A method, device and equipment for analyzing operation data of a pumped storage power station

Abstract

The application discloses a kind of pumped storage equipment operation data analysis method, device and equipment, comprising: obtaining the multiple characteristic quantity data generated in the operation process of pumped storage equipment, multiple characteristic quantity data are preprocessed, and according to the multiple characteristic quantity data after preprocessing, determine the characteristic quantity curve corresponding to pumped storage equipment, if the maximum and minimum of characteristic quantity curve corresponding satisfy preset interval, and the average value of characteristic quantity curve corresponding is in preset range, then according to multiple characteristic quantity data, determine the smoothness of characteristic quantity curve corresponding, according to the smoothness of characteristic quantity curve corresponding, determine the characteristic quantity data analysis result corresponding to pumped storage equipment.The technical scheme of the embodiment of the application can improve the accuracy of pumped storage equipment operation data analysis result.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, apparatus, equipment, and storage medium for analyzing operational data of pumped storage equipment. Background Technology

[0002] Pumped storage hydroelectric power grids are responsible for peak shaving, valley filling, frequency regulation, phase regulation, emergency backup, black start, and system capacity reserve. During operation, pumped storage hydroelectric power grids may experience malfunctions. To prevent damage to other equipment in the power grid, analyzing the operational data of these systems is crucial.

[0003] In existing technologies, when analyzing the operating data of pumped storage equipment, it is usually only necessary to determine whether the characteristic data corresponding to the pumped storage equipment meets the preset data range. If so, it is assumed that the current characteristic data of the pumped storage equipment will not pose a danger.

[0004] However, existing technologies cannot determine the degree of change in feature data. If some feature data changes too drastically, there is a risk of exceeding the upper and lower limits of the data. Secondly, in existing technologies, the upper and lower limits of the data range are determined by only a single data point, resulting in a large error in the data analysis results. Summary of the Invention

[0005] This invention provides a method, apparatus, and equipment for analyzing the operational data of pumped storage equipment, which can improve the accuracy of the analysis results.

[0006] According to one aspect of the present invention, a method for analyzing operational data of a pumped storage hydroelectric power plant is provided, the method comprising:

[0007] Multiple characteristic data generated during the operation of the pumped storage equipment are acquired, the multiple characteristic data are preprocessed, and the characteristic curve corresponding to the pumped storage equipment is determined based on the preprocessed multiple characteristic data.

[0008] If the maximum and minimum values ​​corresponding to the feature quantity curve satisfy a preset interval, and the average value corresponding to the feature quantity curve is within a preset range, then the stability of the feature quantity curve is determined based on the multiple feature quantity data.

[0009] Based on the stability of the characteristic quantity curve, the characteristic quantity data analysis results corresponding to the pumped storage equipment are determined.

[0010] According to another aspect of the present invention, an operational data analysis device for a pumped storage hydroelectric power plant is provided, the device comprising:

[0011] The data acquisition module is used to acquire multiple feature quantity data generated during the operation of the pumped storage equipment, preprocess the multiple feature quantity data, and determine the feature quantity curve corresponding to the pumped storage equipment based on the preprocessed multiple feature quantity data.

[0012] The stability determination module is used to determine the stability of the feature curve based on the multiple feature data if the maximum and minimum values ​​corresponding to the feature curve meet a preset interval and the average value corresponding to the feature curve is within a preset range.

[0013] The data analysis module is used to determine the characteristic quantity data analysis results corresponding to the pumped storage equipment based on the stability of the characteristic quantity curve.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the operation data analysis method for pumped storage equipment according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the operation data analysis method for pumped storage equipment as described in any embodiment of the present invention.

