key heat-generating components and the current internal structural space of the equipment, assess the feasibility and accessibility of installing temperature sensors; Based on the evaluation results, control points were selected and temperature sensors were deployed.
[0007] Preferably, the preset parsing rules in the alarm detection module include: S1. Compare the real-time temperature monitoring data of the monitoring points with preset static temperature thresholds corresponding to different equipment types and monitoring points: If the temperature value exceeds the static temperature threshold, the monitoring point is marked as a potential alarm point; calculate the rate of temperature change of the potential alarm point at the current moment: ; in, This represents the temperature value at the monitoring point at the current sampling time. This is a reference temperature value calculated based on historical data; For a fixed sampling and calculation period; S2. Compare the temperature change rate with the dynamic change rate threshold derived from historical data statistics: If the rate of temperature change If the dynamic change rate exceeds the threshold range, the alarm confidence level is enhanced, a definitive alarm message is generated, and cross-validation analysis is performed by combining temperature data from different points on the same monitored device or related devices. If there is a logical correlation anomaly, a composite alarm detection mechanism is triggered; otherwise, monitoring continues.
[0008] Preferably, the alarm duration is obtained in the alarm detection module as follows: Through formula Calculate the duration of the alarm. ; in, This indicates the time when the current alarm information is acquired within the preset sampling period. This indicates the initial time of the continuous alarm within the previous sampling period; from time... arrive The length from the initial time of the system's historical alarms to the current judgment time is the time interval for the current confirmed alarm. This indicates the real-time temperature value at the monitoring point; This indicates the real-time temperature change rate at the monitoring point; The dynamic weighting function within one sampling period is calculated as follows: ; in, The average temperature of the monitoring points within one sampling period; To correspond to the static temperature threshold of the monitoring point; This is a preset temperature normalization coefficient used to adjust the weighting of the over-temperature amplitude; The rate of temperature change is calculated and determined within a sampling period; This is a preset reference value for the maximum rate of temperature change based on historical data; and The weighting factor parameters are preset for the system and satisfy the following conditions: ,and , All are greater than 0.
[0009] Preferably, the preset sampling period is based on the duration threshold corresponding to the monitored device, and the historical alarm information obtained is from the previous period of the sampling period corresponding to the current alarm information.
[0010] Preferably, the step of the alarm determination module in determining whether the monitored device is experiencing a false alarm includes: Get continuous alarm duration and current alarm temperature value ; Obtain the normal operating temperature baseline curve of the monitored equipment within a preset historical period, and calculate the baseline temperature value corresponding to the current moment. ; The formula for calculating the probability of false alarms is: ;in, The preset duration threshold; This represents the maximum permissible temperature deviation in history. , All are preset weighting coefficients, and ; False alarm probability value Compare with the preset false alarm probability threshold: like If the probability exceeds the false alarm threshold, it is determined to be a false alarm; Otherwise, it will be determined as a non-false alarm.
[0011] Preferably, the high-temperature distribution characteristic parameters of the linkage treatment module include: Obtain the highest temperature value of the core monitoring point that triggered the alarm. ; Based on the 3D model constructed using the sensor deployment module and data from all temperature sensors on the device, the spatial diffusion index of the high-temperature region is calculated. ; Calculate the temperature difference between the two sensors corresponding to the core alarm point and the adjacent normal point, and determine the maximum temperature gradient based on the maximum ratio of the temperature difference between the two sensors to the spatial distance between them. ; The alarm duration is obtained from the alarm detection module. .
[0012] Preferably, the methods for obtaining the comprehensive risk value and determining the alarm level include: The high temperature distribution characteristic parameters are compared with the inherent risk factor of the monitoring equipment. A linear weighted fusion is performed to calculate the comprehensive risk value, specifically as follows: The comprehensive risk value is obtained through formula calculation. : ; , , , , They are respectively , , , , Weighting coefficients; , , , They are respectively , , , The normalization function maps them to the same numerical range; The calculated comprehensive risk value With the preset threshold range Comparison is performed to dynamically determine the alarm level: like < If the monitoring equipment indicates a low risk of high temperature distribution, it is classified as a Level III warning. like ≤ < If the temperature distribution of the monitoring equipment is deemed to be of medium risk, it is classified as a Level II warning. like ≥ If the monitoring equipment is found to have a high risk of high temperature distribution, it is determined to be a Level 1 warning. in , These are the minimum and maximum values of the preset risk thresholds, respectively, based on historical accident data and expert experience analysis.
