Water-turbine generator set guide paddle joint abnormity detection method, device and equipment
By generating a target prediction model and processing the operating data of the hydropower generator set based on the model, real-time detection and early warning of the joint relationship of the guide blades is achieved, and the problem of untimely detection of joint relationships in the existing technology is solved, ensuring that Safe and stable operation of the hydrowheel generator set.
Patent Information
- Application Number
- CN202411841939.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-13
- Publication Date
- 2025-02-11
AI Technical Summary
The prior art is difficult to realize real-time and dynamic detection and early warning of the joint relationship between the guide blades of the hydropower generator set, resulting in the inability to detect and handle joint abnormalities in time, which may lead to the expansion of the fault.
By obtaining the initial operation data of the hydropower generator set, a target prediction model is generated, a blade and blade coordination relationship is described under different active power and head conditions, and subsequent operation data is processed based on this model to achieve blade coordination Detection and early warning of relationships.
Accurate detection of the joint relationship between the guide blades of the hydropower generator set is achieved, and dynamic abnormality warning can be carried out in a timely manner, thereby avoiding the expansion of the fault and ensuring the safe and efficient operation of the hydropower generator set.
Smart Images
Figure CN119412271A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of water turbine abnormality detection, and in particular to a method, device and equipment for detecting abnormality of guide blade linkage of a water turbine generator set. Background Art
[0002] In a hydropower generation system, the coordination between the guide vanes and the blades is one of the core elements for achieving efficient and stable operation of a hydro-turbine generator set. Guide vane coordination refers to the precise control of the opening of the guide vanes and blades to match the water flow conditions under different working conditions, thereby ensuring that the hydro-turbine generator set can operate in the optimal state and maximize energy conversion efficiency. The accuracy of guide vane coordination is affected by many factors, including but not limited to changes in water head, unit load requirements, water flow velocity, water temperature, and the mechanical and electrical conditions of the unit itself.
[0003] In the related art, in order to maintain the guide vane linkage in the best state, modern hydro-turbine generator system usually adopts advanced control systems, such as microcomputer speed governors, to dynamically adjust the opening of the guide vanes and blades through preset linkage curves or real-time calculation models.
[0004] In this way, the detection of guide blade coordination anomalies mostly relies on preset thresholds and planned inspections, and real-time, dynamic detection and early warning cannot be achieved. Summary of the invention
[0005] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.
[0006] To this end, the purpose of the present disclosure is to provide a method, device, electronic device and storage medium for detecting anomalies of guide blade linkage of a hydro-turbine generator set.
[0007] The first aspect of the present disclosure provides a method for detecting abnormality of guide blade linkage of a hydro-turbine generator set, the method comprising:
[0008] Acquire first operating data of the hydro-generator set at a first time, wherein the first operating data at least includes: a first active power of the hydro-generator set, a first water head, and a first guide vane opening and a first blade opening corresponding to the first water head;
[0009] According to the initial operation data, a target prediction model corresponding to the hydro-generator set is generated, wherein the target prediction model is used to describe the coordination relationship between the guide vanes and the blades of the hydro-generator set under different active power and different water head conditions;
[0010] Acquire second operation data of the hydro-generator set at a second time, wherein the first time is earlier than the second time;
[0011] The second operating data is processed based on the target prediction model to obtain the guide blade coordination detection result of the hydro-generator set at the second time.
[0012] The second aspect of the present disclosure provides a device for detecting abnormality of guide blade linkage of a hydro-turbine generator set, the device comprising:
[0013] A first acquisition module is used to acquire first operating data of the hydro-generator set at a first time, wherein the first operating data at least includes: a first active power of the hydro-generator set, a first water head, and a first guide vane opening and a first blade opening corresponding to the first water head;
[0014] A generation module, used for generating a target prediction model corresponding to the hydro-generator set according to the initial operation data, wherein the target prediction model is used for describing the coordination relationship between the guide vanes and the blades of the hydro-generator set under different active power and different water head conditions;
[0015] A second acquisition module is used to acquire second operation data of the hydro-generator set at a second time, wherein the first time is earlier than the second time;
[0016] The processing module is used to process the second operation data based on the target prediction model to obtain the guide blade coordination detection result of the hydro-generator set at the second time.
