CN119803575A - A gate status monitoring method and system for hydropower station - Google Patents
A gate status monitoring method and system for hydropower station Download PDFInfo
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Abstract
本申请提出一种用于水电站的闸门状态监测方法及系统，所述方法包括：获取水电站当前时刻的闸门运行监测数据及闸门的各基准状态阈值，并对所述闸门运行监测数据进行预处理，得到预处理后的闸门运行监测数据；根据所述预处理后的闸门运行监测数据、所述各基准状态阈值和预先建立的状态基准模型确定所述水电站的闸门基准状态偏离量；将所述水电站的闸门基准状态偏离量输入到预先建立的故障预测模型中，得到当前时刻所述水电站的闸门故障诊断结果；所述闸门故障诊断结果包括：闸门的故障模式和风险值。本申请提出的技术方案，能够精确的进行闸门状态监测，大幅提高水电站闸门的安全性、可靠性和运行效率，减少维护成本并延长设备使用寿命。
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 accurately monitor the gate state, greatly improve the safety, reliability and operation efficiency of the hydropower station gate, reduce maintenance costs and extend the service life of the equipment.
Description
Technical Field
The application relates to the technical field of equipment monitoring, in particular to a gate state monitoring method and system for a hydropower station.
Background
The gate in the hydropower station is used as an important water flow control facility and plays key roles of adjusting water level, protecting a dam, controlling the safe operation of power generation equipment and the like. With the ever-increasing scale of hydropower stations and extended operational cycles, the health management of gates presents greater challenges. The traditional gate monitoring method mainly relies on manual inspection and periodic inspection, and although the safe operation of the gate can be ensured to a certain extent, due to the low inspection frequency and the limitation of inspection means, potential faults in the operation process of the gate are often difficult to discover in time. In addition, hydropower stations have complex environments, and the gate may suffer from problems such as jamming, abrasion, corrosion and the like under the influence of long-time high-pressure water flow and environment, and the conventional monitoring method cannot efficiently and accurately identify the potential problems, and the gate is often in a serious stage when a fault occurs.
In order to improve the operation efficiency and safety of the hydropower station gate, in recent years, intelligent monitoring technology is gradually applied to health management of hydropower station equipment. The existing intelligent monitoring system mainly evaluates the running state of the gate through real-time data acquisition, sensor analysis and data processing, but most systems still have great defects in the aspects of fault prediction and diagnosis accuracy, and especially when dealing with complex operating environments and fault modes, false alarms or missing alarms often exist. Meanwhile, most of the prior art focuses on a single monitoring index, lacks a comprehensive method for fusing multiple monitoring data, and does not improve the accuracy and instantaneity of fault diagnosis by updating an optimization model in real time. Therefore, it is highly desirable to provide a solution that can accurately monitor the status of the gate.
Disclosure of Invention
The application provides a gate state monitoring method and system for a hydropower station, which are used for at least solving the technical problem of low gate monitoring precision.
An embodiment of a first aspect of the present application provides a method for monitoring a gate status of a hydropower station, the method including:
Acquiring gate operation monitoring data at the current moment of a hydropower station and all reference state thresholds of a gate, and preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data;
determining the gate reference state deviation amount 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 quantity 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.
Preferably, the preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data includes:
sequentially cleaning, removing abnormal values, supplementing missing data and filtering the gate operation monitoring data to obtain preprocessed gate operation monitoring data;
The gate operation monitoring data comprise external driving force, friction force, water flow force, force generated during opening of the gate, force generated during closing of the gate, vibration frequency, water flow and water level.
Further, the pre-established state reference model includes:
A gate position state model, a mechanical state model, a vibration and abrasion model, and a water flow and environment model;
The gate reference state deviation amount of the hydropower station comprises:
gate position offset, force offset, vibration velocity offset, water flow impact force offset.
Further, the fault prediction model building process includes:
acquiring each reference state value of the gate at each moment in the historical period and a fault mode corresponding to each moment;
Using a machine learning technology, taking each reference state value of the gate at each moment in a history period as input, taking a fault mode corresponding to each moment as output, and carrying out optimization training on an initial fault prediction model to obtain a trained fault prediction model;
The fault modes comprise clamping stagnation, water leakage and abrasion.
Further, the risk value is calculated as follows:
Where f (t) is a risk value at time t, α i is a weight of a gate i-th reference state value, x i (t) is a gate i-th reference state value at time t, and n is a total gate reference state value.
Further, the method further comprises:
Judging whether the risk value is larger than a preset risk threshold, and generating an early warning signal to perform fault early warning when the risk value is larger than the preset risk threshold.
