Methods, devices, equipment and media for locating hydraulic interference sources in pumped storage power stations

Abstract

This invention relates to the field of dynamic analysis and predictive control technology for hydraulic machinery, and discloses a method, device, equipment, and medium for locating hydraulic interference sources in pumped storage power stations. The method includes: constructing a non-uniform density monitoring network for the pumped storage power station; generating a spatial frequency domain matrix of characteristic frequency bands based on the non-uniform density monitoring network, the spatial frequency domain matrix including amplitude distribution and phase distribution; solving for the spatial location result of the interference source using a preset inversion algorithm based on the phase distribution; calculating the turbulent kinetic energy contribution rate based on the spatial location result of the interference source and the amplitude distribution, and conducting an energy risk assessment of the vortex system of the pumped storage power station based on the turbulent kinetic energy contribution rate. This invention achieves precise three-dimensional coordinate locking of hydraulic interference sources, calculates the turbulent kinetic energy contribution rate based on the interference source location result and amplitude distribution, realizes the energy risk assessment of the vortex system of the pumped storage power station, and provides sub-meter-level positioning and high-risk early warning capabilities for hydraulic vibration sources in a multi-machine system of a pumped storage power station.

Description

Technical Field

[0001] This invention relates to the field of dynamic analysis and predictive control technology of hydraulic machinery, specifically to a method, device, equipment, and medium for locating hydraulic interference sources in pumped storage power stations. Background Technology

[0002] Pumped storage power stations frequently undergo operating condition transitions during frequency regulation and peak shaving, inducing transient hydraulic interference phenomena (such as tailrace vortex bands, guide vane gap jets, and Kármán vortex streets at the diversion piers), leading to pressure pulsation amplitude exceeding limits (>0.2 MPa) and structural fatigue failure. Traditional monitoring methods have three major drawbacks:

[0003] (1) Insufficient measurement point coverage: Due to factors such as sensor cost, installation space, complex wiring, signal interference and equipment operation safety, in actual tests, sensors can only be sparsely arranged in extremely limited, easily accessible key sections (such as the inlet of the volute and the outlet of the tailrace pipe) (usually ≤50 points / m). 3 Even lower gradients (such as runner outlet and guide vane gap) cannot capture transient vortex structures in high gradient regions (such as runner outlet and guide vane gap), resulting in a high false negative rate.

[0004] (2) Low positioning accuracy: Existing positioning technology relies heavily on the correlation of signals between a limited number of measurement points. Due to the sparse measurement points and their non-ideal spatial distribution, the signals attenuate and become distorted during propagation. In addition, the phase characteristics of pressure wave propagation are ignored, which often results in the positioning error of the interference source exceeding 5 meters.

[0005] (3) Lack of risk quantification: Traditional monitoring mainly relies on the amplitude of pressure pulsation as a risk criterion. Limited physical sensors cannot directly measure vorticity, nor can they effectively capture these small-scale, high-frequency, and randomly located transient vortex / cavitation structures and their evolution process. It is even more difficult to provide early warning and quantitative assessment of such hidden risks.

[0006] In summary, existing pressure pulsation monitoring technologies employ uniform or empirical point distribution, which cannot accurately capture transient eddy sources (such as gap vortices and Karman vortex streets) and pressure wave reflection sources. Summary of the Invention

[0007] In view of this, the present invention provides a method, device, equipment and medium for locating hydraulic interference sources in pumped storage power stations, so as to solve the problem that transient eddy sources and pressure wave reflection sources cannot be accurately captured due to insufficient point deployment.

[0008] In a first aspect, the present invention provides a method for locating hydraulic interference sources in a pumped storage power station, the method comprising:

[0009] Construct a non-uniform density monitoring network for pumped storage power stations;

[0010] A spatial frequency domain matrix of characteristic frequency bands is generated based on a non-uniform density monitoring network. The spatial frequency domain matrix includes amplitude distribution and phase distribution.

[0011] Based on the phase distribution, a preset inversion algorithm is used to solve the spatial localization results of the interference source;

[0012] The contribution rate of turbulent kinetic energy is calculated based on the spatial location results and amplitude distribution of the interference source, and the energy risk assessment of the vortex system of the pumped storage power station is carried out based on the contribution rate of turbulent kinetic energy.

[0013] This invention provides a method for locating hydraulic interference sources in pumped storage power stations. By constructing a non-uniform density monitoring network for the pumped storage power station, the method ensures signal acquisition density in key interference-sensitive areas, overcoming the technical bottlenecks of traditional methods such as insufficient measurement point coverage, large location errors, and lack of vortex structure risk quantification. Based on the non-uniform density monitoring network, a spatial frequency domain matrix including amplitude and phase distributions is generated for extracting characteristic frequency bands rather than full-frequency domain analysis. This focuses on the frequency range where hydraulic interference is most significant, enabling subsequent phase inversion and energy assessment to be based on a high signal-to-noise ratio data foundation, thus improving the accuracy of feature analysis. Based on the phase distribution, a preset inversion algorithm is used to solve for the spatial coordinates of the interference source, overcoming the limitation of only knowing the region but not the specific location, and achieving precise three-dimensional coordinate locking of the hydraulic interference source. Based on the interference source location results and amplitude distribution, the turbulent kinetic energy contribution rate is calculated, enabling vortex energy risk assessment of the pumped storage power station. This provides sub-meter-level positioning and high-risk early warning capabilities for hydraulic vibration sources in a pumped storage power station's multi-pipe system, solving the problem of inaccurate capture of transient vortex sources and pressure wave reflection sources due to insufficient monitoring points.

[0014] In one alternative implementation, a non-uniform density monitoring network for pumped storage power stations is constructed, including:

[0015] Based on computational fluid dynamics pre-simulation of transient vorticity field and pressure gradient field of pumped storage power station;

[0016] Based on the transient vorticity field and pressure gradient field, the high vorticity intensity region and the low gradient region are determined. Based on the inherent coupling mechanism between pressure pulsation and the high vorticity intensity region and the low gradient region, multiple monitoring points are set in the high vorticity intensity region and the low gradient region according to the preset density constraints. The multiple monitoring points are combined to form a non-uniform density monitoring network for the pumped storage power station.

[0017] This invention provides a method for locating hydraulic interference sources in pumped-storage power stations. The flow field in pumped-storage power stations exhibits strong transient characteristics, and the transient vorticity field and pressure gradient field pre-simulated by CFD can reflect the dynamic changes in the flow field. Based on this, a non-uniform network is deployed, whose monitoring point distribution highly matches the dynamic interference characteristics of the flow field. This avoids the passive situation of traditional fixed uniform point distribution, where some areas have excessive points and others insufficient points 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 has the characteristics of high density in key areas and low redundancy in non-critical areas, directly improving the accuracy of subsequent spatial frequency domain matrix generation.

[0018] In one optional implementation, a spatial frequency domain matrix of characteristic frequency bands is generated based on a non-uniform density monitoring network. The spatial frequency domain matrix includes amplitude distribution and phase distribution, including:

[0019] The pressure pulsation time-domain signals of each monitoring point are collected synchronously, and the spatial frequency domain matrix of the characteristic frequency band is generated by fast Fourier transform of the pressure pulsation time-domain signals of each monitoring point. The formula of the spatial frequency domain matrix is ​​as follows:

[0020] ;

[0021] in, For spatial frequency domain matrix, The amplitude distribution is given by φ, which represents the phase difference relative to the center of the pumped storage power station's turbine runner, expressed in radians. The coordinates of each monitoring point are shown in meters. The characteristic frequency band is expressed in Hz.

