Hydroelectric generating set fault diagnosis method and system
By establishing safety and risk sub-models to compare the history and real-time data of the hydropower unit, the problem of low accuracy in fault detection of hydropower units is solved, efficient fault diagnosis and model updates are achieved, and detection accuracy is improved.
Patent Information
- Application Number
- CN202510479820.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-16
- Publication Date
- 2025-08-05
AI Technical Summary
The hydropower unit has a complex structure, and traditional fault detection methods cannot update the fault type in time, resulting in low detection accuracy and may lead to unit damage and expanding.
Establish a data diagnosis model, including safety sub-models and risk sub-models of different fault types, and accurately judge the operating status and update the model by comparing the historical and real-time data of the hydropower unit.
Accurate detection of hydropower unit failures is realized, the dependence on deep learning technology is reduced, the risk sub-model can be automatically updated, and the accuracy and efficiency of fault detection is improved.
Smart Images
Figure CN120429684A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of hydropower generation, and in particular to a method and system for diagnosing faults of hydropower units. Background Art
[0002] With the global emphasis on environmental protection and sustainable development, hydropower, as a clean, renewable energy technology, has garnered increasing attention and recognition. Compared to fossil fuels, hydropower produces no greenhouse gas emissions and poses less environmental pollution, aligning with global sustainable development trends. Hydropower technology has grown steadily through continuous exploration, innovation, and practice, evolving from its initial simple use to today's large-scale, efficient development. It plays a vital role in the energy sector and will continue to play a key role in future sustainable energy development.
[0003] However, in the process of implementing the technical solutions of the embodiments of the present application, the inventors of the present application discovered that the above technology has at least the following technical problems: The structure of hydropower units is sophisticated and complex. If a fault is not handled in a timely manner, the damage to the hydropower unit may be further expanded. Traditional fault detection methods cannot update the fault type in a timely manner, and the fault detection accuracy is low. Summary of the Invention
[0004] The embodiments of the present invention provide a hydropower unit fault diagnosis method and system, which are used to solve the technical problem of low accuracy in hydropower unit fault detection in the prior art.
[0005] In order to achieve the above object, the present invention provides a method for diagnosing faults of a hydropower unit, comprising: Obtaining historical operation records of the hydropower unit, and establishing a data diagnosis model based on the historical operation records of the hydropower unit, the data diagnosis model including a safety sub-model and risk sub-models corresponding to different fault types; Collecting real-time operating data of the hydropower unit to be analyzed, and establishing a sub-model to be analyzed based on the real-time operating data of the hydropower unit to be analyzed; Analyze the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed; According to the parsing result of the data diagnosis model on the sub-model to be analyzed, it is determined whether to update the data diagnosis model.
[0006] Furthermore, the acquiring of historical operation records of the hydropower unit and establishing a data diagnosis model based on the historical operation records of the hydropower unit include: Extracting all normal operation records from the historical operation records of the hydropower unit; Extract vibration data, temperature data, and pressure data of the core components of the hydropower unit recorded for each normal operation; Preprocessing the vibration data, temperature data, and pressure data of the core components of the hydropower unit extracted from all the normal operation records; According to the pre-processed vibration data, temperature data and pressure data of the core components of the hydropower unit, the safety vibration characteristics, safety temperature characteristics and safety pressure characteristics of the hydropower unit are extracted to build a safety sub-model: ; in, For the security sub-model, For safety vibration characteristics, For safety temperature characteristics, It is a safety pressure characteristic; Extracting all fault operation records from the historical operation records of the hydropower unit; Classifying the fault operation records according to different fault types; Extracting vibration data, temperature data, and pressure data of core components of hydropower units from similar fault operation records, and removing noise and abnormal values from the extracted vibration data, temperature data, and pressure data of core components of hydropower units; According to the processed vibration data, temperature data and pressure data of the core components of the hydropower unit, the risk vibration characteristics, risk temperature characteristics and risk pressure characteristics of the fault type are extracted to construct a risk sub-model of the fault type: ; in, is the risk sub-model of the i-th type of failure, is the risk vibration characteristic of the i-th fault type, is the risk temperature characteristic of the i-th fault type, is the risk pressure characteristic of the i-th fault type.
