Method, device and equipment for positioning hydraulic interference source of pumped storage power station and medium

Abstract

The invention relates to the technical field of dynamic analysis and predictive control of hydraulic machinery, and discloses a method, a device, equipment and a medium for positioning a hydraulic interference source of a pumped storage power station, and the method comprises the steps: constructing a non-uniform density monitoring network of the pumped storage power station; generating a spatial frequency domain matrix of the characteristic frequency band based on the non-uniform density monitoring network, wherein the spatial frequency domain matrix comprises amplitude distribution and phase distribution; based on the phase distribution, a preset inversion algorithm is adopted to solve an interference source space positioning result; and calculating a turbulent energy contribution rate based on the interference source space positioning result and the amplitude distribution, and performing pumped storage power station vortex system energy risk assessment based on the turbulent energy contribution rate. According to the method, the three-dimensional coordinate accurate locking of the hydraulic interference source is realized, the turbulent energy contribution rate is calculated based on the interference source positioning result and the amplitude distribution, the pumped storage power station vortex system energy risk assessment is realized, and the sub-meter positioning and high-risk early warning capabilities are provided for the hydraulic vibration source of the one-pipe multi-machine system of the pumped storage power station.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic analysis and predictive control of hydraulic machinery, and in particular to a method, device, equipment and medium for locating a hydraulic interference source in a pumped storage power station. Background Art

[0002] Pumped-storage power stations frequently experience operating mode transitions during frequency and peak regulation, inducing transient hydraulic interference phenomena (such as vortex bands in the tailwater tube, jets between guide vanes, and Karman vortex streets in the diverter piers). This leads to excessive pressure pulsation amplitudes (>0.2 MPa) and structural fatigue failure. Traditional monitoring methods have three major drawbacks: (1) Insufficient coverage of measurement points: Due to factors such as sensor cost, installation space, wiring complexity, signal interference, and equipment operation safety, in actual tests, sensors can only be sparsely arranged (usually ≤50 points / m) in extremely limited and accessible key sections (such as volute inlet and draft tube outlet). 3 Even lower), it is unable to capture the transient vortex structure in the high gradient area (such as the runner outlet and the guide vane gap), and the missed detection rate is high.

[0003] (2) Low positioning accuracy: Existing positioning technology is highly dependent on the correlation of signals between limited measurement points. Due to the sparseness and poor spatial distribution of measurement points, the signal attenuates and distorts during propagation. In addition, the phase characteristics of pressure wave propagation are ignored, resulting in positioning errors of interference sources often exceeding 5 meters.

[0004] (3) Lack of risk quantification: Traditional monitoring mainly relies on pressure pulsation amplitude as a risk criterion. Limited physical sensors can neither directly measure vorticity nor effectively capture these small-scale, high-frequency, and randomly positioned transient vortex / cavitation structures and their evolution processes. It is even more difficult to provide early warning and quantitative assessment of such hidden risks.

[0005] In summary, the pressure pulsation monitoring in the existing technology adopts uniform point distribution or empirical point distribution, which cannot accurately capture transient vortex sources (such as gap vortex, Karman vortex street) and pressure wave reflection sources. Summary of the Invention

[0006] In view of this, the present invention provides a method, device, equipment and medium for locating hydraulic interference sources in a pumped storage power station to solve the problem of being unable to accurately capture transient vortex sources and pressure wave reflection sources due to insufficient distribution points.

[0007] In a first aspect, the present invention provides a method for locating a hydraulic interference source in a pumped storage power station, the method comprising: Constructing a non-uniform density monitoring network for pumped storage power stations; Generate a spatial frequency domain matrix of characteristic frequency bands based on a non-uniform density monitoring network, the spatial frequency domain matrix includes amplitude distribution and phase distribution; Based on the phase distribution, a preset inversion algorithm is used to solve the spatial positioning result of the interference source; The turbulent kinetic energy contribution rate is calculated based on the spatial positioning results of the interference source and the amplitude distribution, and the vortex energy risk assessment of the pumped storage power station is carried out based on the turbulent kinetic energy contribution rate.

[0008] The present invention provides a method for locating hydraulic interference sources in a pumped-storage power station. By constructing a non-uniform density monitoring network for the pumped-storage power station, the signal acquisition density in key interference-sensitive areas is ensured, overcoming the technical bottlenecks of insufficient measurement point coverage, large positioning errors, and lack of vortex structure risk quantification in traditional methods. A spatial frequency domain matrix including amplitude distribution and phase distribution is generated based on the non-uniform density monitoring network to extract characteristic frequency bands rather than full-frequency domain analysis. This focuses on the frequency range with the most significant hydraulic interference, allowing subsequent phase inversion and energy assessment to be based on high signal-to-noise ratio data, thereby improving feature analysis accuracy. A preset inversion algorithm is used based on the phase distribution to solve the spatial coordinates of the interference source, breaking through the limitation of only knowing the area but not the specific location, and achieving precise locking of the three-dimensional coordinates of the hydraulic interference source. The turbulent kinetic energy contribution rate is calculated based on the interference source positioning results and the amplitude distribution, realizing energy risk assessment of the vortex system of the pumped-storage power station, providing sub-meter positioning and high-risk warning capabilities for the hydraulic vibration source of the one-pipe multi-machine system of the pumped-storage power station, and solving the problem of being unable to accurately capture transient vortex sources and pressure wave reflection sources due to insufficient point distribution.

[0009] In an optional embodiment, a non-uniform density monitoring network for a pumped storage power station is constructed, including: Pre-simulation of transient vorticity and pressure gradient fields of pumped storage power stations based on computational fluid dynamics; Based on the transient vorticity field and pressure gradient field, high vortex intensity areas and low gradient areas are determined. Based on the intrinsic coupling mechanism between pressure pulsation and the high vortex intensity areas and low gradient areas respectively, multiple monitoring points are set in the high vortex intensity areas and low gradient areas according to preset point density constraints, and multiple monitoring points are combined to form a non-uniform density monitoring network for pumped storage power stations.

[0010] This invention provides a method for locating hydraulic interference sources in pumped-storage power stations. The flow field in a pumped-storage power station is highly transient, and the transient vorticity and pressure gradient fields pre-simulated using CFD can reflect the dynamic changes in the flow field. Based on this non-uniform network, the distribution of monitoring points closely matches the dynamic interference characteristics of the flow field. This avoids the passive situation of traditional fixed uniform point distribution, which can lead to an overabundance of points in some areas and a shortage of points in others when the flow field changes. This ensures that the monitoring network maintains stable monitoring capabilities under all operating conditions. The data collected by the non-uniform network is characterized by high density in key areas and low redundancy in non-critical areas, directly improving the accuracy of subsequent spatial-frequency domain matrix generation.

[0011] In an optional embodiment, a spatial frequency domain matrix of a characteristic frequency band is generated based on a non-uniform density monitoring network, where the spatial frequency domain matrix includes an amplitude distribution and a phase distribution, including: The pressure pulsation time domain signal of each monitoring point is collected synchronously, and the pressure pulsation time domain signal of each monitoring point is transformed by fast Fourier transform to generate the spatial frequency domain matrix of the characteristic frequency band. The formula of the spatial frequency domain matrix is ​​as follows: ; in, is the spatial frequency domain matrix, is the amplitude distribution, φ is the phase difference relative to the center of the pumped storage power station rotor, in radians, is the three-axis coordinate of each monitoring point, in meters, is the characteristic band frequency, in Hz.

[0012] The present invention provides a method for locating the hydraulic interference source of a pumped-storage power station, which synchronously collects the pressure pulsation time domain signal of each monitoring point, and uses fast Fourier transform to generate a spatial frequency domain matrix of the characteristic frequency band for the pressure pulsation time domain signal of each monitoring point, thereby realizing three-dimensional fusion of the time domain, frequency domain and spatial domain of the pressure pulsation signal, improving the amount of data information, accurately characterizing the amplitude and phase characteristics of the hydraulic interference, providing a core basis for positioning, adapting to the characteristics of non-uniform monitoring networks, optimizing data utilization and computing efficiency, supporting multi-band collaborative analysis, and adapting to the complex hydraulic environment of pumped-storage power stations.

[0013] In an optional implementation, after generating the spatial frequency domain matrix of the characteristic frequency band, the method for locating a hydraulic interference source of a pumped storage power station further includes: Deploy a pressure sensor array in a pumped storage power station and collect the actual pressure pulsation time domain signal of the pumped storage power station; Based on the digital twin model, the actual pressure pulsation time domain signal is fused and compared with the pressure pulsation time domain signal of each monitoring point in the pre-simulated pumped storage power station to verify the accuracy of the pressure pulsation time domain signal of each monitoring point in the pre-simulated pumped storage power station.

