Hydroelectric generating set full-condition stability state abnormal early warning method, device and equipment

Abstract

The present disclosure provides an abnormal early warning method for full working condition stability state of a hydroelectric generating set. The method comprises: obtaining historical operation data of the hydroelectric generating set in a stability state, determining target water head, target active power and rated power of the hydroelectric generating set corresponding to different historical operation data, and dividing the historical operation data into a plurality of data sets according to the target water head, target active power and rated power corresponding to each historical operation data, wherein each data set has corresponding target water head and target active power, determining a data health value of the hydroelectric generating set when operating at the target water head and target active power corresponding to the data set according to the historical operation data in each data set, and performing abnormal state early warning on the hydroelectric generating set based on the data health value, thereby effectively improving the abnormal early warning effect of the full working condition stability state of the hydroelectric generating set.

Description

Technical Field

[0001] This disclosure relates to the field of early warning technology for hydropower stations, and in particular to an early warning method, device and equipment for abnormal stability status of hydropower units under all operating conditions. Background Technology

[0002] Typical operating conditions of hydropower units may include: stable operation under a given load. When a hydropower unit is in a stable operation under a given load, its operating status is generally relatively stable. The current stability status of the unit is characterized by the operating data performance of different equipment in the hydropower unit under the corresponding head and active power conditions. Therefore, by combining the health values ​​of the operating data of the corresponding equipment in a stable state, abnormal states of different equipment in the hydropower unit can be identified and warned.

[0003] In related technologies, the health value settings for the operating data of hydropower units do not take into account the individual operating conditions of hydropower units, thus making it impossible to identify and warn of abnormal states of hydropower units under all operating conditions. Summary of the Invention

[0004] This disclosure aims to at least partially address the technical problems in the related art.

[0005] Therefore, the purpose of this disclosure is to provide an abnormal early warning method, device, electronic equipment, storage medium, and computer program product for the stability state of a hydropower unit under all operating conditions.

[0006] The method for early warning of abnormal stability of a hydropower unit under all operating conditions according to the first aspect of this disclosure includes: acquiring historical operating data of the hydropower unit when it is in a stable state; determining the target head, target active power, and rated power of the hydropower unit when it operates with different historical operating data; dividing the multiple historical operating data into multiple data sets according to the target head, target active power, and rated power of each historical operating data set, wherein each data set has a corresponding target head and target active power; determining the data health value of the hydropower unit when it operates with the target head and target active power corresponding to the data set according to the historical operating data in each data set, wherein the data health value is the operating data value of the hydropower unit when it operates with the target head and target active power corresponding to the data set and is in a stable state; and issuing an early warning of abnormal state of the hydropower unit based on the data health value.

[0007] The second aspect of this disclosure discloses an abnormal early warning device for the stability state of a hydropower unit under all operating conditions. The device includes: an acquisition module for acquiring historical operating data of the hydropower unit when it is in a stable state; a first determination module for determining the target head, target active power, and rated power of the hydropower unit when it operates with different historical operating data; a processing module for dividing multiple historical operating data into multiple data sets based on the target head, target active power, and rated power corresponding to each historical operating data, wherein each data set has a corresponding target head and target active power; a second determination module for determining a data health value of the hydropower unit when it operates with the target head and target active power corresponding to each data set, based on the historical operating data in each data set, wherein the data health value is the operating data value of the hydropower unit when it operates with the target head and target active power corresponding to the data set and is in a stable state; and an early warning module for issuing an abnormal state warning for the hydropower unit based on the data health value.

[0008] A third aspect of this disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements an abnormal early warning method for the stability state of a hydropower unit under all operating conditions, as proposed in the first aspect of this disclosure.

[0009] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements an anomaly warning method for the stability state of a hydropower unit under all operating conditions as proposed in the first aspect of this disclosure.

[0010] The fifth aspect of this disclosure provides a computer program product that, when executed by an instruction processor, performs an abnormal early warning method for the stability status of a hydropower unit under all operating conditions, as proposed in the first aspect of this disclosure.

[0011] The abnormal early warning method for the stability state of a hydropower unit under all operating conditions provided in the embodiments of this disclosure may include the following beneficial effects: acquiring historical operating data of the hydropower unit when it is in a stable state, determining the target head, target active power, and rated power of the hydropower unit when it is running with different historical operating data, and dividing the multiple historical operating data into multiple data sets according to the target head, target active power, and rated power of the unit corresponding to each historical operating data, wherein each data set has a corresponding target head and target active power, and then determining the target head, target active power, and rated power of the hydropower unit when it is running with different historical operating data. The data health values ​​are the operating data values ​​of the hydropower unit when it is operating at the target head and target active power corresponding to the data set and is in a stable state. The data health values ​​are used to provide early warning of abnormal states for the hydropower unit. This allows for the accurate determination of the data health values ​​of the hydropower unit when it is operating at different target heads and target active power. Based on the data health values, targeted early warning of abnormal states can be provided for hydropower units operating at different heads and active power, thereby effectively improving the early warning effect of abnormal states of the hydropower unit's stability under all operating conditions.