[0019] The technical solution provided by this invention involves acquiring multiple characteristic data points generated during the operation of a pumped-storage hydropower station, preprocessing these data points, and determining a characteristic curve corresponding to the pumped-storage hydropower station based on the preprocessed data. If the maximum and minimum values ​​of the characteristic curves satisfy a preset interval, and the average value of the characteristic curves is within a preset range, then the stability of the characteristic curves is determined based on the data. This method of determining the characteristic data analysis results corresponding to the pumped-storage hydropower station based on the stability of the characteristic curves improves the accuracy of the pumped-storage hydropower station operation data analysis results.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for analyzing the operation data of a pumped storage device according to an embodiment of the present invention;

[0023] Figure 2 This is a flowchart of another method for analyzing the operating data of a pumped storage device according to an embodiment of the present invention;

[0024] Figure 3 This is a flowchart of another method for analyzing the operating data of a pumped storage device according to an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of an operation data analysis device for pumped storage equipment according to an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device for implementing the operation data analysis method of the pumped storage equipment according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Figure 1 This is a flowchart illustrating a method for analyzing operational data of a pumped-storage hydroelectric power plant according to Embodiment 1 of the present invention. This embodiment is applicable to situations involving the analysis of operational data from pumped-storage hydroelectric power plants. The method can be executed by an operational data analysis device for the pumped-storage hydroelectric power plant, which can be implemented in hardware and / or software. This device can be configured in an electronic device (e.g., a terminal or server) with data processing capabilities. Figure 1 As shown, the method includes:

[0030] Step 110: Obtain multiple feature quantity data generated during the operation of the pumped storage equipment; preprocess the multiple feature quantity data based on the multiple feature quantity data; and determine the feature quantity curve corresponding to the pumped storage equipment based on the preprocessed multiple feature quantity data.

[0031] In this step, multiple characteristic data points corresponding to different times during the operation of the pumped storage equipment can be obtained, and the multiple characteristic data points can be preprocessed (e.g., denoising, normalization, etc.). Then, based on the preprocessed multiple characteristic data points, the characteristic curve corresponding to the pumped storage equipment can be determined.

[0032] Step 120: If the maximum and minimum values ​​corresponding to the feature quantity curves satisfy a preset interval, and the average value corresponding to the feature quantity curves is within a preset range, then determine the stability of the feature quantity curves based on the multiple feature quantity data.

[0033] In this embodiment, after determining the characteristic quantity curve corresponding to the pumped storage device, the maximum and minimum values ​​corresponding to the characteristic quantity curve can be obtained, and the maximum value is compared with the upper limit of the preset interval, and the minimum value is compared with the lower limit of the preset interval. If the maximum and minimum values ​​corresponding to the characteristic quantity curve satisfy the preset interval, and the average value corresponding to the characteristic quantity curve is within the preset range, then the stability of the characteristic quantity curve is determined based on multiple characteristic quantity data.

[0034] In one specific implementation, optionally, the degree of change corresponding to the feature curve can be determined based on the difference between the feature data, and the feature curve can be judged whether it has a trend of breaking through the upper and lower limits of the preset range based on the degree of change. Finally, the stability of the feature curve can be determined by combining the judgment result and the degree of change.

[0035] Step 130: Determine the characteristic quantity data analysis results corresponding to the pumped storage equipment based on the stability of the characteristic quantity curve.

[0036] In this embodiment, optionally, after determining the stability of the characteristic quantity curve, if the stability is high, the characteristic quantity data analysis result can be determined to be normal, that is, the operating data of the pumped storage equipment does not have the risk of exceeding the upper and lower limits of the range; conversely, if the stability is low, the characteristic quantity data analysis result can be determined to be abnormal, that is, the operating data of the pumped storage equipment changes drastically and there is a risk of exceeding the upper and lower limits of the range.

[0037] The technical solution provided by this invention involves acquiring multiple characteristic data points generated during the operation of a pumped-storage hydropower station, preprocessing these data points, and determining a characteristic curve corresponding to the pumped-storage hydropower station based on the preprocessed data. If the maximum and minimum values ​​of the characteristic curves satisfy a preset interval, and the average value of the characteristic curves is within a preset range, then the stability of the characteristic curves is determined based on the data. This method of determining the characteristic data analysis results corresponding to the pumped-storage hydropower station based on the stability of the characteristic curves improves the accuracy of the pumped-storage hydropower station operation data analysis results.

