Gate state monitoring method and system for hydropower station

Abstract

The invention provides a gate state monitoring method and system for a hydropower station, and the method comprises the steps: obtaining gate operation monitoring data of the hydropower station at the current moment and each reference state threshold value of a gate, and carrying out the preprocessing of the gate operation monitoring data, and obtaining the preprocessed gate operation monitoring data; determining the gate reference state deviation of the hydropower station according to the preprocessed gate operation monitoring data, the reference state thresholds and a pre-established state reference model; inputting the gate reference state deviation of the hydropower station into a pre-established fault prediction model to obtain a gate fault diagnosis result of the hydropower station at the current moment; the gate fault diagnosis result comprises a gate fault mode and a risk value. According to the technical scheme, the state of the gate can be accurately monitored, the safety, reliability and operation efficiency of the hydropower station gate are greatly improved, the maintenance cost is reduced, and the service life of equipment is prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of equipment monitoring, and in particular to a gate status monitoring method and system for a hydropower station. Background Art

[0002] As an important water flow control facility, the gates in hydropower stations play a key role in regulating water levels, protecting dams, and controlling the safe operation of power generation equipment. With the continuous expansion of the scale of hydropower stations and the extension of their operating cycles, the health management of gates faces greater challenges. Traditional gate monitoring methods mainly rely on manual inspections and regular inspections. Although they can ensure the safe operation of gates to a certain extent, due to the low inspection frequency and the limitations of inspection methods, it is often difficult to timely detect potential faults in the operation of gates. In addition, the environment of hydropower stations is complex, and gates may become stuck, worn, corroded, and other problems under the influence of long-term high-pressure water flow and the environment. Traditional monitoring methods cannot efficiently and accurately identify these potential problems, and when failures occur, they are often already in a more serious stage.

[0003] In order to improve the operating efficiency and safety of hydropower station gates, intelligent monitoring technology has been gradually applied to the health management of hydropower station equipment in recent years. The existing intelligent monitoring system mainly evaluates the operating status of the gate through real-time data acquisition, sensor analysis and data processing, but most systems still have great deficiencies in the accuracy of fault prediction and diagnosis, especially when dealing with complex operating environments and fault modes, there are often false alarms or missed alarms. At the same time, most existing technologies focus on a single monitoring indicator, lack a comprehensive method to integrate multiple monitoring data, and do not improve the accuracy and real-time performance of fault diagnosis by updating the optimization model in real time. Therefore, it is urgent to propose a solution that can accurately monitor the gate status. Summary of the invention

[0004] The present application provides a gate status monitoring method and system for a hydropower station, so as to at least solve the technical problem of low gate monitoring accuracy.

[0005] The first embodiment of the present application provides a gate status monitoring method for a hydropower station, the method comprising:

[0006] Obtaining the gate operation monitoring data of the hydropower station at the current moment and each reference state threshold of the gate, and preprocessing the gate operation monitoring data to obtain the preprocessed gate operation monitoring data;

[0007] Determining a gate reference state deviation of the hydropower station according to the pre-processed gate operation monitoring data, the reference state thresholds and a pre-established state reference model;

[0008] Inputting the gate reference state deviation of the hydropower station into a pre-established fault prediction model to obtain the gate fault diagnosis result of the hydropower station at the current moment;

[0009] The gate fault diagnosis result includes: the gate fault mode and risk value.

[0010] Preferably, the preprocessing of the gate operation monitoring data to obtain the preprocessed gate operation monitoring data includes:

[0011] The gate operation monitoring data is sequentially cleaned, outliers are removed, missing data is supplemented, and filtering is performed to obtain pre-processed gate operation monitoring data;

[0012] The gate operation monitoring data includes: external driving force, friction force, water flow force, force generated when the gate is opened, force generated when the gate is closed, vibration frequency, water flow, and water level.

[0013] Furthermore, the pre-established state reference model includes:

[0014] Gate position state model, mechanical state model, vibration and wear model, water flow and environment model;

[0015] The gate reference state deviation of the hydropower station includes:

[0016] Gate position deviation, force deviation, vibration speed deviation, and water flow impact force deviation.

[0017] Furthermore, the process of establishing the fault prediction model includes:

[0018] Obtain the reference state values ​​of the gate at each moment in the historical period and the corresponding fault mode at each moment;

[0019] By using machine learning technology, the reference state values ​​of the gate at each moment in the historical period are used as input, and the corresponding fault mode at each moment is used as output, the initial fault prediction model is optimized and trained to obtain a trained fault prediction model;

[0020] The failure modes include: sticking, water leakage, and wear.