Further, the method further comprises:
And carrying out self-adaptive optimization training on the pre-established fault prediction model based on the gate fault diagnosis result.
Further, the method further comprises:
Acquiring gate operation monitoring data by using sensors arranged at preset positions;
And judging the fault occurrence position based on the fault mode of the gate in the gate fault diagnosis result and the gate operation monitoring data collected by the sensor arranged at the preset position.
An embodiment of a second aspect of the present application provides a gate status monitoring system for a hydropower station, including:
the acquisition module is used for acquiring gate operation monitoring data at the current moment of the hydropower station and all reference state thresholds of the gate, and preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data;
The determining module is used for 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;
The diagnosis module is used for inputting the gate reference state deviation quantity 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.
An embodiment of the third aspect of the present application proposes a computer readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements a method as described in the embodiment of the first aspect.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
The application provides a gate state monitoring method and a gate state monitoring system for a hydropower station, wherein the method comprises the steps of obtaining gate operation monitoring data at the current moment of the hydropower station and all reference state thresholds of a gate, and preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data; determining the gate reference state deviation amount 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 amount 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, wherein the gate fault diagnosis result comprises a gate fault mode and a risk value. The technical scheme provided by the application can greatly improve the safety, reliability and operation efficiency of the hydropower station gate, reduce maintenance cost and prolong the service life of equipment.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of a gate status monitoring method for a hydropower station according to one embodiment of the application;
FIG. 2 is a first block diagram of a gate status monitoring system for a hydropower station according to one embodiment of the application;
fig. 3 is a second block diagram of a gate status monitoring system for a hydropower station according to an embodiment of the application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.
The gate state monitoring method and system for the hydropower station comprise the steps of obtaining gate operation monitoring data and reference state thresholds of a gate at the current moment of the hydropower station, preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data, determining 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 the pre-established fault prediction model to obtain a gate fault diagnosis result of the hydropower station at the current moment, wherein the gate fault diagnosis result comprises a gate fault mode and a gate risk value. The technical scheme provided by the application can greatly improve the safety, reliability and operation efficiency of the hydropower station gate, reduce maintenance cost and prolong the service life of equipment.
The following describes a gate status monitoring method and a system for a hydropower station according to an embodiment of the application with reference to the accompanying drawings.
Example 1
Fig. 1 is a flowchart of a gate status monitoring method for a hydropower station according to an embodiment of the application, as shown in fig. 1, the method includes:
Step 1, acquiring gate operation monitoring data and each reference state threshold value of a gate at the current moment of a hydropower station, and preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data;
The gate operation monitoring data comprise external driving force, friction force, water flow force, force generated during opening of the gate, force generated during closing of the gate, vibration frequency, water flow and water level.
By considering various factors such as the position, the movement, the mechanical behavior, the vibration and the like of the gate and combining the operation conditions under the specific environment of the hydropower station, a set of state reference models are established, so that each reference state threshold value can be obtained, and a foundation is laid for subsequent fault analysis.
The values or ranges for each of the above variables under normal operating conditions are determined, for example, by historical data and experimental data. And setting reference values, namely, the threshold values of each reference state in normal operation according to the specification, the service condition and the environmental condition of the equipment.
In an embodiment of the present disclosure, the preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data includes:
And cleaning, removing abnormal values, supplementing missing data and filtering the gate operation monitoring data in sequence to obtain the preprocessed gate operation monitoring data.
It should be noted that the number of the substrates, the pretreatment comprises the following steps:
cleaning the data, removing abnormal values, complementing missing data and filtering;
and filtering the collected abnormal data.
The collected monitoring data may have problems of noise, missing value, abnormal fluctuation and the like, so that data preprocessing is required. The data preprocessing comprises data cleaning, abnormal value removal, missing data complement, filtering processing and the like. For outlier data, filtering is performed using methods based on statistical analysis and pattern recognition. For example, by setting a suitable threshold range, data (such as excessive pressure, excessive displacement, etc.) beyond a reasonable range can be detected, and data which does not conform to the actual physical law can be removed. And meanwhile, filling the missing value to ensure the integrity of the data. Through the preprocessing steps, the follow-up analysis and decision are ensured to be based on accurate and clean data, so that false alarm and missing report are avoided.
Step 2, determining 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;
In an embodiment of the present disclosure, the pre-established state reference model includes:
A gate position state model, a mechanical state model, a vibration and abrasion model, and a water flow and environment model;
It should be noted that the gate position state model may describe a position and a movement state of the gate;
Wherein, the gate position state model sets that the motion of gate is driven by external force and is influenced by friction force and water flow force, then the position of gate is described by the following first order dynamics equation:
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, the influence of the friction force is reflected, k1 is the elastic coefficient, and the elastic characteristic of the system is reflected.
