Electrical equipment operation monitoring method and system, electronic equipment and storage medium

Abstract

The invention belongs to the field of electrical equipment monitoring, and discloses an electrical equipment operation monitoring method and system, electronic equipment and a storage medium, and the method comprises the following steps: obtaining real-time monitoring data in an electrical equipment operation process; segmenting the real-time monitoring data by using the sliding window to obtain a plurality of data windows; calculating the change trend of each operation parameter in each data window to obtain change rate characteristics; constructing a state monitoring model based on the historical operation data, and evaluating the equipment operation state based on the change rate characteristics to obtain a window evaluation result; and judging an abnormal window based on a window evaluation result, extracting operation data of the abnormal window, and judging a fault type and a fault position. By fusing multi-dimensional parameter analysis and dynamic window processing, real-time monitoring and accurate fault diagnosis of the operation state of the electrical equipment are realized, the maintenance efficiency and reliability of the equipment are improved, and the method is of great significance to guarantee safe and stable operation of a power system.

Description

Technical Field

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

[0002] In the process of monitoring the operation of electrical equipment, there is a technical contradiction: how to quickly and accurately identify potential signs of equipment failure in the massive amount of real-time monitoring data. During the operation of electrical equipment, a large amount of parameter data such as voltage, current, and power will be generated. These data are collected and transmitted in real time at a frequency of milliseconds. However, the equipment operation status is normal for most of the time, and only the data at very few time points reflect the abnormal status of the equipment. How to identify the key data reflecting the signs of failure from the vast amount of normal data is a technical problem that needs to be solved urgently.

[0003] Traditional threshold alarm methods are difficult to cope with equipment abnormality monitoring under complex working conditions. For example, short-term impact currents during equipment startup or shutdown can easily trigger thresholds and produce false alarms. Moreover, some precursors to faults are reflected in the correlation changes of multiple parameters, which cannot be detected by the threshold alarm of a single parameter. On the other hand, most existing data mining algorithms are aimed at offline data analysis and it is difficult to meet the performance requirements of real-time online monitoring. How to balance the real-time and accuracy of data analysis, build a set of effective algorithm systems, and realize rapid evaluation of equipment operating status and fault warning is a technical bottleneck that needs to be broken through in the field of electrical equipment operation monitoring. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides an electrical equipment operation monitoring method, comprising the following steps:

[0005] Obtain real-time monitoring data during the operation of electrical equipment;

[0006] Using a sliding window to process the real-time monitoring data in sections to obtain a plurality of data windows;

[0007] Calculating the change trend of each operating parameter in each data window to obtain a change rate characteristic;

[0008] Building a status monitoring model based on historical operation data, and evaluating the equipment operation status based on the change rate characteristics to obtain a window evaluation result;

[0009] The abnormal window is determined based on the window evaluation result, and the operation data of the abnormal window is extracted to determine the fault type and fault location.

[0010] Preferably, the segmentation processing method includes:

[0011] Preprocess the real-time monitoring data to remove outliers and noise data, obtaining preprocessed data;

[0012] Determine the moving step size of the sliding window, segment the preprocessed data according to the determined window, and leave an overlapping part between adjacent windows, obtaining a number of the data windows.

[0013] Preferably, the method for obtaining the window evaluation result includes:

[0014] Construct an initial neural network model;

[0015] Obtain the historical operation data of the electrical equipment, and train the initial neural network model based on the historical operation data to obtain the state monitoring model;

[0016] Input the change rate feature into the state monitoring model to obtain the window evaluation result.

[0017] Preferably, the method for judging the fault type and the fault location includes:

[0018] Set an evaluation result threshold, compare the window evaluation result with the evaluation result threshold to obtain the abnormal window;

[0019] Extract the operation data of the electrical equipment within the abnormal window, and extract the data features of the operation data of the electrical equipment;

[0020] Construct an equipment fault feature library, input the data features into the equipment fault feature library for search and matching to obtain the fault type and the fault location.