[0013] The method for high-temperature alarm in substations is as follows: Step 1: Obtain temperature monitoring data for each monitored device within the substation; Step 2: Based on the preset parsing rules, determine whether the temperature monitoring data contains alarm information; Step 3: If an alarm message is detected from any monitored device, obtain the duration of the alarm for that monitored device. Step 4: Determine whether the monitored device is experiencing a false alarm based on the alarm duration and alarm temperature value; Step 5: If the alarm is determined to be a false alarm, the alarm information is initialized; if the alarm is determined to be a true false alarm, the high temperature distribution characteristic parameters are obtained based on the alarm information and the high temperature distribution of the monitored equipment, and a comprehensive risk value is obtained through fusion calculation; the alarm level is dynamically determined based on the comprehensive risk value, and the linkage response plan corresponding to the alarm level is executed.
[0014] The beneficial effects of this invention are: (1) This invention addresses the issues of effectively reducing false alarm rates, quantifying risks, and achieving automatic linkage. It systematically improves the accuracy, reliability, and operability of alarm information. First, it adopts a composite analysis rule that combines static thresholds, dynamic change rates, and equipment correlation to screen out atypical alarm signals from the source. Then, it introduces a false alarm probability calculation model based on weighted duration and temperature deviation to achieve quantitative identification and intelligent filtering of suspicious alarms. Finally, it transforms alarm information into a precise quantified comprehensive risk value by integrating multidimensional high temperature distribution characteristics and inherent equipment risks through a dynamic algorithm, and automatically triggers graded and precise linkage response plans accordingly.
[0015] (2) In order to achieve the goal of comprehensively improving the safety assurance capability and intelligent operation and maintenance level of power distribution stations, this invention achieves accurate perception through optimized deployment based on three-dimensional model and equipment mechanism, realizes early warning and accurate location of high temperature faults through multi-dimensional data fusion analysis, and finally realizes the scientific allocation and efficient utilization of emergency response resources through risk-driven automated hierarchical response mechanism. This not only greatly improves the efficiency of timely discovery, diagnosis and handling of safety hazards, but also promotes the automation process of power distribution operation and maintenance mode, and significantly enhances the proactive safety defense capability and intelligent management level of key power grid infrastructure.
[0016] Of course, any product implementing this invention does not necessarily need to achieve all the advantages described above at the same time. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a module diagram of the high-temperature alarm system for substations according to the present invention; Figure 2 This is a flowchart illustrating the steps of the high-temperature alarm method for power distribution substations according to the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] Please see Figure 1 As shown, the present invention is a high-temperature alarm system for substations, comprising: The temperature monitoring module is used to acquire temperature monitoring data at each monitoring point of each monitored equipment in the substation based on a preset sampling period. The alarm detection module is used to determine whether the temperature monitoring data contains a definite alarm message based on preset parsing rules, and to obtain the alarm duration of the monitored device from the definite alarm message as the alarm detection result. The alarm determination module is used to determine whether the monitored device is experiencing a false alarm based on the alarm duration and alarm temperature value. If it is not a false alarm, proceed to the joint response module; If it is a false alarm, proceed to the initialization module; The linkage response module is used to obtain high temperature distribution characteristic parameters based on the alarm information and the high temperature distribution of the monitored equipment when the alarm is determined to be non-false alarm. It then performs a comprehensive risk value calculation by fusing these parameters with the alarm information and the high temperature distribution of the monitored equipment. Based on the comprehensive risk value, it dynamically determines the alarm level and executes the linkage response plan corresponding to the alarm level. The initialization module is used to initialize the alarm information when the alarm is determined to be a false alarm.
