Workshop local structure vibration influence factor identification method and device and medium

Abstract

The invention relates to the technical field of hydroelectric generation, and discloses a factory building local structure vibration influence factor identification method and device and a medium. Frequency analysis is performed through a target operation signal set and an operation data set, so that a first influence factor range can be preliminarily determined; a maximum information coefficient method is adopted to perform correlation analysis on the vibration response of the local structure of the plant and the mixing amplitude of each influence factor, and the effect strength of each factor on the vibration influence of the local structure of the plant can be considered from the perspective of energy. Meanwhile, a maximum information coefficient method is adopted to perform correlation analysis on the vibration response of the local structure of the factory building and the frequency division amplitude of each influence factor, and an influence factor sequence determined from the perspective of excitation and damping frequency characteristics can be obtained. Furthermore, a plurality of target influence factors of plant local structure vibration can be determined in the first influence factor range through a plurality of first maximum information coefficient values and a plurality of second maximum information coefficient values obtained through correlation analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower generation, and in particular to a method, a device and a medium for identifying factors affecting vibration of a local structure of a plant. Background Art

[0002] The local structures of the pumped-storage power station, such as floor slabs, columns, stairs, etc., will generate corresponding vibrations during the operation of the units. The local structures of the plant buildings of some power stations have large vibration responses. In severe cases, cracks or even damage to the structure may occur, threatening the safe operation of the power station. At the same time, excessive vibrations and corresponding noises also have a certain degree of impact on the health of the operation, inspection and maintenance personnel.

[0003] The pump turbine generator set is the core of the pumped storage power station. In terms of power generation conditions, water flows from the external water diversion pipe into the volute, flows through the fixed guide vanes and movable guide vanes, and the runner, and finally flows into the downstream waterway from the tailwater pipe. Due to the rotation of the runner and the structure, shape, and movement of each component, the pressure in the flow channel of each part has a certain characteristic pulsation. This pressure pulsation is the most fundamental excitation to generate force on the various structural components of the unit, and is further transmitted to the plant structure through the unit structural components connected to the plant foundation, ultimately causing the vibration of the local structure of the plant. According to the basic vibration theory, the vibration system is usually composed of excitation, transmission path, and vibrating object. Therefore, the vibration response of the local structure of the plant of the pumped storage power station is related to the pressure pulsation in the pump turbine, the damping effect of each structure on the transmission path, and its own structure, stiffness, boundary conditions, etc.

[0004] Since there are many water-forged flow channels in the pump-turbine unit, including volute, fixed guide vane, movable guide vane, runner and tailwater pipe, there are pressure pulsations with different characteristics in the area between the runner and movable guide vane, the area between the movable guide vane and the fixed guide vane, in the volute and in the tailwater pipe. These pressure pulsations can be further transmitted to the foundation and plant structure through the structural components of the pump-turbine unit. When the pressure pulsation acts on the structure, the force generated is also reacted by the structure, that is, the damping of each structure on the transmission path plays a certain role in the excitation force.

[0005] When analyzing the factors affecting the vibration response of the local structure of a plant, the general direction can be determined based on the basic theoretical analysis of vibration. The vibration source can be basically determined through frequency analysis. However, there are many links in the vibration system, and the degree of influence of each factor is different.

[0006] At present, the analysis of factors affecting plant structure vibration usually adopts vibration signal analysis method, which mainly includes frequency domain analysis, time domain analysis, wavelet analysis, etc., among which frequency domain analysis is the most commonly used. The main principle of this method is to use Fourier transform to convert time domain vibration signal into frequency domain signal, and determine the vibration source or influencing factor by analyzing the frequency components and distribution in the spectrum diagram.

[0007] Through frequency domain analysis technology, the vibration source or influencing factors can be generally determined. However, due to the large number of components and links in the vibration system, such as the main frequency components of the plant structure vibration signal appearing in the pressure pulsation and vibration signals of multiple parts, frequency domain analysis cannot further determine the size of the influencing factors, and it is also impossible to determine the key influencing factors. Summary of the invention

[0008] In view of this, the present invention provides a method, device and medium for identifying factors affecting the vibration of a local structure of a factory building, so as to solve the problem that the existing frequency domain analysis technology cannot determine the key influencing factors due to the large number of components and links in the vibration system.

[0009] In a first aspect, the present invention provides a method for identifying factors affecting vibration of a local structure of a plant, which is used in a pumped storage power station, wherein the pumped storage power station includes a pump turbine generator set and a plant structure; the method includes:

[0010] When the pumped storage power station is in operation, a target operation signal set and an operation data set are obtained, the operation signal set includes a vibration signal and a pressure pulsation signal, and the operation data set includes plant structure vibration data, unit vibration data of the pump turbine generator set, and unit pressure pulsation data; based on the target operation signal set and the operation data set, after processing by a frequency analysis method, a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set, and a first influencing factor range are obtained, and the target vibration frequency analysis data set includes unit measuring point vibration frequency analysis data and pressure pulsation frequency analysis data; based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform amplitude correlation analysis between the plant local structure vibration response and each influencing factor, and multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values ​​are obtained, and the amplitude correlation analysis includes mixed frequency amplitude correlation analysis and frequency division amplitude correlation analysis; based on the multiple first maximum information coefficient values ​​and the multiple second maximum information coefficient values, multiple target influencing factors of the plant local structure vibration are determined within the first influencing factor range.

[0011] The method for identifying factors affecting vibration of local structure of plant provided by the present invention can preliminarily determine the scope of the first influencing factor by frequency analysis of the target operation signal set and the operation data set. Further, the maximum information coefficient method is used to perform correlation analysis on the vibration response of the local structure of plant and the mixed frequency amplitude of each influencing factor, and the strength of the effect of each factor on the vibration of the local structure of plant can be considered from the energy perspective. At the same time, the maximum information coefficient method is used to perform correlation analysis on the vibration response of the local structure of plant and the frequency division amplitude of each influencing factor, and the ranking of influencing factors determined from the frequency characteristics of excitation and damping can be obtained. Further, multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values ​​obtained by correlation analysis can determine multiple target influencing factors of vibration of local structure of plant within the scope of the first influencing factor. Therefore, by implementing the present invention, the key influencing factors of vibration of local structure of plant can be accurately identified through mixed frequency amplitude correlation analysis and frequency division amplitude correlation analysis, which is more accurate than the existing technical solution, and lays the foundation and points out the direction for solving the problem of vibration of plant structure, the design of subsequent vibration reduction methods and the application of measures.

[0012] In an optional implementation, obtaining a target operation signal set includes:

[0013] Acquire an initial operation signal set; preprocess the initial operation signal set to obtain a target operation signal set.

[0014] The method for identifying factors affecting vibration of the local structure of a plant provided by the present invention improves the signal quality by preprocessing the initial operation signal set, thereby providing support for the accurate identification of key factors affecting vibration of the local structure of a plant in the subsequent process.

[0015] In an optional implementation, based on the target operation signal set and the operation data set, after being processed by a frequency analysis method, a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set and a first influencing factor range are obtained, including:

[0016] Based on the operation data set, the target operation signal set is processed by using the frequency analysis method to obtain the local structure vibration frequency analysis data set of the plant; based on the local structure vibration frequency analysis data set of the plant, the target vibration frequency analysis data set and the range of the first influencing factor are determined.

[0017] The method for identifying factors affecting the vibration of the local structure of a factory building provided by the present invention can convert the originally complex and difficult to intuitively interpret time domain signals into a frequency analysis data set that can reflect the vibration frequency characteristics by processing the target operation signal set using a frequency analysis method. Furthermore, by determining the target vibration frequency analysis data set in combination with the local structure vibration frequency analysis data set of the factory building, the vibration frequency characteristics of different locations can be more comprehensively grasped. At the same time, by determining the scope of the first influencing factor, the scope of investigation is narrowed, focusing on key suspicious influencing factors, avoiding untargeted large-scale investigations, and providing support for the accurate identification of key influencing factors of the subsequent local structure vibration of the factory building.

