Intelligent monitoring method and system for power equipment

Abstract

The invention discloses an intelligent monitoring method and system for power equipment, and relates to the field of power equipment monitoring. An intelligent monitoring system for power equipment comprises a digital twinborn body module, a data acquisition module, a data processing module, an abnormity monitoring module, a fault detection module and an intelligent alarm module. According to the invention, preliminary anomaly monitoring and independent real-time anomaly monitoring are carried out on the data of the power equipment, so that the data volume of data processing is reduced, fault prediction is carried out according to the result, the power equipment needing to be rechecked is obtained according to the fault prediction result, associated anomaly monitoring is carried out on the rechecked power equipment, and the reliability of the power equipment is improved. And finally, intelligent alarm is performed according to the obtained independent anomaly monitoring result, the fault prediction result and the associated anomaly monitoring result, so that the monitoring performance of the power equipment is improved.

Description

Technical Field

[0001] The present invention relates to the field of power equipment monitoring, and in particular to a method and system for intelligent monitoring of power equipment. Background Art

[0002] As modern society becomes increasingly dependent on electricity, the stability and reliability of the power system have become particularly important. Power equipment, such as transformers, circuit breakers, and transmission lines, are core components of the power system, and their normal operation is directly related to the safety and power supply quality of the entire power system. However, as the equipment runs longer, power equipment may fail due to various factors, such as insulation aging, mechanical wear, and overload. Traditional power equipment monitoring methods mainly rely on regular manual inspections and maintenance, which has problems such as low efficiency, poor timeliness, and insufficient accuracy.

[0003] To solve these problems, the power industry has gradually introduced intelligent monitoring technology in recent years. By monitoring the operating status of power equipment in real time, it can realize intelligent evaluation of equipment health status and fault prediction. This intelligent monitoring system can provide more timely and accurate equipment status information and give early warnings before faults occur, thereby effectively reducing power outages and maintenance costs caused by equipment failures. The current intelligent monitoring system has limited data processing capabilities. With the increase in monitoring equipment and the amount of collected data, traditional data processing technologies are difficult to meet the processing needs of large-scale, real-time data. The prediction model is not perfect. The existing fault prediction models are often based on simple rules or a single algorithm, which makes it difficult to fully capture the complexity of equipment failures, resulting in low prediction accuracy. The system integration is not high. Many intelligent monitoring systems are designed for specific equipment or specific faults. They lack the ability to comprehensively monitor the entire power system, resulting in the inability to share data between systems and unsatisfactory monitoring results. Summary of the invention

[0004] The present invention provides a method and system for intelligent monitoring of electric power equipment with high integration, strong data processing capability and high prediction accuracy, so as to meet the higher requirements of modern power systems for safe, reliable and economical operation of equipment.

[0005] An intelligent monitoring method for electric power equipment, comprising: S1. Obtain the equipment information and operation status information of the power equipment, perform collective simulation of the power equipment through the equipment information, obtain the correlation information between the power equipment, use the power equipment operation status information to perform virtual-real mapping, and build a digital twin model of the power equipment operation status; S2. Collect real-time operation data of power equipment at a set period through high-precision sensors; S3. Perform preliminary abnormal analysis on the real-time operation data to obtain the data to be analyzed, and perform noise reduction on the data to be analyzed to obtain noise reduction data; S4. Send the noise reduction data of each power device into the real-time abnormal monitoring model for independent abnormal monitoring to obtain the real-time independent abnormal monitoring results of a single power device; S5. The power equipment for which the preliminary abnormal analysis processing results and the real-time independent abnormal monitoring results are not normal is regarded as the fault equipment to be predicted, and the power equipment associated with the fault equipment to be predicted and the specific association relationship are obtained according to the association information of the fault equipment to be predicted, and the data to be analyzed of the fault equipment to be predicted and the power equipment associated with the fault equipment to be predicted in the current cycle and the previous 9 cycles are obtained; Generate samples to be predicted, take the fault device to be predicted as the main object, and its corresponding 10 cycles of data to be analyzed as the main object data, take each power device that has an association relationship with the main object as an associated object, and the 10 cycles of data to be analyzed corresponding to the associated object as the associated object data, and add a relationship weight to the associated object data according to the type of the association relationship; The samples to be predicted are sent to the fault prediction model for fault prediction to obtain fault prediction results, the power equipment corresponding to the abnormal fault prediction results is obtained as the re-inspection equipment, the noise reduction processing data of the re-inspection equipment and the power equipment associated with it is obtained, which is called re-inspection data, and the re-inspection data is sent to the abnormal monitoring model for associated abnormal monitoring to obtain the associated abnormal monitoring results of the re-inspection equipment; S6. Record the independent abnormal monitoring results, fault prediction results and associated abnormal monitoring results of the power equipment in the intelligent monitoring log, and make intelligent alarms based on the records.