[0021] The present invention also provides an electrical equipment operation monitoring system, which is used to implement the method described in any one of the above, including: a data acquisition module, a data segmentation module, a feature calculation module, an evaluation module, and a fault diagnosis module;

[0022] The data acquisition module is used to obtain real-time monitoring data during the operation of the electrical equipment;

[0023] The data segmentation module uses a sliding window to segment the real-time monitoring data to obtain a number of data windows;

[0024] The feature calculation module is used to calculate the change trend of each operation parameter within each data window to obtain a change rate feature;

[0025] The evaluation module constructs a state monitoring model based on historical operation data, and evaluates the operation state of the equipment based on the change rate feature to obtain a window evaluation result;

[0026] The fault diagnosis module determines abnormal windows based on the window evaluation results, extracts the operation data of the abnormal windows, and determines the fault type and fault location.

[0027] Preferably, the data segmentation module includes: a preprocessing unit and a segmentation unit;

[0028] The preprocessing unit is used to preprocess the real-time monitoring data, remove outliers and noise data, and obtain preprocessed data;

[0029] The segmentation unit is used to determine the moving step of the sliding window, segment the preprocessed data according to the determined window, and leave an overlapping part between adjacent windows to obtain a number of the data windows.

[0030] Preferably, the evaluation module includes: a model construction unit, a model training unit, and an evaluation unit;

[0031] The model construction unit is used to construct an initial neural network model;

[0032] The model training unit is used to obtain the historical operation data of the electrical equipment, and train the initial neural network model based on the historical operation data to obtain the state monitoring model;

[0033] The evaluation unit is used to input the change rate feature into the state monitoring model to obtain the window evaluation result.

[0034] Preferably, the fault diagnosis module includes: a result comparison unit, a data feature extraction unit, and a fault diagnosis unit;

[0035] The result comparison unit is used to set an evaluation result threshold, compare the window evaluation result with the evaluation result threshold to obtain the abnormal window;

[0036] The data feature extraction unit is used to extract the operation data of the electrical equipment within the abnormal window and extract the data features of the operation data of the electrical equipment;

[0037] The fault diagnosis unit is used to construct an equipment fault feature library, input the data features into the equipment fault feature library for search and matching to obtain the fault type and the fault location.

[0038] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the electrical equipment operation monitoring method is implemented.

[0039] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed, implements the method for monitoring the operation of an electrical device.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] The present invention uses a sliding window technique for segmented processing, calculates the parameter change rate characteristics within each window, and compares them with a preset normal operation state model to identify abnormal windows; for abnormal windows, the correlation characteristics between parameters and the correlation coefficient matrix are further extracted; combined with a pre-established fault diagnosis rule base, the specific fault type and location can be quickly determined. This method realizes the real-time monitoring of the operation state of electrical equipment and accurate fault diagnosis by integrating multi-dimensional parameter analysis and dynamic window processing, improves the equipment maintenance efficiency and reliability, and is of great significance for ensuring the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0043] Figure 1 is a schematic flowchart of the method according to an embodiment of the present invention;

[0044] Figure 2 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS:

[0046] 1010, processor; 1020, memory; 1030, input / output interface; 1040, communication interface; 1050, bus. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0048] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the ordinary meanings understood by those of ordinary skill in the field to which the present disclosure belongs. The "first", "second" and similar terms used in the embodiments of the present disclosure do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative position relationships, and when the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0049] Embodiment 1

[0050] In this embodiment, as Figure 1 shown, a method for monitoring the operation of an electrical device includes the following steps:

[0051] S1. Obtain real-time monitoring data during the operation of the electrical device.