[0021] The above technical solution includes a high-temperature alarm system for substations comprising five modules: a temperature monitoring module, an alarm detection module, an alarm judgment module, a linkage response module, and an initialization module. These five modules function as a system, deployed on a local monitoring server within the substation and equipped with a computing program. They are connected via a network to various temperature sensors deployed within the substation (such as wireless temperature sensors installed on switchgear contact arms, cable joints, and busbar connections), environmental sensors, ventilation devices, inspection robots, and fire protection systems. The temperature monitoring module polls and reads temperature sensor data from key points within each switchgear at a fixed sampling period (e.g., every 10 seconds). Each data point includes: device ID, sensor ID, temperature value, and timestamp. Temperature readings from key points of the monitored equipment over several consecutive periods are cached and transmitted to the alarm detection module. Then, the alarm detection module implements the alarm according to preset parsing rules. Specifically, the implementation includes: first, a static threshold comparison is performed by reading real-time data and comparing it with a preset static temperature threshold for that type. Temperatures not exceeding the static temperature threshold in the initial few cycles are not marked. Second, the temperature change rate is calculated and compared with historical dynamic thresholds. For example, if the temperature reaches 79℃ (exceeding 75℃) for the first time in a cycle, it is marked as a potential alarm point. Subsequently, the temperature change rate is calculated. Using the average change rate calculated from the previous three cycles as a reference, it is found that the current change rate reaches 2.0℃ / s, exceeding the maximum dynamic change rate threshold (1.5℃ / s), thus increasing the alarm confidence. Thirdly, differential verification is performed, and the temperature of different contacts in each switchgear is checked simultaneously. The temperature of adjacent switchgear cabinets is also obtained. If only one point shows an abnormal increase, it is determined to be a high temperature anomaly point, and the alarm detection mechanism for the associated anomaly of the check information is triggered. After implementing the preset parsing rules, the alarm information is confirmed, and the alarm duration in the alarm information is obtained. When the determination information that the temperature monitoring data contains alarm information is met, the initial trigger time is recorded. In each subsequent sampling period, the corresponding weighted duration is calculated. During a period of sampling, when the temperature rises and exceeds the maximum value of the temperature change rate, the weight of that period is calculated to obtain the corresponding weighted alarm duration. Next, when the alarm duration reaches a preset duration threshold, the alarm determination module initiates a false alarm determination. The false alarm determination algorithm involves obtaining the current alarm temperature value and the baseline temperature of that location under similar historical conditions (e.g., the historical average under the same load and ambient temperature), substituting them into the false alarm probability formula for calculation, and comparing the obtained false alarm probability value with the false alarm probability threshold. Based on the determination of whether a false alarm exists, the system proceeds to either the linkage handling module or the initialization module. If the alarm is determined to be non-false, the system proceeds to the linkage handling module; if it is determined to be a false alarm, the system automatically proceeds to the initialization module. Finally, the linkage response module extracts high-temperature distribution characteristic parameters, including the highest temperature value at the core monitoring point that triggered the alarm. Based on the 3D model constructed using the sensor deployment module and data from all temperature sensors on the device, the spatial diffusion index of the high-temperature region is calculated. Calculate the temperature difference between the two sensors corresponding to the core alarm point and the adjacent normal point, and determine the maximum temperature gradient based on the maximum ratio of the temperature difference between the two sensors to the spatial distance between them. ; Obtain the alarm duration from the alarm detection module ; through high temperature distribution characteristic parameters and equipment criticality coefficient (the inherent risk coefficient of the monitoring equipment) The comprehensive risk value is obtained through fusion calculation. The alarm level is determined by comparing the comprehensive risk value with the preset threshold range. Alarm information of different levels can provide corresponding processing basis for the execution of linkage processing plans.
