Hydroelectric generating set fault diagnosis method and system based on digital twinning

Abstract

The invention relates to the technical field of unit fault diagnosis, in particular to a hydroelectric generating set fault diagnosis method and system based on digital twinning, and the method comprises the steps: dividing starting stages of a target hydroelectric generating set, analyzing starting transfer-in interference terms, building a filtering channel, evaluating the interference degree, combining real-time data anomaly analysis, and building an early warning model through a sensor algorithm; a digital twinborn model is established based on equipment topology, and a potential fault is predicted in combination with an early warning model, so that the technical problems that temporary normal fluctuation and a hydroelectric generating set fault are difficult to distinguish through an empirical threshold in a hydroelectric generating set starting process and the fault diagnosis analysis is limited are solved; effective separation and quantitative evaluation of various interference factors under different working conditions in the starting stage are realized, and the abnormality in the starting process is accurately captured by constructing a real-time data abnormality analysis module, so that the sensitivity of fault symptom detection is improved, a starting clamping stagnation early warning model is established, and the early warning efficiency is improved. And the fault diagnosis precision of the hydroelectric generating set is comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field related to unit fault diagnosis, and in particular to a hydropower unit fault diagnosis method and system based on digital twins. Background Art

[0002] In the field of modern hydropower generation, the reliability of hydropower units is a key factor in the stable operation of power systems. Common fault diagnosis methods for hydropower units often rely on fixed threshold judgments or algorithm models driven by fault experience data, such as fault trees and other related fault analysis and diagnosis mechanism models, to provide support for the overall status diagnosis and fault analysis of the unit. However, during the startup process, especially in the startup phase of hydropower units, there are limitations to simply relying on fixed threshold judgments or historical fault case analysis, and more refined and intelligent fault identification methods are needed.

[0003] In summary, in the prior art, there are technical problems that it is difficult to distinguish temporary normal fluctuations from hydropower unit failures through empirical thresholds during the startup of the hydropower unit, and the fault diagnosis and analysis has limitations. Summary of the invention

[0004] The present application provides a hydropower unit fault diagnosis method and system based on digital twins, aiming to solve the technical problems in the prior art that it is difficult to distinguish temporary normal fluctuations from hydropower unit failures through empirical thresholds during the startup of the hydropower unit, and that fault diagnosis and analysis have limitations.

[0005] In view of the above problems, the technical solution to implement this application is:

[0006] On the one hand, the present application provides a fault diagnosis method for a hydropower unit based on digital twins, wherein the method comprises: dividing the stages based on the target hydropower unit, determining the startup stage of the target hydropower unit and reading real-time data, wherein the real-time data comprises current, voltage, and active power adjustment values, and the startup stage comprises a hydropower unit standby stage, a hydropower unit kinetic energy climbing stage, and a hydropower unit grid-connected working stage;

[0007] Based on the interference item analysis of the start-up transition in the standby stage of the hydroelectric motor and the kinetic energy climbing stage of the hydroelectric motor, a first interference item set is determined;

[0008] Based on the interference item analysis of the hydroelectric generator kinetic energy climbing stage and the hydroelectric generator grid-connected working stage, a second interference item set is determined;

[0009] Based on the first interference item set and the second interference item set, an interference item filtering channel is established to output the interference degree of the startup phase and extract the time segment in which the interference degree exceeds the interference threshold in the startup phase, wherein the interference threshold includes an absolute threshold and a difference threshold;

[0010] Calculate the unit power mean of the target hydropower unit, and perform abnormal data analysis based on the data distribution of the real-time data. In the time segment when the interference degree exceeds the interference threshold during the startup phase, based on the unit power mean of the target hydropower unit and the abnormal data analysis results, combined with active power regulation feedback, use a sensor algorithm to establish a startup jam warning model;

[0011] Based on the topological structure relationship of the target hydropower unit equipment, a digital twin model is established. Combined with the startup jam warning model, potential faults of the target hydropower unit during the startup process are predicted, and fault diagnosis and alarm are performed based on the potential fault prediction results.

[0012] On the other hand, the present application provides a hydropower unit fault diagnosis system based on digital twins, wherein the system includes: a stage division module, which is used to divide the stages based on the target hydropower unit, determine the startup stage of the target hydropower unit and read real-time data, wherein the real-time data includes current, voltage, and active power adjustment values, and the startup stage includes a hydropower unit standby stage, a hydropower unit kinetic energy climbing stage, and a hydropower unit grid-connected working stage;

[0013] A first interference item analysis module is used to analyze the interference items of starting and switching based on the standby stage of the hydroelectric motor and the kinetic energy climbing stage of the hydroelectric motor, and determine a first interference item set;

[0014] A second interference item analysis module is used to analyze the interference items of the startup and transfer based on the kinetic energy climbing stage of the hydroelectric generator and the grid-connected working stage of the hydroelectric generator, and determine the second interference item set;

[0015] an interference item filtering module, configured to establish an interference item filtering channel based on the first interference item set and the second interference item set, output the interference degree of the startup phase and extract the time segment in which the interference degree exceeds the interference threshold in the startup phase, wherein the interference threshold includes an absolute threshold and a difference threshold;

[0016] An abnormal data analysis module is used to calculate the unit power mean of the target hydropower unit and perform abnormal data analysis based on the data distribution of the real-time data. In the time segment when the interference degree exceeds the interference threshold during the startup phase, based on the unit power mean of the target hydropower unit and the abnormal data analysis results, combined with active power regulation feedback, a sensor algorithm is used to establish a startup jam warning model;

[0017] The fault diagnosis and alarm module is used to establish a digital twin model based on the topological structure relationship of the target hydropower unit equipment, combine the startup jam warning model, predict the potential faults of the target hydropower unit during the startup process, and perform fault diagnosis and alarm based on the potential fault prediction results.

[0018] In summary, one or more technical solutions provided in this application realize the effective separation and quantitative evaluation of various interference factors under different operating conditions during the startup phase. By constructing a real-time data anomaly analysis module, the anomalies in the startup process can be accurately captured, thereby improving the sensitivity of fault sign detection, establishing a startup jam warning model, and comprehensively improving the technical effect of the fault diagnosis accuracy of the hydropower unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a hydropower unit fault diagnosis method based on digital twin is provided for this application;

[0020] Figure 2 A flow chart of adjusting the parameters of the start-up jam warning model in the fault diagnosis method of a hydropower unit based on digital twin is provided for this application;

[0021] Figure 3 A structural schematic diagram of a hydropower unit fault diagnosis system based on digital twin is provided for this application.