[0012] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0013] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0014] Figure 1 This is a flowchart illustrating an abnormal early warning method for the stability state of a hydropower unit under all operating conditions, as proposed in an embodiment of this disclosure.

[0015] Figure 2 This is a schematic diagram of the initial operating data of a hydroelectric generator unit according to an embodiment of this disclosure.

[0016] Figure 3 This is a flowchart illustrating an abnormal early warning method for the stability state of a hydropower unit under all operating conditions, as proposed in another embodiment of this disclosure.

[0017] Figure 4 This is a three-dimensional bar chart of a data set proposed in an embodiment of this disclosure;

[0018] Figure 5 This is a schematic diagram of the structure of an abnormal early warning device for the stability state of a hydropower unit under all operating conditions, according to an embodiment of this disclosure;

[0019] Figure 6A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0020] Embodiments of this disclosure are described in detail below, with examples of embodiments illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are used only to explain this disclosure, and should not be construed as limiting this disclosure. Rather, embodiments of this disclosure include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0021] It should be noted that the processes of data acquisition, collection, storage, use, and processing in this disclosed technical solution comply with relevant laws and regulations and do not violate public order and good morals.

[0022] Figure 1 This is a flowchart illustrating an abnormal early warning method for the stability status of a hydropower unit under all operating conditions, as proposed in an embodiment of this disclosure.

[0023] It should be noted that the execution subject of the abnormal early warning method for the stability status of the hydropower unit under all operating conditions in this embodiment is the abnormal early warning system for the stability status of the hydropower unit under all operating conditions. This device can be implemented by software and / or hardware, and can be configured in an electronic device, which may include, but is not limited to, a terminal, a server, etc.

[0024] like Figure 1 As shown, the abnormal early warning method for the stability state of the hydropower unit under all operating conditions includes:

[0025] S101: Obtain historical operating data of the hydropower unit when it is in a stable state.

[0026] Among them, the stability state can refer to the operating state of the hydropower unit under a given load and without any abnormal operating phenomena, and there are no restrictions on this.

[0027] Historical operating data refers to the operating data of a hydropower unit when it is in a stable state during its historical operation. This historical operating data can include, for example, one or more of the following: X-axis runout of the upper guide bearing, Y-axis runout of the upper guide bearing, X-axis runout of the lower guide bearing, Y-axis runout of the lower guide bearing, X-axis runout of the water guide bearing, Y-axis runout of the water guide bearing, turbine lift-off, X-axis vibration of the upper frame, Y-axis vibration of the upper frame, vertical vibration of the upper frame, X-axis vibration of the stator frame, Y-axis vibration of the stator frame, vertical vibration of the stator frame, and X-axis vibration of the lower frame. Vibrations in the following directions are not limited: Y-axis vibration of the lower frame, vertical vibration of the lower frame, X-axis vibration of the top cover, Y-axis vibration of the top cover, vertical vibration of the top cover, pressure pulsation at the volute inlet, pressure pulsation at the guide vane outlet, pressure pulsation at the bottom of the top cover, pressure pulsation at the tailrace inlet, pressure pulsation at the tailrace outlet, X and Y-axis sway of the generator guide bearing, X and Y-axis sway of the water guide bearing, X and Y-axis vibration of the generator guide bearing, X and Y-axis vibration of the water guide bearing, X, Y-axis vibration of the runner chamber, X, Y, and Z-axis vibration of the combined bearing, X and Y-axis vibration of the guide vane outlet, pressure pulsation at the tailrace inlet, and pressure pulsation of the runner chamber.

[0028] In other words, in this embodiment of the disclosure, the operating status of the hydropower unit can be monitored during the historical operation of the hydropower unit, and when the hydropower unit is found to be in a stable state within a preset time period, all or part of the corresponding operating data of the hydropower unit within the preset time period can be collected as historical operating data, without any limitation.

[0029] Optionally, in some embodiments, obtaining historical operating data of the hydropower unit when it is in a stable state may involve obtaining the initial operating data and rated power of the hydropower unit when it is in a stable state, determining the initial head and initial active power corresponding to the initial operating data, and then determining the historical operating data from multiple initial operating data based on the rated power, initial head and initial active power.

[0030] Among them, the unprocessed operating data obtained when the hydropower unit is in a stable state during its historical operation is the initial operating data.

[0031] When the hydropower unit is running with the initial operating data, the head of the hydropower unit is the initial head, and the active power of the hydropower unit is the initial active power.

[0032] In other words, in this embodiment of the present disclosure, the operating status of the hydropower unit can be monitored during its historical operation. When the hydropower unit is found to be in a stable state within a preset time period, all the corresponding operating data of the hydropower unit within the preset time period are collected as initial operating data. Based on the rated power, initial head and initial active power, historical operating data is determined from multiple initial operating data. Thus, the initial operating data can be filtered, thereby reducing the amount of data and reducing data redundancy.

[0033] In some embodiments, see Figure 2 , Figure 2 This is a schematic diagram of the initial operating data of a hydropower unit proposed in an embodiment of this disclosure. The historical operating data can be determined from the initial operating data by using a statistical calculator with confidence function to remove data spikes from the input initial operating data, thereby obtaining the historical operating data. There are no limitations on this.