The equation describes the position change of the gate under the action of external force drive, friction force and water flow force, and can simulate the motion characteristic of the gate in the normal operation process.
The mechanical state model can describe forces acting during the movement of the gate, including switching forces and resistances;
The mechanical state model, the mechanical characteristics of the gate during operation, are described by the following formula:
F(t)=Fopening(t)+Fclosing(t)
Wherein F (t) is the switching force required by the gate, F opening (t) is the force generated when the gate is opened, and F closing (t) is the force generated when the gate is closed;
the model can help staff evaluate the switching force required by the gate in different operation modes, and compare the switching force with a reference mechanical state to judge whether an abnormality exists, such as excessive friction or operation resistance.
The vibration and wear model may describe vibration and wear conditions present during operation of the gate;
wherein, vibration and wearing model, the vibration state of gate is as follows:
v(t)=a1×sin(ωt)+a2×cos(ωt)+η(t)
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 radian/second, and η (t) is random noise due to the abrasion factor.
The function of the model is to evaluate the vibration characteristics of the gate during use and identify possible structural problems in advance. For example, wear of the gate member may cause frequency variation, or irregular vibration modes may occur.
The water flow and environment model considers the influence of water flow and climate environment factors on the operation of the gate.
Wherein, rivers and environmental model, the model that describes the impact of rivers mechanics to the gate is as follows:
w(t)=β1×Q(t)+β2×H(t)
Wherein w (t) is the impact force generated by water flow, Q (t) is water flow, H (t) is water level, beta 1 is the influence coefficient of water flow force, and beta 2 is the influence coefficient of water head;
The model can predict the working condition of the gate according to the water flow and the water level change, and helps to identify the additional pressure or resistance of the gate caused by environmental change.
By installing the high-precision sensor and the acquisition equipment, the data of the gate in different operation states are monitored and acquired in real time, and the monitored data comprise the data input by all state reference models.
In this embodiment, the input data includes, but is not limited to, the on-off state of the gate, position sensor data, pressure and force sensor feedback, flow and water level data, and the like. The monitored data should not only be limited to the basic position of the gate, but also include details of mechanical stress, vibration, etc. in order to identify potential wear or anomalies.
Further, the gate reference state deviation amount of the hydropower station includes:
gate position offset, force offset, vibration velocity offset, water flow impact force offset.
Step 3, inputting the gate reference state deviation quantity 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.
In an embodiment of the present disclosure, the process for establishing the fault prediction model includes:
acquiring each reference state value of the gate at each moment in the historical period and a fault mode corresponding to each moment;
Using a machine learning technology, taking each reference state value of the gate at each moment in a history period as input, taking a fault mode corresponding to each moment as output, and carrying out optimization training on an initial fault prediction model to obtain a trained fault prediction model;
The fault modes comprise clamping stagnation, water leakage and abrasion.
It should be noted that, the calculation formula of the risk value is as follows:
Where f (t) is a risk value at time t, α i is a weight of a gate i-th reference state value, x i (t) is a gate i-th reference state value at time t, and n is a total gate reference state value.
It should be noted that, the weight of each reference state value may be implemented by the following several methods:
Weight setting based on expert experience. The weighting coefficients may be set manually by expert experience or domain knowledge when there is insufficient historical data or complex algorithm support. For example, based on the operational characteristics of the gate, it may be considered that the position change has a large influence on the fault, and the vibration frequency may be inferior, i.e., the weight of the gate position state model is heavy, and the weight of the vibration and wear model is inferior.
Statistical analysis methods based on data. Statistical analysis (e.g., pearson correlation coefficients, analysis of variance, etc.) is used to calculate the correlation between each state variable and the occurrence of the fault, and thus determine the weighting coefficients. For example, if a variable (e.g., vibration and wear model) has a high correlation with the occurrence of a fault, the weighting coefficient for that variable may be relatively large. That is, a large amount of historical fault data and normal operation data are collected, correlations between each state variable and the occurrence of faults are calculated, and the state variable with the stronger correlation is given to a higher weight coefficient.
Further, the embodiment further includes:
And extracting the real-time monitoring data of the gate state, performing difference comparison with a reference model, and judging whether the problems of clamping stagnation, leakage, abnormal vibration and the like occur. Through comparative analysis, a situation with a significant difference from the normal state can be identified, and a specific diagnosis report can be generated. The core of fault diagnosis is to provide a targeted solution based on known fault patterns and current data.