[0006] As a preferred technical solution of the present invention, the steps of performing preliminary abnormality analysis on real-time operation data to obtain data to be analyzed specifically include: The operation data collected over a period of time is cached, the standard deviation of each data item between the real-time operation data and the operation data in the cache is calculated, the data exceeding the set threshold is regarded as abnormal data, the missing values ​​in the real-time operation data are detected, the abnormal data and missing values ​​are processed using moving smoothing, and the abnormal data and missing values ​​are recorded in the review log.

[0007] As a preferred technical solution of the present invention, the step of performing noise reduction processing on the data to be analyzed to obtain the noise reduction processed data specifically includes: The data to be analyzed of the power equipment is represented by X, and it is divided according to the data type to obtain N modal components. The modal components are represented by Z N ={z 1 ,z 2 ,···,zn} indicates that the energy density Q of the nth modal component is calculated. n and the average period T n , the calculation formula is: , where M 1 Indicates the collection density of equipment operation data; V 2 represents the sampling volume; K Y Indicates the negative amplitude coefficient, t 0 Indicates the time required for data denoising; Calculate the variational modal component noise threshold G h , G h =Q n *T n , detection variational modal component noise threshold G h Whether a mutation occurs. If a mutation occurs, the previous modal component is noise. All the monitored noise is deleted to obtain the noise reduction processing data X ’ .

[0008] As a preferred technical solution of the present invention, the real-time anomaly monitoring model includes a mode selection layer and an anomaly monitoring layer; The mode selection layer is used to select a model according to the input data type. If the input data is the noise reduction processing data of a single power device, it enters the independent abnormality monitoring mode. If the input data is the noise reduction processing data of multiple power devices, it enters the associated abnormality monitoring mode. The abnormal monitoring layer is used to extract fault characteristic parameters from data information, describe the fault characteristic parameters through multi-dimensional feature vectors, and output the abnormal monitoring results of power equipment.

[0009] As a preferred technical solution of the present invention, it also includes building a real-time abnormality monitoring model, and the specific steps are: A1. Acquire real-time operation data of several power equipment, including the real-time operation data of a single power equipment and the real-time operation data of power equipment associated with it, perform preprocessing operations on the real-time operation data and mark abnormal monitoring labels, form the real-time operation data of each power equipment into an independent abnormal monitoring data set, form the real-time operation data of the power equipment and the power equipment associated with it into an associated abnormal monitoring data set, split the independent abnormal monitoring data set and the associated abnormal monitoring data set to obtain an independent abnormal test set, an associated abnormal test set, an independent abnormal training set and an associated abnormal training set; A2. Send the independent anomaly training set and the associated anomaly training set in batches to the initial real-time anomaly monitoring model based on the BP neural network for training, and train the optimized real-time anomaly monitoring model with the anomaly monitoring label as the target; A3. Send the independent anomaly test set and the associated anomaly test set to the optimized real-time anomaly monitoring model for accuracy evaluation. Taking the anomaly monitoring label as the target, adjust the model's hyperparameters to obtain a real-time anomaly monitoring model with an accuracy evaluation within the set range.

[0010] As a preferred technical solution of the present invention, it also includes a fault prediction model, which is based on recurrent neural network training. The goal of the model is to predict the fault prediction results of the samples, and is obtained through training of timing device operation data of several power equipment and power equipment associated with them.

[0011] As a preferred technical solution of the present invention, the steps of constructing a digital twin model of the operating status of power equipment specifically include: B1. Build a physical model for each power equipment, construct a three-dimensional space for storing model objects, perform collective simulation on the physical model, clarify the specifications and equipment parameter data of the power equipment, and add them as attribute values ​​to the physical model; B2. Obtain the correlation information between different power equipment, establish data interface and build physical model association, and construct a digital model of power equipment operation; B3. Use the operating status information of power equipment for virtual-reality mapping, collect data sets uploaded to the cloud platform, including the operating status, working conditions, and sensor monitoring data of power equipment, use data-driven models to achieve mapping of digital virtual bodies, and build a digital twin model of the operating status of power equipment.