[0022] If the initialization module determines that a false alarm is detected, the temporary data will be automatically cleared and the system will enter normal monitoring mode. No linkage action will be triggered after the system enters normal monitoring mode. The corresponding false alarm time can be recorded in the log to provide subsequent optimization thresholds.
[0023] The above five modules enable the system to achieve a complete closed loop, starting from temperature monitoring, analyzing step by step, filtering false alarm information, performing risk quantification analysis, and ultimately triggering precise hierarchical linkage. Compared with traditional threshold alarms, this process can improve the accuracy of alarms. This is because the construction of a weighted duration and false alarm probability model can effectively filter interference while acquiring and integrating multi-dimensional high temperature distribution characteristics. Based on the aforementioned conditions, dynamic risk assessment is achieved, enabling precise classification of alarm levels. And through hierarchical linkage processing plans, efficient allocation of emergency resources is realized.
[0024] As one embodiment of the present invention, it also includes: The sensor deployment module is used to construct a 3D model of the substation through laser scanning and photogrammetry. Based on the operating principles of electrical equipment and the theory of heat transfer, the key heat-generating parts of each device are analyzed and marked in a three-dimensional model; Based on the key heat-generating components and the current internal structural space of the equipment, assess the feasibility and accessibility of installing temperature sensors; Based on the evaluation results, control points were selected and temperature sensors were deployed.
[0025] In the above technical solution, a sensor deployment module enables effective, reliable, and maintainable temperature monitoring of key heat-generating components of the equipment without affecting normal operation or damaging the original structure. The first step involves constructing a 3D model of the substation and switchgear using laser scanning and photogrammetry. This ensures that the point cloud data collected during data acquisition is subsequently imported into 3D modeling software for further analysis, generating realistic 3D models of key equipment components. These models clearly demonstrate the spatial layout, relative distances, and outlines of critical components such as the internal structure and contours of the cabinet. The second step involves analyzing and marking key heat-generating components based on conventional electrical and thermal theories. Different colored virtual labels are then automatically applied to the 3D model. Alternatively, the theoretically critical heat-generating components can be marked semi-automatically; for example, all contact connections can be marked as red highlighted areas; and the feasibility of sensors can be evaluated based on the critical heat-generating components and structural space. For each marked critical heat-generating component, spatial analysis and evaluation can be performed in a 3D model. These all involve conventional spatial feasibility analysis methods, and a corresponding quantitative scoring table is generated based on the feasibility analysis. Finally, based on the evaluation results, control points are selected and corresponding sensors are deployed. All of these are planned in the early stage of monitoring data acquisition, and an optimized list of sensor control points is output in advance to ensure the acquisition of accurate information on the 3D coordinates and sensor type of each point.
[0026] As an embodiment of the present invention, the preset parsing rules in the alarm detection module include: S1. Compare the real-time temperature monitoring data of the monitoring points with preset static temperature thresholds corresponding to different equipment types and monitoring points: If the temperature value exceeds the static temperature threshold, the monitoring point is marked as a potential alarm point; calculate the rate of temperature change of the potential alarm point at the current moment: ; in, This represents the temperature value at the monitoring point at the current sampling time. This is a reference temperature value calculated based on historical data; For a fixed sampling and calculation period; S2. Compare the temperature change rate with the dynamic change rate threshold derived from historical data statistics: If the rate of temperature change If the dynamic change rate exceeds the threshold range, the alarm confidence level is enhanced, a definitive alarm message is generated, and cross-validation analysis is performed by combining temperature data from different points on the same monitored device or related devices. If there is a logical correlation anomaly, a composite alarm detection mechanism is triggered; otherwise, monitoring continues.