[0017] The electronic device proposed in the third aspect embodiment of the present disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for detecting abnormal coordination of the guide blades of a hydro-turbine generator set proposed in the first aspect embodiment of the present disclosure is implemented.
[0018] The fourth aspect embodiment of the present disclosure proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for detecting abnormal coordination of guide blades of a hydro-turbine generator set as proposed in the first aspect embodiment of the present disclosure.
[0019] The fifth aspect embodiment of the present disclosure proposes a computer program product. When the instructions in the computer program product are executed by a processor, the method for detecting abnormal coordination of the guide blades of a hydro-turbine generator set proposed in the first aspect embodiment of the present disclosure is executed.
[0020] The present disclosure provides a method, device, electronic device and storage medium for detecting anomaly of guide blade linkage of a hydro-turbine generator set, which have at least the following beneficial effects: by acquiring first operating data of the hydro-turbine generator set at a first time, wherein the first operating data at least includes: a first active power of the hydro-turbine generator set, a first water head, and a first guide vane opening and a first paddle opening corresponding to the first water head, generating a target prediction model corresponding to the hydro-turbine generator set according to the initial operating data, wherein the target prediction model is used to describe the linkage relationship between the guide vanes and the paddle blades of the hydro-turbine generator set under different active power and different water head conditions, acquiring second operating data of the hydro-turbine generator set at a second time, wherein the first time is earlier than the second time, processing the second operating data based on the target prediction model to obtain a guide blade linkage detection result of the hydro-turbine generator set at the second time, thereby being able to accurately detect the abnormal state of the guide blade linkage relationship of the hydro-turbine generator set, and realizing real-time and dynamic guide blade linkage abnormality warning, thereby avoiding the expansion of faults of the hydro-turbine generator set and ensuring safe and efficient operation of the hydro-turbine generator set.
[0021] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:
[0023] Figure 1 It is a flow chart of a method for detecting abnormality of guide blade linkage of a hydro-turbine generator set proposed in an embodiment of the present disclosure;
[0024] Figure 2 It is a flow chart of a method for detecting abnormality of guide blade linkage of a hydro-turbine generator set proposed in another embodiment of the present disclosure;
[0025] Figure 3 It is a flow chart of a method for detecting abnormality of guide blade linkage of a hydro-turbine generator set proposed in another embodiment of the present disclosure;
[0026] Figure 4 It is a structural schematic diagram of a device for detecting abnormality of guide blade linkage of a hydro-turbine generator set proposed in an embodiment of the present disclosure;
[0027] Figure 5 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION
[0028] Embodiments of the present disclosure 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 only used to explain the present disclosure, and are not to be construed as limitations of the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications, and equivalents that fall within the spirit and connotation of the appended claims.
[0029] Figure 1 It is a flow chart of a method for detecting anomalies of guide blade linkage of a hydro-turbine generator set proposed in an embodiment of the present disclosure.
[0030] Among them, the coordination between the guide vanes and the blades is one of the core elements to achieve efficient and stable operation of the turbine. The coordination of the guide vanes and the blades refers to the precise control of the opening of the guide vanes and the blades to match the water flow conditions under different working conditions, thereby ensuring that the turbine can operate in the optimal state and maximize the energy conversion efficiency. The accuracy of the guide vane coordination is affected by many factors, including but not limited to changes in water head, unit load requirements, water flow velocity, water temperature, and the mechanical and electrical conditions of the unit itself.
[0031] Among them, it should be noted that the executor of the method for detecting the abnormality of the guide blade linkage of the hydro-turbine generator set in this embodiment is a device for detecting the abnormality of the guide blade linkage of the hydro-turbine generator set. The device can be implemented by software and / or hardware. The device can be configured in an electronic device. The electronic device can include but is not limited to a terminal, a server, etc. For example, the terminal can be a mobile phone, a handheld computer, etc.
[0032] like Figure 1 As shown, the method for detecting abnormality of guide blade linkage of a hydro-turbine generator set comprises:
[0033] S101: Acquire first operating data of the hydro-generator set at a first time, wherein the first operating data at least includes: a first active power of the hydro-generator set, a first water head, and a first guide vane opening and a first blade opening corresponding to the first water head.
[0034] The first time is any historical time period or time point, without any restriction.