[0012] As a preferred technical solution of the present invention, the real-time operation data includes voltage, current, temperature, vibration and humidity parameters of the power equipment.

[0013] As a preferred technical solution of the present invention, intelligent alarm is performed according to the recorded situation, and the specific steps are as follows: C1. Obtain independent abnormal monitoring results, fault prediction results and associated abnormal monitoring results in the intelligent monitoring log; C2. If one or more of the three results are abnormal, but not all are abnormal, a yellow level alarm will be issued, a yellow warning light will be lit, and the yellow alarm result will be recorded; C3. If all three results are abnormal, a red level alarm will be triggered, with an audible alarm and a red warning light; C4. Associate the actual result of the abnormality of the power equipment with the record, perform weight calculation according to the weight of the record of the actual result of the abnormality of the power equipment, and issue a red level alarm for the record whose weight exceeds the set threshold.

[0014] An intelligent monitoring system for electric power equipment, comprising: The digital twin module is used to obtain the equipment information and operation status information of the power equipment, perform collective simulation of the power equipment through the equipment information, obtain the correlation information between the power equipment, use the power equipment operation status information to perform virtual-real mapping, and build a digital twin model of the power equipment operation status; A data acquisition module is used to collect real-time operation data of power equipment at a set period through high-precision sensors; The data processing module performs preliminary abnormal analysis on the real-time operation data to obtain the data to be analyzed, and performs noise reduction on the data to be analyzed to obtain the noise reduction processed data; The abnormality monitoring module is used to send the noise reduction processing data of each power device into the real-time abnormality monitoring model for independent abnormality monitoring, and obtain the real-time independent abnormality monitoring results of a single power device; send the re-inspection data into the abnormality monitoring model for associated abnormality monitoring, and obtain the associated abnormality monitoring results of the re-inspected device; A fault prediction module is used to take the power equipment for which the preliminary abnormality analysis processing results and the real-time independent abnormality monitoring results are not normal as the fault equipment to be predicted, obtain the power equipment associated with the fault equipment to be predicted and the specific association relationship according to the association information of the fault equipment to be predicted, and obtain the data to be analyzed of the fault equipment to be predicted and the power equipment associated with the fault equipment to be predicted in the current cycle and the previous 9 cycles; Generate samples to be predicted, take the fault device to be predicted as the main object, and its corresponding 10 cycles of data to be analyzed as the main object data, take each power device that has an association relationship with the main object as an associated object, and the 10 cycles of data to be analyzed corresponding to the associated object as the associated object data, and add a relationship weight to the associated object data according to the type of the association relationship; The samples to be predicted are sent to the fault prediction model for fault prediction to obtain fault prediction results, and the power equipment corresponding to the abnormal fault prediction results is obtained as the re-inspection equipment, and the data to be analyzed of the re-inspection equipment and the power equipment associated with it are obtained, which is called re-inspection data; The intelligent alarm module is used to record the independent abnormal monitoring results, fault prediction results and associated abnormal monitoring results of the power equipment in the intelligent monitoring log, and to make intelligent alarms according to the recorded conditions.

[0015] The present invention has the following advantages: 1. The present invention reduces the amount of data processing by performing preliminary abnormality monitoring and independent real-time abnormality monitoring on the data of power equipment, and performs fault prediction based on the results. The power equipment that needs to be re-inspected is obtained based on the fault prediction results, and associated abnormality monitoring is performed on the re-inspected power equipment. Finally, intelligent alarms are performed based on the obtained independent abnormality monitoring results, fault prediction results and associated abnormality monitoring results, thereby improving the monitoring performance of the power equipment.