[0022] Explanation of reference numerals: stage division module M100, first interference item analysis module M200, second interference item analysis module M300, interference item filtering module M400, abnormal data analysis module M500, fault diagnosis alarm module M600. DETAILED DESCRIPTION

[0023] Embodiment 1

[0024] The present application is described in detail below with reference to the accompanying drawings. Figure 1 As shown, the present application provides a fault diagnosis method for a hydropower unit based on digital twins, wherein the method comprises:

[0025] S1: Based on the target hydropower unit, the start-up phase of the target hydropower unit is determined and real-time data is read, wherein the real-time data includes current, voltage, and active power adjustment values, and the start-up phase includes a hydropower unit standby phase, a hydropower unit kinetic energy climbing phase, and a hydropower unit grid-connected working phase;

[0026] There are limitations to simply relying on fixed thresholds or historical fault case analysis. For example, the stress state of each component changes significantly during the startup process, and the real-time monitoring data fluctuates greatly. The threshold comparison cannot accurately distinguish between temporary fluctuations during the normal startup process and real fault signals. The startup phase involves a variety of different operating mode conversions, and the types and causes of possible faults in each mode are different.

[0027] By dividing the stages, the whole startup process from the standby stage to the kinetic energy climbing stage and then to the grid-connected working stage was analyzed in detail, with special attention paid to the interference item analysis during the startup sequence. An interference item filtering channel was established to accurately quantify the interference degree during the startup stage. Combined with the unit power mean calculation and abnormal data analysis of real-time data, a startup jam warning model was constructed using the sensor algorithm. Fault diagnosis and alarm were performed throughout the startup process to ensure the safe operation of the target hydropower unit during the startup stage.

[0028] According to the work flow and operating procedures of the target hydropower unit, the operating state of the target hydropower unit is clearly divided into three main startup stages: the hydropower unit standby stage, the hydropower unit kinetic energy climbing stage and the hydropower unit grid-connected working stage; the synchronous acquisition equipment of the target hydropower unit is configured, and the synchronous acquisition equipment includes relevant electrical parameter acquisition equipment such as voltmeters and ammeters;

[0029] It should be noted that in the subdivided startup phases corresponding to the hydropower generator standby phase, the hydropower generator kinetic energy climbing phase, and the hydropower generator grid-connected working phase, the analysis of real-time data and fault diagnosis have different focuses: in the hydropower generator standby phase, detect and confirm whether the basic electrical parameters of the unit are within the normal range, such as checking whether the current and voltage remain constant and fluctuate around the rated value, and monitor the initial state and reaction speed of the active power regulation device in the standby state to ensure that corresponding adjustments can be made quickly after the start-up command is issued. In addition, in the hydropower generator standby phase, attention should also be paid to non-electrical parameters such as upstream water flow and water head, which will affect the selection of the unit start-up timing and the safety of initial operation;

[0030] During the stage of hydroelectric generator kinetic energy climbing, as the turbine blades begin to capture the energy of the water flow, the focus of monitoring turns to the dynamic trend of current and voltage as the speed increases, analyzing whether they change according to the expected performance curve, as well as the followability and accuracy of the active power regulation system, that is, whether it can effectively convert mechanical energy into electrical energy and adjust it in time according to load demand. At the same time, observe the changes in physical quantities such as unit vibration and noise, as well as thermodynamic parameters such as temperature and pressure, to check whether there are early signs of failure that may lead to reduced efficiency or component wear;

[0031] During the grid-connected operation phase of the hydropower generator, after the unit is successfully connected to the grid, the real-time data monitoring focuses on the current and voltage quality under the grid synchronization conditions to ensure that they meet the harmonic, frequency and voltage fluctuation limits specified by the grid access regulations. In addition, the active power regulation system is monitored to accurately track the dispatching instructions under actual load conditions to maintain the stability of the output power, and the reactive power regulation capability is examined to ensure that the unit has good dynamic response performance and stability within the full range of operating conditions. At the same time, the overall efficiency of the unit is comprehensively considered, and the impact of factors such as the change in the water level difference between the upper and lower basins and the coordinated operation of upstream and downstream equipment is comprehensively considered. Through the digital twin model, simulation analysis is carried out to prevent the risk of system-level failures in advance;

[0032] Therefore, the focus of reading real-time data is slightly different in each stage. Specifically, when the target hydropower unit is in the standby stage, read the real-time data to monitor the basic levels of current and voltage and the preparatory state of active power regulation during standby; after the target hydropower unit enters the kinetic energy climbing stage, continue to collect and record the changes in real-time data, pay attention to the dynamic changes of current and voltage as the water flow energy increases, and how the active power regulation value responds to the unit speed increase process; after the target hydropower unit enters the grid-connected working stage, continue to monitor and record real-time data, focusing on the stable operating status after grid connection, including whether the current and voltage meet the grid specifications, whether the active power regulation reaches the preset value and whether there is fluctuation, etc. The startup stage of the hydropower unit is refined into the hydropower standby stage, the hydropower kinetic energy climbing stage, and the hydropower grid-connected working stage, to provide support for subsequent analysis.