[0007] Furthermore, the collecting of real-time operating data of the hydropower unit to be analyzed and establishing a sub-model to be analyzed based on the real-time operating data of the hydropower unit to be analyzed include: Obtaining vibration data, temperature data, and pressure data of the current core components of the hydropower unit to be analyzed; According to the vibration data, temperature data and pressure data of the current core components of the hydropower unit to be analyzed, the vibration characteristics, temperature characteristics and pressure characteristics of the hydropower unit to be analyzed are extracted to construct a sub-model to be analyzed: ; in, is the sub-model to be analyzed, To analyze the vibration characteristics of the hydropower unit, The temperature characteristics of the hydropower unit to be analyzed are: The pressure characteristics of the hydropower unit to be analyzed.
[0008] Furthermore, parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Extracting the security sub-model in the data diagnosis model; Calculating whether the sub-model to be analyzed is similar to the safety sub-model; if the sub-model to be analyzed is similar to the safety sub-model, determining that the hydropower unit to be analyzed is in a normal operating state; If the sub-model to be analyzed is not similar to the safety sub-model, extracting risk sub-models corresponding to different fault types in the data diagnosis model; Calculate whether the sub-model to be analyzed is similar to the risk sub-models corresponding to different fault types respectively; if the sub-model to be analyzed is similar to the risk sub-model, determine that the hydropower unit to be analyzed is in a fault operation state of the fault type corresponding to the risk sub-model similar to the sub-model to be analyzed.
[0009] Furthermore, parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Pre-set safety thresholds; Extract the security sub-model and calculate the safety factor between the security sub-model and the sub-model to be analyzed: ; in, is the safety factor between the safety sub-model and the sub-model to be analyzed; If the safety factor is greater than or equal to the safety threshold, it is determined that the sub-model to be analyzed is similar to the safety sub-model; If the safety factor is less than the safety threshold, it is determined that the sub-model to be analyzed is not similar to the safety sub-model.
[0010] Furthermore, parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Pre-set risk thresholds; When the safety factor is less than the safety threshold, the risk sub-models of all fault types are extracted respectively, and the risk factor between each risk sub-model and the sub-model to be analyzed is calculated respectively: ; in, is the risk factor between the risk sub-model of the i-th fault type and the sub-model to be analyzed; If the risk factor between the risk sub-model of the i-th fault type and the sub-model to be analyzed is less than or equal to the risk threshold, it is determined that the sub-model to be analyzed is similar to the risk sub-model of the i-th fault type; If the risk factor between the risk sub-model of the i-th fault type and the sub-model to be analyzed is greater than the risk threshold, it is determined that the sub-model to be analyzed is not similar to the risk sub-model of the i-th fault type.
[0011] Furthermore, parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Presetting a first warning value and a second warning value, wherein the first warning value is smaller than the second warning value; Pre-set level 1 fault warning, level 2 fault warning, and level 3 fault warning; When the risk factor between the risk sub-model of the i-th type of fault and the sub-model to be analyzed is less than or equal to the risk threshold, calculating a warning value between the risk factor between the risk sub-model of the i-th type of fault and the sub-model to be analyzed and the risk threshold; If the warning value is less than or equal to the first warning value, a first-level fault warning of the i-th fault type is issued; If the warning value is greater than the first warning value and the warning value is less than or equal to the second warning value, a second-level fault warning of the i-th fault type is issued; If the warning value is greater than the second warning value, a third-level fault warning of the i-th fault type is issued.
[0012] Furthermore, judging whether to update the data diagnosis model according to the parsing result of the sub-model to be analyzed by the data diagnosis model includes: If the sub-model to be analyzed is not similar to the safety sub-model, and the sub-model to be analyzed is not similar to the risk sub-models corresponding to the different fault types, then the sub-model to be analyzed is determined to be a risk sub-model of a new fault type and is updated into the data diagnosis model.