[0052] In this embodiment, the real-time monitoring of electrical devices is the key to ensuring their safe and stable operation. By collecting parameters such as voltage, current, and power, the operation status of the device can be comprehensively grasped. Taking a transformer as an example, real-time monitoring of its winding temperature, oil temperature, load and other data helps to detect potential faults in a timely manner. The selection of the data collection frequency is crucial. For large generator sets, data may need to be collected every millisecond to capture transient changes; while for distribution transformers, collecting data once per second is sufficient. Too high a collection frequency will increase the storage and processing burden, and too low a frequency may miss key information.

[0053] S2. Use a sliding window to segment the real-time monitoring data to obtain a number of data windows.

[0054] The method of segmenting the data includes: preprocessing the real-time monitoring data to remove outliers and noise data to obtain preprocessed data; determining the moving step of the sliding window, segmenting the preprocessed data according to the determined window, and leaving an overlapping part between adjacent windows to obtain a number of data windows.

[0055] In this embodiment, the sliding window technique is an effective method for processing real-time monitoring data. By setting an appropriate window size, the temporal correlation of the data can be captured. In the preprocessing stage, it is crucial to remove outliers and noisy data. Taking current data as an example, the median filtering method can be used to remove sudden spike interferences. Suppose the normal current range is 10 - 20 amperes. If an instantaneous value of 100 amperes appears, it may be caused by equipment startup or external interference and should be identified and removed to ensure the accuracy of subsequent analysis. After that, a 5-minute sliding window can be set to slide once per second with a step size of 1 minute. This can not only ensure the real-time nature of the data but also capture the current fluctuation trend in the short term. Determine the moving step size of the sliding window, segment the preprocessed data according to the determined window, and leave an overlapping part between adjacent windows to obtain several data windows; the moving step size of the sliding window determines the degree of data overlap.

[0056] S3. Calculate the change trend of each operating parameter within each data window to obtain the change rate feature.

[0057] In this embodiment, numerical calculations are performed on each operating parameter within each data window to obtain the initial value and the end value of the parameter. For example, within a 5-minute data window, for the voltage parameter, record the voltage value at the start moment of the window as the initial value and the voltage value at the end moment of the window as the end value. Calculate the change amount of each operating parameter within the data window. The change amount is equal to the end value minus the initial value. Still taking voltage as an example, if the initial voltage is 220V and the end voltage is 222V, then the change amount of the voltage is 2V. Calculate the change rate of each operating parameter within the data window. The change rate is equal to the change amount divided by the initial value and then multiplied by 100%. In the above voltage example, the change rate of the voltage is (2 / 220) × 100% ≈ 0.91%. For each operating parameter, take the calculated change rate as the change rate feature of the parameter within this data window. Combine the change rate features of all operating parameters to form the comprehensive change rate feature vector of this data window for subsequent state evaluation and analysis.

[0058] S4. Construct a state monitoring model based on historical operation data and evaluate the operating state of the equipment based on the change rate feature to obtain the window evaluation result.

[0059] The methods for obtaining the window evaluation result include: constructing an initial neural network model; obtaining the historical operation data of the electrical equipment, training the initial neural network model based on the historical operation data to obtain the state monitoring model; inputting the change rate feature into the state monitoring model to obtain the window evaluation result.