[0014] The present invention provides a method for locating hydraulic interference sources in a pumped-storage power station. After generating a spatial frequency domain matrix of a characteristic frequency band, the method deploys a pressure sensor array to collect actual signals and fuses and compares them with digital twin pre-simulation signals, verifies the accuracy of pre-simulation data, corrects the deviation of the original digital twin model, verifies the rationality of the monitoring point layout, optimizes the non-uniform density network, and ultimately improves the accuracy, reliability and engineering application value of the hydraulic interference source positioning method.

[0015] In an optional implementation, based on the phase distribution, a preset inversion algorithm is used to solve the interference source spatial positioning result, including: Calculate the pressure wave propagation velocity field based on the phase distribution using a preset phase lag equation; Based on the pressure wave propagation velocity field, a phase difference equation is constructed between the phase difference of each monitoring point and the phase difference of a preset phase reference point; Perform Taylor expansion linearization on the phase difference equation to obtain the linearized phase difference equation; The linearized phase difference equation is converted into a least squares problem, and based on the least squares problem, a preset inversion algorithm is used to iteratively solve the interference source spatial positioning result, which includes the interference source spatial coordinates and the impact radius.

[0016] The present invention provides a method for locating hydraulic interference sources in a pumped-storage power station. Based on the inversion algorithm of phase distribution, mathematical modeling, linear simplification, and least squares iteration are used to accurately calculate the spatial coordinates and influence radius of the hydraulic interference source. This method not only provides core data for flow field diagnosis and risk assessment, but also solves the nonlinear and multivariable problems of interference source positioning in complex hydraulic environments, providing scientific support for the safe operation and precise operation and maintenance of pumped-storage power stations.

[0017] In an optional embodiment, the turbulent kinetic energy contribution rate is calculated based on the spatial positioning result and amplitude distribution of the interference source, and the vortex energy risk assessment of the pumped storage power station is performed based on the turbulent kinetic energy contribution rate, including: extracting a three-dimensional amplitude field of the amplitude distribution and identifying high-amplitude regions of the three-dimensional amplitude field; The volume of the spatially connected domain is extracted from the high-amplitude region, and the contribution rate of the turbulent kinetic energy is calculated based on the volume of the spatially connected domain; When the turbulent kinetic energy contribution rate is greater than the preset contribution rate threshold, the high-amplitude area corresponding to the turbulent kinetic energy contribution rate is marked as a high-risk area, and the equivalent radius of the high-risk area is calculated; Risk assessment of vortex energy in pumped storage power stations is conducted based on high-risk areas and corresponding equivalent radii.

[0018] The present invention provides a method for locating the source of hydraulic interference in a pumped-storage power station. The vortex energy risk assessment converts the potential hazards of hydraulic interference into measurable, traceable, and controllable indicators by quantifying the contribution of turbulent kinetic energy, marking high-risk areas, and defining the scope of impact. It not only provides accurate early warning for the safe operation of the pumped-storage power station, but also optimizes the allocation of operation and maintenance resources, promotes the iterative upgrade of the digital twin model, and ultimately achieves the goal of knowing, controlling, and preventing risks.

[0019] In an optional embodiment, extracting a three-dimensional amplitude field of the amplitude distribution and identifying a high-amplitude region of the three-dimensional amplitude field includes: The spatial frequency domain matrix is ​​scanned in a sliding window of a preset interference characteristic frequency band, and the peak amplitude of each spatial point in the spatial frequency domain matrix within the interference characteristic frequency band is extracted to obtain a three-dimensional amplitude field; A three-dimensional amplitude field whose peak amplitude within the interference characteristic frequency band is greater than a preset amplitude threshold is identified as a high-amplitude region.

[0020] The present invention provides a method for locating hydraulic interference sources in a pumped-storage power station, which extracts three-dimensional amplitude fields and identifies high-amplitude areas. By focusing on characteristic frequency bands, standardizing thresholds, and strengthening spatial correlation, it provides an accurate, objective, and efficient analysis basis for vortex energy risk assessment, ensuring that the risk assessment results can truly reflect the spatial distribution and energy intensity of hydraulic interference, and providing a reliable basis for the safety management and control of pumped-storage power stations.

[0021] In a second aspect, the present invention provides a device for locating a hydraulic interference source of a pumped storage power station, the device comprising: Intelligent point distribution module, used to build a non-uniform density monitoring network for pumped storage power stations; A spatial frequency analysis engine module is used to generate a spatial frequency domain matrix of characteristic frequency bands based on a non-uniform density monitoring network. The spatial frequency domain matrix includes amplitude distribution and phase distribution. The interference source positioning module is used to solve the interference source spatial positioning result based on the phase distribution and the preset inversion algorithm; The risk assessment terminal module is used to calculate the turbulent kinetic energy contribution rate based on the spatial positioning result of the interference source and the amplitude distribution, and to perform vortex energy risk assessment of the pumped storage power station based on the turbulent kinetic energy contribution rate.

[0022] In an optional embodiment, the intelligent point distribution module includes: A pre-simulation unit, used to pre-simulate the transient vorticity field and pressure gradient field of a pumped storage power station based on computational fluid dynamics; The intelligent point distribution unit is used to determine the high vortex intensity area and the low gradient area based on the transient vortex field and the pressure gradient field, and based on the intrinsic coupling mechanism between the pressure pulsation and the high vortex intensity area and the low gradient area respectively, set multiple monitoring points in the high vortex intensity area and the low gradient area according to the preset point distribution density constraint conditions, and combine multiple monitoring points to form a non-uniform density monitoring network of the pumped storage power station.

[0023] In an optional embodiment, the spatial frequency analysis engine module includes: The spatial frequency domain matrix generation unit is used to synchronously collect the pressure pulsation time domain signal of each monitoring point, and use fast Fourier transform to generate the spatial frequency domain matrix of the characteristic frequency band for the pressure pulsation time domain signal of each monitoring point. The formula of the spatial frequency domain matrix is ​​as follows: ; in, is the spatial frequency domain matrix, is the amplitude distribution, φ is the phase difference relative to the center of the pumped storage power station rotor, in radians, is the three-axis coordinate of each monitoring point, in meters, is the characteristic band frequency, in Hz.

[0024] In an optional embodiment, the device further comprises: The twin-measured data fusion module is used to deploy a pressure sensor array in a pumped-storage power station and collect the actual pressure pulsation time domain signal of the pumped-storage power station; based on the digital twin model, the actual pressure pulsation time domain signal is fused and compared with the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped-storage power station to verify the accuracy of the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped-storage power station.

[0025] In an optional implementation, the interference source locating module includes: A pressure wave propagation velocity field calculation unit, configured to calculate the pressure wave propagation velocity field based on the phase distribution using a preset phase lag equation; A phase difference equation construction unit, configured to construct a phase difference equation between the phase difference at each monitoring point and the phase difference at a preset phase reference point based on the pressure wave propagation velocity field; A linearization processing unit, used for performing Taylor expansion linearization processing on the phase difference equation to obtain a linearized phase difference equation; The interference source positioning unit is used to convert the linearized phase difference equation into a least squares problem, and based on the least squares problem, a preset inversion algorithm is used to iteratively solve the interference source spatial positioning result, which includes the interference source spatial coordinates and the influence radius.

[0026] In an optional embodiment, the risk assessment terminal module includes: a high-amplitude region identification unit, configured to extract a three-dimensional amplitude field of the amplitude distribution and identify high-amplitude regions of the three-dimensional amplitude field; A turbulent kinetic energy contribution rate calculation unit is used to extract the volume of the spatially connected domain from the high-amplitude region and calculate the turbulent kinetic energy contribution rate based on the volume of the spatially connected domain; A high-risk area marking and equivalent radius calculation unit is used to mark the high-amplitude area corresponding to the turbulent kinetic energy contribution rate as a high-risk area when the turbulent kinetic energy contribution rate is greater than a preset contribution rate threshold, and calculate the equivalent radius of the high-risk area; The risk assessment unit is used to perform risk assessment on the vortex energy of the pumped storage power station based on the high-risk area and the corresponding equivalent radius.

[0027] In an optional embodiment, the high-amplitude region identification unit includes: The three-dimensional amplitude field extraction subunit is used to scan the spatial frequency domain matrix in a preset interference characteristic frequency band sliding window, extract the peak amplitude of each spatial point in the spatial frequency domain matrix within the interference characteristic frequency band, and obtain a three-dimensional amplitude field; The high-amplitude region identification subunit is used to identify a three-dimensional amplitude field whose peak amplitude within the interference characteristic frequency band is greater than a preset amplitude threshold as a high-amplitude region.

[0028] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for locating hydraulic interference sources in a pumped-storage power station according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0029] 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 locating hydraulic interference sources in a pumped-storage power station according to the first aspect or any corresponding embodiment thereof.