[0021] Furthermore, the risk value is calculated as follows:

[0022]

[0023] Where f(t) is the risk value at time t, α i is the weight of the gate's ith reference state value, x i (t) is the i-th reference state value of the gate at time t, and n is the total number of reference state values ​​of the gate.

[0024] Furthermore, the method further comprises:

[0025] It is determined whether the risk value is greater than a preset risk threshold, and when the risk value is greater than the preset risk threshold, a warning signal is generated to provide a fault warning.

[0026] Furthermore, the method further comprises:

[0027] The pre-established fault prediction model is adaptively optimized and trained based on the gate fault diagnosis result.

[0028] Furthermore, the method further comprises:

[0029] Use sensors placed at preset locations to collect gate operation monitoring data;

[0030] The fault location is determined based on the gate fault mode in the gate fault diagnosis result and the gate operation monitoring data collected by the sensor arranged at the preset position.

[0031] The second embodiment of the present application provides a gate status monitoring system for a hydropower station, comprising:

[0032] An acquisition module is used to acquire the gate operation monitoring data of the hydropower station at the current moment and each reference state threshold of the gate, and pre-process the gate operation monitoring data to obtain the pre-processed gate operation monitoring data;

[0033] A determination module, used to determine the gate reference state deviation of the hydropower station according to the pre-processed gate operation monitoring data, the reference state thresholds and a pre-established state reference model;

[0034] A diagnosis module, used for inputting the gate reference state deviation of the hydropower station into a pre-established fault prediction model to obtain a gate fault diagnosis result of the hydropower station at the current moment;

[0035] The gate fault diagnosis result includes: the gate fault mode and risk value.

[0036] A third aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect of the present application.

[0037] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0038] The present application proposes a gate state monitoring method and system for a hydropower station, the method comprising: obtaining the gate operation monitoring data and each reference state threshold of the gate at the current moment of the hydropower station, and preprocessing the gate operation monitoring data to obtain the preprocessed gate operation monitoring data; determining the gate reference state deviation of the hydropower station according to the preprocessed gate operation monitoring data, each reference state threshold and a pre-established state reference model; inputting the gate reference state deviation of the hydropower station into a pre-established fault prediction model to obtain the gate fault diagnosis result of the hydropower station at the current moment; the gate fault diagnosis result includes: the gate fault mode and risk value. The technical solution proposed in the present application can greatly improve the safety, reliability and operation efficiency of the hydropower station gate, reduce maintenance costs and extend the service life of the equipment.

[0039] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0041] Figure 1 A flow chart of a gate status monitoring method for a hydropower station provided according to an embodiment of the present application;

[0042] Figure 2 A first structural diagram of a gate status monitoring system for a hydropower station provided according to an embodiment of the present application;

[0043] Figure 3 This is a second structural diagram of a gate status monitoring system for a hydropower station provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0045] The present application proposes a gate state monitoring method and system for a hydropower station, the method comprising: obtaining the gate operation monitoring data and each reference state threshold of the gate at the current moment of the hydropower station, and preprocessing the gate operation monitoring data to obtain the preprocessed gate operation monitoring data; determining the gate reference state deviation of the hydropower station according to the preprocessed gate operation monitoring data, each reference state threshold and a pre-established state reference model; inputting the gate reference state deviation of the hydropower station into a pre-established fault prediction model to obtain the gate fault diagnosis result of the hydropower station at the current moment; the gate fault diagnosis result includes: the gate fault mode and risk value. The technical solution proposed in the present application can greatly improve the safety, reliability and operation efficiency of the hydropower station gate, reduce maintenance costs and extend the service life of the equipment.

[0046] A gate status monitoring method and system for a hydropower station according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0047] Embodiment 1

[0048] Figure 1 A flow chart of a gate status monitoring method for a hydropower station provided according to an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0049] Step 1: obtaining the gate operation monitoring data of the hydropower station at the current moment and each reference state threshold of the gate, and preprocessing the gate operation monitoring data to obtain the preprocessed gate operation monitoring data;

[0050] It should be noted that the gate operation monitoring data includes: external driving force, friction force, water flow force, force generated when opening the gate, force generated when closing the gate, vibration frequency, water flow, and water level.

[0051] It should be noted that by considering multiple factors such as the gate's position, movement, mechanical behavior, vibration, etc., and combining the operating conditions under the specific environment of the hydropower station, a set of state benchmark models is established, and the threshold values ​​of each benchmark state can be obtained, laying the foundation for subsequent fault analysis.