It should be noted that, the fault prediction model firstly trains the historical operation data of the gate through the machine learning technology, and the training data comprises the standard state in normal operation and various known fault modes, including sample data of clamping stagnation, water leakage and abrasion;
the input data of the fault prediction model is directly derived from the output of the state variable and the state reference model, and a multi-dimensional data set with time sequence characteristics is formed by combining historical operation data.
And the output data of the fault prediction model provides risk prediction factors and potential fault information, and provides scientific basis for fault prediction and management.
And obtaining a risk value of f (t) through real-time data input into the fault prediction model, providing an early warning signal based on the risk value, judging whether the gate is in a fault critical state, and identifying potential faults of the gate according to a state variable abnormal value output by a certain model in the state reference model.
Further, the method further comprises:
Judging whether the risk value is larger than a preset risk threshold, and generating an early warning signal to perform fault early warning when the risk value is larger than the preset risk threshold.
Further, the method further comprises:
And carrying out self-adaptive optimization training on the pre-established fault prediction model based on the gate fault diagnosis result.
It should be noted that, according to the result of fault diagnosis, the new training data is used as the fault prediction model to continuously adjust and optimize the diagnosis model, so as to improve the accuracy and response speed of the future fault prediction model. Through the self-adaptive learning mode, the system can continuously improve the fault diagnosis precision in long-term use.
Further, the method further comprises:
Acquiring gate operation monitoring data by using sensors arranged at preset positions;
And judging the fault occurrence position based on the fault mode of the gate in the gate fault diagnosis result and the gate operation monitoring data collected by the sensor arranged at the preset position.
It should be noted that, according to the failure prediction model, a failure mode is identified based on a known failure mode;
According to the identified fault mode, the system accurately judges the position of the fault through the comparison of the data source of the sensor and the historical data.
The system can accurately judge the position of fault occurrence through the comparison of the data sources (such as a temperature sensor, a vibration sensor, a displacement sensor and the like) of the sensor and the historical data. For example, if the vibration sensor data is abnormal and the temperature sensor data is normal, the system may presume that a certain mechanical component of the gate is malfunctioning, and if the displacement sensor shows abnormality and the other sensors are normal, it may be that a moving component of the gate is stuck.
Meanwhile, after fault location, the system generates real-time feedback based on the diagnosis result and the historical maintenance record, and recommends repair measures. For example, if it is found that the gate is out of position due to excessive water flow pressure, the system may suggest increasing the adjustment of the water flow pressure or adjusting the tightness of the gate. If the machine fails due to wear, replacement of the components or lubrication may be recommended.
In summary, the gate state monitoring method for the hydropower station has the advantages that 1. The defects in the prior art are overcome by establishing a state reference model of the gate, implementing multi-mode data fusion, monitoring in real time and combining a fault prediction model. 2. The method not only can realize comprehensive monitoring of the running state of the gate, but also can identify potential fault risks in advance through a fault prediction model, and the problems of false alarm and missing report in the traditional monitoring method are avoided. 3. Through accurate fault diagnosis and location, the position and the type of fault occurrence can be rapidly determined, and efficient maintenance and repair are further realized. 4. And dynamically adjusting the prediction model according to the real-time monitoring data and the fault diagnosis result, thereby continuously improving the accuracy of fault diagnosis and the timeliness of system response. Finally, the method can greatly improve the safety, reliability and operation efficiency of the hydropower station gate, reduce maintenance cost and prolong the service life of equipment.
Example two
Fig. 2 is a block diagram of a gate status monitoring system for a hydropower station according to an embodiment of the application, as shown in fig. 2, the system includes:
The acquisition module 100 is configured to acquire gate operation monitoring data at a current moment of the hydropower station and each reference state threshold value of the gate, and perform preprocessing on the gate operation monitoring data to obtain preprocessed gate operation monitoring data;
the gate operation monitoring data comprise external driving force, friction force, water flow force, force generated during opening of the gate, force generated during closing of the gate, vibration frequency, water flow and water level.
The determining module 200 is configured to determine a gate reference state deviation amount of the hydropower station according to the preprocessed gate operation monitoring data, the reference state thresholds and a pre-established state reference model;
Wherein the pre-established state reference model comprises:
A gate position state model, a mechanical state model, a vibration and abrasion model, and a water flow and environment model;
The gate reference state deviation amount of the hydropower station comprises:
gate position offset, force offset, vibration velocity offset, water flow impact force offset.
The diagnosis module 300 is used for inputting the gate reference state deviation amount 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.