[0030] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the method for locating a hydraulic interference source in a pumped-storage power station according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 is a flow chart of a method for locating a hydraulic interference source in a pumped storage power station according to an embodiment of the present invention; Figure 2 is a flow chart of another method for locating a hydraulic interference source in a pumped storage power station according to an embodiment of the present invention; Figure 3 is a flow chart of another method for locating a hydraulic interference source in a pumped storage power station according to an embodiment of the present invention; Figure 4 is a schematic diagram of a high-amplitude region identification process according to an embodiment of the present invention; Figure 5 is a flow chart of another method for locating a hydraulic interference source in a pumped storage power station according to an embodiment of the present invention; Figure 6 is a real-time diagram of a pumped storage unit according to an embodiment of the present invention; Figure 7 2. It is a schematic diagram of the arrangement of a tailwater pipe monitoring network in a pumped storage unit according to an embodiment of the present invention; Figure 8 is a structural block diagram of a device for locating a hydraulic interference source in a pumped storage power station according to an embodiment of the present invention; Figure 9 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying 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 making creative efforts shall fall within the scope of protection of the present invention.

[0034] Traditional vibration monitoring methods have the disadvantage of insufficient measurement point coverage. Conventional CFD (Computational Fluid Dynamics) analysis only sets points on a small number of cross sections (such as inlet / outlet), and cannot construct spatial frequency domain distribution and evenly distribute points (≤50 points / m 3 ) cannot capture the transient vortex structure in high gradient areas (such as the runner outlet and guide vane gap), and the missed detection rate is high. Traditional pressure pulsation monitoring adopts uniform distribution or empirical distribution, and its pressure pulsation monitoring mainly relies on the pressure pulsation amplitude as the risk criterion, which seriously ignores the inherent coupling mechanism between pressure pulsation and the core characteristics of the flow field (such as vortex field and turbulent energy field). Pressure pulsation is the result and external manifestation of the dynamic changes of flow field structures such as vortex motion and flow separation. Limited physical sensors can neither directly measure the vortex amount nor effectively capture these small-scale, high-frequency, and randomly positioned transient vortex / cavitation structures and their evolution process. It is even more difficult to provide early warning and quantitative evaluation of such hidden risks. When monitoring the pressure pulsation amplitude, the vortex energy contribution rate ( E vortex ), which can easily lead to the problem of not being able to accurately capture transient vortex sources (such as gap vortex, Karman vortex street) and pressure wave reflection sources (such as water hammer wave).

[0035] An embodiment of the present invention provides a method for locating hydraulic interference sources in a pumped-storage power station. By constructing a non-uniform density monitoring network, extracting three-dimensional frequency domain features, and quantitatively evaluating vortex energy, high-precision spatial positioning and risk assessment of hydraulic vibration sources are achieved, effectively solving the problem of being unable to accurately capture transient vortex sources and pressure wave reflection sources due to insufficient point distribution.

[0036] According to an embodiment of the present invention, an embodiment of a method for locating a hydraulic interference source in a pumped storage power station 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.

[0037] In this embodiment, a method for locating hydraulic interference sources in a pumped storage power station is provided, which can be used in a computer device to perform CFD pre-simulation and state control on a pumped storage unit in a pumped storage power station. Figure 1 FIG. 1 is a flow chart of a method for locating a hydraulic interference source in a pumped storage power station according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps: Step S101: constructing a non-uniform density monitoring network for a pumped storage power station.

[0038] Specifically, based on the computational fluid dynamics (CFD) simulation of a real pumped storage power station, the internal flow field of the pumped storage power station is deeply analyzed. By obtaining the transient vorticity field Ω and pressure gradient field | |, can accurately locate areas with significant hydraulic characteristics.

[0039] In the high vortex area, the condition is met or The setting of monitoring points is extremely critical in these areas. These areas are often prone to hydraulic interference, such as the tailwater pipe cone section, where the water flow is prone to form complex vortex structures, affecting the stable operation of the unit; the runner outlet area, where the water flow state behind the blade outlet edge is directly related to the energy conversion efficiency; the guide vane gap area, where the high-speed water flow in the gap will produce strong vortices and secondary flows; the tail of the diversion pier, which is prone to produce special flow states such as Karman vortex streets. Therefore, in these high vortex intensity areas, the monitoring point density is set to be greater than or equal to 300 points / m 3 , that is, arrange sensor arrays at each monitoring point to ensure that subtle flow field changes can be captured. Taking the runner outlet area as an example, 0.2D behind the blade outlet edge t (D t Within the range of (= the runner diameter), 8 rings × 32 points are evenly distributed along the circumference, which can effectively capture the blade passing frequency pulsation and provide accurate data for subsequent analysis of blade operating status and hydraulic interference.

[0040] In low gradient areas, the flow field is relatively stable and the density of monitoring points can be appropriately reduced to less than or equal to 100 points / m 3To avoid wasting resources while ensuring monitoring of the overall flow field trend. The total number of monitoring points in the entire monitoring network is controlled between 8,000 and 12,000. While ensuring comprehensive monitoring, it also takes into account the feasibility of data processing and transmission, realizing the construction of a non-uniform density monitoring network.

[0041] Step S102: generating a spatial frequency domain matrix of a characteristic frequency band based on the non-uniform density monitoring network, wherein the spatial frequency domain matrix includes an amplitude distribution and a phase distribution.

[0042] Specifically, in the monitoring and analysis of hydraulic disturbances in pumped storage power stations, the spatial frequency domain matrix is ​​a multidimensional data structure that integrates spatial position information and frequency domain characteristics, and is used to quantify the distribution of hydraulic pulsation intensity at different spatial points in the flow field within a specific frequency range.

[0043] The spatial-frequency matrix is ​​a three-dimensional complex matrix indexed by spatial coordinates (x, y, z) as rows and columns and frequency f as another dimension. Each element in the matrix represents the hydraulic pulsation amplitude (or energy) at a spatial point at frequency f, intuitively reflecting the intensity of the hydraulic disturbance at that spatial location at a specific frequency. By analyzing the spatial and frequency dimensions of the matrix, the amplitude and phase distributions can be extracted, respectively.

[0044] Phase distribution refers to the spatial distribution of the phase angle (or phase difference) of hydraulic pulsation signals at different spatial points in the flow field at a specific frequency. It reflects the time difference or spatial lag relationship of the signal during propagation and is used for interference source location, propagation path analysis, and dynamic consistency verification.

[0045] Amplitude distribution refers to the intensity (amplitude) distribution of hydraulic pulsation signals (such as pressure pulsation and velocity pulsation) at different spatial points in the flow field in the time domain or frequency domain. It is used to quantify pulsation intensity, identify high-risk areas, and evaluate energy characteristics.

[0046] Step S103: Based on the phase distribution, a preset inversion algorithm is used to solve the interference source spatial positioning result.

[0047] Specifically, the default inversion algorithm is the phase gradient inversion algorithm. In flow field monitoring, acoustic imaging, or structural dynamic analysis, the phase gradient inversion algorithm is a numerical method that analyzes the spatial variation of the signal phase (phase gradient) to infer the core characteristics of the physical field (such as wave source location, propagation direction, and velocity field distribution). Its core logic is that the spatial gradient of the phase is mathematically related to the propagation characteristics of the physical field (such as wave number, flow velocity, and vortex rotation direction). By quantifying the gradient characteristics, the hidden physical parameters can be inverted.

[0048] Interference source spatial localization results refer to the final output of the specific location coordinates (usually relative to a reference coordinate system) of hydraulic interference sources (such as noise, vibration, and flow field vortex sources) in three-dimensional space, determined through phase gradient inversion, along with their core characteristic parameters (such as intensity, frequency, and propagation direction). This is the core achievement of interference source localization technology and is directly used to assess the impact range of interference.

[0049] The three-dimensional phase distribution at the target characteristic frequency is extracted from the spatial frequency domain matrix, and the phase difference of each monitoring point relative to the reference point (such as the center of the wheel) is clarified to form a phase space map. The phase gradient inversion algorithm is used to reversely infer the position of the interference source by analyzing the spatial variation law of the phase, and the spatial positioning result of the interference source is output.

[0050] Step S104 , calculating the turbulent kinetic energy contribution rate based on the interference source spatial positioning result and the amplitude distribution, and performing an energy risk assessment of the vortex system of the pumped storage power station based on the turbulent kinetic energy contribution rate.

[0051] Specifically, the turbulent kinetic energy contribution rate refers to the ratio of the turbulent kinetic energy of a specific area in the flow field (such as the vortex concentration area around the interference source) to the total turbulent kinetic energy of the flow field. It is a key indicator for quantifying the degree of local energy concentration. This ratio can be used to intuitively judge the weight of the influence of local vortex energy on the overall flow field energy. In engineering scenarios such as pumped-storage power stations, a high turbulent kinetic energy contribution rate often means that the vortex energy in the area is active, which may cause risks such as unit vibration and efficiency reduction. Therefore, it has become an important quantitative basis for assessing vortex energy risks.

