Pole tower partial discharge detection method and system based on acoustic imager

Abstract

The invention relates to the technical field of power detection, in particular to a tower partial discharge detection method and system based on an acoustic imager. The method comprises the following steps: acquiring tower acoustic data acquired by an acoustic imager; analyzing the tower acoustic data, and determining an acoustic characteristic spectrum; based on a preset discharge recognition algorithm, analyzing the acoustic characteristic spectrum, and determining a partial discharge type; matching the partial discharge type with a preset detection mode to obtain a matching relationship; and according to the matching relationship, adjusting the detection frequency band of the acoustic imager and the like. A multi-dimensional discrimination basis is provided for partial discharge type identification, the partial discharge type is matched with a preset detection mode, the synchronization rate of intermittent discharge detection time is improved, the omission ratio is reduced, positioning errors in a high-humidity environment can be eliminated, equivalent propagation distance calculation errors are reduced, and the accuracy of partial discharge detection is improved. And the high-precision positioning requirement in a complex environment is met.

Description

Technical Field

[0001] The present application relates to the field of power detection technology, and in particular to a tower partial discharge detection method and system based on an acoustic imager. Background Art

[0002] With the advancement of smart grid construction, tower partial discharge detection technology is gradually transitioning from traditional contact-based detection to non-contact, intelligent detection. The current mainstream detection method primarily uses drones equipped with acoustic imagers for aerial inspections, using microphone arrays to collect discharge acoustic signals and acoustic source localization technology to spatially identify discharge points.

[0003] However, in the current power grid system, the accurate detection of partial discharge phenomena on towers has the problem of a high misjudgment rate of atypical discharge patterns. Therefore, how to identify atypical discharge conditions has become a significant defect in existing technologies. Summary of the Invention

[0004] The present application provides a tower partial discharge detection method and system based on an acoustic imager to solve the above-mentioned problems.

[0005] In a first aspect, the present application provides a method for detecting partial discharge of a tower using an acoustic imager, the method comprising: Acquiring tower acoustic data collected by the acoustic imager; analyzing the tower acoustic data to determine an acoustic characteristic spectrum; Analyzing the acoustic signature spectrum based on a preset discharge recognition algorithm to determine the type of partial discharge; Matching the partial discharge type with a preset detection mode to obtain a matching relationship; and adjusting the detection frequency band of the acoustic imager according to the matching relationship; The acoustic imager is controlled to operate according to the adjusted detection frequency band and to obtain compensated acoustic data in real time. Based on a preset sound wave attenuation compensation model, the compensated acoustic data is analyzed to determine the location coordinates of partial discharge on the tower.

[0006] By the scheme, the tower acoustic data collected by the acoustic imager is acquired, which helps to eliminate the data space-time dislocation problem caused by the change of flight attitude, and improve the sound source positioning accuracy. Analyzing the tower acoustic data, the acoustic characteristic map is determined, which provides multi-dimensional discrimination basis for partial discharge type identification, and reduces the misjudgment risk in the characteristic overlapping area. Based on the preset discharge identification algorithm, the acoustic characteristic map is analyzed to determine the partial discharge type, which helps to reduce the misjudgment rate of atypical discharge, and avoids the type misclassification caused by single feature misjudgment. The partial discharge type is matched with the preset detection mode to obtain a matching relationship, which helps to improve the synchronization rate of the detection time of intermittent discharge and reduce the missed detection rate. According to the matching relationship, the detection frequency band of the acoustic imager is adjusted, which helps to eliminate the random interference caused by the change of wind speed and improve the data signal-to-noise ratio. The acoustic imager is controlled to work according to the adjusted detection frequency band, and the compensation acoustic data is acquired in real time. Based on the preset sound wave attenuation compensation model, the compensation acoustic data is analyzed to determine the position coordinates of the tower partial discharge, which helps to eliminate the positioning error in high humidity environment, reduce the equivalent propagation distance calculation error, and meet the high-precision positioning demand in complex environment.

[0007] Optionally, the adjusting the detection frequency band of the acoustic imager according to the matching relationship comprises: determining a detection mode according to the matching relationship; determining a center frequency and a bandwidth of the acoustic imager according to the detection mode; acquiring an initial detection frequency band; and determining a current filtering parameter according to the center frequency, the bandwidth and the initial detection frequency band.

[0008] Optionally, the analyzing the acoustic characteristic map based on the preset discharge identification algorithm to determine the partial discharge type comprises: analyzing the acoustic characteristic map based on the preset discharge identification algorithm to determine a frequency spectrum feature; analyzing the frequency spectrum feature to determine a main frequency component, a harmonic distribution and an energy concentration frequency band; determining the partial discharge type according to the main frequency component, the harmonic distribution and the energy concentration frequency band.

[0009] Optionally, the establishing of the preset detection mode comprises: acquiring a plurality of known detection data; analyzing the known detection data to determine a detection feature and a discharge type; clustering the detection features by using a data statistical algorithm to obtain a detection feature set; calling detection parameters of the known detection data; analyzing the detection feature set based on the discharge type to determine the effectiveness of the detection parameters; According to the effectiveness, a preset detection mode is obtained; the preset detection mode includes the discharge type and the detection parameter.

[0010] Optionally, the determination of the partial discharge type according to the main frequency component, the harmonic distribution and the energy concentration frequency band includes: The peak frequency of the main frequency component is extracted. The harmonic distribution is analyzed to determine the number of harmonics and the harmonic energy proportion. The energy concentration degree is calculated based on the energy concentration frequency band. The peak frequency, the number of harmonics, the energy concentration degree and the harmonic energy proportion are input into a preset discharge type decision model to output the partial discharge type.

[0011] Optionally, the determination of the position coordinates of the tower partial discharge based on the preset sound wave attenuation compensation model and the analysis of the compensation acoustic data includes: The flight environment of the bearing device of the acoustic imager at the current time is obtained. The flight environment is analyzed to determine the current flight height, environmental temperature and humidity and terrain distribution. The terrain shielding coefficient is determined according to the terrain distribution. The theoretical sound pressure value is determined by analyzing the compensation acoustic data according to a preset sound wave propagation model. The measured sound pressure value is determined by analyzing the compensation acoustic data. The position coordinates of the tower partial discharge are determined based on the preset sound wave attenuation compensation model and the analysis of the theoretical sound pressure value and the measured sound pressure value.

[0012] Optionally, the partial discharge type includes intermittent discharge; before the control of the acoustic imager working according to the adjusted detection frequency band, the method further includes: When the intermittent discharge mode is detected, the multi-cycle frequency band scanning mode of the acoustic imager is activated, and a periodic travel signal is sent to the bearing device. The periodic frequency band scanning data of the acoustic imager is obtained; the energy change trend of the characteristic frequency band in adjacent cycles is determined by analyzing the periodic frequency band scanning data. The optimal detection time window is determined according to the energy change trend of the characteristic frequency band in adjacent cycles.

[0013] Optionally, the determination of the theoretical sound pressure value by analyzing the compensation acoustic data according to the preset sound wave propagation model includes: The current detection geographical condition is determined by analyzing the flight environment. The weather state is determined by analyzing the environmental temperature and humidity and the current detection geographical condition. analyze the weather state to determine weather noise influence; analyze the compensation acoustic data to determine a specific frequency band in which the weather noise influence exists; eliminate the weather noise in the specific frequency band according to a preset dynamic filter to obtain actual compensation acoustic data; analyze the actual compensation acoustic data according to the sound wave propagation model to determine a theoretical sound pressure value.

[0014] Optionally, the analysis of the weather state to determine weather noise influence comprises: analyze the weather state to determine weather noise; analyze the weather noise to determine time-domain impact characteristics and frequency-domain resonance characteristics; determine weather noise influence according to the time-domain impact characteristics and the frequency-domain resonance characteristics.

