Water-turbine generator set servomotor abnormity early warning method, device, equipment and medium

Abstract

The invention provides a hydroelectric generating set servomotor abnormity early warning method, device and equipment and a medium, and the method comprises the steps that initial parameter sets of a hydroelectric generating set at a plurality of preset time points are collected, and the initial parameter sets comprise the initial guide vane opening degree, the initial paddle opening degree and the unit active power; data preprocessing is conducted on the initial parameter set to obtain a target parameter set, and the target parameter set comprises the target guide vane opening degree, the target paddle opening degree and the unit active power; grouping the plurality of target parameter groups to obtain a to-be-analyzed parameter group; determining an abnormal parameter group from a plurality of target parameter groups in the to-be-analyzed parameter groups based on a preset threshold array; and performing servomotor abnormity early warning based on the abnormal parameter group. Therefore, automatic monitoring of the abnormal state of the servomotor can be achieved, abnormal early warning of the servomotor is accurately achieved, and the early warning effect is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of servomotor status analysis and evaluation, and in particular to a method, device, equipment and medium for early warning of abnormality of a servomotor of a hydro-turbine generator set. Background Art

[0002] In the comprehensive operation and maintenance system of hydro-turbine generator sets, abnormal monitoring of servomotors is one of the important links to ensure the stable operation of the units. The servomotor is responsible for regulating the direction and flow of water flow in the unit, and its normal operation is crucial to power generation efficiency and equipment safety. Typical servomotor abnormalities include guide vane deviation, guide vane deviation, chamber oil leakage, etc. These abnormal conditions may cause significant changes in the working parameters of the servomotor, thereby affecting the overall performance and safety of the unit. The opening of the guide vanes and blades is the key working parameter of the servomotor. Under normal operating conditions, these openings should be kept within a specific range to ensure effective regulation of the water flow and the stability of the unit. However, the deviation of the guide vanes from opening or closing will lead to uneven flow, which may cause unit vibration, reduced efficiency, and even equipment damage. Therefore, real-time monitoring of these parameters is particularly important.

[0003] In the related art, when performing abnormal warning of the relay of the hydro-turbine generator set, it relies on manual experience, and it is impossible to accurately locate the abnormal state of the relay, and the warning effect is poor. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the purpose of the present invention is to propose a method, device, computer equipment and storage medium for abnormal warning of a hydro-turbine generator set relay, thereby enabling automatic monitoring of the abnormal state of the relay and accurate relay abnormal warning, thereby effectively improving the warning effect.

[0006] To achieve the above-mentioned purpose, the first aspect of the present disclosure provides a method for early warning of abnormality of a servomotor of a hydro-turbine generator set, comprising:

[0007] Collecting initial parameter groups of the hydro-generator set at multiple preset time points, wherein the initial parameter groups include: initial guide vane opening, initial blade opening and unit active power;

[0008] Performing data preprocessing on the initial parameter group to obtain a target parameter group, wherein the target parameter group includes: a target guide vane opening, a target blade opening, and the active power of the unit;

[0009] Grouping the plurality of target parameter groups to obtain a parameter group to be analyzed;

[0010] Determine an abnormal parameter group from a plurality of the target parameter groups in the parameter groups to be analyzed based on a preset threshold array;

[0011] A relay abnormality warning is performed based on the abnormal parameter group.

[0012] To achieve the above-mentioned purpose, a hydro-generator servomotor abnormality warning device provided in a second aspect of the present disclosure includes:

[0013] A collection module, used for collecting the initial parameter group of the hydro-generator set at multiple preset time points, wherein the initial parameter group includes: initial guide vane opening, initial blade opening and unit active power;

[0014] A first processing module is used to perform data preprocessing on the initial parameter group to obtain a target parameter group, wherein the target parameter group includes: a target guide vane opening, a target blade opening and the active power of the unit;

[0015] A second processing module, used for grouping the plurality of target parameter groups to obtain a parameter group to be analyzed;

[0016] A determination module, configured to determine an abnormal parameter group from a plurality of target parameter groups in the parameter group to be analyzed based on a preset threshold array;

[0017] The early warning module is used to provide an early warning of relay abnormality based on the abnormal parameter group.

[0018] The computer device proposed in the third aspect 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 abnormal warning method for the relay of a hydro-turbine generator set proposed in the first aspect of the present disclosure is implemented.