[0052] If the turbulent kinetic energy contribution rate is lower than the preset safety threshold, it indicates that the vortex energy is dispersed, and the impact on the unit vibration and efficiency is small, and the risk level is low; if the turbulent kinetic energy contribution rate exceeds the preset safety threshold, it means that the local vortex energy is highly concentrated, which may cause water pulsation, fatigue damage of the runner blades and other problems. The risk level is high, and it is necessary to further define the scope of the high-risk area (such as the equivalent radius) and put forward control suggestions.

[0053] The method for locating hydraulic interference sources in a pumped-storage power station provided in this embodiment establishes a non-uniform density monitoring network for the pumped-storage power station to ensure signal acquisition density in key interference-sensitive areas. This overcomes the technical bottlenecks of traditional methods, such as insufficient measurement point coverage, large positioning errors, and a lack of quantified vortex structure risk. Based on the non-uniform density monitoring network, a spatial frequency domain matrix, including amplitude and phase distributions, is generated to extract characteristic frequency bands rather than full-frequency domain analysis. This focuses on the frequency range with the most significant hydraulic interference, enabling subsequent phase inversion and energy assessment to be based on high-signal-to-noise ratio data, improving feature resolution accuracy. A preset inversion algorithm (phase gradient inversion) is used based on the phase distribution to determine the spatial coordinates of the interference source, breaking the limitation of only knowing the region but not the specific location. This allows for precise three-dimensional positioning of the hydraulic interference source. The turbulent kinetic energy contribution rate is calculated based on the interference source positioning results and amplitude distribution, enabling energy risk assessment of the vortex system in the pumped-storage power station. This provides sub-meter positioning and high-risk early warning capabilities for hydraulic vibration sources in a single-pipe, multi-machine system in a pumped-storage power station, addressing the inability to accurately capture transient vortex sources and pressure wave reflection sources due to insufficient point distribution.

[0054] In this embodiment, a method for locating hydraulic interference sources in a pumped storage power station is provided, which can be used in a computer device to perform CFD pre-simulation and state control on a pumped storage unit in a pumped storage power station. Figure 2 FIG. 1 is a flow chart of a method for locating a hydraulic interference source in a pumped storage power station according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps: Step S201: constructing a non-uniform density monitoring network for a pumped storage power station.

[0055] Specifically, the above step S201 includes: Step S2011 , pre-simulating the transient vorticity field and pressure gradient field of the pumped storage power station based on computational fluid dynamics.

[0056] Specifically, the CFD pre-simulation is based on a digital twin model, which is linked to the real pumped storage power station in the following ways: Geometric mapping: The pumped storage power station building information model (BIM model, Building Information Modeling) is converted into the CFD calculation domain. The mesh size of the key area (runner, guide vane, bifurcation pipe) is ≤1.5mm, and the boundary layer y + ≤1.

[0057] Working condition matching: Load real-time data (head H, flow Q, speed n) from the Supervisory Control and Data Acquisition (SCADA) system to ensure that the error between the CFD boundary conditions and the actual operating conditions is ≤3%.

[0058] After completing the above steps, the transient vorticity field Ω and pressure gradient field |▽p| of the real pumped storage power station are pre-simulated based on computational fluid dynamics (CFD).

[0059] Step S2012: determine the high vortex intensity area and the low gradient area based on the transient vortex field and the pressure gradient field, and set multiple monitoring points in the high vortex intensity area and the low gradient area according to the preset point density constraint conditions based on the intrinsic coupling mechanism between the pressure pulsation and the high vortex intensity area and the low gradient area respectively, and combine the multiple monitoring points to form a non-uniform density monitoring network of the pumped storage power station.

[0060] Specifically, the aforementioned content states that there is an intrinsic coupling mechanism between pressure pulsation and core flow field characteristics (such as the vorticity field and turbulent kinetic energy field). Pressure pulsation is the result and external manifestation of dynamic changes in flow field structures such as vortex motion and flow separation. The intrinsic coupling mechanisms between pressure pulsation and high vortex intensity areas and low gradient areas, respectively, include: In meeting the conditions or In high vortex intensity areas / pressure mutation areas, the monitoring point density is set to be greater than or equal to 300 points / m 3 ; Set the density to be less than or equal to 100 points / m in low gradient areas 3 The total number of monitoring points is 10 4 Magnitude to 10 5 Magnitude.

[0061] Furthermore, the high vortex intensity area includes the tailwater tube cone section, the runner outlet area, the guide vane gap area and the tail of the diversion pier, and its monitoring point density is 300 to 500 points / m 3 The monitoring points are placed using an intelligent placement algorithm that performs the following operations: Input the transient vorticity field of CFD calculation and pressure gradient field ; Preset point density setting constraints: (1); Output monitoring point coordinate set: (2); in, is the point density (unit: points / m 3 ), is the vorticity field, is the pressure gradient, is the coordinate of monitoring point i, .

[0062] Based on the calculations of formulas (1) and (2) above and relevant CFD calculation experience, the density layout of monitoring points in key areas inside the pumped storage unit is recommended as shown in Table 1 below: Table 1 Density arrangement of monitoring points in key areas inside pumped storage units

[0063] The density layout recommendations for monitoring points in key areas of the pressure piping system are shown in Table 2 below: Table 2 Density layout of monitoring points in key areas of pressure piping system

[0064] Step S202: Generate a spatial frequency domain matrix of a characteristic frequency band based on the non-uniform density monitoring network. The spatial frequency domain matrix includes an amplitude distribution and a phase distribution.

[0065] Specifically, the above step S202 includes: Step a: synchronously collect the pressure pulsation time domain signal of each monitoring point, and use fast Fourier transform to generate the spatial frequency domain matrix of the characteristic frequency band for the pressure pulsation time domain signal of each monitoring point.

[0066] Among them, step a is run under the GPU (Graphic Processing Unit, graphics processor) parallel architecture, processing 10 4 Magnitude to 10 5 The calculation time for the Fast Fourier Transform (FFT) of a monitoring point is ≤ 10 minutes. The details of the Fast Fourier Transform processing can be implemented by referring to the relevant technologies and will not be repeated here.

[0067] The characteristic frequency band is the hydraulic interference frequency band unique to the one-pipe multi-machine system, and also includes: Water hammer frequency band: 0.1 Hz ≤ f ≤ 2 Hz.

[0068] Unit interference frequency band: 0.3 ≤ f ≤ 0.8 ,in Frequency conversion for the unit.

[0069] The spatial frequency domain matrix also becomes a three-dimensional complex matrix, and its formula is expressed as follows: (3); in, is the spatial frequency domain matrix, is the amplitude distribution, is the phase distribution, φ is the phase difference relative to the center of the pumped storage power station rotor, in radians, is the three-axis coordinate of each monitoring point, i.e., the spatial point, in m. is the characteristic band frequency, in Hz.

[0070] Step S203: deploy a pressure sensor array in the pumped-storage power station and collect the actual pressure pulsation time domain signal of the pumped-storage power station; based on the digital twin model, fuse and compare the actual pressure pulsation time domain signal with the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped-storage power station to verify the accuracy of the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped-storage power station.

[0071] Specifically, the pressure sensor array is a micro pressure sensor array with an accuracy of ±0.1% FS (FS is femtosecond) and a sampling rate of ≥2 kHz.

[0072] In a pumped-storage power station, an array of pressure sensors is initially deployed in key flow path areas (such as the runner, guide vanes, and tailwater tube) to collect real-time pressure pulsation time-domain signals during actual operation. Simultaneously, relying on the pumped-storage power station's digital twin model, pre-simulated pressure pulsation time-domain signals are obtained for each monitoring point. By fusing and comparing the measured and simulated signals, the consistency between the two is specifically verified in key indicators such as characteristic frequency, amplitude variation trends, and phase relationships. This verifies the accuracy of the pre-simulated signals and provides a reliable data foundation for subsequent interference source location and risk assessment.

[0073] After generating the spatial frequency domain matrix of the characteristic frequency band, the actual signal is collected by deploying a pressure sensor array and fused and compared with the digital twin pre-simulation signal to verify the accuracy of the pre-simulation data, correct the deviation of the original digital twin model, verify the rationality of the monitoring point layout, and optimize the non-uniform density network, ultimately improving the accuracy, reliability and engineering application value of the hydraulic interference source positioning method.