[0013] Furthermore, a normal operation image of the hydropower unit and fault operation images corresponding to different fault types can be generated according to the data diagnosis model of the hydropower unit; A current operation image of the hydropower unit to be analyzed is generated according to the sub-model to be analyzed.
[0014] In order to achieve the above object, the present invention also provides a hydropower unit fault diagnosis system, comprising: A data training module is used to obtain historical operation records of the hydropower unit and establish a data diagnosis model based on the historical operation records of the hydropower unit, wherein the data diagnosis model includes a safety sub-model and risk sub-models corresponding to different fault types; A data acquisition module is used to collect real-time operating data of the hydropower unit to be analyzed, and to establish a sub-model to be analyzed based on the real-time operating data of the hydropower unit to be analyzed; A data analysis module, configured to analyze the sub-model to be analyzed according to the data diagnosis model, and determine the operating status of the hydropower unit to be analyzed; An updating module is used to determine whether to update the data diagnosis model according to the parsing result of the data diagnosis model on the sub-model to be analyzed.
[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method and system for diagnosing faults of hydropower units. A safety sub-model and several risk sub-models are established based on the historical operation records of the hydropower units. A sub-model to be analyzed is established for the hydropower unit to be analyzed. The sub-model to be analyzed is analyzed by using the safety sub-model and the several risk sub-models, so that the operating status of the hydropower unit to be analyzed is accurately obtained, and the risk sub-model is automatically updated. For new types of faults, there is no need to re-select model training, thereby reducing dependence on deep learning technology. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings: Figure 1 A schematic flow chart of a method for diagnosing faults of a hydropower unit according to an embodiment of the present invention is shown; Figure 2 A structural diagram of a hydropower unit fault diagnosis system according to an embodiment of the present invention is shown. DETAILED DESCRIPTION
[0017] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following embodiments are used to illustrate the present invention but are not intended to limit the scope of the present invention.
[0018] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.
[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.
[0020] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.
[0021] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.
[0022] like Figure 1 As shown, an embodiment of the present invention discloses a method for diagnosing faults of a hydropower unit, comprising: S110, obtaining historical operation records of the hydropower unit, and establishing a data diagnosis model based on the historical operation records of the hydropower unit, the data diagnosis model including a safety sub-model and risk sub-models corresponding to different fault types; S120, collecting real-time operating data of the hydropower unit to be analyzed, and establishing a sub-model to be analyzed based on the real-time operating data of the hydropower unit to be analyzed; S130, analyzing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed; S140 : Determine whether to update the data diagnosis model based on the analysis result of the sub-model to be analyzed by the data diagnosis model.
[0023] In some embodiments of the present application, obtaining historical operation records of a hydropower unit and establishing a data diagnosis model based on the historical operation records of the hydropower unit include: Extract all normal operation records from the historical operation records of hydropower units; Extract vibration data, temperature data, and pressure data of the core components of the hydropower unit for each normal operation record; Pre-process the vibration data, temperature data, and pressure data of the core components of the hydropower unit extracted from all normal operation records; Based on the pre-processed vibration data, temperature data, and pressure data of the core components of the hydropower unit, the safety vibration characteristics, safety temperature characteristics, and safety pressure characteristics of the hydropower unit are extracted to build a safety sub-model: ; in, For the security sub-model, For safety vibration characteristics, For safety temperature characteristics, It is a safety pressure characteristic; Extract all fault operation records from the historical operation records of hydropower units; Classify fault operation records according to different fault types; Extract vibration data, temperature data, and pressure data of core components of hydropower units from similar fault operation records, and remove noise and abnormal values from the extracted vibration data, temperature data, and pressure data of core components of hydropower units; Based on the processed vibration data, temperature data, and pressure data of the core components of the hydropower unit, the risk vibration characteristics, risk temperature characteristics, and risk pressure characteristics of the fault type are extracted to construct a risk sub-model for the fault type: ; in, is the risk sub-model of the i-th type of failure, is the risk vibration characteristic of the i-th fault type, is the risk temperature characteristic of the i-th fault type, is the risk pressure characteristic of the i-th fault type.