[0019] 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, the abnormal warning method for the relay of a hydro-turbine generator set proposed in the first aspect embodiment of the present disclosure is implemented.

[0020] The fifth aspect of the present disclosure provides a computer program product. When the instructions in the computer program product are executed by a processor, the abnormal warning method for the relay of a hydro-turbine generator set provided in the first aspect of the present disclosure is executed.

[0021] The present disclosure provides a method, device, computer equipment and storage medium for abnormal warning of a servomotor of a hydro-turbine generator set. The method collects the initial parameter groups of the hydro-turbine generator set at multiple preset time points, wherein the initial parameter groups include: initial guide vane opening, initial blade opening and active power of the unit; performs data preprocessing on the initial parameter groups to obtain target parameter groups, wherein the target parameter groups include: target guide vane opening, target blade opening and active power of the unit; groups multiple target parameter groups to obtain parameter groups to be analyzed; determines abnormal parameter groups from multiple target parameter groups in the parameter groups to be analyzed based on a preset threshold array; and performs abnormal warning of the servomotor based on the abnormal parameter groups. Thus, it is possible to realize automatic monitoring of abnormal states of servomotors and accurately realize abnormal warning of servomotors, thereby effectively improving the warning effect.

[0022] 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

[0023] 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:

[0024] Figure 1 It is a flow chart of a method for early warning of abnormality of a servomotor of a hydro-turbine generator set proposed in one embodiment of the present disclosure;

[0025] Figure 2 is a flow chart of a method for early warning of abnormality of a servomotor of a hydro-turbine generator set proposed in another embodiment of the present disclosure;

[0026] Figure 3 is a schematic diagram of a relay abnormal warning process according to the present disclosure;

[0027] Figure 4 is a relay warning calculation flow chart proposed in the present disclosure;

[0028] Figure 5 It is a structural schematic diagram of an abnormal warning device for a servomotor of a hydro-turbine generator set proposed in an embodiment of the present disclosure;

[0029] Figure 6 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure are described in detail below, examples of which 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.

[0031] Figure 1 It is a flow chart of a method for warning abnormality of a servomotor of a hydro-turbine generator set proposed in an embodiment of the present disclosure.

[0032] Among them, it should be noted that the executor of the abnormal warning method for the relay of the turbine generator set in this embodiment is the abnormal warning device for the relay of the turbine generator set, which can be implemented by software and / or hardware. The device can be configured in a computer device, and the computer 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.

[0033] like Figure 1 As shown, the abnormal warning method for the servomotor of a hydro-turbine generator set includes:

[0034] S101: Collecting initial parameter groups of a hydro-generator set at a plurality of preset time points, wherein the initial parameter group includes: initial guide vane opening, initial blade opening and set active power.

[0035] The preset time point may refer to a preset parameter collection time point.

[0036] Among them, 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. The initial guide vane opening refers to the collected and determined guide vane opening of the turbine.

[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. The initial blade opening refers to the collected and determined turbine blade opening.

[0038] In the embodiment of the present disclosure, corresponding sensors can be configured for the guide vane opening, the blade opening and the active power of the unit, so as to detect and obtain the initial guide vane opening, the initial blade opening and the active power of the unit.

[0039] S102: performing data preprocessing on the initial parameter group to obtain a target parameter group, wherein the target parameter group includes: a target guide vane opening, a target blade opening, and a unit active power.

[0040] The target parameter group refers to the parameter group obtained after data preprocessing of the initial parameter group, and the target guide vane opening and target blade opening refer to the guide vane opening value and blade opening value obtained after data preprocessing of the initial guide vane opening and initial blade opening.

[0041] It is understandable that in the process of obtaining the initial guide vane opening and the initial blade opening values, abnormal data may be generated due to line interference, loose sensors, etc. Therefore, in the embodiment of the present disclosure, the initial parameter group can be preprocessed to obtain a target parameter group to ensure the practicality and reliability of the obtained target parameter group.

[0042] S103: Grouping multiple target parameter groups to obtain a parameter group to be analyzed.

[0043] The parameter group to be analyzed refers to a combination obtained by grouping multiple target parameter groups. Each parameter group to be analyzed includes multiple target parameter groups.