[0035] Among them, the operating data of the hydro-turbine generator set collected at the first time is the first operating data, and the first operating data can be the operating parameters of the hydro-turbine generator set at the first time, including: the first active power of the hydro-turbine generator set, the first water head, and the first guide vane opening and the first blade opening corresponding to the first water head, etc., without limitation.
[0036] The guide vane opening is a parameter that characterizes the position of the guide vane during the flow regulation process of the turbine. During the operation of the turbine, the governor changes the speed, direction and flow of the water entering the runner by adjusting the guide vane opening, that is, adjusting the outlet angle of the guide vane, thereby adjusting the output power of the turbine generator set to meet the needs of load changes in the power system.
[0037] The blade opening refers to the angle between the blade and the shaft. This angle is a key factor in determining the degree of momentum exchange when water flows through the rotor, and has an important impact on the performance and efficiency of the turbine.
[0038] Among them, water head refers to the potential energy generated by the upstream and downstream water level difference when water flows through the turbine. It represents the potential energy (potential energy) of water and is a necessary condition for the normal operation of the turbine.
[0039] S102: Generate a target prediction model corresponding to the hydro-turbine generator set based on the initial operation data, wherein the target prediction model is used to describe the coordination relationship between the guide vanes and the blades of the hydro-turbine generator set under different active power and different water head conditions.
[0040] Among them, the target prediction model can be used to predict the blade opening of the hydro-turbine generator set according to the operating data of the hydro-turbine generator set, so that the blade opening can be jointly predicted later to determine whether there is an abnormality in the coordination relationship between the guide blades. The target prediction model can be an artificial intelligence model or a digital model, and there is no restriction on this.
[0041] That is to say, in the embodiment of the present disclosure, an initial artificial intelligence model, sample operation data and labeled blade opening may be obtained, and then the initial artificial intelligence model obtained above may be iteratively trained according to the mapping relationship between the sample operation data and the labeled blade opening until the model converges, so as to obtain a target prediction model.
[0042] In other embodiments, a target prediction model corresponding to the hydro-turbine generator set is generated based on the initial operating data. Alternatively, a large model is obtained and the large model is specifically tuned based on the initial operating data to obtain the target prediction model, and there is no limitation on this.
[0043] Optionally, in some embodiments, a target prediction model corresponding to the hydro-turbine generator set is generated based on the initial operating data. An initial prediction model corresponding to the hydro-turbine generator set is constructed based on the initial operating data, and the initial prediction model is verified to obtain a model verification result. When the model verification result indicates that the initial prediction model has passed the verification, the initial prediction model is determined as the target prediction model.
[0044] That is to say, in the embodiments of the present disclosure, the initial operating data can be processed based on the interpolation method to construct an initial prediction model corresponding to the hydro-turbine generator set, and then the initial prediction model can be verified to obtain a model verification result. When the model verification result indicates that the initial prediction model has passed the verification, the initial prediction model is determined as the target prediction model. Thus, by verifying the model, the accuracy and reliability of the target prediction model can be ensured.
[0045] In some embodiments, the initial prediction model is verified by obtaining verification data and inputting the verification data into the initial prediction model, recording the interval time from when the model starts processing the verification data to when it outputs the result, and when the interval time is less than a preset time threshold, determining that the model verification result is a passed model verification, and when the interval time is greater than or equal to the preset time threshold, determining that the model verification result is a failed model verification, thereby ensuring that the processing efficiency of the model meets the standards.
[0046] In other embodiments, the initial prediction model is verified by obtaining verification data and inputting the verification data into the initial prediction model, recording whether the model can perform processing on the verification data, and outputting the result. If the model successfully outputs the result, the model verification result is determined to be the model verification passed; if the model fails to successfully output the result, the model verification result is determined to be the model verification failed, and there is no limitation on this.
[0047] Optionally, in some embodiments, the initial prediction model is verified to obtain a model verification result, which can be by obtaining reference operating data corresponding to the hydro-turbine generator set, wherein the reference operating data includes: reference active power, reference water head, and a reference guide vane opening and a reference blade opening corresponding to the reference water head, and the reference active power, the reference water head, and the reference guide vane opening corresponding to the reference water head are input into the initial prediction model together to obtain a first predicted blade opening output by the initial prediction model, and determine a first deviation value between the reference blade opening and the first predicted blade opening. When the first deviation value is greater than or equal to a preset deviation threshold, determine that the model verification result is a model verification pass, and when the first deviation value is less than the preset deviation threshold, determine that the model verification result is a model verification failure.