[0015] In a second aspect, the present application provides a tower partial discharge detection system based on an acoustic imager, which comprises: a data analysis module configured to acquire tower acoustic data collected by the acoustic imager, and analyze the tower acoustic data to determine an acoustic characteristic map; a map analysis module configured to analyze the acoustic characteristic map based on a preset discharge identification algorithm to determine a partial discharge type; a frequency band adjustment module configured to match the partial discharge type with a preset detection mode to obtain a matching relationship, and adjust a detection frequency band of the acoustic imager according to the matching relationship; a coordinate determination module configured to control the acoustic imager to work according to the adjusted detection frequency band, acquire compensation acoustic data in real time, analyze the compensation acoustic data based on a preset sound wave attenuation compensation model, and determine a position coordinate of tower partial discharge.

[0016] Optionally, when the frequency band adjustment module adjusts the detection frequency band of the acoustic imager according to the matching relationship, it is configured to: determine a detection mode according to the matching relationship; determine a center frequency and a bandwidth of the acoustic imager according to the detection mode; acquire an initial detection frequency band, and determine a current filter parameter according to the center frequency, the bandwidth, and the initial detection frequency band.

[0017] Optionally, when the map analysis module analyzes the acoustic characteristic map based on a preset discharge identification algorithm to determine a partial discharge type, it is configured to: analyze the acoustic characteristic map based on a preset discharge identification algorithm to determine a frequency spectrum characteristic; analyzing the spectrum features to determine a main frequency component, a harmonic distribution, and an energy concentration frequency band; determining a partial discharge type according to the main frequency component, the harmonic distribution, and the energy concentration frequency band.

[0018] Optionally, the tower partial discharge detection system based on the acoustic imager further comprises a mode determination module configured to: acquire a plurality of known detection data, analyze the known detection data to determine detection features and a discharge type; cluster the detection features by using a data statistical algorithm to obtain a detection feature set; retrieve detection parameters of the known detection data; analyze the detection feature set based on the discharge type to determine effectiveness of the detection parameters; obtain a preset detection mode according to the effectiveness; the preset detection mode comprises the discharge type and the detection parameters.

[0019] Optionally, when the atlas analysis module determines a partial discharge type according to the main frequency component, the harmonic distribution, and the energy concentration frequency band, the atlas analysis module is configured to: extract a peak frequency of the main frequency component; analyze the harmonic distribution to determine a number of harmonics and a harmonic energy proportion; calculate an energy concentration degree based on the energy concentration frequency band; input the peak frequency, the number of harmonics, the energy concentration degree, and the harmonic energy proportion into a preset discharge type decision model to output a partial discharge type.

[0020] Optionally, when the coordinate determination module analyzes the compensated acoustic data based on a preset sound wave attenuation compensation model to determine a location coordinate of the tower partial discharge, the coordinate determination module is configured to: acquire a flight environment of a bearing device of the acoustic imager at a current time; analyze the flight environment to determine a current flight height, an environmental temperature and humidity, and a terrain distribution; determine a terrain shielding coefficient according to the terrain distribution; analyze the compensated acoustic data according to a preset sound wave propagation model to determine a theoretical sound pressure value; analyze the compensated acoustic data to determine a measured sound pressure value; analyze the theoretical sound pressure value and the measured sound pressure value based on a preset sound wave attenuation compensation model to determine a location coordinate of the tower partial discharge.

[0021] Optionally, the tower partial discharge detection system based on the acoustic imager further comprises a window determination module configured to: activate a multi-cycle frequency band scanning mode of the acoustic imager when the intermittent discharge mode is detected, and send a periodic travel signal to the bearing device; acquire periodic frequency band scanning data of the acoustic imager; analyze the periodic frequency band scanning data to determine an energy change trend of a characteristic frequency band in adjacent cycles; determine an optimal detection time window according to the energy change trend of the characteristic frequency band in adjacent cycles.

[0022] Optionally, when the coordinate determination module determines the theoretical sound pressure value by analyzing the compensated acoustic data according to a preset sound wave propagation model, the method further includes: analyzing the flight environment to determine a current detection geographical condition; analyzing the environmental temperature and humidity and the current detection geographical condition to determine a weather state; analyzing the weather state to determine a weather noise influence; analyzing the compensated acoustic data to determine a specific frequency band in which the weather noise influence exists; eliminating the weather noise in the specific frequency band according to a preset dynamic filter to obtain actual compensated acoustic data; analyzing the actual compensated acoustic data according to the sound wave propagation model to determine a theoretical sound pressure value.

[0023] Optionally, when the coordinate determination module analyzes the weather state to determine a weather noise influence, the method further includes: analyzing the weather state to determine a weather noise; analyzing the weather noise to determine a time-domain impact feature and a frequency-domain resonance characteristic; determining a weather noise influence according to the time-domain impact feature and the frequency-domain resonance characteristic. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0025] Figure 1 An application scenario schematic diagram provided by an embodiment of the present application; Figure 2 A flowchart of a tower partial discharge detection method based on an acoustic imager provided by an embodiment of the present application; Figure 3 A tower partial discharge detection system structure schematic diagram based on an acoustic imager provided by an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0027] In addition, the term "and / or" in this document merely describes an association relationship of associated objects, and indicates that there can be three relationships, for example, A and / or B can represent three cases of A existing alone, A and B existing simultaneously, and B existing alone. In addition, the character " / " in this document generally represents an "or" relationship between the front and rear associated objects unless otherwise specified.

[0028] The embodiments of the present application will be further described below with reference to the drawings of the specification.

[0029] In the current power grid system, the accurate detection of the partial discharge phenomenon of the tower has a high misjudgment rate of the atypical discharge mode, therefore, how to realize the identification of the atypical discharge condition becomes a significant defect existing in the prior art.

[0030] Based on this, the application provides a tower partial discharge detection method and system based on an acoustic imager, tower acoustic data collected by the acoustic imager is acquired; the tower acoustic data is analyzed to determine an acoustic feature map; based on a preset discharge identification algorithm, the acoustic feature map is analyzed to determine a partial discharge type; the partial discharge type is matched with a preset detection mode to obtain a matching relationship; according to the matching relationship, the detection frequency band of the acoustic imager is adjusted; the acoustic imager is controlled to work according to the adjusted detection frequency band, and real-time compensation acoustic data is acquired, based on a preset sound wave attenuation compensation model, the compensation acoustic data is analyzed to determine the position coordinates of the tower partial discharge. Acquiring the tower acoustic data collected by the acoustic imager helps to eliminate the data space dislocation problem caused by the change of flight attitude, and improves the sound source positioning accuracy. Analyzing the tower acoustic data to determine the acoustic feature map provides multi-dimensional discrimination basis for partial discharge type identification, and reduces the misjudgment risk of the feature overlap area. Based on the preset discharge identification algorithm, the acoustic feature map is analyzed to determine the partial discharge type, which helps to reduce the misjudgment rate of atypical discharge, and avoids the type error caused by single feature misjudgment. Matching the partial discharge type with the preset detection mode to obtain the matching relationship helps to improve the synchronization rate of the detection time of intermittent discharge and reduce the missed detection rate. According to the matching relationship, the detection frequency band of the acoustic imager is adjusted, which helps to eliminate the random interference caused by the change of wind speed and improve the data signal-to-noise ratio. The acoustic imager is controlled to work according to the adjusted detection frequency band, and real-time compensation acoustic data is acquired, based on a preset sound wave attenuation compensation model, the compensation acoustic data is analyzed to determine the position coordinates of the tower partial discharge, which helps to eliminate the positioning error in high humidity environment, reduce the equivalent propagation distance calculation error, and meet the high-precision positioning demand in complex environment.