[0033] S2: analyzing the interference items of starting up based on the standby phase of the hydroelectric generator and the kinetic energy rising phase of the hydroelectric generator, and determining a first set of interference items;

[0034] S3: analyzing the interference items of the startup and transfer based on the hydroelectric generator kinetic energy climbing stage and the hydroelectric generator grid-connected working stage, and determining the second interference item set;

[0035] S4: establishing an interference item filtering channel based on the first interference item set and the second interference item set, outputting the interference degree of the startup phase and extracting the time segment in which the interference degree exceeds the interference threshold in the startup phase, wherein the interference threshold includes an absolute threshold and a difference threshold;

[0036] Integrate and analyze the data of each detailed startup stage to find the transition points between the detailed startup stages, especially pay attention to the possible interference items in the startup transition process, such as the changes in the transition from standby to ramp-up, and from ramp-up to grid-connected stage. Further, determine the start and end time points of the hydropower unit standby stage, hydropower unit kinetic energy ramp-up stage and grid-connected working stage, and continuously collect key parameter data such as current, voltage, active power regulation value, etc. in this process through real-time monitoring;

[0037] Transition analysis from the standby stage to the kinetic energy climbing stage. Specifically, analyze the real-time data changes from the end of the standby stage to the beginning of the kinetic energy climbing stage, and identify factors that may affect smooth startup during this period, such as sudden current fluctuations, voltage instability, etc.; according to the pre-defined interference factor categories, filter out interference variables that have a significant impact in this stage and group them into the first interference item set;

[0038] Analyze the transition from the kinetic energy climbing stage to the grid-connected working stage. Specifically, when the kinetic energy climbing stage is about to end and the unit is about to be connected to the grid, analyze the change trend of the real-time data again to find the interference sources that may cause grid connection difficulties or have a negative impact on the stability of the unit; adopt the same method as the transition analysis process from the standby stage to the kinetic energy climbing stage, and include the identified key interference factors into the second interference item set;

[0039] An algorithm model for processing interference items is configured, wherein the interference item filtering channel can perform real-time calculation and analysis based on the variables included in the first interference item set and the second interference item set; for continuous processing of real-time data, each interference factor is quantitatively scored using the interference item filtering channel, and a continuous interference degree signal curve is established based on the scoring results;

[0040] Integrate the interference signal of the entire startup phase to obtain the interference level value in each time period; set the absolute threshold value (such as the maximum allowable instantaneous interference amount) and the difference threshold value (such as the relative change amplitude of the interference amount at adjacent time points) based on engineering practice experience and historical failure cases; check the interference level curve to find out the multiple time segments in which the interference level exceeds the absolute threshold and the difference threshold in the interference threshold within the startup phase time limit, and record the multiple time segments in which the interference level exceeds the absolute threshold and the difference threshold in the interference threshold within the startup phase time limit;

[0041] In summary, during the startup phase of the target hydropower unit, the real-time data of each detailed startup phase is dynamically monitored, and an effective method is used to quantify the degree of interference, and then determine when the preset interference threshold is exceeded, thereby accurately extracting the problem period that may be caused by various interferences during the startup process of the hydropower unit, and providing support for targeted fault diagnosis and alarm.

[0042] S5: Calculate the unit power mean of the target hydropower unit, and perform abnormal data analysis based on the data distribution of the real-time data. In the time segment when the interference degree exceeds the interference threshold during the startup phase, based on the unit power mean of the target hydropower unit and the abnormal data analysis results, combined with active power regulation feedback, use a sensor algorithm to establish a startup jam warning model;

[0043] S6: Based on the topological structure relationship of the target hydropower unit equipment, a digital twin model is established, and combined with the startup jam warning model, potential faults of the target hydropower unit during the startup process are predicted, and fault diagnosis alarms are performed based on the potential fault prediction results.

[0044] Based on the real-time data of the target hydropower unit during the startup phase, the average active power of each time interval (such as second level) is calculated to obtain the power mean curve per unit time; the overall distribution characteristics of the real-time data during the startup phase are analyzed, including the trend, peak value, valley value and fluctuation range of the data; reasonable statistical thresholds are set to detect whether there are abnormal data points that deviate from the normal range according to the distribution of real-time data; in the time period when the identified interference level exceeds the preset threshold, special attention is paid to the occurrence and characteristics of abnormal data;

[0045] Use the perceptron algorithm or other machine learning methods suitable for fault warning, take the unit power mean data, the results of abnormal data analysis, and the feedback information of the active power regulation system as input features, train the model through the training data set, so that it can identify the patterns that may cause startup jams or delays, and further calibrate the model parameters to ensure that the model can accurately predict the risk of startup jams when new real-time data arrives;

[0046] Create a virtual digital twin model based on the actual physical structure, electrical system and control logic of the target hydropower unit; the digital twin model should be able to reflect the interaction and dynamic response between the internal components of the unit and have simulation capabilities; integrate the startup jam warning model into the digital twin model so that the virtual model can reflect the actual unit status in real time;

[0047] A digital twin model with an integrated startup jam warning model is used to simulate various scenarios of the target hydropower unit during the startup process. Furthermore, during the simulation process, potential fault scenarios that may occur are predicted, such as rotor imbalance, excessive electromagnetic vibration, and increased bearing wear. The simulation results are analyzed to sort and prioritize the causes and degrees of faults that may cause startup jams. When the digital twin model predicts that the probability of a potential fault exceeds a certain threshold, a fault diagnosis alarm mechanism is triggered, and a startup jam warning model is established to comprehensively improve the fault diagnosis accuracy of the hydropower unit during the startup phase.

[0048] Furthermore, the present application method comprises:

[0049] Obtain the upstream and downstream water level information of the target hydropower unit, and establish a comprehensive diagnosis system based on the topological structure of the target hydropower unit equipment;

[0050] Based on the integrated diagnosis system and the operation data of the upper and lower level equipment, the upstream water level information and the downstream water level information are compared to obtain the availability of the alarm result;

[0051] The alarm result availability is used as identification information and added to the potential fault prediction result.

[0052] Install or connect data acquisition equipment, set up water level monitoring sensors in the upstream and downstream areas of the target hydropower unit, and continuously collect real-time water level information; synchronously obtain water level data in the upstream and downstream basins, and integrate and store them; further, analyze the network topology of the target hydropower unit and its upstream and downstream related equipment, clarify the correlation and impact path between the equipment; understand the logical connection and operation rules between the hydropower unit and the upstream reservoir, downstream river, gate and other control facilities;

[0053] According to the equipment topology relationship, combined with the physical characteristics and operating principles of the hydropower unit, a comprehensive diagnostic system covering various environmental factors such as water level, flow velocity, flow, equipment status, etc. is established; the comprehensive diagnostic system should be able to consider the impact of water level changes on unit performance, load distribution and potential failures, and be able to integrate the operating data of equipment at different levels.

