Electrical equipment operation fault monitoring system

Abstract

The invention discloses an electrical equipment operation fault monitoring system, and belongs to the technical field of electrical equipment fault monitoring. Through combination of various sensors, comprehensive real-time monitoring of electrical equipment is realized, running state data of the equipment is captured in real time, comprehensiveness and accuracy of the data are ensured, potential equipment fault types can be accurately identified through digital processing and deep feature extraction, the accuracy and timeliness of fault diagnosis are effectively improved, and the fault diagnosis efficiency is improved. Operation data of multiple devices are integrated through the industrial Internet of Things, the range and precision of fault diagnosis are improved through data sharing and multi-device correlation analysis, faults jointly caused by the multiple devices are comprehensively analyzed and positioned, an intelligent fault diagnosis and optimization scheme is provided for a complex device group, and the fault diagnosis efficiency is improved. The data aggregation optimization unit effectively optimizes the overall operation state of the equipment by adjusting the operation parameters of the equipment, avoids the problems of load imbalance and the like, and improves the operation efficiency of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment fault monitoring, and in particular to an electrical equipment operation fault monitoring system. Background Art

[0002] In modern power systems and industrial equipment, electrical equipment is a key component, and its operating stability and safety are directly related to the normal operation of the entire system. However, electrical equipment is prone to failure during long-term operation due to factors such as overload, aging, poor contact, and changes in ambient temperature. This not only affects the operating efficiency of the equipment, but may also lead to major safety accidents. Traditional electrical equipment fault detection methods mostly rely on regular inspections and manual inspections, which are not only inefficient, but also prone to missed detections and false detections, making it difficult to achieve real-time monitoring and accurate diagnosis of equipment failures.

[0003] With the development of sensor technology, data processing technology and Internet of Things technology, electrical equipment fault monitoring is gradually developing towards intelligence and automation. Some existing monitoring systems collect key parameters such as current, voltage and temperature of equipment by installing sensors, and use basic signal processing and analysis methods to provide fault warnings.

[0004] However, these traditional systems are mostly limited to the monitoring of a single device, lack collaborative diagnosis and optimization management among multiple devices or groups of devices, and are unable to fully utilize the correlation information between devices for fault prediction and operation optimization. Summary of the invention

[0005] The object of the present invention is to provide an electrical equipment operation fault monitoring system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an electrical equipment operation fault monitoring system, comprising: Data acquisition module for: The operating status data is collected in real time by sensors installed on the electrical equipment, wherein the operating status data includes current signals, voltage signals and ambient temperature signals, and the sensors include current sensors, voltage sensors and temperature sensors; Data processing and analysis module, used for: Build a fault database, digitize the collected signals, extract features and perform statistical analysis on the data, and identify potential fault types and locations by matching the fault database with real-time operating status data; Distributed collaborative diagnosis module for: Build a data management platform, connect electrical equipment into a collaborative network through the industrial Internet of Things, supplement the monitoring blind spots of individual electrical equipment through the operating status data of adjacent electrical equipment, and generate operation optimization plans and fault diagnosis warnings for the collaborative network through data aggregation and multi-device correlation analysis; Telecommunications module for: Remotely transmit fault information to the maintenance personnel terminal through the wireless module and issue an alarm in real time; Alarm display module, used for: When a fault is detected, an alarm signal is issued, and the operating status and diagnostic results are displayed in real time on the display screen and mobile interface; Data storage module for: The operating data and the diagnostic results are stored according to data types, which include real-time data, historical data, and diagnostic results.

[0007] Furthermore, the data acquisition module includes: Current sensor unit for: The current sensor is used to collect the current signal of the electrical equipment in real time. The current sensor is installed in the current loop of the electrical equipment. The current sensor obtains the current signal of the equipment in operation and transmits it to the data processing and analysis module; Voltage sensor unit for: The voltage sensor is used to collect the voltage signal of the electrical equipment in real time. The voltage sensor is installed at the voltage measurement point of the electrical equipment. The voltage sensor obtains the voltage signal of the equipment in operation and transmits it to the data processing and analysis module; Temperature sensor unit for: The temperature sensors are used to collect the ambient temperature signals of key parts of the electrical equipment in real time, including electrical connectors, cable channels and transformers.

[0008] Furthermore, the temperature sensor unit is also used for: Real-time extraction of temperature values ​​of electrical connectors, cable channels and transformers; Acquire a first temperature correlation coefficient between the electrical structure and the cable channel by using the temperature value of the electrical connector and the temperature value of the cable channel collected each time; The first temperature correlation coefficient is obtained by the following formula: ; Among them, R 01 represents the first temperature correlation coefficient; T q and T d Respectively represent the temperature value of the electrical connector and the temperature value of the cable channel; T qmax and Tqmin They represent the maximum and minimum temperature values ​​corresponding to the historical maximum variation range of the electrical connector; T dmax and T dmin They represent the maximum and minimum temperature values ​​corresponding to the historical maximum variation range of the cable channel; T dp Indicates the average temperature of the cable channel; T dd Indicates the temperature standard deviation of the cable channel; T qd represents the temperature standard deviation of the electrical connector; σ represents the first adjustment coefficient, which is used to control the slope of the arctan function, and the first adjustment coefficient is determined by experiments and actual application requirements; P 01 Indicates the current actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the electrical connector; Obtaining a second temperature correlation coefficient between the cable channel and the transformer using the temperature values ​​of the cable channel and the transformer collected each time; The second temperature correlation coefficient is obtained by the following formula: ; Among them, R 02 represents the second temperature correlation coefficient; T y and T d Respectively represent the temperature value of the transformer and the temperature value of the cable channel; T dmax Indicates the maximum temperature value corresponding to the historical maximum variation range of the cable channel; T dd Indicates the temperature standard deviation of the cable channel; T yd Indicates the temperature standard deviation of the transformer; ΔT d Indicates the temperature span value corresponding to the historical maximum change range of the cable channel; ΔT y Indicates the temperature span value corresponding to the historical maximum change range of the transformer; P 02 Indicates the current actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the transformer; The temperature collection frequency corresponding to the cable channel is adjusted using the first temperature correlation coefficient and the second temperature correlation coefficient.

