Comprehensive monitoring system for intelligent power equipment

Abstract

The invention, which relates to the technical field of power equipment monitoring, discloses an intelligent power equipment integrated monitoring system comprising a sensor module, a data acquisition module, a data analysis module, a fault diagnosis module, a remote monitoring module and an auxiliary module. The system has the advantages that the temperature, vibration and noise data of the dry-type transformer and the motor are monitored in real time through the sensor module, the change trend of the states is analyzed in combination with an algorithm of the data analysis module, potential faults can be recognized in advance, and therefore more efficient equipment management is achieved, preventive maintenance is promoted, and the system is suitable for popularization and application. The fault rate of the equipment is further reduced, the overall operation efficiency of the equipment is improved, and corresponding processing modes can be adopted according to abnormities of different degrees by adopting judgment rules of multilevel fault severity in the data analysis and fault diagnosis module.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to an intelligent power equipment comprehensive monitoring system. Background Art

[0002] In the power system, dry-type transformers and motors are key equipment, and their operating status directly affects the stability and safety of the entire system. However, traditional equipment maintenance methods often rely on regular manual inspections and post-failure repairs. This method is not only inefficient, but also difficult to effectively prevent failures before they occur. With the rapid development of sensor technology and data analysis technology, real-time monitoring and preventive maintenance have gradually become important trends in power equipment management.

[0003] Most traditional power equipment monitoring systems can only monitor a single parameter, such as temperature or vibration. This single parameter monitoring method is often difficult to fully reflect the operating status of the equipment and can easily lead to missed detection or misjudgment of faults. Therefore, we propose an intelligent power equipment comprehensive monitoring system. Summary of the invention

[0004] The purpose of the present invention is to provide a comprehensive monitoring system for intelligent power equipment.

[0005] In order to solve the problems raised in the above background technology, the present invention provides the following technical solutions: a comprehensive monitoring system for intelligent power equipment, comprising the following modules:

[0006] Sensor modules for monitoring temperature, vibration and noise data of dry-type transformers and motors;

[0007] The data acquisition module is used to collect the temperature, vibration and noise data monitored by the sensor module and pre-process the data;

[0008] The data analysis module is used to receive the data processed by the data acquisition module and analyze the operation status of the equipment through the data;

[0009] The fault diagnosis module is used to receive the analysis results of the data analysis module and diagnose the equipment status according to the analysis results. If there is abnormal data, the type and severity of the fault are determined, and the specific location and cause of the fault are determined in combination with the preset fault diagnosis rules;

[0010] Remote monitoring module, used for users to remotely view the operating status, monitoring data and fault information of the equipment;

[0011] Auxiliary module, used to provide stable and reliable power supply, save historical data and test reports, and provide a human-computer interaction interface.

[0012] As a further solution of the present invention: The sensor module includes a temperature sensor unit, a vibration sensor unit, and a noise sensor unit. The temperature sensor unit is used to monitor the winding temperature and oil temperature of the dry-type transformer, as well as the winding temperature and bearing temperature of the motor. The vibration sensor unit is used to monitor the vibration states of the dry-type transformer and the motor. The noise sensor unit is used to monitor the noise states of the dry-type transformer and the motor.

[0013] As a further solution of the present invention: The data analysis module includes an algorithm execution unit. The algorithm execution unit is used to analyze the change trends of temperature, vibration, and noise states. The expression of the temperature data analysis algorithm is:

[0014]

[0015] Where, T n represents the weighted moving average temperature of the nth node, N is the window size, T i is the temperature value at the ith time point, ω i is the weight corresponding to the ith time point. The expression of the vibration data analysis algorithm is:

[0016]

[0017] Where, x t is the vibration value at the current moment, p is the order of the autoregressive model, φ q is the autoregressive coefficient, representing the influence degree of the vibration value at the qth past time point on the vibration value at the current moment. x t-q is the vibration value at the qth past time point, m is the error term, and n is the constant term, representing the average level of vibration. The noise data analysis algorithm uses the wavelet transform algorithm. The algorithm execution unit will analyze the change trends of temperature, vibration, and noise states in real time and transmit the analysis results to the fault diagnosis module.

[0018] As a further solution of the present invention: The specific fault diagnosis rule is that, according to the abnormal data type and the source device in the analysis results, the real-time data of the sensors on the corresponding device are obtained. The abnormal types are noise abnormality, temperature abnormality, and vibration abnormality. The severity levels are divided into minor abnormality, moderate abnormality, and severe abnormality. The judgment method for the severity level of noise abnormality is that, set Q as the average value of the noise intensity during normal operation, and P as the current noise intensity. If Q×1.2 < P ≤ Q×1.5, it means the noise intensity is slightly abnormal. If Q×1.2 < P ≤ Q×1.8, it means the noise intensity is moderately abnormal. If P > Q×1.8, it means the noise intensity is severely abnormal.