[0034] Optionally, in some embodiments, historical operating data is determined from multiple initial operating data based on rated power, initial head, and initial active power. This can be achieved by deleting the initial operating data corresponding to the largest initial head, the initial operating data corresponding to the smallest initial head, the initial operating data with initial active power greater than the rated power, and the initial operating data with initial active power less than zero from the multiple initial operating data. In this way, historical operating data can be accurately determined from multiple initial operating data without affecting the abnormal early warning of the stability of the hydropower unit under all operating conditions.

[0035] In other words, after obtaining multiple initial operating data, the embodiments of this disclosure may delete the initial operating data corresponding to the initial head with the largest value, the initial operating data corresponding to the initial head with the smallest value, the initial operating data with initial active power greater than the rated power, and the initial operating data with initial active power less than zero, and use the remaining initial operating data after deletion as historical operating data.

[0036] S102: Determine the target head, target active power, and rated power of the hydropower unit when it is running with different historical operating data.

[0037] When the hydropower unit is running based on historical operating data, the head of the hydropower unit is the target head, the active power of the hydropower unit is the target active power, and the rated active power of the hydropower unit is the rated power of the unit.

[0038] After obtaining historical operating data of the hydropower unit when it is in a stable state, the embodiments of this disclosure can obtain historical operating data of the hydropower unit when it is in a stable state.

[0039] In other words, in this embodiment of the present disclosure, the head, active power and rated active power of the hydropower unit can be monitored when the hydropower unit is running with historical operating data, so as to determine the target head, target active power and rated power of the hydropower unit when running with different historical operating data.

[0040] In this embodiment of the disclosure, the target head and the corresponding target active power can be understood as a head and active power operating condition, that is, the operating condition in which the hydropower unit operates with the target head and the corresponding target active power. In this embodiment of the disclosure, the target head and target active power corresponding to the operation of the hydropower unit with different historical operating data can be determined. Then, the historical operating data at different heads and corresponding active powers can be combined to determine the data health value at different head and active power operating conditions, thereby realizing targeted abnormal state early warning for hydropower units at different head and active power operating conditions. For details, please refer to the following embodiments, which will not be repeated here.

[0041] S103: Based on the target head, target active power and rated power of the unit corresponding to each historical operating data, the multiple historical operating data are divided into multiple data sets, wherein each data set has a corresponding target head and target active power.

[0042] Each data set has a corresponding target head and target active power, which can be understood as the historical operating data in each data set belonging to a certain head and active power condition.

[0043] In this embodiment of the disclosure, after determining the target head, target active power, and rated power of the hydropower unit when it operates with different historical operating data, multiple historical operating data can be divided into multiple data sets according to the target head, target active power, and rated power of the unit corresponding to each historical operating data.

[0044] In some embodiments, based on the target head, target active power, and rated power of the unit corresponding to each historical operating data, multiple historical operating data are divided into multiple data sets. This can be achieved by grouping historical operating data corresponding to the same head, target active power, and rated power into one data set to obtain multiple data sets, and there is no limitation on this.

[0045] In other embodiments, multiple historical operating data are divided into multiple data sets based on the target head, target active power, and rated power of the unit corresponding to each historical operating data. Alternatively, multiple historical operating data can be filtered based on the rated power of the unit. That is, historical operating data with a target active power greater than the rated power of the unit can be deleted, and then multiple target head ranges and corresponding target active power ranges can be divided. Historical operating data falling within each target head range and corresponding target active power range can be combined into a single data set. There are no restrictions on this.

[0046] S104: Based on the historical operating data in each data set, determine the data health value of the hydropower unit when it is operating at the target head and target active power corresponding to the data set.

[0047] Among them, the data health value is the operating data value of a hydropower unit that is operating at the target head and target active power corresponding to the data set and is in a stable state.

[0048] In this embodiment of the disclosure, after dividing multiple historical operating data into multiple data sets according to the target head, target active power and rated power of the unit corresponding to each historical operating data, it is possible to: determine the data health value of the hydropower unit when it is running at the target head and target active power corresponding to the data set based on the historical operating data in each data set.

[0049] In some embodiments, the data health value of the hydropower unit is determined based on the historical operating data in each data set when it is operating at the target head and target active power corresponding to the data set. This can be done by determining the average value of the historical operating data in each data set and using the average value of the historical operating data in each data set as the data health value, or by inputting the historical operating data in each data set into a pre-trained neural network model, which processes the historical operating data in each data set and outputs the corresponding data health value. There is no limitation on this method.

[0050] S105: Provide early warning of abnormal conditions for hydropower units based on data health values.

[0051] In this embodiment of the disclosure, after determining the data health value of the hydropower unit when it is running with the target head and target active power corresponding to the data set based on the historical operating data in each data set, abnormal state warnings for the hydropower unit can be issued based on the data health value.

[0052] In some embodiments, abnormal state warnings for hydropower units based on data health values ​​can be generated by acquiring the operating data of the hydropower unit, determining the head and active power conditions corresponding to the operating data, and generating an abnormal warning of the overall stability state of the hydropower unit when the acquired operating data of the hydropower unit is greater than the data health value under the head and active power conditions. There are no restrictions on this.