[0016] 2. The present invention constructs a digital twin model of power equipment and performs correlation analysis on the power equipment based on the correlation relationship of the model, thereby realizing joint analysis of multiple power equipment and improving the integration performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a schematic diagram of the structure of an intelligent monitoring system for electric power equipment used in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0019] Embodiment 1, a method for intelligent monitoring of electric power equipment, comprising: S1. Obtain the equipment information and operation status information of the power equipment, perform collective simulation of the power equipment through the equipment information, obtain the correlation information between the power equipment, use the power equipment operation status information to perform virtual-real mapping, and build a digital twin model of the power equipment operation status; The steps of constructing the digital twin model of the operating status of the power equipment in step S1 specifically include: B1. Build a physical model for each power equipment, construct a three-dimensional space for storing model objects, perform collective simulation on the physical model, clarify the specifications and equipment parameter data of the power equipment, and add them as attribute values ​​to the physical model; B2. Obtain the correlation information between different power equipment, establish data interface and build physical model association, and construct a digital model of power equipment operation; B3. Use the operating status information of power equipment for virtual-reality mapping, collect data sets uploaded to the cloud platform, including the operating status, working conditions, and sensor monitoring data of power equipment, use data-driven models to achieve mapping of digital virtual bodies, and build a digital twin model of the operating status of power equipment.

[0020] S2. Collect real-time operation data of power equipment at a set period through high-precision sensors; Real-time operating data includes the voltage, current, temperature, vibration, and humidity parameters of the power equipment. The data is obtained through the sensors of the operating equipment. The sensor data is first transmitted to the local computer and then uploaded to the data center through the network for processing. The data center can be a large computer room or a cloud platform.

[0021] S3. Perform preliminary abnormal analysis on the real-time operation data to obtain the data to be analyzed, and perform noise reduction on the data to be analyzed to obtain noise reduction data; The steps of performing preliminary abnormal analysis on real-time operation data to obtain the data to be analyzed specifically include: The operation data collected over a period of time is cached, the standard deviation of each data item between the real-time operation data and the operation data in the cache is calculated, the data exceeding the set threshold is regarded as abnormal data, the missing values ​​in the real-time operation data are detected, the abnormal data and missing values ​​are processed using moving smoothing, and the abnormal data and missing values ​​are recorded in the review log.

[0022] The moving smoothing method used is 5-point smoothing, that is, the average value of 4 data, 2 data before and after the processing point, is used to replace the processing point data; The preliminary anomaly analysis process removes outliers and missing values ​​in the data, reducing the abnormal interference received during anomaly processing; The steps of performing noise reduction processing on the data to be analyzed and obtaining the noise reduction processed data specifically include: The data to be analyzed of the power equipment is represented by X, and it is divided according to the data type to obtain N modal components. The modal components are represented by Z N ={z 1 ,z 2 ,···,z n} indicates that the energy density Q of the nth modal component is calculated. n and the average period T n , the calculation formula is: , where M 1 Indicates the collection density of equipment operation data; V 2 represents the sampling volume; K Y Indicates the negative amplitude coefficient, t 0 Indicates the time required for data denoising; Calculate the variational modal component noise threshold G h , G h =Q n *T n , detection variational modal component noise threshold G h Whether a mutation occurs. If a mutation occurs, the previous modal component is noise. All the monitored noise is deleted to obtain the noise reduction processing data X ’ ; Abnormal data is further removed through noise reduction processing, allowing the model to conduct more targeted analysis of data changes; S4. Send the noise reduction data of each power device into the real-time abnormal monitoring model for independent abnormal monitoring to obtain the real-time independent abnormal monitoring results of a single power device; The real-time anomaly monitoring model includes a mode selection layer and an anomaly monitoring layer; The mode selection layer is used to select a model according to the input data type. If the input data is the noise reduction processing data of a single power device, it enters the independent abnormality monitoring mode. If the input data is the noise reduction processing data of multiple power devices, it enters the associated abnormality monitoring mode. The abnormal monitoring layer is used to extract fault characteristic parameters from data information, describe the fault characteristic parameters through multi-dimensional feature vectors, and output the abnormal monitoring results of power equipment.

