State evaluation method, system and device based on SCADA and TMR measurement data and medium

Abstract

The invention discloses a state evaluation method, system and device based on SCADA and TMR measurement data and a medium, and relates to the field of power plant station device state monitoring and intelligent operation and maintenance, and the method comprises the steps: collecting electrical parameters, building a device collection end analysis model, and completing the dynamic synchronization and structured storage of multi-source data; preprocessing the electrical parameters to obtain a basic feature set; based on the basic feature set, constructing an equipment state classification model to comprehensively analyze the operation indexes of the plant station, and obtaining the overall operation state grade of the plant station; and generating an execution strategy according to the overall operation state grade of the plant station. According to the method, dynamic synchronization of SCADA and TMR data is realized by constructing a time deviation correction model, and the fusion problem of inconsistent multi-source heterogeneous data acquisition time sequences is effectively solved; and a state evaluation model with a self-learning capability is further introduced, so that the precision of equipment operation state identification and the environmental adaptability of the model are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of plant condition monitoring technology, specifically to condition assessment methods, systems, equipment, and media based on SCADA and TMR measurement data. Background Technology

[0002] As a crucial hub in the power system, the safe operation of power plants is essential to the overall reliability and stability of the power grid. However, power plant equipment is diverse and complex, including core equipment such as main transformers, busbars, and high-voltage switches. During operation, these devices are susceptible to performance degradation and potential failures due to various factors such as load, ambient temperature, and operating conditions. At the same time, the electrical parameters monitored by the SCADA system are highly coupled with the physical state of the equipment, increasing the complexity of condition monitoring. If the operating status of these devices cannot be obtained and effectively assessed in a timely manner, hidden dangers may accumulate, ultimately threatening the safety of the power plant and the entire power system. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by this invention is that existing status assessment methods suffer from problems such as single data sources, asynchronous time, weak abnormal data processing capabilities, lack of adaptability in assessment models, fixed maintenance strategies, and lack of interactive intelligence, making it difficult to achieve accurate assessment and efficient maintenance of plant equipment operating status. How to achieve unified synchronization of SCADA and TMR multi-source data, high-quality feature extraction, self-learning assessment models, and interactive hierarchical maintenance is the key technical problem that this invention aims to solve.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a status assessment method based on SCADA and TMR measurement data, comprising: real-time acquisition of electrical parameters through a SCADA system; establishing an analysis model at the equipment acquisition end to achieve dynamic synchronization and structured storage of multi-source data; preprocessing the synchronized and structured electrical parameters to obtain a basic feature set; constructing an equipment status classification model based on the basic feature set to comprehensively analyze the plant's operating indicators and obtain the overall operating status level of the plant; and generating an execution strategy based on the overall operating status level of the plant, combined with the equipment health level and the content of the strategy library.

[0006] As a preferred embodiment of the condition assessment method based on SCADA and TMR measurement data described in this invention, the step of real-time acquisition of electrical parameters through the SCADA system includes: Temperature rise in transformer windings is measured using temperature sensing equipment, and key thermal parameters of the transformer are monitored in real time using temperature monitoring sensors.

[0007] Partial discharge sensors are installed in areas of high signal concentration near transformer windings or tanks to monitor and collect characteristics of typical insulation defects inside the transformer.

[0008] As a preferred embodiment of the SCADA and TMR measurement data-based state assessment method of the present invention, the step of establishing an analysis model at the device acquisition end to complete the dynamic synchronization and structured storage of multi-source data includes: A correction model is established based on the offset relationship between each data acquisition unit and the standard clock source, and dynamic time synchronization is used to achieve unified alignment of multi-channel data.

[0009] The corrected electrical parameters are stored in a standardized manner according to the equipment identification and time sequence data, forming a time-series database with time consistency and equipment uniqueness.

[0010] As a preferred embodiment of the condition assessment method based on SCADA and TMR measurement data described in this invention, the preprocessing of the electrical parameters after synchronization and structured storage includes: The collected electrical parameters are cleaned and repaired to eliminate noise, remove anomalies, and fill in missing values.

[0011] Align electrical parameters and perform time alignment and integration of heterogeneous data from different sources.