[0038] Figure 2 This is a flowchart illustrating a method for analyzing operational data of a pumped storage device according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above embodiment. Figure 2 As shown, the method includes:

[0039] Step 210: Obtain multiple feature quantity data generated during the operation of the pumped storage equipment, preprocess the multiple feature quantity data, and determine the feature quantity curve corresponding to the pumped storage equipment based on the preprocessed multiple feature quantity data.

[0040] In one embodiment of this example, preprocessing the plurality of feature data includes: filtering out abnormal feature data from the plurality of feature data and removing the abnormal feature data; if the plurality of feature data includes missing values, then supplementing the missing values.

[0041] In this embodiment, filtering abnormal feature data from the plurality of feature data includes: dividing the plurality of feature data into a plurality of data groups according to the corresponding acquisition time; obtaining the maximum and minimum values ​​corresponding to each data group; and filtering abnormal feature data from the plurality of feature data based on the number of each maximum and minimum value in the corresponding data group and adjacent data groups, and the difference between the maximum and minimum value and the second maximum and minimum value in the corresponding data group.

[0042] In one specific embodiment, optionally, multiple feature data can be divided into multiple data groups according to a preset duration and the acquisition time corresponding to each feature data, and then the maximum and minimum values ​​corresponding to each data group can be obtained. If the number of extreme values ​​(maximum or minimum values) in a certain data group is less than a preset number threshold in the data group and adjacent data groups, and the difference between the extreme value and the second extreme value in the data group is greater than a preset multiple of the range (i.e., the difference between the maximum and minimum values), then the extreme value can be regarded as abnormal feature data.

[0043] Optionally, the preset quantity threshold can be 3, and the preset multiple can be 1 / 10. The specific values ​​can be adjusted according to the actual situation, and this embodiment does not impose any restrictions on them.

[0044] In another embodiment of this example, when filling in missing values, multiple feature data adjacent to the missing value can be selected as reference values, and a fitting curve can be generated based on the multiple reference values. Finally, the missing value is filled in based on the fitting curve.

[0045] Step 220: If the maximum and minimum values ​​corresponding to the feature quantity curves satisfy a preset interval, and the average value corresponding to the feature quantity curves is within a preset range, then obtain the average value corresponding to the multiple feature quantity data and the slope of the trend line corresponding to the feature quantity curves.

[0046] In this step, optionally, a trend line corresponding to the characteristic curve can be fitted based on the peak points in the characteristic curve, and the slope of the trend line can be calculated.

[0047] Step 230: Determine the difference between the average value and the preset average value, and determine whether the feature curve is a safe curve based on the product of the difference and the slope.

[0048] In this embodiment, the preset mean can be a specific numerical range, such as [μ a μ b In this step, the difference between the average value and the maximum and minimum values ​​in the range can be calculated, and the product of the difference and the slope can be calculated.

[0049] In a specific embodiment, if the product is greater than a preset threshold within a preset time period, the feature curve can be considered a dangerous curve; conversely, if the product is less than or equal to the preset threshold within a preset time period, the feature curve can be considered a safe curve.

[0050] Step 240: If the feature curve is a safe curve, then determine the stability of the feature curve based on the multiple feature data.

[0051] Step 250: Determine the characteristic quantity data analysis results corresponding to the pumped storage equipment based on the stability of the characteristic quantity curve.