[0018] In an optional implementation, determining a target vibration frequency analysis dataset and a first influencing factor range based on a local structure vibration frequency analysis dataset of a plant includes:

[0019] Based on the vibration frequency analysis dataset of the local structure of the plant, the vibration source and the transmission path are determined; based on the vibration source and the transmission path, the range of the first influencing factor and the locations of multiple measuring points are determined; based on the vibration frequency analysis dataset of the local structure of the plant, the target vibration frequency analysis dataset at the locations of multiple measuring points is determined.

[0020] The method for identifying factors affecting vibration of local structures of a plant provided by the present invention determines the vibration source through the data set of vibration frequency analysis of local structures of a plant, and can accurately find out which equipment (such as water pumps, turbines, motors, etc.) or structural parts first caused the vibration, thus avoiding the waste of resources and time consumption caused by blindly checking all possible factors. Furthermore, by determining the transmission path, the diffusion range and the affected area of ​​the vibration can be determined, and then the corresponding first influencing factor range and the positions of multiple measuring points can be determined, providing support for the accurate identification of key influencing factors of the subsequent local structure vibration of the plant.

[0021] In an optional implementation, based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform amplitude correlation analysis between the plant local structure vibration response and each influencing factor, and multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values ​​are obtained, including:

[0022] Based on the local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform correlation analysis on the mixed frequency amplitude of the local structure vibration response of the plant and the mixed frequency amplitude of each influencing factor, and multiple first maximum information coefficient values ​​are obtained; based on the local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform correlation analysis on the divided frequency amplitude of the local structure vibration response of the plant and the divided frequency amplitude of each influencing factor, and multiple second maximum information coefficient values ​​are obtained.

[0023] The method for identifying factors affecting vibration of local structure of a plant provided by the present invention uses the maximum information coefficient method to perform correlation analysis on the vibration response of the local structure of the plant and the mixed frequency amplitude of each influencing factor, and can determine the strength of the influence of each factor on the vibration of the local structure of the plant from the energy perspective. At the same time, the maximum information coefficient method is used to perform correlation analysis on the vibration response of the local structure of the plant and the frequency division amplitude of each influencing factor, and the ranking of influencing factors determined from the frequency characteristics of excitation and damping can be obtained. Through two correlation analyses and determining multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values, support is provided for the subsequent accurate identification of key influencing factors of the vibration of the local structure of the plant.

[0024] In an optional implementation, based on the multiple first maximum information coefficient values ​​and the multiple second maximum information coefficient values, multiple target influencing factors of the local structural vibration of the plant are determined within the first influencing factor range, including:

[0025] Based on multiple first maximum information coefficient values, the influencing factors within the first influencing factor range are sorted to obtain a sorted second influencing factor range; based on multiple second maximum information coefficient values, the influencing factors within the first influencing factor range are sorted to obtain a sorted third influencing factor range; based on the second influencing factor range and the third influencing factor range, multiple target influencing factors of the local structural vibration of the factory building are determined.

[0026] The method for identifying factors affecting the vibration of the local structure of a plant provided by the present invention can sort the factors within the first influencing factor range by using multiple first maximum information coefficient values, and can ultimately improve the vibration level of the plant structure from the perspective of reducing the energy of each influencing factor. At the same time, by sorting the factors within the first influencing factor range by using multiple second maximum information coefficient values, the most fundamental cause of the vibration of the plant structure can be determined from the perspective of the frequency characteristics of the influencing factors. Furthermore, by combining the two sorting results, the key factors affecting the vibration of the local structure of the plant can be accurately determined.

[0027] In a second aspect, the present invention provides a device for identifying factors affecting vibration of a local structure of a plant, which is used in a pumped storage power station, wherein the pumped storage power station includes a pump turbine generator set and a plant structure; the device includes:

[0028] The acquisition module is used to acquire a target operation signal set and an operation data set when the pumped storage power station is in operation. The operation signal set includes a vibration signal and a pressure pulsation signal. The operation data set includes plant structure vibration data, unit vibration data of a pump turbine generator set, and unit pressure pulsation data; the processing module is used to obtain a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set, and a first influencing factor range based on the target operation signal set and the operation data set through a frequency analysis method. The target vibration frequency analysis data set includes unit measurement point vibration frequency analysis data and pressure pulsation frequency analysis data; the analysis module is used to perform amplitude correlation analysis between the plant local structure vibration response and each influencing factor using the maximum information coefficient method based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, and obtain multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values. The amplitude correlation analysis includes mixed frequency amplitude correlation analysis and frequency division amplitude correlation analysis; the determination module is used to determine multiple target influencing factors of plant local structure vibration within the first influencing factor range based on the multiple first maximum information coefficient values ​​and the multiple second maximum information coefficient values.

[0029] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for identifying factors affecting vibration of the local structure of a factory building according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0030] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for identifying factors affecting vibration of the local structure of a factory building according to the first aspect or any corresponding embodiment thereof.

[0031] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for identifying factors affecting vibration of a local structure of a plant according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 is a flow chart of a method for identifying factors affecting vibration of a local structure of a plant according to an embodiment of the present invention;

[0034] Figure 2 is a flow chart of another method for identifying factors affecting vibration of a local structure of a plant according to an embodiment of the present invention;

[0035] Figure 3 is a flow chart of another method for identifying factors affecting vibration of a local structure of a plant according to an embodiment of the present invention;

[0036] Figure 4 is a schematic diagram of a pumped storage power station according to an embodiment of the present invention;

[0037] Figure 5 is a schematic diagram of a partitioning scheme according to an embodiment of the present invention;

[0038] Figure 6 is a schematic diagram of another partitioning scheme according to an embodiment of the present invention;

[0039] Figure 7 is another schematic diagram of a partitioning scheme according to an embodiment of the present invention;

[0040] Figure 8 is a structural block diagram of a device for identifying factors affecting vibration of a local structure of a plant according to an embodiment of the present invention;

[0041] Fig. 9 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0043] When analyzing the factors affecting the vibration response of the local structure of the plant, the general direction can be determined based on the basic theoretical analysis of vibration. The vibration source can be basically determined through frequency analysis. However, there are many links in the vibration system, and the influence of each factor varies. Therefore, using scientific methods to evaluate and sort the influence of each factor is conducive to identifying the key factors causing the vibration of the plant structure and providing a clear positioning for the subsequent vibration reduction and anti-vibration methods and measures.

[0044] At present, the analysis of factors affecting plant structure vibration usually adopts vibration signal analysis method, which mainly includes frequency domain analysis, time domain analysis, wavelet analysis, etc., among which frequency domain analysis is the most commonly used. The main principle of this method is to use Fourier transform to convert time domain vibration signal into frequency domain signal, and determine the vibration source or influencing factor by analyzing the frequency components and distribution in the spectrum diagram.

[0045] In a pumped storage power station, the vibration response signal of a local structure of a plant is first analyzed in the frequency domain to obtain its main frequency components. Then, the pressure pulsation signals of the main parts of the unit, such as the bladeless area, volute, tailwater pipe, etc., are analyzed in the frequency domain to obtain the main frequency and other main frequency components of each pressure pulsation, usually the second main frequency or the third main frequency. On this basis, the main frequency of the plant structure vibration and the main frequency components of each pressure pulsation are compared, and the vibration source and influencing factors are determined according to the basic vibration theory.