[0074] Step S204: Based on the phase distribution, a preset inversion algorithm is used to solve the interference source spatial positioning result. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0075] Step S205: Calculate the turbulent kinetic energy contribution rate based on the interference source spatial positioning result and amplitude distribution, and perform vortex energy risk assessment of the pumped storage power station based on the turbulent kinetic energy contribution rate. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0076] This embodiment provides a method for locating hydraulic interference sources in pumped-storage power stations. The flow field in a pumped-storage power station is highly transient, and the transient vorticity and pressure gradient fields pre-simulated by CFD can reflect the dynamic changes in the flow field. Based on this non-uniform network, the distribution of monitoring points closely matches the dynamic interference characteristics of the flow field. This avoids the passive situation of traditional fixed uniform point distribution, which can lead to excess points in some areas and insufficient points in others when the flow field changes. This ensures that the monitoring network maintains stable monitoring capabilities under all operating conditions. The data collected by the non-uniform network is characterized by high density in key areas and low redundancy in non-critical areas, which directly improves the accuracy of subsequent spatial-frequency domain matrix generation. The pressure pulsation time domain signal of each monitoring point is collected synchronously, and the fast Fourier transform is used to generate the spatial frequency domain matrix of the characteristic frequency band for the pressure pulsation time domain signal of each monitoring point, so as to realize the three-dimensional fusion of the time domain, frequency domain and spatial domain of the pressure pulsation signal, improve the amount of data information, accurately characterize the amplitude and phase characteristics of hydraulic interference, provide the core basis for positioning, adapt to the characteristics of non-uniform monitoring networks, optimize data utilization and computing efficiency, support multi-band collaborative analysis, and adapt to the complex hydraulic environment of pumped storage power stations.

[0077] In this embodiment, a method for locating a hydraulic interference source in a pumped storage power station is provided, which can be used in the above-mentioned computer device to perform CFD pre-simulation and state control on a pumped storage unit in a pumped storage power station. Figure 3 FIG. 1 is a flow chart of a method for locating a hydraulic interference source in a pumped storage power station according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps: Step S301: Construct a non-uniform density monitoring network for a pumped storage power station. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0078] Step S302: Generate a spatial frequency domain matrix of characteristic frequency bands based on the non-uniform density monitoring network. The spatial frequency domain matrix includes amplitude distribution and phase distribution. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0079] Step S303: Based on the phase distribution, a preset inversion algorithm is used to solve the interference source spatial positioning result.

[0080] Specifically, the above step S303 includes: Step S3031 : Calculate the pressure wave propagation velocity field based on the phase distribution using a preset phase lag equation.

[0081] Specifically, the three-dimensional phase distribution at the target characteristic frequency is extracted from the spatial frequency domain matrix, and the phase difference of each monitoring point relative to the reference point (such as the center of the wheel) is determined to form a phase space spectrum, that is, a phase distribution map .

[0082] Using the preset phase lag equation Calculate the pressure wave propagation velocity field (c is the wave velocity, Δs is the distance between monitoring points, and Δt is the arrival time difference of waves with the same frequency). The phase lag equation, a mathematical expression describing the relationship between the phase difference and spatial distance between different monitoring points in the flow field and the interference source, is the core equation for locating interference sources based on phase distribution. The phase lag equation stems from the law of wave propagation: when a pressure wave propagates from an interference source to different monitoring points, the longer the propagation distance, the later the signal arrives, and the greater the phase lag (the phase difference is directly proportional to the propagation path length).

[0083] Furthermore, first, the pressure pulsation phase data of each monitoring point is extracted from the signal collected by the pressure sensor array to construct a flow field phase distribution map; second, the preset phase lag equation (such as the one that describes the relationship between phase difference, propagation distance, and wave velocity) is called, and the known characteristic frequency f of the phase difference between different monitoring points and the spatial coordinates of the monitoring points (i.e., the distance difference) are substituted into the equation; finally, the pressure wave velocity c on each propagation path is inferred by solving the equation, and the global pressure wave propagation velocity field is generated by combining the spatial interpolation method, providing key parameters for subsequent interference source positioning and flow field characteristic analysis.

[0084] Step S3032: constructing a phase difference equation between the phase difference at each monitoring point and the phase difference at a preset phase reference point based on the pressure wave propagation velocity field.

[0085] Specifically, the phase difference equation is expressed as follows: (4); in, For the k Monitoring point phase difference, is the position vector, is the characteristic frequency (Hz), c is the wave speed (m / s), Represents the position vector of the kth monitoring point. The subscript k is used to distinguish different monitoring points (such as the kth sensor in the array), and the vector symbol indicates that it is a vector describing the spatial position (containing coordinate information, such as (x, y, z) in three-dimensional space). The position vector of the wave source (signal source). The subscript source clearly indicates the source of the signal (such as an acoustic wave source, an electromagnetic wave source, etc.). It is also a vector describing the spatial position. Assuming there are N monitoring points (N ≥ 4), the equation for each monitoring point is: (5).

[0086] Step S3033: perform Taylor expansion linearization processing on the phase difference equation to obtain a linearized phase difference equation.

[0087] Specifically, because the equation contains nonlinear terms , Taylor expansion linearization is required: Set the initial guess coordinates (Usually take the point with the most delayed phase), and convert the distance function into At the first-order expansion: (6); Define the distance vector: (7); (8); The system of equations simplifies to: (9).

[0088] Step S3034: convert the linearized phase difference equation into a least squares problem, and iteratively solve the interference source spatial positioning result based on the least squares problem using a preset inversion algorithm. The interference source spatial positioning result includes the interference source spatial coordinates and the influence radius.

[0089] Convert the linearized phase difference equation into a least squares problem, that is, rewrite it into matrix form : (10); in: : N×3 direction cosine matrix, : Coordinate correction amount, : N×1 constant vector, 、 、 All are intermediate variables.

[0090] Iterative solution: ; (11); in, , is the convergence threshold, the spatial coordinates (X, Y, Z) of the interference source and the impact radius R≤2.5m, where the spatial coordinates of the interference source are expressed as a three-dimensional position vector: (12); is the unknown quantity to be solved, which represents the precise spatial position of the interference source in the flow field.

[0091] Step S304 , calculating the turbulent kinetic energy contribution rate based on the interference source spatial positioning result and the amplitude distribution, and performing an energy risk assessment of the vortex system of the pumped storage power station based on the turbulent kinetic energy contribution rate.

[0092] Specifically, the above step S304 includes: Step S3041 , extracting the three-dimensional amplitude field of the amplitude distribution, and identifying high-amplitude regions of the three-dimensional amplitude field.

[0093] In some optional implementations, step S3041 includes: Step b1, scanning the spatial frequency domain matrix in a preset interference characteristic frequency band sliding window, extracting the peak amplitude of each spatial point in the spatial frequency domain matrix within the interference characteristic frequency band, and obtaining a three-dimensional amplitude field.

[0094] Specifically, the steps of extracting the three-dimensional amplitude field are as follows: Input: 3D complex matrix .

[0095] Operation: In the characteristic frequency band of hydraulic interference (such as ) Sliding window scanning, extracting each spatial point Peak amplitude within the characteristic frequency band .

[0096] Output: 3D amplitude field , the formula is as follows: (13); in, They respectively represent the lower and upper limits of the frequency range of the hydraulic interference characteristic frequency band. The frequency range of the frequency band is not fixed. For the unit, the interference frequency band will change with the actual rotation frequency.

[0097] Step b2: identifying the three-dimensional amplitude field whose peak amplitude within the interference characteristic frequency band is greater than a preset amplitude threshold as a high-amplitude region.

[0098] like Figure 4 As shown, the preset amplitude threshold is .

[0099] The peak amplitude is greater than The 3D amplitude field of the image is identified as high-amplitude regions, all high-amplitude regions are marked as candidate regions, and all high-amplitude regions are output using a morphological closing operation. Morphological Closing is a fundamental operation in digital image processing and morphological analysis, primarily used to fill small holes in an image, connect adjacent object edges, or smooth object contours while keeping the overall shape and size of the object essentially unchanged.

[0100] The method for locating hydraulic interference sources in a pumped-storage power station provided in this embodiment extracts three-dimensional amplitude fields and identifies high-amplitude areas. By focusing on characteristic frequency bands, standardizing thresholds, and strengthening spatial correlations, it provides an accurate, objective, and efficient analysis basis for vortex energy risk assessment, ensuring that the risk assessment results can truly reflect the spatial distribution and energy intensity of hydraulic interference, and providing a reliable basis for the safety management and control of pumped-storage power stations.

[0101] Step S3042: extract the volume of the spatially connected domain from the high-amplitude region, and calculate the turbulent kinetic energy contribution rate based on the volume of the spatially connected domain.

[0102] Specifically, extract the volume of the spatially connected domain The specific process is as follows: The three-dimensional amplitude field data are spatially gridded (e.g., divided into 10mm×10mm×10mm cubic grids according to the coordinates of the monitoring points), and high-amplitude grid cells in the grid whose amplitude exceeds a preset threshold (e.g., 1.2 times the average amplitude of the design operating condition) are retained to form a preliminary high-amplitude discrete point set.