[0019] As a further solution of the present invention: In the fault diagnosis module, the judgment method for the severity level of temperature abnormality is that, set T Vis the average temperature of the equipment during normal operation, T D is the current temperature, K is T D and T V If 1℃≤K<5℃, it means the temperature is slightly abnormal; 5℃≤K<15℃, it means the temperature is moderately abnormal; if K≥15℃, it means the temperature is severely abnormal.

[0020] As a further solution of the present invention: In the fault diagnosis module, the vibration abnormality severity judgment method is to set Z V is the average vibration amplitude of the equipment during normal operation, Z C is the current vibration amplitude, F is Z C With Z V If the deviation value is 0mm / s≤F<0.1mm / s, it means that the vibration is slightly abnormal; if 0.1mm / s≤F<0.3mm / s, it means that the vibration is moderately abnormal; if F≥0.3mm / s, it means that the vibration is seriously abnormal.

[0021] As a further solution of the present invention: the method for handling minor abnormalities is to focus on monitoring the abnormal equipment, the method for handling moderate abnormalities is to send a text message containing preliminary diagnostic suggestions to a preset maintenance personnel team, and the method for handling serious abnormalities is to immediately stop the equipment, send an alarm to the maintenance personnel via text message, and provide the location of the abnormal equipment and fault information.

[0022] As a further solution of the present invention: the remote monitoring module includes a data transmission unit and a terminal display unit, the data transmission unit is used to transmit the operating status, monitoring data and fault information of the equipment to the user's remote terminal through the network, and the terminal display unit is used to display the operating status and related information of the equipment on the terminal device.

[0023] As a further solution of the present invention: the auxiliary module includes a power supply unit, a data storage unit and a human-computer interaction unit, the power supply unit is used to provide a stable power supply, the data storage unit is used for the historical data generated by the system, and the human-computer interaction unit is used to provide a human-computer interaction interface.

[0024] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. The present invention uses a sensor module to monitor the temperature, vibration and noise data of dry-type transformers and motors in real time, and combines the algorithm of the data analysis module to analyze the changing trends of these states, so as to identify potential faults in advance, thereby achieving more efficient equipment management, promoting preventive maintenance, and thus reducing equipment failure rate and improving the overall operation efficiency of the equipment;

[0026] 2. The present invention adopts multi-level fault severity judgment rules in the data analysis and fault diagnosis module, and can adopt corresponding processing methods according to different degrees of abnormalities. For the classification and processing of minor, moderate and severe abnormalities, the operation and maintenance personnel can respond accurately, ensure the stable operation of the equipment, and improve the timeliness and effectiveness of fault response;

[0027] 3. The present invention sets up a remote monitoring module so that users can access the operating status, monitoring data and fault information of the equipment through the network anytime and anywhere, which improves the convenience of equipment management and supports remote diagnosis, helping operation and maintenance personnel to quickly obtain information and make decisions, further reducing equipment downtime and improving system availability and operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The figure is a flow chart of the monitoring system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] 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.

[0030] Embodiment 1:

[0031] Please refer to the attached Figure 1 The present invention provides a comprehensive monitoring system for intelligent power equipment, comprising the following modules:

[0032] Sensor modules for monitoring temperature, vibration and noise data of dry-type transformers and motors;

[0033] The data acquisition module is used to collect the temperature, vibration and noise data monitored by the sensor module and pre-process the data;

[0034] The data analysis module is used to receive the data processed by the data acquisition module and analyze the operation status of the equipment through the data;

[0035] The fault diagnosis module is used to receive the analysis results of the data analysis module and diagnose the equipment status according to the analysis results. If there is abnormal data, the type and severity of the fault are determined, and the specific location and cause of the fault are determined in combination with the preset fault diagnosis rules;

[0036] Remote monitoring module, used for users to remotely view the operating status, monitoring data and fault information of the equipment;

[0037] Auxiliary module, used to provide stable and reliable power supply, save historical data and test reports, and provide a human-computer interaction interface;

[0038] The sensor module includes a temperature sensor unit, a vibration sensor unit and a noise sensor unit. The temperature sensor unit is used to monitor the winding temperature and oil temperature of the dry-type transformer and the winding temperature and bearing temperature of the motor. The vibration sensor unit is used to monitor the vibration state of the dry-type transformer and the motor. The noise sensor unit is used to monitor the noise state of the dry-type transformer and the motor.