[0046] Other methods, such as time domain analysis, directly analyze the waveform of the vibration signal, and identify the vibration source and influencing factors through the time domain characteristics such as the peak value of the signal. It is suitable for identifying periodic vibration and impact vibration. Wavelet analysis combines the advantages of time domain and frequency domain analysis, and can capture the time domain and frequency domain information of the signal at the same time. It is more suitable for vibration source identification of non-stationary signals and transient signals. Time domain analysis is more used to judge the intensity of vibration, and wavelet analysis is used for specific transient processes. In comparison, frequency domain analysis is most commonly used in identifying vibration influencing factors.

[0047] Through frequency domain analysis technology, the vibration source or influencing factors can be generally determined. However, due to the large number of components and links in the vibration system, such as the main frequency components of the plant structure vibration signal appearing in the pressure pulsation and vibration signals of multiple parts, frequency domain analysis cannot further determine the size of the influencing factors, and it is also impossible to determine the key influencing factors.

[0048] According to an embodiment of the present invention, an embodiment of a method for identifying factors affecting vibration of a local structure of a factory building is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0049] In this embodiment, a method for identifying factors affecting vibration of a local structure of a plant is provided, a pumped storage power station, further, the pumped storage power station comprises a pump turbine generator set and a plant structure; Figure 1 is a flow chart of a method for identifying factors affecting vibration of a local structure of a plant according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0050] Step S101, when the pumped storage power station is in operation, a target operation signal set and an operation data set are obtained.

[0051] Among them, the target operation signal set may include vibration signals and pressure pulsation signals; the operation data set may include plant structure vibration data, unit vibration data of the pump and turbine generator set, and unit pressure pulsation data.

[0052] Specifically, sensors, including pressure pulsation sensors and vibration sensors, can be arranged on the pump turbine generator set and local structures of the plant in the pumped storage power station to synchronously collect pressure pulsation and vibration signals during operation.

[0053] Among them, pressure pulsation sensors can be installed in key locations where pressure changes are likely to occur, such as the inlet and outlet pipes and volute of the unit; vibration sensors can be installed on key structural components such as the bearing seat, casing, main shaft, etc. of the pump and turbine generator unit, as well as at key nodes of local structures such as the main beams and columns and foundations of the plant building.

[0054] Furthermore, the operation data set can be obtained through field testing of the pumped storage power station. The field testing needs to be carried out under different operating conditions of the pumped storage power station, such as the start-up, stable operation, different load states (low load, medium load, high load) and shutdown process of the unit.

[0055] Step S102, based on the target operation signal set and the operation data set, a frequency analysis method is used to obtain a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set and a first influencing factor range.

[0056] Among them, the local structural vibration frequency analysis data set of the factory building may include amplitude and frequency; the frequency analysis method may be a fast Fourier transform (FFT) method, a wavelet transform method, etc., which is not specifically limited in this embodiment as long as the analysis requirements are met.

[0057] Furthermore, the local structure vibration frequency analysis data set of the factory building represents the vibration of the local structure of the factory building. The data set obtained after frequency analysis may contain the vibration information of each local structure of the factory building (such as beams, columns, foundations, etc.) at different frequencies, mainly including vibration amplitude and frequency.

[0058] Furthermore, the target vibration frequency analysis data set may include unit measurement point vibration frequency analysis data and pressure pulsation frequency analysis data, mainly including vibration amplitude and frequency.

[0059] Among them, the measuring points are the locations pre-arranged at the key parts of the units and key nodes of the plant structure of the pumped storage power station for measuring vibration conditions.

[0060] Specifically, by processing the target operation signal set and the operation data set using a frequency analysis method, the corresponding plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set can be analyzed and obtained.

[0061] Furthermore, the corresponding range of the first influencing factor can be preliminarily determined by combining the plant local structure vibration frequency analysis dataset and the target vibration frequency analysis dataset.

[0062] Step S103, based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform amplitude correlation analysis between the plant local structure vibration response and each influencing factor to obtain multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values.

[0063] Among them, the Maximum Information Cofficient (MIC) method represents a statistical method for measuring the correlation between two variables, which can effectively handle various types of relationships, including linear and nonlinear relationships.

[0064] Furthermore, the amplitude correlation analysis may include frequency mixing amplitude correlation analysis and frequency division amplitude correlation analysis.

[0065] Specifically, since there is a non-linear relationship between the local structural vibration of the plant, the unit vibration data and the pressure pulsation data, the maximum information coefficient method is used in this embodiment to perform the mixing amplitude correlation analysis and the frequency division amplitude correlation analysis of the local structural vibration response of the plant and each influencing factor, and obtain the corresponding multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values ​​(MIC values) for characterizing the strong and weak correlation between each influencing factor and the vibration response.

[0066] Step S104: determining a plurality of target influencing factors of the local structural vibration of the plant within the first influencing factor range based on the plurality of first maximum information coefficient values ​​and the plurality of second maximum information coefficient values.

[0067] Specifically, the MIC value is between [0, 1], and the larger the value, the stronger the correlation between the two variables. Therefore, by obtaining multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values, multiple key influencing factors of the local structural vibration of the plant, i.e., multiple target influencing factors, can be determined within the range of the first influencing factor.

[0068] The method for identifying factors affecting vibration of the local structure of the plant provided in this embodiment can preliminarily determine the range of the first influencing factor by performing frequency analysis on the target operation signal set and the operation data set. Further, the maximum information coefficient method is used to perform correlation analysis on the vibration response of the local structure of the plant and the mixed frequency amplitude of each influencing factor, and the strength of the influence of each factor on the vibration of the local structure of the plant can be considered from the energy perspective. At the same time, the maximum information coefficient method is used to perform correlation analysis on the vibration response of the local structure of the plant and the frequency division amplitude of each influencing factor, and the ranking of the influencing factors determined from the frequency characteristics of excitation and damping can be obtained. Further, the multiple first maximum information coefficient values ​​and the multiple second maximum information coefficient values ​​obtained by the correlation analysis can determine multiple target influencing factors of the vibration of the local structure of the plant within the range of the first influencing factor. Therefore, by implementing the present invention, the key influencing factors of the vibration of the local structure of the plant can be accurately identified through the correlation analysis of the mixed frequency amplitude and the correlation analysis of the frequency division amplitude, which is more accurate than the existing technical solution, and lays the foundation and points out the direction for solving the problem of plant structure vibration, the design of subsequent vibration reduction methods and the application of measures.

[0069] In this embodiment, a method for identifying factors affecting vibration of a local structure of a plant is provided, a pumped storage power station, further, the pumped storage power station comprises a pump turbine generator set and a plant structure; Figure 2 is a flow chart of a method for identifying factors affecting vibration of a local structure of a plant according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0070] Step S201: When the pumped storage power station is in operation, a target operation signal set and an operation data set are obtained. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0071] Step S202, based on the target operation signal set and the operation data set, a frequency analysis method is used to obtain a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set and a first influencing factor range.

[0072] Specifically, the above step S202 includes:

[0073] Step S2021, based on the operation data set, the target operation signal set is processed using a frequency analysis method to obtain a plant local structure vibration frequency analysis data set.

[0074] Specifically, by collecting the plant structure vibration data, unit vibration data and unit pressure pulsation data, the signals in the target operation signal set are analyzed and calculated to obtain the frequency components in each signal and form a corresponding plant local structure vibration frequency analysis data set.

[0075] In an optional implementation, taking the fast Fourier transform (FFT) method as an example, the frequency analysis process may specifically include:

[0076] (1) Data segmentation (optional operation):

[0077] Depending on the total length of the data collection and the accuracy requirements of the analysis, you can decide whether to segment the data. If the data collection time is long, such as continuously collecting data for several hours or even longer, in order to more carefully observe the changes in signal frequency characteristics in different time periods and avoid problems such as excessive consumption of computing resources caused by processing a large amount of data at one time, the data can be segmented at certain time intervals.