[0022] This invention provides a method for locating hydraulic interference sources in pumped storage power stations. It simultaneously collects pressure pulsation time-domain signals from each monitoring point and uses Fast Fourier Transform to generate a spatial frequency domain matrix of characteristic frequency bands from these signals. This achieves three-dimensional fusion of the pressure pulsation signals in the time, frequency, and spatial domains, increasing the amount of data information and accurately characterizing the amplitude and phase features of the hydraulic interference. This provides a core basis for location analysis, adapts to the characteristics of non-uniform monitoring networks, optimizes data utilization and computational efficiency, supports multi-band collaborative analysis, and is suitable for the complex hydraulic environment of pumped storage power stations.

[0023] In one alternative implementation, after generating the spatial frequency domain matrix of the characteristic frequency band, the method for locating hydraulic interference sources in a pumped storage power station further includes:

[0024] A pressure sensor array is deployed in the pumped storage power station to collect the actual pressure pulsation time-domain signal of the pumped storage power station;

[0025] 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 based on the digital twin model, so as to verify the accuracy of the pressure pulsation time-domain signal of each monitoring point of the pre-simulated pumped storage power station.

[0026] This invention provides a method for locating hydraulic interference sources in pumped storage power stations. After generating a spatial frequency domain matrix of characteristic frequency bands, the method collects actual signals by deploying a pressure sensor array and fuses and compares them with digital twin pre-simulated signals to verify the accuracy of the pre-simulated data, correct the deviation of the original digital twin model, verify the rationality of the monitoring point layout, optimize the non-uniform density network, and ultimately improve the accuracy, reliability, and engineering application value of the hydraulic interference source location method.

[0027] In one optional implementation, based on the phase distribution, a preset inversion algorithm is used to solve for the spatial localization result of the interference source, including:

[0028] The pressure wave propagation velocity field is calculated based on the phase distribution using a pre-defined phase lag equation.

[0029] A phase difference equation is constructed 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.

[0030] The phase difference equation is linearized by Taylor expansion to obtain the linearized phase difference equation.

[0031] The linearized phase difference equation is transformed into a least squares problem, and a preset inversion algorithm is used to iteratively solve the spatial localization result of the interference source based on the least squares problem. The spatial localization result of the interference source includes the spatial coordinates of the interference source and the radius of influence.

[0032] This invention provides a method for locating hydraulic interference sources in pumped storage power stations. Based on the phase distribution inversion algorithm, through mathematical modeling, linearization simplification, and least squares iteration, it achieves accurate solution of the spatial coordinates and influence radius of the hydraulic interference source. This not only provides core data for flow field diagnosis and risk assessment, but also solves the nonlinear and multivariable problems of interference source location in complex hydraulic environments, providing scientific support for the safe operation and precise maintenance of pumped storage power stations.

[0033] In one optional implementation, the turbulent kinetic energy contribution rate is calculated based on the spatial location results and amplitude distribution of the interference source, and an energy risk assessment of the vortex system of the pumped storage power station is conducted based on the turbulent kinetic energy contribution rate, including:

[0034] Extract the three-dimensional amplitude field of the amplitude distribution and identify the high amplitude region of the three-dimensional amplitude field;

[0035] 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;

[0036] When the contribution rate of turbulent kinetic energy is greater than the preset contribution rate threshold, the high amplitude area corresponding to the contribution rate of turbulent kinetic energy is marked as a high-risk area, and the equivalent radius of the high-risk area is calculated.

[0037] Risk assessment of vortex energy in pumped storage power stations is conducted based on high-risk areas and their corresponding equivalent radii.

[0038] This invention provides a method for locating hydraulic interference sources in pumped storage power stations. The vortex energy risk assessment transforms 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 warning 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 making risks known, controllable, and preventable.

[0039] In one optional implementation, extracting the three-dimensional amplitude field of the amplitude distribution and identifying high-amplitude regions of the three-dimensional amplitude field includes:

[0040] The spatial frequency domain matrix is ​​scanned within a preset interference characteristic frequency band sliding window, 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.

[0041] Three-dimensional amplitude fields with peak amplitudes greater than a preset amplitude threshold within the interference characteristic frequency band are identified as high amplitude regions.

[0042] This invention provides a method for locating hydraulic interference sources in pumped storage power stations. The method extracts a three-dimensional amplitude field and identifies high-amplitude regions. By focusing on characteristic frequency bands, standardizing thresholds, and strengthening spatial correlation, it provides an accurate, objective, and efficient analytical basis for vortex energy risk assessment. This ensures that the risk assessment results can truly reflect the spatial distribution and energy intensity of hydraulic interference, and provides a reliable basis for the safety management of pumped storage power stations.

[0043] Secondly, the present invention provides a device for locating hydraulic interference sources in a pumped storage power station, the device comprising:

[0044] The intelligent deployment module is used to construct a non-uniform density monitoring network for pumped storage power stations;

[0045] The 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.

[0046] The interference source localization module is used to solve the spatial localization result of the interference source based on the phase distribution and using a preset inversion algorithm;

[0047] The risk assessment terminal module is used to calculate the turbulent kinetic energy contribution rate based on the spatial positioning results of the interference source and the amplitude distribution, and to conduct a vortex energy risk assessment of the pumped storage power station based on the turbulent kinetic energy contribution rate.

[0048] In one optional implementation, the intelligent deployment module includes:

[0049] The pre-simulation unit is used to pre-simulate the transient vorticity field and pressure gradient field of a pumped storage power station based on computational fluid dynamics.

[0050] The intelligent monitoring unit is used to determine the high vorticity zone and low gradient zone based on the transient vorticity field and pressure gradient field. Based on the inherent coupling mechanism between pressure pulsation and the high vorticity zone and low gradient zone, multiple monitoring points are set in the high vorticity zone and low gradient zone according to the preset monitoring point density constraints. The multiple monitoring points are combined to form a non-uniform density monitoring network for the pumped storage power station.

[0051] In one alternative implementation, the spatial frequency analysis engine module includes:

[0052] The spatial frequency domain matrix generation unit is used to synchronously acquire the pressure pulsation time-domain signals of each monitoring point, and to generate a spatial frequency domain matrix of characteristic frequency bands by applying a fast Fourier transform to the pressure pulsation time-domain signals of each monitoring point. The formula for the spatial frequency domain matrix is ​​as follows:

[0053] ;

[0054] in, For spatial frequency domain matrix, The amplitude distribution is given by φ, which represents the phase difference relative to the center of the pumped storage power station's turbine runner, expressed in radians. The coordinates of each monitoring point are shown in meters (m). The characteristic frequency band is expressed in Hz.

[0055] In one alternative embodiment, the device further includes:

[0056] 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 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.

[0057] In one optional implementation, the interference source localization module includes:

[0058] 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.

[0059] The phase difference equation construction unit is used to construct the phase difference equation between each monitoring point and the preset phase reference point based on the pressure wave propagation velocity field.

[0060] The linearization unit is used to perform Taylor expansion linearization on the phase difference equation to obtain the linearized phase difference equation;

[0061] The interference source localization unit is used to convert the linearized phase difference equation into a least squares problem, and to iteratively solve the spatial localization result of the interference source using a preset inversion algorithm based on the least squares problem. The spatial localization result of the interference source includes the spatial coordinates of the interference source and its influence radius.

[0062] In one optional implementation, the risk assessment terminal module includes:

[0063] A 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.

[0064] 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.

[0065] 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 to calculate the equivalent radius of the high-risk area.

[0066] The risk assessment unit is used to conduct risk assessments on the vortex energy of pumped storage power stations based on high-risk areas and their corresponding equivalent radii.

[0067] In one optional implementation, the high-amplitude region identification unit includes:

[0068] The three-dimensional amplitude field extraction sub-unit is used to scan the spatial frequency domain matrix in a sliding window of a preset interference characteristic frequency band, extract the peak amplitude of each spatial point in the spatial frequency domain matrix in the interference characteristic frequency band, and obtain the three-dimensional amplitude field.