[0012] After preprocessing, a basic feature set containing the operating status of all devices is generated.

[0013] As a preferred embodiment of the condition assessment method based on SCADA and TMR measurement data described in this invention, the step of constructing an equipment condition classification model to comprehensively analyze the plant's operating indicators includes: Based on the basic feature set, an equipment status classification model is constructed to comprehensively analyze the plant operation indicators.

[0014] The equipment status classification model uses the support vector machine method to distinguish different equipment statuses.

[0015] The support vectors and classification hyperplane are determined by solving the dual problem, and the final state classification result is output using the decision function.

[0016] As a preferred embodiment of the status assessment method based on SCADA and TMR measurement data described in this invention, the acquisition of the overall operating status level of the plant includes: Each piece of equipment is assigned a corresponding weight based on its importance to the operation of the plant.

[0017] By combining the health scores and weights of each piece of equipment, the overall impact on the plant is calculated.

[0018] By combining equipment status and operational performance scores using a weighted comprehensive method, an overall health score for the plant is obtained.

[0019] As a preferred embodiment of the status assessment method based on SCADA and TMR measurement data described in this invention, the step of generating execution strategies based on the overall operating status level of the plant, combined with equipment health levels and strategy library content, includes: A tiered maintenance strategy is developed based on the plant's operational status level.

[0020] When the status is healthy, perform routine inspections and remote monitoring.

[0021] When the status is slightly abnormal, optimize the operating parameters and shorten the inspection cycle.

[0022] When the status is abnormal, locate the problematic device, repair the faulty component, and optimize load distribution.

[0023] When the status is severely abnormal, immediately shut down for inspection and maintenance, replace equipment or components, and conduct in-depth analysis in conjunction with external experts.

[0024] This invention provides a condition assessment system based on SCADA and TMR measurement data.

[0025] To address the aforementioned technical problems, this invention provides the following technical solution: a status assessment system based on SCADA and TMR measurement data, comprising: a multi-source data synchronous acquisition and management module, a data preprocessing feature construction module, and a status assessment strategy interaction module.

[0026] The multi-source data synchronous acquisition and management module collects electrical parameters in real time through the SCADA system, establishes an analysis model at the equipment acquisition end, and completes the dynamic synchronization and structured storage of multi-source data.

[0027] The data preprocessing feature construction module performs preprocessing on the electrical parameters after synchronization and structured storage to obtain a basic feature set.

[0028] The status assessment strategy interaction module, based on the basic feature set, constructs an equipment status classification model to comprehensively analyze the plant's operating indicators and obtain the overall operating status level of the plant.

[0029] Execution strategies are generated based on the overall operating status level of the plant, combined with the equipment health level and the content of the strategy library.

[0030] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the state assessment method based on SCADA and TMR measurement data.

[0031] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the state assessment method based on SCADA and TMR measurement data.

[0032] The beneficial effects of this invention are as follows: The condition assessment method based on SCADA and TMR measurement data provided by this invention offers one advantage per solution: By constructing an equipment time deviation correction model and achieving dynamic synchronization of SCADA and TMR data, the problem of inconsistent acquisition times and difficulty in fusion of multi-source data is effectively solved. By introducing a condition assessment model with self-learning capabilities, accurate classification of equipment operating status and dynamic adaptation of the model are achieved, resulting in stronger responsiveness. By establishing an operation and maintenance strategy library and a matching interactive work order generation mechanism, maintenance instructions are made real-time and adjustable, with faster response and more flexible configuration. This invention achieves better results in terms of the timeliness of multi-source data fusion, the accuracy of equipment status identification, and the closed-loop nature of intelligent maintenance response. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 The first embodiment of the present invention provides an overall flowchart of a state assessment method based on SCADA and TMR measurement data.

[0035] Figure 2 The first embodiment of the present invention provides an overall framework diagram of a state assessment system based on SCADA and TMR measurement data. Detailed Implementation

[0036] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0037] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a state assessment method based on SCADA and TMR measurement data, including: S1. Real-time acquisition of electrical parameters through the SCADA system, establishment of equipment acquisition terminal analysis model to complete dynamic synchronization and structured storage of multi-source data.