[0060] In this embodiment, a multi-layer perceptron is selected as the initial model, including: 1 input layer, 2 hidden layers, and 1 output layer. The number of neurons in the input layer should match the dimension of the change rate feature. The number of neurons in the hidden layer can be optimized according to experience or by methods such as cross-validation. The number of neurons in the output layer is determined according to the number of classifications of the device operating state. At the same time, a suitable activation function is selected for each neuron. For example, the ReLU function is used for the hidden layer, and the softmax function is used for the output layer to introduce non-linearity so that the model can better learn the complex relationships in the data. Then, historical operation data of the electrical equipment is collected from the database or data management system of the electrical equipment. These data should cover the operation parameter records of the equipment under normal operation, different fault states, and various working conditions, such as the change data of parameters such as voltage, current, power, and temperature over time. At the same time, the data is labeled to clarify the device operation state category corresponding to each data sample, such as fault types such as normal operation, overload, short circuit, and insulation aging, for use in the subsequent supervised learning process of model training. The labeled historical operation data is divided into a training set, a validation set, and a test set according to the ratio of 7:2:1. The training set is used to train the initial neural network model. The weights and bias parameters of the network are continuously adjusted through the backpropagation algorithm to minimize the loss function of the model. During the training process, the batch gradient descent method is used to update the network parameters to obtain a preliminarily trained model. Then, the validation set is used to monitor the training process of the model to prevent overfitting. When the performance of the model on the validation set no longer improves or signs of overfitting appear, the training is stopped, and the model parameters at this time are recorded as the trained state monitoring model. The change rate features within each calculated data window are organized into a format matching the input layer of the model and input into the state monitoring model as the input data of the model to evaluate the device operation state represented by each data window and obtain the window evaluation result.

[0061] S5. Based on the window evaluation result, judge the abnormal window, extract the operation data of the abnormal window, and judge the fault type and fault location.

[0062] The methods for judging the fault type and fault location include: setting an evaluation result threshold, comparing the window evaluation result with the evaluation result threshold to obtain the abnormal window; extracting the operation data of the electrical equipment within the abnormal window and extracting the data features of the operation data of the electrical equipment; constructing an equipment fault feature library, inputting the data features into the equipment fault feature library for search and matching to obtain the fault type and fault location.

[0063] In this embodiment, according to the distribution of window evaluation results under the normal operating state of the electrical equipment, combined with the equipment operation experience and industry standards, a reasonable evaluation result threshold is determined. For example, for transformer equipment, if the window evaluation result shows that the health index of the equipment operating state is lower than 0.8, it is considered that there may be an abnormality in this window, and 0.8 is set as the evaluation result threshold. Traverse the evaluation results of all data windows and compare them with the set evaluation result threshold one by one. If the evaluation result of a certain data window is lower than the threshold, it is determined that this window is an abnormal window, and its position and time range in the data sequence are recorded for subsequent further analysis. According to the position and time range of the abnormal window, the complete electrical equipment operation data within this abnormal window is extracted from the original real-time monitoring data, including the specific values of various operation parameters such as voltage, current, power, temperature, etc., providing a detailed basis for subsequent judgment of the fault type and fault location. Feature extraction is performed on the operation data within the extracted abnormal window, and features such as the change trend, fluctuation amplitude, and correlation of various operation parameters are analyzed. The typical operation data features of the electrical equipment under different fault types and fault locations are collected, and a comprehensive equipment fault feature library is constructed through various methods such as expert experience, historical fault case analysis, and fault simulation experiments. The data feature vectors extracted from the abnormal window are compared with the feature vectors in the equipment fault feature library one by one to find the fault feature vector with the most similar or highest matching degree. Specifically, methods such as Euclidean distance and cosine similarity can be used to measure the similarity between the data feature vector and the feature vector in the fault feature library, and the fault type and fault location corresponding to the fault feature vector with the highest matching degree are found, so as to achieve accurate judgment of the electrical equipment fault.

[0064] It should be noted that the method of the embodiment of the present disclosure can be executed by a single device, such as a computer or a server, etc. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In this case of a distributed scenario, one of these multiple devices can only execute one or more steps of the method of the embodiment of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0065] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention. The actions or steps recited in the claims can be executed in a different order from that in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or consecutive order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] Embodiment 2

[0067] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure further provides an electrical equipment operation monitoring system, including: a data acquisition module, a data segmentation module, a feature calculation module, an evaluation module, and a fault diagnosis module.

[0068] The data acquisition module is used to obtain real-time monitoring data during the operation of the electrical equipment.

[0069] The data segmentation module uses a sliding window to segment the real-time monitoring data to obtain a number of data windows.