[0052] For example, historical data and experimental data are used to determine the value or range of each variable under normal operating conditions. According to equipment specifications, usage and environmental conditions, the benchmark value during normal operation, i.e., each benchmark state threshold, is set.

[0053] In the embodiment of the present disclosure, the preprocessing of the gate operation monitoring data to obtain the preprocessed gate operation monitoring data includes:

[0054] The gate operation monitoring data is cleaned, outliers are removed, missing data are supplemented, and filtered in turn to obtain pre-processed gate operation monitoring data.

[0055] It should be noted that preprocessing includes:

[0056] Clean the data, remove outliers, fill in missing data, and perform filtering;

[0057] Filter the collected abnormal data.

[0058] The collected monitoring data may have problems such as noise, missing values, abnormal fluctuations, etc., so data preprocessing is required. Data preprocessing includes data cleaning, removing outliers, completing missing data, and filtering. For abnormal data, use methods based on statistical analysis and pattern recognition to filter. For example, by setting a suitable threshold range, data that exceeds a reasonable range (such as excessive pressure, excessive displacement, etc.) can be detected, and data that does not conform to actual physical laws can be eliminated. At the same time, missing values ​​are filled to ensure data integrity. Through these preprocessing steps, subsequent analysis and decision-making are based on accurate and clean data, thereby avoiding false positives and false negatives.

[0059] Step 2: determining the gate reference state deviation of the hydropower station according to the pre-processed gate operation monitoring data, the reference state thresholds and the pre-established state reference model;

[0060] In the embodiment of the present disclosure, the pre-established state reference model includes:

[0061] Gate position state model, mechanical state model, vibration and wear model, water flow and environment model;

[0062] It should be noted that the gate position state model can describe the position and motion state of the gate;

[0063] Among them, the gate position state model assumes that the movement of the gate is driven by external force and is affected by friction and water flow force, and the position of the gate is described by the following first-order dynamic equation:

[0064]

[0065] Where x(t) is the gate position, F external (t) is the external driving force, F friction (t) is the friction force, F water (t) is the water flow force, γ1 is the damping coefficient, which reflects the influence of friction, and k1 is the elastic coefficient, which reflects the elastic characteristics of the system.

[0066] This equation describes the position change of the gate under the action of external force, friction and water flow force, and can simulate the movement characteristics of the gate during normal operation.

[0067] The mechanical state model can describe the forces acting during the gate movement, including the switching force and the resistance;

[0068] Among them, the mechanical state model, the mechanical characteristics experienced by the gate during operation are described by the following formula:

[0069] F(t)=F opening (t)+F closing (t)

[0070] Where F(t) is the opening and closing force required by the gate, F opening (t) is the force generated when the gate is opened, F closing (t) is the force generated when the gate is closed;

[0071] The model can help staff evaluate the opening and closing force required for the gate in different operating modes and compare it with the baseline mechanical state to determine whether there are anomalies, such as excessive friction or operating resistance.

[0072] The vibration and wear model can describe the vibration and wear state existing in the gate operation process;

[0073] Among them, the vibration and wear model, the vibration state of the gate is as follows:

[0074] v(t)=a 1 ×sin(ωt)+a 2 ×cos(ωt)+η(t)

[0075] Where v(t) is the vibration velocity at time t, a 1 is the first vibration amplitude coefficient, a 2 is the second vibration amplitude coefficient, ω is the angular velocity in radians per second, and η(t) is the random noise caused by wear factors.

[0076] The purpose of this model is to evaluate the vibration characteristics of the gate during use and identify possible structural problems in advance. For example, wear of gate components can cause frequency changes or irregular vibration patterns.

[0077] The water flow and environment model takes into account the impact of environmental factors such as water flow and climate on gate operation.

[0078] Among them, the water flow and environment model, the model describing the impact of water flow mechanics on the gate is as follows:

[0079] w(t)=β 1 ×Q(t)+β2 ×H(t)

[0080] Where w(t) is the impact force generated by the water flow, Q(t) is the water flow, H(t) is the water level, and β 1 is the influence coefficient of water flow force, β 2 is the influence coefficient of water head;

[0081] The model can predict the operating conditions of the gates based on water flow and water level changes, helping to identify additional pressure or resistance on the gates caused by environmental changes.