[0069] The high-amplitude region identification subunit is used to identify three-dimensional amplitude fields with peak amplitudes greater than a preset amplitude threshold within the interference characteristic frequency band as high-amplitude regions.

[0070] Thirdly, the present invention provides a computer device, comprising: 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 computer instructions to perform the pumped storage power station hydraulic interference source location method described in the first aspect or any corresponding embodiment.

[0071] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing 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 described above.

[0072] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause 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 described above. Attached Figure Description

[0073] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0074] Figure 1 This is a flowchart illustrating the method for locating hydraulic interference sources in a pumped storage power station according to an embodiment of the present invention.

[0075] Figure 2 This is a flowchart illustrating another method for locating hydraulic interference sources in a pumped storage power station according to an embodiment of the present invention.

[0076] Figure 3 This is a flowchart illustrating another method for locating hydraulic interference sources in a pumped storage power station according to an embodiment of the present invention.

[0077] Figure 4 This is a schematic diagram of the high-amplitude region identification process according to an embodiment of the present invention;

[0078] Figure 5 This is a flowchart illustrating a method for locating hydraulic interference sources in a pumped storage power station according to an embodiment of the present invention.

[0079] Figure 6 This is a real-world diagram of a pumped storage unit according to an embodiment of the present invention;

[0080] Figure 7 This is a schematic diagram of the tailrace pipe monitoring network layout in a pumped storage unit according to an embodiment of the present invention;

[0081] Figure 8 This is a structural block diagram of a pumped storage power station hydraulic interference source locating device according to an embodiment of the present invention;

[0082] Figure 9 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0083] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0084] Traditional vibration monitoring methods suffer from insufficient measurement point coverage. Conventional CFD (Computational Fluid Dynamics) analysis only sets up points on a small number of cross-sections (such as inlet / outlet), failing to construct a spatial frequency domain distribution and achieve uniform point distribution (≤50 points / m). 3 Traditional pressure pulsation monitoring methods fail to capture transient vortex structures in high-gradient regions (such as runner outlets and guide vane gaps), resulting in a high false negative rate. These methods rely primarily on pressure pulsation amplitude as a risk criterion, severely neglecting the inherent coupling mechanism between pressure pulsation and core flow field characteristics (such as vorticity and turbulent kinetic energy fields). Pressure pulsation is the result and external manifestation of dynamic changes in flow field structures such as vortex motion and flow separation. Limited physical sensors cannot directly measure vorticity, nor can they effectively capture these small-scale, high-frequency, and randomly located transient vortex / cavitation structures and their evolution. Furthermore, they struggle to provide early warning and quantitative assessment of such hidden risks. Moreover, the monitoring of pressure pulsation amplitude does not correlate with the vortex energy contribution rate. E vortex This can easily lead to problems in accurately capturing transient eddy sources (such as gap vortices and Karman vortex streets) and pressure wave reflection sources (such as water hammer waves).

[0085] This invention provides a method for locating hydraulic interference sources in pumped storage power stations. By constructing a non-uniform density monitoring network, extracting three-dimensional frequency domain features, and quantifying vortex energy, it achieves high-precision spatial positioning and risk assessment of hydraulic vibration sources, effectively solving the problem of inaccurate capture of transient vortex sources and pressure wave reflection sources due to insufficient monitoring points.

[0086] According to an embodiment of the present invention, a method for locating hydraulic interference sources in a pumped storage power station is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0087] This embodiment provides a method for locating hydraulic interference sources in a pumped storage power station. This method can be used in computer equipment to perform CFD pre-simulation and state control of the pumped storage units in the power station. Figure 1 This is a flowchart of a method for locating hydraulic interference sources in a pumped storage power station according to an embodiment of the present invention, as follows: Figure 1 As shown, the process includes the following steps:

[0088] Step S101: Construct a non-uniform density monitoring network for pumped storage power stations.

[0089] Specifically, based on computational fluid dynamics (CFD) pre-simulation of a real pumped storage power station, an in-depth analysis of the internal flow field of the pumped storage power station is conducted. This is achieved by obtaining the transient vorticity field Ω and the pressure gradient field | | It can accurately locate areas with significant hydraulic characteristics.

[0090] In the region of high vortex intensity, i.e., the condition is met. or In these high-vortex areas, the placement of monitoring points is extremely critical. These areas are often prone to hydraulic interference, such as the tailrace cone section, where water flow easily forms complex vortex structures, affecting the stable operation of the unit; the runner outlet area, where the water flow state behind the blade outlet edge directly affects energy conversion efficiency; the guide vane gap area, where high-speed water flow generates strong vortices and secondary flows; and the tail of the diversion pier, where special flow patterns such as Karman vortex streets are easily generated. Therefore, in these high-vortex areas, the monitoring point density is set to greater than or equal to 300 points / m². 3 This involves deploying a sensor array at each monitoring point to ensure the capture of subtle changes in the flow field. Taking the runner outlet region as an example, a sensor array is placed 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 frequency pulsation of the blades passing through, providing accurate data for subsequent analysis of the blade operating status and hydraulic disturbances.

[0091] In the low gradient region, the flow field is relatively stable, and the monitoring point density can be appropriately reduced to less than or equal to 100 points / m². 3 To avoid wasting resources while ensuring monitoring of the overall flow field trend, the total number of monitoring points in the entire monitoring network was controlled between 8,000 and 12,000. This ensured comprehensive monitoring while also considering the feasibility of data processing and transmission, thus realizing the construction of a non-uniform density monitoring network.

[0092] Step S102: Generate a spatial frequency domain matrix of the characteristic frequency band based on the non-uniform density monitoring network. The spatial frequency domain matrix includes amplitude distribution and phase distribution.

[0093] Specifically, in the monitoring and analysis of hydraulic interference in pumped storage power stations, the spatial frequency domain matrix is ​​a multi-dimensional data structure that integrates spatial location information and frequency domain characteristics. It is used to quantify the distribution of hydraulic pulsation intensity at different spatial points in the flow field within a specific frequency range.

[0094] The spatial frequency domain matrix is ​​a three-dimensional complex matrix with spatial coordinates (x, y, z) as row / column indices and frequency f as another dimension. Each element in the matrix represents the amplitude (or energy) of hydraulic fluctuations at a spatial point at frequency f, intuitively reflecting the intensity of hydraulic disturbance at that spatial location at a specific frequency. By analyzing the spatial and frequency dimensions of the matrix, the amplitude distribution and phase distribution can be extracted respectively.

[0095] Phase distribution refers to the spatial distribution of the phase angle (or phase difference) of hydraulic pulsation signals at different spatial points in a 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 localization, propagation path analysis, and dynamic consistency verification.

[0096] Amplitude distribution refers to the intensity (amplitude) distribution of hydraulic pulsation signals (such as pressure pulsation and velocity pulsation) at different spatial points in a flow field in the time or frequency domain. It is used to quantify pulsation intensity, identify high-risk areas, and assess energy characteristics.

[0097] Step S103: Based on the phase distribution, the spatial positioning result of the interference source is solved using a preset inversion algorithm.

[0098] Specifically, the preset 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 infers the core characteristics of the physical field (such as wave source location, propagation direction, velocity field distribution, etc.) by analyzing the spatial variation law of the signal phase (phase gradient). Its core logic is that there is a mathematical correlation between the spatial gradient of the phase and the propagation characteristics of the physical field (such as wave number, flow velocity, vortex rotation direction), and the hidden physical parameters can be inverted by quantifying the gradient characteristics.

[0099] The spatial localization result of interference sources refers to the final output of the specific location coordinates (usually based on a reference coordinate system) of hydraulic interference sources (such as noise sources, vibration sources, flow field vortex sources, etc.) in three-dimensional space, determined through phase gradient inversion, as well as their core characteristic parameters (such as intensity, frequency, propagation direction, etc.). It is the core achievement of interference source localization technology and is directly used to assess the range of interference impact.