The fault prediction model building process comprises the following steps:
acquiring each reference state value of the gate at each moment in the historical period and a fault mode corresponding to each moment;
Using a machine learning technology, taking each reference state value of the gate at each moment in a history period as input, taking a fault mode corresponding to each moment as output, and carrying out optimization training on an initial fault prediction model to obtain a trained fault prediction model;
The fault modes comprise clamping stagnation, water leakage and abrasion.
The risk value is calculated as follows:
Where f (t) is a risk value at time t, α i is a weight of a gate i-th reference state value, x i (t) is a gate i-th reference state value at time t, and n is a total gate reference state value.
In the embodiment of the present disclosure, the obtaining module 100 is further configured to:
And cleaning, removing abnormal values, supplementing missing data and filtering the gate operation monitoring data in sequence to obtain the preprocessed gate operation monitoring data.
In an embodiment of the present disclosure, as shown in fig. 3, the system further includes an early warning module 400;
the early warning module 400 is configured to determine whether the risk value is greater than a preset risk threshold, and generate an early warning signal to perform fault early warning when the risk value is greater than the preset risk threshold.
In an embodiment of the present disclosure, as shown in fig. 3, the system further includes an optimization module 500;
the optimizing module 500 is configured to perform adaptive optimization training on the pre-established fault prediction model based on the gate fault diagnosis result.
In an embodiment of the present disclosure, as shown in FIG. 3, the system further includes a fault location identification module 600;
the fault location recognition module 600 is configured to collect gate operation monitoring data by using sensors disposed at preset locations;
the fault location identifying module 600 is further configured to determine a fault location based on a fault mode of the gate in the gate fault diagnosis result and the gate operation monitoring data collected by the sensor disposed at the preset location.
In summary, the gate state monitoring system for the hydropower station provided by the embodiment can greatly improve the safety, reliability and operation efficiency of the gate of the hydropower station, reduce maintenance cost and prolong the service life of equipment.
Example III
In order to implement the above-described embodiments, the present disclosure also proposes a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, implements a method as described in embodiment one.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. A gate status monitoring method for a hydropower station, the method comprising:
Acquiring gate operation monitoring data at the current moment of a hydropower station and all reference state thresholds of a gate, and preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data;
determining the gate reference state deviation amount 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 quantity 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.
2. The method of claim 1, wherein preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data comprises:
sequentially cleaning, removing abnormal values, supplementing missing data and filtering the gate operation monitoring data to obtain preprocessed gate operation monitoring data;
The gate operation monitoring data comprise external driving force, friction force, water flow force, force generated during opening of the gate, force generated during closing of the gate, vibration frequency, water flow and water level.
3. The method of claim 2, wherein the pre-established state reference model comprises:
A gate position state model, a mechanical state model, a vibration and abrasion model, and a water flow and environment model;
The gate reference state deviation amount of the hydropower station comprises:
gate position offset, force offset, vibration velocity offset, water flow impact force offset.
4. The method of claim 3, wherein the fault prediction model creation process comprises:
acquiring each reference state value of the gate at each moment in the historical period and a fault mode corresponding to each moment;
Using a machine learning technology, taking each reference state value of the gate at each moment in a history period as input, taking a fault mode corresponding to each moment as output, and carrying out optimization training on an initial fault prediction model to obtain a trained fault prediction model;
The fault modes comprise clamping stagnation, water leakage and abrasion.
5. The method of claim 4, wherein the risk value is calculated as:
Where f (t) is a risk value at time t, α i is a weight of a gate i-th reference state value, x i (t) is a gate i-th reference state value at time t, and n is a total gate reference state value.
6. The method of claim 5, wherein the method further comprises:
Judging whether the risk value is larger than a preset risk threshold, and generating an early warning signal to perform fault early warning when the risk value is larger than the preset risk threshold.
7. The method of claim 6, wherein the method further comprises:
And carrying out self-adaptive optimization training on the pre-established fault prediction model based on the gate fault diagnosis result.
8. The method of claim 7, wherein the method further comprises:
Acquiring gate operation monitoring data by using sensors arranged at preset positions;
And judging the fault occurrence position based on the fault mode of the gate in the gate fault diagnosis result and the gate operation monitoring data collected by the sensor arranged at the preset position.
9. A gate condition monitoring system for a hydropower station, the system comprising:
the acquisition module is used for acquiring gate operation monitoring data at the current moment of the hydropower station and all reference state thresholds of the gate, and preprocessing the gate operation monitoring data to obtain preprocessed gate operation monitoring data;
The determining module is used for 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;
The diagnosis module is used for inputting the gate reference state deviation quantity 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.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-8.
Priority Applications (1)
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