[0044] In the embodiment of the present disclosure, when a plurality of target parameter groups are grouped to obtain a parameter group to be analyzed, the plurality of target parameter groups may be sorted and grouped according to a time series to obtain a parameter group to be analyzed.

[0045] S104: Determine an abnormal parameter group from a plurality of target parameter groups in the parameter group to be analyzed based on a preset threshold array.

[0046] The preset threshold array refers to an array consisting of threshold values ​​configured for multiple parameters in the target parameter group.

[0047] The abnormal parameter group refers to a parameter group that may have abnormalities among multiple target parameter groups.

[0048] That is to say, in the embodiment of the present disclosure, a corresponding warning threshold can be pre-configured for each parameter, a preset threshold array is composed of multiple warning thresholds, and then the abnormal parameter group is determined from multiple target parameter groups in the parameter group to be analyzed based on the preset threshold array.

[0049] S105: Perform relay abnormality warning based on the abnormal parameter group.

[0050] That is, in the embodiment of the present disclosure, after determining the abnormal parameter group from multiple target parameter groups in the parameter group to be analyzed based on the preset threshold array, a relay abnormality warning can be performed based on the abnormal parameter group to ensure the accuracy and real-time nature of the warning.

[0051] In this embodiment, the initial parameter groups of the hydro-generator set at multiple preset time points are collected, wherein the initial parameter groups include: initial guide vane opening, initial blade opening and active power of the set; data preprocessing is performed on the initial parameter groups to obtain target parameter groups, wherein the target parameter groups include: target guide vane opening, target blade opening and active power of the set; multiple target parameter groups are grouped to obtain parameter groups to be analyzed; abnormal parameter groups are determined from multiple target parameter groups in the parameter groups to be analyzed based on a preset threshold array; relay abnormality warning is performed based on the abnormal parameter groups. In this way, the abnormal state of the relay can be automatically monitored, and the relay abnormality warning can be accurately realized, thereby effectively improving the warning effect.

[0052] Figure 2 It is a flow chart of a method for warning abnormality of a servomotor of a hydro-turbine generator set proposed in another embodiment of the present disclosure.

[0053] like Figure 2 As shown, the abnormal warning method for the servomotor of a hydro-turbine generator set includes:

[0054] S201: Collecting initial parameter groups of the hydro-generator set at multiple preset time points, wherein the initial parameter group includes: initial guide vane opening, initial blade opening and unit active power.

[0055] The description of S201 can be specifically referred to the above embodiment, which will not be repeated here.

[0056] S202: Determine the guide vane opening average value, guide vane opening standard deviation, blade opening average value, and blade opening standard deviation of multiple initial parameter groups.

[0057] That is to say, in the embodiment of the present disclosure, after collecting the initial parameter groups of the hydro-turbine generator set at multiple preset time points, the guide vane opening average value, guide vane opening standard deviation, blade opening average value and blade opening standard deviation of the multiple initial parameter groups can be determined, thereby providing reliable reference information for the subsequent determination of the target guide vane opening corresponding to the initial guide vane opening.

[0058] S203: Determine a target guide vane opening corresponding to the initial guide vane opening according to the guide vane opening average value and the guide vane opening standard deviation.

[0059] In the embodiment of the present disclosure, when determining the target guide vane opening corresponding to the initial guide vane opening based on the average guide vane opening value and the standard deviation of the guide vane opening, the average guide vane opening value, the standard deviation of the guide vane opening and the initial guide vane opening may be input into a pre-trained machine learning model to obtain the corresponding target guide vane opening. Alternatively, the target guide vane opening corresponding to the initial guide vane opening may be determined based on the average guide vane opening value and the standard deviation of the guide vane opening based on a method combining numbers and shapes, and there is no limitation to this.

[0060] Optionally, in some embodiments, when determining the target guide vane opening corresponding to the initial guide vane opening according to the guide vane opening average value and the guide vane opening standard deviation, it can be: if the difference between the initial guide vane opening and the guide vane opening average value in the initial parameter group is less than or equal to three times the guide vane opening standard deviation, the initial guide vane opening is used as the target guide vane opening; if the difference between the initial guide vane opening and the guide vane opening average value in the initial parameter group is greater than three times the guide vane opening standard deviation, the leading guide vane opening is used as the target guide vane opening, wherein the leading guide vane opening is the target guide vane opening in the leading parameter group, and the leading parameter group is an adjacent target parameter group whose acquisition time point is earlier than the initial parameter group. In this way, the cleaning and padding processing of the initial guide vane opening can be achieved to ensure the reliability of the obtained target guide vane opening.