[0100] The three-dimensional phase distribution of the target characteristic frequency is extracted from the spatial frequency domain matrix. The phase difference between each monitoring point and the reference point (such as the center of the wheel) is determined to form a phase space map. The phase gradient inversion algorithm is used to infer the location of the interference source by analyzing the spatial variation law of the phase, and output the spatial positioning result of the interference source.

[0101] Step S104: Calculate the turbulent kinetic energy contribution rate based on the spatial location results and amplitude distribution of the interference source, and conduct an energy risk assessment of the vortex system of the pumped storage power station based on the turbulent kinetic energy contribution rate.

[0102] Specifically, the turbulent kinetic energy contribution rate refers to the proportion of turbulent kinetic energy in a specific region of the flow field (such as the vortex concentration area around the disturbance 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 proportion can be used to intuitively determine the influence weight 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 indicates that the vortex energy in that region is active, which may lead to risks such as unit vibration and efficiency reduction. Therefore, it has become an important quantitative basis for assessing the risk of vortex energy.

[0103] If the contribution rate of turbulent kinetic energy is lower than the preset safety threshold, it indicates that the vortex energy is dispersed and has little impact on unit vibration and efficiency, with a low risk level. If the contribution rate of turbulent kinetic energy exceeds the preset safety threshold, it indicates that the local vortex energy is highly concentrated, which may cause problems such as water flow pulsation and turbine blade fatigue damage, with a high risk level. It is necessary to further define the high-risk area (such as the equivalent radius) and propose control recommendations.

[0104] The method for locating hydraulic interference sources in pumped storage power stations provided in this embodiment overcomes the technical bottlenecks of traditional methods, such as insufficient measurement point coverage, large positioning errors, and lack of vortex structure risk quantification, by constructing a non-uniform density monitoring network for the pumped storage power station to ensure signal acquisition density in key interference-sensitive areas. Based on the non-uniform density monitoring network, a spatial frequency domain matrix including amplitude and phase distributions is generated for extracting characteristic frequency bands rather than full-frequency domain analysis, focusing on the frequency range where hydraulic interference is most significant. This allows subsequent phase inversion and energy assessment to be based on a high signal-to-noise ratio data foundation, improving the accuracy of feature analysis. Based on the phase distribution, a preset inversion algorithm (phase gradient inversion) is used to solve for the spatial coordinates of the interference source, overcoming the limitation of only knowing the region but not the specific location, achieving precise three-dimensional coordinate locking of the hydraulic interference source. Based on the interference source location results and amplitude distribution, the turbulent kinetic energy contribution rate is calculated, enabling vortex energy risk assessment of the pumped storage power station. This provides sub-meter-level positioning and high-risk early warning capabilities for hydraulic vibration sources in a multi-machine system of a pumped storage power station, solving the problem of insufficient point deployment leading to inaccurate capture of transient vortex sources and pressure wave reflection sources.

[0105] This embodiment provides a method for locating hydraulic interference sources in a pumped storage power station. This method can be used with computer equipment to perform CFD pre-simulation and state control of the pumped storage units in the power station. Figure 2 This is a flowchart of a method for locating hydraulic interference sources in a pumped storage power station according to an embodiment of the present invention, as follows: Figure 2 As shown, the process includes the following steps:

[0106] Step S201: Construct a non-uniform density monitoring network for pumped storage power stations.

[0107] Specifically, step S201 includes:

[0108] Step S2011: Based on computational fluid dynamics, pre-simulate the transient vorticity field and pressure gradient field of the pumped storage power station.

[0109] Specifically, the CFD pre-simulation is built based on a digital twin model, which is associated with the real pumped storage power station in the following ways:

[0110] Geometric mapping: The Building Information Modeling (BIM) model of the pumped storage power station is converted into a CFD computational domain. The mesh size of key areas (runner, guide vanes, and bifurcation pipes) is ≤1.5mm, and the boundary layer y + ≤1.

[0111] Operating condition matching: Load real-time data (head H, flow rate Q, rotational 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%.

[0112] After the above steps are completed, 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).

[0113] Step S2012: Based on the transient vorticity field and pressure gradient field, determine the high vorticity intensity region and the low gradient region. Based on the inherent coupling mechanism between pressure pulsation and the high vorticity intensity region and the low gradient region, set up multiple monitoring points in the high vorticity intensity region and the low gradient region according to the preset density constraint conditions. Combine the multiple monitoring points to form a non-uniform density monitoring network for the pumped storage power station.

[0114] Specifically, as described above, there is an inherent coupling mechanism between pressure pulsations and core flow field characteristics (such as vorticity field and turbulent kinetic energy field). Pressure pulsations are the result and external manifestation of dynamic changes in flow field structure such as vortex motion and flow separation. The inherent coupling mechanisms between pressure pulsations and high vortex intensity regions and low gradient regions include:

[0115] Under the condition or In areas with high vortex intensity / pressure abrupt changes, the monitoring point density should be greater than or equal to 300 points / m². 3 In low gradient regions, set a density of less than or equal to 100 points / m. 3 The total number of monitoring points is 10 4 Quantity up to 10 5 Magnitude.

[0116] Furthermore, the high vortex intensity zone includes the tailrace cone section, the runner outlet area, the guide vane gap area, and the tail of the diversion pier, with a monitoring point density of 300 to 500 points / m. 3 The deployment of monitoring points is achieved through an intelligent deployment algorithm, which performs the following operations:

[0117] Input transient vorticity field calculated by CFD and pressure gradient field ;

[0118] Preset density settings constraints:

[0119] (1);

[0120] Output monitoring point coordinate set:

[0121] (2);

[0122] in, Point density (unit: points / m) 3 ), For vorticity field, For pressure gradient, Let i be the coordinates of the monitoring point. .

[0123] Based on the calculations of formulas (1) and (2) above, and relevant CFD calculation experience, the recommended density layout of monitoring points in key areas inside pumped storage units is shown in Table 1 below:

[0124] Table 1. Density Layout of Monitoring Points in Key Areas Inside Pumped Storage Units

[0125]

[0126] The recommended density of monitoring points in critical areas of pressure piping systems is shown in Table 2 below:

[0127] Table 2. Density Layout of Monitoring Points in Key Areas of Pressure Piping Systems

[0128]

[0129] Step S202: Generate a spatial frequency domain matrix of the characteristic frequency band based on the non-uniform density monitoring network. The spatial frequency domain matrix includes amplitude distribution and phase distribution.

[0130] Specifically, step S202 includes:

[0131] Step a: Synchronously acquire the time-domain signals of pressure pulsation at each monitoring point, and use Fast Fourier Transform to generate a spatial frequency domain matrix of the characteristic frequency band from the time-domain signals of pressure pulsation at each monitoring point.

[0132] Step a runs on a GPU (Graphics Processing Unit) parallel architecture, processing 10... 4 Quantity up to 10 5 The computation time for a large number of monitoring points using the Fast Fourier Transform (FFT) is ≤10 minutes. For detailed Fast Fourier Transform processing, please refer to relevant technical documentation; further details will not be elaborated here.

[0133] The characteristic frequency band is the hydraulic interference band unique to a single-pipe multi-machine system, and also includes:

[0134] Water hammer wave frequency band: 0.1 Hz ≤ f ≤ 2 Hz.

[0135] Unit interference frequency band: 0.3 ≤ f ≤ 0.8 ,in This refers to the frequency rotation of the generator unit.

[0136] The spatial frequency domain matrix is ​​also known as a three-dimensional complex matrix, and its formula is expressed as follows:

[0137] (3);

[0138] in, For spatial frequency domain matrix, For amplitude distribution, The phase distribution is given by φ, which represents the phase difference relative to the center of the pumped storage power station's runner, expressed in radians. The coordinates of each monitoring point are represented by three axes, i.e., spatial points, in meters (m). The characteristic frequency band is expressed in Hz.