[0038] S2. Perform preprocessing on the electrical parameters after synchronization and structured storage to obtain the basic feature set.

[0039] S3. Based on the basic feature set, construct an equipment status classification model to comprehensively analyze the operating indicators of the plant and obtain the overall operating status level of the plant.

[0040] S4. Generate execution strategies based on the overall operating status level of the plant, combined with the equipment health level and strategy library content.

[0041] First, the SCADA system is used to achieve dynamic synchronization and structured storage of multi-source electrical parameters, laying a high-quality data foundation for subsequent analysis. Then, a basic feature set is constructed through data preprocessing, effectively improving data quality and usability. On this basis, the operating indicators are comprehensively analyzed through the equipment status classification model to accurately assess the overall operating status level of the plant. Finally, based on the assessment results, combined with the equipment health level and strategy library, execution strategies are intelligently generated, improving operation and maintenance efficiency and the scientific nature of decision-making, and realizing a complete intelligent management closed loop from status monitoring to maintenance execution.

[0042] Example 2, an embodiment of the present invention, provides a state assessment method based on SCADA and TMR measurement data, based on the previous embodiment, including: In this embodiment, the device acquisition end analysis model in S1, namely the time deviation model, specifically includes acquiring real-time operating data through a SCADA system, including voltage, current, frequency, active power, and reactive power. TMR data is obtained by measuring the winding temperature rise using fiber optic temperature sensors or resistance thermometers embedded in the transformer windings, and by using temperature monitoring sensors (such as platinum resistance thermometers) installed in the transformer tank to detect the insulating oil temperature. Partial discharge sensors (such as ultrasonic sensors or high-frequency partial discharge sensors) are installed in signal concentration areas near the transformer windings or tank to monitor the amplitude, frequency, and waveform of partial discharges.

[0043] In one alternative implementation, the device acquisition end analysis model can be a software synchronization model based on data feature matching. First, by identifying common characteristic events in the SCADA and TMR systems (such as current surges caused by switching actions), a pattern recognition algorithm is used to calculate the timestamp offset, a dynamic deviation model is established, and subsequent data timestamps are compensated in real time to achieve multi-source data synchronization.

[0044] In another optional implementation, the device acquisition end analysis model can also be a synchronization model based on hardware time synchronization and data buffering. The specific steps are as follows: by configuring high-precision clock modules for all acquisition units and connecting them to a unified time synchronization network, a precise timestamp is added when the data is generated and temporarily stored in a local buffer. The central service collects multi-source data within the same time window, sorts and reassembles it according to the timestamp, and then stores it in a time-series database to achieve strict time synchronization.

[0045] Furthermore, the real-time acquisition of electrical parameters via the SCADA system in S1 includes steps A1-A2: A1. Measure the temperature rise of the transformer windings using temperature sensing equipment, and use temperature monitoring sensors to monitor the key thermal parameters of the transformer in real time.

[0046] A2. Install partial discharge sensors in areas with concentrated signals near transformer windings or tanks to monitor and collect characteristics of typical insulation defects inside the transformer.

[0047] Furthermore, the establishment of the device acquisition end analysis model in S1 to complete the dynamic synchronization and structured storage of multi-source data includes steps B1-B2: B1. A correction model is established based on the offset relationship between each data acquisition unit and the standard clock source, and dynamic time synchronization is used to achieve unified alignment of multi-channel data.

[0048] B2. Standardize and store the corrected electrical parameters according to equipment identification and time sequence data to form a time-series database with time consistency and equipment uniqueness.

[0049] In this embodiment, dynamic time synchronization in B1, i.e., adjusting timestamps, specifically includes: establishing a time synchronization system based on the IEEE 1588 precision clock protocol; deploying clock deviation monitoring modules in each acquisition terminal; achieving microsecond-level time synchronization through a master-slave clock architecture; using a sliding time window mechanism to dynamically buffer multi-source data; and combining the least squares method to calculate the relative clock drift rate of each acquisition unit in real time; using a Kalman filter algorithm to predict and compensate for clock deviations, and performing dynamic correction during the data timestamp marking stage; and simultaneously establishing an abnormal clock jump detection mechanism, automatically activating a backup clock source when the clock deviation exceeds a threshold, ensuring that the timing consistency of multi-channel data can still be maintained under communication delay fluctuations, ultimately achieving sub-millisecond-level synchronization accuracy of the entire station data acquisition system.