[0009] Furthermore, the adjusting the temperature acquisition frequency corresponding to the cable channel by using the first temperature correlation coefficient and the second temperature correlation coefficient includes: Compare the first temperature correlation coefficient with a preset first correlation coefficient threshold to obtain a first comparison result; Comparing the second temperature correlation coefficient with a preset second correlation coefficient threshold to obtain a second comparison result; When the first comparison result shows that the first temperature correlation coefficient exceeds the preset first correlation coefficient threshold, or the second comparison result shows that the second temperature correlation coefficient exceeds the preset second correlation coefficient threshold, the temperature correlation coefficient exceeding the corresponding correlation coefficient threshold is used as the target temperature correlation coefficient; The target temperature correlation coefficient is used to adjust the temperature acquisition frequency of the cable channel, and the temperature sensor corresponding to the cable channel is controlled to perform acquisition control according to the adjusted temperature acquisition frequency of the cable channel; The adjusted cable channel temperature acquisition frequency is obtained by the following formula: ; Among them, f t Indicates the adjusted cable channel temperature acquisition frequency; f b represents the cable channel temperature acquisition frequency before adjustment; R represents the target temperature correlation coefficient; θ represents the correlation coefficient threshold corresponding to the target temperature correlation coefficient; a represents the base of the preset logarithmic function.

[0010] Furthermore, the method of adjusting the temperature acquisition frequency corresponding to the cable channel by using the first temperature correlation coefficient and the second temperature correlation coefficient further includes: Compare the first temperature correlation coefficient with a preset first correlation coefficient threshold to obtain a first comparison result; Comparing the second temperature correlation coefficient with a preset second correlation coefficient threshold to obtain a second comparison result; When the first comparison result shows that the first temperature correlation coefficient exceeds the preset first correlation coefficient threshold, and the second comparison result shows that the second temperature correlation coefficient exceeds the preset second correlation coefficient threshold, the first temperature correlation coefficient and the second temperature correlation coefficient are retrieved; The temperature acquisition frequency of the cable channel is adjusted by using the first temperature correlation coefficient and the second temperature correlation coefficient, and the temperature sensor corresponding to the cable channel is controlled to perform acquisition control according to the adjusted temperature acquisition frequency of the cable channel; The adjusted cable channel temperature acquisition frequency is obtained by the following formula: ; Among them, f t Indicates the adjusted cable channel temperature acquisition frequency; f b Indicates the cable channel temperature acquisition frequency before adjustment; R 01 Represents the first temperature correlation coefficient; R 02 represents the second temperature correlation coefficient; θ 01 represents the preset first correlation coefficient threshold; θ 02 represents the preset second correlation coefficient threshold; P 01Indicates the actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the electrical connector; P 02 Indicates the actual current temperature change rate of the cable channel corresponding to each increase in the preset unit temperature of the transformer.

[0011] Furthermore, the data processing and analysis module includes: Digital processing unit for: Receive the running status data from the data acquisition module and convert it into a digital signal, perform basic filtering on the digitized signal and remove noise, and transmit the digitized running status data to the feature extraction unit; Feature extraction unit, used to: According to the characteristics of the current signal, voltage signal and ambient temperature signal, fault-related features are preset, and the fault-related features include the amplitude, frequency characteristics, peak value and average value of the waveform. Based on the changes of the current signal, voltage signal and ambient temperature signal in the time domain, amplitude fluctuation, overload and abnormal waveform features are extracted; Extract the frequency components of the signal through Fourier transform and identify periodic and transient fault signals; Statistical features of the signal are extracted, including mean, standard deviation, skewness, and kurtosis.

[0012] Furthermore, the data processing and analysis module further includes: Fault mode matching unit for: Obtain historical fault data, operating condition data, and fault feature samples of electrical equipment, generate a fault database, match the features of real-time operating status data with the fault patterns in the fault database, identify the equipment fault type of the electrical equipment based on the matching results, the equipment fault type includes overload, short circuit, poor contact, and overheating, and estimate the probability of the fault occurring.

[0013] Locate the specific location of the fault through the spatial distribution characteristics of the data, the spatial distribution characteristics are the distribution positions of the temperature sensor and the current sensor, generate a fault report based on the matching results, and output it to the alarm display module and the remote communication module; Real-time data statistics unit, used for: Perform regular statistics on the real-time collected operating status data, calculate the mean, maximum, minimum and change rate, and evaluate the health status of the electrical equipment based on the results of regular statistics to generate a health score for the equipment operation.

[0014] Furthermore, the distributed collaborative diagnosis module includes: Equipment data sharing unit for: Connect multiple electrical devices to a centralized data management platform through the Industrial Internet of Things, identify the data of different electrical devices through timestamps, and align the data between different electrical devices; Collect and integrate operating status data from multiple electrical devices in real time through data sharing; Companion diagnostic identification unit for: Matching the operating status data of each electrical device with the fault database, identifying the fault mode caused by multiple electrical devices, and identifying the fault types of different equipment groups according to the fault modes between multiple electrical devices, wherein the equipment group fault types include systemic faults, local overloads, and load imbalances; Combined with the spatial distribution characteristics of electrical equipment, the specific location of equipment group faults is located through data cross-comparison, and the results of collaborative diagnosis are transmitted to the alarm display module and remote communication module in real time.

[0015] Furthermore, the distributed collaborative diagnosis module further includes: Data aggregation optimization unit for: Aggregate the operating status data of multiple electrical devices to generate a global data view, which includes the real-time status of each electrical device and the environment around the device; An optimization objective function is set based on the operation objectives of the equipment, wherein the operation objectives include load balancing, temperature control, and power consumption optimization, and the operation parameters of the equipment are optimized by optimizing the objective function to adjust the workload and operating temperature parameters of the electrical equipment; The corresponding adjustment strategy is generated based on the optimization results and sent to each electrical device for execution through control signals. Continuous optimization is performed based on the feedback from the electrical devices and the global data view.