[0053] Optionally, in some embodiments, providing an abnormal state warning for the hydropower unit based on data health values ​​may involve obtaining the current operating data of the hydropower unit in its current operating state, including the current water head and current active power, and determining a target health value corresponding to the current operating data based on the current water head and current active power. The target health value is the operating data value of the hydropower unit operating at the current water head and current active power and in a stable state. Then, a device identifier corresponding to the current operating data is determined, whereby the device identifier is used to identify the hydropower unit device corresponding to the current operating data. If the current operating data is greater than the target health value, an abnormal state warning for the hydropower unit device is generated.

[0054] The current operating data can be one or more of the following: X-axis runout of the upper guide bearing, Y-axis runout of the upper guide bearing, X-axis runout of the lower guide bearing, Y-axis runout of the lower guide bearing, X-axis runout of the water guide bearing, Y-axis runout of the water guide bearing, turbine lift, X-axis vibration of the upper frame, Y-axis vibration of the upper frame, vertical vibration of the upper frame, X-axis vibration of the stator frame, Y-axis vibration of the stator frame, vertical vibration of the stator frame, X-axis vibration of the lower frame, Y-axis vibration of the lower frame, vertical vibration of the lower frame, and top cover. X-axis vibration, Y-axis vibration of the top cover, vertical vibration of the top cover, pressure pulsation at the volute inlet, pressure pulsation at the guide vane outlet, pressure pulsation at the bottom of the top cover, pressure pulsation at the tailrace inlet, pressure pulsation at the tailrace outlet, X and Y oscillations of the generator guide bearing, X and Y oscillations of the water guide bearing, X and Y vibrations of the generator guide bearing, X and Y vibrations of the water guide bearing, X, Y vibrations of the runner chamber, X, Y, and Z axial vibrations of the combined bearing, X and Y vibrations of the guide vane outlet, pressure pulsation at the tailrace inlet, pressure pulsation of the runner chamber, etc., are not restricted.

[0055] The hydropower unit equipment can specifically include, for example, the upper frame, upper guide bearing, and tailrace inlet of a mixed-flow turbine generator set, without any restrictions.

[0056] The equipment identifier can be used to identify a unique hydropower unit. This equipment identifier can be, for example, the number or name of the hydropower unit, and there are no restrictions on this.

[0057] In other words, in this embodiment of the disclosure, the operation of the hydropower unit can be monitored in real time to collect the current operating data, current head and current active power of the hydropower unit in the current operating state.

[0058] The target health value is the operating data value of a hydropower unit that is able to operate in a stable state with the current head and current active power.

[0059] In this embodiment of the present disclosure, after obtaining the current operating data of the hydropower unit in its current operating state, including the current head and current active power, the operating conditions of the head and active power corresponding to the current head and current active power can be determined. That is, the data health values ​​corresponding to the target head and target active power corresponding to the current head and current active power can be determined, and the aforementioned determined health values ​​can be used as the target health values. Thus, the target health value that conforms to the operating condition of the current head and active power can be accurately determined.

[0060] In this embodiment of the disclosure, the device identifier corresponding to the current operating data can also be determined, and if the current operating data is greater than the target health value, an abnormal status warning for the hydropower unit equipment is generated, thereby facilitating the identification of the hydropower unit equipment that sends the abnormal status warning, and thus facilitating the subsequent maintenance work of the hydropower unit.

[0061] In this embodiment of the disclosure, since the target health value is consistent with the current operating condition of head and active power, the impact of operating condition differences on the abnormal early warning of the stability state of the hydropower unit under all operating conditions can be eliminated. In this way, the abnormal early warning requirement of the stability state of the hydropower unit under all operating conditions (head-active power) can be met, and the abnormal early warning effect of the stability state of the hydropower unit under all operating conditions can be effectively improved.

[0062] In this embodiment, by acquiring historical operating data of the hydropower unit when it is in a stable state, the target head, target active power, and rated power of the hydropower unit corresponding to different historical operating data are determined. Based on the target head, target active power, and rated power corresponding to each historical operating data point, multiple historical operating data are divided into multiple data sets, each with a corresponding target head and target active power. Then, based on the historical operating data in each data set, a data health value is determined for the hydropower unit operating at the target head and target active power corresponding to the data set. The data health value is the operating data value of the hydropower unit operating at the target head and target active power corresponding to the data set and in a stable state. Based on the data health value, abnormal state warnings are issued for the hydropower unit. This allows for accurate determination of the data health value of the hydropower unit operating at different target heads and target active power, enabling targeted abnormal state warnings for hydropower units operating at different heads and active power, thereby effectively improving the abnormal warning effect of the hydropower unit's overall stability state.

[0063] Figure 3 This is a flowchart illustrating an abnormal early warning method for the stability status of a hydropower unit under all operating conditions, as proposed in another embodiment of this disclosure.

[0064] like Figure 3 As shown, the abnormal early warning method for the stability state of the hydropower unit under all operating conditions includes:

[0065] S301: Obtain historical operating data of the hydropower unit when it is in a stable state.

[0066] S302: Determine the target head, target active power, and rated power of the hydropower unit when it is running with different historical operating data.