[0078] For example, if the plant structure vibration data collected for 1 hour is divided into 6 segments, each segment is 10 minutes long, and then FFT analysis is performed on each segment. Of course, if the collected data is shorter or you want to observe the frequency characteristics from an overall perspective, you can also skip segmentation and directly perform FFT calculation on the overall data.

[0079] (2) FFT calculation and spectrum drawing:

[0080] For each section (or the whole) of plant structure vibration data, unit vibration data, and unit pressure pulsation data, the FFT algorithm is used to calculate and convert the time domain signal into a frequency domain signal. In this process, the FFT algorithm can output the amplitude information corresponding to each frequency point, and usually draw a spectrum with frequency as the horizontal axis and amplitude as the vertical axis.

[0081] Furthermore, by observing the spectrum, we can clearly see the amplitude distribution of each signal at different frequencies, find out the main frequency peaks and the corresponding amplitudes. For example, in the spectrum of the plant structure vibration data, if a high amplitude peak is found at a certain frequency (such as 30Hz), this may mean that the plant structure has a more obvious vibration response at this frequency, which may be related to the operating frequency of a certain equipment or the resonant frequency of the structure, and further analysis and investigation is needed.

[0082] (3) Frequency component extraction and recording:

[0083] The key frequency components in each signal and the corresponding amplitude, phase (if phase information is required) and other information can be extracted from the spectrum diagram and recorded in detail. Furthermore, these data can be organized in a table format. For example, for the plant structure vibration data, the column headings of the table can include "measurement point location (such as a beam column number)", "frequency value (Hz)", "amplitude (specific unit, such as mm / s 2)” and “Phase (degrees)”, etc., record these characteristic parameters of each measuring point at different frequencies line by line to form a preliminary record of frequency analysis results.

[0084] (4) Data integration and summary: The frequency component information of the factory building structure vibration data at each measuring point obtained through the above frequency analysis process can be integrated and summarized to form a complete factory building local structure vibration frequency analysis data set containing key characteristic parameters such as vibration amplitude and frequency of different local structures of the factory building (key parts such as beams, columns, foundations, etc.) at different frequencies.

[0085] Step S2022: determining a target vibration frequency analysis data set and a first influencing factor range based on the vibration frequency analysis data set of the local structure of the plant.

[0086] Specifically, the target vibration frequency analysis dataset and the first influencing factor range can be further determined in combination with the local structure vibration frequency analysis dataset of the plant.

[0087] In some optional implementations, the above step S2022 includes:

[0088] Step a1: Determine the vibration source and transmission path based on the vibration frequency analysis data set of the local structure of the plant.

[0089] Step a2: determining the first influencing factor range and the positions of multiple measuring points based on the vibration source and the transmission path.

[0090] Step a3: determining a target vibration frequency analysis data set at multiple measuring point locations based on the vibration frequency analysis data set of the local structure of the plant.

[0091] Firstly, the corresponding vibration source can be preliminarily determined by frequency analysis of the local structure vibration frequency analysis data set of the plant, which includes the vibration amplitude and frequency of different local structures of the plant at different frequencies.

[0092] Specifically, the unit pressure pulsation is considered to be the source (excitation source) of the unit vibration and plant structure vibration, so the focus is on comparing the unit pressure pulsation measurement point data and the frequency components of other related measurement points (such as unit vibration measurement points and plant structure vibration measurement points). If a specific frequency is detected at a certain unit pressure pulsation measurement point (for example, the water pressure pulsation at a certain measurement point contains a 90Hz frequency), and then the same frequency is found at other vibration measurement points of the unit (such as the top cover vibration also contains a 90Hz frequency) and subsequent plant structure vibration measurement points (continue to a certain position on the plant floor if the vibration also contains a 90Hz frequency), then the source corresponding to this frequency is likely to be related to the unit pressure pulsation.

[0093] Furthermore, since the known sources are all in the internal flow channel of the pump-turbine unit, the specific location where the frequency (90Hz) appears is further searched in the pressure pulsation measurement point data at different key positions of the internal flow channel of the unit (such as the volute, bladeless area, top cover and runner, tailwater pipe, etc.). By comparing the pressure pulsation frequency characteristics of these different flow channel positions, if it is found that the pressure pulsation measurement point at the volute position has a more prominent amplitude or a higher frequency at this frequency, then it can be preliminarily determined that the vibration source is likely to be closely related to the water flow pressure pulsation at the volute, and it is preliminarily determined as the vibration source.

[0094] Secondly, through the data set of vibration frequency analysis of the local structure of the factory, we can find out the path along which the vibration source is transmitted. Furthermore, the positions with the same frequency components can be basically connected in series to form a transmission path.

[0095] Specifically, starting from the location of the possible vibration source that has been preliminarily determined (such as the volute mentioned above), find other measurement points with the same frequency component (90Hz), and try to connect these locations in series according to the structural connection relationship and the physically reasonable propagation order. For example, if it is found that the frequency first appears at the vibration measurement point of the top cover of the unit, and then appears at a vibration measurement point at a certain position on the floor of the plant, then it can be basically determined that the path of this vibration is from the measurement point position of the pressure pulsation (volute) → top cover → floor. According to this idea, all the measurement point positions with the same key frequency in the data set are sorted out and connected in series, and multiple possible vibration transmission paths are gradually outlined.

[0096] Furthermore, in the process of connecting the vibration transmission paths in series, the actual structural characteristics of the plant and the unit and the layout of the measuring points must be fully considered. For example, based on the floor structure of the plant, the distribution of beams and columns, and the connecting components between the unit and the plant, the reasonable propagation direction of the vibration in these structures and the possible locations it may pass through can be determined. If two adjacent measuring points have the same frequency components, but there is no direct structural connection or physically reasonable propagation path between them, it is necessary to further analyze whether there are missing measuring points or other indirect transmission links. By supplementing and improving this information, the determined vibration transmission path can be more in line with the actual physical situation and more accurate and reliable.

[0097] Then, after determining the vibration source and transmission path, it is equivalent to determining the position of each measuring point on the transmission path, and knowing the factors that ultimately cause the vibration of the factory building, and thus determining the corresponding first influencing factor range.

[0098] Finally, the vibration frequency analysis data of the corresponding measuring points at multiple measuring point locations can be obtained in the local structure vibration frequency analysis data set of the plant, that is, the target vibration frequency analysis data set contains the vibration frequency analysis data and pressure pulsation frequency analysis data of multiple target measuring points at different frequencies, mainly including vibration amplitude and frequency.

[0099] Step S203, based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform amplitude correlation analysis between the plant local structure vibration response and each influencing factor to obtain multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values.

[0100] Specifically, the above step S203 includes:

[0101] Step S2031, based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform correlation analysis on the mixing amplitude of the plant local structure vibration response and the mixing amplitude of each influencing factor to obtain multiple first maximum information coefficient values.

[0102] Specifically, the mixed frequency amplitude of the local structure vibration of the plant contained in the local structure vibration frequency analysis data set of the plant and the mixed frequency amplitude of the pressure pulsation of each measuring point in the target vibration frequency analysis data set are obtained.

[0103] Furthermore, the mixed frequency amplitude of the local structural vibration of the plant is taken as the variable Y, and the corresponding influencing factors, namely, the mixed frequency amplitude of the pressure pulsation of each measuring point are taken as variables X1, X2, X3...Xn respectively.

[0104] Furthermore, the maximum information coefficient method is used to perform correlation analysis on the combination of two different variables such as Y and X1, Y and X2, etc., and the influence of each influencing factor X1, X2, ..., Xn on Y is obtained, that is, multiple first maximum information coefficient values ​​are obtained.