[0139] 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.

[0140] Specifically, the pressure sensor array is a miniature pressure sensor array with an accuracy of ±0.1%FS (FS stands for femtosecond) and a sampling rate of ≥2kHz.

[0141] In pumped-storage power stations, pressure sensor arrays are first deployed in key flow areas (such as the runner, guide vanes, and tailrace) to collect real-time time-domain signals of pressure pulsations during actual operation. Simultaneously, using a digital twin model of the pumped-storage power station, pre-simulated time-domain signals of pressure pulsations at each monitoring point are obtained. By fusing and comparing the measured and simulated signals, the consistency of key indicators such as characteristic frequency, amplitude variation trend, and phase relationship is verified. This validates the accuracy of the pre-simulated signals and provides a reliable data foundation for subsequent interference source localization and risk assessment.

[0142] After generating the spatial frequency domain matrix of the characteristic frequency band, the actual signals are collected by deploying a pressure sensor array and fused with the digital twin pre-simulated signals for comparison. This verifies the accuracy of the pre-simulated 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 location method.

[0143] Step S204: Based on the phase distribution, a preset inversion algorithm is used to solve for the spatial localization result of the interference source. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0144] Step S205: Calculate the turbulent kinetic energy contribution rate based on the spatial location results and amplitude distribution of the interference source, and conduct an energy risk assessment of the vortex system of the pumped storage power station based on the turbulent kinetic energy contribution rate. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0145] The method for locating hydraulic interference sources in pumped storage power stations provided in this embodiment addresses the strong transient nature of the flow field in pumped storage power stations. The transient vorticity field and pressure gradient field pre-simulated by CFD can reflect the dynamic changes in the flow field. Based on this, a non-uniform network is deployed, whose monitoring point distribution highly matches the dynamic interference characteristics of the flow field. This avoids the passive situation of traditional fixed uniform point distribution, where some areas have excessive points and others insufficient points when the flow field changes. This ensures the monitoring network maintains stable monitoring capabilities under all operating conditions. The data collected by the non-uniform network has high density in key areas and low redundancy in non-key areas, directly improving the accuracy of subsequent spatial frequency domain matrix generation. The system synchronously collects time-domain signals of pressure pulsations from each monitoring point and uses Fast Fourier Transform to generate a spatial frequency domain matrix of characteristic frequency bands from the time-domain signals of each monitoring point. This achieves three-dimensional fusion of the time-domain, frequency-domain, and spatial-domain signals of pressure pulsations, increases the amount of data information, accurately characterizes the amplitude and phase features of hydraulic interference, provides core basis for positioning, adapts to the characteristics of non-uniform monitoring networks, optimizes data utilization and computational efficiency, supports multi-frequency band collaborative analysis, and is suitable for the complex hydraulic environment of pumped storage power stations.

[0146] This embodiment provides a method for locating hydraulic interference sources in a pumped storage power station. This method can be used with the aforementioned computer equipment to perform CFD pre-simulation and state control of the pumped storage units in the power station. Figure 3 This is a flowchart of a method for locating hydraulic interference sources in a pumped storage power station according to an embodiment of the present invention, as follows: Figure 3 As shown, the process includes the following steps:

[0147] Step S301: Construct a non-uniform density monitoring network for the pumped storage power station. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0148] Step S302: Generate a spatial frequency domain matrix of the characteristic frequency band based on the non-uniform density monitoring network. The spatial frequency domain matrix includes amplitude distribution and phase distribution. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0149] Step S303: Based on the phase distribution, the spatial positioning result of the interference source is solved using a preset inversion algorithm.

[0150] Specifically, step S303 includes:

[0151] Step S3031: Calculate the pressure wave propagation velocity field based on the phase distribution using a preset phase lag equation.

[0152] Specifically, the three-dimensional phase distribution at the target characteristic frequency is extracted from the spatial frequency domain matrix to clarify the phase difference between each monitoring point and the reference point (such as the center of the rotor), thus forming a phase space map, i.e., a phase distribution map. .

[0153] Using a pre-defined phase lag equation The propagation velocity field of the pressure wave is calculated (c is the wave velocity, Δs is the distance between monitoring points, and Δt is the arrival time difference of the same frequency wave). The phase lag equation is a mathematical expression describing the relationship between the phase difference and spatial distance between different monitoring points and the interference source in the flow field. It is the core equation for locating the interference source based on the phase distribution. The phase lag equation originates from the wave propagation law: when a pressure wave propagates from the interference source to different monitoring points, the farther the propagation distance and the later the signal arrival time, the greater the phase lag (the phase difference is directly proportional to the propagation path length).

[0154] Furthermore, firstly, pressure pulsation phase data of each monitoring point are extracted from the signals collected by the pressure sensor array to construct a flow field phase distribution map; secondly, a preset phase lag equation (such as one describing the relationship between phase difference and 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 (i.e., distance difference) of the monitoring points are substituted into the equation; finally, the pressure wave velocity c on each propagation path is derived by solving the equation, and a global pressure wave propagation velocity field is generated by combining spatial interpolation methods, providing key parameters for subsequent interference source location and flow field characteristic analysis.

[0155] Step S3032: Construct a phase difference equation between the phase difference of each monitoring point and the phase difference of the preset phase reference point based on the pressure wave propagation velocity field.

[0156] Specifically, the phase difference equation is expressed by the following formula:

[0157] (4);

[0158] in, For the first k Phase difference at monitoring points For position vectors, The characteristic frequency (Hz) c Wave speed (m / s) This represents the position vector of the k-th monitoring point. The subscript k distinguishes different monitoring points (e.g., the k-th sensor in an array), and the vector symbol indicates that it is a vector describing spatial location (containing coordinate information, such as (x, y, z) in three-dimensional space). This represents the position vector of the wave source (signal source). The subscript "source" explicitly indicates that it is the source of the signal (such as a sound source, electromagnetic wave source, etc.), and it is also a vector describing spatial location. Assuming there are N monitoring points (N≥4), the equation for each monitoring point is as follows:

[0159] (5).

[0160] Step S3033: Perform Taylor expansion and linearization on the phase difference equation to obtain the linearized phase difference equation.

[0161] Specifically, because the equation contains nonlinear terms Taylor expansion linearization is required:

[0162] Set initial guess coordinates (Usually, the point with the longest phase lag is chosen), and the distance function is applied at... First-order expansion:

[0163] (6);

[0164] Define the distance vector:

[0165] (7);

[0166] (8);

[0167] The system of equations simplifies to:

[0168] (9).

[0169] Step S3034: The linearized phase difference equation is converted into a least squares problem, and the spatial positioning result of the interference source is iteratively solved using a preset inversion algorithm based on the least squares problem. The spatial positioning result of the interference source includes the spatial coordinates of the interference source and the radius of influence.

[0170] The linearized phase difference equation is transformed into a least squares problem, i.e., rewritten in matrix form. :

[0171] (10);

[0172] in: : N×3 directional cosine matrix Coordinate correction amount : N×1 constant vector, , , All of these are intermediate variables.

[0173] Iterative solution:

[0174] ;

[0175] (11);

[0176] in, , To achieve the convergence threshold, the spatial coordinates (X,Y,Z) of the interference source and the radius of influence R are ≤2.5m, where the spatial coordinates of the interference source are represented as a three-dimensional position vector:

[0177] (12);

[0178] The unknown quantity to be solved represents the precise spatial location of the disturbance source in the flow field.

[0179] Step S304: Calculate the turbulent kinetic energy contribution rate based on the spatial location results and amplitude distribution of the interference source, and conduct an energy risk assessment of the vortex system of the pumped storage power station based on the turbulent kinetic energy contribution rate.