[0103] A three-dimensional connectivity analysis algorithm (such as the 6-neighborhood or 26-neighborhood method) is used to identify spatially connected regions where high-amplitude points are concentrated. If two grid cells share a face, edge, or vertex and are both high-amplitude cells, they are considered to be the same connected domain. A morphological closing operation is performed to fill small holes within the connected domain (such as isolated low-amplitude gaps caused by sparse monitoring points) to ensure the morphological integrity of the connected domain.

[0104] Count the number of grid cells contained in each connected domain, combined with the volume of a single grid cell (such as 10 -6 m 3 ), the volume of the spatial connected domain is accumulated (for example, a connected domain contains 500 grid cells, then the volume is 500×10 -6 = 5×10 -4 m 3 For connected domains that are too small (e.g., less than 1×10 -5 m 3 ) to filter out interference from noise or local disturbances.

[0105] Calculation of turbulent kinetic energy contribution rate: (14); in, ; It is the turbulence intensity output by CFD pre-simulation. In turbulence statistical theory, it is usually defined as the root mean square of the turbulent fluctuating velocity component.

[0106] Step S3043: When the turbulent kinetic energy contribution rate is greater than a preset contribution rate threshold, the high-amplitude region corresponding to the turbulent kinetic energy contribution rate is marked as a high-risk area, and the equivalent radius of the high-risk area is calculated.

[0107] Specifically, the preset contribution rate threshold is set at 15%, and the specific criteria for determining high-risk areas are as follows: (15); To satisfy or , calculate the equivalent geometric center and equivalent radius: (16); in, is the equivalent radius of the high-risk area, is the volume of the spatially connected domain.

[0108] In actual calculation, the equivalent geometric center adopts the weighted summation form of the CFD grid, and the formula is as follows: (17); in, Indicates that in the connected domain The total number of CFD grids included; ) indicates that in the connected domain The first j Grid center coordinates; Indicates the j The volume of the grid (unit: m 3 ), 、 、 are the equivalent geometric center coordinates.

[0109] Identify high-risk areas (such as the vortex belt at the runner outlet) in advance to avoid blade cracks or structural resonance accidents and extend the life of the pumped storage unit.

[0110] Step S3044: Perform risk assessment on the vortex energy of the pumped storage power station based on the high-risk areas and the corresponding equivalent radii.

[0111] Specifically, first, clarify the geometric center position and equivalent radius (reflecting the risk diffusion range) of the high-risk area where the turbulent kinetic energy contribution rate exceeds the threshold; second, analyze whether the high-risk area covers key components such as the runner and guide vanes, and compare the energy intensity within the equivalent radius with the safety limit of the components; finally, divide the risk levels into low, medium and high according to the degree of coverage, energy magnitude and whether there is a frequency resonance risk, to provide a basis for formulating targeted control measures (such as optimizing operating parameters or maintenance).

[0112] It should be noted that the risk assessment terminal module outputs a three-dimensional interactive map, marking high-risk areas >15% with red contour lines, and annotating the interference source coordinates and impact radius.

[0113] The method for locating hydraulic interference sources in a pumped-storage power station provided in this embodiment uses a phase distribution-based inversion algorithm to accurately determine the spatial coordinates and impact radius of the hydraulic interference source through mathematical modeling, linear simplification, and least squares iteration. This not only provides core data for flow field diagnosis and risk assessment, but also solves the nonlinear and multivariable problems of locating interference sources in complex hydraulic environments, providing scientific support for the safe operation and precise operation and maintenance of pumped-storage power stations. The vortex energy risk assessment converts the potential hazards of hydraulic interference into measurable, traceable, and controllable indicators by quantifying the contribution of turbulent kinetic energy, marking high-risk areas, and defining the scope of influence. This not only provides accurate early warnings for the safe operation of pumped-storage power stations, but also optimizes the allocation of operation and maintenance resources, promotes the iterative upgrade of digital twin models, and ultimately achieves the goal of knowing, controlling, and preventing risks.

[0114] As one or more specific application embodiments of the present invention, combined with Figures 5 to 7 The method for locating the hydraulic interference source of a pumped storage power station provided by the present invention is further described in detail. The specific process is as follows: The embodiment of the present invention provides a method for locating a hydraulic interference source of a pumped storage power station. Figure 5 As shown, the following steps are included: Step 1: First, a non-uniform density monitoring network is constructed based on the vortex field and pressure gradient field pre-simulated by computational fluid dynamics (CFD). 300 to 500 points / m are set in high vortex intensity areas / pressure mutation areas such as the draft tube cone section and the runner outlet. 3 High-density monitoring points, low gradient area density ≤ 100 points / m 3 , with a total of 8,000 to 12,000 monitoring points.

[0115] Step 2: Generate the pressure pulsation signal by synchronously collecting the pressure pulsation signal and performing fast Fourier transform, including the amplitude distribution A and the phase distribution The three-dimensional complex matrix is ​​used to extract the water hammer wave frequency band (0.1 to 2 Hz) and the unit interference frequency band (0.3f n to 0.8f n ) characteristics.

[0116] Step 3: Use the phase distribution to invert the pressure wave propagation velocity field, accurately solve the spatial coordinates and influence radius of the interference source through the phase gradient iterative algorithm, and output the interference source positioning result.

[0117] Step 4: Calculate the turbulent kinetic energy contribution rate of the vortex core region based on the amplitude field, mark the high-risk area, and output its geometric center and equivalent radius.

[0118] This invention overcomes the technical bottlenecks of insufficient measurement point coverage, large positioning errors, and lack of vortex structure risk quantification of traditional methods through non-uniform point distribution guided by digital twin models, GPU-accelerated spatial frequency domain analysis, and vortex energy quantitative assessment. It provides sub-meter positioning and high-risk early warning capabilities for the hydraulic vibration source of the one-pipe multi-machine system of a pumped-storage power station.

[0119] like Figure 6 As shown, taking the actual situation diagram of the pumped storage unit as an example, the method for locating the hydraulic interference source of the pumped storage power station provided by this embodiment is further described in detail: Step 1) CFD pre-simulation parameters: Working conditions: head 420m, flow 280m 3 / s, speed 300rpm (speed f n =5Hz).

[0120] Digital twin model construction: CFD model is generated through BIM geometry mapping, with the following requirements: grid size of key areas ≤ 1.5 mm; boundary layer y + ≤1; load SCADA real-time data, working condition error ≤3%.

[0121] Twin-measured data fusion: A micro pressure sensor array (accuracy ±0.1% FS, sampling rate ≥ 2 kHz) was deployed in a real power station and compared with the spatial frequency domain matrix.

[0122] Step 2) Monitoring point layout (taking tailwater pipe as an example): Identification results of the tailwater tube cone section: Maximum vortex ( threshold); pressure gradient (>500 Pa / m threshold).

[0123] The monitoring points layout of the tailwater pipe cone section is shown in Table 3 below: Table 3 Layout of monitoring points in the tailwater pipe cone section

[0124] The schematic diagram of the tailwater pipe monitoring network layout in the pumped storage unit is as follows: Figure 7 shown.

[0125] Data acquisition: Synchronous acquisition duration: 80 seconds (covering 400 wheel cycles).

[0126] Characteristic frequency band analysis: low-frequency vortex bands f 1=0.4~2Hz (amplitude 0.28MPa).

[0127] Generate a three-dimensional complex matrix of characteristic frequency bands .

[0128] Step 3) Interference source location: Target frequency: f = 2 Hz (dominant frequency of the low-frequency vortex in the draft tube).

[0129] Reference point: P0 (0,0,0) (center of cone entrance, phase reference =0°).

[0130] The monitoring point data (some key points) are shown in Table 4 below: Table 4 Monitoring point data (some key points)

[0131] Pressure wave velocity: c = 1500m / s (speed of sound in water medium).

[0132] Convergence threshold: ε = 0.05 m.

[0133] The solution steps of the phase gradient inversion algorithm are as follows: (1) Construct the phase difference equation: Establish the wave equation for each monitoring point: ; Substituting the reference point φ0=0°, convert to radians: ; (2) Linearization processing: Initial guess point selection: Take the point P6 (0.05, -0.12, 1.8) with the most delayed phase as the initial guess coordinate: ; Calculate the distance vector: Definition: .

[0134] Take P1 as an example: ; ; Taylor expansion linear approximation: ; Substitute into the phase difference equation: ; in, .

[0135] (3) Construct the least squares problem ( ):matrix Construct (direction cosine matrix), vector B is constructed.