[0031] Figure 1An application scenario provided by the present application is shown in the following. When a local discharge of a tower pole is detected by using a drone carrying an acoustic imager, the method provided by the present application is applied. Specifically, the method provided by the present application is applied in any server, and the server interacts with the acoustic imager to obtain tower acoustic data collected by the acoustic imager, which helps to eliminate the problem of spatial and temporal dislocation of data caused by changes in flight posture and improve the accuracy of sound source positioning. By analyzing the tower acoustic data, the acoustic feature map is determined, which provides a multi-dimensional basis for identifying the type of partial discharge and reduces the risk of misjudgment in the feature overlap area. Based on a preset discharge identification algorithm, the acoustic feature map is analyzed to determine the type of partial discharge, which helps to reduce the misjudgment rate of atypical discharge and avoid type misclassification caused by single feature misjudgment. The type of partial discharge is matched with a preset detection mode to obtain a matching relationship, which helps to improve the synchronization rate of the detection time of intermittent discharge and reduce the missed detection rate. According to the matching relationship, the detection frequency band of the acoustic imager is adjusted, which helps to eliminate random interference caused by changes in wind speed and improve the signal-to-noise ratio of data. The acoustic imager is controlled to work according to the adjusted detection frequency band, and compensation acoustic data is obtained in real time through the acoustic imager. Based on a preset sound wave attenuation compensation model, the compensation acoustic data is analyzed to determine the position coordinates of the local discharge of the tower pole, which helps to eliminate the positioning error in a high-humidity environment, reduce the calculation error of the equivalent propagation distance, and meet the demand for high-precision positioning in a complex environment.

[0032] The specific implementation can refer to the following embodiments.

[0033] Figure 2 A flowchart of a tower pole local discharge detection method based on an acoustic imager is provided for an embodiment of the present application. The method of the present embodiment can be applied to the server in the above scenario. As shown in the following, Figure 2 The method comprises the following steps. S201, obtaining tower acoustic data collected by an acoustic imager; analyzing the tower acoustic data to determine an acoustic feature map; The acoustic imager can be a drone-mounted detection device carrying a 128-channel EMS microphone array.

[0034] The tower acoustic data can be original acoustic signal data obtained by the acoustic imager during the inspection process.

[0035] The acoustic feature map can be a feature expression generated by time-frequency analysis of the tower acoustic data.

[0036] Specifically, in the process of unmanned aerial vehicle inspection, the 128-channel EMS microphone array is used to collect real-time acoustic data of the tower. A dynamic frequency band division algorithm is constructed: first, based on the sliding time window, the short-time Fourier transform spectrum is calculated, and the energy mutation point is detected as a potential discharge event; second, the detected event segment is decomposed by wavelet packet; then, the correlation between each sub-band and the discharge type is evaluated by mutual information entropy, and the sub-band with the highest discrimination is automatically selected as the characteristic frequency band; finally, the acoustic feature spectrum containing time-frequency energy distribution, harmonic structure and pulse repetition rate is generated.

[0037] S202, based on the preset discharge recognition algorithm, analyze the acoustic feature spectrum, and determine the partial discharge type; The preset discharge recognition algorithm can be an algorithm pre-set for decoupling mixed discharge characteristics and outputting discharge type judgment results. It is pre-stored in the server and called when used.

[0038] The partial discharge type can be a discharge mode category caused by insulation defects of power equipment.

[0039] Specifically, the acoustic feature spectrum is classified by a deep convolutional neural network using the convolutional feature extraction principle in image recognition and the statistical pattern recognition theory in time-frequency analysis: first, the time-frequency domain feature matrix is received by the network input layer; second, the statistical features of the discharge pulse phase distribution spectrum are fused in the middle layer; then, the output layer sets a mixed discharge type judgment module; further, when the confidence difference between corona discharge and surface discharge is small, secondary feature extraction is started: first, the zero-crossing rate of the discharge pulse rising edge and the high-frequency oscillation decay coefficient are analyzed, and the pattern is separated by support vector machine; finally, the partial discharge type containing probability weight is output.

[0040] S203, match the partial discharge type with the preset detection mode to obtain a matching relationship; and adjust the detection frequency band of the acoustic imager according to the matching relationship; The preset detection mode can be a pre-set detection parameter set, including discharge type and detection parameter. It is pre-stored in the server and called when used.

[0041] The matching relationship can be the corresponding association relationship between the partial discharge type and the preset detection mode.

[0042] The detection frequency band can be the frequency range currently worked by the acoustic imager.

[0043] Specifically, the optimal detection parameter matching relationship is selected from a preset detection mode library: firstly, a high-frequency narrow-band mode for corona discharge; secondly, a wide-frequency multi-order mode for surface discharge; and thirdly, a double-frequency band alternating sampling strategy in a mixed discharge mode. The local oscillator frequency of the acoustic imager is adjusted in real time through a digital down converter, and the signal-to-noise ratio of the target frequency band is improved by controlling a variable gain amplifier; for the mixed discharge mode, a multi-band parallel processing architecture is adopted, and a multi-phase filter parallel architecture in digital signal processing and a hardware acceleration principle of a field programmable gate array (FPGA) are combined to realize synchronous operation of independent digital filter channels.

[0044] In S204, the acoustic imager is controlled to work according to the adjusted detection frequency band, and compensated acoustic data are acquired in real time. Based on a preset sound wave attenuation compensation model, the compensated acoustic data are analyzed to determine the position coordinates of the partial discharge of the tower.

[0045] The detection frequency band operation can be an operation state in which the acoustic imager performs acoustic signal acquisition and processing according to the adjusted center frequency and bandwidth parameters.

[0046] The compensated acoustic data can be acoustic data acquired after the detection frequency band is adjusted.

[0047] The preset sound wave attenuation compensation model can be a pre-set dynamic sound pressure attenuation calculation model. The model is pre-stored in a server and is called when used.

[0048] The partial discharge of the tower can be a non-penetrating discharge phenomenon on the surface or inside of a power transmission tower due to insulation deterioration.

[0049] The position coordinates can be three-dimensional space coordinates of a discharge point.

[0050] Specifically, based on the atmospheric absorption attenuation model of the International Telecommunication Union, combined with the geometric acoustic correction of the ray tracing method for terrain shielding effect, and through real-time correction of the sound velocity gradient by the temperature and humidity parameters and correction of the Doppler frequency shift by the wind speed parameter, a preset sound wave attenuation compensation model is established for accurate positioning: firstly, the temperature and humidity and wind speed data of the meteorological sensor are accessed in real time to establish a sound velocity correction model; secondly, the ray tracing method is used to simulate the reflection or diffraction path of sound waves in complex terrain according to the pose data of the unmanned aerial vehicle and the three-dimensional coordinates of the tower to calculate the equivalent propagation distance; finally, an improved TDOA algorithm based on the nonlinear relationship between sound wave propagation time delay and path in the wave equation is used, combined with the time difference positioning principle in array signal processing, a propagation path compensation factor is introduced on the basis of traditional time delay estimation, and the space coordinates of the discharge point are solved by the Levenberg-Marquardt nonlinear optimization algorithm based on the nonlinear least squares optimization theory, and finally the position coordinates are output.