[0016] Furthermore, the alarm display module includes: Fault alarm generating unit, for: Different alarm levels are set according to the fault type, where minor faults are set as warnings and serious faults generate emergency alarms. When a device fails, an alarm signal is generated, which includes the fault type, device location, and fault severity. The alarm signal is transmitted to the maintenance personnel terminal through the remote communication module; Operation status display unit, used for: Based on the real-time collected operating status data, charts and curves are generated and displayed on the device display and mobile interface, showing the diagnosis results of equipment faults. Based on the changes in the operating status data of electrical equipment, the operating data and fault diagnosis results are dynamically updated.

[0017] Furthermore, the data storage module includes: Real-time data storage unit for: Receive the real-time transmission of electrical equipment operating status data from the data acquisition module and record it in time series. Each piece of operating status data includes the equipment ID, acquisition time, sensor type and corresponding measurement value, and write the real-time operating status data into the storage system; Historical data storage unit, used for: Some important indicators in the real-time operation status data and historical diagnostic data are stored regularly, and the important indicators include current, voltage and temperature. The fault diagnosis result data generated by the data processing and analysis module and the distributed collaborative diagnosis module are stored regularly.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention captures the operating status data of electrical equipment through the comprehensive application of multiple sensors, reflects the operating status of the equipment in real time, effectively avoids the monitoring blind spots that may occur in traditional monitoring systems, ensures the comprehensiveness and timeliness of the equipment status, and effectively identifies potential faults of the equipment through in-depth analysis such as digital processing, feature extraction, and pattern matching of the collected data. At the same time, through matching with the fault database, the system can estimate the probability of fault occurrence and accurately locate the fault location in real time, provide accurate diagnostic results for subsequent maintenance of the equipment, significantly reduce equipment downtime, effectively avoid excessive damage to the equipment, and extend the service life of the equipment.

[0019] 2. Through the industrial Internet of Things, the present invention can upload data from multiple electrical devices in real time and aggregate them to a centralized management platform. The system utilizes data sharing and time alignment mechanisms between devices to eliminate the limitations of single device monitoring. By integrating the operating data of each device, the system can identify failure modes caused by multiple devices in real time, and accurately locate device group failures in combination with spatial distribution characteristics, so that the system can provide comprehensive fault diagnosis in a complex device group environment, which is not only limited to the failure of a single device, but can also effectively identify multiple potential problems such as systemic failures, local overloads, and load imbalances.

[0020] 3. The present invention analyzes the related faults between various electrical equipments, thereby improving the accuracy of fault detection. The data aggregation optimization unit can set optimization goals by performing a global analysis of the real-time status of multiple devices, and adjust the equipment operating parameters according to the optimization results to reduce equipment load imbalance or overload. It not only improves the fault diagnosis efficiency of the equipment group, but also can predict and warn before a fault occurs, effectively optimizing the overall operating status of the equipment group and improving the reliability and work efficiency of electrical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1It is a schematic diagram of the modules of the electrical equipment operation fault monitoring system of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 , the present invention provides the following technical solutions: An electrical equipment operation fault monitoring system, comprising: Data acquisition module for: The operating status data is collected in real time through sensors installed on the electrical equipment. The operating status data includes current signals, voltage signals and ambient temperature signals. The sensors include current sensors, voltage sensors and temperature sensors. Data processing and analysis module, used for: Build a fault database, digitize the collected signals, extract features and perform statistical analysis on the data, and identify potential fault types and locations by matching the fault database with real-time operating status data; Distributed collaborative diagnosis module for: Build a data management platform, connect electrical equipment into a collaborative network through the industrial Internet of Things, supplement the monitoring blind spots of individual electrical equipment through the operating status data of adjacent electrical equipment, and generate operation optimization plans and fault diagnosis warnings for the collaborative network through data aggregation and multi-device correlation analysis; Telecommunications module for: Remotely transmit fault information to the maintenance personnel terminal through the wireless module and issue an alarm in real time; Alarm display module, used for: When a fault is detected, an alarm signal is issued, and the operating status and diagnostic results are displayed in real time on the display screen and mobile interface; Data storage module for: The operating data and the diagnostic results are stored according to the data type, which includes real-time data, historical data, and diagnostic results.

[0024] In the above embodiment, by combining data collection, data processing and analysis, and distributed collaborative diagnosis, real-time monitoring, fault diagnosis and early warning of the operating status of electrical equipment can be achieved, which has significant beneficial effects. Through the system-integrated sensor module, the equipment can continuously and accurately collect multiple operating status data such as current, voltage and temperature, thereby ensuring the comprehensiveness and timeliness of the data. The data processing and analysis module identifies the type and location of potential faults according to preset fault modes, detects potential faults in advance and provides accurate diagnostic information. The distributed collaborative diagnosis module further optimizes the collaborative work and data sharing between devices through the collaborative mechanism of the industrial Internet of Things, improves the fault diagnosis accuracy and operating efficiency of the entire equipment group, helps to quickly locate problems and guide equipment maintenance, and reduces equipment downtime and the frequency of failures.

[0025] Data acquisition module, including: Current sensor unit for: The current sensor is used to collect the current signal of the electrical equipment in real time. The current sensor is installed in the current loop of the electrical equipment. The current sensor obtains the current signal of the equipment in operation and transmits it to the data processing and analysis module; Voltage sensor unit for: The voltage sensor is used to collect the voltage signal of the electrical equipment in real time. The voltage sensor is installed at the voltage measurement point of the electrical equipment. The voltage sensor obtains the voltage signal of the equipment in operation and transmits it to the data processing and analysis module; Temperature sensor unit for: Temperature sensors are used to collect real-time ambient temperature signals from key parts of electrical equipment, including electrical connectors, cable channels, and transformers.