[0039] The data analysis module includes an algorithm execution unit, which is used to analyze the changing trends of temperature, vibration and noise status. The expression of the temperature data analysis algorithm is:

[0040]

[0041] Among them, T n represents the weighted moving average temperature of the nth node, N is the window size, T i is the temperature value at the i-th time point, ω i is the weight corresponding to the i-th time point, and the expression of the vibration data analysis algorithm is:

[0042]

[0043] Among them, x t is the vibration value at the current moment, p is the order of the autoregressive model, φ q is the autoregressive coefficient, which indicates the influence of the vibration value at the qth time point in the past on the vibration value at the current moment, x t-q is the vibration value at the qth time point in the past, m is the error term, n is the constant term, which represents the average level of vibration. The noise data analysis algorithm adopts the wavelet transform algorithm. The algorithm execution unit will analyze the changing trends of temperature, vibration and noise status in real time, and transmit the analysis results to the fault diagnosis module.

[0044] Specific workflow: In a certain power plant, the intelligent power equipment comprehensive monitoring system of the present invention is installed to monitor the dry-type transformers and motors in the plant in real time. The temperature sensor unit in the sensor module accurately monitors the winding temperature and oil temperature of the dry-type transformer and the winding temperature and bearing temperature of the motor. The vibration sensor unit and the noise sensor unit also continuously collect the vibration state and noise state of the equipment respectively.

[0045] For example, during a period of monitoring, the data analysis module analyzes the temperature data through the weighted moving average temperature algorithm and discovers that the temperature change trend of a certain dry-type transformer is abnormal. By comparing with historical data, the average temperature of this transformer during normal operation is 45°C. During the current time period, the temperature gradually rises. After calculation, the moving average temperature reaches 50°C, and the temperature deviation is 5°C. According to the judgment rules of the system, this is within the range of minor abnormality. At this time, the system automatically strengthens the monitoring frequency of this device and deeply analyzes the reason for the temperature change. After further investigation, it is found that due to partial blockage of the heat dissipation channel, the heat dissipation is poor and the temperature gradually rises;

[0046] By promptly discovering and handling this problem, it avoids potential equipment failures that may be caused by further temperature increase, reduces the equipment failure rate. According to statistics, after installing this monitoring system, the equipment failure rate of this factory has decreased by 30% compared with before, significantly improving the overall optimized operation efficiency of the equipment.

[0047] Furthermore, by the sensor module to real-time monitor the temperature, vibration and noise data of the dry-type transformer and the motor, and combining with the algorithm of the data analysis module to analyze the change trend of these states, it can identify potential failures in advance, thus achieving more efficient equipment management, promoting preventive maintenance, and further reducing the equipment failure rate and improving the overall operation efficiency of the equipment.

[0048] Embodiment 2:

[0049] Based on Embodiment 1, the fault diagnosis rules are specifically as follows. According to the abnormal data type and the source device in the analysis result, obtain the real-time data of the sensors on the corresponding device. The abnormal types are noise abnormality, temperature abnormality and vibration abnormality. The severity levels are divided into minor abnormality, moderate abnormality and severe abnormality. The judgment method for the severity level of noise abnormality is to set Q as the average noise intensity during normal operation and P as the current noise intensity. If Q×1.2 < P ≤ Q×1.5, it indicates a minor noise intensity abnormality. If Q×1.2 < P ≤ Q×1.8, it indicates a moderate noise intensity abnormality. If P > Q×1.8, it indicates a severe noise intensity abnormality;

[0050] In the fault diagnosis module, the judgment method for the severity level of temperature abnormality is to set T V as the average temperature during normal operation of the device, T D as the current temperature, and K as the deviation between T D and T V If 1°C ≤ K < 5°C, it indicates a minor temperature abnormality. If 5°C ≤ K < 15°C, it indicates a moderate temperature abnormality. If K ≥ 15°C, it indicates a severe temperature abnormality;

[0051] In the fault diagnosis module, the judgment method for the severity level of vibration abnormality is to set Z Vis the average vibration amplitude of the equipment during normal operation, Z C is the current vibration amplitude, F is Z C With Z V If the deviation value is 0mm / s≤F<0.1mm / s, it means that the vibration is slightly abnormal; if 0.1mm / s≤F<0.3mm / s, it means that the vibration is moderately abnormal; if F≥0.3mm / s, it means that the vibration is seriously abnormal.

[0052] Specific workflow: In a large industrial enterprise, the intelligent power equipment integrated monitoring system played an important role. Once, the vibration sensor unit detected that the vibration of a motor was abnormal. After the autoregressive vibration analysis algorithm in the data analysis module processed the vibration data, it was found that the current vibration value deviated greatly from the average vibration value during normal operation.