[0180] Specifically, step S304 includes:

[0181] Step S3041: Extract the three-dimensional amplitude field of the amplitude distribution and identify the high amplitude region of the three-dimensional amplitude field.

[0182] In some optional implementations, step S3041 above includes:

[0183] Step b1: Scan the spatial frequency domain matrix within a preset interferometric characteristic frequency band sliding window, extract the peak amplitude of each spatial point in the spatial frequency domain matrix within the interferometric characteristic frequency band, and obtain the three-dimensional amplitude field.

[0184] Specifically, the steps for extracting the three-dimensional amplitude field are as follows:

[0185] Input: a three-dimensional complex matrix .

[0186] Operation: In the hydraulic interferometric characteristic frequency band (e.g. Sliding window scan to extract each spatial point Peak amplitude within the characteristic frequency band .

[0187] Output: Three-dimensional amplitude field The formula is expressed as follows:

[0188] (13);

[0189] in, These represent the lower and upper limits of the frequency range of the hydraulic interference characteristic frequency band, respectively. The frequency range of the frequency band is not fixed; for the unit, the interference frequency band will change with the actual frequency.

[0190] Step b2: Identify the three-dimensional amplitude field with peak amplitude greater than the preset amplitude threshold within the interference characteristic frequency band as a high amplitude region.

[0191] like Figure 4As shown, the preset amplitude threshold is .

[0192] The peak amplitude is greater than The three-dimensional amplitude field is identified as a high-amplitude region, and all high-amplitude regions are marked as candidate regions. Morphological closing is then used to output all high-amplitude regions. Morphological closing is a fundamental operation in digital image processing and morphological analysis, mainly used to fill small holes in an image, connect the edges of adjacent objects, or smooth the contours of objects, while maintaining the overall shape and size of the objects essentially unchanged.

[0193] The method for locating hydraulic interference sources in pumped storage power stations provided in this embodiment extracts the three-dimensional amplitude field and identifies high-amplitude regions. By focusing on characteristic frequency bands, standardizing thresholds, and strengthening spatial correlation, it provides an accurate, objective, and efficient analytical basis for vortex energy risk assessment. This ensures that the risk assessment results can truly reflect the spatial distribution and energy intensity of hydraulic interference, providing a reliable basis for the safety management of pumped storage power stations.

[0194] 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.

[0195] Specifically, extract the volume of the spatially connected domain. The specific process is as follows:

[0196] The three-dimensional amplitude field data is 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 that exceed a preset threshold (e.g., 1.2 times the average amplitude under design conditions) are retained to form a preliminary set of high-amplitude discrete points.

[0197] A three-dimensional connectivity analysis algorithm (such as the 6-neighbor or 26-neighbor method) is used to identify interconnected regions in the space where high-amplitude points are concentrated: if two grid cells share a face, edge, or vertex, and both are high-amplitude cells, they are determined to be the same connected region. Morphological closing operations are used to fill the small holes inside the connected region (such as isolated low-amplitude gaps caused by sparse monitoring points) to ensure the integrity of the connected region's shape.

[0198] Count the number of grid cells contained in each connected component, and combine this with the volume of a single grid cell (e.g., 10). -6 m 3 The volume of the spatially connected domain is obtained by summing these elements (for example, if a connected domain contains 500 grid cells, then the volume is 500 × 10⁻⁶). -6 = 5×10 -4 m 3 For connected components with excessively small volumes (e.g., less than 1×10⁻⁶),-5 m 3 Filtering is performed to eliminate interference from noise or local disturbances.

[0199] Calculation of turbulent kinetic energy contribution rate:

[0200] (14);

[0201] in, ; The turbulence intensity output by the CFD pre-simulation is usually defined as the root mean square of the turbulent pulsating velocity component in turbulence statistical theory.

[0202] Step S3043: When the turbulent kinetic energy contribution rate is greater than the preset contribution rate threshold, mark the high amplitude area corresponding to the turbulent kinetic energy contribution rate as a high-risk area, and calculate the equivalent radius of the high-risk area.

[0203] Specifically, the preset contribution rate threshold is set at 15%, and the specific criteria for determining high-risk areas are as follows:

[0204] (15);

[0205] To satisfy or Calculate the equivalent geometric center and equivalent radius of the vortex core region:

[0206] (16);

[0207] in, The equivalent radius of the high-risk area. Let V be the volume of the spatially connected domain.

[0208] The equivalent geometric center is calculated using a weighted summation of the CFD mesh, as shown in the following formula:

[0209] (17);

[0210] in, Indicates in connected components The total number of CFD meshes contained within; ) indicates in the connected component The first j The center coordinates of each grid; Indicates the first j Volume of each grid cell (unit: m³) 3 ), , , These are the coordinates of the equivalent geometric center.

[0211] Early identification of high-risk areas (such as the turbine runner outlet vortex zone) can prevent blade cracks or structural resonance accidents and extend the life of pumped storage units.

[0212] Step S3044: Conduct a risk assessment of the vortex energy of the pumped storage power station based on the high-risk area and the corresponding equivalent radius.

[0213] Specifically, firstly, the geometric center and equivalent radius (reflecting the risk diffusion range) of the high-risk area where the turbulent kinetic energy contribution rate exceeds the threshold are identified; secondly, it is analyzed whether the high-risk area covers key components such as the runner and guide vanes, and the energy intensity within the equivalent radius is compared with the safety limits of the components; finally, based on the coverage, energy level, and whether there is a risk of frequency resonance, low, medium, and high risk levels are classified to provide a basis for formulating targeted control measures (such as optimizing operating parameters or maintenance).

[0214] 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 indicating the coordinates of the interference source and its radius of influence.

[0215] The method for locating hydraulic interference sources in pumped storage power stations provided in this embodiment, based on a phase distribution inversion algorithm, achieves accurate determination of the spatial coordinates and influence radius of the hydraulic interference source through mathematical modeling, linearization 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 challenges of interference source location in complex hydraulic environments, providing scientific support for the safe operation and precise maintenance of pumped storage power stations. The vortex energy risk assessment, by quantifying turbulent kinetic energy contribution, marking high-risk areas, and defining the scope of influence, transforms the potential hazards of hydraulic interference into measurable, traceable, and controllable indicators. This not only provides accurate early warning 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 making risks known, controllable, and preventable.

[0216] As one or more specific application embodiments of the present invention, combined with Figures 5 to 7 The method for locating hydraulic interference sources in pumped storage power stations provided by this invention will be further described in detail below.

[0217] This invention provides a method for locating hydraulic interference sources in pumped storage power stations, such as... Figure 5 As shown, it includes the following steps:

[0218] Step 1: First, a non-uniform density monitoring network is constructed based on the vorticity field and pressure gradient field pre-simulated by Computational Fluid Dynamics (CFD). 300-500 points / m are set in high vorticity areas / pressure abrupt change areas such as the tailrace cone section and the runner outlet. 3High-density monitoring points, low-gradient area density ≤100 points / m 3 A total of 8,000 to 12,000 monitoring points will be set up.

[0219] Step 2: By synchronously acquiring pressure pulsation signals and performing a fast Fourier transform, a signal containing amplitude distribution A and phase distribution A is generated. The three-dimensional complex matrix was used to extract the water hammer wave frequency band (0.1 to 2 Hz) and the unit interference frequency band (0.3 Hz). n up to 0.8f n ) characteristics.

[0220] Step 3: Use the phase distribution to invert the pressure wave propagation velocity field, and use the phase gradient iterative algorithm to accurately solve for the spatial coordinates and influence radius of the interference source, and output the interference source location result.

[0221] 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.