[0024] In this embodiment, a safety sub-model and several risk sub-models are established based on the historical operation records of the hydropower unit, and each risk sub-model corresponds to a fault type; It also contains several sub-features, representing the vibration features extracted from different core components. It also contains several sub-features, representing the temperature features extracted from different core components. It also contains several sub-features, which represent the pressure features extracted from different core components; 、 、 Same thing.
[0025] In this embodiment, real-time operating data of the hydropower unit to be analyzed is collected, and a sub-model to be analyzed is established based on the real-time operating data of the hydropower unit to be analyzed, including: Obtain the vibration data, temperature data, and pressure data of the core components of the hydropower unit to be analyzed; Based on the vibration data, temperature data and pressure data of the current core components of the hydropower unit to be analyzed, the vibration characteristics, temperature characteristics and pressure characteristics of the hydropower unit to be analyzed are extracted to build the sub-model to be analyzed: ; in, is the sub-model to be analyzed, To analyze the vibration characteristics of the hydropower unit, The temperature characteristics of the hydropower unit to be analyzed are: The pressure characteristics of the hydropower unit to be analyzed.
[0026] In this embodiment, 、 、 Contains the same number of sub-features, 、 、 Contains the same number of sub-features, 、 、 Contains the same number of sub-features.
[0027] In some embodiments of the present application, parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Extract the security sub-model in the data diagnosis model; Calculate whether the sub-model to be analyzed is similar to the safety sub-model. If the sub-model to be analyzed is similar to the safety sub-model, it is determined that the hydropower unit to be analyzed is in normal operation; If the sub-model to be analyzed is not similar to the safety sub-model, the risk sub-model corresponding to different fault types in the data diagnosis model is extracted; Calculate whether the sub-model to be analyzed is similar to the risk sub-models corresponding to different fault types. If the sub-model to be analyzed is similar to the risk sub-model, it is determined that the hydropower unit to be analyzed is in a fault operation state of the fault type corresponding to the risk sub-model similar to the sub-model to be analyzed.
[0028] In this embodiment, the safety sub-model is used to determine whether the hydropower unit to be analyzed is operating normally. If not, the fault type of the hydropower unit to be analyzed is determined based on several risk sub-models.
[0029] The beneficial effect of the above technical solution is: preliminary analysis is performed through the safety sub-model, thereby avoiding redundant analysis when the hydropower unit to be analyzed is operating normally, and saving resource allocation.
[0030] In some embodiments of the present application, parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Pre-set safety thresholds; Extract the safety sub-model and calculate the safety factor between the safety sub-model and the sub-model to be analyzed: ; in, is the safety factor between the safety sub-model and the sub-model to be analyzed; If the safety factor is greater than or equal to the safety threshold, it is judged that the sub-model to be analyzed is similar to the safety sub-model; If the safety factor is less than the safety threshold, it is determined that the sub-model to be analyzed is not similar to the safety sub-model.
[0031] In this embodiment, the sub-model to be analyzed is parsed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed, including: Pre-set risk thresholds; When the safety factor is less than the safety threshold, the risk sub-models of all fault types are extracted respectively, and the risk factor between each risk sub-model and the sub-model to be analyzed is calculated respectively: ; in, is the risk factor between the risk sub-model of the i-th fault type and the sub-model to be analyzed; If the risk factor between the risk sub-model of the i-th fault type and the sub-model to be analyzed is less than or equal to the risk threshold, then the sub-model to be analyzed is judged to be similar to the risk sub-model of the i-th fault type; If the risk factor between the risk sub-model of the i-th fault type and the sub-model to be analyzed is greater than the risk threshold, it is determined that the sub-model to be analyzed is not similar to the risk sub-model of the i-th fault type.