[0048] The reference operating data may be used to verify the initial prediction model. The reference operating data may be part of the first operating data or other data, and there is no limitation on this.
[0049] The reference operating data include: reference active power, reference water head, and reference guide vane opening and reference blade opening corresponding to the reference water head.
[0050] The deviation value between the reference blade opening and the first predicted blade opening is the first deviation value.
[0051] Among them, the preset deviation threshold can be adaptively set in combination with the actual operating conditions and safety requirements of the unit in the actual business scenario, and there is no restriction on this.
[0052] That is to say, in the embodiment of the present disclosure, the reference active power, the reference water head, and the reference guide vane opening corresponding to the reference water head can be input into the initial prediction model together to obtain the first predicted blade opening output by the initial prediction model, and then the first deviation value between the reference blade opening and the first predicted blade opening is determined. When the first deviation value is greater than or equal to the preset deviation threshold, the model verification result is determined to be the model verification passed. When the first deviation value is less than the preset deviation threshold, the model verification result is determined to be the model verification failed. In this way, it can be ensured that the deviation between the predicted blade opening of the model and the actual blade opening is within an acceptable range, thereby ensuring the accuracy of the model prediction.
[0053] S103: Acquire second operating data of the hydro-generator set at a second time, wherein the first time is earlier than the second time.
[0054] The second time is after the first time, and the second time may be, for example, the current time, which is not limited.
[0055] That is to say, in the embodiments of the present disclosure, the second active power, the second water head, and the second guide vane opening and the second blade opening corresponding to the second water head of the hydro-turbine generator set may be collected at the second time, and the second active power, the second water head, and the second guide vane opening and the second blade opening corresponding to the second water head of the hydro-turbine generator set may be used as the second operating data. Then, the second operating data may be combined to determine the guide vane coordination detection result of the hydro-turbine generator set at the second time. For details, please refer to the subsequent embodiments.
[0056] S104: Processing the second operating data based on the target prediction model to obtain a guide blade coordination detection result of the hydro-generator set at a second time.
[0057] In the disclosed embodiment, after obtaining the second operating data of the hydro-turbine generator set at the second time, the second operating data can be processed based on the target prediction model to obtain the guide blade coordination detection result of the hydro-turbine generator set at the second time.
[0058] Optionally, in some embodiments, the second operating data is processed based on the target prediction model to obtain the guide blade coordination detection result of the hydro-turbine generator set at the second time. The second active power, the second water head, and the second guide vane opening corresponding to the second water head of the hydro-turbine generator set are input into the target prediction model to obtain the second predicted blade opening output by the target prediction model, and determine the second deviation value between the second predicted blade opening and the second blade opening. When the second deviation value is greater than or equal to the preset deviation threshold, it is determined that the guide blade coordination detection result is an abnormal guide blade coordination relationship.
[0059] The deviation between the second predicted blade opening and the second blade opening is the second deviation value.
[0060] That is to say, in the embodiment of the present disclosure, the second active power, the second water head, and the second guide vane opening corresponding to the second water head of the hydro-generator set can be input into the target prediction model together to obtain the second predicted blade opening output by the target prediction model, and determine the second deviation value between the second predicted blade opening and the second blade opening. When the second deviation value is greater than or equal to the preset deviation threshold, it is determined that the guide blade coordination detection result is that the guide blade coordination relationship is abnormal. When the second deviation value is less than the preset deviation threshold, it is determined that the guide blade coordination detection result is that the guide blade coordination relationship is normal.
[0061] In the disclosed embodiment, when it is determined that the guide blade coordination detection result is an abnormal guide blade coordination relationship, an alarm message can be triggered to indicate that an abnormal guide blade coordination relationship event has occurred in the hydro-turbine generator set at the second time, thereby prompting the operation and maintenance personnel to start fault maintenance work in time, thereby avoiding the expansion of the fault, ensuring the safe and efficient operation of the turbine, preventing the unit from operating in non-coordinated conditions for a long time, and improving the unit's operating stability and efficiency.