[0026] In the above embodiment, through the precise configuration of current sensors, voltage sensors and temperature sensors, the operating status of the electrical equipment is comprehensively monitored, key operating data is obtained, and real-time current, voltage and temperature signals are provided during the operation of the electrical equipment. The timeliness and accuracy of the data are ensured through high-frequency acquisition, covering all operating links of the electrical equipment, especially key factors such as temperature, load, power, etc., effectively avoiding the monitoring blind spots that may exist in traditional monitoring methods. By configuring sensors in the core parts of the electrical equipment, abnormal operating information of the equipment can be fed back in real time, further improving the accuracy of fault warning and diagnosis.

[0027] Specifically, the temperature sensor unit is further used for: Real-time extraction of temperature values ​​of electrical connectors, cable channels and transformers; Acquire a first temperature correlation coefficient between the electrical structure and the cable channel by using the temperature value of the electrical connector and the temperature value of the cable channel collected each time; The first temperature correlation coefficient is obtained by the following formula: ; Among them, R 01 represents the first temperature correlation coefficient; T q and T d Respectively represent the temperature value of the electrical connector and the temperature value of the cable channel; T qmax and T qmin They represent the maximum and minimum temperature values ​​corresponding to the historical maximum variation range of the electrical connector; T dmax and T dmin They represent the maximum and minimum temperature values ​​corresponding to the historical maximum variation range of the cable channel; T dp Indicates the average temperature of the cable channel; T dd Indicates the temperature standard deviation of the cable channel; T qd represents the temperature standard deviation of the electrical connector; σ represents the first adjustment coefficient, which is used to control the slope of the arctan function, and the first adjustment coefficient is determined by experiments and actual application requirements; P 01 Indicates the current actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the electrical connector; Obtaining a second temperature correlation coefficient between the cable channel and the transformer using the temperature values ​​of the cable channel and the transformer collected each time; The second temperature correlation coefficient is obtained by the following formula: ; Among them, R 02 represents the second temperature correlation coefficient; T y and T d Respectively represent the temperature value of the transformer and the temperature value of the cable channel; T dmax Indicates the maximum temperature value corresponding to the historical maximum variation range of the cable channel; T dd Indicates the temperature standard deviation of the cable channel; T yd Indicates the temperature standard deviation of the transformer; ΔT d Indicates the temperature span value corresponding to the historical maximum change range of the cable channel; ΔT y Indicates the temperature span value corresponding to the historical maximum change range of the transformer; P 02 Indicates the current actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the transformer; The temperature collection frequency corresponding to the cable channel is adjusted using the first temperature correlation coefficient and the second temperature correlation coefficient.

[0028] The technical effect of the above technical solution is: by extracting the temperature values ​​of electrical connectors, cable channels and transformers in real time, abnormal temperature conditions can be discovered in time to prevent equipment failures or safety accidents caused by excessive temperature. The temperature correlation coefficient (first temperature correlation coefficient and second temperature correlation coefficient) is used to quantify the degree of temperature interaction between the electrical structure, cable channel and transformer, providing a scientific basis for subsequent frequency adjustment. The formula takes into account the historical temperature change range (maximum value, minimum value, average value, standard deviation, etc.) of the electrical connector and cable channel, as well as the temperature span value, so that the calculation of the temperature correlation coefficient is more accurate. The first adjustment coefficient (σ) is introduced to control the slope of the arctan function, which can be flexibly adjusted according to experimental and actual application requirements, thereby improving the applicability and accuracy of the formula. The temperature acquisition frequency of the cable channel is dynamically adjusted according to the size of the first temperature correlation coefficient and the second temperature correlation coefficient. When the temperature correlation is high, the acquisition frequency is increased to monitor the temperature change more accurately; when the temperature correlation is low, the acquisition frequency is reduced to save resources and costs. This dynamic adjustment strategy helps to balance monitoring accuracy and resource consumption, and improve the overall performance and efficiency of the system. Through real-time monitoring and accurate temperature correlation analysis, potential temperature anomalies and potential faults can be discovered in time, so that corresponding preventive measures can be taken to improve the reliability and safety of the equipment. Dynamic adjustment of the temperature acquisition frequency can also help to timely discover and deal with temperature anomalies to avoid equipment damage or safety accidents caused by overheating. This technical solution provides operation and maintenance personnel with more accurate and timely temperature monitoring data and analysis results, which helps to optimize operation and maintenance strategies and management processes. By reducing unnecessary temperature acquisition and data transmission, the system's energy consumption and operation and maintenance costs are reduced.

[0029] In summary, the above technical solution improves the reliability and safety of the power system, optimizes operation and maintenance management, and reduces operation and maintenance costs by real-time monitoring of temperature values, accurate calculation of temperature correlation coefficients, and dynamic adjustment of temperature acquisition frequency.

[0030] Specifically, the adjusting the temperature acquisition frequency corresponding to the cable channel by using the first temperature correlation coefficient and the second temperature correlation coefficient includes: Compare the first temperature correlation coefficient with a preset first correlation coefficient threshold to obtain a first comparison result; Comparing the second temperature correlation coefficient with a preset second correlation coefficient threshold to obtain a second comparison result; When the first comparison result shows that the first temperature correlation coefficient exceeds the preset first correlation coefficient threshold, or the second comparison result shows that the second temperature correlation coefficient exceeds the preset second correlation coefficient threshold, the temperature correlation coefficient exceeding the corresponding correlation coefficient threshold is used as the target temperature correlation coefficient; The target temperature correlation coefficient is used to adjust the temperature acquisition frequency of the cable channel, and the temperature sensor corresponding to the cable channel is controlled to perform acquisition control according to the adjusted temperature acquisition frequency of the cable channel; The adjusted cable channel temperature acquisition frequency is obtained by the following formula: ; Among them, f t Indicates the adjusted cable channel temperature acquisition frequency; f b represents the cable channel temperature acquisition frequency before adjustment; R represents the target temperature correlation coefficient; θ represents the correlation coefficient threshold corresponding to the target temperature correlation coefficient; a represents the base of the preset logarithmic function.