[0053] During normal operation, the average vibration amplitude of the motor is 0.05mm / s, while the current vibration amplitude has reached 0.15mm / s. According to the system's judgment rules, this is a moderate abnormality. The system immediately sends a text message containing preliminary diagnostic suggestions to the preset maintenance team. After receiving the text message, the maintenance personnel quickly rushed to the site for inspection. After detailed inspection, it was determined that the motor bearings had a certain degree of wear. Since this problem was discovered and handled in time, the fault was avoided from further deterioration.

[0054] In another monitoring, the noise sensor unit detected that the noise intensity of a dry-type transformer increased abnormally. After analysis, the current noise intensity was twice the average noise intensity during normal operation, which was a serious abnormality. The system immediately stopped the equipment and sent an alarm to the maintenance personnel via SMS. At the same time, the location of the abnormal equipment and fault information were provided. The maintenance personnel quickly took measures to repair it, avoiding a major accident.

[0055] This multi-level fault severity judgment and classification process allows operation and maintenance personnel to respond accurately, improving the timeliness and effectiveness of fault response and ensuring stable operation of the equipment.

[0056] Furthermore, by adopting multi-level fault severity judgment rules in the data analysis and fault diagnosis module, corresponding processing methods can be adopted according to different degrees of abnormalities. The classification and processing of minor, moderate and severe abnormalities allows operation and maintenance personnel to respond accurately, ensure the stable operation of the equipment, and improve the timeliness and effectiveness of fault response.

[0057] Embodiment three:

[0058] Based on the second embodiment, the handling method of minor abnormalities is to focus on monitoring the abnormal equipment, the handling method of moderate abnormalities is to send a text message containing preliminary diagnosis suggestions to the preset maintenance personnel team, and the handling method of serious abnormalities is to immediately stop the equipment and send an alarm to the maintenance personnel via text message, and provide the location of the abnormal equipment and fault information;

[0059] The remote monitoring module includes a data transmission unit and a terminal display unit. The data transmission unit is used to transmit the operating status, monitoring data and fault information of the equipment to the user's remote terminal through the network. The terminal display unit is used to display the operating status and related information of the equipment on the terminal device.

[0060] The auxiliary module includes a power supply unit, a data storage unit and a human-computer interaction unit. The power supply unit is used to provide a stable power supply, the data storage unit is used for the historical data generated by the system, and the human-computer interaction unit is used to provide a human-computer interaction interface.

[0061] Specific workflow: In a widely distributed power distribution network, the remote monitoring module of the intelligent power equipment comprehensive monitoring system plays a huge role. Managers can access the equipment's operating status, monitoring data and fault information anytime and anywhere through the network;

[0062] For example, during a routine inspection, the manager discovered through the terminal display unit of the remote monitoring module that the temperature of a dry-type transformer in a remote area showed a slight upward trend. By checking the detailed data, it was found that the current temperature deviated from the average temperature during normal operation by about 3°C, which was in the slightly abnormal range. The manager immediately arranged for local maintenance personnel to focus on monitoring the transformer.

[0063] In another scenario, a motor experienced moderately abnormal vibration. The remote monitoring module promptly sent fault information to the manager, who could quickly organize professionals to conduct remote diagnosis based on the preliminary diagnostic suggestions provided by the system and arrange for nearby maintenance personnel to go to the site for processing.

[0064] The remote monitoring module greatly improves the convenience of equipment management and supports remote diagnosis, enabling operation and maintenance personnel to quickly obtain information and make decisions. According to statistics, after using this monitoring system, the power distribution network has reduced equipment downtime by 40%, and the system availability and operational efficiency have been significantly improved.

[0065] Furthermore, by setting up a remote monitoring module, users can access the equipment's operating status, monitoring data and fault information through the network anytime and anywhere, which improves the convenience of equipment management and supports remote diagnosis, helping operation and maintenance personnel to quickly obtain information and make decisions, further reducing equipment downtime and improving system availability and operational efficiency.

[0066] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A comprehensive monitoring system for intelligent power equipment, characterized in that: It includes the following modules: The sensor module is used to monitor the temperature, vibration and noise data of the dry-type transformer and the motor; The data acquisition module is used to collect the temperature, vibration and noise data monitored by the sensor module and preprocess the data; The data analysis module is used to receive the data processed by the data acquisition module and analyze the operating conditions of the equipment through the data; The fault diagnosis module is used to receive the analysis results of the data analysis module and diagnose the equipment status according to the analysis results. If there are abnormal data, it judges the type and severity of the fault, and determines the specific location and cause of the fault in combination with the preset fault diagnosis rules; The remote monitoring module is used for users to remotely view the operating status, monitoring data and fault information of the equipment; The auxiliary module is used to provide a stable and reliable power supply, store historical data and test reports, and provide a man-machine interaction interface.