[0023] The final output is the real-time abnormal monitoring result of the power equipment, and the output calculation formula is:

[0024] In the formula Indicates the abnormal result of the output. is the mapping function representing the i-th data, represents the output feature vector; Build a real-time anomaly monitoring model. The specific steps are: A1. Acquire real-time operation data of several power equipment, including the real-time operation data of a single power equipment and the real-time operation data of power equipment associated with it, perform preprocessing operations on the real-time operation data and mark abnormal monitoring labels, form the real-time operation data of each power equipment into an independent abnormal monitoring data set, form the real-time operation data of the power equipment and the power equipment associated with it into an associated abnormal monitoring data set, split the independent abnormal monitoring data set and the associated abnormal monitoring data set to obtain an independent abnormal test set, an associated abnormal test set, an independent abnormal training set and an associated abnormal training set; The sample objects in the associated anomaly monitoring data set include main objects and associated objects, which are the current power equipment and the power equipment associated with it. The associated objects have associated weights, which are obtained by the type of associated relationship. A2. Send the independent anomaly training set and the associated anomaly training set in batches to the initial real-time anomaly monitoring model based on the BP neural network for training, and train the optimized real-time anomaly monitoring model with the anomaly monitoring label as the target; The initial real-time anomaly monitoring model includes 1 integrated input layer, 8 hidden layers and 1 integrated output layer. The integrated input layer is used to receive input data. The first 3 layers of the 8 hidden layers are used to analyze the characteristic relationship between the real-time operation data of the current power equipment, and the last 5 layers are used to analyze the characteristic relationship between the real-time operation data of the current power equipment and the power equipment associated with it. When the mode is the independent anomaly monitoring mode, only the first 3 layers of the hidden layer are open. When the mode is the associated anomaly monitoring mode, the data is analyzed through all hidden layers. The integrated output layer is used to output the real-time anomaly monitoring results. A3. Send the independent anomaly test set and the associated anomaly test set to the optimized real-time anomaly monitoring model for accuracy evaluation. Taking the anomaly monitoring label as the target, adjust the model's hyperparameters to obtain a real-time anomaly monitoring model with an accuracy evaluation within the set range.

[0025] S5. The power equipment for which the preliminary abnormal analysis processing results and the real-time independent abnormal monitoring results are not normal is regarded as the fault equipment to be predicted, and the power equipment associated with the fault equipment to be predicted and the specific association relationship are obtained according to the association information of the fault equipment to be predicted, and the data to be analyzed of the fault equipment to be predicted and the power equipment associated with the fault equipment to be predicted in the current cycle and the previous 9 cycles are obtained; Generate samples to be predicted, take the fault device to be predicted as the main object, and its corresponding 10 cycles of data to be analyzed as the main object data, take each power device that has an association relationship with the main object as an associated object, and the 10 cycles of data to be analyzed corresponding to the associated object as the associated object data, and add a relationship weight to the associated object data according to the type of the association relationship; The specific relationship weight needs to be calculated based on the distance between devices and the type of association relationship. The specific formula is:

[0026] Where d is the distance between devices, D is the standard value of the distance, and α j is the type weight of the association relationship; The samples to be predicted are sent to the fault prediction model for fault prediction to obtain fault prediction results, the power equipment corresponding to the abnormal fault prediction results is obtained as the re-inspection equipment, the noise reduction processing data of the re-inspection equipment and the power equipment associated with it is obtained, which is called re-inspection data, and the re-inspection data is sent to the abnormal monitoring model for associated abnormal monitoring to obtain the associated abnormal monitoring results of the re-inspection equipment; The fault prediction model is based on recurrent neural network training. The goal of the model is to predict the fault prediction results of the samples. It is trained through the timing equipment operation data of several power equipment and the power equipment associated with them.

[0027] The fault prediction model adopted by the method includes 1 integrated input layer, 4 hidden layers and 1 integrated output layer; the integrated input layer is used to receive the data of the input prediction sample; the 4 hidden layers are used to analyze the connection between the data of the input prediction sample, and the integrated output layer is used to output the fault prediction result; In a recurrent neural network, the states of different layers are represented by time steps, and the dependencies in the data are obtained by propagating the recurrent connections between different layers in the time steps. The hidden layer state is determined by the hidden state of the previous time step and the current input. The specific formula is:

[0028] in, represents the hidden state of the time step, tanh represents the hyperbolic tangent activation function, represents the weight of the input layer, Indicates the current input. represents the state of the input layer, represents the weight of the hidden layer, represents the hidden state at the previous time step, Represents the state quantity of the hidden layer; The state of the output layer is determined by the current hidden state. The specific formula is:

[0029] in, Indicates the current output, represents the weight of the output layer, Indicates the current hidden state. is the bias term of the output layer; S6. Record the independent abnormal monitoring results, fault prediction results and associated abnormal monitoring results of the power equipment in the intelligent monitoring log, and make intelligent alarms based on the records.