[0061] It can be understood that in the embodiment of the present disclosure, when determining whether the initial guide vane opening is used as the target guide vane opening, it can also be determined whether the difference between the initial guide vane opening and the average guide vane opening in the initial parameter group is less than or equal to X times the standard deviation of the guide vane opening, where X can take any value according to the application scenario and there is no limitation on this.

[0062] S204: Determine a target blade opening corresponding to the initial blade opening according to the blade opening average value and the blade opening standard deviation.

[0063] Optionally, in some embodiments, when determining the target blade opening corresponding to the initial blade opening according to the blade opening average value and the blade opening standard deviation, if the difference between the initial blade opening and the blade opening average value in the initial parameter group is less than or equal to three times the blade opening standard deviation, the initial blade opening is used as the target blade opening; if the difference between the initial blade opening and the blade opening average value in the initial parameter group is greater than three times the blade opening standard deviation, the leading blade opening is used as the target blade opening, wherein the leading blade opening is the target blade opening in the leading parameter group, and the leading parameter group is an adjacent target parameter group whose acquisition time point is earlier than the initial parameter group. In this way, the cleaning and filling processing of the initial blade opening can be achieved to ensure the reliability of the obtained target blade opening.

[0064] It can be understood that in the embodiment of the present disclosure, when determining whether the initial blade opening is used as the target blade opening, it can also be determined whether the difference between the initial blade opening in the initial parameter group and the average blade opening is less than or equal to X times the standard deviation of the blade opening, where X can take any value according to the application scenario and there is no limitation on this.

[0065] S205: The target guide vane opening, target blade opening and unit active power associated with the initial parameter group are combined as a target parameter group.

[0066] That is to say, in the disclosed embodiment, after collecting the initial parameter groups of the hydro-generator set at multiple preset time points, the average guide vane opening, standard deviation of the guide vane opening, average blade opening, and standard deviation of the blade opening of multiple initial parameter groups can be determined; according to the average guide vane opening and standard deviation of the guide vane opening, the target guide vane opening corresponding to the initial guide vane opening is determined; according to the average blade opening and standard deviation of the blade opening, the target blade opening corresponding to the initial blade opening is determined; the target guide vane opening, target blade opening, and active power of the unit associated with the initial parameter group are combined as the target parameter group. Thus, the practicality and reliability of the obtained target parameter group can be effectively improved through data cleaning and completion processing.

[0067] S206: Grouping multiple target parameter groups to obtain parameter groups to be analyzed.

[0068] The description of S206 can be specifically referred to the above embodiment, which will not be repeated here.

[0069] S207: When there is a unit active power greater than the startup power threshold in the parameter group to be analyzed, determine the extreme difference values ​​of the active powers of multiple units.

[0070] The startup power threshold refers to a threshold configured for the active power of the unit for determining whether the unit is in the startup state. The startup power threshold may be 5MW, for example, or may be flexibly configured according to the application scenario, without limitation.

[0071] S208: When the extreme difference value is less than or equal to the steady-state power threshold, determine the first comparison result of the target guide vane opening and the guide vane opening threshold, the second comparison result of the target guide vane opening and the guide vane closing threshold, the third comparison result of the target blade opening and the blade opening threshold, and the fourth comparison result of the target blade opening and the blade closing threshold in each target parameter group.

[0072] The steady-state power threshold refers to a threshold configured for the extreme difference values ​​of the active power of multiple units and is used to determine whether the units are in a stable working state.

[0073] The guide vane opening threshold refers to a threshold configured for the guide vane opening and used to determine whether the guide vane is in a biased opening state.

[0074] The guide vane partially closed threshold refers to a threshold configured for the guide vane opening and used to determine whether the guide vane is in a partially closed state.

[0075] The blade deviation threshold refers to a threshold configured for the blade opening and used to determine whether the blade is in a deviated state.

[0076] The blade partially closed threshold refers to a threshold configured for the blade opening and used to determine whether the blade is in a partially closed state.

[0077] S209: Determine an abnormal parameter group according to the first comparison result, the second comparison result, the third comparison result, and the fourth comparison result.