[0136] ; (4) Iterative solution: First iteration: Solve a system of linear equations: ; Calculation results: ; Update guess coordinates: ; Residuals: ; Second iteration: As the starting point, recalculate the distance vector (taking P3 as an example): ; ; renew , and then solve the B matrix: , and update the space coordinates: ; Residuals: (convergence).

[0137] Positioning result: Interference source coordinates (keep two decimal places): (0.08, -0.15, 1.38) m.

[0138] Step 4) Vortex Energy Risk Assessment: Based on the amplitude distribution , calculate the contribution rate of turbulent kinetic energy in the vortex core region ,mark Areas >15% are high-risk vibration areas.

[0139] Feature band amplitude extraction ( ); High amplitude area: (volume ); Calculation of turbulent kinetic energy contribution rate and determination of high-risk areas: It is a high-risk vibration area; Equivalent radius: .

[0140] The embodiments of the present invention have the following key points and beneficial effects: (1) Intelligent deployment of non-uniform monitoring network: Vorticity field based on CFD pre-simulation ( ) and the pressure gradient field ( ) Dynamically adjust the density of monitoring points to break the limitations of traditional uniform distribution.

[0141] High vortex strong area ( ) or high voltage transformer area ( >500Pa / m) density ≥300 points / m 3 , low gradient area ≤ 100 points / m 3 , to accurately capture transient hydraulic disturbances.

[0142] (2) Spatial frequency domain matrix construction method: Generate complex matrix through synchronous acquisition + 3D FFT: ; Contains both amplitude A and phase The spatial distribution of the data provides a holographic data basis for positioning.

[0143] (3) Phase gradient inversion positioning algorithm: Phase difference equation: ; The phase difference equation is used to invert and infer the coordinates of the interference source, and the nonlinear positioning problem is solved through Taylor expansion linearization + least squares iteration (convergence condition ‖ΔP‖<0.05m).

[0144] (5) Vortex energy risk assessment model: Definition of turbulent kinetic energy contribution As a vibration risk indicator, combined with the characteristic frequency band amplitude threshold ( >0.2MPa) and energy threshold ( ), intelligently mark high-risk vibration areas and calculate equivalent radius .

[0145] (6) Digital twin drive implementation: CFD model and BIM geometry mapping (grid ≤ 1.5mm, y + ≤1), load SCADA real-time data (operating condition error ≤3%), and realize dynamic coupling between virtual and physical fields.

[0146] In this embodiment, a device for locating a hydraulic interference source of a pumped storage power station is also provided. The device is used to implement the above-mentioned embodiments and preferred embodiments. The details that have been described will not be repeated here. 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, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0147] This embodiment provides a device for locating hydraulic interference sources in a pumped storage power station. Figure 8 Shown, including: The intelligent point distribution module 801 is used to construct a non-uniform density monitoring network for a pumped storage power station.

[0148] The spatial frequency analysis engine module 802 is used to generate a spatial frequency domain matrix of a characteristic frequency band based on a non-uniform density monitoring network. The spatial frequency domain matrix includes amplitude distribution and phase distribution.

[0149] The interference source positioning module 803 is used to solve the interference source spatial positioning result based on the phase distribution using a preset inversion algorithm.

[0150] The risk assessment terminal module 804 is used to calculate the turbulent kinetic energy contribution rate based on the interference source spatial positioning result and the amplitude distribution, and perform vortex energy risk assessment of the pumped storage power station based on the turbulent kinetic energy contribution rate.

[0151] In some optional implementations, the intelligent point distribution module 801 includes: The pre-simulation unit is used to pre-simulate the transient vorticity field and pressure gradient field of the pumped storage power station based on computational fluid dynamics.

[0152] The intelligent point distribution unit is used to determine the high vortex intensity area and the low gradient area based on the transient vortex field and the pressure gradient field, and based on the intrinsic coupling mechanism between the pressure pulsation and the high vortex intensity area and the low gradient area respectively, set multiple monitoring points in the high vortex intensity area and the low gradient area according to the preset point distribution density constraint conditions, and combine multiple monitoring points to form a non-uniform density monitoring network of the pumped storage power station.

[0153] In some optional implementations, the spatial frequency analysis engine module 802 includes: The spatial frequency domain matrix generation unit is used to synchronously collect the pressure pulsation time domain signal of each monitoring point, and use fast Fourier transform to generate the spatial frequency domain matrix of the characteristic frequency band for the pressure pulsation time domain signal of each monitoring point. The formula of the spatial frequency domain matrix is ​​as follows: ; in, is the spatial frequency domain matrix, is the amplitude distribution, φ is the phase difference relative to the center of the pumped storage power station rotor, in radians, is the three-axis coordinate of each monitoring point, in meters, is the characteristic band frequency, in Hz.

[0154] In some optional implementations, the device for locating a hydraulic interference source of a pumped storage power station further includes: The twin-measured data fusion module is used to deploy a pressure sensor array in a pumped-storage power station and collect the actual pressure pulsation time domain signal of the pumped-storage power station; based on the digital twin model, the actual pressure pulsation time domain signal is fused and compared with the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped-storage power station to verify the accuracy of the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped-storage power station.

[0155] In some optional implementations, the interference source locating module 803 includes: The pressure wave propagation velocity field calculation unit is used to calculate the pressure wave propagation velocity field based on the phase distribution using a preset phase lag equation.

[0156] The phase difference equation construction unit is used to construct a phase difference equation between the phase difference of each monitoring point and the phase difference of a preset phase reference point based on the pressure wave propagation velocity field.

[0157] The linearization processing unit is used to perform Taylor expansion linearization processing on the phase difference equation to obtain a linearized phase difference equation.

[0158] The interference source positioning unit is used to convert the linearized phase difference equation into a least squares problem, and based on the least squares problem, a preset inversion algorithm is used to iteratively solve the interference source spatial positioning result, which includes the interference source spatial coordinates and the influence radius.

[0159] In some optional implementations, the risk assessment terminal module 804 includes: The high-amplitude region identification unit is used to extract the three-dimensional amplitude field of the amplitude distribution and identify the high-amplitude region of the three-dimensional amplitude field.

[0160] The turbulent kinetic energy contribution rate calculation unit is used to extract the volume of the spatially connected domain from the high-amplitude region and calculate the turbulent kinetic energy contribution rate based on the volume of the spatially connected domain.

[0161] The high-risk area marking and equivalent radius calculation unit is used to mark the high-amplitude area corresponding to the turbulent kinetic energy contribution rate as a high-risk area when the turbulent kinetic energy contribution rate is greater than the preset contribution rate threshold, and calculate the equivalent radius of the high-risk area.

[0162] The risk assessment unit is used to perform risk assessment on the vortex energy of the pumped storage power station based on the high-risk area and the corresponding equivalent radius.

[0163] In some optional implementations, the high-amplitude region identification unit includes: The three-dimensional amplitude field extraction subunit is used to scan the spatial frequency domain matrix in the preset interference characteristic frequency band sliding window, extract the peak amplitude of each spatial point in the spatial frequency domain matrix within the interference characteristic frequency band, and obtain the three-dimensional amplitude field.

[0164] The high-amplitude region identification subunit is used to identify a three-dimensional amplitude field whose peak amplitude within the interference characteristic frequency band is greater than a preset amplitude threshold as a high-amplitude region.

[0165] 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.

[0166] The pumped storage power station hydraulic interference source locating device 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.

[0167] The embodiment of the present invention also provides a computer device having the above Figure 8 The hydraulic interference source positioning device of the pumped storage power station shown.

[0168] See also Figure 9 , Figure 9 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 9 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory 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 and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 9 A processor 10 is taken as an example.

[0169] 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 an application-specific 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.

[0170] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0171] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on 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 located 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.

[0172] 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.

[0173] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 9 The bus connection is taken as an example.

[0174] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device. Examples include a touch screen, keypad, mouse, trackpad, touchpad, pointing stick, one or more mouse buttons, trackball, joystick, etc. The output device 40 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibration motors). Such display devices include, but are not limited to, liquid crystal displays, light emitting diodes, monitors, and plasma displays. In some optional embodiments, the display device may be a touch screen.

[0175] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, 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 drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. 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.

[0176] A portion 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 form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. 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 that can be accessed by the computer.

[0177] Although the embodiments of the present invention have been described with reference to 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. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for locating hydraulic interference sources in a pumped storage power station, characterized in that: The method comprises: Constructing a non-uniform density monitoring network for pumped storage power stations; generating a spatial frequency domain matrix of characteristic frequency bands based on the non-uniform density monitoring network, wherein the spatial frequency domain matrix includes amplitude distribution and phase distribution; Based on the phase distribution, a preset inversion algorithm is used to solve the spatial positioning result of the interference source; The turbulent kinetic energy contribution rate is calculated based on the spatial positioning result of the interference source and the amplitude distribution, and the vortex energy risk assessment of the pumped storage power station is performed based on the turbulent kinetic energy contribution rate.