[0062] In the disclosed embodiment, by acquiring the first operating data of the hydro-turbine generator set at the first time, wherein the first operating data at least includes: the first active power of the hydro-turbine generator set, the first water head, and the first guide vane opening and the first blade opening corresponding to the first water head, a target prediction model corresponding to the hydro-turbine generator set is generated according to the initial operating data, wherein the target prediction model is used to describe the coordination relationship between the guide vanes and the blades of the hydro-turbine generator set under different active power and different water head conditions. The second operating data of the hydro-turbine generator set at the second time is acquired, wherein the first time is earlier than the second time, and the second operating data is processed based on the target prediction model to obtain the guide vane coordination detection result of the hydro-turbine generator set at the second time, thereby accurately detecting the abnormal state of the guide vane coordination relationship of the hydro-turbine generator set, realizing real-time and dynamic guide vane coordination abnormality warning, thereby avoiding the expansion of the fault of the hydro-turbine generator set and ensuring the safe and efficient operation of the hydro-turbine generator set.
[0063] Figure 2 It is a flow chart of a method for detecting anomalies of guide blade linkage of a hydro-turbine generator set proposed in another embodiment of the present disclosure.
[0064] like Figure 2 As shown, the method for detecting abnormality of guide blade linkage of a hydro-turbine generator set comprises:
[0065] S201: Acquire the initial operation data of the hydro-generator set at the first time.
[0066] The unprocessed operation data of the hydro-turbine generator set obtained at the first time is the initial operation data.
[0067] In the embodiment of the present disclosure, the real-time data of the measuring point of the hydro-generator set may be collected at the first time, and the real-time data of the measuring point is specifically shown in the following Table 1:
[0068] Table 1
[0069] Serial number Measuring point name Measuring point data 1 Unit 1 water head Water head 2 Unit 1 guide vane opening Guide vane opening 3 Unit 1 blade opening Blade opening 4 Active power of unit 1 Active Power 5 Unit 1 generator outlet switch circuit breaker Unit power on / off status
[0070] S202: Preprocessing the initial operation data to obtain candidate operation data, wherein the candidate operation data includes: unit on / off state data, candidate active power, candidate water head, and candidate guide vane opening and candidate blade opening corresponding to the candidate water head.
[0071] The candidate operating data include: unit on / off status data, candidate active power, candidate water head, and candidate guide vane opening and candidate blade opening corresponding to the candidate water head.
[0072] In the embodiment of the present disclosure, after the initial operation data of the hydro-generator set is obtained at the first time, the initial operation data may be preprocessed to obtain candidate operation data. The data preprocessing process includes:
[0073] Step 1: Calculate the mean and standard deviation of the guide vane and blade measurement point data of each unit;
[0074] Step 2: Filter out the data of the guide vane and blade measurement points that differ from the average value by more than 3 standard deviations;
[0075] Step 3: Filter out the data selected in step 2;
[0076] Step 4: Complement the above filtered data: Complement the guide vane and blade monitoring data by pre-complementing the data, or consider the data loss of the guide vane and blade measurement point data due to data link problems, and perform data completion on all data measurement points by pre-complementing the data at the second level.
[0077] S203: Determine first operation data from candidate operation data.
[0078] In the embodiment of the present disclosure, after the initial operation data is preprocessed to obtain candidate operation data, the first operation data may be determined from the candidate operation data.
[0079] Optionally, in some embodiments, determining the first operating data from the candidate operating data may be that when the candidate active power is less than an active power threshold and the unit on / off status data indicates that the unit is in the on state, the candidate active power, the candidate water head, and the candidate guide vane opening and the candidate blade opening corresponding to the candidate water head in the candidate operating data are determined as the first operating data, wherein the active power threshold is used to indicate that the hydro-turbine generator set is in a stable operating state.
[0080] That is to say, in the embodiment of the present disclosure, when the candidate active power is less than the active power threshold and the unit on / off status data indicates that the unit is in the on state, the candidate active power, the candidate water head, and the candidate guide vane opening and the candidate blade opening corresponding to the candidate water head in the candidate operating data may be determined as the first operating data.
[0081] S204: Generate a target prediction model corresponding to the hydro-turbine generator set according to the initial operation data, wherein the target prediction model is used to describe the coordination relationship between the guide vanes and the blades of the hydro-turbine generator set under different active power and different water head conditions.
[0082] S205: Acquire second operating data of the hydro-generator set at a second time, wherein the first time is earlier than the second time.