[0078] That is to say, in the embodiment of the present disclosure, after a plurality of target parameter groups are grouped to obtain a parameter group to be analyzed, when there is a unit active power greater than the startup power threshold in the parameter group to be analyzed, the extreme difference values ​​of the active power of the plurality of units can be determined; when the extreme difference value is less than or equal to the steady-state power threshold, the first comparison result of the target guide vane opening and the guide vane bias threshold, the second comparison result of the target guide vane opening and the guide vane bias threshold, the third comparison result of the target blade opening and the blade bias threshold, and the fourth comparison result of the target blade opening and the blade bias threshold in each target parameter group are determined; according to the first comparison result, the second comparison result, the third comparison result, and the fourth comparison result, the abnormal parameter group is determined. Thus, the abnormal parameter group can be accurately and quickly determined from the plurality of target parameter groups based on the first comparison result, the second comparison result, the third comparison result, and the fourth comparison result.

[0079] S210: Performing a relay abnormality warning based on the first comparison result, the second comparison result, the third comparison result, and the fourth comparison result corresponding to the abnormal parameter group.

[0080] That is to say, in the embodiment of the present disclosure, after determining the abnormal parameter group, the relay abnormality warning can be performed based on the first comparison result, the second comparison result, the third comparison result and the fourth comparison result corresponding to the abnormal parameter group. Thus, accurate warning of the relay abnormality can be achieved based on the first comparison result, the second comparison result, the third comparison result and the fourth comparison result.

[0081] In this embodiment, the guide vane opening average value, guide vane opening standard deviation, blade opening average value and blade opening standard deviation of multiple initial parameter groups are determined; the target guide vane opening corresponding to the initial guide vane opening is determined according to the guide vane opening average value and the guide vane opening standard deviation; the target blade opening corresponding to the initial blade opening is determined according to the blade opening average value and the blade opening standard deviation; the target guide vane opening, target blade opening and unit active power associated with the initial parameter group are combined as the target parameter group. Thus, the practicality and reliability of the obtained target parameter group can be effectively improved through data cleaning and completion processing. When there is a unit active power greater than the startup power threshold in the parameter group to be analyzed, the extreme difference values ​​of the active power of multiple units are determined; when the extreme difference value is less than or equal to the steady-state power threshold, the first comparison result of the target guide vane opening and the guide vane bias threshold, the second comparison result of the target guide vane opening and the guide vane bias threshold, the third comparison result of the target blade opening and the blade bias threshold, and the fourth comparison result of the target blade opening and the blade bias threshold in each target parameter group are determined; according to the first comparison result, the second comparison result, the third comparison result and the fourth comparison result, the abnormal parameter group is determined. Thus, the abnormal parameter group can be accurately and quickly determined from multiple target parameter groups based on the first comparison result, the second comparison result, the third comparison result and the fourth comparison result. The relay abnormality warning is performed based on the first comparison result, the second comparison result, the third comparison result and the fourth comparison result corresponding to the abnormal parameter group. Thus, accurate warning of the abnormality of the relay can be achieved based on the first comparison result, the second comparison result, the third comparison result and the fourth comparison result.

[0082] Based on the above embodiments, Figure 3 As shown, Figure 3 It is a schematic diagram of the relay abnormality warning process proposed in the present disclosure. The data involved in the present disclosure comes from the real-time collected data of the unit monitoring system. By integrating the timestamps and fields of the data, the integrated data of the guide blade opening monitoring is obtained. Subsequently, these data are cleaned to obtain processed integrated data for the guide blade opening monitoring. After cleaning, the data is input into the relay abnormality warning model in real time. The model tracks, calculates and extracts the characteristic indicators of the guide blade opening abnormality, and finally generates the calculation and extraction results of the guide blade opening abnormality indicators of the turbine generator set, providing a reliable data basis for the relay abnormality warning and fault diagnosis. Among them, the steps are as follows:

[0083] 1.1 Data sources and data cleaning and completion

[0084] 1.1.1 Data Source

[0085] The data involved in this disclosure comes from the unit monitoring system, mainly including the guide blade opening measurement point, external parameter input, time parameter and calculated extracted characteristic measurement point data, etc. The main contents include but are not limited to (taking unit 1 as an example):

[0086] Guide vane opening of units 1-n, blade opening of units 1-n, and active power of the units.