[0031] The technical effect of the above technical solution is: by comparing the temperature correlation coefficient with the preset correlation coefficient threshold, the solution can flexibly identify scenes with a high degree of temperature correlation. Once any temperature correlation coefficient exceeds its corresponding threshold, the system can respond quickly and adjust the exceeded correlation coefficient as the target temperature correlation coefficient. By adjusting the temperature acquisition frequency of the cable channel using the target temperature correlation coefficient, unnecessary frequent acquisition can be avoided, thereby saving system resources. At the same time, dynamically adjusting the acquisition frequency according to the actual temperature correlation situation can improve the pertinence and efficiency of data acquisition. The adjusted cable channel temperature acquisition frequency formula combines multiple parameters such as the target temperature correlation coefficient, the correlation coefficient threshold, and the base of the logarithmic function, and can achieve precise control of the temperature acquisition frequency. This helps to more accurately monitor the temperature changes of the cable channel and discover potential problems in a timely manner. The base a of the logarithmic function in the solution is a preset parameter that can be adjusted according to actual conditions. This makes the solution highly adaptable and can be optimized according to different application scenarios and requirements. By dynamically adjusting the temperature acquisition frequency, the solution can more effectively monitor the temperature state of the cable channel and promptly discover and handle temperature anomalies. This helps improve the stability and safety of the entire power system and prevent equipment failures or safety accidents caused by excessive temperatures. The solution has clear logic and is easy to understand and implement. At the same time, the solution is relatively easy to maintain and manage because it uses simple mathematical formulas and preset parameters for adjustment.

[0032] In summary, the above technical solution achieves accurate monitoring and control of cable channel temperature changes by flexibly comparing the temperature correlation coefficient with the preset threshold and dynamically adjusting the temperature acquisition frequency, thereby improving the resource utilization, stability and safety of the system. At the same time, the solution has strong adaptability and is easy to implement and maintain, providing strong support for the reliable operation of the power system.

[0033] Specifically, the adjusting the temperature acquisition frequency corresponding to the cable channel by using the first temperature correlation coefficient and the second temperature correlation coefficient also includes: Compare the first temperature correlation coefficient with a preset first correlation coefficient threshold to obtain a first comparison result; Comparing the second temperature correlation coefficient with a preset second correlation coefficient threshold to obtain a second comparison result; When the first comparison result shows that the first temperature correlation coefficient exceeds the preset first correlation coefficient threshold, and the second comparison result shows that the second temperature correlation coefficient exceeds the preset second correlation coefficient threshold, the first temperature correlation coefficient and the second temperature correlation coefficient are retrieved; The temperature acquisition frequency of the cable channel is adjusted by using the first temperature correlation coefficient and the second temperature correlation coefficient, and the temperature sensor corresponding to the cable channel is controlled to perform acquisition control according to the adjusted temperature acquisition frequency of the cable channel; The adjusted cable channel temperature acquisition frequency is obtained by the following formula: ; Among them, f t Indicates the adjusted cable channel temperature acquisition frequency; f b Indicates the cable channel temperature acquisition frequency before adjustment; R 01 Represents the first temperature correlation coefficient; R 02 represents the second temperature correlation coefficient; θ 01 represents the preset first correlation coefficient threshold; θ 02 represents the preset second correlation coefficient threshold; P 01 Indicates the actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the electrical connector; P 02 Indicates the actual current temperature change rate of the cable channel corresponding to each increase in the preset unit temperature of the transformer.

[0034] The technical effect of the above technical solution is: the solution requires that the temperature acquisition frequency is adjusted only when the first temperature correlation coefficient and the second temperature correlation coefficient simultaneously exceed their respective preset thresholds. This dual-condition triggering mechanism avoids misjudgment caused by a single factor and improves the accuracy and reliability of the adjustment. By considering the first temperature correlation coefficient and the second temperature correlation coefficient at the same time, the solution can more comprehensively reflect the temperature influence relationship between the electrical connector, the cable channel and the transformer. This helps to formulate a more reasonable temperature acquisition strategy and improve the pertinence and effectiveness of data acquisition. According to the real-time changes of the first temperature correlation coefficient and the second temperature correlation coefficient, the temperature acquisition frequency of the cable channel is dynamically adjusted. When the temperature correlation is high, the acquisition frequency is increased to more accurately monitor the temperature change; when the temperature correlation is low, the acquisition frequency is reduced to save resources and costs. This dynamic adjustment strategy helps to improve monitoring efficiency while maintaining the stability and security of the system. The current actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the electrical connector and the transformer is introduced into the formula, which enables the adjusted acquisition frequency to more accurately reflect the impact of the actual temperature change on the cable channel temperature acquisition demand. Enhanced adaptability and flexibility of the solution. Since the solution can quickly adjust the acquisition frequency according to the real-time changes of the temperature correlation coefficient, the system can respond to temperature changes more quickly, detect potential problems in time and take measures. This helps to improve the response speed and overall performance of the system. By dynamically adjusting the temperature acquisition frequency, unnecessary frequent acquisition and data transmission is avoided, thereby reducing the system's energy consumption and operation and maintenance costs.

[0035] In summary, the above technical solution realizes accurate monitoring and control of cable channel temperature changes by comprehensively considering temperature-related factors, dynamically adjusting the acquisition frequency, and introducing actual temperature change rate, thereby improving the monitoring efficiency, stability and safety of the system and reducing operation and maintenance costs.

[0036] Data processing and analysis modules include: Digital processing unit for: Receive the running status data from the data acquisition module and convert it into a digital signal, perform basic filtering on the digitized signal and remove noise, and transmit the digitized running status data to the feature extraction unit; Feature extraction unit, used to: According to the characteristics of the current signal, voltage signal and ambient temperature signal, the fault-related features are preset, including the amplitude, frequency characteristics, peak value and average value of the waveform. Based on the changes of the current signal, voltage signal and ambient temperature signal in the time domain, the amplitude fluctuation, overload and abnormal waveform features are extracted; Extract the frequency components of the signal through Fourier transform and identify periodic and transient fault signals; Extract statistical features of the signal, including mean, standard deviation, skewness, and kurtosis; Fault mode matching unit for: Obtain historical fault data, operating condition data, and fault feature samples of electrical equipment, generate a fault database, match the features of real-time operating status data with the fault patterns in the fault database, and identify the equipment fault type of the electrical equipment based on the matching results. Equipment fault types include overload, short circuit, poor contact, and overheating, and estimate the probability of fault occurrence.