[0050] In one alternative implementation, dynamic time synchronization can be achieved through software synchronization using data feature matching. Specifically, this includes: first, monitoring characteristic events in the SCADA and TMR systems; then, identifying the time offset of the same event in different systems using a dynamic time warping algorithm; next, establishing a time deviation prediction model based on historical offset sequences; and finally, dynamically compensating the timestamps of the collected data according to the model output.

[0051] In another alternative implementation, dynamic time synchronization can also be achieved through hardware time synchronization and data buffer synchronization. Specifically, all acquisition units are configured with hardware clock modules supporting the PTP protocol to establish a unified time synchronization network with microsecond-level precision; high-precision timestamps are applied to data during acquisition and temporarily stored in a circular buffer; a time alignment service collects data packets of the same time window in each buffer at fixed intervals; finally, the multi-source data is sorted and reorganized according to the precise timestamps to generate strictly synchronized time-series data records.

[0052] In this embodiment, the standardized data storage in B2, i.e., the structured storage, is implemented through the following steps: First, a unique logical device identifier is created for each physical device (such as a transformer or circuit breaker), and it is associated and mapped with all relevant SCADA measurement points and TMR sensors. Next, a standardized data attribute system is defined, assigning uniform data types, units, and quality codes to various parameters such as voltage, current, and temperature. Then, the data stream, after time synchronization processing, is organized according to a three-dimensional structure of "logical device - data attribute - timestamp," and labeled with data source and quality tags. Finally, the processed data is written into a dedicated time-series database, forming a structured data set that combines time-series characteristics with device topology relationships, providing a unified and standardized data service interface for upper-layer applications.

[0053] In one alternative implementation, standardized data storage can be based on a tiered storage architecture using data lake technology. The specific steps are as follows: First, a raw data pool is established, persistently storing all raw data packets from all collection sources without processing to ensure data traceability. Second, a standard processing layer is established, using configured scripts for data cleaning, format conversion, and unit normalization to process the raw data into standardized intermediate data. Subsequently, in the application integration layer, the intermediate data is encapsulated into directly usable data views or feature tables based on specific analysis topics (such as status assessment and performance calculation). Finally, a unified metadata service is used to catalog and manage the entire data chain, supporting on-demand data retrieval and access.

[0054] In another alternative implementation, standardized data storage can also be based on an event-triggered snapshot storage mode. This involves: first, the system continuously monitors the data stream, and when it identifies critical events such as switch changes, limit alarms, or planned operations, it immediately triggers the storage mechanism; then, using the event time as a baseline, it automatically collects and correlates multi-source, multi-type data (including electrical quantities, status quantities, environmental quantities, etc.) from all relevant devices within a time window before and after the event, forming a complete data snapshot; next, each snapshot is assigned a unique event ID, and metadata such as event type, associated devices, and time range is recorded in a structured manner; finally, these context-rich data snapshots are stored and managed as independent, standardized data units, greatly improving the efficiency of subsequent event analysis and human-computer interaction diagnostics.

[0055] Furthermore, the preprocessing of the electrical parameters after synchronization and structured storage in S2 includes steps C1-C3: C1. Clean and repair the collected electrical parameters to eliminate noise, remove anomalies, and fill in missing values.

[0056] C2. Align electrical parameters and perform time alignment and integration of heterogeneous data from different sources.

[0057] C3. After preprocessing, a basic feature set containing the operating status of all devices is generated.

[0058] Specifically, the synchronized SCADA and TMR raw data are subjected to sliding window denoising and abrupt change anomaly identification processing. Data completion is performed by combining time series interpolation and equipment behavior model, and a multi-dimensional index set containing state trends and fault characteristics is extracted.