2. The method according to claim 1, characterized in that The construction of the non-uniform density monitoring network for the pumped storage power station includes: Pre-simulation of transient vorticity and pressure gradient fields of pumped storage power stations based on computational fluid dynamics; Based on the transient vorticity field and the pressure gradient field, high vortex intensity areas and low gradient areas are determined, and based on the intrinsic coupling mechanism between the pressure pulsation and the high vortex intensity areas and the low gradient areas, multiple monitoring points are set in the high vortex intensity areas and the low gradient areas according to preset point density constraints, and the multiple monitoring points are combined to form a non-uniform density monitoring network for the pumped storage power station.

3. The method according to claim 2, characterized in that The generating of a spatial frequency domain matrix of characteristic frequency bands based on the non-uniform density monitoring network includes: The pressure pulsation time domain signals of each monitoring point are synchronously collected, and the pressure pulsation time domain signals of each monitoring point are subjected to fast Fourier transform to generate a spatial frequency domain matrix of the characteristic frequency band. The formula of the spatial frequency domain matrix is ​​as follows: ； in, is the spatial frequency domain matrix, is the amplitude distribution, is the phase difference relative to the center of the pumped storage power station rotor, in radians, is the three-axis coordinate of each monitoring point, in meters, is the characteristic band frequency, in Hz.

4. The method according to claim 3, characterized in that After generating the spatial frequency domain matrix of the characteristic frequency bands, the method further includes: Deploying a pressure sensor array in the pumped storage power station and collecting actual pressure pulsation time domain signals of the pumped storage power station; Based on the digital twin model, the actual pressure pulsation time domain signal is fused and compared with the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped storage power station to verify the accuracy of the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped storage power station.

5. The method according to claim 1, wherein Based on the phase distribution, a preset inversion algorithm is used to solve the interference source spatial positioning result, including: Calculating the pressure wave propagation velocity field using a preset phase lag equation based on the phase distribution; Constructing a phase difference equation between the phase difference of each monitoring point and the phase difference of a preset phase reference point based on the pressure wave propagation velocity field; Performing Taylor expansion linearization processing on the phase difference equation to obtain a linearized phase difference equation; The linearized phase difference equation is converted into a least squares problem, and a preset inversion algorithm is used based on the least squares problem to iteratively solve the interference source spatial positioning result, where the interference source spatial positioning result includes the interference source spatial coordinates and the influence radius.

6. The method according to claim 1, characterized in that The calculation of the turbulent kinetic energy contribution rate based on the interference source spatial positioning result and the amplitude distribution, and the performance of the vortex energy risk assessment of the pumped storage power station based on the turbulent kinetic energy contribution rate include: extracting a three-dimensional amplitude field of the amplitude distribution and identifying high-amplitude regions of the three-dimensional amplitude field; Extracting a volume of a spatially connected domain from the high-amplitude region, and calculating a turbulent kinetic energy contribution rate based on the volume of the spatially connected domain; When the turbulent kinetic energy contribution rate is greater than a preset contribution rate threshold, marking the high-amplitude area corresponding to the turbulent kinetic energy contribution rate as a high-risk area, and calculating the equivalent radius of the high-risk area; A risk assessment of the vortex energy of the pumped storage power station is performed based on the high-risk areas and the corresponding equivalent radii.

7. The method according to claim 6, characterized in that The extracting of the three-dimensional amplitude field of the amplitude distribution and identifying a high-amplitude region of the three-dimensional amplitude field comprises: Scanning the spatial frequency domain matrix in a preset interference characteristic frequency band sliding window, extracting the peak amplitude of each spatial point in the spatial frequency domain matrix within the interference characteristic frequency band, and obtaining a three-dimensional amplitude field; A three-dimensional amplitude field whose peak amplitude within the interference characteristic frequency band is greater than a preset amplitude threshold is identified as a high-amplitude region.

8. A device for locating hydraulic interference sources in a pumped storage power station, characterized in that: The device comprises: Intelligent point distribution module, used to build a non-uniform density monitoring network for pumped storage power stations; A spatial frequency analysis engine module, configured to generate a spatial frequency domain matrix of characteristic frequency bands based on the non-uniform density monitoring network, wherein the spatial frequency domain matrix includes an amplitude distribution and a phase distribution; An interference source positioning module is used to solve the interference source spatial positioning result based on the phase distribution using a preset inversion algorithm; The risk assessment terminal module is used to calculate the turbulent kinetic energy contribution rate based on the spatial positioning result of the interference source and the amplitude distribution, and to perform vortex energy risk assessment of the pumped storage power station based on the turbulent kinetic energy contribution rate.

9. The device according to claim 8, characterized in that The intelligent point distribution module includes: A pre-simulation unit, used to pre-simulate the transient vorticity field and pressure gradient field of a pumped storage power station based on computational fluid dynamics; An intelligent point distribution unit is used to determine high vortex intensity areas and low gradient areas based on the transient vortex field and pressure gradient field, and based on the intrinsic coupling mechanism between pressure pulsation and the high vortex intensity areas and low gradient areas respectively, set multiple monitoring points in the high vortex intensity areas and the low gradient areas according to preset point distribution density constraints, and combine the multiple monitoring points to form a non-uniform density monitoring network for the pumped storage power station.

10. The device according to claim 8, characterized in that The spatial frequency analysis engine module includes: The spatial frequency domain matrix generation unit is used to synchronously collect the pressure pulsation time domain signal of each monitoring point, and use fast Fourier transform to generate the spatial frequency domain matrix of the characteristic frequency band for the pressure pulsation time domain signal of each monitoring point. The formula of the spatial frequency domain matrix is ​​as follows: ； in, is the spatial frequency domain matrix, is the amplitude distribution, φ is the phase difference relative to the center of the pumped storage power station rotor, in radians, is the three-axis coordinate of each monitoring point, in meters, is the characteristic band frequency, in Hz.

11. The device according to claim 8, characterized in that The device further comprises: The twin-measured data fusion module is used to deploy a pressure sensor array in the pumped-storage power station and collect the actual pressure pulsation time domain signal of the pumped-storage power station; based on the digital twin model, the actual pressure pulsation time domain signal is fused and compared with the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped-storage power station to verify the accuracy of the pressure pulsation time domain signal of each monitoring point of the pre-simulated pumped-storage power station.

12. The device according to claim 8, characterized in that The interference source positioning module includes: a pressure wave propagation velocity field calculation unit, configured to calculate the pressure wave propagation velocity field based on the phase distribution using a preset phase lag equation; a phase difference equation construction unit, configured to construct a phase difference equation between the phase difference at each monitoring point and the phase difference at a preset phase reference point based on the pressure wave propagation velocity field; a linearization processing unit, configured to perform Taylor expansion linearization processing on the phase difference equation to obtain a linearized phase difference equation; The interference source positioning unit is used to convert the linearized phase difference equation into a least squares problem, and based on the least squares problem, adopt a preset inversion algorithm to iteratively solve the interference source spatial positioning result, wherein the interference source spatial positioning result includes the interference source spatial coordinates and the influence radius.

13. The device according to claim 8, characterized in that The risk assessment terminal module includes: a high-amplitude region identification unit, configured to extract a three-dimensional amplitude field of the amplitude distribution and identify a high-amplitude region of the three-dimensional amplitude field; a turbulent kinetic energy contribution rate calculation unit, configured to extract a spatially connected domain volume from the high-amplitude region and calculate a turbulent kinetic energy contribution rate based on the spatially connected domain volume; a high-risk area marking and equivalent radius calculation unit, configured to mark a high-amplitude area corresponding to the turbulent kinetic energy contribution rate as a high-risk area when the turbulent kinetic energy contribution rate is greater than a preset contribution rate threshold, and calculate the equivalent radius of the high-risk area; The risk assessment unit is used to perform risk assessment on the vortex energy of the pumped storage power station based on the high-risk area and the corresponding equivalent radius.

14. The device according to claim 13, characterized in that The high-amplitude region identification unit includes: A three-dimensional amplitude field extraction subunit is used to scan the spatial frequency domain matrix in a preset interference characteristic frequency band sliding window, extract the peak amplitude of each spatial point in the spatial frequency domain matrix within the interference characteristic frequency band, and obtain a three-dimensional amplitude field; The high-amplitude region identification subunit is configured to identify a three-dimensional amplitude field whose peak amplitude within the interference characteristic frequency band is greater than a preset amplitude threshold as a high-amplitude region.

15. 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 locating a hydraulic interference source of a pumped storage power station according to any one of claims 1 to 7 by executing the computer instructions.

16. 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 locating a hydraulic interference source of a pumped-storage power station according to any one of claims 1 to 7.

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