[0027] In the above technical solution, the acquisition method of real-time monitoring data stream is defined by preset parsing rules. This process consists of two steps: Step S1 involves identifying and marking potential alarm points by calculating the temperature change rate and marking cases exceeding the static threshold as potential alarm points, and then using a formula... Calculate and obtain the rate of temperature change Step S2 involves analyzing and cross-validating the dynamic rate of change. The calculated temperature rate of change is compared to a preset dynamic rate of change threshold to determine the changes at potential alarm points within the corresponding time period of the monitoring sampling cycle. This is then compared to a preset dynamic rate of change threshold range. If the rate of change exceeds the upper limit of this range, the abnormally rapid increase strongly indicates an abnormally rapid temperature rise. The system thus significantly increases its confidence in the location of the monitoring device (potential alarm point), and simultaneously generates a confirmed alarm message to be verified. Finally, to verify the authenticity of the alarm message, cross-validation analysis is used to determine if there are other abrupt changes or interference. Real-time temperature values from other related points are obtained. If the analysis results contradict the logical association of normal equipment operation, excluding instantaneous changes in sunlight or ventilation interruptions, the system determines that there is a "logical association anomaly" and generates a confirmed alarm message. Conversely, if the changes do not exceed the dynamic threshold range and there is no logical association anomaly, the system continues to the next monitoring cycle for the potential alarm point.
[0028] As an embodiment of the present invention, the alarm duration is obtained in the alarm detection module as follows: Through formula Calculate the duration of the alarm. ; in, This indicates the time when the current alarm information is acquired within the preset sampling period. This indicates the initial time of the continuous alarm within the previous sampling period; from time... arrive The length from the initial time of the system's historical alarms to the current judgment time is the time interval for the current confirmed alarm. This indicates the real-time temperature value at the monitoring point; This indicates the real-time temperature change rate at the monitoring point; The dynamic weighting function within one sampling period is calculated as follows: ; in, The average temperature of the monitoring points within one sampling period; To correspond to the static temperature threshold of the monitoring point; This is a preset temperature normalization coefficient used to adjust the weighting of the over-temperature amplitude; The rate of temperature change is calculated and determined within a sampling period; This is a preset reference value for the maximum rate of temperature change based on historical data; and The weighting factor parameters are preset for the system and satisfy the following conditions: ,and , All are greater than 0.
[0029] The above technical solution, by introducing a dynamic weighting function for integration, offers several significant advantages over traditional simple timing methods, greatly improving the system's intelligence and alarm accuracy. Specifically, unlike traditional over-temperature range analysis which ignores the varying over-temperature amplitudes over time and the logic that a larger over-temperature amplitude equates to a higher risk, this invention utilizes a weighting function... The duration of each moment is determined based on the current overheat level. and rate of change Weighting is applied, and the greater the temperature exceedance, the faster the temperature rises; the larger the weighting function, the longer the equivalent duration contributed by that period. Furthermore, by comprehensively calculating the rate of temperature anomaly deterioration... Normalized value of the severity of the current temperature deviating from the safe threshold The system can capture risks from continuous alarm information. The longer the alarm lasts, the more the current risk level can be presented in real time, serving as an important characteristic value for feedback on the evolution of high temperature risks.
[0030] As one embodiment of the present invention, the preset sampling period is based on the duration threshold corresponding to the monitored device, and the historical alarm information in the previous period of the sampling period corresponding to the current alarm information is obtained.
[0031] In the above technical solution, the setting dynamically binds the sampling period and the duration threshold of each device, which essentially distinguishes the time markers of brief interference and continuous fault alerts. Different time scales are used for analysis of different faults, which greatly improves the accuracy of risk assessment, while optimizing false alarm filtering and trend judgment. Furthermore, by migrating historical alarm information of the entire cycle, it can provide a preliminary reference, and then verify the results based on the factual trend of temperature change rate.
[0032] As an embodiment of the present invention, the step of the alarm determination module in determining whether the monitored device is experiencing a false alarm includes: Get continuous alarm duration and current alarm temperature value ; Obtain the normal operating temperature baseline curve of the monitored equipment within a preset historical period, and calculate the baseline temperature value corresponding to the current moment. ; The formula for calculating the probability of false alarms is: ;in, The preset duration threshold; This represents the maximum permissible temperature deviation in history. , All are preset weighting coefficients, and ; False alarm probability value Compare with the preset false alarm probability threshold: like If the probability exceeds the false alarm threshold, it is determined to be a false alarm; Otherwise, it will be determined as a non-false alarm.