[0059] Further steps involve data denoising. For the collected SCADA and TMR data, a moving average or smoothing algorithm is used to remove short-term fluctuations and noise (such as high-frequency interference or measurement errors).

[0060] Outlier handling involves checking for abnormal data outside the operating range (such as excessively high or low voltage, current, or temperature), marking them as outliers, deleting them, and correcting the abnormal data.

[0061] Missing value completion: For missing values ​​in the collected data caused by equipment or communication failures, interpolation (such as fitting previous and subsequent data or linear completion) or filling with default values ​​is used to fill in the missing values ​​to ensure the integrity of the time series.

[0062] Align SCADA data and TMR data, and use interpolation or resampling methods to unify the time axis to ensure that data obtained from different devices match at the same time point.

[0063] After preprocessing, a basic feature set containing the operating status of all devices is generated.

[0064] Furthermore, the state evaluation model is built based on SVM, as detailed below: The input is based on the feature set x and the label y∈{1,2,3,4}, where 1 represents healthy, 2 represents sub-healthy, 3 represents abnormal, and 4 represents severe abnormal.

[0065] The original optimization problem is transformed into a dual problem using the Lagrange multiplier method, which facilitates its solution. in, Let be the Lagrange multiplier, corresponding to the weight of each sample, and m represent the total number of samples. Let be the Lagrange multiplier corresponding to the j-th training sample. Let represent the true class label of the i-th training sample. Let the j-th training sample be the true class label. Represented as a kernel function, Let it be represented as the feature vector of the i-th training sample. Let it be represented as the feature vector of the j-th training sample.

[0066] Find the support vectors, where the support vectors are... For samples with a value greater than 0, determine the classification hyperplane and calculate the bias b: Decision function: Output the classification result y=argmax(f(x)).

[0067] For scattered missing data caused by communication interruptions or equipment failures, three-point linear interpolation is used for rapid completion. For segments continuously missing for more than three sampling periods, ARIMA-based time series prediction or default safety values ​​are introduced to minimize the impact of gaps on subsequent analysis. Then, data from different sampling frequencies are uniformly resampled to the target frequency. Linear or spline interpolation is used for low-frequency signals, and mean aggregation is performed for high-frequency signals. All timestamps are corrected to achieve precise alignment of all data at the same absolute moment. Finally, multi-dimensional features including time domain (mean, standard deviation, skewness, kurtosis), frequency domain (FFT spectral peaks and amplitude), and transient (rate of change, energy envelope) dimensions are extracted from the preprocessed, smooth, and continuous time series signal to construct an indicator vector that comprehensively reflects the equipment's health status and potential fault symptoms. This design not only significantly improves the reliability and accuracy of subsequent feature extraction and model training, but also uses algorithms with low computational overhead, is easy to deploy in embedded real-time, and provides strong support for subsequent fault location and system maintenance by marking and tracking outliers and missing values.

[0068] Furthermore, in S3, the construction of the equipment status classification model is used to comprehensively analyze the plant's operating indicators, including steps D1-D3: D1. Based on the basic feature set, construct an equipment status classification model to comprehensively analyze the plant operation indicators.

[0069] D2. The equipment status classification model uses the support vector machine method to distinguish different equipment statuses.

[0070] D3. Determine the support vectors and classification hyperplane by solving the dual problem, and output the final state classification result using the decision function.

[0071] Furthermore, obtaining the overall operating status level of the plant in S3 includes steps E1-E3: E1. Assign appropriate weights to each piece of equipment based on its importance to the plant operation.

[0072] E2. Calculate the overall impact on the plant by combining the health scores and weights of each device.

[0073] E3. By combining the equipment status and operating performance scores using a weighted comprehensive method, the overall health score of the plant is obtained.

[0074] Furthermore, based on the health status of the plant equipment, a comprehensive analysis of the plant's operational indicators is conducted to obtain the overall operational status level of the plant, as detailed below: Each piece of equipment is assigned a different weight based on its importance to the plant's operation. .

[0075] For example: the main transformer accounts for 50% of the weight, high-voltage switchgear accounts for 20%, and auxiliary equipment accounts for 30%.

[0076] Based on the health scores of each device With weight The overall impact of computing equipment on the plant: Where N represents the number of plants and stations.