[0054] Linked analysis of upstream and downstream water level information and real-time operating data of hydropower units and other related equipment to identify the relationship between water level fluctuations and equipment operating status; set a series of judgment conditions and thresholds, compare the actual water level with the preset safety range or ideal working range, calculate the reliability and effectiveness of the current alarm result, that is, the availability of the alarm result; further, use statistics, machine learning or expert systems to learn and verify a large amount of historical data to improve the accuracy of the alarm availability;

[0055] The availability of alarm results is taken as an important reference indicator, quantified and attached to the existing potential fault prediction results; if the availability of alarm results is high, the credibility of the corresponding potential fault prediction is enhanced; if the availability is low, the weight of the prediction result is reduced or other possible causes are further investigated; in the final fault prediction report, not only the fault type and possibility are included, but also the availability of alarm results related to water level will be displayed as an important basis for auxiliary decision-making; when the potential fault prediction results show a higher risk, combined with the availability of alarm results, early warning signals are issued in a timely manner, so as to check the relevant risks in advance and improve the stability of the target hydropower unit.

[0056] Furthermore, if Figure 2 As shown, the upstream water level information and downstream water level information of the target hydropower unit are obtained, and the method of the present application also includes:

[0057] Connecting to a data acquisition module to collect watershed monitoring data, wherein the watershed monitoring data includes turbidity, flow rate, and flow velocity of the upstream and downstream watersheds of the target hydropower unit;

[0058] The watershed monitoring data is combined with the upstream water level information and the downstream water level information, and the real-time data is combined to obtain a comprehensive situation data combination;

[0059] By combining the comprehensive situation data, analyzing the potential failure influencing factors of the changes in the watershed monitoring data on the startup phase of the target hydropower unit;

[0060] Based on the potential fault influencing factors, the startup jam warning model parameters are adjusted.

[0061] Install corresponding water quality monitoring instruments (turbidity meters), flow meters, flow rate meters and other data acquisition equipment at key locations in the upstream and downstream basins of the target hydropower units, connect the above data acquisition modules, start the automated data acquisition process, and obtain and record parameter data such as turbidity, flow rate and flow rate in the upstream and downstream basins in real time;

[0062] Integrate the real-time acquired basin monitoring data, including turbidity, flow, flow velocity, etc., with the existing upstream water level information and downstream water level information; perform data alignment to ensure the consistency of data of each dimension in the same time series; after the basin monitoring data is merged with the upstream water level information and downstream water level information, it is combined with the real-time data to form a comprehensive situation data combination that fully reflects the overall hydrological status of the basin where the target hydropower unit is located;

[0063] Use statistical analysis methods or machine learning algorithms to analyze the time-varying trends of various parameters in the comprehensive situation data combination and their interactions; study how specific hydrological conditions (such as abnormally high / low water levels, sudden changes in water flow, water quality deterioration, etc.) affect the normal operation of hydropower units during the startup phase, identify potential fault influencing factors that may induce startup jams or other faults, identify which changes in hydrological parameters have an impact on the startup jam warning model, and incorporate potential fault influencing factors into the startup jam warning model as the basis for adjusting model parameters;

[0064] According to the correlation between potential fault influencing factors and the probability of fault occurrence, the specific parameters of the startup jam warning model are optimized; through backtesting, cross-validation and other means, it is verified whether the model parameters can more accurately predict the startup failure risk of the target hydropower unit under different hydrological conditions after adjustment; the model parameters are continuously iterated until the model accuracy meets the expected performance on the historical data set, thereby enhancing the early warning capability of potential faults in the startup phase of the hydropower unit.

[0065] Furthermore, based on the data distribution of the real-time data, abnormal data analysis is performed, and the method of the present application includes:

[0066] Extracting data distribution characteristics of the real-time data, selecting kernel functions and penalty factors, and establishing a single-classification OCSVN framework;

[0067] Based on the single-classification OCSVN framework, the model parameters are optimized through training sample data to determine the abnormal data analysis channel;

[0068] The interference item filtering channel is connected to the abnormal data analysis channel to obtain an abnormal data processing model.

[0069] Calculate statistical data (such as mean, variance, kurtosis, skewness, etc.), frequency distribution histogram, correlation matrix, etc. to obtain the data distribution characteristics of real-time data, which are used to characterize the normal behavior and potential patterns of the data; construct a single-class OCSVN (One-Class Support Vector Network) framework: according to the distribution characteristics of real-time data, select a suitable kernel function, such as linear kernel, Gaussian kernel (RBF), polynomial kernel, etc., to map the original data in a high-dimensional space; determine the penalty factor, which is used to balance the complexity of the model and the generalization ability. Too large or too small a penalty factor will affect the performance of the model; use the selected kernel function and penalty factor to initialize a single-class support vector machine framework. OCSVN is mainly used to identify the boundaries of normal samples in the data set in order to find abnormal data that deviates from the normal range.

[0070] Prepare training sample data, which should contain real-time data points representing normal working conditions; use the training sample data to train under the OCSVN framework, and adjust the model parameters through optimization algorithms (such as Lagrange multiplier method, SMO algorithm, etc.), with the goal of finding the optimal hyperplane that maximizes the distance between the data points and the decision boundary. Furthermore, it involves solving the support vector problem, aiming to make the training data as tightly enclosed as possible in the region defined by the hyperplane, while keeping the unlabeled abnormal data away from the region;

[0071] After the model training is completed, based on the optimized parameters, the model will be able to distinguish between normal and abnormal data. At this time, the OCSVN model constitutes an abnormal data analysis channel; by predicting new real-time data points, the model will classify them into normal or abnormal categories. Abnormal data points are those data that the model considers to be significantly deviated from the normal distribution;

[0072] The interference item filtering channel based on the startup phase that has been constructed is connected to the abnormal data analysis channel, wherein the interference item filtering channel first screens the real-time data to eliminate or weaken the fluctuations caused by conventional interference factors in the startup phase; the abnormal data analysis channel then processes the filtered data to further identify possible fault signals in the abnormal startup phase; the interference item filtering channel is connected to the abnormal data analysis channel to form an abnormal data processing model, so that the abnormal data processing model can effectively reduce noise interference while accurately detecting abnormal phenomena that may cause startup jams or other faults of the hydropower unit.

[0073] Furthermore, in combination with the topological structure relationship of the target hydropower unit equipment, a comprehensive diagnostic system is established, and the present application method also includes:

[0074] Based on the startup phase of the target hydroelectric unit, analyzing the real-time data and extracting key operating parameters;

[0075] Based on the start-up transfer period corresponding to the first interference item set and in combination with the key operating parameters, a first time window segment is set, wherein the first time window segment includes a non-shielded activation instruction of the abnormal data processing model;

[0076] The first time window segments in the start-up transition period of the first interference item set are inserted into the abnormal data analysis channel to capture abnormal data in the start-up transition period corresponding to the first interference item set.