[0032] In this embodiment, the risk factors between several risk sub-models and the sub-model to be analyzed are calculated respectively. For example, if there are 1, 2, ...i, ..., n risk sub-models of fault types, and after further detection, it is found that the risk sub-model of the i-th fault type and the risk sub-model of the i+1-th fault type are similar to the sub-model to be analyzed, then it is determined that the hydropower unit to be analyzed is in the fault operation state of the i-th fault type and the i+1-th fault type.
[0033] In some embodiments of the present application, parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: A first warning value and a second warning value are preset, and the first warning value is smaller than the second warning value; Pre-set level 1 fault warning, level 2 fault warning, and level 3 fault warning; When the risk factor between the risk sub-model of the i-th type of fault and the sub-model to be analyzed is less than or equal to the risk threshold, calculate the warning value between the risk factor of the risk sub-model of the i-th type of fault and the sub-model to be analyzed and the risk threshold; If the warning value is less than or equal to the first warning value, a first-level fault warning of the i-th fault type is issued; If the warning value is greater than the first warning value and the warning value is less than or equal to the second warning value, a second-level fault warning of the i-th fault type is issued; If the warning value is greater than the second warning value, a third-level fault warning of the i-th fault type is issued.
[0034] In this embodiment, the warning value is the absolute value of the difference between the risk factor and the risk threshold of the risk sub-model of the i-th type of fault and the sub-model to be analyzed. Only when it is confirmed that the hydropower unit to be analyzed is in a faulty operating state of the i-th type of fault, the warning value between the risk factor and the risk threshold of the risk sub-model of the i-th type of fault and the sub-model to be analyzed is calculated; the first-level alarm is the highest level of risk factor, the second-level alarm risk factor is less than the first-level alarm, and the third-level alarm risk factor is less than the second-level alarm.
[0035] The beneficial effect of the above technical solution is: according to different levels of alarms, operators are reminded to pay different levels of attention to hydropower units. When multiple fault types exist at the same time, the fault types corresponding to higher-level alarms are handled first.
[0036] In some embodiments of the present application, determining whether to update the data diagnosis model based on the parsing result of the sub-model to be analyzed by the data diagnosis model includes: If the sub-model to be analyzed is not similar to the safety sub-model, and the sub-model to be analyzed is not similar to the risk sub-models corresponding to different fault types, then the sub-model to be analyzed is determined to be a risk sub-model for a new fault type and is updated into the data diagnosis model.
[0037] The beneficial effects of the above technical solution are: it can enrich the risk sub-model categories in the data diagnosis model during detection and can more effectively track the fault types of hydropower units.
[0038] In some embodiments of the present application, a normal operating image of the hydropower unit and fault operating images corresponding to different fault types may be generated according to the hydropower unit data diagnosis model; Generate the current operation image of the hydropower unit to be analyzed based on the sub-model to be analyzed.
[0039] In this embodiment, the generated image information can intuitively display the operating status of the hydropower unit, making it easier for operating personnel to monitor the working status of the hydropower unit.
[0040] In order to further illustrate the technical idea of the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.
[0041] Correspondingly, such as Figure 2 As shown, the present application also provides a hydropower unit fault diagnosis system, comprising: The data training module is used to obtain the historical operation records of the hydropower unit and establish a data diagnosis model based on the historical operation records of the hydropower unit. The data diagnosis model includes a safety sub-model and risk sub-models corresponding to different fault types; A data acquisition module is used to collect real-time operating data of the hydropower unit to be analyzed, and to establish a sub-model to be analyzed based on the real-time operating data of the hydropower unit to be analyzed; The data analysis module is used to analyze the sub-model to be analyzed according to the data diagnosis model and determine the operating status of the hydropower unit to be analyzed; The update module is used to determine whether to update the data diagnosis model based on the analysis results of the data diagnosis model on the sub-model to be analyzed.
[0042] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.