[0077] Weighted comprehensive score of operating indicators: By combining equipment scores and operational performance scores, a comprehensive plant health score is derived: Where α and β are the weights for equipment status and performance considerations, respectively.

[0078] In this embodiment, the execution strategy in S4 is to formulate a graded maintenance strategy based on the plant's operating status level. Specifically, when the status is healthy (plant comprehensive health score 90-100), routine inspections and remote monitoring are performed to maintain good operation. When the status is slightly abnormal (plant comprehensive health score 75-89), the focus is on checking equipment with low scores, optimizing operating parameters, and shortening the inspection cycle. When the status is abnormal (plant comprehensive health score 50-74), the problematic equipment is located, faulty components are quickly repaired, and load distribution is optimized. When the status is severely abnormal (plant comprehensive health score below 50), the plant is immediately shut down for maintenance, equipment or components are replaced if necessary, and in-depth analysis is conducted in conjunction with external experts to ensure safe and reliable operation recovery.

[0079] In one optional implementation, the execution strategy can be a preventative maintenance strategy based on dynamic risk assessment. The specific implementation steps are as follows: the system continuously assesses the health status and operational risks of the equipment, and automatically adjusts the maintenance plan when potential fault characteristics are identified. For low-risk conditions, data monitoring and trend analysis are mainly implemented; when the risk level increases, predictive maintenance processes are initiated, including equipment performance optimization and preventative maintenance; if the risk further increases, targeted maintenance plans are implemented, including component replacement and system parameter adjustment; when a high-risk threshold is reached, an emergency maintenance plan is immediately activated, taking measures such as equipment isolation and system reconfiguration, and organizing experts to conduct root cause analysis of the fault.

[0080] In another optional implementation, the execution strategy can also be a resource-optimized collaborative maintenance strategy. The specific implementation steps are as follows: deeply integrate maintenance decisions with the resource management system, and intelligently allocate maintenance resources according to the equipment status level. Under normal operating conditions, basic inspection resources are allocated according to plan; when minor anomalies occur, the system automatically optimizes resource allocation, dispatches additional professional personnel, and prepares necessary spare parts; when a clear fault is detected, a multi-departmental collaboration mechanism is activated to coordinate and allocate technical teams and repair equipment; in emergency situations, a rapid response mechanism is automatically activated to prioritize the maintenance resources of critical equipment, while coordinating external support to ensure efficient progress of maintenance work.

[0081] Example 3, referring to Figure 2 This is one embodiment of the present invention, which provides a state assessment system based on SCADA and TMR measurement data, including: a multi-source data synchronous acquisition and management module, a data preprocessing feature construction module, and a state assessment strategy interaction module.

[0082] The multi-source data synchronous acquisition and management module collects electrical parameters in real time through the SCADA system, establishes an analysis model at the equipment acquisition end, and completes the dynamic synchronization and structured storage of multi-source data.

[0083] The data preprocessing feature construction module performs preprocessing on the electrical parameters after synchronization and structured storage to obtain the basic feature set.

[0084] The status assessment strategy interaction module, based on the basic feature set, constructs an equipment status classification model to comprehensively analyze the plant's operating indicators and obtain the overall operating status level of the plant.

[0085] Execution strategies are generated based on the overall operating status level of the plant, combined with the equipment health level and the content of the strategy library.

[0086] This embodiment also provides an electronic device applicable to the state assessment method based on SCADA and TMR measurement data, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the state assessment method based on SCADA and TMR measurement data as proposed in the above embodiment.

[0087] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the state assessment method based on SCADA and TMR measurement data as proposed in the above embodiments.

[0088] The storage medium proposed in this embodiment and the state assessment method based on SCADA and TMR measurement data proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0089] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A condition assessment method based on SCADA and TMR measurement data, characterized in that, include: By collecting electrical parameters in real time through the SCADA system, an analysis model of the equipment acquisition end is established to achieve dynamic synchronization and structured storage of multi-source data; Preprocessing is performed on the electrical parameters after synchronization and structured storage to obtain the basic feature set; Based on the basic feature set, an equipment status classification model is constructed to comprehensively analyze the operating indicators of the plant and obtain the overall operating status level of the plant. Execution strategies are generated based on the overall operating status level of the plant, combined with the equipment health level and the content of the strategy library.