[0077] Furthermore, the present application method also includes:

[0078] Based on the start-up transfer period corresponding to the second interference item set and in combination with the key operating parameters, a second time window segment is set, wherein the second time window segment includes a non-shielded activation instruction of the abnormal data processing model;

[0079] The second time window segments in the start-up transition period of the second interference item set are inserted into the abnormal data analysis channel to capture the abnormal data in the start-up transition period corresponding to the second interference item set.

[0080] During the transition from the standby stage to the kinetic energy climbing stage, for the start-up stage of the target hydropower unit, according to the actual operating status of the target hydropower unit and the relationship between the equipment, extract key operating parameters from the real-time data, such as rotor speed, torque, generator efficiency, excitation current, etc.; conduct in-depth analysis of the key operating parameters to understand their dynamic change rules in different time periods during the start-up stage, and find out the characteristic values ​​closely related to the start-up performance;

[0081] Determine the startup transition period corresponding to the first interference item set, that is, the startup transition period is the period that is most susceptible to interference from the external environment or internal conditions; use historical data and field measured data to analyze the influence characteristics and specific manifestations of the first interference item set in the startup transition stage (such as entering the kinetic energy climbing stage from the standby stage); combine theoretical analysis and actual observation results, set a specific time window segment for the startup transition period corresponding to the first interference item set; within the corresponding time window segment, special attention should be paid to abnormal data, so a non-shielded activation instruction is set, which means that the abnormal data processing model is in a state of enhanced sensitivity or priority processing during this time period;

[0082] Insert an abnormal data analysis channel. Specifically, integrate the information of the set first time window segment into the workflow of the abnormal data analysis channel. When the real-time data falls into the time window segment, the abnormal data processing model will automatically activate and increase the response level to ensure timely and accurate capture and analysis of abnormal data within the startup transition period. Within the first time window segment, the abnormal data processing model will conduct an in-depth scan and judgment of the real-time data to identify data points that exceed the normal range or do not conform to the expected rules. Furthermore, for the captured suspected abnormal data, a secondary diagnosis will be performed through the abnormal data processing model. In short, in the specific startup phase and critical time window, the detection and diagnosis of abnormal behavior of the target hydropower unit will be strengthened, thereby improving the system's fault prediction accuracy and early warning capabilities.

[0083] In the same way, during the transition from the kinetic energy climbing stage to the grid-connected working stage, the start-up transition period corresponding to the second interference item set is another key transition stage, that is, from the kinetic energy climbing stage of the hydropower generator to the grid-connected working stage; determine which interference factors in this stage may significantly affect the performance of the unit or cause potential failures, and form the second interference item set;

[0084] During the startup transition period corresponding to the second interference item set, the key operating parameters in the real-time data are carefully studied, including but not limited to parameters such as rotation speed, voltage fluctuation, frequency change, and mechanical vibration; by analyzing the characteristics and trends of these parameters in this period, the key operating parameters closely related to the second interference item are found; according to the timing and duration of the possible abnormality caused by the second interference item set, the second time window segment is defined; within the second time window segment, a non-shielded activation instruction of the abnormal data processing model is set, which means that within the second time window segment, the model will give the highest level of response and processing priority to the abnormal data;

[0085] Insert an abnormal data analysis channel, specifically, embed the relevant parameter configuration of the second time window segment into the existing abnormal data analysis channel; design system logic so that when the real-time data enters the second time window segment, the abnormal data processing model can automatically switch to the non-shielded activation mode; monitor the data flow of the target hydropower unit in the startup transition period corresponding to the second interference item set in real time; when the real-time data deviates from the normal range or shows an atypical pattern within the second time window segment, the abnormal data processing model responds quickly and accurately captures the abnormal data; in short, effectively monitor and warn the potential fault risks of the target hydropower unit in the transition process from the kinetic energy climbing stage to the grid-connected working stage during the startup process, thereby improving the ability of fault diagnosis and prevention.

[0086] Furthermore, in combination with the topological structure relationship of the target hydropower unit equipment, a comprehensive diagnostic system is established, and the present application method also includes:

[0087] Based on the grid-side controller and each machine-side rectifier of the target hydropower unit, monitoring the startup interaction signal and obtaining multi-address control link information;

[0088] During the hydroelectric generator standby stage, the hydroelectric generator kinetic energy climbing stage, and the hydroelectric generator grid-connected working stage, the rotor speed range is analyzed to obtain the speed change law and torque characteristic curve during the startup process;

[0089] Based on the speed variation law and torque characteristic curve during the startup process, fault location is performed in combination with the multi-access control link information.

[0090] Determine the internal equipment composition of the target hydropower unit and their physical connections and logical control relationships, and establish a detailed equipment topology model, including the grid-side controller, each machine-side rectifier, and the interfaces and communication paths between them; deploy a sensor network and data acquisition system to monitor and record the startup interaction signals between the grid-side controller and each machine-side rectifier in real time. The startup interaction signals contain important information reflecting the system status and control instructions;

[0091] By parsing and analyzing the startup interaction signals, we can extract the multi-address control link information, that is, how different devices work together, transmit commands, and respond to the status, so as to understand the control flow and potential problems in the entire startup process;

[0092] In different stages of hydropower unit startup (standby stage, kinetic energy climbing stage, grid-connected working stage), the rotor speed data is continuously collected, and the change of torque output is paid attention to; the speed range of each stage is carefully analyzed to clarify key indicators such as the speed of speed increase, stable point and mutation point, and a speed change law model during the startup process is constructed accordingly;

[0093] Synchronously record and analyze the characteristic curve of torque changing with speed, including the maximum torque point, torque fluctuation, and whether there are abnormal peaks or valleys, so as to characterize the performance and health of the equipment; combine the acquired multi-access control link information with the speed change law and torque characteristic curve, and preliminarily judge the links or components that may fail by comparing the expected behavior under normal working conditions with the actual observed behavior differences;

[0094] Based on the comprehensive diagnostic system, the fault location is further refined through data analysis technology (such as pattern recognition, machine learning, etc.). For example, if an abnormality is found in the torque curve and at the same time a control link reacts laggingly or incorrectly, it may point to a fault or performance degradation in a specific component (such as the excitation system or speed regulation device). Fault reports and diagnostic suggestions are generated to guide on-site maintenance personnel to conduct targeted inspections and repairs to ensure the safe and efficient operation of the hydropower unit.