[0033] In the above technical solution, the duration is obtained by currently... and current alarm temperature value It also obtains the normal operating temperature baseline curve of the monitored equipment within a preset historical period and calculates the baseline temperature value corresponding to the current moment. ; through calculation formula The duration of time and temperature deviation are combined; the weighted duration of the current alarm is calculated. The shorter the value, the higher the value. The larger the value, the less stable and continuous the current situation has been established, and the higher the probability of a false alarm; on the other hand, the current temperature compared to historical normal baselines... The larger the deviation, the greater the deviation relative to the historical maximum permissible deviation. The larger this value, the better; this only measures the "degree of deviation from the normal baseline," not the "degree of exceeding the absolute threshold." A temperature value may exceed the absolute threshold, but if its deviation from the equipment's historical normal behavior under that operating condition is not significant, it may still be judged as a false alarm; therefore, by outputting a false alarm probability value... The output no longer uses a simple "yes" / no" decision, but instead quantifies the confidence level and uses preset weighting coefficients. , The system can flexibly adjust its focus based on the type of device (for example, increasing the weighting of the time dimension for devices susceptible to transient interference). The strategy is adjustable by comparing it with the preset false alarm probability threshold, thus balancing sensitivity and reliability.
[0034] As an embodiment of the present invention, the high-temperature distribution characteristic parameters of the linkage treatment module include: Obtain the highest temperature value of the core monitoring point that triggered the alarm. ; Based on the 3D model constructed using the sensor deployment module and data from all temperature sensors on the device, the spatial diffusion index of the high-temperature region is calculated. ; Calculate the temperature difference between the two sensors corresponding to the core alarm point and the adjacent normal point, and determine the maximum temperature gradient based on the maximum ratio of the temperature difference between the two sensors to the spatial distance between them. ; The alarm duration is obtained from the alarm detection module. .
[0035] In the above technical solution, high-temperature distribution characteristic parameters are obtained by constructing multi-dimensional fault thermal information. These high-temperature distribution characteristic parameters include... , , , The risk value is obtained by linearly fusing the high temperature distribution characteristic parameters and the inherent risk coefficient of the monitoring equipment.
[0036] As an embodiment of the present invention, the method for obtaining the comprehensive risk value and the method for determining the alarm level include: The high temperature distribution characteristic parameters are compared with the inherent risk factor of the monitoring equipment. A linear weighted fusion is performed to calculate the comprehensive risk value, specifically as follows: The comprehensive risk value is obtained through formula calculation. : ; , , , , They are respectively , , , , Weighting coefficients; , , , They are respectively , , , The normalization function maps them to the same numerical range; The calculated comprehensive risk value With the preset threshold range Comparison is performed to dynamically determine the alarm level: like < If the monitoring equipment indicates a low risk of high temperature distribution, it is classified as a Level III warning. like ≤ < If the temperature distribution of the monitoring equipment is deemed to be of medium risk, it is classified as a Level II warning. like ≥ If the monitoring equipment is found to have a high risk of high temperature distribution, it is determined to be a Level 1 warning. in , These are the minimum and maximum values of the preset risk thresholds, respectively, based on historical accident data and expert experience analysis.
[0037] In the above technical solution, it should be noted that after dynamically determining the alarm level, the linkage response plan includes: for a level 3 warning, automatically activating the enhanced ventilation device in the equipment area and highlighting it on the monitoring interface; for a level 2 alarm, automatically dispatching an inspection robot to the alarm point to perform image and infrared verification, while simultaneously notifying the nearest inspection personnel; for a level 1 emergency alarm, automatically performing electrical operations to reduce the load on the equipment, activating the automatic fire extinguishing device preparatory program, and simultaneously sending emergency alarm information containing location and contingency plan to multiple levels of management personnel.