[0051] By the scheme, the tower acoustic data collected by the acoustic imager is acquired, which helps to eliminate the data space-time dislocation problem caused by the change of flight attitude, and improve the sound source positioning accuracy. The acoustic data of the tower is analyzed to determine the acoustic characteristic map, which provides multi-dimensional discrimination basis for partial discharge type identification, and reduces the misjudgment risk in the characteristic overlap area. Based on the preset discharge identification algorithm, the acoustic characteristic map is analyzed to determine the type of partial discharge, which helps to reduce the misjudgment rate of atypical discharge, and avoids the type error caused by single feature misjudgment. The partial discharge type is matched with the preset detection mode to obtain a matching relationship, which helps to improve the synchronization rate of the detection time of intermittent discharge and reduce the missed detection rate. According to the matching relationship, the detection frequency band of the acoustic imager is adjusted, which helps to eliminate the random interference caused by the change of wind speed and improve the data signal-to-noise ratio. The acoustic imager is controlled to work according to the adjusted detection frequency band, and the compensation acoustic data is acquired in real time. Based on the preset sound wave attenuation compensation model, the compensation acoustic data is analyzed to determine the position coordinates of the tower partial discharge, which helps to eliminate the positioning error in high humidity environment, reduce the equivalent propagation distance calculation error, and meet the high-precision positioning demand in complex environment.

[0052] In some embodiments, according to the matching relationship, the detection mode is determined; according to the detection mode, the center frequency and bandwidth of the acoustic imager are determined; the initial detection frequency band is acquired; and according to the center frequency, bandwidth and initial detection frequency band, the current filtering parameter is determined.

[0053] The detection mode can be a working strategy suitable for the current state selected after matching the partial discharge type with the preset detection mode.

[0054] The center frequency can be the core frequency point of the acoustic imager for signal acquisition and analysis under the current detection mode.

[0055] The bandwidth can be an effective detection frequency range based on the center frequency.

[0056] The initial detection frequency band can be an initial value of the adjustable frequency band calculated based on the hardware performance of the acoustic imager and the environmental parameters.

[0057] The current filtering parameter can be a combination of parameter sets for real-time control of signal extraction and noise suppression characteristics of the target frequency band.

[0058] Specifically, according to the real-time detected discharge pulse phase distribution map and acoustic wave spectrum characteristics, a discharge type identification code and an environmental parameter vector are input into a preset detection mode, a mode matching degree calculation is performed, a detection mode is output, and a corresponding parameter configuration unit is activated. Then, the center frequency reference value in the target detection mode is read from the parameter configuration unit; then, the frequency band is aligned based on the initial detection frequency band, the center frequency is dynamically corrected through the frequency band offset compensation algorithm, and it is ensured that the target frequency band covers the main energy area of the discharge signal. Subsequently, the wide frequency fast scanning module of the acoustic imager is started, and the full frequency band acoustic wave signal is collected through the 128 channel EMS microphone array; then, the original signal is subjected to synchronous sampling and fast Fourier transform for frequency spectrum analysis, and the frequency band interval with signal energy exceeding the noise baseline is extracted and defined as the initial detection frequency band. Finally, a parameter fusion algorithm based on the filter parameter dynamic adjustment mechanism of environmental feedback is used to weight and fuse the target center frequency, bandwidth and initial detection frequency band, thereby generating the current filtering parameters.

[0059] Through the scheme, the detection mode is determined according to the matching relationship, which helps to eliminate the misjudgment rate of atypical discharge. According to the detection mode, the center frequency and bandwidth of the acoustic imager are determined, which helps to improve the effective signal energy capture rate and suppress the raindrop collision noise interference of non-target frequency band. The initial detection frequency band is obtained, which helps to eliminate the frequency band blind area problem caused by the time window misalignment of intermittent discharge, ensures the capture of the characteristic frequency band at the moment of discharge active period, and reduces the missed detection rate in the wind speed change area. According to the center frequency, bandwidth and initial detection frequency band, the current filtering parameters are determined, which helps to realize the adaptive contraction and expansion of frequency band selection and avoid the signal fragmentation problem caused by acoustic wave reflection in complex terrain.

[0060] In some embodiments, based on a preset discharge recognition algorithm, the acoustic characteristic map is analyzed to determine the spectrum characteristics; the spectrum characteristics are analyzed to determine the main frequency component, harmonic distribution and energy concentrated frequency band; and the local discharge type is determined according to the main frequency component, harmonic distribution and energy concentrated frequency band.

[0061] The spectrum characteristics can be the distribution characteristics of the acoustic wave signal after frequency domain conversion.

[0062] The main frequency component can be a set of significant frequency points in the acoustic characteristic map whose energy proportion exceeds the total energy.

[0063] The harmonic distribution can be the energy amplitude of the integer multiple frequency points of the main frequency component and the relative proportion relationship with the fundamental frequency.

[0064] The energy concentrated frequency band can be the maximum coverage interval in the acoustic characteristic map where the energy of the continuous frequency points is significantly higher than the environmental noise baseline.

[0065] Specifically, the time-domain acoustic wave signals collected by the 128-channel EMS microphone array are input into a preset discharge identification algorithm, a frequency domain energy distribution map is generated based on fast Fourier transform, and a time-frequency joint feature is extracted synchronously by using short-time Fourier transform to form a frequency spectrum feature containing a frequency-time-energy three-dimensional mapping. Then, based on the frequency spectrum feature, an energy threshold screening peak detection algorithm is established by establishing a dynamic energy baseline based on historical noise sample statistics, the noise resistance is improved by combining the gradient change and continuity criterion, the frequency point set with high energy proportion is determined, and the main frequency component is defined; based on the main frequency component, the energy attenuation slope of the integer multiple frequency points of the main frequency component is identified by the fundamental frequency locking mechanism of the harmonic tracking algorithm to determine the harmonic distribution; the energy cumulative distribution of the full frequency band is counted, and the continuous frequency band interval with high energy density is defined as the energy concentrated frequency band. The main frequency component, harmonic component and energy concentrated frequency band form a feature vector; through laboratory simulation of typical discharge types and collection of acoustic spectrum features, statistical characteristic analysis is performed combined with field measurement data to construct a preset detection mode based on the multi-dimensional features of main frequency-harmonic-energy distribution; the cosine similarity algorithm is used to calculate the matching degree of the feature vector and the preset detection mode, and the mode with high similarity is selected as the candidate type by combining the discharge feature dimension weight optimization; for intermittent discharge signals, the mode matching result is counted across the scanning period, and the majority voting mechanism is used to determine the final partial discharge type.

[0066] Through the scheme, based on the preset discharge identification algorithm, the acoustic characteristic map is analyzed to determine the frequency spectrum feature, which helps to improve the signal-to-noise ratio of the original signal. Analyzing the frequency spectrum feature to determine the main frequency component, harmonic distribution and energy concentrated frequency band helps to improve the separability of different discharge type features in the frequency spectrum overlap area and avoid feature omission caused by static frequency band division. According to the main frequency component, harmonic distribution and energy concentrated frequency band, the partial discharge type is determined, the superposition characteristics of corona and surface discharge in the mixed discharge mode are effectively distinguished, and the energy dispersion type discharge missing detection problem caused by fixed frequency band analysis is eliminated.

[0067] In some embodiments, a plurality of known detection data is obtained; the known detection data is analyzed to determine detection features and discharge types; a data statistical algorithm is used to cluster the detection features to obtain a detection feature set; detection parameters of the known detection data are called; based on the discharge types, the detection feature set is analyzed to determine the effectiveness of the detection parameters; and the preset detection mode is obtained according to the effectiveness.

[0068] The known detection data can be a set of partial discharge acoustic wave signal samples.

[0069] The detection feature can be a sound wave characteristic in the sound wave signal obtained by detection.

[0070] The discharge type can be a partial discharge mode embodied in the known detection data.

[0071] The data mining algorithm can be a mathematical method for feature analysis and pattern classification.

[0072] The detection parameter can be a core parameter configured when performing discharge detection.

[0073] The effectiveness can be an evaluation index of performance of the detection parameter under the discharge type and the environmental condition.