[0037] The specific location of the fault is located through the spatial distribution characteristics of the data. The spatial distribution characteristics are the distribution positions of the temperature sensor and the current sensor. A fault report is generated based on the matching results and output to the alarm display module and the remote communication module; Real-time data statistics unit, used for: Perform regular statistics on the real-time collected operating status data, calculate the mean, maximum, minimum and change rate, and evaluate the health status of the electrical equipment based on the results of regular statistics to generate a health score for the equipment operation.

[0038] In the above embodiment, the original sensor signal is converted through digital processing, feature extraction and pattern matching technology to provide a reliable basis for fault diagnosis. By extracting the time domain features of signals such as current, voltage, temperature, etc. to extract the frequency features, it is possible to efficiently identify periodic faults and transient fault signals, further enhancing the breadth and depth of fault detection. Through the established fault database, the system can perform pattern matching with real-time data, accurately identify potential equipment fault types, such as overload, short circuit, poor contact, etc., and even predict the probability of fault occurrence. Its high efficiency and accuracy can identify potential faults in real time and accurately, minimize equipment damage or downtime, and improve the reliability and operation and maintenance efficiency of the system.

[0039] Distributed collaborative diagnosis module, including: Equipment data sharing unit for: Connect multiple electrical devices to a centralized data management platform through the Industrial Internet of Things, identify the data of different electrical devices through timestamps, and align the data between different electrical devices; Collect and integrate operating status data from multiple electrical devices in real time through data sharing; Companion diagnostic identification unit for: Match the operating status data of each electrical device with the fault database to identify the fault mode caused by multiple electrical devices. According to the fault mode between multiple electrical devices, the fault types of different equipment groups are identified. The fault types of equipment groups include systemic faults, local overloads and load imbalances. Combined with the spatial distribution characteristics of electrical equipment, the specific location of equipment group faults is located through data cross-comparison, and the results of collaborative diagnosis are transmitted to the alarm display module and remote communication module in real time; Data aggregation optimization unit for: Aggregate the operating status data of multiple electrical devices to generate a global data view, which includes the real-time status of each electrical device and the environment around the device; Set optimization objective functions based on the operation objectives of the equipment. The operation objectives include load balancing, temperature control, and power consumption optimization. Optimize the operation parameters of the equipment by optimizing the objective functions and adjust the workload and operating temperature parameters of the electrical equipment. The corresponding adjustment strategy is generated based on the optimization results and sent to each electrical device for execution through control signals. Continuous optimization is performed based on the feedback from the electrical devices and the global data view.

[0040] In the above embodiment, multiple electrical devices are connected to a centralized management platform through the industrial Internet of Things to achieve data sharing and collaborative diagnosis. The limitations of single device monitoring are eliminated through the complementary status data of adjacent devices, and the fault diagnosis capability of the entire electrical equipment group is improved. Through the device data sharing unit, the operating data of multiple devices are aligned through timestamps to form a unified data view, which provides basic support for collaborative diagnosis. The collaborative diagnosis identification unit can identify the failure modes caused by multiple devices based on the data of multiple devices, such as systemic failures, local overloads, load imbalances, etc., consider the occurrence mechanism of the failure from the system level, accurately locate the specific location of the equipment failure, provide more comprehensive and accurate fault reports and maintenance suggestions, and improve the fault response capability of the equipment cluster. Through data aggregation and optimization, potential failure losses are reduced and the overall operating efficiency is improved.

[0041] Alarm display module, including: Fault alarm generating unit, for: Different alarm levels are set according to the fault type. Minor faults are set as warnings, and serious faults generate emergency alarms. When a device fails, an alarm signal is triggered. The alarm signal includes the fault type, device location, and fault severity. The alarm signal is transmitted to the maintenance personnel terminal through the remote communication module. Operation status display unit, used for: Based on the real-time collected operating status data, charts and curves are generated and displayed on the device display and mobile interface, showing the diagnosis results of equipment faults. Based on the changes in the operating status data of electrical equipment, the operating data and fault diagnosis results are dynamically updated.

[0042] In the above embodiment, when a device failure is detected in real time, an alarm signal can be quickly generated, and the operating status and fault diagnosis results of the equipment can be displayed in real time through the display screen and the mobile terminal interface. Through the fault alarm generation unit and the operating status display unit, rapid feedback of fault information is achieved. When a device fails, the alarm signal can be transmitted to the maintenance personnel terminal in a timely manner, and the real-time status, diagnostic results and fault type of the equipment can be displayed through clear and intuitive charts, curves, etc., so that maintenance personnel can make judgments and repairs quickly, effectively improving the fault response time, helping enterprises to quickly restore equipment operation, and reducing losses caused by failures. The dynamic update function of the alarm display module can continuously track the health status of the equipment according to changes in real-time data, and adjust the display content in time, providing maintenance personnel with real-time and accurate decision-making basis.

[0043] Data storage module, including: Real-time data storage unit for: Receive the real-time transmission of electrical equipment operating status data from the data acquisition module and record it in time series. Each piece of operating status data includes the equipment ID, acquisition time, sensor type and corresponding measurement value, and write the real-time operating status data into the storage system; Historical data storage unit, used for: Some important indicators in the real-time operation status data and historical diagnostic data are stored regularly. Important indicators include current, voltage and temperature. The fault diagnosis result data generated by the data processing and analysis module and the distributed collaborative diagnosis module are stored regularly.