[0052] The technical solution provided by this invention acquires multiple characteristic data generated during the operation of a pumped storage power station, preprocesses the multiple characteristic data, and determines the characteristic curve corresponding to the pumped storage power station based on the preprocessed multiple characteristic data. If the maximum and minimum values ​​of the characteristic curves meet a preset interval, and the average value of the characteristic curves is within a preset range, then the average value of the multiple characteristic data and the slope of the characteristic curves are acquired. The difference between the average value and the preset mean is determined, and the product of the difference and the slope is used to determine whether the characteristic curve is a safe curve. If so, the stability of the characteristic curves is determined based on the multiple characteristic data. The technical means of determining the characteristic data analysis results corresponding to the pumped storage power station based on the stability of the characteristic curves is compared with the prior art, which only judges whether the characteristic data corresponding to the pumped storage power station meets the preset data range. This method focuses on the volatility of the pumped storage characteristic curves, assesses which curves are more stable, and which curves are more likely to break through the upper and lower limits, thereby improving the accuracy of the pumped storage power station operation data analysis results.

[0053] Figure 3 This is a flowchart of a method for analyzing operational data of a pumped storage device according to Embodiment 3 of the present invention. This embodiment is a further refinement of the above embodiment. Figure 3 As shown, the method includes:

[0054] Step 310: Obtain multiple feature quantity data generated during the operation of the pumped storage equipment, preprocess the multiple feature quantity data, and determine the feature quantity curve corresponding to the pumped storage equipment based on the preprocessed multiple feature quantity data.

[0055] Step 320: If the maximum and minimum values ​​corresponding to the feature quantity curves satisfy a preset interval, and the average value corresponding to the feature quantity curves is within a preset range, then determine the dispersion, upper mean, and lower mean corresponding to the feature quantity curves based on the multiple feature quantity data.

[0056] In this embodiment, multiple feature data points above the average value in the feature curve can be obtained, and the upper mean can be calculated based on these multiple feature data points. Similarly, multiple feature data points below the average value in the feature curve can also be obtained, and the lower mean can be calculated based on these multiple feature data points.

[0057] Optionally, in this step, the proportion of each feature data point in all feature data points can be calculated, and based on the calculation results of the corresponding proportions of each feature data point, a distribution curve corresponding to the pumped storage equipment can be plotted. The dispersion of the feature curve can then be determined based on the distribution curve. Specifically, if the peak value of the distribution curve is higher, it indicates that the data set has a larger mode, the data is more concentrated, and the dispersion is lower.

[0058] In one embodiment of this example, determining the dispersion of the feature curve based on the plurality of feature data includes: determining the difference between each feature data and the average value; and determining the dispersion of the feature curve based on each difference and a preset weight corresponding to each feature data.

[0059] In a specific embodiment, it is assumed that the pumped storage device generates n characteristic data T during operation. i If i∈[l,n], then the average value μ corresponding to the n feature data can be calculated. i :

[0060]

[0061] Then, the multiple feature data are divided into multiple data groups according to a preset number (assuming it is 10), and the dispersion D corresponding to each data group is calculated using the following formula. i :

[0062]

[0063] Here, is the preset weight, which can be set to 1 / 10. The specific value can be adjusted according to the actual situation, and this embodiment does not impose any restrictions on it.

[0064] In this embodiment, the dispersion (D1, D2, D3...D) corresponding to multiple data groups is calculated using the above method. n After that, the multiple discrete values ​​can be weighted and summed according to their corresponding weights to obtain the discrete value D corresponding to the feature curve, as shown in the following formula:

[0065] D = D1*w1 + D2*w2 + D3*w3 + ... + D n *w n

[0066] Among them, since the characteristic data of the pumped storage equipment in the early stage has little impact on the data in the later stage of the curve, while the data of the pumped storage equipment in the later stage has a greater impact on the current change of the curve, the preset weights corresponding to each characteristic data in the characteristic curve gradually increase.

[0067] In one specific embodiment, the preset weight w corresponding to each data group i It can exhibit arithmetic or geometric progressions. For example: w n =w n-1 +d, or w n =αw n-1 Where d and a are constants, w1 + w2 + ... + w n =1.

[0068] In this embodiment, the dispersion can be used to represent the degree of divergence between the characteristic data of the pumped storage device and the average value of the characteristic data.

[0069] Step 330: Determine the distance threshold corresponding to the feature curve based on the average value corresponding to the multiple feature data and the distance between the average value and the upper and lower average values ​​respectively.