[0083] S206: Processing the second operating data based on the target prediction model to obtain a guide blade coordination detection result of the hydro-generator set at the second time.
[0084] The specific description of S204-S206 can be found in the above embodiment, which will not be repeated here.
[0085] In the disclosed embodiment, by acquiring the initial operating data of the hydro-turbine generator set at a first time, the initial operating data is preprocessed to obtain candidate operating data, wherein the candidate operating data includes: unit on / off status data, candidate active power, candidate water head, and candidate guide vane opening and candidate blade opening corresponding to the candidate water head, and the first operating data is determined from the candidate operating data, thereby ensuring that the first operating data is available, and effectively assisting the execution of the subsequent hydro-turbine generator set guide blade coordination abnormality detection method, and generating a target prediction model corresponding to the hydro-turbine generator set according to the initial operating data, and obtaining the second operating data of the hydro-turbine generator set at a second time, wherein the first time is earlier than the second time, and the second operating data is processed based on the target prediction model to obtain the guide blade coordination detection result of the hydro-turbine generator set at the second time.
[0086] Figure 3 FIG. 1 is a flow chart of a method for detecting abnormality of guide blade linkage of a hydro-turbine generator set according to another embodiment of the present disclosure. Figure 3 As shown, a preset deviation threshold and a second operating data may be obtained, and the predicted blade opening may be determined, and when the deviation value between the predicted blade opening and the actual blade opening is greater than or equal to the preset deviation threshold, a guide blade linkage abnormality warning may be issued, and at the same time, the abnormal warning name, abnormal warning time, warning level, and warning abnormality value may be output. When the deviation value between the predicted blade opening and the actual blade opening is less than the preset deviation threshold, no warning may be output, and the indicator value may be recorded in the time series library for guide blade linkage relationship analysis.
[0087] Figure 4 It is a structural schematic diagram of a device for detecting an abnormality of a guide blade linkage of a hydro-turbine generator set proposed in one embodiment of the present disclosure.
[0088] like Figure 4 As shown, the hydraulic turbine generator set guide blade coordinated abnormality detection device 40 includes:
[0089] A first acquisition module 401 is used to acquire first operating data of the hydro-generator set at a first time, wherein the first operating data at least includes: a first active power of the hydro-generator set, a first water head, and a first guide vane opening and a first blade opening corresponding to the first water head;
[0090] A generating module 402 is used to generate a target prediction model corresponding to the hydro-generator set according to the initial operation data, wherein the target prediction model is used to describe the coordination relationship between the guide vanes and the blades of the hydro-generator set under different active power and different water head conditions;
[0091] A second acquisition module 403 is used to acquire second operation data of the hydro-generator set at a second time, wherein the first time is earlier than the second time;
[0092] The processing module 404 is used to process the second operation data based on the target prediction model to obtain the guide blade coordination detection result of the hydro-generator set at the second time.
[0093] In some embodiments of the present disclosure, the generating module 402 is further configured to:
[0094] According to the initial operation data, an initial prediction model corresponding to the hydro-generator set is constructed;
[0095] Verify the initial prediction model to obtain the model verification result;
[0096] When the model verification result indicates that the initial prediction model has passed the verification, the initial prediction model is determined as the target prediction model.
[0097] In some embodiments of the present disclosure, the generating module 402 is further configured to:
[0098] Acquire reference operating data corresponding to the hydro-generator set, wherein the reference operating data includes: reference active power, reference water head, and reference guide vane opening and reference blade opening corresponding to the reference water head;
[0099] Inputting the reference active power, the reference water head, and the reference guide vane opening corresponding to the reference water head into the initial prediction model to obtain a first predicted blade opening output by the initial prediction model;
[0100] determining a first deviation value between a reference blade opening and a first predicted blade opening;
[0101] When the first deviation value is greater than or equal to the preset deviation threshold, determining that the model verification result is model verification passed;
[0102] When the first deviation value is less than the preset deviation threshold, it is determined that the model verification result is that the model verification fails.