[0044] In the above embodiment, through the real-time data storage unit, the module can continuously store real-time data from the sensor and provide detailed timestamp and device identification information for each data record to ensure the traceability of the data. The historical data storage unit regularly saves key operating indicators and fault diagnosis results, so that the system can trace the historical data of the fault when a fault occurs, providing an important basis for analyzing the cause of the fault and optimizing equipment maintenance. Through efficient data storage and backup mechanisms, the security and availability of the data are guaranteed, providing strong data support for the system. Combined with efficient query and access interfaces, maintenance personnel can quickly retrieve historical data, conduct fault analysis, trend prediction and equipment optimization, providing a scientific basis for the long-term healthy operation of electrical equipment.

[0045] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An electrical equipment operation fault monitoring system, characterized in that: include: Data acquisition module for: The operating status data is collected in real time by sensors installed on the electrical equipment, wherein the operating status data includes current signals, voltage signals and ambient temperature signals, and the sensors include current sensors, voltage sensors and temperature sensors; Data processing and analysis module, used for: Build a fault database, digitize the collected signals, extract features and perform statistical analysis on the data, and identify potential fault types and locations by matching the fault database with real-time operating status data; Distributed collaborative diagnosis module for: Build a data management platform, connect electrical equipment into a collaborative network through the industrial Internet of Things, supplement the monitoring blind spots of individual electrical equipment through the operating status data of adjacent electrical equipment, and generate operation optimization plans and fault diagnosis warnings for the collaborative network through data aggregation and multi-device correlation analysis; Telecommunications module for: Remotely transmit fault information to the maintenance personnel terminal through the wireless module and issue an alarm in real time; Alarm display module, used for: When a fault is detected, an alarm signal is issued, and the operating status and diagnostic results are displayed in real time on the display screen and mobile interface; Data storage module for: The operating data and the diagnostic results are stored according to data types, which include real-time data, historical data, and diagnostic results.

2. An electrical equipment operation fault monitoring system as claimed in claim 1, characterized in that: The data acquisition module comprises: Current sensor unit for: The current sensor is used to collect the current signal of the electrical equipment in real time. The current sensor is installed in the current loop of the electrical equipment. The current sensor obtains the current signal of the equipment in operation and transmits it to the data processing and analysis module; Voltage sensor unit for: The voltage sensor is used to collect the voltage signal of the electrical equipment in real time. The voltage sensor is installed at the voltage measurement point of the electrical equipment. The voltage sensor obtains the voltage signal of the equipment in operation and transmits it to the data processing and analysis module; Temperature sensor unit for: The temperature sensors are used to collect the ambient temperature signals of key parts of the electrical equipment in real time, including electrical connectors, cable channels and transformers.

3. An electrical equipment operation fault monitoring system as claimed in claim 1, characterized in that: The temperature sensor unit is further used for: Real-time extraction of temperature values ​​of electrical connectors, cable channels and transformers; Acquire a first temperature correlation coefficient between the electrical structure and the cable channel by using the temperature value of the electrical connector and the temperature value of the cable channel collected each time; The first temperature correlation coefficient is obtained by the following formula: ； Among them, R 01 represents the first temperature correlation coefficient; T q and T d Respectively represent the temperature value of the electrical connector and the temperature value of the cable channel; T qmax and T qmin They represent the maximum and minimum temperature values ​​corresponding to the historical maximum variation range of the electrical connector; T dmax and T dmin They represent the maximum and minimum temperature values ​​corresponding to the historical maximum variation range of the cable channel; T dp Indicates the average temperature of the cable channel; T dd Indicates the temperature standard deviation of the cable channel; T qd represents the temperature standard deviation of the electrical connector; σ represents the first adjustment coefficient, which is used to control the slope of the arctan function; P 01 Indicates the current actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the electrical connector; Obtaining a second temperature correlation coefficient between the cable channel and the transformer using the temperature values ​​of the cable channel and the transformer collected each time; The second temperature correlation coefficient is obtained by the following formula: ； Among them, R 02 represents the second temperature correlation coefficient; T y and T d Respectively represent the temperature value of the transformer and the temperature value of the cable channel; T dmax Indicates the maximum temperature value corresponding to the historical maximum variation range of the cable channel; T dd Indicates the temperature standard deviation of the cable channel; T yd Indicates the temperature standard deviation of the transformer; ΔT d Indicates the temperature span value corresponding to the historical maximum change range of the cable channel; ΔT y Indicates the temperature span value corresponding to the historical maximum change range of the transformer; P 02 Indicates the current actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the transformer; The temperature collection frequency corresponding to the cable channel is adjusted using the first temperature correlation coefficient and the second temperature correlation coefficient.

4. An electrical equipment operation fault monitoring system as claimed in claim 3, characterized in that: The adjusting the temperature acquisition frequency corresponding to the cable channel by using the first temperature correlation coefficient and the second temperature correlation coefficient includes: Compare the first temperature correlation coefficient with a preset first correlation coefficient threshold to obtain a first comparison result; Comparing the second temperature correlation coefficient with a preset second correlation coefficient threshold to obtain a second comparison result; When the first comparison result shows that the first temperature correlation coefficient exceeds the preset first correlation coefficient threshold, or the second comparison result shows that the second temperature correlation coefficient exceeds the preset second correlation coefficient threshold, the temperature correlation coefficient exceeding the corresponding correlation coefficient threshold is used as the target temperature correlation coefficient; The target temperature correlation coefficient is used to adjust the temperature acquisition frequency of the cable channel, and the temperature sensor corresponding to the cable channel is controlled to perform acquisition control according to the adjusted temperature acquisition frequency of the cable channel; The adjusted cable channel temperature acquisition frequency is obtained by the following formula: ； Among them, f t Indicates the adjusted cable channel temperature acquisition frequency; f b represents the cable channel temperature acquisition frequency before adjustment; R represents the target temperature correlation coefficient; θ represents the correlation coefficient threshold corresponding to the target temperature correlation coefficient; a represents the base of the preset logarithmic function; Compare the first temperature correlation coefficient with a preset first correlation coefficient threshold to obtain a first comparison result; Comparing the second temperature correlation coefficient with a preset second correlation coefficient threshold to obtain a second comparison result; When the first comparison result shows that the first temperature correlation coefficient exceeds the preset first correlation coefficient threshold, and the second comparison result shows that the second temperature correlation coefficient exceeds the preset second correlation coefficient threshold, the first temperature correlation coefficient and the second temperature correlation coefficient are retrieved; The temperature acquisition frequency of the cable channel is adjusted by using the first temperature correlation coefficient and the second temperature correlation coefficient, and the temperature sensor corresponding to the cable channel is controlled to perform acquisition control according to the adjusted temperature acquisition frequency of the cable channel; The adjusted cable channel temperature acquisition frequency is obtained by the following formula: ； Among them, f t Indicates the adjusted cable channel temperature acquisition frequency; f b Indicates the cable channel temperature acquisition frequency before adjustment; R 01 Represents the first temperature correlation coefficient; R 02 represents the second temperature correlation coefficient; θ 01 represents the preset first correlation coefficient threshold; θ 02 represents the preset second correlation coefficient threshold; P 01 Indicates the actual temperature change rate of the cable channel corresponding to each increase of the preset unit temperature of the electrical connector; P 02 Indicates the actual current temperature change rate of the cable channel corresponding to each increase in the preset unit temperature of the transformer.