[0074] Specifically, through a laboratory high-voltage discharge simulation platform and on-site tower measurement, sound wave signal samples containing corona discharge, surface discharge, internal discharge and mixed discharge modes are obtained. Then, the frequency domain energy distribution atlas is generated by applying fast Fourier transform to the sound wave signal samples, and the frequency-time-energy three-dimensional mapping is extracted by simultaneously using short-time Fourier transform, so as to determine the detection features; subsequently, the sound wave signal samples are subjected to band-pass filtering and environmental noise suppression, and the discharge type is labeled. Then, the data mining algorithm is used, taking the center frequency of the main frequency component, the harmonic component and the energy concentrated frequency band width as the clustering dimensions, determining the contour coefficient through the average distance from the sample to other samples in the same cluster and the minimum average distance from the sample to the samples in other clusters, and then setting the contour coefficient threshold value by statistical distribution of the contour coefficient of the laboratory samples, so as to divide the feature space into three categories of corona discharge cluster, surface discharge cluster and mixed discharge cluster; further, for intermittent discharge data, the time dimension analysis is increased, the signal is segmented according to the time window, the frequency of the main frequency component in each window is counted, and the periodic discharge mode is identified in combination with hierarchical clustering. Then, three detection parameters corresponding to each type of discharge are extracted from the historical database, namely, the dynamic frequency range adjustment parameter, the sound wave attenuation compensation parameter and the intermittent discharge detection parameter. Further, the leave-one-out cross-validation method is used to balance the demand for deviation-variance, the detection feature set is divided into a training set and a test set, and the type recognition accuracy and positioning error are used to verify the effectiveness of the parameters. The effectiveness is bound with the corresponding feature vector and discharge type to generate a standard template; finally, the cross-validation method is used to evaluate the pattern matching accuracy, the samples are randomly divided as a training set to construct a template library and a test set to verify the classification accuracy, so as to obtain a preset detection mode.

[0075] By the scheme, a plurality of known detection data are acquired, which helps to eliminate the misjudgment problem of atypical discharge caused by insufficient single frequency band data. Analyzing the known detection data helps to determine the detection features and discharge types, which helps to reduce the misjudgment rate of mixed discharge mode and eliminate the feature-position correlation error caused by positioning deviation in unmanned aerial vehicle inspection. Using data statistical algorithm, the detection features are clustered to effectively distinguish the core feature distribution difference of corona, surface and internal discharge samples, and improve the mode compatibility under non-standard working conditions. Retrieving the detection parameters of the known detection data helps to overcome the parameter overfitting risk caused by noise interference of the field measurement data. Based on the discharge type, the effectiveness of the detection parameters is determined, which helps to reduce the misjudgment rate of atypical discharge. According to the effectiveness, the preset detection mode is obtained, which helps to eliminate the mode mismatch problem caused by the preset fixed frequency band.

[0076] In some embodiments, the peak frequency of the main frequency component is extracted; the harmonic distribution is analyzed to determine the number of harmonics and the harmonic energy proportion; the energy concentration degree is calculated based on the energy concentration frequency band; the peak frequency, the number of harmonics, the energy concentration degree and the harmonic energy proportion are input into a preset discharge type decision model to output the partial discharge type.

[0077] The peak frequency can be the frequency point with the maximum energy value in the main frequency component.

[0078] The number of harmonics can be the total number of frequency components that are integer multiples of the peak frequency.

[0079] The harmonic energy proportion can be the proportion of the sum of the second harmonic and the third harmonic energy to the total energy of the full frequency band signal.

[0080] The energy concentration degree can be the ratio of the continuous frequency interval bandwidth covering the total energy to the peak frequency.

[0081] The preset discharge type decision model can be a classification model preset to output corona discharge, surface discharge, internal discharge or mixed discharge type. It is pre-stored in a server and called when used.

[0082] Specifically, a significant frequency component of the energy value, i.e., a main frequency component, is identified; a frequency corresponding to a maximum energy value is selected as a peak frequency. Based on the peak frequency, positions of second and third harmonics are determined; energy values at the peak frequency, the second and third harmonics are extracted; and a proportion of energy of each harmonic to energy of a fundamental frequency is calculated. Based on the peak frequency, an energy concentration frequency band, i.e., an energy concentration degree, is defined by extending to boundary points where energy accumulations reach total energy on both sides. A four-dimensional feature vector is composed of the peak frequency, the number of harmonics, the energy concentration degree, and the proportion of harmonic energy. If the four-dimensional feature vector matches a preset discharge type decision model based on a multi-dimensional feature fusion theory in pattern recognition, which realizes discharge type mapping by combining statistical classification and hierarchical clustering, a corresponding type is directly output. If the matching degree is insufficient, hierarchical clustering analysis is triggered. First, a signal is segmented according to a time window, and gradient change features of a time-frequency matrix are extracted. Then, the gradient change features of the time-frequency matrix are used as clustering input, and Ward minimum variance method is used for secondary clustering to identify periodic discharge patterns. Finally, initial matching and auxiliary clustering results are integrated to output a final partial discharge type.

[0083] By the scheme, the peak frequency of the main frequency component is extracted, which helps to eliminate the feature aliasing problem caused by fixed frequency band division. The number of harmonics and the proportion of harmonic energy are determined by analyzing the harmonic distribution, which helps to improve the detection rate of mixed discharge. The energy concentration degree is calculated based on the energy concentration frequency band, which helps to reflect the characteristics of local concentrated discharge and represent the concurrent features of multiple types of discharge. The peak frequency, the number of harmonics, the energy concentration degree, and the proportion of harmonic energy are input into the preset discharge type decision model to output the partial discharge type, which helps to overcome the defect that single fundamental frequency features are easily affected by environmental interference, avoid misjudgment of high-frequency noise as surface discharge, realize effective separation of mixed discharge and single discharge type, and reduce the misjudgment rate.

[0084] In some embodiments, a flight environment of a bearing device of an acoustic imager at a current time is acquired; the flight environment is analyzed to determine a current flight height, environmental temperature and humidity, and terrain distribution; a terrain shielding coefficient is determined according to the terrain distribution; a theoretical sound pressure value is determined by analyzing compensated acoustic data according to a preset sound wave propagation model; a measured sound pressure value is determined by analyzing the compensated acoustic data; and position coordinates of a tower partial discharge are determined based on the theoretical sound pressure value, the measured sound pressure value, and the terrain shielding coefficient according to a preset sound wave attenuation compensation model.

[0085] The bearing device can be a drone carrying the acoustic imager.

[0086] The flight environment can be a set of environmental parameters of a space currently occupied by the drone.

[0087] The current flight height can be a real-time measured vertical altitude of the drone.

[0088] The ambient temperature and humidity can be the air temperature and relative humidity of the environment in which the UAV is located.

[0089] The terrain distribution can be a classification result of geographical features in the detection area of the UAV.

[0090] The terrain shielding coefficient can be a correction parameter quantifying the shielding effect of the terrain on the sound wave propagation path.

[0091] The preset sound wave propagation model can be a mathematical model preset to describe the attenuation law of sound wave propagation in the air. It is pre-stored in the server and called when used.

[0092] The theoretical sound pressure value can be an expected sound pressure value.

[0093] The measured sound pressure value can be the sound pressure data actually measured by the acoustic imager and filtered and calibrated.