[0070] The data segmentation module includes: a preprocessing unit and a segmentation unit; the preprocessing unit is used to preprocess the real-time monitoring data, remove outliers and noise data, and obtain preprocessed data; the segmentation unit is used to determine the moving step of the sliding window, segment the preprocessed data according to the determined window, and leave an overlapping part between adjacent windows to obtain a number of data windows.

[0071] The feature calculation module is used to calculate the change trend of each operating parameter within each data window to obtain a change rate feature.

[0072] The evaluation module constructs a state monitoring model based on historical operation data, and evaluates the operation state of the equipment based on the change rate feature to obtain a window evaluation result.

[0073] The evaluation module includes: a model construction unit, a model training unit, and an evaluation unit; the model construction unit is used to construct an initial neural network model; the model training unit is used to obtain the historical operation data of the electrical equipment, train the initial neural network model based on the historical operation data to obtain a state monitoring model; the evaluation unit is used to input the change rate feature into the state monitoring model to obtain a window evaluation result.

[0074] The fault diagnosis module determines abnormal windows based on the window evaluation results, extracts the operation data of the abnormal windows, and determines the fault type and fault location.

[0075] The fault diagnosis module includes: a result comparison unit, a data feature extraction unit, and a fault diagnosis unit; the result comparison unit is used to set an evaluation result threshold, compare the window evaluation results with the evaluation result threshold to obtain abnormal windows; the data feature extraction unit is used to extract the operation data of the electrical equipment within the abnormal windows and extract the data features of the operation data of the electrical equipment; the fault diagnosis unit is used to construct an equipment fault feature library, input the data features into the equipment fault feature library for search and matching to obtain the fault type and fault location.

[0076] The system of the above embodiment is used to implement the corresponding electrical equipment operation monitoring method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0077] It should be noted that the above electrical equipment operation monitoring system is embodied in the form of functional units. The term "module" here can be implemented in software and / or hardware forms, and no specific limitation is made thereto.

[0078] For example, the "module" can be a software program, a hardware circuit, or a combination of the two that implements the above functions. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a proprietary processor, or a group of processors, etc.) for executing one or more software or firmware programs, a memory, a combined logic circuit, and / or other suitable components that support the described functions.

[0079] Embodiment III

[0080] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the electrical equipment operation monitoring method described in any of the above embodiments.

[0081] Figure 2 Fig. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0082] The processor 1010 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0083] The memory 1020 can be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0084] The input / output interface 1030 is used to connect to the input / output module to implement information input and output. The input / output module can be configured as a component in the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Among them, the input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0085] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to implement communication interaction between this device and other devices. Among them, the communication module can implement communication through a wired method (such as USB (Universal Serial Bus), network cable, etc.) or can also implement communication through a wireless method (such as a mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0086] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0087] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, this device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solutions of the embodiments of this specification, and do not necessarily include all the components shown in the figure.

[0088] The system of the above embodiments is used to implement the corresponding electrical equipment operation monitoring method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated herein.

[0089] Embodiment 4

[0090] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the electrical equipment operation monitoring method as described in any of the foregoing embodiments.

[0091] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0092] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the electrical equipment operation monitoring method as described in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated herein.

[0093] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the concept of the present disclosure, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of brevity.

[0094] In addition, for simplicity of explanation and discussion, and so as not to make the embodiments of the present disclosure difficult to understand, well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that details regarding the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be entirely within the understanding of those skilled in the art). In cases where specific details (such as circuits) are set forth to describe exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be practiced without these specific details or with variations of these specific details. Accordingly, these descriptions should be regarded as illustrative rather than restrictive.

[0095] Although the present disclosure has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0096] Therefore, the units of the examples described in the embodiments of the present application can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0097] The embodiments of the present disclosure are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for monitoring the operation of electrical equipment, characterized in that: The following steps are involved: Obtain real-time monitoring data during the operation of electrical equipment; Using a sliding window to process the real-time monitoring data in sections to obtain a plurality of data windows; Calculating the change trend of each operating parameter in each data window to obtain a change rate characteristic; Building a status monitoring model based on historical operation data, and evaluating the equipment operation status based on the change rate characteristics to obtain a window evaluation result; The abnormal window is determined based on the window evaluation result, and the operation data of the abnormal window is extracted to determine the fault type and fault location.