5. The electrical equipment operation fault monitoring system according to claim 1, characterized in that: The data processing and analysis module comprises: Digital processing unit for: Receive the running status data from the data acquisition module and convert it into a digital signal, perform basic filtering on the digitized signal and remove noise, and transmit the digitized running status data to the feature extraction unit; Feature extraction unit, used to: According to the characteristics of the current signal, voltage signal and ambient temperature signal, fault-related features are preset, and the fault-related features include the amplitude, frequency characteristics, peak value and average value of the waveform. Based on the changes of the current signal, voltage signal and ambient temperature signal in the time domain, amplitude fluctuation, overload and abnormal waveform features are extracted; Extract the frequency components of the signal through Fourier transform and identify periodic and transient fault signals; Statistical features of the signal are extracted, including mean, standard deviation, skewness, and kurtosis.

6. An electrical equipment operation fault monitoring system as claimed in claim 5, characterized in that: The data processing and analysis module further includes: Fault mode matching unit for: Acquire historical fault data, operating condition data, and fault feature samples of electrical equipment, generate a fault database, match the features of real-time operating status data with the fault patterns in the fault database, identify the equipment fault type of the electrical equipment based on the matching results, the equipment fault type includes overload, short circuit, poor contact, and overheating, and estimate the probability of the fault occurring; Locate the specific location of the fault through the spatial distribution characteristics of the data, the spatial distribution characteristics are the distribution positions of the temperature sensor and the current sensor, generate a fault report based on the matching results, and output it to the alarm display module and the remote communication module; Real-time data statistics unit, used for: Perform regular statistics on the real-time collected operating status data, calculate the mean, maximum, minimum and change rate, and evaluate the health status of the electrical equipment based on the results of regular statistics to generate a health score for the equipment operation.

7. An electrical equipment operation fault monitoring system as claimed in claim 1, characterized in that: The distributed collaborative diagnosis module includes: Device data sharing unit for: Connect multiple electrical devices to a centralized data management platform through the Industrial Internet of Things, identify the data of different electrical devices through timestamps, and align the data between different electrical devices; Collect and integrate operating status data from multiple electrical devices in real time through data sharing; Companion diagnostic identification unit for: Matching the operating status data of each electrical device with the fault database, identifying the fault mode caused by multiple electrical devices, and identifying the fault types of different equipment groups according to the fault modes between multiple electrical devices, wherein the equipment group fault types include systemic faults, local overloads, and load imbalances; Combined with the spatial distribution characteristics of electrical equipment, the specific location of equipment group faults is located through data cross-comparison, and the results of collaborative diagnosis are transmitted to the alarm display module and remote communication module in real time.

8. An electrical equipment operation fault monitoring system as claimed in claim 7, characterized in that: The distributed collaborative diagnosis module further includes: Data aggregation optimization unit for: Aggregate the operating status data of multiple electrical devices to generate a global data view, which includes the real-time status of each electrical device and the environment around the device; An optimization objective function is set based on the operation objectives of the equipment, wherein the operation objectives include load balancing, temperature control, and power consumption optimization, and the operation parameters of the equipment are optimized by optimizing the objective function to adjust the workload and operating temperature parameters of the electrical equipment; The corresponding adjustment strategy is generated based on the optimization results and sent to each electrical device for execution through control signals. Continuous optimization is performed based on the feedback from the electrical devices and the global data view.

9. An electrical equipment operation fault monitoring system as claimed in claim 1, characterized in that: The alarm display module comprises: Fault alarm generating unit, for: Different alarm levels are set according to the fault type, where minor faults are set as warnings and serious faults generate emergency alarms. When a device fails, an alarm signal is generated, which includes the fault type, device location, and fault severity. The alarm signal is transmitted to the maintenance personnel terminal through the remote communication module; Operation status display unit, used for: Based on the real-time collected operating status data, charts and curves are generated and displayed on the device display and mobile interface, showing the diagnosis results of equipment faults. Based on the changes in the operating status data of electrical equipment, the operating data and fault diagnosis results are dynamically updated.

10. An electrical equipment operation fault monitoring system as claimed in claim 1, characterized in that: The data storage module comprises: Real-time data storage unit for: Receive the real-time transmission of electrical equipment operating status data from the data acquisition module and record it in time series. Each piece of operating status data includes the equipment ID, acquisition time, sensor type and corresponding measurement value, and write the real-time operating status data into the storage system; Historical data storage unit, used for: Some important indicators in the real-time operation status data and historical diagnostic data are stored regularly, and the important indicators include current, voltage and temperature. The fault diagnosis result data generated by the data processing and analysis module and the distributed collaborative diagnosis module are stored regularly.

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