[0103] In some embodiments of the present disclosure, the second operating data includes: a second active power of the hydro-generator set, a second water head, and a second guide vane opening and a second blade opening corresponding to the second water head;
[0104] The processing module 404 is further used for:
[0105] The second active power, the second water head, and the second guide vane opening corresponding to the second water head of the hydro-generator set are input into the target prediction model to obtain the second predicted blade opening output by the target prediction model;
[0106] determining a second deviation value between the second predicted blade opening and the second blade opening;
[0107] When the second deviation value is greater than or equal to the preset deviation threshold, it is determined that the guide blade coordination detection result is that the guide blade coordination relationship is abnormal.
[0108] In some embodiments of the present disclosure, the first acquisition module 401 is further configured to:
[0109] Obtain the initial operating data of the hydro-generator set at the first time;
[0110] Preprocessing the initial operation data to obtain candidate operation data, wherein the candidate operation data includes: unit on / off state data, candidate active power, candidate water head, and candidate guide vane opening and candidate blade opening corresponding to the candidate water head;
[0111] First operating data is determined from the candidate operating data.
[0112] In some embodiments of the present disclosure, the first acquisition module 401 is further configured to:
[0113] When the candidate active power is less than the active power threshold and the unit on / off status data indicates that the unit is in the on state, the candidate active power, candidate water head, and candidate guide vane opening and candidate blade opening corresponding to the candidate water head in the candidate operating data are determined as the first operating data, wherein the active power threshold is used to indicate that the hydro-turbine generator set is in a stable operating state.
[0114] It should be noted that the aforementioned explanation of the method for detecting anomaly of the guide blade linkage of a hydro-turbine generator set is also applicable to the device for detecting anomaly of the guide blade linkage of a hydro-turbine generator set in this embodiment, and will not be repeated here.
[0115] In the disclosed embodiment, by acquiring the first operating data of the hydro-turbine generator set at the first time, wherein the first operating data at least includes: the first active power of the hydro-turbine generator set, the first water head, and the first guide vane opening and the first blade opening corresponding to the first water head, a target prediction model corresponding to the hydro-turbine generator set is generated according to the initial operating data, wherein the target prediction model is used to describe the coordination relationship between the guide vanes and the blades of the hydro-turbine generator set under different active power and different water head conditions, and the second operating data of the hydro-turbine generator set at the second time is acquired, wherein the first time is earlier than the second time, and the second operating data is processed based on the target prediction model to obtain the guide blade coordination detection result of the hydro-turbine generator set at the second time, thereby accurately detecting the abnormal state of the guide blade coordination relationship of the hydro-turbine generator set, realizing real-time and dynamic guide blade coordination abnormality warning, thereby avoiding the expansion of the fault of the hydro-turbine generator set and ensuring the safe and efficient operation of the hydro-turbine generator set.
[0116] Figure 5 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 5 The electronic device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.
[0117] like Figure 5 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).
[0118] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.
[0119] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.
[0120] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 Not shown, often called a "hard drive").
[0121] although Figure 5Not shown, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (Compact Disc Read Only Memory; hereinafter referred to as: CD-ROM), a digital versatile disc read only memory (Digital Video Disc Read Only Memory; hereinafter referred to as: DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.
[0122] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.
[0123] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), may also communicate with one or more devices that enable the human body to interact with the electronic device 12, and / or communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. In addition, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 through a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0124] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the method for detecting the abnormality of the guide blade linkage of the turbine generator set mentioned in the above-mentioned embodiment.
[0125] In order to implement the above-mentioned embodiments, the present disclosure also proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for detecting abnormal coordination of guide blades of a hydro-turbine generator set proposed in the aforementioned embodiments of the present disclosure is implemented.
[0126] In order to implement the above embodiments, the present disclosure further proposes a computer program product. When the instruction processor in the computer program product is executed, the method for detecting abnormality of the guide blade coordination of a hydro-turbine generator set proposed in the above embodiments of the present disclosure is executed.
[0127] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.
[0128] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
[0129] It should be noted that, in the description of the present disclosure, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, unless otherwise specified, the meaning of "plurality" is two or more.
[0130] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure 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 those skilled in the art to which the embodiments of the present disclosure belong.
[0131] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0132] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.
[0133] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.
[0134] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.
[0135] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" 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 disclosure. In this specification, the schematic representation of the above terms does 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.
[0136] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.