[0105] When the maximum information coefficient method is used for correlation analysis, the calculation formula is as shown in the following relationship (1):

[0106]

[0107] Wherein: MIC(X; Y) represents the first maximum information coefficient value between variable X and variable Y; I(X; Y) represents the mutual information between variable X and variable Y, which is used to measure the mutual dependence between variable X and variable Y; a×b represents dividing the data space into a grid with a rows and b columns, which can be used to calculate the dependency relationship between variable X and variable Y; I(X; Ya×b) represents the mutual information between variable X and variable Y when the data space is divided into a grid with a rows and b columns; B represents a pre-set parameter used to limit the size of the grid division.

[0108] Step S2032, based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform correlation analysis on the frequency division amplitude of the plant local structure vibration response and the frequency division amplitude of each influencing factor to obtain multiple second maximum information coefficient values.

[0109] Specifically, when performing correlation analysis on the frequency division amplitude, the frequency of the local structural vibration of the plant is first analyzed to clarify the main frequency components f1, f2...fn and their frequency division amplitudes. That is, the main frequency components f1, f2...fn and their frequency division amplitudes can be determined based on the local structural vibration frequency analysis data set of the plant.

[0110] The specific order of n is determined according to the actual situation, with obvious impact as the principle.

[0111] Furthermore, the amplitude of the frequency division f1 of the plant structure vibration is taken as the variable Y, and the amplitudes of the corresponding frequency divisions in the vibration and pressure pulsation of the measuring points in the locked target vibration frequency analysis data set are taken as variables X1, X2, X3...Xn.

[0112] Furthermore, the maximum information coefficient method shown in the above relationship (1) is used to perform correlation analysis on the combinations of two different variables such as Y and X1, Y and X2, etc., and the influence of each influencing factor X1, X2, ..., Xn on the frequency division f1 is obtained, that is, multiple second maximum information coefficient values ​​are obtained.

[0113] Step S204: Based on the multiple first maximum information coefficient values ​​and the multiple second maximum information coefficient values, multiple target influencing factors of the local structural vibration of the plant are determined within the first influencing factor range. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0114] The method for identifying factors affecting vibration of the local structure of a plant provided in this embodiment can convert the originally complex and difficult to intuitively interpret time domain signals into frequency analysis data sets that can reflect the vibration frequency characteristics by processing the target operation signal set by the frequency analysis method. Further, the target vibration frequency analysis data set is determined in combination with the vibration frequency analysis data set of the local structure of the plant, so that the vibration frequency characteristics of different positions can be more comprehensively grasped. At the same time, by determining the scope of the first influencing factor, the scope of investigation is narrowed, focusing on key suspicious influencing factors, and avoiding untargeted large-scale investigation. Further, the maximum information coefficient method is used to perform correlation analysis on the vibration response of the local structure of the plant and the mixed frequency amplitude of each influencing factor, and the strength of the effect of each factor on the vibration of the local structure of the plant can be considered from the energy perspective. At the same time, the maximum information coefficient method is used to perform correlation analysis on the vibration response of the local structure of the plant and the frequency division amplitude of each influencing factor, and the ranking of influencing factors determined from the frequency characteristics of excitation and damping can be obtained. Through two correlation analyses and determining multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values, the key influencing factors of the vibration of the local structure of the plant can be accurately identified.

[0115] In this embodiment, a method for identifying factors affecting vibration of a local structure of a plant is provided, a pumped storage power station, further, the pumped storage power station comprises a pump turbine generator set and a plant structure; Figure 3 is a flow chart of a method for identifying factors affecting vibration of a local structure of a plant according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0116] Step S301, when the pumped storage power station is in operation, obtain the target operation signal set and operation data set. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0117] In an optional implementation, obtaining the target operation signal set includes:

[0118] Step b1, obtaining an initial operation signal set.

[0119] Step b2, preprocessing the initial operation signal set to obtain a target operation signal set.

[0120] Specifically, after the initial operating signal set including vibration signals and pressure pulsation signals is obtained through the pressure pulsation sensor and the vibration sensor, the initial operating signal set can be preprocessed, including removing outliers and denoising, so as to improve the signal quality of the obtained target operating signal set.

[0121] Step S302: Based on the target operation signal set and the operation data set, a frequency analysis method is used to obtain a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set and a first influencing factor range. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0122] Step S303: Based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform amplitude correlation analysis on the plant local structure vibration response and each influencing factor, and a plurality of first maximum information coefficient values ​​and a plurality of second maximum information coefficient values ​​are obtained. Figure 2 Step S203 of the illustrated embodiment will not be described in detail here.

[0123] Step S304: determining a plurality of target influencing factors of the local structural vibration of the plant within the first influencing factor range based on the plurality of first maximum information coefficient values ​​and the plurality of second maximum information coefficient values.

[0124] Specifically, the above step S304 includes:

[0125] Step S3041: sort the influencing factors within the first influencing factor range based on the multiple first maximum information coefficient values ​​to obtain a sorted second influencing factor range.

[0126] Specifically, the first maximum information coefficient value is used to characterize the strength of the effect of various factors considered from an energy perspective on the local structural vibration of the factory building. Therefore, by sorting the influencing factors within the first influencing factor range in descending order according to the first maximum information coefficient value, the second influencing factor range from strong to weak can be obtained, in which the effect of various factors considered from an energy perspective on the local structural vibration of the factory building is obtained.

[0127] Furthermore, in the second range of influencing factors, the factors ranked in the front are the factors that have a greater impact on the local structural vibration of the plant from the energy perspective, while the factors ranked in the back are the factors with relatively smaller impacts.

[0128] Step S3042: sort the influencing factors within the first influencing factor range based on the multiple second maximum information coefficient values ​​to obtain a sorted third influencing factor range.

[0129] Specifically, by sorting the influencing factors within the first influencing factor range in descending order according to the multiple second maximum information coefficient values, the influencing factor sorting determined from the perspective of frequency characteristics of excitation and damping can be obtained, that is, the corresponding third influencing factor range can be obtained.

[0130] Furthermore, in the third range of influencing factors, the influencing factors ranked at the top have a stronger influence on the local structural vibration of the plant in terms of the frequency characteristics of excitation and damping, while the influencing factors ranked at the bottom are relatively weaker.

[0131] Step S3043: determining a plurality of target influencing factors of the local structural vibration of the plant based on the second influencing factor range and the third influencing factor range.

[0132] Specifically, by comprehensively analyzing the ranking of the influencing factors in the second influencing factor range and the third influencing factor range, the root cause of the local structural vibration of the plant can be further determined, that is, multiple target influencing factors can be determined.

[0133] In an optional implementation, multiple target influencing factors are determined in the following manner:

[0134] (1) Select the top-ranked factors from the second influencing factor range. For example, if the second influencing factor range includes factors A, B, C, D, E, etc., and the order of influence from the energy perspective is A>B>C>D>E, the top-ranked factors (such as A, B, C) can usually be selected as the focus. These factors have a greater impact on the local structural vibration of the plant at the energy level.

[0135] (2) Select the top-ranked factors from the third influencing factor range. For example, if the third influencing factor range includes factors F, G, H, I, J, etc., and the order of influence of the frequency characteristics of excitation and damping is F>G>H>I>J, the top-ranked factors (such as F, G, H) can be selected as important considerations. These factors have a greater impact on the local structural vibration of the plant in terms of frequency characteristics.

[0136] (3) Comprehensively compare the key factors extracted from the second influencing factor range with the key factors extracted from the third influencing factor range. The following situations may occur:

[0137] a. Overlapping factors: Some factors rank high in both the second and third influencing factors. These overlapping factors are likely to be critical factors affecting the local structural vibration of the plant and should be prioritized as target influencing factors.

[0138] b. Partially overlapping factors: For some factors, only one of them ranks high in the two ranges. For these factors, it is necessary to further analyze their influence mechanism on the local structural vibration of the plant from their respective perspectives (energy or frequency characteristics) and comprehensively judge their importance.