[0094] Specifically, the flight environment at the current time is obtained through the UAV onboard navigation equipment. Then, based on the flight environment, the current flight height is obtained through RTK positioning, and the ambient temperature and relative humidity are collected by synchronously calling the temperature and humidity sensor; further, the terrain elevation data around the current coordinate point is parsed by calling the geographic information database, and the terrain distribution is generated. Based on the terrain distribution data, the ray tracing algorithm constructed based on the terrain elevation data in the geographic information database is used to simulate the sound wave propagation path using the geometric acoustics principle, and the number of truncations and shielding angles of the obstacles in the path are counted; based on the theory of acoustic shielding effect, the shielding angle and the obstacle density are combined to correct the terrain shielding coefficient. Then, the sound source intensity and propagation time in the compensated acoustic data are input into the classical sound wave spherical diffusion attenuation model, and the preset sound wave propagation model is superimposed with the atmospheric absorption attenuation term and the terrain shielding correction term; further, the current environmental sound speed is calculated according to the international standard sound speed formula; then, the theoretical sound pressure value is fused by the attenuation formula with the output of the preset sound wave propagation model. Subsequently, the compensated acoustic data is subjected to time domain windowing processing, and the sound pressure peak value of each microphone channel is extracted; the effective sound pressure value is calculated by the energy integral method obtained by adding the Hanning window to the time domain signal, and converted into decibel value, so as to determine the measured sound pressure value. Further, the theoretical sound pressure value and the measured sound pressure value are compared, and the sound pressure attenuation deviation is calculated; if the sound pressure attenuation deviation is low, the sound source positioning result is directly used; if the sound pressure attenuation deviation is high, the sound pressure attenuation deviation is input into the position based on the preset sound wave attenuation compensation model, and the distance correction amount is generated; based on the time difference of arrival positioning result of the microphone array, the distance correction amount is superimposed to obtain the position coordinate.

[0095] By the scheme, the flight environment of the bearing device of the acoustic imager at the current time is acquired, which helps to eliminate the defect of the influence of the change of temperature and humidity on the sound wave propagation speed without being considered. Analyzing the flight environment, the current flight height, the environmental temperature and humidity and the terrain distribution are determined, which helps to avoid the deviation of the sound source positioning caused by the height measurement error. According to the terrain distribution, the terrain shielding coefficient is determined, which helps to eliminate the deviation of the sound pressure attenuation caused by ignoring the terrain reflection and shielding, and significantly improves the positioning robustness of the complex terrain. According to the preset sound wave propagation model, the compensation acoustic data is analyzed, and the theoretical sound pressure value is determined, which helps to break through the limitation of the attenuation coefficient and eliminate the distortion problem of the theoretical value caused by the sudden change of the sound speed in the rain and fog weather. Analyzing the compensation acoustic data, the measured sound pressure value is determined, which avoids the sound pressure measurement fluctuation caused by noise pollution. Based on the preset sound wave attenuation compensation model, the position coordinates of the tower partial discharge are determined by analyzing the theoretical sound pressure value and the measured sound pressure value, which effectively suppresses the abnormal channel data interference caused by multiple reflections or path shielding, reduces the positioning error in the complex terrain, and solves the problem of rigid positioning weight distribution.

[0096] In some embodiments, when the intermittent discharge mode is detected, a multi-cycle frequency band scanning mode of the acoustic imager is activated, and a periodic travel signal is sent to the bearing device; the periodic frequency band scanning data of the acoustic imager is acquired; the energy change trend of the characteristic frequency band in the adjacent cycle is determined by analyzing the periodic frequency band scanning data; and the optimal detection time window is determined according to the energy change trend of the characteristic frequency band in the adjacent cycle.

[0097] The intermittent discharge mode can be a non-continuous, periodic or quasi-periodic discharge phenomenon.

[0098] The multi-cycle frequency band scanning mode can be a scanning mode dynamically configured according to the intermittent discharge pulse cycle.

[0099] The periodic travel signal can be a uniform speed displacement control instruction sent to the bearing device.

[0100] The periodic frequency band scanning data can be time-frequency matrix data generated in the multi-cycle frequency band scanning mode.

[0101] The adjacent cycle can be two scanning cycles continuously in time sequence in the multi-cycle frequency band scanning.

[0102] The characteristic frequency band can be a frequency band region in the energy peak frequency point in the periodic frequency band scanning data.

[0103] The energy change trend can be a quantitative fluctuation rule of the energy ratio of the characteristic frequency band in the adjacent cycle.

[0104] The optimal detection time window can be a continuous time period selected according to the energy stable trend.

[0105] Specifically, when an intermittent discharge spectrum pattern is detected, a pattern recognition flag is triggered. Then, based on the intermittent discharge pulse period, the acoustic imager's multi-cycle frequency band scanning mode is dynamically configured: using the discharge center frequency as the reference, the extended bandwidth as the scanning range, and the number of consecutive scanning cycles set. Subsequently, a periodic travel signal is sent to the carrier device. Finally, within each scanning cycle, full-band sound pressure data is collected and stored as a time-frequency matrix. The start timestamp of each cycle and the spatial coordinates of the carrier device are marked to generate spatiotemporally aligned periodic frequency band scanning data for the acoustic imager. Based on this periodic frequency band scanning data, the energy ratio of the same frequency band in adjacent cycles is calculated. If the same frequency band energy ratio is too high and the peak frequency offset between adjacent cycles is low, an energy increase trend is determined. If the same frequency band energy ratio is too low and the peak frequency standard deviation is high, an energy decrease trend is determined. Finally, if the energy change trend is increasing, the time interval in which the energy is predicted to reach its maximum value in the next scanning cycle is set as the optimal detection time window, so that the optimal detection time window can be used to acquire the tower acoustic data in the above embodiment.

[0106] Through this solution, when an intermittent discharge pattern is detected, the multi-cycle frequency band scanning mode of the acoustic imager is activated, and a periodic travel signal is sent to the carrier device, which helps to avoid errors in the detection frequency band setting due to pattern misjudgment, while suppressing random noise interference, avoiding inaccurate acoustic signal acquisition due to fluctuations in the device's moving speed or path obstruction, and improving the spatial alignment accuracy of multi-cycle data. Acquiring the periodic frequency band scanning data of the acoustic imager helps to provide a high-precision, traceable raw data basis for the analysis of energy change trends in adjacent cycles. Analyzing the periodic frequency band scanning data and determining the energy change trend of the characteristic frequency bands in adjacent cycles helps to eliminate the interference of non-target signals on the detection window selection. Based on the energy change trend of the characteristic frequency bands in adjacent cycles, the optimal detection time window is determined, providing a high-confidence time benchmark for the acoustic imager detection frequency band adjustment.

[0107] In some embodiments, the flight environment is analyzed to determine the current detection geographical conditions; the ambient temperature and humidity and the current detection geographical conditions are analyzed to determine the weather conditions; the weather conditions are analyzed to determine the impact of weather noise; the compensated acoustic data is analyzed to determine the specific frequency bands affected by weather noise; based on a preset dynamic filter, the weather noise in the specific frequency band is eliminated to obtain actual compensated acoustic data; based on the sound wave propagation model, the actual compensated acoustic data is analyzed to determine the theoretical sound pressure value.

[0108] The current detection geographical condition may be a set of terrain feature parameters at the current location of the UAV.

[0109] The weather state can be a discrete label that characterizes the acoustic propagation characteristics of the current air medium.

[0110] The weather noise influence can be an abnormal energy of a specific frequency band of the acoustic signal.

[0111] The specific frequency band can be a frequency band range of the acoustic data contaminated by the weather noise.

[0112] The preset dynamic filter can be a set of frequency domain filter parameters preset and bound to the weather state label. The preset set is stored in a server in advance and is called when used.

[0113] The weather noise can be an acoustic signal interference caused by a meteorological condition.

[0114] The actual compensated acoustic data can be the acoustic signal after the weather noise is eliminated by the dynamic filter, and the original signal characteristics of the non-noise frequency band are retained.

[0115] The sound wave propagation model can be a physical model for calculating a theoretical sound pressure value.