[0082] It should be noted that by installing high-precision sensors and acquisition equipment, the data of the gate in different operating states can be monitored and collected in real time. The monitored data includes the data input by all state benchmark models.

[0083] In this embodiment, the input data includes but is not limited to the gate's open and closed state, position sensor data, pressure and force sensor feedback, flow and water level data, etc. The monitored data is not limited to the basic position of the gate, but should also include details such as mechanical stress and vibration to identify potential wear or abnormalities.

[0084] Furthermore, the gate reference state deviation of the hydropower station includes:

[0085] Gate position deviation, force deviation, vibration speed deviation, and water flow impact force deviation.

[0086] Step 3: inputting the gate reference state deviation of the hydropower station into a pre-established fault prediction model to obtain the gate fault diagnosis result of the hydropower station at the current moment;

[0087] The gate fault diagnosis result includes: the gate fault mode and risk value.

[0088] In the embodiment of the present disclosure, the process of establishing the fault prediction model includes:

[0089] Obtain the reference state values ​​of the gate at each moment in the historical period and the corresponding fault mode at each moment;

[0090] By using machine learning technology, the reference state values ​​of the gate at each moment in the historical period are used as input, and the corresponding fault mode at each moment is used as output, the initial fault prediction model is optimized and trained to obtain a trained fault prediction model;

[0091] The failure modes include: sticking, water leakage, and wear.

[0092] It should be noted that the calculation formula of the risk value is as follows:

[0093]

[0094] Where f(t) is the risk value at time t, α i is the weight of the gate's ith reference state value, x i (t) is the i-th reference state value of the gate at time t, and n is the total number of reference state values ​​of the gate.

[0095] It should be noted that the weight of each benchmark state value can be achieved through the following methods:

[0096] Weight setting based on expert experience. When there is not enough historical data or complex algorithm support, the weight coefficient can be manually set through expert experience or domain knowledge. For example, based on the working characteristics of the gate, it may be considered that the position change has a greater impact on the fault, while the vibration frequency may be second, that is, the gate position state model has a greater weight, and the vibration and wear model has a second weight.

[0097] Data-based statistical analysis method. Use statistical analysis (such as Pearson correlation coefficient, variance analysis, etc.) to calculate the correlation between each state variable and the occurrence of a fault, and then determine the weight coefficient. For example, if a variable (such as a vibration and wear model) has a high correlation with the occurrence of a fault, then the weight coefficient of the variable will be relatively large. That is, collect a large amount of historical fault data and normal operation data, calculate the correlation between each state variable and the occurrence of a fault, and assign a higher weight coefficient to the state variable with a stronger correlation.

[0098] Furthermore, this embodiment also includes:

[0099] The real-time monitoring data of the gate status is extracted and compared with the baseline model to determine whether problems such as sticking, leakage, abnormal vibration, etc. have occurred. Through comparative analysis, situations that are significantly different from the normal state can be identified and a specific diagnostic report can be generated. The core of fault diagnosis is to provide targeted solutions based on known fault modes and current data.

[0100] It should be noted that the fault prediction model is first trained on the gate’s historical operating data using machine learning technology. The training data includes the baseline state during normal operation and various known fault modes, including sample data of sticking, leakage, and wear.

[0101] The input data of the fault prediction model comes directly from the state variables and the output of the state benchmark model, combined with the historical operation data to form a multidimensional data set with time series characteristics.

[0102] The output data of the fault prediction model provides risk prediction factors and potential failure information, providing a scientific basis for fault prediction and management.

[0103] By inputting real-time data into the fault prediction model, the risk value of f(t) is obtained, and an early warning signal is provided based on the risk value to determine whether the gate is in a critical state of failure. The potential faults of the gate are identified based on the abnormal value of the state variable output by a model in the state benchmark model.

[0104] Furthermore, the method further comprises:

[0105] It is determined whether the risk value is greater than a preset risk threshold, and when the risk value is greater than the preset risk threshold, a warning signal is generated to provide a fault warning.

[0106] Furthermore, the method further comprises:

[0107] The pre-established fault prediction model is adaptively optimized and trained based on the gate fault diagnosis result.

[0108] It should be noted that the results of fault diagnosis are used as new training data for the fault prediction model to continuously adjust and optimize the diagnosis model, thereby improving the accuracy and response speed of future fault prediction models. Through this adaptive learning method, the system can continuously improve the accuracy of fault diagnosis in long-term use.