[0139] (4) Determine the final multiple target influencing factors through comprehensive comparison results. For example, after comprehensive comparison, it is found that factor A (from the energy perspective) and factor F (from the frequency characteristic perspective) are both very important, and factor B (from the energy perspective) and factor G (from the frequency characteristic perspective) also have a certain correlation. Then factors A, B, F, G, etc. can be determined as multiple target influencing factors of the local structural vibration of the plant.

[0140] The method for identifying factors affecting vibration of the local structure of a plant provided in this embodiment improves the signal quality of the target operating signal set by preprocessing the initial operating signal set, and then the first influencing factor range can be preliminarily determined by frequency analysis of the target operating signal set and the operating data set. Further, the maximum information coefficient method is used to perform correlation analysis on the vibration response of the local structure of the plant and the mixed frequency amplitude of each influencing factor, and the strength of the effect of each factor on the vibration of the local structure of the plant can be considered from the energy perspective. At the same time, the maximum information coefficient method is used to perform correlation analysis on the vibration response of the local structure of the plant and the frequency division amplitude of each influencing factor, and the ranking of influencing factors determined from the frequency characteristics of excitation and damping can be obtained. By sorting the influencing factors within the first influencing factor range through multiple first maximum information coefficient values, the vibration level of the plant structure can be finally improved from the perspective of reducing the energy of each influencing factor. At the same time, by sorting the influencing factors within the first influencing factor range through multiple second maximum information coefficient values, the most fundamental cause of the vibration of the plant structure can be determined from the frequency characteristics of the influencing factors. Further, combining the two sorting results, the key influencing factors of the vibration of the local structure of the plant can be accurately determined.

[0141] In one example, a method for identifying factors affecting vibration of a local structure of a pumped storage power station building based on correlation analysis is provided, which specifically includes:

[0142] (1) Sensors, including pressure pulsation and vibration sensors, are placed on the pump-turbine generator sets and local structures of the powerhouse in the pumped-storage power station to synchronously collect pressure pulsation and vibration signals during operation. The signals are preprocessed, including removing outliers and denoising, to improve signal quality.

[0143] (2) Extract key characteristic parameters of vibration response and pressure pulsation, including amplitude and frequency.

[0144] (3) First, frequency analysis is performed on the plant structure vibration data, unit vibration data, and unit pressure pulsation data to preliminarily determine the vibration source and transmission path, and lock the scope of influencing factors and specific measurement point locations.

[0145] Specifically, the plant structure vibration, unit vibration and pressure pulsation are all obtained through on-site testing, and the collected signals are calculated to obtain the frequency. Since the unit pressure pulsation is the source of the unit vibration and plant structure vibration, that is, the excitation source, the frequency components in each signal can be seen through frequency characteristic analysis. For example, if the water pressure pulsation at a certain measuring point or position contains a 90Hz frequency, and the vibration of a certain unit vibration measuring point such as the top cover also contains a 90Hz frequency, if the vibration at a certain position of the plant floor also contains a 90Hz frequency, it can be preliminarily determined that the path of the vibration is from the measuring point of the pressure pulsation → the top cover → the floor.

[0146] Furthermore, the source is in the flow channel inside the pump-turbine unit, that is, the pulsation generated by the water flow continues to be transmitted to the structural parts, causing vibration. However, the flow channel is also divided into many locations, mainly the volute, bladeless area, top cover and runner, tailwater pipe, etc. Similarly, there are many vibration measurement points for the unit, such as the top cover, upper and lower frames, etc., and there are more measurement points for plant vibration, covering the floors, columns, stairs, etc. on each floor.

[0147] Furthermore, by analyzing the signals obtained through field tests and using frequency characteristics, we can determine the path along which the vibration source is transmitted, and locations with the same frequency components can be basically connected in series to form a transmission path.

[0148] (4) Classify the correlation analysis of the test results of the determined measuring points, mainly including the analysis of amplitude and frequency. Since there is a non-linear relationship between the local structural vibration of the plant, the unit vibration data and the pressure pulsation data, this patent uses the Maximum Information Cofficient (MIC) method to perform correlation analysis between variables, obtain the strong and weak correlation between each influencing factor and the vibration response, and then determine whether each factor is a key influencing factor. The value of MIC is between [0, 1]. The larger the value, the stronger the correlation between the two variables.

[0149] The test results, i.e. the vibration or pressure pulsation at each position, contain two parts, namely the amplitude reflecting the energy size and the frequency reflecting the speed of change. The classified correlation analysis is to conduct correlation analysis on the amplitude and the frequency.

[0150] Specifically, the correlation analysis includes:

[0151] (4.1) During the amplitude analysis, the mixed frequency amplitude of the local structural vibration of the plant is taken as the variable Y, and its influencing factors, i.e., the mixed frequency amplitude of the pressure pulsation of the locked measuring point, are variables X1, X2, X3, ..., Xn. Correlation analysis is performed on the combinations of two different variables such as Y and X1, Y and X2, ..., to obtain the degree of influence of each influencing factor X1, X2, ..., Xn on Y.

[0152] (4.2) During the frequency analysis, the frequency of the local structural vibration of the plant is first analyzed to identify the main frequency components f1, f2, ... fn and their frequency division amplitudes. The specific n to several orders are determined according to the actual situation, with the principle of obvious influence. Then analyze the corresponding frequency division and amplitude of each influencing factor. The amplitude of the frequency division f1 of the plant structure vibration is taken as the variable Y, and the amplitude of the corresponding frequency division in the vibration and pressure pulsation of the locked measuring point is taken as the variable X1, X2, X3 ... Xn. Similar to the amplitude correlation analysis process in (5), the correlation analysis is performed on the combination of two different variables to identify the strength of the influence of each influencing factor on the frequency division f1. The other frequency division analysis processes are the same.

[0153] (5) The correlation analysis of the mixed frequency amplitude determines the order of various influencing factors from the perspective of energy, which can ultimately improve the vibration level of the plant structure from the perspective of reducing the energy of each influencing factor. The correlation analysis of the frequency division amplitude determines the most fundamental cause of the plant structure vibration from the perspective of the frequency characteristics of the influencing factors.

[0154] This example provides a method for identifying factors affecting the vibration of the local structure of a pumped storage power plant based on correlation analysis. Based on the measured vibration data of the pumped storage power plant unit and plant, the vibration source and influencing factors of the local structure vibration of the plant are preliminarily determined in the frequency domain analysis technology. The maximum information coefficient method is used to perform correlation analysis on the mixed frequency amplitude of the local structure vibration response of the plant and the mixed frequency amplitude of each influencing factor. The calculated MIC values ​​are sorted from large to small, that is, the strength of the influence of each factor on the local structure vibration of the plant from the energy perspective is obtained. Similarly, the correlation analysis is performed on the main frequency division amplitude of the local structure vibration of the plant and the corresponding frequency division amplitude of each influencing factor, which can obtain the ranking of influencing factors determined from the frequency characteristics of excitation and damping, and can more accurately determine the root cause of the local structure vibration of the plant.

[0155] Through the correlation analysis of these two parts, the key influencing factors of the local structural vibration of the factory building can be identified, which is more accurate than the existing technical solutions. It lays the foundation and points out the direction for solving the problem of factory building structure vibration, the design of subsequent vibration reduction methods and the application of measures.

[0156] In an optional embodiment, Figure 4Taking the pumped storage power station shown in the figure as an example, the pump turbine includes flow components such as volute, guide vane, runner, tailwater pipe, etc. When water flows through the flow components of the unit, different pressure pulsations will be generated at different positions under different operating conditions. The main focus positions include the volute inlet, the guide vane and the runner blades, the tailwater pipe inlet, etc. The pressure pulsation will act on the structural components, such as the top cover above the runner, the upper and lower frames of the generator, the stator base, etc. At the same time, for example, since the volute and the tailwater pipe are connected to the surrounding rock and other foundation structures of the powerhouse, the internal pressure pulsation will also be transmitted to the surrounding rock and other foundations of the powerhouse through these channels, and continue to be transmitted through the concrete, eventually leading to the vibration of local structures such as the floor slabs, columns and stairs connected to each other on each floor of the powerhouse.