[0030] In step S6, intelligent alarm is performed according to the recorded situation, and the specific steps are as follows: C1. Obtain independent abnormal monitoring results, fault prediction results and associated abnormal monitoring results in the intelligent monitoring log; C2. If one or more of the three results are abnormal, but not all are abnormal, a yellow level alarm will be issued, a yellow warning light will be lit, and the yellow alarm result will be recorded; C3. If all three results are abnormal, a red level alarm will be triggered, with an audible alarm and a red warning light; C4. Associate the actual result of the abnormality of the power equipment with the record, perform weight calculation according to the weight of the record of the actual result of the abnormality of the power equipment, and issue a red level alarm for the record whose weight exceeds the set threshold.

[0031] The weight of the recorded situation is composed of an initial value and an adjusted value, and the weight of the initial value and the adjusted value will change over time, thus achieving the purpose of dynamically adjusting the intelligent alarm; the specific formula is:

[0032] In the formula are the weights of the initial value and the adjusted value, The value of is larger in the early stage, which can reduce the deviation caused by a small amount of abnormality. It decreases over time, ensuring the accuracy of long-term dependence. U is the number of abnormal true results, and T is the time; Example 2, an intelligent monitoring system for electric power equipment, see Figure 1 As shown, including: The digital twin module is used to obtain the equipment information and operation status information of the power equipment, perform collective simulation of the power equipment through the equipment information, obtain the correlation information between the power equipment, use the power equipment operation status information to perform virtual-real mapping, and build a digital twin model of the power equipment operation status; A data acquisition module is used to collect real-time operation data of power equipment at a set period through high-precision sensors; The data processing module performs preliminary abnormal analysis on the real-time operation data to obtain the data to be analyzed, and performs noise reduction on the data to be analyzed to obtain the noise reduction processed data; The abnormality monitoring module is used to send the noise reduction processing data of each power device into the real-time abnormality monitoring model for independent abnormality monitoring, and obtain the real-time independent abnormality monitoring results of a single power device; send the re-inspection data into the abnormality monitoring model for associated abnormality monitoring, and obtain the associated abnormality monitoring results of the re-inspected device; A fault prediction module is used to take the power equipment for which the preliminary abnormality analysis processing results and the real-time independent abnormality monitoring results are not normal as the fault equipment to be predicted, obtain the power equipment associated with the fault equipment to be predicted and the specific association relationship according to the association information of the fault equipment to be predicted, and obtain the data to be analyzed of the fault equipment to be predicted and the power equipment associated with the fault equipment to be predicted in the current cycle and the previous 9 cycles; Generate samples to be predicted, take the fault device to be predicted as the main object, and its corresponding 10 cycles of data to be analyzed as the main object data, take each power device that has an association relationship with the main object as an associated object, and the 10 cycles of data to be analyzed corresponding to the associated object as the associated object data, and add a relationship weight to the associated object data according to the type of the association relationship; The samples to be predicted are sent to the fault prediction model for fault prediction to obtain fault prediction results, and the power equipment corresponding to the abnormal fault prediction results is obtained as the re-inspection equipment, and the data to be analyzed of the re-inspection equipment and the power equipment associated with it are obtained, which is called re-inspection data; The intelligent alarm module is used to record the independent abnormal monitoring results, fault prediction results and associated abnormal monitoring results of the power equipment in the intelligent monitoring log, and to make intelligent alarms according to the recorded conditions.