[0070] In this embodiment, the distance threshold can be used to reflect the degree of change in the feature quantity curve.

[0071] In a specific embodiment, it is assumed that the average value of multiple feature data is u. i The upper mean of the characteristic curve is u. + The lower mean is u - The distance threshold Δ corresponding to the feature curve can be calculated using the following formula:

[0072]

[0073] Step 340: Determine the stationarity of the feature curve based on the dispersion and distance threshold corresponding to the feature curve.

[0074] In this step, optionally, the discreteness and distance threshold corresponding to the feature curve can be processed linearly or nonlinearly to obtain the stationarity corresponding to the feature curve.

[0075] In one embodiment of this example, determining the stationarity of the feature curve based on its dispersion and distance threshold includes: weighting and summing the dispersion and distance threshold of the feature curve according to the target weights corresponding to the dispersion and distance threshold, respectively, to obtain the stationarity of the feature curve.

[0076] In a specific embodiment, assuming the target weight corresponding to the dispersion D is α and the target weight corresponding to the distance threshold Δ is b, the stationarity ∑ corresponding to the feature curve can be calculated using the following formula. j :

[0077] ∑ j =αD+bΔ

[0078] Where α+b=1, optional, both a and b can be 0.5, the specific values ​​can be preset according to the actual situation, and this embodiment does not limit them.

[0079] Step 350: Compare the stability of the feature curve with a preset stability threshold.

[0080] Step 360: Based on the comparison results, determine the characteristic data analysis results corresponding to the pumped storage equipment.

[0081] In this embodiment, optionally, if the stationarity corresponding to the feature curve is greater than or equal to a preset stationarity threshold, the feature data analysis result can be determined to be normal; conversely, if the stationarity corresponding to the feature curve is less than the preset stationarity threshold, the feature data analysis result can be determined to be abnormal.

[0082] The advantage of this setup is that the results of characteristic data analysis can be intuitively determined based on data comparison. Specifically, the stability threshold can be determined based on the materials of the pumped storage equipment and its environment.

[0083] Based on the above implementation method, after determining the stationarity A corresponding to the characteristic curve, it is also possible to determine the stationarity A and the average value μ corresponding to the characteristic curve. i and the preset mean [μ] a μ b The safety value corresponding to the characteristic curve is calculated using the following formula.

[0084]

[0085] The technical solution provided by this invention acquires multiple characteristic data generated during the operation of a pumped storage hydropower station, preprocesses these data, and determines a characteristic curve corresponding to the station based on the preprocessed data. If the maximum and minimum values ​​of the characteristic curve satisfy a preset interval and the average value is within a preset range, the dispersion, upper mean, and lower mean of the characteristic curve are determined based on the data. A distance threshold is also determined for the characteristic curve. Based on the dispersion and distance threshold, the stability of the characteristic curve is determined. The stability is compared with a preset stability threshold, and the analysis results of the characteristic data corresponding to the pumped storage hydropower station are determined based on the comparison results. Compared to existing technologies that only determine whether the characteristic data of the pumped storage hydropower station meets a preset data range, this method emphasizes the volatility of the characteristic curves, assesses which curves are more stable, and identifies which curves are more likely to exceed their upper and lower limits. This improves the accuracy of the analysis results of the pumped storage hydropower station's operating data.

[0086] Figure 4 This is a schematic diagram of the structure of an operation data analysis device for a pumped storage power plant provided in Embodiment 4 of the present invention, as shown below. Figure 4 As shown, the device includes: a data acquisition module 410, a stability determination module 420, and a data analysis module 430.

[0087] The data acquisition module 410 is used to acquire multiple feature data generated during the operation of the pumped storage equipment, preprocess the multiple feature data, and determine the feature curve corresponding to the pumped storage equipment based on the preprocessed multiple feature data.

