Adaptive frequency selection method and device for partial discharge narrowband monitoring, equipment and storage medium

Abstract

The application discloses a kind of partial discharge narrowband monitoring adaptive frequency selection method, device, equipment and storage medium, belong to partial discharge narrowband monitoring technical field.The application is by determining the wideband electromagnetic wave signal to be detected, and the wideband electromagnetic wave signal is detected according to the preset narrowband width continuously, and the narrowband signal power dataset is obtained;Extract multiple interference frequency bands in the narrowband signal power dataset, obtain interference frequency band set, wherein the interference frequency band is the frequency band that each kind of communication signal in the current detection environment generates partial discharge monitoring interference to wideband electromagnetic wave signal;According to interference frequency band set, remove interference frequency band in multiple narrowband frequency bands corresponding to wideband electromagnetic wave signal, obtain multiple narrowband frequency bands for partial discharge monitoring, i.e.determine interference frequency band in the current detection environment first, and then determine multiple narrowband frequency bands for partial discharge monitoring, so that partial discharge monitoring system can adapt to interference frequency band of different measurement environments.

Description

Technical Field

[0001] This invention relates to the field of partial discharge narrowband monitoring technology, and particularly to an adaptive frequency selection method, apparatus, equipment, and storage medium for partial discharge narrowband monitoring. Background Technology

[0002] In various scenarios of industrial production and daily life, the power system plays a vital role as a basic energy infrastructure. To ensure the normal operation of the power system, it is necessary to monitor various electrical equipment in the power system in real time, so as to detect electrical equipment faults in a timely manner and carry out corresponding maintenance in advance.

[0003] The aging and potential defects of various electrical equipment can trigger partial discharge, which in turn accelerates the deterioration of insulation materials, creating a vicious cycle. Each partial discharge leads to a further decline in the insulation performance of the equipment, thus threatening the stable operation of the power system. Therefore, monitoring partial discharge phenomena in electrical equipment has become an important means of detecting the insulation status of electrical equipment and ensuring its safe operation. UHF (Ultra High Frequency) partial discharge detection technology is typically used to achieve real-time monitoring of the frequency band of partial discharge in equipment. Relying on the better noise suppression capabilities of narrowband frequency domain signal processing, the partial discharge frequency band with a higher signal-to-noise ratio can be detected in environments with noise and external interference.

[0004] However, in different measurement environments, the frequency bands of interference signal distribution and the frequency bands of different partial discharge types are different. Many existing narrowband partial discharge monitoring systems are usually only configured with a few fixed narrowbands. Their fixed narrowbands cannot be well adapted to different detection environments, resulting in poor partial discharge frequency band monitoring performance of existing partial discharge monitoring systems with fixed monitoring narrowbands in different measurement environments. Summary of the Invention

[0005] The main objective of this application is to provide an adaptive frequency selection method, apparatus, device, and storage medium for partial discharge narrowband monitoring, aiming to solve the technical problem that existing partial discharge monitoring systems with fixed monitoring narrowbands have poor monitoring performance in different measurement environments.

[0006] To achieve the above objectives, this application provides an adaptive frequency selection method for narrowband partial discharge monitoring, the adaptive frequency selection method for narrowband partial discharge monitoring comprising the following steps:

[0007] The broadband electromagnetic wave signal to be detected is identified, and the broadband electromagnetic wave signal is continuously narrowband power detected according to a preset narrowband width to obtain a narrowband signal power dataset.

[0008] Multiple interference frequency bands are extracted from the narrowband signal power dataset to obtain an interference frequency band set, wherein the interference frequency bands are the frequency bands that generate partial discharge monitoring interference to the broadband electromagnetic wave signal corresponding to various communication signals in the current detection environment;

[0009] Based on the set of interference frequency bands, the interference frequency bands in the multiple narrowband frequency bands corresponding to the broadband electromagnetic wave signal are removed to obtain multiple narrowband frequency bands for partial discharge monitoring.

[0010] In one embodiment, the step of continuously narrowband power detection of the broadband electromagnetic wave signal according to a preset narrowband width to obtain a narrowband signal power dataset includes:

[0011] The broadband electromagnetic wave signal is continuously narrowband power detected according to a preset narrowband width, and the process is repeated a preset number of times to obtain a corresponding number of narrowband signal power data subsets.

[0012] The step of extracting multiple interference frequency bands from the narrowband signal power dataset to obtain an interference frequency band set includes:

[0013] By using a preset narrowband power fusion calculation rule, the interference frequency bands in each subset of narrowband signal power data are extracted to obtain the interference frequency band subset corresponding to the preset number of times.

[0014] The interference frequency band set is obtained by performing an intersection operation on the subset of interference frequency bands corresponding to the preset number of times.

[0015] In one embodiment, the step of extracting interference frequency bands from each subset of narrowband signal power data using a preset narrowband power fusion calculation rule to obtain the interference frequency band subset corresponding to the preset number of sets includes:

[0016] The preset narrowband power fusion calculation rules include weighted average threshold judgment rules, adjustable multiquantile sequence filtering rules, and narrowband sequence MAD operation rules.