[0095] Furthermore, according to the potential fault prediction result, a fault diagnosis alarm is performed, and the present application method also includes:

[0096] Establish fault chain computing power constraints by presetting standard information;

[0097] Extract key fault indicators based on the fault maintenance records of the target hydropower units;

[0098] Based on the digital twin model and the start-up jam warning model, a deep simulation is performed with the key fault indicators as the center to obtain fault chain data, wherein the fault chain data includes gearbox fault chain data and transmission chain fault chain data;

[0099] Based on the fault chain data, using the fault chain computing power constraint, multi-dimensional fault propagation path tolerance deduction is performed to evaluate the operation fault risk index;

[0100] The potential fault prediction result is marked with a risk level according to the operational fault risk index.

[0101] Refer to GB / T 15468-2020 "Basic Technical Conditions for Hydraulic Turbines" and GB / T 38334-2019 "Technical Specifications for Black Start of Hydropower Stations", refine the technical and safety requirements related to fault diagnosis, define the conditions and restriction rules for fault chain triggering, and set fault chain computing power constraints that meet industry standards. Fault chain computing power constraints can be used to limit the scope of fault impact and the possibility of fault spread, ensuring the accuracy and compliance of subsequent analysis;

[0102] Review the historical fault maintenance records of the target hydropower unit and extract representative key fault indicators, such as fault frequency, severity, related components, fault occurrence stage and other important information; use the established digital twin model to accurately simulate the actual operating status of the target hydropower unit, combine the startup jam warning model, conduct in-depth dynamic simulation on the extracted key fault indicators, and analyze the possible chain effects caused by different fault scenarios;

[0103] Through simulation calculations, the fault chain data of key components such as gearbox failure and transmission chain failure are obtained, including but not limited to the possibility of failure, the probability of chain failure, and the expected loss. Based on the acquired fault chain data, under the preset fault chain computing power constraints, advanced computing methods are used to perform multi-dimensional deductions on the possible propagation paths of the faults, evaluate the propagation range and consequences of each fault state, and then quantify the operational fault risk index that may be caused by various fault conditions. The operational fault risk index comprehensively reflects the risk factors of multiple aspects such as the probability of failure, potential losses, and operational stability.

[0104] Based on the calculated operational failure risk index, the potential failure prediction results are divided into different risk levels, such as low risk, medium risk, high risk or emergency risk levels; for each predicted potential failure, a corresponding risk level label is assigned to form an intuitive risk warning report, and this information is transmitted to the monitoring system and decision-making platform in a timely manner, triggering the corresponding level of alarm notification, providing a scientific basis for operation and maintenance personnel to take preventive measures or emergency response.

[0105] In summary, the beneficial effects of the embodiments of the present application are:

[0106] 1. Real-time monitoring and accurate analysis of various state parameters and environmental variables during the startup phase of the hydropower unit, effectively filtering out irrelevant interference, collecting real-time data and conducting comprehensive situation data analysis, effectively identifying potential fault influencing factors that cause startup jams, and building a startup jam warning model, combined with multi-source data fusion and machine learning algorithms, to achieve synchronous warning of faults occurring during the startup process of the hydropower unit.

[0107] 2. A highly simulated operating environment for hydropower units is established using the digital twin model. Starting from the equipment topology, the correlation between components is deeply analyzed, the interaction between equipment and the linkage effect of the entire system are fully considered, and the accuracy and coverage of fault diagnosis are improved.

[0108] 3. Through studying the fault modes under different operating conditions during the startup phase of the hydropower unit, combining the multi-address control link information and the speed and torque characteristic curves, the interference factors and abnormal data are processed in a refined manner to improve the accuracy of fault judgment. Through deep simulation and fault chain computing power constraints, the fault risks are quantitatively evaluated and graded.

[0109] 4. Due to the adoption of the startup phase based on the target hydropower unit, the real-time data is analyzed and the key operating parameters are extracted; based on the startup transition period corresponding to the first interference item set, combined with the key operating parameters, the first time window segment is set, and the first time window segment includes the non-shielded activation instruction of the abnormal data processing model; the first time window segment in the startup transition period of the first interference item set is inserted into the abnormal data analysis channel to capture the abnormal data in the startup transition period corresponding to the first interference item set. In the specific startup phase and key time window, the detection and diagnosis of abnormal behavior of the target hydropower unit are strengthened, thereby improving the fault prediction accuracy and early warning capability of the system.

[0110] Embodiment 2

[0111] Based on the same inventive concept as the hydropower unit fault diagnosis method based on digital twin in the aforementioned embodiment, Figure 3 As shown, the embodiment of the present application provides a hydropower unit fault diagnosis system based on digital twins, wherein the system includes:

[0112] The stage division module M100 is used to divide the stages based on the target hydropower unit, determine the start-up stage of the target hydropower unit and read the real-time data, wherein the real-time data includes the current, voltage, and active power adjustment values, and the start-up stage includes the hydropower unit standby stage, the hydropower unit kinetic energy climbing stage, and the hydropower unit grid-connected working stage;

[0113] The first interference item analysis module M200 is used to analyze the interference items of the start-up based on the standby stage of the hydroelectric motor and the kinetic energy climbing stage of the hydroelectric motor, and determine the first interference item set;

[0114] The second interference item analysis module M300 is used to analyze the interference items of the startup and transfer based on the hydroelectric generator kinetic energy climbing stage and the hydroelectric generator grid-connected working stage, and determine the second interference item set;

[0115] The interference item filtering module M400 is used to establish an interference item filtering channel based on the first interference item set and the second interference item set, output the interference degree of the startup phase and extract the time segment in which the interference degree exceeds the interference threshold in the startup phase, wherein the interference threshold includes an absolute threshold and a difference threshold;

[0116] The abnormal data analysis module M500 is used to calculate the unit power mean of the target hydropower unit and perform abnormal data analysis based on the data distribution of the real-time data. In the time segment when the interference degree exceeds the interference threshold during the startup phase, based on the unit power mean of the target hydropower unit and the abnormal data analysis results, combined with active power regulation feedback, a sensor algorithm is used to establish a startup jam warning model;

[0117] The fault diagnosis and alarm module M600 is used to establish a digital twin model based on the topological structure relationship of the target hydropower unit equipment, combine the startup jam warning model, predict the potential faults of the target hydropower unit during the startup process, and perform fault diagnosis and alarm based on the potential fault prediction results.