[0088] The stability determination module 420 is used to determine the stability of the feature curve based on the multiple feature data if the maximum and minimum values ​​corresponding to the feature curve meet a preset interval and the average value corresponding to the feature curve is within a preset range.

[0089] The data analysis module 430 is used to determine the characteristic quantity data analysis results corresponding to the pumped storage equipment based on the stability of the characteristic quantity curve.

[0090] The technical solution provided by this invention involves acquiring multiple characteristic data points generated during the operation of a pumped-storage hydropower station, preprocessing these data points, and determining a characteristic curve corresponding to the pumped-storage hydropower station based on the preprocessed data. If the maximum and minimum values ​​of the characteristic curves satisfy a preset interval, and the average value of the characteristic curves is within a preset range, then the stability of the characteristic curves is determined based on the data. This method of determining the characteristic data analysis results corresponding to the pumped-storage hydropower station based on the stability of the characteristic curves improves the accuracy of the pumped-storage hydropower station operation data analysis results.

[0091] Based on the above embodiments, the data acquisition module 410 includes:

[0092] An abnormal data removal unit is used to filter abnormal feature data from the plurality of feature data and remove the abnormal feature data.

[0093] A missing value supplementation unit is used to supplement missing values ​​if the plurality of feature data includes missing values.

[0094] The data group partitioning unit is used to divide the multiple feature data into multiple data groups according to the corresponding acquisition time.

[0095] An abnormal data filtering unit is used to obtain the maximum and minimum values ​​corresponding to each data group, and to filter abnormal feature data from the multiple feature data based on the number of each maximum and minimum value in the corresponding data group and adjacent data groups, and the difference between the maximum and minimum value and the second maximum and minimum value in the corresponding data group.

[0096] The stability determination module 420 includes:

[0097] The slope acquisition unit is used to acquire the average value corresponding to the multiple feature quantity data and the slope of the trend line corresponding to the feature quantity curve.

[0098] The curve judgment unit is used to determine the difference between the average value and the preset average value, and to determine whether the feature curve is a safe curve based on the product of the difference and the slope; if so, it determines the stability of the feature curve based on the multiple feature data.

[0099] The dispersion determination unit is used to determine the dispersion, upper mean, and lower mean of the feature curve based on the multiple feature data.

[0100] The distance threshold determination unit is used to determine the distance threshold corresponding to the feature curve based on the distance between the average value corresponding to the multiple feature data and the upper and lower average values, respectively.

[0101] The stability acquisition unit is used to determine the stability of the feature curve based on the dispersion and distance threshold corresponding to the feature curve.

[0102] A difference determination unit is used to determine the difference between each of the feature data and the average value;

[0103] The difference processing unit is used to determine the dispersion of the feature curve based on each difference and a preset weight corresponding to each feature data.

[0104] The data analysis module 430 includes:

[0105] The stability comparison unit is used to compare the stability corresponding to the feature curve with a preset stability threshold.

[0106] The analysis result determination unit is used to determine the characteristic quantity data analysis results corresponding to the pumped storage equipment based on the comparison results.

[0107] The above-described apparatus can execute the methods provided in all the foregoing embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the above methods. Technical details not described in detail in the embodiments of the present invention can be found in the methods provided in all the foregoing embodiments of the present invention.

[0108] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0109] like Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0110] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0111] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for analyzing operational data of pumped storage facilities.

[0112] In some embodiments, the pumped-storage hydroelectric power plant operation data analysis method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the pumped-storage hydroelectric power plant operation data analysis method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the pumped-storage hydroelectric power plant operation data analysis method by any other suitable means (e.g., by means of firmware).