[0043] While the present invention has been described above with reference to exemplary embodiments, various modifications may be made and equivalent components may be substituted without departing from the scope of the present invention. In particular, the various features of the disclosed embodiments may be combined with one another in any manner, provided no structural conflicts exist. These combinations are not fully described in this specification for reasons of space and resource conservation.
[0044] Those skilled in the art will understand that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will still be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.
Claims
1. A method for diagnosing faults of a hydropower unit, characterized in that: include: Obtaining historical operation records of the hydropower unit, and establishing a data diagnosis model based on the historical operation records of the hydropower unit, the data diagnosis model including a safety sub-model and risk sub-models corresponding to different fault types; Collecting real-time operating data of the hydropower unit to be analyzed, and establishing a sub-model to be analyzed based on the real-time operating data of the hydropower unit to be analyzed; Analyze the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed; According to the parsing result of the data diagnosis model on the sub-model to be analyzed, it is determined whether to update the data diagnosis model.
2. A method for diagnosing faults of a hydropower unit according to claim 1, characterized in that: The obtaining of historical operation records of the hydropower unit and establishing a data diagnosis model based on the historical operation records of the hydropower unit includes: Extracting all normal operation records from the historical operation records of the hydropower unit; Extract vibration data, temperature data, and pressure data of the core components of the hydropower unit recorded for each normal operation; Preprocessing the vibration data, temperature data, and pressure data of the core components of the hydropower unit extracted from all the normal operation records; According to the pre-processed vibration data, temperature data and pressure data of the core components of the hydropower unit, the safety vibration characteristics, safety temperature characteristics and safety pressure characteristics of the hydropower unit are extracted to build a safety sub-model: ; in, For the security sub-model, For safety vibration characteristics, For safety temperature characteristics, It is a safety pressure characteristic; Extracting all fault operation records from the historical operation records of the hydropower unit; Classifying the fault operation records according to different fault types; Extracting vibration data, temperature data, and pressure data of core components of hydropower units from similar fault operation records, and removing noise and abnormal values from the extracted vibration data, temperature data, and pressure data of core components of hydropower units; According to the processed vibration data, temperature data and pressure data of the core components of the hydropower unit, the risk vibration characteristics, risk temperature characteristics and risk pressure characteristics of the fault type are extracted to construct a risk sub-model of the fault type: ; in, is the risk sub-model of the i-th type of failure, is the risk vibration characteristic of the i-th fault type, is the risk temperature characteristic of the i-th fault type, is the risk pressure characteristic of the i-th fault type.
3. A method for diagnosing faults of a hydropower unit according to claim 1, characterized in that: The collecting of real-time operating data of the hydropower generating units to be analyzed and establishing a sub-model to be analyzed based on the real-time operating data of the hydropower generating units to be analyzed include: Obtaining vibration data, temperature data, and pressure data of the current core components of the hydropower unit to be analyzed; According to the vibration data, temperature data and pressure data of the current core components of the hydropower unit to be analyzed, the vibration characteristics, temperature characteristics and pressure characteristics of the hydropower unit to be analyzed are extracted to construct a sub-model to be analyzed: ; in, is the sub-model to be analyzed, To analyze the vibration characteristics of the hydropower unit, The temperature characteristics of the hydropower unit to be analyzed are: The pressure characteristics of the hydropower unit to be analyzed.
4. A method for diagnosing faults of a hydropower unit according to claims 1-3, characterized in that: The step of parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Extracting the security sub-model in the data diagnosis model; Calculating whether the sub-model to be analyzed is similar to the safety sub-model; if the sub-model to be analyzed is similar to the safety sub-model, determining that the hydropower unit to be analyzed is in a normal operating state; If the sub-model to be analyzed is not similar to the safety sub-model, extracting risk sub-models corresponding to different fault types in the data diagnosis model; Calculate whether the sub-model to be analyzed is similar to the risk sub-models corresponding to different fault types respectively; if the sub-model to be analyzed is similar to the risk sub-model, determine that the hydropower unit to be analyzed is in a fault operation state of the fault type corresponding to the risk sub-model similar to the sub-model to be analyzed.