[0067] For a detailed description of S301-S302, please refer to the above embodiments, which will not be repeated here.

[0068] S303: Based on the preset head interval, the initial data range from the target head with the smallest value to the target head with the largest value is divided into multiple first data ranges.

[0069] In this embodiment of the disclosure, S303 and S304 can be specifically illustrated in conjunction with Table 1. Table 1 is a table of operating condition range intervals proposed in an embodiment of the disclosure (Table 1 will be illustrated with an example of a unit rated power of 1 / 10Pr with a preset head interval of 1m and a preset multiple, but this is not a limitation):

[0070] Table 1

[0071]

[0072]

[0073] Where Hmin is the minimum target head, Hmax is the maximum target head, and Pr is the rated power of the unit.

[0074] In this embodiment of the disclosure, referring to Table 1 above, the initial data range H from the target head with the smallest value to the target head with the largest value is divided into multiple first data ranges based on a preset head interval. This can be done by referring to the first column of Table 1, that is, dividing the initial data range from the target head with the smallest value to the target head with the largest value with a preset head interval of 1m, to obtain multiple first data ranges in the head dimension such as [Hmin,Hmin+1], [Hmin+1,Hmin+2]...[Hmax-1,Hmax].

[0075] S304: Divide each first data range into power intervals based on a preset multiple of the unit's rated power to obtain multiple second data ranges.

[0076] In this embodiment of the disclosure, referring to Table 1 above, each first data range is divided into multiple second data ranges by using a preset multiple of the unit's rated power as the power interval. This can be achieved by using 1 / 10Pr as the power interval and sequentially dividing the multiple first data ranges shown in the first column of Table 1 into 10 second data ranges, such as [0, 1 / 10Pr], [1 / 10Pr, 2 / 10Pr], ..., [9 / 10Pr, Pr], based on the first data range of [Hmin, Hmin+1]. The head range corresponding to the second data range can be, for example, [Hmin+1, Hmin+2], [0, 1 / 10Pr], without limitation.

[0077] S305: Divide the historical running data into multiple sets based on the second data range.

[0078] After obtaining the operating condition range interval division table as shown in Table 1 above, this embodiment of the disclosure can divide multiple historical operating data according to the second data range to obtain multiple data sets, see [link to relevant documentation]. Figure 4 , Figure 4 This is a three-dimensional bar chart of a dataset proposed in one embodiment of this disclosure. Figure 4 Each three-dimensional cylindrical cube can be considered as a data set.

[0079] In some embodiments, multiple historical running data are divided according to the second data range to obtain multiple data sets. This can be done by sequentially traversing all the historical running data to determine which corresponding second data range shown in Table 1 above each historical running data belongs to, and then taking all the historical running data that fall within the second data range as a data set. There is no limitation on this.

[0080] Optionally, in some embodiments, dividing multiple historical operating data according to the second data range to obtain multiple data sets can be achieved by sorting multiple historical operating data based on the data generation time corresponding to the historical operating data to obtain an operating data sequence, and dividing the operating data sequence into multiple operating data subsequences, wherein each operating data subsequence contains a preset number of historical operating data, and when the power difference between the target active power corresponding to the last historical operating data and the target active power corresponding to the first historical operating data in the operating data subsequence is less than a power difference threshold, the historical operating data falling within the second data range in the operating data subsequence is determined as a data set.

[0081] In other words, in this embodiment of the present disclosure, multiple historical operating data can be sorted from largest to smallest according to the data generation time corresponding to the historical operating data to obtain an operating data sequence. The operating data sequence can be divided into multiple operating data subsequences by a preset number of data points. For example, the operating data sequence can be divided into multiple operating data subsequences by a preset number of data points (e.g., 3 data points). When the power difference between the target active power corresponding to the last historical operating data and the target active power corresponding to the first historical operating data in the operating data subsequence is less than the power difference threshold (e.g., 2MW, which can be set according to the dead zone of the hydropower unit and is not limited thereto), the historical operating data falling within the second data range in the operating data subsequence is determined as a data set. When the power difference between the target active power corresponding to the last historical operating data and the target active power corresponding to the first historical operating data in the operating data subsequence is greater than the power difference threshold, the operating data subsequence is skipped and the next operating data subsequence is traversed. This is not limited thereto.

[0082] S306: Sort multiple historical rows of data in the data set based on the data generation time corresponding to the historical running data, and determine the running data difference between the last historical running data and the first historical running data in the sorted data set.

[0083] In this embodiment of the disclosure, after dividing multiple historical running data according to the second data range to obtain multiple data sets, multiple historical rows of data in the data sets can be sorted based on the data generation time corresponding to the historical running data, and the running data difference between the last historical running data and the first historical running data in the sorted data set can be determined.

[0084] S307: Determine the average and maximum values ​​of all historical running data in each dataset, and determine the ratio between the maximum and the average values.