[0113] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0115] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0116] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0117] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0118] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0119] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0120] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for analyzing operational data of a pumped storage hydroelectric power plant, characterized in that, The method includes: Multiple characteristic data generated during the operation of the pumped storage equipment are acquired, the multiple characteristic data are preprocessed, and the characteristic curve corresponding to the pumped storage equipment is determined based on the preprocessed multiple characteristic data. If the maximum and minimum values ​​corresponding to the feature quantity curve satisfy a preset interval, and the average value corresponding to the feature quantity curve is within a preset range, then the stability of the feature quantity curve is determined based on the multiple feature quantity data. Based on the stability of the characteristic quantity curve, determine the characteristic quantity data analysis results corresponding to the pumped storage equipment; Based on the multiple feature data, the stationarity corresponding to the feature curve is determined, including: Obtain the average value corresponding to the multiple feature data, and the slope of the trend line corresponding to the feature curve; Determine the difference between the average value and the preset average value, and determine whether the feature curve is a safe curve based on the product of the difference and the slope. If so, then the stability of the feature curve is determined based on the multiple feature data. Based on the multiple feature data, the stationarity corresponding to the feature curve is determined, including: Based on the multiple feature data, determine the dispersion, upper mean, and lower mean corresponding to the feature curve; Based on the average value corresponding to the multiple feature data, the distance threshold corresponding to the feature curve is determined by the distance between the average value and the upper and lower average values, respectively. The stationarity of the feature curve is determined based on the dispersion and distance threshold corresponding to the feature curve.

2. The method according to claim 1, characterized in that, Preprocessing of the multiple feature data includes: Filter out abnormal feature data from the multiple feature data, and remove the abnormal feature data; If the multiple feature data include missing values, then the missing values ​​are supplemented.

3. The method according to claim 2, characterized in that, Filtering out anomalous feature data from the plurality of feature data includes: The multiple feature data are divided into multiple data groups according to their corresponding acquisition times; Obtain the maximum and minimum values ​​corresponding to each data group, and filter out abnormal feature data from the multiple feature data based on the number of each maximum and minimum value in the corresponding data group and adjacent data groups, and the difference between the maximum and minimum value and the second maximum and minimum value in the corresponding data group.

4. The method according to claim 1, characterized in that, Based on the multiple feature quantity data, the dispersion corresponding to the feature quantity curve is determined, including: Determine the difference between each of the aforementioned feature data and the average value; The dispersion of the feature curve is determined based on the differences and the preset weights corresponding to the feature data.

5. The method according to claim 1, characterized in that, Based on the stability corresponding to the characteristic quantity curve, the characteristic quantity data analysis results corresponding to the pumped storage equipment are determined, including: The stability of the feature curve is compared with a preset stability threshold. Based on the comparison results, the characteristic quantity data analysis results corresponding to the pumped storage equipment are determined.

6. A device for analyzing the operational data of a pumped storage hydroelectric power plant, characterized in that, The device includes: The data acquisition module is used to acquire multiple feature quantity data generated during the operation of the pumped storage equipment, preprocess the multiple feature quantity data, and determine the feature quantity curve corresponding to the pumped storage equipment based on the preprocessed multiple feature quantity data. The stability determination module is used to determine the stability of the feature curve based on the multiple feature data if the maximum and minimum values ​​corresponding to the feature curve meet a preset interval and the average value corresponding to the feature curve is within a preset range. The data analysis module is used to determine the characteristic quantity data analysis results corresponding to the pumped storage equipment based on the stability of the characteristic quantity curve. The stability determination module is specifically used for: Obtain the average value corresponding to the multiple feature data, and the slope of the trend line corresponding to the feature curve; Determine the difference between the average value and the preset average value, and determine whether the feature curve is a safe curve based on the product of the difference and the slope. If so, then the stability of the feature curve is determined based on the multiple feature data. The stability determination module further includes: The dispersion determination unit is used to determine the dispersion, upper mean, and lower mean of the feature curve based on the multiple feature data. The distance threshold determination unit is used to determine the distance threshold corresponding to the feature curve based on the distance between the average value corresponding to the multiple feature data and the upper and lower average values, respectively. The stability acquisition unit is used to determine the stability of the feature curve based on the dispersion and distance threshold corresponding to the feature curve.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the operation data analysis method for the pumped storage equipment according to any one of claims 1-5.

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