[0116] Specifically, a geographic information database is called to match preset terrain elevation data based on GPS coordinates to obtain the current detected geographic condition of the flight environment of the unmanned aerial vehicle. Real-time environment temperature and relative humidity data are collected by a temperature and humidity sensor array, and an air medium sound attenuation factor is calculated by using an international standardization organization air sound attenuation model in combination with the current detected geographic condition. The weather state is output according to a relationship matrix of the air medium sound attenuation factor and the geographic condition. A preset noise spectrum library is generated by statistical classification based on a meteorological acoustic characteristic measurement database, and the weather noise influence is generated by matching the preset noise spectrum library with the weather state. The time-domain acoustic signal is converted into a frequency band energy distribution spectrum by performing FFT transformation on the compensated acoustic data. Then, a noise threshold is set based on the frequency band energy mean value of a weather interference-free period in historical data and a critical value preset according to sound source detection requirements, and a specific frequency band in which the energy exceeds the noise threshold is identified from the weather noise influence and is recorded as the specific frequency band. According to the specific frequency band range, a preset dynamic filter with a linear phase is loaded to perform amplitude suppression on the specific frequency band in the compensated acoustic data, and actual compensated acoustic data is generated. The actual compensated acoustic data is input into the sound wave propagation model, and a theoretical sound pressure value is calculated in combination with the propagation distance and the air medium sound attenuation factor.

[0117] By the scheme, the flight environment is analyzed, the current detection geographic condition is determined, the multipath interference caused by terrain reflection is excluded, the physical environment basis of acoustic data analysis is ensured to be consistent with the real propagation condition. The environment temperature and humidity and the current detection geographic condition are analyzed, the weather state is determined, the physical environment parameter support is provided for weather noise influence identification. The weather state is analyzed, the weather noise influence is determined, and the effective signal loss caused by global filtering is avoided. The compensated acoustic data is analyzed, the specific frequency band with weather noise influence is determined, the noise positioning and energy amplitude quantization are realized, and the frequency band boundary condition is provided for dynamic filter parameter selection. According to the preset dynamic filter, the weather noise in the specific frequency band is eliminated, the actual compensated acoustic data is obtained, the noise energy is suppressed, and the frequency domain purity of the compensated data is ensured. According to the sound wave propagation model, the actual compensated acoustic data is analyzed, the theoretical sound pressure value is determined, and the comparison analysis and verification data compensation effectiveness of the sound source positioning or abnormality detection are helped.

[0118] In some embodiments, the weather state is analyzed, the weather noise is determined; the weather noise is analyzed, the time domain impact feature and the frequency domain resonance characteristic are determined; and the weather noise influence is determined according to the time domain impact feature and the frequency domain resonance characteristic.

[0119] The time domain impact feature can be a set of time domain parameters of transient pulse events in the weather noise.

[0120] The frequency domain resonance characteristic can be a set of specific frequency bands in which the energy of the weather noise is significantly higher than that of the background noise in the frequency domain.

[0121] Specifically, a preset weather state is called, the current weather state type is determined based on the joint matching of the temperature and humidity sensor data and the current detection geographic condition, and the corresponding weather noise is indexed. Then, according to the weather state, a preset noise spectrum library is called, the noise frequency band and the time domain impact feature parameter corresponding to the weather state are extracted; the peak value of the time domain waveform of the compensated acoustic data is detected, the impact pulse interval and amplitude are counted, and if the impact pulse interval matches the time domain impact feature parameter, it is determined that there is a time domain impact feature of weather noise; at the same time, the compensated acoustic data is subjected to FFT transformation to generate a frequency energy distribution spectrum, identify the frequency band with energy exceeding the noise threshold, and screen out the frequency band intersecting with the noise frequency band, which is marked as the frequency domain resonance characteristic frequency band. Finally, the frequency bands satisfying the time domain impact feature and the frequency domain resonance characteristic are combined, and the non-overlapping frequency bands are removed to generate the final weather noise influence.

[0122] By the scheme, the weather state is analyzed, the weather noise is determined, the accurate classification of the weather type is realized, and the category basis is provided for noise characteristic matching. The weather noise is analyzed, the time domain impact feature and the frequency domain resonance characteristic are determined, which helps to exclude non-weather interference, reduce the misjudgment rate, and avoid the interference of high-frequency or low-frequency environmental noise. According to the time domain impact feature and the frequency domain resonance characteristic, the weather noise influence is determined, which helps to accurately limit the noise frequency band range to be suppressed, and ensures the pertinence and effectiveness of noise elimination.

[0123] Figure 3 A structure schematic diagram of a tower partial discharge detection system based on an acoustic imager provided by an embodiment of the present application is shown in FIG. 1. Figure 3 As shown in the figure, the tower partial discharge detection system 300 based on the acoustic imager of the embodiment includes a data analysis module 301, a graph analysis module 302, a frequency band adjustment module 303, and a coordinate determination module 304.

[0124] The data analysis module 301 is configured to acquire tower acoustic data collected by the acoustic imager, analyze the tower acoustic data, and determine an acoustic feature graph. The graph analysis module 302 is configured to analyze the acoustic feature graph based on a preset discharge identification algorithm, determine a partial discharge type, and analyze the acoustic feature graph based on the preset discharge identification algorithm. The frequency band adjustment module 303 is configured to match the partial discharge type with a preset detection mode, obtain a matching relationship, adjust a detection frequency band of the acoustic imager according to the matching relationship, and adjust the detection frequency band of the acoustic imager according to the matching relationship. The coordinate determination module 304 is configured to control the acoustic imager to work according to the adjusted detection frequency band, acquire compensation acoustic data in real time, analyze the compensation acoustic data based on a preset sound wave attenuation compensation model, and determine a position coordinate of tower partial discharge.

[0125] Optionally, when the frequency band adjustment module 303 adjusts the detection frequency band of the acoustic imager according to the matching relationship, the frequency band adjustment module 303 is configured to: determine a detection mode according to the matching relationship; determine a center frequency and a bandwidth of the acoustic imager according to the detection mode; acquire an initial detection frequency band, and determine a current filtering parameter according to the center frequency, the bandwidth, and the initial detection frequency band.

[0126] Optionally, when the graph analysis module 302 analyzes the acoustic feature graph based on the preset discharge identification algorithm to determine a partial discharge type, the graph analysis module 302 is configured to: analyze the acoustic feature graph based on the preset discharge identification algorithm to determine a frequency spectrum feature; analyze the frequency spectrum feature to determine a main frequency component, a harmonic distribution, and an energy concentration frequency band; According to the main frequency component, the harmonic distribution and the energy concentration frequency band, a partial discharge type is determined.

[0127] Optionally, the tower partial discharge detection system based on the acoustic imager further comprises a mode determination module 305, configured to: Obtain a plurality of known detection data; analyze the known detection data to determine a detection feature and a discharge type; Use a data statistical algorithm to cluster the detection feature to obtain a detection feature set; Retrieve a detection parameter of the known detection data; Based on the discharge type, analyze the detection feature set to determine the effectiveness of the detection parameter; According to the effectiveness, a preset detection mode is obtained; the preset detection mode comprises the discharge type and the detection parameter.

[0128] Optionally, when the atlas analysis module 302 determines the partial discharge type according to the main frequency component, the harmonic distribution and the energy concentration frequency band, it is configured to: Extract a peak frequency of the main frequency component; Analyze the harmonic distribution to determine a harmonic number and a harmonic energy proportion; Based on the energy concentration frequency band, calculate an energy concentration degree; Input the peak frequency, the harmonic number, the energy concentration degree and the harmonic energy proportion into a preset discharge type decision model to output a partial discharge type.

[0129] Optionally, when the coordinate determination module 304 analyzes the compensation acoustic data based on a preset sound wave attenuation compensation model to determine the position coordinates of the tower partial discharge, it is configured to: Obtain a flight environment of a bearing device of the acoustic imager at the current time; Analyze the flight environment to determine a current flight height, an environmental temperature and humidity and a terrain distribution; According to the terrain distribution, determine a terrain shielding coefficient; According to a preset sound wave propagation model, analyze the compensation acoustic data to determine a theoretical sound pressure value; Analyze the compensation acoustic data to determine a measured sound pressure value; Based on a preset sound wave attenuation compensation model, analyze the theoretical sound pressure value, the measured sound pressure value and the terrain shielding coefficient to determine the position coordinates of the tower partial discharge.