[0038] Please see Figure 2 As shown, it also includes a method for high-temperature alarm in substations, the method being: Step 1: Obtain temperature monitoring data for each monitored device within the substation; Step 2: Based on the preset parsing rules, determine whether the temperature monitoring data contains alarm information; Step 3: If an alarm message is detected from any monitored device, obtain the duration of the alarm for that monitored device. Step 4: Determine whether the monitored device is experiencing a false alarm based on the alarm duration and alarm temperature value; Step 5: If the alarm is determined to be a false alarm, the alarm information is initialized; if the alarm is determined to be a true false alarm, the high temperature distribution characteristic parameters are obtained based on the alarm information and the high temperature distribution of the monitored equipment, and a comprehensive risk value is obtained through fusion calculation; the alarm level is dynamically determined based on the comprehensive risk value, and the linkage response plan corresponding to the alarm level is executed.
[0039] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, and therefore described more simply; relevant parts can be referred to the descriptions of the method embodiments.
[0040] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they should all fall within the protection scope of the present invention.
Claims
1. A high-temperature alarm system for substations, characterized in that, include: The temperature monitoring module is used to acquire temperature monitoring data at each monitoring point of each monitored equipment in the substation based on a preset sampling period. The alarm detection module is used to determine whether the temperature monitoring data contains definite alarm information based on preset parsing rules, and to obtain the alarm duration of the monitored device from the definite alarm information as the alarm detection result. The alarm determination module is used to determine whether the monitored device is experiencing a false alarm based on the alarm duration and alarm temperature value. If it is not a false alarm, proceed to the joint response module; If it is a false alarm, proceed to the initialization module; The linkage response module is used to obtain high temperature distribution characteristic parameters and perform fusion calculation to obtain a comprehensive risk value when the alarm is determined to be non-false alarm, based on the alarm information and the high temperature distribution of the monitored device. The alarm level is dynamically determined based on the comprehensive risk value, and the corresponding emergency response plan is executed. An initialization module is used to initialize the alarm information when it is determined to be a false alarm.
2. The high-temperature alarm system for substations according to claim 1, characterized in that, Also includes: The sensor deployment module is used to construct a 3D model of the substation through laser scanning and photogrammetry. Based on the operating principles of electrical equipment and the theory of heat transfer, the key heat-generating components of each device are analyzed and marked in the three-dimensional model; Based on the aforementioned key heat-generating components and the current internal structural space of the equipment, the feasibility and accessibility of installing a temperature sensor are evaluated. Based on the evaluation results, control points were selected and temperature sensors were deployed.
3. The high-temperature alarm system for substations according to claim 1, characterized in that, The preset parsing rules in the alarm detection module include: S1. Compare the real-time temperature monitoring data of the monitoring points with preset static temperature thresholds corresponding to different equipment types and monitoring points: If the temperature value exceeds the static temperature threshold, the monitoring point is marked as a potential alarm point; the rate of temperature change of the potential alarm point at the current moment is calculated: ； in, The temperature value at the monitoring point at the current sampling time; This is a reference temperature value calculated based on historical data; For a fixed sampling and calculation period; S2. Compare the temperature change rate with a dynamic change rate threshold derived from historical data statistics: If the rate of temperature change If the dynamic change rate exceeds the threshold range, the alarm confidence level is enhanced, a definitive alarm message is generated, and cross-validation analysis is performed by combining temperature data from different points on the same monitored device or related devices. If there is a logical correlation anomaly, a composite alarm detection mechanism is triggered; otherwise, monitoring continues.
4. The high-temperature alarm system for substations according to claim 3, characterized in that, The alarm duration is obtained in the alarm detection module as follows: Through formula Calculate the duration of the alarm. ; in, This indicates the time when the current alarm information is acquired within the preset sampling period. This indicates the initial time of the continuous alarm within the previous sampling period; from time... arrive The length from the initial time of the system's historical alarms to the current judgment time is the time interval for the current confirmed alarm. This indicates the real-time temperature value at the monitoring point; This indicates the real-time temperature change rate at the monitoring point; The dynamic weighting function within one sampling period is calculated as follows: ； in, The average temperature of the monitoring points within one sampling period; To correspond to the static temperature threshold of the monitoring point; This is a preset temperature normalization coefficient used to adjust the weighting of the over-temperature amplitude; The rate of temperature change is calculated and determined within a sampling period; This is a preset reference value for the maximum rate of temperature change based on historical data; and The weighting factor parameters are preset for the system and satisfy the following conditions: ,and , All are greater than 0.