[0109] Furthermore, the method further comprises:

[0110] Use sensors placed at preset locations to collect gate operation monitoring data;

[0111] The fault location is determined based on the gate fault mode in the gate fault diagnosis result and the gate operation monitoring data collected by the sensor arranged at the preset position.

[0112] It should be noted that according to the fault prediction model, the fault mode is identified based on the known fault mode;

[0113] Based on the identified fault mode, the system accurately determines the location of the fault by comparing the sensor data source with historical data.

[0114] By comparing the data sources of sensors (such as temperature sensors, vibration sensors, displacement sensors, etc.) with historical data, the system can accurately determine the location of the fault. For example, if the vibration sensor data is abnormal, but the temperature sensor data is normal, the system may infer that a mechanical part of the gate is faulty; if the displacement sensor shows an abnormality, but other sensors are normal, it may be that the moving parts of the gate are stuck.

[0115] At the same time, after the fault is located, the system will generate real-time feedback based on the diagnostic results and historical maintenance records, and recommend repair measures. For example, if it is found that the abnormal position of the gate is caused by excessive water pressure, the system may recommend increasing the regulation of water pressure or adjusting the sealing of the gate. If it is a mechanical failure caused by wear, it may be recommended to replace the parts or perform lubrication.

[0116] In summary, the gate status monitoring method for a hydropower station proposed in this embodiment has the following advantages: 1. By establishing a gate status benchmark model, implementing multimodal data fusion, real-time monitoring and combining a fault prediction model, the deficiencies in the prior art are overcome. 2. This method can not only realize comprehensive monitoring of the gate operation status, but also identify potential fault risks in advance through a fault prediction model, avoiding the problems of false alarms and missed alarms in traditional monitoring methods. 3. Through accurate fault diagnosis and positioning, the location and type of the fault can be quickly determined, thereby achieving efficient maintenance and repair. 4. The prediction model is dynamically adjusted according to real-time monitoring data and fault diagnosis results, thereby continuously improving the accuracy of fault diagnosis and the timeliness of system response. Ultimately, this method can greatly improve the safety, reliability and operating efficiency of hydropower station gates, reduce maintenance costs and extend the service life of equipment.

[0117] Embodiment 2

[0118] Figure 2 FIG. 1 is a structural diagram of a gate status monitoring system for a hydropower station provided according to an embodiment of the present application, such as Figure 2 As shown, the system comprises:

[0119] The acquisition module 100 is used to acquire the gate operation monitoring data of the hydropower station at the current moment and each reference state threshold of the gate, and pre-process the gate operation monitoring data to obtain the pre-processed gate operation monitoring data;

[0120] Among them, the gate operation monitoring data includes: external driving force, friction force, water flow force, force generated when opening the gate, force generated when closing the gate, vibration frequency, water flow, and water level.

[0121] A determination module 200, for determining a gate reference state deviation of the hydropower station according to the preprocessed gate operation monitoring data, the reference state thresholds and a pre-established state reference model;

[0122] Wherein, the pre-established state reference model includes:

[0123] Gate position state model, mechanical state model, vibration and wear model, water flow and environment model;

[0124] The gate reference state deviation of the hydropower station includes:

[0125] Gate position deviation, force deviation, vibration speed deviation, and water flow impact force deviation.

[0126] The diagnosis module 300 is used to input the gate reference state deviation of the hydropower station into a pre-established fault prediction model to obtain the gate fault diagnosis result of the hydropower station at the current moment;

[0127] The gate fault diagnosis result includes: the gate fault mode and risk value.

[0128] The process of establishing the fault prediction model includes:

[0129] Obtain the reference state values ​​of the gate at each moment in the historical period and the corresponding fault mode at each moment;

[0130] By using machine learning technology, the reference state values ​​of the gate at each moment in the historical period are used as input, and the corresponding fault mode at each moment is used as output, the initial fault prediction model is optimized and trained to obtain a trained fault prediction model;

[0131] The failure modes include: sticking, water leakage, and wear.

[0132] The risk value is calculated as follows:

[0133]

[0134] Where f(t) is the risk value at time t, α i is the weight of the gate's ith reference state value, x i (t) is the i-th reference state value of the gate at time t, and n is the total number of reference state values ​​of the gate.

[0135] In the embodiment of the present disclosure, the acquisition module 100 is further used for:

[0136] The gate operation monitoring data is cleaned, outliers are removed, missing data are supplemented, and filtered in turn to obtain pre-processed gate operation monitoring data.