2. The method for monitoring the operation of electrical equipment according to claim 1, characterized in that: The segmentation processing method includes: Preprocessing the real-time monitoring data to remove abnormal values ​​and noise data to obtain preprocessed data; The moving step length of the sliding window is determined, and the preprocessed data is segmented according to the determined windows, and overlapping parts are left between adjacent windows to obtain a plurality of data windows.

3. The method for monitoring the operation of electrical equipment according to claim 1, characterized in that: The method for obtaining the window evaluation result includes: Build an initial neural network model; Acquire the historical operation data of the electrical equipment, and train the initial neural network model based on the historical operation data to obtain the state monitoring model; The change rate feature is input into the condition monitoring model to obtain the window evaluation result.

4. The method for monitoring the operation of electrical equipment according to claim 1, characterized in that: The method for determining the fault type and the fault location includes: Setting an evaluation result threshold, comparing the window evaluation result with the evaluation result threshold, and obtaining the abnormal window; Extracting the electrical equipment operation data within the abnormal window, and extracting data features of the electrical equipment operation data; Construct an equipment fault feature library, input the data feature into the equipment fault feature library for search and matching, and obtain the fault type and the fault location.

5. An electrical equipment operation monitoring system, the system being used to implement the method according to any one of claims 1 to 4, characterized in that: include: Data acquisition module, data segmentation module, feature calculation module, evaluation module and fault diagnosis module; The data acquisition module is used to obtain real-time monitoring data during the operation of the electrical equipment; The data segmentation module uses a sliding window to segment the real-time monitoring data to obtain a number of data windows; The feature calculation module is used to calculate the change trend of each operating parameter in each data window to obtain a change rate feature; The evaluation module constructs a status monitoring model based on historical operation data, and evaluates the equipment operation status based on the change rate characteristics to obtain a window evaluation result; The fault diagnosis module determines the abnormal window based on the window evaluation result, extracts the operation data of the abnormal window, and determines the fault type and fault location.

6. An electrical equipment operation monitoring system according to claim 5, characterized in that: The data segmentation module includes: a pre-processing unit and a segmentation unit; The preprocessing unit is used to preprocess the real-time monitoring data, remove abnormal values ​​and noise data, and obtain preprocessed data; The segmentation unit is used to determine the moving step of the sliding window, segment the preprocessed data according to the determined windows, and leave overlapping parts between adjacent windows to obtain a plurality of data windows.

7. An electrical equipment operation monitoring system according to claim 5, characterized in that: The evaluation module includes: a model building unit, a model training unit and an evaluation unit; The model building unit is used to build an initial neural network model; The model training unit is used to obtain the historical operation data of the electrical equipment, and train the initial neural network model based on the historical operation data to obtain the state monitoring model; The evaluation unit is used to input the change rate feature into the state monitoring model to obtain the window evaluation result.

8. The electrical equipment operation monitoring system according to claim 5, characterized in that: The fault diagnosis module includes: a result comparison unit, a data feature extraction unit and a fault diagnosis unit; The result comparison unit is used to set an evaluation result threshold, compare the window evaluation result with the evaluation result threshold, and obtain the abnormal window; The data feature extraction unit is used to extract the electrical equipment operation data within the abnormal window, and extract the data features of the electrical equipment operation data; The fault diagnosis unit is used to construct an equipment fault feature library, input the data feature into the equipment fault feature library for search and matching, and obtain the fault type and the fault location.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for monitoring the operation of electrical equipment as claimed in any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method for monitoring the operation of electrical equipment according to any one of claims 1 to 4 is implemented.

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