2. According to claim 1, a comprehensive monitoring system for intelligent power equipment is characterized in that: The sensor module includes a temperature sensor unit, a vibration sensor unit and a noise sensor unit. The temperature sensor unit is used to monitor the winding temperature and oil temperature of the dry-type transformer and the winding temperature and bearing temperature of the motor. The vibration sensor unit is used to monitor the vibration states of the dry-type transformer and the motor. The noise sensor unit is used to monitor the noise states of the dry-type transformer and the motor.

3. The intelligent power equipment comprehensive monitoring system according to claim 1 is characterized in that: The data analysis module includes an algorithm execution unit. The algorithm execution unit is used to analyze the change trends of the temperature, vibration and noise states. The expression of the temperature data analysis algorithm is: Among them, T n represents the weighted moving average temperature of the nth node, N is the window size, T i is the temperature value at the i-th time point, ω i is the weight corresponding to the i-th time point, and the expression of the vibration data analysis algorithm is: Among them, x t is the vibration value at the current moment, p is the order of the autoregressive model, φ q is the autoregressive coefficient, which indicates the influence of the vibration value at the qth time point in the past on the vibration value at the current moment, x t-q is the vibration value at the qth time point in the past, m is the error term, n is the constant term, which represents the average level of vibration. The noise data analysis algorithm adopts the wavelet transform algorithm. The algorithm execution unit will analyze the changing trends of temperature, vibration and noise status in real time, and transmit the analysis results to the fault diagnosis module.

4. The intelligent power equipment comprehensive monitoring system according to claim 1 is characterized in that: The specific fault diagnosis rules are as follows: according to the abnormal data type and the source equipment in the analysis results, the real-time data of the sensors on the corresponding equipment are obtained. The abnormal types are noise abnormality, temperature abnormality and vibration abnormality. The severity levels are divided into minor abnormality, moderate abnormality and severe abnormality. The judgment method for the severity of noise abnormality is: set Q as the average noise intensity during normal operation, and P as the current noise intensity. If Q×1.2 < P ≤ Q×1.5, it indicates a minor noise intensity abnormality. If Q×1.2 < P ≤ Q×1.8, it indicates a moderate noise intensity abnormality. If P > Q×1.8, it indicates a severe noise intensity abnormality.

5. The intelligent power equipment comprehensive monitoring system according to claim 4 is characterized in that: In the fault diagnosis module, the severity of temperature anomaly is determined by setting T V is the average temperature of the equipment during normal operation, T D is the current temperature, K is T D and T V If 1℃≤K<5℃, it means the temperature is slightly abnormal; 5℃≤K<15℃, it means the temperature is moderately abnormal; if K≥15℃, it means the temperature is severely abnormal.

6. The intelligent power equipment comprehensive monitoring system according to claim 4 is characterized in that: In the fault diagnosis module, the vibration abnormality severity is determined by setting Z V is the average vibration amplitude of the equipment during normal operation, Z C is the current vibration amplitude, F is Z C With Z V If the deviation value is 0mm / s≤F<0.1mm / s, it means that the vibration is slightly abnormal; if 0.1mm / s≤F<0.3mm / s, it means that the vibration is moderately abnormal; if F≥0.3mm / s, it means that the vibration is seriously abnormal.

7. The intelligent power equipment comprehensive monitoring system according to claim 4 is characterized in that: The processing method for minor abnormality is to focus on monitoring the abnormal equipment. The processing method for moderate abnormality is to send a text message containing preliminary diagnosis suggestions to the preset maintenance personnel team. The processing method for severe abnormality is to immediately stop the equipment, send an alarm to the maintenance personnel through a text message, and provide the location and fault information of the abnormal equipment.

8. The intelligent power equipment comprehensive monitoring system according to claim 1 is characterized in that: The remote monitoring module includes a data transmission unit and a terminal display unit. The data transmission unit is used to transmit the operating status, monitoring data and fault information of the equipment to the user's remote terminal through the network. The terminal display unit is used to display the operating conditions and relevant information of the equipment on the terminal device.

9. The intelligent power equipment comprehensive monitoring system according to claim 1, characterized in that: The auxiliary module includes a power supply unit, a data storage unit and a man-machine interaction unit. The power supply unit is used to provide a stable power supply. The data storage unit is used to store the historical data generated by the system. The man-machine interaction unit is used to provide a man-machine interaction interface.

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