[0017] By using the weighted average threshold judgment rule, each subset of narrowband signal power data is judged to obtain the mean narrowband set;

[0018] The adjustable quantile sequence filtering rules are used to filter each subset of narrowband signal power data to obtain a quantile narrowband set.

[0019] The MAD operation rules for the narrowband sequence are used to calculate the power data subset of each narrowband signal to obtain the MAD narrowband set.

[0020] Perform a union operation on the mean narrowband set, quantile narrowband set, and MAD narrowband set corresponding to each dataset to obtain the interference frequency band subset corresponding to the preset number of groups.

[0021] In one embodiment, the step of judging each subset of narrowband signal power data according to the weighted average threshold judgment rule to obtain the mean narrowband set includes:

[0022] Determine the power value of the broadband signal in each subset of narrowband signal power data, obtain the narrowband average bandwidth specified by the relevant user, and calculate the average narrowband power based on the power value and the narrowband average bandwidth.

[0023] The average power of multiple narrowband segments in the broadband signal of each subset of narrowband signal power data is obtained by averaging the power of multiple narrowband segments.

[0024] Calculate the average of the narrowband power mean and the average of the multiple narrowband power mean;

[0025] Signal frequency bands with power values ​​greater than the average value in each subset of narrowband signal power data are placed into the mean narrowband set.

[0026] In one embodiment, the step of placing the signal frequency bands with power values ​​greater than the average value in each subset of narrowband signal power data into the mean narrowband set includes:

[0027] Obtain the debugging coefficients specified by the relevant user;

[0028] Signal frequency bands whose power values ​​are greater than the product of the average value and the tuning coefficient in each set of narrowband signal power data subsets are placed into the mean narrowband set.

[0029] In one embodiment, the step of filtering each subset of narrowband signal power data using the adjustable quantile sequence filtering rule to obtain a quantile narrowband set includes:

[0030] The data in each narrowband signal power data subset are sorted, and the quantile coefficients adaptively set by the relevant users according to their actual needs are obtained.

[0031] Based on the quantile coefficients, the data in the sorted narrowband signal power data subset are filtered to obtain the quantile narrowband set.

[0032] In one embodiment, the step of calculating the MAD narrowband set by using the narrowband sequence MAD operation rule for each subset of narrowband signal power data includes:

[0033] Calculate the median of the data in each subset of narrowband signal power data, calculate the deviation of each data point from the median, and calculate the median of the absolute deviations;

[0034] If the deviation value is greater than the product of the median of the absolute deviation and the coefficient of the discrete threshold point specified by the relevant user, then the data corresponding to the deviation value is determined to be an outlier, and the MAD narrowband set is obtained based on the outlier.

[0035] Furthermore, to achieve the above objectives, this application also provides an adaptive frequency selection device for partial discharge narrowband monitoring, the adaptive frequency selection device for partial discharge narrowband monitoring comprising:

[0036] The detection module is used to determine the broadband electromagnetic wave signal to be detected, and to continuously detect the power of the broadband electromagnetic wave signal according to a preset narrowband width to obtain a narrowband signal power dataset.

[0037] The extraction module is used to extract multiple interference frequency bands from the narrowband signal power dataset to obtain an interference frequency band set, wherein the interference frequency bands are the frequency bands that generate partial discharge monitoring interference to the broadband electromagnetic wave signal corresponding to various communication signals in the current detection environment;

[0038] The processing module is used to remove the interfering frequency bands from the multiple narrowband frequency bands corresponding to the broadband electromagnetic wave signal according to the set of interfering frequency bands, so as to obtain multiple narrowband frequency bands for partial discharge monitoring.

[0039] In addition, to achieve the above objectives, this application also provides an adaptive frequency selection device for partial discharge narrowband monitoring. The adaptive frequency selection device for partial discharge narrowband monitoring includes: a memory, a processor, and an adaptive frequency selection program for partial discharge narrowband monitoring stored in the memory and executable on the processor. The adaptive frequency selection program for partial discharge narrowband monitoring is configured to implement the steps of the adaptive frequency selection method for partial discharge narrowband monitoring as described above.

[0040] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing an adaptive frequency selection program for partial discharge narrowband monitoring, wherein the adaptive frequency selection program for partial discharge narrowband monitoring, when executed by a processor, implements the steps of the adaptive frequency selection method for partial discharge narrowband monitoring as described above.