[0033] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A method for intelligent monitoring of electric power equipment, characterized in that: include: S1. Obtain the equipment information and operation status information of the power equipment, perform collective simulation of the power equipment through the equipment information, obtain the correlation information between the power equipment, use the power equipment operation status information to perform virtual-real mapping, and build a digital twin model of the power equipment operation status; S2. Collect real-time operation data of power equipment at a set period through high-precision sensors; S3. Perform preliminary abnormal analysis on the real-time operation data to obtain the data to be analyzed, and perform noise reduction on the data to be analyzed to obtain noise reduction data; S4. Send the noise reduction data of each power device into the real-time abnormal monitoring model for independent abnormal monitoring to obtain the real-time independent abnormal monitoring results of a single power device; S5. The power equipment for which the preliminary abnormal analysis processing results and the real-time independent abnormal monitoring results are not normal is regarded as the fault equipment to be predicted, and the power equipment associated with the fault equipment to be predicted and the specific association relationship are obtained according to the association information of the fault equipment to be predicted, and the data to be analyzed of the fault equipment to be predicted and the power equipment associated with the fault equipment to be predicted in the current cycle and the previous 9 cycles are obtained; Generate samples to be predicted, take the fault device to be predicted as the main object, and its corresponding 10 cycles of data to be analyzed as the main object data, take each power device that has an association relationship with the main object as an associated object, and the 10 cycles of data to be analyzed corresponding to the associated object as the associated object data, and add a relationship weight to the associated object data according to the type of the association relationship; The samples to be predicted are sent to the fault prediction model for fault prediction to obtain fault prediction results, the power equipment corresponding to the abnormal fault prediction results is obtained as the re-inspection equipment, the noise reduction processing data of the re-inspection equipment and the power equipment associated with it is obtained, which is called re-inspection data, and the re-inspection data is sent to the abnormal monitoring model for associated abnormal monitoring to obtain the associated abnormal monitoring results of the re-inspection equipment; S6. Record the independent abnormal monitoring results, fault prediction results and associated abnormal monitoring results of the power equipment in the intelligent monitoring log, and make intelligent alarms based on the records.

2. The method for intelligent monitoring of electric power equipment according to claim 1, characterized in that: The steps of performing preliminary abnormal analysis on real-time operation data to obtain the data to be analyzed specifically include: The operation data collected over a period of time is cached, the standard deviation of each data item between the real-time operation data and the operation data in the cache is calculated, the data exceeding the set threshold is regarded as abnormal data, the missing values ​​in the real-time operation data are detected, the abnormal data and missing values ​​are processed using moving smoothing, and the abnormal data and missing values ​​are recorded in the review log.

3. The method for intelligent monitoring of electric power equipment according to claim 1, characterized in that: The steps of performing noise reduction processing on the data to be analyzed and obtaining the noise reduction processed data specifically include: The data to be analyzed of the power equipment is represented by X, and it is divided according to the data type to obtain N modal components. The modal components are represented by Z N ={z1,z2,···,z n } indicates that the energy density Q of the nth modal component is calculated. n and the average period T n , the calculation formula is: , where M1 represents the collection density of equipment operation data; V2 represents the sampling volume; K Y represents the negative amplitude coefficient, and t0 represents the time required for data noise reduction processing; Calculate the variational modal component noise threshold G h , G h =Q n *T n , detection variational modal component noise threshold G h Whether a mutation occurs. If a mutation occurs, the previous modal component is noise. All the monitored noise is deleted to obtain the noise reduction processing data X ’ .

4. The method for intelligent monitoring of electric power equipment according to claim 1, characterized in that: The real-time anomaly monitoring model includes a mode selection layer and an anomaly monitoring layer; The mode selection layer is used to select a model according to the input data type. If the input data is the noise reduction processing data of a single power device, it enters the independent abnormality monitoring mode. If the input data is the noise reduction processing data of multiple power devices, it enters the associated abnormality monitoring mode. The abnormal monitoring layer is used to extract fault characteristic parameters from data information, describe the fault characteristic parameters through multi-dimensional feature vectors, and output the abnormal monitoring results of power equipment.

5. The method for intelligent monitoring of electric power equipment according to claim 4, characterized in that: It also includes building a real-time anomaly monitoring model. The specific steps are: A1. Acquire real-time operation data of several power equipment, including the real-time operation data of a single power equipment and the real-time operation data of power equipment associated with it, perform preprocessing operations on the real-time operation data and mark abnormal monitoring labels, form the real-time operation data of each power equipment into an independent abnormal monitoring data set, form the real-time operation data of the power equipment and the power equipment associated with it into an associated abnormal monitoring data set, split the independent abnormal monitoring data set and the associated abnormal monitoring data set to obtain an independent abnormal test set, an associated abnormal test set, an independent abnormal training set and an associated abnormal training set; A2. Send the independent anomaly training set and the associated anomaly training set in batches to the initial real-time anomaly monitoring model based on the BP neural network for training, and train the optimized real-time anomaly monitoring model with the anomaly monitoring label as the target; A3. Send the independent anomaly test set and the associated anomaly test set to the optimized real-time anomaly monitoring model for accuracy evaluation. Taking the anomaly monitoring label as the target, adjust the model's hyperparameters to obtain a real-time anomaly monitoring model with an accuracy evaluation within the set range.