Claims
1. A method for detecting abnormality of guide blade linkage of a hydro-turbine generator set, characterized in that: The method comprises: Acquire first operating data of the hydro-generator set at a first time, wherein the first operating data at least includes: a first active power of the hydro-generator set, a first water head, and a first guide vane opening and a first blade opening corresponding to the first water head; Generate a target prediction model corresponding to the hydro-generator set according to the initial operation data, wherein the target prediction model is used to describe the coordination relationship between the guide vanes and the blades of the hydro-generator set under different active power and different water head conditions; Acquire second operating data of the hydro-generator set at a second time, wherein the first time is earlier than the second time; The second operating data is processed based on the target prediction model to obtain a guide blade coordination detection result of the hydro-generator set at the second time.
2. The method according to claim 1, characterized in that The step of generating a target prediction model corresponding to the hydro-generator set according to the initial operation data comprises: Constructing an initial prediction model corresponding to the hydro-generator set according to the initial operation data; Verifying the initial prediction model to obtain a model verification result; When the model verification result indicates that the initial prediction model has passed the verification, the initial prediction model is determined as the target prediction model.
3. The method according to claim 2, characterized in that The verifying the initial prediction model to obtain a model verification result includes: Acquire reference operating data corresponding to the hydro-generator set, wherein the reference operating data includes: reference active power, reference water head, and reference guide vane opening and reference blade opening corresponding to the reference water head; Inputting the reference active power, the reference water head, and the reference guide vane opening corresponding to the reference water head into the initial prediction model to obtain a first predicted blade opening output by the initial prediction model; determining a first deviation value between the reference blade opening and the first predicted blade opening; When the first deviation value is greater than or equal to a preset deviation threshold, determining that the model verification result is model verification passed; When the first deviation value is less than the preset deviation threshold, it is determined that the model verification result is that the model verification fails.
4. The method according to claim 1, characterized in that The second operation data includes: a second active power of the hydro-generator set, a second water head, and a second guide vane opening and a second blade opening corresponding to the second water head; The processing of the second operating data based on the target prediction model to obtain the guide blade coordination detection result of the hydro-generator set at the second time includes: Inputting the second active power, the second water head, and the second guide vane opening corresponding to the second water head of the hydro-generator set into the target prediction model to obtain the second predicted blade opening output by the target prediction model; determining a second deviation value between the second predicted blade opening and the second blade opening; When the second deviation value is greater than or equal to a preset deviation threshold, it is determined that the guide blade coordination detection result is that the guide blade coordination relationship is abnormal.
5. The method according to claim 1, characterized in that The obtaining of the first operating data of the hydro-generator set at the first time includes: Acquiring initial operation data of the hydro-generator set at the first time; Preprocessing the initial operation data to obtain candidate operation data, wherein the candidate operation data includes: unit on / off state data, candidate active power, candidate water head, and candidate guide vane opening and candidate blade opening corresponding to the candidate water head; The first operation data is determined from the candidate operation data.
6. The method according to claim 5, characterized in that The determining the first operation data from the candidate operation data includes: When the candidate active power is less than the active power threshold and the unit on / off status data indicates that the unit is in the on state, the candidate active power, candidate water head, and candidate guide vane opening and candidate blade opening corresponding to the candidate water head in the candidate operating data are determined as the first operating data, wherein the active power threshold is used to indicate that the hydro-turbine generator set is in a stable operating state.
7. A device for detecting abnormality of guide blade linkage of a hydro-turbine generator set, characterized in that: The device comprises: A first acquisition module is used to acquire first operating data of the hydro-generator set at a first time, wherein the first operating data at least includes: a first active power of the hydro-generator set, a first water head, and a first guide vane opening and a first blade opening corresponding to the first water head; A generating module, used for generating a target prediction model corresponding to the hydro-generator set according to the initial operation data, wherein the target prediction model is used for describing the coordination relationship between the guide vanes and the blades of the hydro-generator set under different active power and different water head conditions; A second acquisition module is used to acquire second operating data of the hydro-generator set at a second time, wherein the first time is earlier than the second time; A processing module is used to process the second operating data based on the target prediction model to obtain a guide blade coordination detection result of the hydro-generator set at the second time.
8. The device according to claim 7, characterized in that The generating module is further used for: Constructing an initial prediction model corresponding to the hydro-generator set according to the initial operation data; Verifying the initial prediction model to obtain a model verification result; When the model verification result indicates that the initial prediction model has passed the verification, the initial prediction model is determined as the target prediction model.
9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.
10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: in, The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.