5. A method for diagnosing faults of a hydropower unit according to claim 4, characterized in that: The step of parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Pre-set safety thresholds; Extract the security sub-model and calculate the safety factor between the security sub-model and the sub-model to be analyzed: ; in, is the safety factor between the safety sub-model and the sub-model to be analyzed; If the safety factor is greater than or equal to the safety threshold, it is determined that the sub-model to be analyzed is similar to the safety sub-model; If the safety factor is less than the safety threshold, it is determined that the sub-model to be analyzed is not similar to the safety sub-model.
6. A method for diagnosing faults of a hydropower unit according to claim 5, characterized in that: The step of parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Pre-set risk thresholds; When the safety factor is less than the safety threshold, the risk sub-models of all fault types are extracted respectively, and the risk factor between each risk sub-model and the sub-model to be analyzed is calculated respectively: ; in, is the risk factor between the risk sub-model of the i-th fault type and the sub-model to be analyzed; If the risk factor between the risk sub-model of the i-th fault type and the sub-model to be analyzed is less than or equal to the risk threshold, it is determined that the sub-model to be analyzed is similar to the risk sub-model of the i-th fault type; If the risk factor between the risk sub-model of the i-th fault type and the sub-model to be analyzed is greater than the risk threshold, it is determined that the sub-model to be analyzed is not similar to the risk sub-model of the i-th fault type.
7. A method for diagnosing faults of a hydropower unit according to claim 6, characterized in that: The step of parsing the sub-model to be analyzed according to the data diagnosis model to determine the operating status of the hydropower unit to be analyzed includes: Presetting a first warning value and a second warning value, wherein the first warning value is smaller than the second warning value; Pre-set level 1 fault warning, level 2 fault warning, and level 3 fault warning; When the risk factor between the risk sub-model of the i-th type of fault and the sub-model to be analyzed is less than or equal to the risk threshold, calculating a warning value between the risk factor between the risk sub-model of the i-th type of fault and the sub-model to be analyzed and the risk threshold; If the warning value is less than or equal to the first warning value, a first-level fault warning of the i-th fault type is issued; If the warning value is greater than the first warning value and the warning value is less than or equal to the second warning value, a second-level fault warning of the i-th fault type is issued; If the warning value is greater than the second warning value, a third-level fault warning of the i-th fault type is issued.
8. A method for diagnosing faults of a hydropower unit according to claim 4, characterized in that: The determining whether to update the data diagnosis model according to the parsing result of the sub-model to be analyzed by the data diagnosis model includes: If the sub-model to be analyzed is not similar to the safety sub-model, and the sub-model to be analyzed is not similar to the risk sub-models corresponding to the different fault types, then the sub-model to be analyzed is determined to be a risk sub-model of a new fault type and is updated into the data diagnosis model.
9. A method for diagnosing faults of a hydropower unit according to claim 1, characterized in that: The normal operation image of the hydropower unit and the fault operation images corresponding to different fault types can be generated according to the data diagnosis model of the hydropower unit; A current operation image of the hydropower unit to be analyzed is generated according to the sub-model to be analyzed.
10. A hydropower unit fault diagnosis system, characterized in that: include: A data training module is used to obtain historical operation records of the hydropower unit and establish a data diagnosis model based on the historical operation records of the hydropower unit, wherein the data diagnosis model includes a safety sub-model and risk sub-models corresponding to different fault types; A data acquisition module is used to collect real-time operating data of the hydropower unit to be analyzed, and to establish a sub-model to be analyzed based on the real-time operating data of the hydropower unit to be analyzed; A data analysis module, configured to analyze the sub-model to be analyzed according to the data diagnosis model, and determine the operating status of the hydropower unit to be analyzed; An updating module is used to determine whether to update the data diagnosis model according to the parsing result of the data diagnosis model on the sub-model to be analyzed.