[0118] Furthermore, the hydropower unit fault diagnosis system based on digital twin is also used to perform the following method:

[0119] Obtain the upstream and downstream water level information of the target hydropower unit, and establish a comprehensive diagnosis system based on the topological structure of the target hydropower unit equipment;

[0120] Based on the integrated diagnosis system and the operation data of the upper and lower level equipment, the upstream water level information and the downstream water level information are compared to obtain the availability of the alarm result;

[0121] The alarm result availability is used as identification information and added to the potential fault prediction result.

[0122] Furthermore, the hydropower unit fault diagnosis system based on digital twin is also used to perform the following method:

[0123] Connecting to a data acquisition module to collect watershed monitoring data, wherein the watershed monitoring data includes turbidity, flow rate, and flow velocity of the upstream and downstream watersheds of the target hydropower unit;

[0124] The watershed monitoring data is combined with the upstream water level information and the downstream water level information, and the real-time data is combined to obtain a comprehensive situation data combination;

[0125] By combining the comprehensive situation data, analyzing the potential failure influencing factors of the changes in the watershed monitoring data on the startup phase of the target hydropower unit;

[0126] Based on the potential fault influencing factors, the startup jam warning model parameters are adjusted.

[0127] Furthermore, the abnormal data analysis module M500 is used to perform the following method:

[0128] Extracting data distribution characteristics of the real-time data, selecting kernel functions and penalty factors, and establishing a single-classification OCSVN framework;

[0129] Based on the single-classification OCSVN framework, the model parameters are optimized through training sample data to determine the abnormal data analysis channel;

[0130] The interference item filtering channel is connected to the abnormal data analysis channel to obtain an abnormal data processing model.

[0131] Furthermore, the hydropower unit fault diagnosis system based on digital twin is also used to perform the following method:

[0132] Based on the startup phase of the target hydroelectric unit, analyzing the real-time data and extracting key operating parameters;

[0133] Based on the start-up transfer period corresponding to the first interference item set and in combination with the key operating parameters, a first time window segment is set, wherein the first time window segment includes a non-shielded activation instruction of the abnormal data processing model;

[0134] The first time window segments in the start-up transition period of the first interference item set are inserted into the abnormal data analysis channel to capture abnormal data in the start-up transition period corresponding to the first interference item set.

[0135] Furthermore, the hydropower unit fault diagnosis system based on digital twin is also used to perform the following method:

[0136] Based on the start-up transfer period corresponding to the second interference item set and in combination with the key operating parameters, a second time window segment is set, wherein the second time window segment includes a non-shielded activation instruction of the abnormal data processing model;

[0137] The second time window segments in the start-up transition period of the second interference item set are inserted into the abnormal data analysis channel to capture the abnormal data in the start-up transition period corresponding to the second interference item set.

[0138] Furthermore, the hydropower unit fault diagnosis system based on digital twin is also used to perform the following method:

[0139] Based on the grid-side controller and each machine-side rectifier of the target hydropower unit, monitoring the startup interaction signal and obtaining multi-address control link information;

[0140] During the hydroelectric generator standby stage, the hydroelectric generator kinetic energy climbing stage, and the hydroelectric generator grid-connected working stage, the rotor speed range is analyzed to obtain the speed change law and torque characteristic curve during the startup process;

[0141] Based on the speed variation law and torque characteristic curve during the startup process, fault location is performed in combination with the multi-access control link information.

[0142] Furthermore, the fault diagnosis and alarm module M600 is used to execute the following method:

[0143] Establish fault chain computing power constraints by presetting standard information;

[0144] Extract key fault indicators based on the fault maintenance records of the target hydropower units;

[0145] Based on the digital twin model and the start-up jam warning model, a deep simulation is performed with the key fault indicators as the center to obtain fault chain data, wherein the fault chain data includes gearbox fault chain data and transmission chain fault chain data;

[0146] Based on the fault chain data, using the fault chain computing power constraint, multi-dimensional fault propagation path tolerance deduction is performed to evaluate the operation fault risk index;

[0147] The potential fault prediction result is marked with a risk level according to the operational fault risk index.

[0148] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor, and no unnecessary restrictions are made here.

[0149] Furthermore, the above-mentioned technical scheme only reflects the preferred technical scheme of the technical scheme of the embodiment of the present application. Some changes that may be made to some parts thereof by technicians in this technical field all reflect the novel principles of the embodiment of the present application. Obviously, technicians in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A hydropower unit fault diagnosis method based on digital twins, characterized in that: The method comprises: Based on the target hydropower unit, the start-up phase of the target hydropower unit is determined and real-time data is read, wherein the real-time data includes current, voltage, and active power adjustment values, and the start-up phase includes a hydropower unit standby phase, a hydropower unit kinetic energy climbing phase, and a hydropower unit grid-connected working phase; Based on the interference item analysis of the start-up transition in the standby stage of the hydroelectric motor and the kinetic energy climbing stage of the hydroelectric motor, a first interference item set is determined; Based on the interference item analysis of the hydroelectric generator kinetic energy climbing stage and the hydroelectric generator grid-connected working stage, a second interference item set is determined; Based on the first interference item set and the second interference item set, an interference item filtering channel is established to output the interference degree of the startup phase and extract the time segment in which the interference degree exceeds the interference threshold in the startup phase, wherein the interference threshold includes an absolute threshold and a difference threshold; Calculate the unit power mean of the target hydropower unit, and perform abnormal data analysis based on the data distribution of the real-time data. In the time segment when the interference degree exceeds the interference threshold during the startup phase, based on the unit power mean of the target hydropower unit and the abnormal data analysis results, combined with active power regulation feedback, use a sensor algorithm to establish a startup jam warning model; Based on the topological structure relationship of the target hydropower unit equipment, a digital twin model is established. Combined with the startup jam warning model, potential faults of the target hydropower unit during the startup process are predicted, and fault diagnosis and alarm are performed based on the potential fault prediction results.