[0087] 1.1.2 Data cleaning and completion

[0088] Due to line interference, loose sensors and other reasons, there are abnormal data in the data, so the data needs to be cleaned. The data cleaning process is as follows:

[0089] (1) Cleaning is performed based on the monitoring data of the guide blade opening of the auxiliary equipment of the hydro-turbine generator set: according to the distribution of values ​​in the data records of each guide blade opening measurement point, outlier values ​​in the data records of each measurement point are deleted. The data cleaning process is as follows:

[0090] Step 1: Calculate the mean value and standard deviation of the data of each guide blade opening measurement point;

[0091] Step 2: Filter out the data of the guide blade opening measurement points whose difference from the average value exceeds 3 times the standard deviation;

[0092] Step 3: Filter out the data selected in step 2.

[0093] (2) Considering the data requirements for the calculation of the guide blade opening abnormality index, it is necessary to perform the following processing:

[0094] Step 1: Complement the data filtered out by the above (1): Complement the guide blade opening monitoring data by pre-complementing the data;

[0095] Step 2: Considering the data loss of the guide blade opening measurement point due to data link problems, all data measurement points are supplemented at the second level using the pre-complement method.

[0096] 1.2 Relay abnormal warning algorithm

[0097] The extraction of characteristic indicators of guide vane opening and blade opening after the power of the hydro-turbine generator set is stabilized can provide sign indicators for faults such as guide vane deviation from opening or closing, help accurately locate relay faults, realize relay abnormality warning, and provide strong support for relay status inspection and maintenance.

[0098] 1.2.1 Algorithm Input

[0099] The algorithm input includes two parts: real-time data of measurement points and algorithm parameters. The channel real-time data includes the guide blade opening and the unit active power data. The algorithm parameters are a set of important parameters that can be configured by the algorithm.

[0100] (1) Real-time data of measuring points

[0101] The operator input includes the guide blade opening and the time parameters corresponding to the unit active power. Taking one unit as an example, the details are shown in Table 1:

[0102] Table 1

[0103] Serial number Measuring point name Remark 1 Unit 1 guide vane opening 2 Unit 1 blade opening 3 Active power of unit 1

[0104] (2) Algorithm parameters

[0105] In order to increase the flexibility of the algorithm and ensure its configurability, so as to facilitate its promotion and application in various power plants, some parameters of the algorithm are adjusted according to the data situation and calculation effect, and the important parameters of the algorithm are condensed into a configurable parameter set of the algorithm, including the common fields of the input power plant site for configuring different power plant data, as shown in Table 2.

[0106] Table 2

[0107] Serial number Field Name meaning 1 threshold_open Threshold for judging whether the guide vane opening / blade opening of the unit is biased 2 threshold_close Threshold for judging whether the guide vane opening / blade opening of the unit is closed 3 std_threshold Power deviation threshold for judging whether the unit is in a stable operating state 4 startup_threshold Threshold for judging whether the unit is turned on

[0108] 1.2.2 Algorithm Logic

[0109] In general, the algorithm logic mainly includes five steps: data reading, data preprocessing, index calculation, data writing and collection, and data output. By reading the guide blade opening parameters, the data is grouped, sorted, processed and screened, and the output relay abnormal warning characteristic indicators are calculated. Figure 4 As shown, Figure 4 It is a relay warning calculation flow chart proposed in the present disclosure, and the specific steps are as follows:

[0110] Step 1: Input data reading. Traverse all input measurement points, and input the guide vane opening and blade opening of units 1-m, the active power, active power threshold, biased opening and biased closing threshold and other external parameter input data of units 1-m in turn.

[0111] Step 2: Preprocessing of parameter input data. Separate the strings of external parameter input, unit active power measurement points and corresponding thresholds by commas and store them as new strings for subsequent indexing.

[0112] Step 3: Analyze and process the input status data. For the input data, first group them by unit, traverse each group in turn, and then sort and group them by time.

[0113] Step 4: Unit start and stop judgment. Read the unit active power P, traverse each group of data grouped by time, and when the active power is greater than 5MW, it is considered that the unit is in the start state, retain the data and execute the next step; otherwise, no processing is performed.

[0114] Step 5: Data screening and filtering. For a group of data after grouping, read the active power P of the unit, traverse each group of data after time grouping, and perform stable working condition screening: if the difference between the maximum and minimum power values ​​in the time period does not exceed the threshold, it is considered that the unit is in a stable working condition, retain the data and execute the next step; otherwise, filter the data under the time grouping.