[0157] Furthermore, based on the correlation analysis-based identification method for influencing factors of local structural vibration of pumped storage power station buildings provided in this example, Figure 4 The pumped storage power station shown provides a specific identification process:

[0158] (1) Through on-site measurements, the amplitude and frequency of the pressure pulsation in the pump-turbine can be obtained, including the volute inlet, the bladeless area (between the guide vanes and the blades), the top cover and the runner, and the tailwater pipe inlet; the vibration amplitude and frequency of the upper and lower frames and the top cover of the unit; and the vibration amplitude and frequency of the measuring points arranged at typical locations of the floor slabs, columns, and stairs on each floor of the plant.

[0159] (2) Taking a unit with a rated speed of 300r / min as an example, its rotation frequency fn is 300 / 60=5Hz, and the number of runner blades is 9. For a pump turbine, the pressure pulsation amplitude in the bladeless area is the largest, reflecting that the excitation energy there is the largest. The pressure pulsation in the flow channel inside the unit is transmitted from there to the upstream, i.e., the volute, and to the downstream, i.e., the tailwater pipe. Usually, the pulsation amplitude gradually decreases, but if superposition or resonance occurs in the flow channel such as the volute, the pulsation amplitude will increase. For the pulsation in the bladeless area, its frequency is related to the runner blades, i.e., the number of blades × fn. For this example, it is 9×5Hz=45Hz.

[0160] (3) Field measurement data show that the vibration frequency of the generator layer, which is the top floor of the factory building, is 90 Hz. The vibration source and transmission path are analyzed.

[0161] First, the test results show that the first main frequency of the pressure pulsation in the bladeless area of ​​the pump-turbine is 45Hz, the first main frequency of the volute inlet is 90Hz, the first main frequency of the tailwater pipe inlet is 5Hz, the first main frequency of the upper and lower frames is 5Hz, the first main frequency of the top cover vibration is 45Hz, the vibration main frequency of the turbine layer floor is 90Hz, the vibration main frequency of the turbine layer columns is 90Hz, the vibration main frequency of the busbar layer floor and columns is also 90Hz, and the vibration main frequency of the stairs between the turbine layer and the busbar layer, and between the busbar layer and the generator layer is also 90Hz.

[0162] Through frequency analysis, it can be preliminarily determined that the vibration of the turbine floor is transmitted by the volute, that is, from the volute to the foundation. Although the frame is also connected to the foundation, it can be seen from the frequency that the vibration of the floor is not transmitted through the frame, but mainly through the volute → foundation → floor.

[0163] Further research into the unit revealed that the second main frequency of the pressure pulsation in the bladeless area is 90Hz, which means that although the pulsating pressure in the bladeless area is mainly composed of the energy corresponding to the 45Hz frequency, in the process of transmitting upstream, the energy corresponding to the 45Hz frequency division begins to decrease, while the energy corresponding to the 90Hz frequency division is enhanced. This is related to the structure of the volute, and the fluctuations may be superimposed there, showing a main frequency of 90Hz. The energy of this frequency continues to be transmitted to the foundation, to the floor slabs and columns of the turbine layer, to the floor slabs, columns and stairs of the busbar layer, and finally to the floor slabs of the generator layer. Therefore, through frequency analysis, it can be preliminarily determined that the vibration source is the pressure pulsation in the bladeless area, and the transmission path is bladeless area → volute → floor slab → column / stairs → floor slab.

[0164] Furthermore, the influencing factors initially identified include pressure pulsation in the bladeless area, pressure pulsation at the volute inlet, vibration of floor slabs on each floor, vibration of columns and stairs, etc. The specific measurement point locations are the relevant test locations initially arranged.

[0165] (4) During the amplitude analysis, the mixed frequency amplitude of the vibration of the generator floor (including the first main frequency, the second main frequency, etc.) is taken as the variable Y, and the mixed frequency amplitudes of the influencing factors, i.e. the locked measuring points (pressure pulsation in the bladeless area, pressure pulsation at the volute inlet, vibration of the turbine floor and columns, vibration of the busbar floor and columns, and vibration of the stairs between floors) are respectively taken as variables X1, X2, X3, ..., Xn. Correlation analysis is performed on the combinations of different variables such as Y and X1, Y and X2, ..., etc., respectively, to obtain the degree of influence of each influencing factor X1, X2, ..., Xn on Y.

[0166] (5) The maximum information coefficient (MIC) method was used to analyze the correlation between variables. The calculation formula is shown in the above equation (1).

[0167] Specifically, taking the correlation analysis between Y value and X1 value as an example, B takes the value of n 0.6 , where n is the number of data. Further, when calculating the maximum information coefficient of Y and X1, there are 19 data in total, so B takes the value of 6. a and b can be 2, 2; 2, 3; 3, 2 respectively.

[0168] Further, different partitioning schemes are adopted, such as Figures 5 to 7As shown. Similarly, different partitioning schemes can produce different I(X; Y|a×b) values:

[0169] (1) Figure 5 As shown, a=2, b=2: maxI(X;Y|a×b)=0.155;

[0170] (2) Figure 6 As shown, a=2, b=3: maxI(X;Y|a×b)=0.069;

[0171] (3) Figure 7 As shown, a=3, b=2: maxI(X;Y|a×b)=0.019.

[0172] Furthermore, the maximum MIC value is taken as 0.155, and then normalized, max MIC / log(min(a,b)), and finally the normalized max MIC value is 0.515, that is, the maximum information coefficient value MIC1 between Y and X1 is 0.515.

[0173] By analogy, we can get the maximum information coefficient values ​​MIC2, MIC3, ... MICn between Y and X2, X3, ... Xn, and sort them according to their values. The larger the value, the greater the influence of the corresponding variable X on Y.

[0174] (6) Through the above process, the influencing factors can also be ranked according to frequency data analysis.

[0175] (7) The above correlation analysis of amplitude is to determine the ranking of various influencing factors from the perspective of energy, which can ultimately improve the vibration level of the plant structure from the perspective of reducing the energy of each influencing factor. The correlation analysis of the frequency division amplitude is to determine the most fundamental cause of the plant structure vibration from the perspective of the frequency characteristics of the influencing factors.

[0176] In this embodiment, a device for identifying factors affecting vibration of local structural structures of a plant is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0177] This embodiment provides a device for identifying factors affecting vibration of a local structure of a plant, which is used in a pumped-storage power station. The pumped-storage power station includes a pump-turbine generator set and a plant structure; Figure 8 As shown, the device comprises:

[0178] The acquisition module 801 is used to acquire the target operation signal set and operation data set when the pumped storage power station is in operation. The operation signal set includes a vibration signal and a pressure pulsation signal. The operation data set includes plant structure vibration data, unit vibration data of the pump-turbine generator set, and unit pressure pulsation data.

[0179] The processing module 802 is used to obtain a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set and a first influencing factor range based on the target operation signal set and the operation data set through frequency analysis method. The target vibration frequency analysis data set includes the unit measuring point vibration frequency analysis data and the pressure pulsation frequency analysis data.

[0180] The analysis module 803 is used to perform amplitude correlation analysis of the vibration response of the local structure of the plant and various influencing factors based on the local structure vibration frequency analysis data set and the target vibration frequency analysis data set by using the maximum information coefficient method to obtain multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values. The amplitude correlation analysis includes mixing amplitude correlation analysis and frequency division amplitude correlation analysis.