[0130] Optionally, the tower partial discharge detection system based on the acoustic imager further comprises a window determination module 306, configured to: When the intermittent discharge mode is detected, a multi-cycle frequency band scanning mode of the acoustic imager is activated, and a periodic travel signal is sent to the bearing device; Periodic frequency band scanning data of the acoustic imager is acquired, and the periodic frequency band scanning data is analyzed to determine an energy change trend of a characteristic frequency band in adjacent cycles; According to the energy change trend of the characteristic frequency band in adjacent cycles, a best detection time window is determined.

[0131] Optionally, when the coordinate determination module 304 analyzes the compensation acoustic data according to the preset sound wave propagation model to determine the theoretical sound pressure value, it is used for: The flight environment is analyzed to determine the current detection geographical condition; The environmental temperature and humidity and the current detection geographical condition are analyzed to determine the weather state; The weather state is analyzed to determine the weather noise influence; The compensation acoustic data is analyzed to determine a specific frequency band where the weather noise influence exists; According to a preset dynamic filter, the weather noise of the specific frequency band is eliminated to obtain actual compensation acoustic data; According to the sound wave propagation model, the actual compensation acoustic data is analyzed to determine the theoretical sound pressure value.

[0132] Optionally, when the coordinate determination module 304 analyzes the weather state to determine the weather noise influence, it is used for: The weather state is analyzed to determine the weather noise; The weather noise is analyzed to determine the time domain impact characteristics and the frequency domain resonance characteristics; According to the time domain impact characteristics and the frequency domain resonance characteristics, the weather noise influence is determined.

[0133] The system of the embodiment can be used to execute the method of any of the above embodiments, and has similar implementation principles and technical effects, which will not be described here.

Claims

1. A tower partial discharge detection method based on acoustic imaging instrument, characterized in that: include: Acquiring tower acoustic data collected by the acoustic imager; Analyzing the tower acoustic data to determine an acoustic signature spectrum; Analyzing the acoustic signature spectrum based on a preset discharge recognition algorithm to determine the type of partial discharge; Matching the partial discharge type with a preset detection mode to obtain a matching relationship; and adjusting the detection frequency band of the acoustic imager according to the matching relationship; The acoustic imager is controlled to operate according to the adjusted detection frequency band and to obtain compensated acoustic data in real time. Based on a preset sound wave attenuation compensation model, the compensated acoustic data is analyzed to determine the location coordinates of partial discharge on the tower.

2. The method according to claim 1, characterized in that The adjusting the detection frequency band of the acoustic imager according to the matching relationship includes: Determining a detection mode according to the matching relationship; determining a center frequency and a bandwidth of the acoustic imager according to the detection mode; Acquire an initial detection frequency band; and determine current filtering parameters according to the center frequency, the bandwidth, and the initial detection frequency band.

3. The method according to claim 2, characterized in that The step of analyzing the acoustic characteristic spectrum based on a preset discharge identification algorithm to determine the type of partial discharge includes: Analyzing the acoustic characteristic spectrum based on a preset discharge recognition algorithm to determine the spectrum characteristics; Analyze the spectrum characteristics to determine the main frequency components, harmonic distribution and energy concentration frequency bands; The type of partial discharge is determined according to the main frequency component, the harmonic distribution and the energy concentration frequency band.

4. The method according to claim 3, characterized in that The determining of the partial discharge type according to the main frequency component, the harmonic distribution and the energy concentration frequency band includes: Extracting the peak frequency of the main frequency component; Analyzing the harmonic distribution to determine the number of harmonics and the proportion of harmonic energy; Calculating energy concentration based on the energy concentrated frequency band; The peak frequency, the number of harmonics, the energy concentration and the harmonic energy ratio are input into a preset discharge type decision model to output a partial discharge type.

5. The method according to claim 1, wherein The establishment of the preset detection mode includes: Acquire a number of known detection data; analyze the known detection data to determine detection characteristics and discharge types; Clustering the detection features using a data statistical algorithm to obtain a detection feature set; Retrieving detection parameters of the known detection data; Analyzing the detection feature set based on the discharge type to determine the validity of the detection parameters; A preset detection mode is obtained according to the effectiveness; the preset detection mode includes the discharge type and the detection parameters.

6. The method according to claim 1, characterized in that The step of analyzing the compensated acoustic data based on a preset acoustic wave attenuation compensation model to determine the location coordinates of the partial discharge on the tower includes: Obtaining the current flight environment of the device carrying the acoustic imager; Analyze the flight environment to determine the current flight altitude, ambient temperature and humidity, and terrain distribution; determining a terrain shielding coefficient according to the terrain distribution; Analyzing the compensated acoustic data according to a preset sound wave propagation model to determine a theoretical sound pressure value; Analyzing the compensated acoustic data to determine a measured sound pressure value; Based on a preset sound wave attenuation compensation model, the theoretical sound pressure value, the measured sound pressure value and the terrain shielding coefficient are analyzed to determine the location coordinates of the partial discharge on the tower.

7. The method according to claim 6, characterized in that The partial discharge type includes intermittent discharge; before controlling the acoustic imager to operate according to the adjusted detection frequency band, the method further includes: When an intermittent discharge pattern is detected, activating a multi-cycle frequency band scanning mode of the acoustic imager and sending a periodic travel signal to the carrier device; Acquiring periodic frequency band scanning data of the acoustic imager; analyzing the periodic frequency band scanning data to determine energy change trends of characteristic frequency bands in adjacent periods; The optimal detection time window is determined based on the energy change trend of the characteristic frequency bands in adjacent cycles.

8. The method according to claim 6, characterized in that Analyzing the compensated acoustic data according to a preset sound wave propagation model to determine a theoretical sound pressure value includes: Analyze the flight environment and determine the current detection geographical conditions; Analyze the ambient temperature and humidity and the current detected geographical conditions to determine weather conditions; Analyzing the weather conditions to determine the impact of weather noise; Analyzing the compensated acoustic data to determine a specific frequency band affected by the weather noise; Eliminating weather noise in the specific frequency band according to a preset dynamic filter to obtain actual compensated acoustic data; The actual compensated acoustic data is analyzed according to the sound wave propagation model to determine a theoretical sound pressure value.

9. The method according to claim 8, characterized in that The analyzing the weather conditions and determining the impact of weather noise includes: analyzing the weather conditions to determine weather noise; Analyzing the weather noise to determine time domain impact characteristics and frequency domain resonance characteristics; The impact of weather noise is determined based on the time domain impact characteristics and the frequency domain resonance characteristics.

10. A tower partial discharge detection system based on acoustic imaging instrument, characterized in that: The method according to any one of claims 1 to 9, characterized in that it includes: A data analysis module is used to obtain the tower acoustic data collected by the acoustic imager; analyze the tower acoustic data to determine the acoustic characteristic spectrum; A spectrum analysis module, configured to analyze the acoustic characteristic spectrum based on a preset discharge identification algorithm to determine the type of partial discharge; a frequency band adjustment module, configured to match the partial discharge type with a preset detection mode to obtain a matching relationship; and adjust the detection frequency band of the acoustic imager according to the matching relationship; The coordinate determination module is used to control the acoustic imager to operate according to the adjusted detection frequency band, and to obtain compensated acoustic data in real time. Based on a preset sound wave attenuation compensation model, the compensated acoustic data is analyzed to determine the location coordinates of the partial discharge of the tower.

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