[0115] Step 6: Calculate the guide blade opening data deviation characteristic index, and perform alarm judgment on the data status when the machine is turned on. According to the guide blade opening and blade opening ID characteristic fields, traverse and extract the guide blade opening data, and group them by guide blade opening and blade opening; there are two warning situations: ① Filter the guide blade opening deviation exceeding ±0.5% under stable load state, and analyze and warn the abnormal deviation data; ② When the guide blade opening changes continuously under stable load state, filter out the continuous change segment with a deviation exceeding ±0.5% for analysis and warning.

[0116] Step 7: Traverse all the data after grouping in step 3, execute steps 4-7, calculate various feature indicators, and write them out.

[0117] 1.2.3 Algorithm Output

[0118] Calculate and output the id, time, v and other values ​​of each guide blade opening index, output to the corresponding measurement point, and store it in the InfluxDB library. Taking Unit 1 as an example, the measurement points corresponding to each index are shown in Table 3:

[0119] Table 3

[0120]

[0121] Figure 5 It is a structural schematic diagram of a hydro-turbine generator set servomotor abnormality warning device proposed in one embodiment of the present disclosure.

[0122] like Figure 5 As shown, the hydro-generator relay abnormality warning device 50 comprises:

[0123] The acquisition module 501 is used to acquire the initial parameter group of the hydro-generator set at multiple preset time points, wherein the initial parameter group includes: initial guide vane opening, initial blade opening and active power of the set;

[0124] The first processing module 502 is used to perform data preprocessing on the initial parameter group to obtain a target parameter group, wherein the target parameter group includes: a target guide vane opening, a target blade opening, and a unit active power;

[0125] The second processing module 503 is used to group the multiple target parameter groups to obtain a parameter group to be analyzed;

[0126] A determination module 504, configured to determine an abnormal parameter group from a plurality of target parameter groups in the parameter group to be analyzed based on a preset threshold array;

[0127] The warning module 505 is used to provide a relay abnormality warning based on the abnormal parameter group.

[0128] It should be noted that the aforementioned explanation of the abnormal warning method for the servomotor of a hydro-turbine generator set is also applicable to the abnormal warning device for the servomotor of a hydro-turbine generator set of the present embodiment, and will not be described in detail here.

[0129] In this embodiment, the initial parameter groups of the hydro-generator set at multiple preset time points are collected, wherein the initial parameter groups include: initial guide vane opening, initial blade opening and active power of the set; data preprocessing is performed on the initial parameter groups to obtain target parameter groups, wherein the target parameter groups include: target guide vane opening, target blade opening and active power of the set; multiple target parameter groups are grouped to obtain parameter groups to be analyzed; abnormal parameter groups are determined from multiple target parameter groups in the parameter groups to be analyzed based on a preset threshold array; relay abnormality warning is performed based on the abnormal parameter groups. In this way, the abnormal state of the relay can be automatically monitored, and the relay abnormality warning can be accurately realized, thereby effectively improving the warning effect.

[0130] Figure 6 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Figure 6 The computer 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.

[0131] like Figure 6 As shown, the computer device 12 is in the form of a general-purpose computing device. The components of the computer 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).

[0132] 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.

[0133] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0134] 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 computer 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 6 Not shown, often called a "hard drive").

[0135] although Figure 6 Not 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.

[0136] 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.

[0137] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a human body to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. In addition, the computer 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) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 via 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 computer 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.

[0138] 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 abnormal warning method for the servomotor of the hydro-turbine generator set mentioned in the above-mentioned embodiment.

[0139] In order to implement the above 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 abnormal warning method for the relay of a hydro-turbine generator set proposed in the above embodiments of the present disclosure is implemented.

[0140] 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 abnormal warning method for the servomotor of a hydro-turbine generator set proposed in the above embodiments of the present disclosure is executed.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0149] 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.

[0150] 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 early warning of abnormality of a servomotor of a hydro-turbine generator set, characterized in that: include: Collecting initial parameter groups of the hydro-generator set at multiple preset time points, wherein the initial parameter groups include: initial guide vane opening, initial blade opening and unit active power; Performing data preprocessing on the initial parameter group to obtain a target parameter group, wherein the target parameter group includes: a target guide vane opening, a target blade opening, and the active power of the unit; Grouping the plurality of target parameter groups to obtain a parameter group to be analyzed; Determine an abnormal parameter group from a plurality of the target parameter groups in the parameter groups to be analyzed based on a preset threshold array; A relay abnormality warning is performed based on the abnormal parameter group.