[0222] This invention overcomes the technical bottlenecks of traditional methods, such as insufficient measurement point coverage, large positioning errors, and lack of vortex structure risk quantification, through non-uniform point distribution guided by digital twin models, GPU-accelerated spatial frequency domain analysis, and vortex energy quantification assessment. It provides sub-meter-level positioning and high-risk early warning capabilities for hydraulic vibration sources in pumped storage power station multi-machine systems.

[0223] like Figure 6 As shown in the diagram, taking a pumped storage unit as an example, the method for locating hydraulic interference sources in a pumped storage power station provided in this embodiment will be further explained in detail:

[0224] Step 1) CFD pre-simulation parameters:

[0225] Operating conditions: head 420m, flow rate 280m³ / h 3 / s, rotational speed 300rpm (frequency f) n =5Hz).

[0226] Digital twin model construction: The CFD model is generated through BIM geometric mapping, with the following requirements: key area mesh size ≤ 1.5 mm; boundary layer y + ≤1; Load SCADA real-time data, operating condition error ≤3%.

[0227] Twin-measured data fusion: Deploy a miniature pressure sensor array (accuracy ±0.1%FS, sampling rate ≥2 kHz) in a real power plant and compare it with the spatial frequency domain matrix.

[0228] Step 2) Monitoring point layout (taking the tailrace pipe as an example):

[0229] Tailwater pipe cone section identification results: Maximum vorticity ( Threshold); pressure gradient (>500 Pa / m threshold).

[0230] The monitoring points for the tailrace cone section are shown in Table 3 below:

[0231] Table 3 Layout of Monitoring Points for Tailwater Pipe Cone Section

[0232]

[0233] A schematic diagram of the tailrace pipe monitoring network layout in a pumped storage unit is shown below. Figure 7 As shown.

[0234] Data Acquisition: Synchronous acquisition duration: 80 seconds (covering 400 rotation cycles).

[0235] Characteristic frequency band analysis: Low-frequency vortex band f 1 = 0.4~2Hz (amplitude 0.28MPa).

[0236] Generate a three-dimensional complex matrix of characteristic frequency bands .

[0237] Step 3) Locating the interference source:

[0238] Target frequency: f = 2 Hz (dominant frequency of the low-frequency vortex band in the tailrace pipe).

[0239] Reference point: P0(0,0,0) (Center of the tapered tube inlet, phase reference) =0°).

[0240] The monitoring data (partial key points) is shown in Table 4 below:

[0241] Table 4 Monitoring Point Data (Partial Key Points)

[0242]

[0243] Pressure wave velocity: c = 1500m / s (velocity of sound in water).

[0244] Convergence threshold: ε = 0.05 m.

[0245] The solution steps for the phase gradient inversion algorithm are as follows:

[0246] (1) Construct the phase difference equation:

[0247] Establish a wave equation for each monitoring point:

[0248] ;

[0249] Substituting the reference point ϕ0=0°, convert to radians:

[0250] ;

[0251] (2) Linearization process:

[0252] Initial guess point selection: The point with the most phase lag, P6 (0.05, -0.12, 1.8), is selected as the initial guess coordinates.

[0253] ;

[0254] Calculate the distance vector: Definition: .

[0255] Taking P1 as an example:

[0256] ;

[0257] ;

[0258] Taylor expansion linear approximation:

[0259] ;

[0260] Substitute into the phase difference equation:

[0261] ;

[0262] in, .

[0263] (3) Constructing the least squares problem ( ):matrix Construct the (direction cosine matrix), and construct vector B.

[0264] ;

[0265] (4) Iterative solution:

[0266] First iteration:

[0267] Solve the system of linear equations: ;

[0268] Calculation results: ;

[0269] Updated guessed coordinates:

[0270] ;

[0271] Residual: ;

[0272] Second iteration: with Starting from point P3, recalculate the distance vector (using P3 as an example):

[0273] ;

[0274] ;

[0275] renew Solving for matrix B: And update the spatial coordinates:

[0276] ;

[0277] Residual: (convergence).

[0278] Location result: Coordinates of the interference source (rounded to two decimal places): (0.08, -0.15, 1.38) m.

[0279] Step 4) Vortex system energy risk assessment:

[0280] Based on amplitude distribution Calculate the turbulent kinetic energy contribution rate of the vortex core region. ,mark >15% of the area is a high-risk area for vibration.

[0281] Feature band amplitude extraction ( );

[0282] High amplitude area: (volume );

[0283] Calculation of turbulent kinetic energy contribution rate and identification of high-risk areas: This is a high-risk area for vibration.

[0284] Equivalent radius: .

[0285] The embodiments of the present invention have the following key points and beneficial effects:

[0286] (1) Intelligent deployment of non-uniform monitoring network:

[0287] vorticity field based on CFD pre-simulation ( ) and pressure gradient field ( The density of monitoring points can be dynamically adjusted to break the limitations of traditional uniform distribution.

[0288] High vortex intensity region ( ) or high-voltage transformer area ( >500Pa / m) Density ≥300 points / m 3 Low gradient region ≤100 points / m 3 This enables precise capture of transient hydraulic disturbances.

[0289] (2) Spatial frequency domain matrix construction method:

[0290] A complex matrix is ​​generated through synchronous acquisition and 3D FFT:

[0291] ;

[0292] It includes both amplitude A and phase. The spatial distribution provides a holographic data foundation for positioning.

[0293] (3) Phase gradient inversion positioning algorithm:

[0294] Phase difference equation:

[0295] ;

[0296] The coordinates of the interference source are inverted using the phase difference equation, and the problem of nonlinear positioning is solved by Taylor expansion linearization and least squares iteration (convergence condition ‖ΔP‖<0.05m).

[0297] (5) Vortex system energy risk assessment model:

[0298] Define turbulent kinetic energy contribution rate As a vibration risk indicator, it is combined with the characteristic frequency band amplitude threshold ( >0.2MPa) and energy threshold ( Intelligent marking of high-risk vibration areas and calculation of equivalent radius .

[0299] (6) Digital twin-driven implementation:

[0300] CFD model and BIM geometry mapping (mesh ≤ 1.5mm, y + ≤1), load SCADA real-time data (operating condition error ≤3%) to achieve dynamic coupling of virtual and physical fields.

[0301] This embodiment also provides a hydraulic interference source locating device for a pumped storage power station. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0302] This embodiment provides a device for locating hydraulic interference sources in a pumped storage power station, such as... Figure 8 As shown, it includes:

[0303] The intelligent deployment module 801 is used to construct a non-uniform density monitoring network for pumped storage power stations.

[0304] The spatial frequency analysis engine module 802 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.

[0305] The interference source localization module 803 is used to solve the spatial localization result of the interference source based on the phase distribution and using a preset inversion algorithm.

[0306] The risk assessment terminal module 804 is used to calculate the turbulent kinetic energy contribution rate based on the spatial positioning results of the interference source and the amplitude distribution, and to conduct a vortex energy risk assessment of the pumped storage power station based on the turbulent kinetic energy contribution rate.

[0307] In some alternative implementations, the intelligent deployment module 801 includes:

[0308] The pre-simulation unit is used to pre-simulate the transient vorticity field and pressure gradient field of a pumped storage power station based on computational fluid dynamics.

[0309] The intelligent monitoring unit is used to determine the high vorticity zone and low gradient zone based on the transient vorticity field and pressure gradient field. Based on the inherent coupling mechanism between pressure pulsation and the high vorticity zone and low gradient zone, multiple monitoring points are set in the high vorticity zone and low gradient zone according to the preset monitoring point density constraints. The multiple monitoring points are combined to form a non-uniform density monitoring network for the pumped storage power station.