[0085] In other words, in this embodiment of the disclosure, the average value v_avg_avg of all historical running data in each dataset can be determined, and the maximum value v_max_max of all historical running data in each dataset can be determined, and then the ratio between the maximum value and the average value can be determined. Then, the average value, maximum value, and ratio between the maximum value and the average value of all historical operating data in each dataset can be combined to trigger the execution of an anomaly warning method for the stability status of the hydropower unit under all operating conditions. For details, please refer to the following embodiments, which will not be repeated here.

[0086] S308: Based on the differences, ratios, averages, and maximum values ​​of the operating data, determine the data health value of the hydropower unit when it is operating with the target head and target active power corresponding to the data set.

[0087] In this embodiment of the disclosure, the average value and the maximum value of all historical operating data in each data set are determined, and the ratio between the maximum value and the average value is determined. Then, based on the difference in operating data, the ratio, the average value and the maximum value, the data health value of the hydropower unit is determined when it is running with the target head and target active power corresponding to the data set.

[0088] Optionally, in some embodiments, the data health value of the hydropower unit is determined based on the difference, ratio, average, and maximum value of the operating data. This can be achieved by using the sum of the maximum value and the difference of the operating data as the data health value when the ratio is greater than or equal to the ratio threshold, or by determining the product between the average value and the ratio threshold when the ratio is less than the ratio threshold, and using the sum of the product and the difference of the operating data as the data health value.

[0089] The ratio threshold can be, for example, 110%, and there is no restriction on it.

[0090] In other words, in the embodiments of this disclosure, it can be in When the ratio is greater than or equal to the threshold, the sum of the maximum value and the difference between the running data is used as the data health value. When the value is less than the ratio threshold, the product between the average value and the ratio threshold is determined, and the sum of the product and the difference in the running data is taken as the data health value.

[0091] S309: Provide early warning of abnormal conditions for hydropower units based on data health values.

[0092] For a detailed description of S309, please refer to the above embodiments, which will not be repeated here.

[0093] In this embodiment, historical operating data of the hydropower unit in a stable state is acquired, and the target head, target active power, and rated power of the unit corresponding to different historical operating data are determined. Then, based on a preset head interval, the initial data range from the minimum to the maximum target head is divided into multiple first data ranges. Each first data range is further divided into multiple second data ranges using a preset multiple of the unit's rated power as the power interval. Multiple historical operating data are then divided according to the second data ranges to obtain multiple data sets. Finally, multiple historical rows of data in the data sets are sorted based on the data generation time corresponding to the historical operating data, and the last historical operating data and the first historical row of data in the sorted data set are determined. The difference between operating data is used to determine the average and maximum values ​​of all historical operating data in each data set, and the ratio between the maximum and average values ​​is determined. Based on the difference, ratio, average, and maximum values, the data health value of the hydropower unit is determined when it is operating at the target head and target active power corresponding to the data set. Based on the data health value, abnormal state warnings are issued for the hydropower unit. Since the target health value is consistent with the current operating condition of head and active power, the impact of operating condition differences on the abnormal warning of the hydropower unit's overall stability can be eliminated. This can meet the abnormal warning requirements of the hydropower unit's overall stability under all operating conditions (head-active power), effectively improving the abnormal warning effect of the hydropower unit's overall stability.

[0094] Figure 5 This is a schematic diagram of the structure of an abnormal early warning device for the stability status of a hydropower unit under all operating conditions, as proposed in an embodiment of this disclosure.

[0095] like Figure 5 As shown, the abnormal early warning device 50 for the stability status of the hydropower unit under all operating conditions includes:

[0096] The acquisition module 501 is used to acquire historical operating data of the hydropower unit when it is in a stable state.

[0097] The first determining module 502 is used to determine the target head, target active power and rated power of the hydropower unit when it is running with different historical operating data;

[0098] The processing module 503 is used to divide multiple historical operating data into multiple data sets according to the target head, target active power and rated power of the unit corresponding to each historical operating data. Each data set has a corresponding target head and target active power.

[0099] The second determining module 504 is used to determine the data health value of the hydropower unit when it is running at the target head and target active power corresponding to the data set, based on the historical operating data in each data set. The data health value is the operating data value of the hydropower unit when it is running at the target head and target active power corresponding to the data set and is in a stable state.

[0100] The early warning module 505 is used to provide early warning of abnormal conditions of hydropower units based on data health values.

[0101] In some embodiments of this disclosure, the processing module 503 is further configured to:

[0102] Based on the preset head interval, the initial data range from the target head with the smallest value to the target head with the largest value is divided into multiple first data ranges;

[0103] The first data range is divided into multiple second data ranges by using the rated power of the unit as a preset multiple as the power interval;

[0104] The historical operational data is divided into multiple sets based on the second data range.

[0105] In some embodiments of this disclosure, the processing module 503 is further configured to:

[0106] Based on the data generation time corresponding to the historical operation data, multiple historical operation data are sorted to obtain the operation data sequence;

[0107] The running data sequence is divided into multiple running data subsequences, where each running data subsequence contains a preset number of historical running data.

[0108] If the power difference between the target active power corresponding to the last historical running data in the running data subsequence and the target active power corresponding to the first historical running data is less than the power difference threshold, then the historical running data falling within the second data range in the running data subsequence will be determined as a data set.