2. The condition assessment method based on SCADA and TMR measurement data as described in claim 1, characterized in that: The real-time acquisition of electrical parameters through the SCADA system includes, Temperature rise of transformer windings is measured by temperature sensing equipment, and key thermal parameters of the transformer are monitored in real time by temperature monitoring sensors. Partial discharge sensors are installed in areas of high signal concentration near transformer windings or tanks to monitor and collect characteristics of typical insulation defects inside the transformer.

3. The condition assessment method based on SCADA and TMR measurement data as described in claim 2, characterized in that: The establishment of the device acquisition terminal analysis model to achieve dynamic synchronization and structured storage of multi-source data includes, A correction model is established based on the offset relationship between each data acquisition unit and the standard clock source, and dynamic time synchronization is used to achieve unified alignment of multi-channel data. The corrected electrical parameters are stored in a standardized manner according to the equipment identification and time sequence data, forming a time-series database with time consistency and equipment uniqueness.

4. The condition assessment method based on SCADA and TMR measurement data as described in claim 3, characterized in that: The preprocessing of electrical parameters after synchronization and structured storage includes, The collected electrical parameters are cleaned and repaired to eliminate noise, remove anomalies, and fill in missing values. Align electrical parameters and perform time alignment and integration of heterogeneous data from different sources; After preprocessing, a basic feature set containing the operating status of all devices is generated.

5. The condition assessment method based on SCADA and TMR measurement data as described in claim 4, characterized in that: The constructed equipment status classification model comprehensively analyzes the plant's operational indicators, including: Based on the basic feature set, an equipment status classification model is constructed to comprehensively analyze the plant operation indicators; The equipment status classification model uses the support vector machine method to distinguish different equipment statuses; The support vectors and classification hyperplane are determined by solving the dual problem, and the final state classification result is output using the decision function.

6. The condition assessment method based on SCADA and TMR measurement data as described in claim 5, characterized in that: The acquisition of the overall operating status level of the plant includes Based on the importance of the equipment to the operation of the plant, assign corresponding weights to each piece of equipment; By combining the health scores and weights of each piece of equipment, the overall impact on the plant is calculated. By combining equipment status and operational performance scores using a weighted comprehensive method, an overall health score for the plant is obtained.

7. The condition assessment method based on SCADA and TMR measurement data as described in claim 6, characterized in that: The process of generating execution strategies based on the overall operating status level of the plant, combined with equipment health levels and strategy library content, includes: Based on the plant's operational status level, a tiered maintenance strategy is formulated: When the status is healthy, perform routine inspections and remote monitoring; When the status is slightly abnormal, optimize the operating parameters and shorten the inspection cycle; When the status is abnormal, locate the problematic device, repair the faulty component, and optimize load distribution; When the status is severely abnormal, immediately shut down for inspection and maintenance, replace equipment or components, and conduct in-depth analysis in conjunction with external experts.

8. A condition assessment system based on SCADA and TMR measurement data, employing the condition assessment method based on SCADA and TMR measurement data as described in any one of claims 1 to 7, characterized in that, include: Multi-source data synchronous acquisition and management module, data preprocessing feature construction module, and status assessment strategy interaction module; The multi-source data synchronous acquisition and management module collects electrical parameters in real time through the SCADA system, establishes an analysis model at the equipment acquisition end, and completes the dynamic synchronization and structured storage of multi-source data. The data preprocessing feature construction module performs preprocessing on the electrical parameters after synchronization and structured storage to obtain a basic feature set; The status assessment strategy interaction module, based on the basic feature set, constructs an equipment status classification model to comprehensively analyze the plant's operating indicators and obtain the overall operating status level of the plant. Execution strategies are generated based on the overall operating status level of the plant, combined with the equipment health level and the content of the strategy library.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the state assessment method based on SCADA and TMR measurement data as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the state assessment method based on SCADA and TMR measurement data as described in any one of claims 1 to 7.

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