2. The method according to claim 1, characterized in that The performing data preprocessing on the initial parameter group to obtain a target parameter group includes: Determining a guide vane opening average value, a guide vane opening standard deviation, a blade opening average value, and a blade opening standard deviation of a plurality of the initial parameter groups; Determining a target guide vane opening corresponding to the initial guide vane opening according to the guide vane opening average value and the guide vane opening standard deviation; Determining a target blade opening corresponding to the initial blade opening according to the blade opening average value and the blade opening standard deviation; The target guide vane opening, the target blade opening, and the unit active power associated with the initial parameter group are combined as the target parameter group.

3. The method according to claim 2, characterized in that Determining a target guide vane opening corresponding to the initial guide vane opening according to the guide vane opening average value and the guide vane opening standard deviation includes: If the difference between the initial guide vane opening and the average guide vane opening in the initial parameter group is less than or equal to three times the standard deviation of the guide vane opening, the initial guide vane opening is used as the target guide vane opening; If the difference between the initial guide vane opening and the guide vane opening average value in the initial parameter group is greater than three times the standard deviation of the guide vane opening, the leading guide vane opening is used as the target guide vane opening, wherein the leading guide vane opening is the target guide vane opening in the leading parameter group, and the leading parameter group is an adjacent target parameter group whose collection time point is earlier than that of the initial parameter group.

4. The method according to claim 2, characterized in that The determining, according to the blade opening average value and the blade opening standard deviation, a target blade opening corresponding to the initial blade opening comprises: If the difference between the initial blade opening and the average blade opening in the initial parameter group is less than or equal to three times the standard deviation of the blade opening, the initial blade opening is used as the target blade opening; If the difference between the initial blade opening in the initial parameter group and the average blade opening is greater than three times the standard deviation of the blade opening, the leading blade opening is used as the target blade opening, wherein the leading blade opening is the target blade opening in the leading parameter group, and the leading parameter group is an adjacent target parameter group whose collection time point is earlier than that of the initial parameter group.

5. The method according to claim 1, characterized in that The preset threshold array includes: a startup power threshold, a steady-state power threshold, a guide vane bias-open threshold, a guide vane bias-close threshold, a blade bias-open threshold, and a blade bias-close threshold; Wherein, the determining of the abnormal parameter group from the plurality of target parameter groups in the parameter group to be analyzed based on a preset threshold array includes: When there is a case in the parameter group to be analyzed where the active power of the unit is greater than the startup power threshold, determining the extreme difference values ​​of the active powers of the multiple units; When the extreme difference value is less than or equal to the steady-state power threshold, determining a first comparison result of the target guide vane opening and the guide vane opening threshold, a second comparison result of the target guide vane opening and the guide vane closing threshold, a third comparison result of the target blade opening and the blade opening threshold, and a fourth comparison result of the target blade opening and the blade closing threshold in each target parameter group; The abnormal parameter group is determined according to the first comparison result, the second comparison result, the third comparison result, and the fourth comparison result.

6. The method according to claim 5, characterized in that The relay abnormality warning based on the abnormal parameter group includes: A relay abnormality warning is performed based on the first comparison result, the second comparison result, the third comparison result and the fourth comparison result corresponding to the abnormal parameter group.

7. A hydro-generator relay abnormality warning device, characterized in that: include: A collection module, used for collecting the initial parameter group of the hydro-generator set at multiple preset time points, wherein the initial parameter group includes: initial guide vane opening, initial blade opening and active power of the set; A first processing module is used to perform data preprocessing on the initial parameter group to obtain a target parameter group, wherein the target parameter group includes: a target guide vane opening, a target blade opening and the active power of the unit; A second processing module, used for grouping the plurality of target parameter groups to obtain a parameter group to be analyzed; A determination module, configured to determine an abnormal parameter group from a plurality of target parameter groups in the parameter group to be analyzed based on a preset threshold array; The early warning module is used to provide an early warning of relay abnormality based on the abnormal parameter group.

8. A computer 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.

9. 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.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

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