[0109] In some embodiments of this disclosure, the second determining module 504 is further configured to:

[0110] Based on the data generation time corresponding to the historical running data, sort multiple historical rows of data in the data set, and determine the running data difference between the last historical running data and the first historical running data in the sorted data set.

[0111] Determine the average and maximum values ​​of all historical running data in each dataset, and determine the ratio between the maximum value and the average value;

[0112] Based on the differences, ratios, averages, and maximum values ​​of the operating data, the data health value of the hydropower unit is determined when it is operating at the target head and target active power corresponding to the data set.

[0113] In some embodiments of this disclosure, the second determining module 504 is further configured to:

[0114] If the ratio is greater than or equal to the ratio threshold, the sum of the maximum value and the difference in running data will be used as the data health value.

[0115] If the ratio is less than the ratio threshold, the product between the average and the ratio threshold is determined, and the sum of the product and the difference in the running data is taken as the data health value.

[0116] In some embodiments of this disclosure, the warning module 505 is further configured to:

[0117] Acquire the current operating data of the hydropower unit in its current operating state, including the current head and current active power.

[0118] Based on the current head and current active power, determine the target health value corresponding to the current operating data. The target health value is the operating data value of the hydropower unit that can operate in a stable state with the current head and current active power.

[0119] Determine the equipment identifier corresponding to the current operating data, wherein the equipment identifier is used to identify the hydropower unit equipment corresponding to the current operating data;

[0120] If the current operating data is greater than the target health value, an abnormal status warning for the hydropower unit equipment will be generated.

[0121] In some embodiments of this disclosure, the acquisition module 501 is further configured to:

[0122] Acquire the initial operating data and rated power of the hydropower unit when it is in a stable state;

[0123] Determine the initial head and initial active power corresponding to the initial operating data;

[0124] Historical operating data are determined from multiple initial operating data based on rated power, initial head, and initial active power.

[0125] In some embodiments of this disclosure, the acquisition module 501 is further configured to:

[0126] The initial operating data is obtained by deleting the initial operating data corresponding to the largest initial head, the initial operating data corresponding to the smallest initial head, the initial operating data with an initial active power greater than the rated power, and the initial operating data with an initial active power less than zero from multiple initial operating data.

[0127] With the above Figures 1 to 4 Corresponding to the abnormal early warning method for the stability state of a hydropower unit under all operating conditions provided in the embodiments, this disclosure also provides an abnormal early warning device for the stability state of a hydropower unit under all operating conditions. Since the abnormal early warning device for the stability state of a hydropower unit under all operating conditions provided in the embodiments of this disclosure is similar to the one described above... Figures 1 to 4 The abnormal early warning method for the stability status of a hydropower unit under all operating conditions provided in the embodiments corresponds to the method for abnormal early warning of the stability status of a hydropower unit under all operating conditions provided in the embodiments of this disclosure. Therefore, the implementation method for abnormal early warning of the stability status of a hydropower unit under all operating conditions is also applicable to the abnormal early warning device for the stability status of a hydropower unit under all operating conditions provided in the embodiments of this disclosure. It will not be described in detail in the embodiments of this disclosure.

[0128] In this embodiment, by acquiring historical operating data of the hydropower unit when it is in a stable state, the target head, target active power, and rated power of the hydropower unit corresponding to different historical operating data are determined. Based on the target head, target active power, and rated power corresponding to each historical operating data point, multiple historical operating data are divided into multiple data sets. Each data set has a corresponding target head and target active power. Then, based on the historical operating data in each data set, the data health value of the hydropower unit when operating at the target head and target active power corresponding to the data set is determined. The data health value is the operating data value of the hydropower unit operating at the target head and target active power corresponding to the data set and in a stable state. Based on the data health value, abnormal state warnings are issued for the hydropower unit. This allows for accurate determination of the data health value of the hydropower unit when operating at different target heads and target active power, enabling targeted abnormal state warnings for hydropower units operating at different heads and active power, thereby effectively improving the abnormal warning effect of the hydropower unit's overall stability.

[0129] To implement the above embodiments, this disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the abnormal early warning method for the stability status of a hydropower unit under all operating conditions as proposed in the foregoing embodiments of this disclosure.

[0130] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements an abnormal early warning method for the stability state of a hydropower unit under all operating conditions as proposed in the foregoing embodiments of this disclosure.

[0131] To implement the above embodiments, this disclosure also proposes a computer program product that, when the instruction processor in the computer program product is executed, performs an abnormal early warning method for the stability status of a hydropower unit under all operating conditions as proposed in the foregoing embodiments of this disclosure.

[0132] Figure 6 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0133] like Figure 6 As shown, the electronic device is represented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0134] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0135] Electronic devices typically include a variety of computer-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, and removable and non-removable media.

[0136] 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 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 6 Not shown; usually referred to as a "hard drive".

[0137] although Figure 6 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0138] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0139] The electronic device can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the electronic device, and / or with any device that enables the electronic device to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, the electronic device can also communicate with one or more networks (e.g., Local Area Network (LAN), Wide Area Network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of the electronic device via bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0140] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the abnormal early warning method for the stability state of the hydropower unit under all operating conditions mentioned in the foregoing embodiments.