[0181] The determination module 804 is used to determine multiple target influencing factors of the local structural vibration of the plant within the first influencing factor range based on the multiple first maximum information coefficient values ​​and the multiple second maximum information coefficient values.

[0182] In some optional implementations, acquiring the target operation signal set in the acquisition module 801 includes: acquiring an initial operation signal set; and preprocessing the initial operation signal set to obtain the target operation signal set.

[0183] In some optional implementations, the processing module 802 includes:

[0184] The processing submodule is used to process the target operation signal set based on the operation data set using the frequency analysis method to obtain the plant local structure vibration frequency analysis data set.

[0185] The first determination submodule is used to determine a target vibration frequency analysis data set and a first influencing factor range based on a local structure vibration frequency analysis data set of a plant.

[0186] In some optional implementations, the determining submodule includes:

[0187] The first determination unit is used to determine the vibration source and the transmission path based on the vibration frequency analysis data set of the local structure of the plant.

[0188] The second determining unit is used to determine a target vibration frequency analysis data set at a plurality of measuring point positions based on the vibration frequency analysis data set of a local structure of a plant.

[0189] In some optional implementations, the analysis module 803 includes:

[0190] The first analysis submodule is used to perform correlation analysis between the mixed frequency amplitude of the local structure vibration response of the plant and the mixed frequency amplitude of each influencing factor based on the local structure vibration frequency analysis data set and the target vibration frequency analysis data set by using the maximum information coefficient method to obtain multiple first maximum information coefficient values.

[0191] The second analysis submodule is used to perform correlation analysis between the frequency division amplitude of the local structure vibration response of the plant and the frequency division amplitude of each influencing factor based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set by using the maximum information coefficient method to obtain multiple second maximum information coefficient values.

[0192] In some optional implementations, the determining module 804 includes:

[0193] The first sorting submodule is used to sort the influencing factors within the first influencing factor range based on multiple first maximum information coefficient values ​​to obtain a sorted second influencing factor range.

[0194] The second sorting submodule is used to sort the influencing factors within the first influencing factor range based on multiple second maximum information coefficient values ​​to obtain a sorted third influencing factor range.

[0195] The second determination submodule is used to determine multiple target influencing factors of the local structural vibration of the plant based on the second influencing factor range and the third influencing factor range.

[0196] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0197] The device for identifying factors affecting the vibration of the local structure of a factory building in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0198] The embodiment of the present invention also provides a computer device having the above Figure 8 The device for identifying factors affecting local structural vibration of a factory building is shown.

[0199] See also Fig. 9 , Fig. 9 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig. 9As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig. 9 A processor 10 is taken as an example.

[0200] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0201] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0202] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0203] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0204] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0205] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0206] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0207] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for identifying factors affecting vibration of a local structure of a plant, used in a pumped storage power station, wherein the pumped storage power station comprises a pump turbine generator set and a plant structure; characterized in that: The method comprises: When the pumped storage power station is in operation, a target operation signal set and an operation data set are obtained, wherein the operation signal set includes a vibration signal and a pressure pulsation signal, and the operation data set includes plant structure vibration data, unit vibration data of the pump turbine generator set, and unit pressure pulsation data; Based on the target operation signal set and the operation data set, a frequency analysis method is used to obtain a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set and a first influencing factor range, wherein the target vibration frequency analysis data set includes unit measurement point vibration frequency analysis data and pressure pulsation frequency analysis data; Based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, a maximum information coefficient method is used to perform amplitude correlation analysis between the plant local structure vibration response and various influencing factors to obtain a plurality of first maximum information coefficient values ​​and a plurality of second maximum information coefficient values, wherein the amplitude correlation analysis includes a mixed frequency amplitude correlation analysis and a divided frequency amplitude correlation analysis; Based on the multiple first maximum information coefficient values ​​and the multiple second maximum information coefficient values, multiple target influencing factors of the local structural vibration of the plant are determined within the range of the first influencing factors.

2. The method according to claim 1, characterized in that Get the target running signal set, including: Get the initial run signal set; The initial operation signal set is preprocessed to obtain the target operation signal set.

3. The method according to claim 1, characterized in that Based on the target operation signal set and the operation data set, after being processed by a frequency analysis method, a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set and a first influencing factor range are obtained, including: Based on the operation data set, the target operation signal set is processed using the frequency analysis method to obtain the local structure vibration frequency analysis data set of the plant; Based on the plant local structure vibration frequency analysis dataset, the target vibration frequency analysis dataset and the first influencing factor range are determined.

4. The method according to claim 3, characterized in that Determining the target vibration frequency analysis dataset and the first influencing factor range based on the local structure vibration frequency analysis dataset of the plant includes: Determine the vibration source and transmission path based on the local structural vibration frequency analysis data set of the plant; Based on the vibration source and the transmission path, determining the first influencing factor range and multiple measuring point positions; Based on the plant local structure vibration frequency analysis dataset, the target vibration frequency analysis dataset at the plurality of measuring point locations is determined.

5. The method according to claim 1, characterized in that Based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform amplitude correlation analysis of the plant local structure vibration response and each influencing factor, and multiple first maximum information coefficient values ​​and multiple second maximum information coefficient values ​​are obtained, including: Based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, a maximum information coefficient method is used to perform correlation analysis between the mixed frequency amplitude of the plant local structure vibration response and the mixed frequency amplitude of each influencing factor to obtain the multiple first maximum information coefficient values; Based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, the maximum information coefficient method is used to perform correlation analysis on the frequency division amplitude of the plant local structure vibration response and the frequency division amplitude of each influencing factor to obtain the multiple second maximum information coefficient values.

6. The method according to claim 1, characterized in that Based on the multiple first maximum information coefficient values ​​and the multiple second maximum information coefficient values, multiple target influencing factors of the local structural vibration of the plant are determined within the range of the first influencing factors, including: Based on the multiple first maximum information coefficient values, sorting the influencing factors within the first influencing factor range to obtain a sorted second influencing factor range; Based on the multiple second maximum information coefficient values, sorting the influencing factors within the first influencing factor range to obtain a sorted third influencing factor range; Based on the second influencing factor range and the third influencing factor range, the multiple target influencing factors of the local structural vibration of the plant are determined.

7. A device for identifying factors affecting vibration of local structure of a plant, used in a pumped storage power station, wherein the pumped storage power station comprises a pump turbine generator set and a plant structure; characterized in that: The device comprises: an acquisition module, used for acquiring a target operation signal set and an operation data set when the pumped storage power station is in operation, wherein the operation signal set includes a vibration signal and a pressure pulsation signal, and the operation data set includes plant structure vibration data, unit vibration data of the pump turbine generator set, and unit pressure pulsation data; A processing module, configured to obtain a plant local structure vibration frequency analysis data set, a target vibration frequency analysis data set and a first influencing factor range based on the target operation signal set and the operation data set through frequency analysis method, wherein the target vibration frequency analysis data set includes unit measuring point vibration frequency analysis data and pressure pulsation frequency analysis data; An analysis module, for performing amplitude correlation analysis of the vibration response of the local structure of the plant and various influencing factors using a maximum information coefficient method based on the plant local structure vibration frequency analysis data set and the target vibration frequency analysis data set, to obtain a plurality of first maximum information coefficient values ​​and a plurality of second maximum information coefficient values, wherein the amplitude correlation analysis includes a mixed frequency amplitude correlation analysis and a divided frequency amplitude correlation analysis; A determination module is used to determine multiple target influencing factors of the local structural vibration of the plant within the range of the first influencing factors based on the multiple first maximum information coefficient values ​​and the multiple second maximum information coefficient values.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for identifying factors affecting vibration of a local structure of a plant according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for identifying factors affecting vibration of the local structure of a plant according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the method for identifying factors affecting vibration of a local structure of a plant according to any one of claims 1 to 6.

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