[0310] In some alternative implementations, the spatial frequency analysis engine module 802 includes:

[0311] The spatial frequency domain matrix generation unit is used to synchronously acquire the pressure pulsation time-domain signals from each monitoring point, and to generate a spatial frequency domain matrix of characteristic frequency bands from the pressure pulsation time-domain signals of each monitoring point using a fast Fourier transform. The formula for the spatial frequency domain matrix is ​​as follows:

[0312] ;

[0313] in, For spatial frequency domain matrix, The amplitude distribution is given by φ, which represents the phase difference relative to the center of the pumped storage power station's turbine runner, expressed in radians. The coordinates of each monitoring point are shown in meters. The characteristic frequency band is expressed in Hz.

[0314] In some optional embodiments, the pumped storage power station hydraulic interference source locating device further includes:

[0315] 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 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.

[0316] In some alternative implementations, the interference source localization module 803 includes:

[0317] 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.

[0318] The phase difference equation construction unit is used to construct the phase difference equation between each monitoring point and the preset phase reference point based on the pressure wave propagation velocity field.

[0319] The linearization unit is used to perform Taylor expansion linearization on the phase difference equation to obtain the linearized phase difference equation.

[0320] The interference source localization unit is used to convert the linearized phase difference equation into a least squares problem, and to iteratively solve the spatial localization result of the interference source using a preset inversion algorithm based on the least squares problem. The spatial localization result of the interference source includes the spatial coordinates of the interference source and its influence radius.

[0321] In some optional implementations, the risk assessment terminal module 804 includes:

[0322] 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.

[0323] 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.

[0324] 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 to calculate the equivalent radius of the high-risk area.

[0325] The risk assessment unit is used to conduct risk assessments on the vortex energy of pumped storage power stations based on high-risk areas and their corresponding equivalent radii.

[0326] In some optional implementations, the high-amplitude region identification unit includes:

[0327] The three-dimensional amplitude field extraction sub-unit is used to scan the spatial frequency domain matrix within 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 the three-dimensional amplitude field.

[0328] The high-amplitude region identification subunit is used to identify three-dimensional amplitude fields with peak amplitudes greater than a preset amplitude threshold within the interference characteristic frequency band as high-amplitude regions.

[0329] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0330] In this embodiment, the pumped storage power station hydraulic interference source locating device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0331] This invention also provides a computer device having the above-described features. Figure 8 The device shown is a hydraulic interference source locator for a pumped storage power station.

[0332] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device provided in 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 the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 9 Take a processor 10 as an example.

[0333] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0334] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0335] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0336] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0337] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 9 Taking the example of a connection between China and Israel via a bus.

[0338] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0339] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0340] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0341] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall 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 includes: Construct a non-uniform density monitoring network for pumped storage power stations; Based on the non-uniform density monitoring network, a spatial frequency domain matrix of characteristic frequency bands is generated, and the spatial frequency domain matrix includes amplitude distribution and phase distribution. Based on the phase distribution, a preset inversion algorithm is used to solve for the spatial localization result of the interference source; The turbulent kinetic energy contribution rate is calculated based on the spatial location 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.

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

3. The method according to claim 2, characterized in that, The generation of the spatial frequency domain matrix of the characteristic frequency band based on the non-uniform density monitoring network includes: The pressure pulsation time-domain signals of each monitoring point are collected synchronously, and a spatial frequency domain matrix of the characteristic frequency band is generated by applying a fast Fourier transform to the pressure pulsation time-domain signals of each monitoring point. The formula for the spatial frequency domain matrix is ​​as follows: ； in, For spatial frequency domain matrix, For amplitude distribution, The phase difference relative to the center of the pumped storage power station's turbine runner, expressed in radians. The coordinates of each monitoring point are shown in meters (m). The characteristic frequency band is expressed in Hz.

4. The method according to claim 3, characterized in that, After generating the spatial frequency domain matrix of the characteristic frequency band, the method further includes: A pressure sensor array is deployed in the pumped storage power station to collect the actual pressure pulsation time-domain signal of the pumped storage power station. 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 based on the digital twin model, so as 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, characterized in that, Based on the phase distribution, a preset inversion algorithm is used to solve for the spatial localization result of the interference source, including: The pressure wave propagation velocity field is calculated 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 the preset phase reference point; The phase difference equation is linearized by Taylor expansion 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 to iteratively solve the spatial localization result of the interference source based on the least squares problem. The spatial localization result of the interference source includes the spatial coordinates of the interference source and its influence radius.

6. The method according to claim 1, characterized in that, The calculation of the turbulent kinetic energy contribution rate based on the spatial location results of the interference source and the amplitude distribution, and the assessment of the vortex energy risk of the pumped storage power station based on the turbulent kinetic energy contribution rate, include: Extract the three-dimensional amplitude field of the amplitude distribution and identify the high amplitude region of the three-dimensional amplitude field; 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; 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 region, and the equivalent radius of the high-risk region is calculated. Risk assessment of vortex energy in pumped storage power stations is conducted based on the high-risk areas and their corresponding equivalent radii.

7. The method according to claim 6, characterized in that, The step of extracting the three-dimensional amplitude field of the amplitude distribution and identifying the 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. The three-dimensional amplitude field with a peak amplitude greater than a preset amplitude threshold within the interference characteristic frequency band 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 includes: The intelligent deployment module is used to construct a non-uniform density monitoring network for pumped storage power stations; The spatial frequency analysis engine module is used 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 amplitude distribution and phase distribution. An interference source localization module is used to solve for the spatial localization result of the interference source based on the phase distribution and 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 results of the interference source and the amplitude distribution, and to conduct a vortex energy risk assessment of the pumped storage power station based on the turbulent kinetic energy contribution rate.

9. The apparatus according to claim 8, characterized in that, The intelligent deployment module includes: The pre-simulation unit is 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 monitoring unit is used to determine the high vorticity region and the low gradient region based on the transient vorticity field and the pressure gradient field, and based on the inherent coupling mechanism between the pressure pulsation and the high vorticity region and the low gradient region, set up multiple monitoring points in the high vorticity region and the low gradient region according to the preset monitoring point density constraints, and combine the multiple monitoring points to form a non-uniform density monitoring network for the pumped storage power station.

10. The apparatus 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 acquire the pressure pulsation time-domain signals of each monitoring point, and to generate a spatial frequency domain matrix of characteristic frequency bands by applying a fast Fourier transform to the pressure pulsation time-domain signals of each monitoring point. The formula for the spatial frequency domain matrix is ​​as follows: ； in, For spatial frequency domain matrix, The amplitude distribution is given by φ, which represents the phase difference relative to the center of the pumped storage power station's turbine runner, expressed in radians. The coordinates of each monitoring point are shown in meters (m). The characteristic frequency band is expressed in Hz.

11. The apparatus according to claim 8, characterized in that, The device further includes: 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 apparatus according to claim 8, characterized in that, The interference source localization module 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. 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. A linearization processing unit is used to perform Taylor expansion linearization on the phase difference equation to obtain a linearized phase difference equation. The interference source localization unit is used to convert the linearized phase difference equation into a least squares problem, and to iteratively solve the spatial localization result of the interference source using a preset inversion algorithm based on the least squares problem. The spatial localization result of the interference source includes the spatial coordinates of the interference source and its influence radius.

13. The apparatus according to claim 8, characterized in that, The risk assessment terminal module includes: A 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; 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. 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 a preset contribution rate threshold, and to calculate the equivalent radius of the high-risk area. The risk assessment unit is used to conduct risk assessment of the vortex energy of the pumped storage power station based on the high-risk area and the corresponding equivalent radius.

14. The apparatus according to claim 13, characterized in that, 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 in the interference characteristic frequency band, and obtain the three-dimensional amplitude field. The high-amplitude region identification subunit is used to identify three-dimensional amplitude fields with peak amplitudes greater than a preset amplitude threshold within the interference feature frequency band as high-amplitude regions.

15. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for locating hydraulic interference sources in a pumped storage power station as described in any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the method for locating hydraulic interference sources in a pumped storage power station as described in any one of claims 1 to 7.

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