[0141] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0142] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

[0143] It should be noted that in the description of this disclosure, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0144] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0145] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0146] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0147] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0148] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0149] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0150] Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for abnormal early warning of a full-load stability state of a hydroelectric generating unit, characterized in that, The method includes: Acquire historical operating data of hydropower units when they are in a stable state; Determine the target head, target active power, and rated power of the hydropower unit when it operates with different historical operating data; Based on the target head, target active power, and rated power of the unit corresponding to each of the historical operating data, the multiple historical operating data are divided into multiple data sets, wherein each data set has a corresponding target head and target active power; Based on the data generation time corresponding to the historical operating data, multiple historical rows of data in the data set are sorted, and the operating data difference between the last historical operating data and the first historical operating data in the sorted data set is determined; the average value and the maximum value of all historical operating data in each data set are determined, and the ratio between the maximum value and the average value is determined; if the ratio is greater than or equal to a ratio threshold, the sum of the maximum value and the operating data difference is taken as the data health value of the hydropower unit when operating at the target head and the target active power corresponding to the data set; if the ratio is less than the ratio threshold, the product between the average value and the ratio threshold is determined, and the sum of the product and the operating data difference is taken as the data health value, wherein the data health value is the operating data value of the hydropower unit when operating at the target head and the target active power corresponding to the data set and being able to be in the stable state; Based on the data health value, an abnormal state warning is issued for the hydropower unit.

2. The method of claim 1, wherein, The method involves dividing the historical operating data into multiple data sets based on the target head, target active power, and rated power of the unit corresponding to each historical operating data set, including: Based on a preset head interval, the initial data range from the target head with the smallest value to the target head with the largest value is divided into multiple first data ranges; The first data range is divided into multiple second data ranges by using the rated power of the unit as a preset multiple as the power interval; The historical operation data is divided into multiple sets according to the second data range.

3. The method of claim 2, wherein, The step of dividing the historical operational data into multiple sets based on the second data range to obtain multiple data sets includes: Based on the data generation time corresponding to the historical operation data, sort multiple historical operation data to obtain an operation data sequence; The running data sequence is divided into multiple running data subsequences, wherein each running data subsequence contains a preset number of historical running data; If the power difference between the target active power corresponding to the last historical operating data in the operating data subsequence and the target active power corresponding to the first historical operating data is less than the power difference threshold, then the historical operating data falling within the second data range in the operating data subsequence is determined as a data set.

4. The method of claim 1, wherein, The method of providing early warning of abnormal states for the hydropower unit based on the data health value includes: Obtain the current operating data of the hydropower unit in its current operating state, including the current head and current active power. Based on the current water head and the current active power, a target health value corresponding to the current operating data is determined, wherein the target health value is the operating data value of the hydropower unit operating at the current water head and the current active power and being able to be in the stable state; Determine the device identifier corresponding to the current operating data, wherein the device identifier is used to identify the hydropower unit equipment corresponding to the current operating data; If the current operating data is greater than the target health value, an abnormal status warning for the hydropower unit equipment is generated.

5. The method of claim 1, wherein, The acquisition of historical operating data of the hydropower unit when it is in a stable state includes: Obtain the initial operating data and rated power of the hydropower unit when it is in a stable state; Determine the initial head and initial active power corresponding to the initial operating data; Based on the rated power, the initial head, and the initial active power, the historical operating data is determined from a plurality of initial operating data.

6. The method of claim 5, wherein, The step of determining the historical operating data from multiple initial operating data based on the rated power, the initial head, and the initial active power includes: The historical operating data is obtained by deleting the initial operating data corresponding to the largest initial head, the initial operating data corresponding to the smallest initial head, the initial operating data where the initial active power is greater than the rated power, and the initial operating data where the initial active power is less than zero from the plurality of initial operating data.

7. A device for abnormal early warning of the stability state of a hydroelectric generating unit in all operating conditions, characterized in that it comprises: The device includes: The acquisition module is used to acquire historical operating data of the hydropower unit when it is in a stable state. The first determining module is used to determine the target head, target active power and rated power of the hydropower unit when it is running with different historical operating data. The processing module is used to divide multiple historical operating data into multiple data sets according to the target head, the target active power and the rated power of the unit corresponding to each historical operating data, wherein each data set has a corresponding target head and target active power; The second determining module is used to sort multiple historical rows of data in the data set based on the data generation time corresponding to the historical operating data, and determine the operating data difference between the last historical operating data and the first historical operating data in the sorted data set; determine the average and maximum values ​​of all historical operating data in each data set, and determine the ratio between the maximum value and the average value; if the ratio is greater than or equal to a ratio threshold, the sum of the maximum value and the operating data difference is used as the data health value of the hydropower unit when operating at the target head and the target active power corresponding to the data set; if the ratio is less than the ratio threshold, the product between the average value and the ratio threshold is determined, and the sum of the product and the operating data difference is used as the data health value, wherein the data health value is the operating data value of the hydropower unit when operating at the target head and the target active power corresponding to the data set and being able to be in the stable state; The early warning module is used to provide early warning of abnormal conditions of the hydropower unit based on the data health value.

8. An electronic device, comprising: 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 to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

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