6. The method for intelligent monitoring of electric power equipment according to claim 1, characterized in that: It also includes a fault prediction model, which is based on recurrent neural network training. The goal of the model is to predict the fault prediction results of the samples, and is obtained through training with the timing equipment operation data of several power equipment and the power equipment associated with them.

7. The method for intelligent monitoring of electric power equipment according to claim 1, characterized in that: The steps to build a digital twin model of the operating status of power equipment include: B1. Build a physical model for each power equipment, construct a three-dimensional space for storing model objects, perform collective simulation on the physical model, clarify the specifications and equipment parameter data of the power equipment, and add them as attribute values ​​to the physical model; B2. Obtain the correlation information between different power equipment, establish data interface and build physical model association, and construct a digital model of power equipment operation; B3. Use the operating status information of power equipment for virtual-reality mapping, collect data sets uploaded to the cloud platform, including the operating status, working conditions, and sensor monitoring data of power equipment, use data-driven models to achieve mapping of digital virtual bodies, and build a digital twin model of the operating status of power equipment.

8. The method for intelligent monitoring of electric power equipment according to claim 1, characterized in that: Real-time operating data includes voltage, current, temperature, vibration and humidity parameters of power equipment.

9. The method for intelligent monitoring of electric power equipment according to claim 1, characterized in that: Intelligent alarm is carried out according to the recorded situation. The specific steps are as follows: C1. Obtain independent abnormal monitoring results, fault prediction results and associated abnormal monitoring results in the intelligent monitoring log; C2. If one or more of the three results are abnormal, but not all are abnormal, a yellow level alarm will be issued, a yellow warning light will be lit, and the yellow alarm result will be recorded; C3. If all three results are abnormal, a red level alarm will be triggered, with an audible alarm and a red warning light; C4. Associate the actual result of the abnormality of the power equipment with the record, perform weight calculation according to the weight of the record of the actual result of the abnormality of the power equipment, and issue a red level alarm for the record whose weight exceeds the set threshold.

10. An intelligent monitoring system for electric power equipment, characterized in that: The system applies an intelligent monitoring method for electric power equipment according to any one of claims 1 to 9, including: The digital twin module is used to obtain the equipment information and operation status information of the power equipment, perform collective simulation of the power equipment through the equipment information, obtain the correlation information between the power equipment, use the power equipment operation status information to perform virtual-real mapping, and build a digital twin model of the power equipment operation status; A data acquisition module is used to collect real-time operation data of power equipment at a set period through high-precision sensors; The data processing module performs preliminary abnormal analysis on the real-time operation data to obtain the data to be analyzed, and performs noise reduction on the data to be analyzed to obtain the noise reduction processed data; The abnormality monitoring module is used to send the noise reduction processing data of each power device into the real-time abnormality monitoring model for independent abnormality monitoring, and obtain the real-time independent abnormality monitoring results of a single power device; send the re-inspection data into the abnormality monitoring model for associated abnormality monitoring, and obtain the associated abnormality monitoring results of the re-inspected device; A fault prediction module is used to take the power equipment for which the preliminary abnormality analysis processing results and the real-time independent abnormality monitoring results are not normal as the fault equipment to be predicted, obtain the power equipment associated with the fault equipment to be predicted and the specific association relationship according to the association information of the fault equipment to be predicted, and obtain the data to be analyzed of the fault equipment to be predicted and the power equipment associated with the fault equipment to be predicted in the current cycle and the previous 9 cycles; Generate samples to be predicted, take the fault device to be predicted as the main object, and its corresponding 10 cycles of data to be analyzed as the main object data, take each power device that has an association relationship with the main object as an associated object, and the 10 cycles of data to be analyzed corresponding to the associated object as the associated object data, and add a relationship weight to the associated object data according to the type of the association relationship; The samples to be predicted are sent to the fault prediction model for fault prediction to obtain fault prediction results, and the power equipment corresponding to the abnormal fault prediction results is obtained as the re-inspection equipment, and the data to be analyzed of the re-inspection equipment and the power equipment associated with it are obtained, which is called re-inspection data; The intelligent alarm module is used to record the independent abnormal monitoring results, fault prediction results and associated abnormal monitoring results of the power equipment in the intelligent monitoring log, and to make intelligent alarms according to the recorded conditions.

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