5. The high-temperature alarm system for substations according to claim 4, characterized in that, The preset sampling period is based on the duration threshold corresponding to the monitored device, and the data obtained is the historical alarm information within the previous period of the current alarm information.
6. The high-temperature alarm system for substations according to claim 4, characterized in that, The alarm determination module determines whether the monitored device is experiencing a false alarm by including the following steps: Obtain the duration of the continuous alarm and current alarm temperature value ; Obtain the normal operating temperature baseline curve of the monitored equipment within a preset historical period, and calculate the baseline temperature value corresponding to the current moment. ; The formula for calculating the probability of false alarms is: ;in, The preset duration threshold; This represents the maximum permissible temperature deviation in history. , All are preset weighting coefficients, and ; False alarm probability value Compare with the preset false alarm probability threshold: like If the probability exceeds the false alarm threshold, it is determined to be a false alarm; Otherwise, it will be determined as a non-false alarm.
7. The high-temperature alarm system for substations according to claim 1, characterized in that, The high-temperature distribution characteristic parameters of the linkage treatment module include: Obtain the highest temperature value of the core monitoring point that triggered the alarm. ; Based on the 3D model of the substation constructed using the sensor deployment module and the data from all temperature sensors on the device, the spatial diffusion index of the high-temperature area is calculated. ; Calculate the temperature difference between the two sensors corresponding to the core alarm point and the adjacent normal point, and determine the maximum temperature gradient based on the maximum ratio of the temperature difference between the two sensors to the spatial distance between them. ; The alarm duration is obtained from the alarm detection module. .
8. The high-temperature alarm system for substations according to claim 7, characterized in that, The methods for obtaining the comprehensive risk value and determining the alarm level include: The high temperature distribution characteristic parameters are compared with the inherent risk coefficient of the monitoring equipment. A linear weighted fusion is performed to calculate the comprehensive risk value, specifically as follows: The comprehensive risk value is obtained through formula calculation. : ； , , , , They are respectively , , , , Weighting coefficients; , , , They are respectively , , , The normalization function maps them to the same numerical range; The calculated comprehensive risk value With the preset threshold range Comparison is performed to dynamically determine the alarm level: like < If the monitoring equipment indicates a low risk of high temperature distribution, it is classified as a Level III warning. like ≤ < If the temperature distribution of the monitoring equipment is deemed to be of medium risk, it is classified as a Level II warning. like ≥ If the monitoring equipment is found to have a high risk of high temperature distribution, it is determined to be a Level 1 warning. in , These are the minimum and maximum values of the preset risk thresholds, respectively, based on historical accident data and expert experience analysis.
9. A method for high-temperature alarm in substations, characterized in that, The method is applied to the high-temperature alarm system for substations as described in any one of claims 1-8, wherein the method is as follows: Step 1: Obtain temperature monitoring data for each monitored device within the substation; Step 2: Based on preset parsing rules, determine whether the temperature monitoring data contains alarm information; Step 3: If an alarm message is detected from any monitored device, obtain the duration of the alarm for that monitored device. Step 4: Determine whether the monitored device is experiencing a false alarm based on the alarm duration and alarm temperature value. Step 5: If the alarm is determined to be a false alarm, the alarm information is initialized; if the alarm is determined to be a true false alarm, the high temperature distribution characteristic parameters are obtained based on the alarm information and the high temperature distribution of the monitored device, and a comprehensive risk value is obtained through fusion calculation. The alarm level is dynamically determined based on the comprehensive risk value, and the corresponding emergency response plan is executed.