2. The hydropower unit fault diagnosis method based on digital twin according to claim 1 is characterized in that: The method comprises: Obtain the upstream and downstream water level information of the target hydropower unit, and establish a comprehensive diagnosis system based on the topological structure of the target hydropower unit equipment; Based on the integrated diagnosis system and the operation data of the upper and lower level equipment, the upstream water level information and the downstream water level information are compared to obtain the availability of the alarm result; The alarm result availability is used as identification information and added to the potential fault prediction result.

3. The hydropower unit fault diagnosis method based on digital twin according to claim 2 is characterized in that: Acquiring upstream water level information and downstream water level information of the target hydropower unit, the method further includes: Connecting to a data acquisition module to collect watershed monitoring data, wherein the watershed monitoring data includes turbidity, flow rate, and flow velocity of the upstream and downstream watersheds of the target hydropower unit; The watershed monitoring data is combined with the upstream water level information and the downstream water level information, and the real-time data is combined to obtain a comprehensive situation data combination; By combining the comprehensive situation data, analyzing the potential failure influencing factors of the changes in the watershed monitoring data on the startup phase of the target hydropower unit; Based on the potential fault influencing factors, the startup jam warning model parameters are adjusted.

4. The hydropower unit fault diagnosis method based on digital twin according to claim 2 is characterized in that: Based on the data distribution of the real-time data, abnormal data analysis is performed, and the method includes: Extracting data distribution characteristics of the real-time data, selecting kernel functions and penalty factors, and establishing a single-classification OCSVN framework; Based on the single-classification OCSVN framework, the model parameters are optimized through training sample data to determine the abnormal data analysis channel; The interference item filtering channel is connected to the abnormal data analysis channel to obtain an abnormal data processing model.

5. The hydropower unit fault diagnosis method based on digital twin according to claim 4 is characterized in that: In combination with the topological structure relationship of the target hydropower unit equipment, a comprehensive diagnosis system is established, and the method further includes: Based on the startup phase of the target hydroelectric unit, analyzing the real-time data and extracting key operating parameters; Based on the start-up transfer period corresponding to the first interference item set and in combination with the key operating parameters, a first time window segment is set, wherein the first time window segment includes a non-shielded activation instruction of the abnormal data processing model; The first time window segments in the start-up transition period of the first interference item set are inserted into the abnormal data analysis channel to capture abnormal data in the start-up transition period corresponding to the first interference item set.

6. The hydropower unit fault diagnosis method based on digital twin according to claim 5 is characterized in that: The method further comprises: Based on the start-up transfer period corresponding to the second interference item set and in combination with the key operating parameters, a second time window segment is set, wherein the second time window segment includes a non-shielded activation instruction of the abnormal data processing model; The second time window segments in the start-up transition period of the second interference item set are inserted into the abnormal data analysis channel to capture the abnormal data in the start-up transition period corresponding to the second interference item set.

7. The hydropower unit fault diagnosis method based on digital twin according to claim 2 is characterized in that: In combination with the topological structure relationship of the target hydropower unit equipment, a comprehensive diagnosis system is established, and the method further includes: Based on the grid-side controller and each machine-side rectifier of the target hydropower unit, monitoring the startup interaction signal and obtaining multi-address control link information; During the hydroelectric generator standby stage, the hydroelectric generator kinetic energy climbing stage, and the hydroelectric generator grid-connected working stage, the rotor speed range is analyzed to obtain the speed change law and torque characteristic curve during the startup process; Based on the speed variation law and torque characteristic curve during the startup process, fault location is performed in combination with the multi-access control link information.

8. The hydropower unit fault diagnosis method based on digital twin according to claim 7 is characterized in that: Providing a fault diagnosis alarm based on the potential fault prediction result, the method further includes: Establish fault chain computing power constraints by presetting standard information; Extract key fault indicators based on the fault maintenance records of the target hydropower units; Based on the digital twin model and the start-up jam warning model, a deep simulation is performed with the key fault indicators as the center to obtain fault chain data, wherein the fault chain data includes gearbox fault chain data and transmission chain fault chain data; Based on the fault chain data, using the fault chain computing power constraint, multi-dimensional fault propagation path tolerance deduction is performed to evaluate the operation fault risk index; The potential fault prediction result is marked with a risk level according to the operational fault risk index.

9. The hydropower unit fault diagnosis system based on digital twin is characterized by: For implementing the fault diagnosis method of a hydropower unit based on digital twins according to any one of claims 1 to 8, the system comprises: A stage division module is used to divide the stages based on the target hydropower unit, determine the start-up stage of the target hydropower unit and read real-time data, wherein the real-time data includes current, voltage, and active power adjustment values, and the start-up stage includes a hydropower unit standby stage, a hydropower unit kinetic energy climbing stage, and a hydropower unit grid-connected working stage; A first interference item analysis module is used to analyze the interference items of starting and switching based on the standby stage of the hydroelectric motor and the kinetic energy climbing stage of the hydroelectric motor, and determine a first interference item set; A second interference item analysis module is used to analyze the interference items of the startup and transfer based on the kinetic energy climbing stage of the hydroelectric generator and the grid-connected working stage of the hydroelectric generator, and determine the second interference item set; an interference item filtering module, configured to establish an interference item filtering channel based on the first interference item set and the second interference item set, output the interference degree of the startup phase and extract the time segment in which the interference degree exceeds the interference threshold in the startup phase, wherein the interference threshold includes an absolute threshold and a difference threshold; An abnormal data analysis module is used to calculate the unit power mean of the target hydropower unit and perform abnormal data analysis based on the data distribution of the real-time data. In the time segment when the interference degree exceeds the interference threshold during the startup phase, based on the unit power mean of the target hydropower unit and the abnormal data analysis results, combined with active power regulation feedback, a sensor algorithm is used to establish a startup jam warning model; The fault diagnosis and alarm module is used to establish a digital twin model based on the topological structure relationship of the target hydropower unit equipment, combine the startup jam warning model, predict the potential faults of the target hydropower unit during the startup process, and perform fault diagnosis and alarm based on the potential fault prediction results.

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