[0137] In the embodiments of the present disclosure, Figure 3 As shown, the system further includes: an early warning module 400;

[0138] The warning module 400 is used to determine whether the risk value is greater than a preset risk threshold, and when the risk value is greater than the preset risk threshold, generate a warning signal for fault warning.

[0139] In the embodiments of the present disclosure, Figure 3 As shown, the system further includes: an optimization module 500;

[0140] The optimization module 500 is used to perform adaptive optimization training on the pre-established fault prediction model based on the gate fault diagnosis result.

[0141] In the embodiments of the present disclosure, Figure 3 As shown, the system further includes: a fault location identification module 600;

[0142] The fault location identification module 600 is used to collect gate operation monitoring data using sensors arranged at preset locations;

[0143] The fault location identification module 600 is also used to determine the fault location based on the gate fault mode in the gate fault diagnosis result and the gate operation monitoring data collected by the sensor arranged at the preset position.

[0144] In summary, the gate status monitoring system for a hydropower station proposed in this embodiment can greatly improve the safety, reliability and operating efficiency of the hydropower station gates, reduce maintenance costs and extend the service life of the equipment.

[0145] Embodiment 3

[0146] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0147] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0148] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0149] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A gate status monitoring method for a hydropower station, characterized in that: The method comprises: Obtaining the gate operation monitoring data of the hydropower station at the current moment and each reference state threshold of the gate, and preprocessing the gate operation monitoring data to obtain the preprocessed gate operation monitoring data; Determining a gate reference state deviation of the hydropower station according to the pre-processed gate operation monitoring data, the reference state thresholds and a pre-established state reference model; Inputting the gate reference state deviation of the hydropower station into a pre-established fault prediction model to obtain the gate fault diagnosis result of the hydropower station at the current moment; The gate fault diagnosis result includes: the gate fault mode and risk value.

2. The method according to claim 1, characterized in that The preprocessing of the gate operation monitoring data to obtain the preprocessed gate operation monitoring data includes: The gate operation monitoring data is sequentially cleaned, outliers are removed, missing data is supplemented, and filtering is performed to obtain pre-processed gate operation monitoring data; The gate operation monitoring data includes: external driving force, friction force, water flow force, force generated when the gate is opened, force generated when the gate is closed, vibration frequency, water flow, and water level.

3. The method according to claim 2, characterized in that The pre-established state reference model includes: Gate position state model, mechanical state model, vibration and wear model, water flow and environment model; The gate reference state deviation of the hydropower station includes: Gate position deviation, force deviation, vibration speed deviation, and water flow impact force deviation.

4. The method according to claim 3, characterized in that The process of establishing the fault prediction model includes: Obtain the reference state values ​​of the gate at each moment in the historical period and the corresponding fault mode at each moment; By using machine learning technology, the reference state values ​​of the gate at each moment in the historical period are used as input, and the corresponding fault mode at each moment is used as output, the initial fault prediction model is optimized and trained to obtain a trained fault prediction model; The failure modes include: sticking, water leakage, and wear.

5. The method according to claim 4, characterized in that The risk value is calculated as follows: Where f(t) is the risk value at time t, α i is the weight of the gate's ith reference state value, x i (t) is the i-th reference state value of the gate at time t, and n is the total number of reference state values ​​of the gate.

6. The method according to claim 5, characterized in that The method further comprises: It is determined whether the risk value is greater than a preset risk threshold, and when the risk value is greater than the preset risk threshold, a warning signal is generated to provide a fault warning.

7. The method according to claim 6, characterized in that The method further comprises: The pre-established fault prediction model is adaptively optimized and trained based on the gate fault diagnosis result.

8. The method according to claim 7, characterized in that The method further comprises: Use sensors placed at preset locations to collect gate operation monitoring data; The fault location is determined based on the gate fault mode in the gate fault diagnosis result and the gate operation monitoring data collected by the sensor arranged at the preset position.

9. A gate status monitoring system for a hydropower station, characterized in that: The system comprises: An acquisition module is used to acquire the gate operation monitoring data of the hydropower station at the current moment and each reference state threshold of the gate, and pre-process the gate operation monitoring data to obtain the pre-processed gate operation monitoring data; A determination module, used to determine the gate reference state deviation of the hydropower station according to the pre-processed gate operation monitoring data, the reference state thresholds and a pre-established state reference model; A diagnosis module, used for inputting the gate reference state deviation of the hydropower station into a pre-established fault prediction model to obtain a gate fault diagnosis result of the hydropower station at the current moment; The gate fault diagnosis result includes: the gate fault mode and risk value.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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