[0041] One or more technical solutions proposed in this application have at least the following technical effects: determining the broadband electromagnetic wave signal to be detected, and performing continuous narrowband power detection on the broadband electromagnetic wave signal according to a preset narrowband width to obtain a narrowband signal power dataset; extracting multiple interference frequency bands from the narrowband signal power dataset to obtain an interference frequency band set, wherein the interference frequency bands are frequency bands corresponding to various communication signals in the current detection environment that generate partial discharge monitoring interference to the broadband electromagnetic wave signal; removing the interference frequency bands from the multiple narrowband frequency bands corresponding to the broadband electromagnetic wave signal according to the interference frequency band set to obtain multiple narrowband frequency bands for partial discharge monitoring. Specifically, when determining the broadband electromagnetic wave signal to be detected, first performing continuous narrowband power detection on its corresponding signal according to a certain narrowband width, and dividing it into a narrowband signal power dataset composed of multiple narrowband signal powers, and then extracting from this dataset... The narrowband signal power dataset extracts multiple interference frequency bands that generate partial discharge (PD) monitoring interference to broadband electromagnetic signals from various communication signals in the current detection environment, forming an interference frequency band set. Based on this set, the corresponding interference frequency bands are removed, thus selecting multiple narrowband frequency bands for PD monitoring. This avoids the frequency bands that interfere with PD monitoring from communication signals. In subsequent PD monitoring, the selected narrowband frequency bands are used as the key monitoring targets, improving the adaptability of the PD monitoring system to different environments and communication signal interference conditions. This enables the PD monitoring system to perform accurate PD monitoring on narrowband frequency bands under different environmental interference conditions. In other words, by configuring different narrowband frequency bands for PD monitoring in different measurement environments, the PD monitoring effect of the PD system on PD frequency bands in different environments is improved. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 A flowchart illustrating the first embodiment of the adaptive frequency selection method for partial-range narrowband monitoring in this application;

[0045] Figure 2 A flowchart illustrating the second embodiment of the adaptive frequency selection method for partial-amplitude narrowband monitoring in this application;

[0046] Figure 3This is a schematic diagram of the process of extracting interference frequency bands through multiple consecutive sampling in an embodiment of this application;

[0047] Figure 4 A flowchart illustrating the third embodiment of the adaptive frequency selection method for partial-amplitude narrowband monitoring in this application;

[0048] Figure 5 This is a schematic diagram of the process for obtaining the mean narrowband set in an embodiment of this application;

[0049] Figure 6 This is a schematic diagram of the process for obtaining the quantile narrowband set in an embodiment of this application;

[0050] Figure 7 This is a schematic diagram of the process for obtaining the MAD narrowband set in an embodiment of this application;

[0051] Figure 8 This is a schematic diagram of the narrowband power fusion calculation process in an embodiment of this application;

[0052] Figure 9 This is a schematic diagram of the module structure of the adaptive frequency selection device for partial discharge narrowband monitoring according to an embodiment of this application;

[0053] Figure 10 This is a schematic diagram of the device structure of the hardware operating environment involved in the adaptive frequency selection method for partial discharge narrowband monitoring in the embodiments of this application.

[0054] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0055] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0056] like Figure 1 As shown, in the first embodiment, the adaptive frequency selection method for partial discharge narrowband monitoring includes the following steps:

[0057] S10, determine the broadband electromagnetic wave signal to be detected, and perform continuous narrowband power detection on the broadband electromagnetic wave signal according to the preset narrowband width to obtain the narrowband signal power dataset;

[0058] S20, extract multiple interference frequency bands from the narrowband signal power dataset to obtain an interference frequency band set, wherein the interference frequency bands are the frequency bands that generate partial discharge monitoring interference to the broadband electromagnetic wave signal corresponding to various communication signals in the current detection environment;

[0059] S30, based on the set of interference frequency bands, remove the interference frequency bands from the multiple narrowband frequency bands corresponding to the broadband electromagnetic wave signal to obtain multiple narrowband frequency bands for partial discharge monitoring.

[0060] It should be noted that UHF partial discharge detection technology is a general technology in electromagnetic and acoustic partial discharge detection methods. The nominal UHF detection frequency band of UHF partial discharge detection technology is 300MHz~3GHz, which has the characteristics of wide detection frequency and rich electromagnetic wave signals. Existing UHF (ultra-high frequency) partial discharge detection technology mainly uses signal detection to detect and analyze broadband UHF signals. However, because it uses broadband signal reception, the 300MHz~3GHz detection frequency band is prone to interference from communication signals such as mobile phone communication, TV communication, and satellite communication, resulting in a low signal-to-noise ratio of broadband detection signals and large interference in partial discharge signal diagnosis.

[0061] In order to effectively improve the signal-to-noise ratio of partial discharge signals and reduce environmental signal interference in the UHF band of 300MHz~3GHz, narrowband frequency domain signal processing is adopted, which has better noise suppression capability and higher signal-to-noise ratio in environments with noise and external interference.

[0062] Therefore, in this embodiment, many existing narrowband partial discharge detection systems typically only have a few fixed narrowband segments. Under different measurement environments, due to the different frequency bands of interference signal distribution and the different frequency bands of different partial discharge types, the fixed narrowband segments cannot adapt well to different detection environments. To solve this problem, this embodiment proposes a selection scheme for partial discharge narrowband frequency bands that adapt to the interference frequency bands of the environment. Specifically, it involves first determining some interference frequency bands through the power data of broadband electromagnetic wave signals, and then selecting multiple narrowband frequency bands that can be used for partial discharge monitoring from the narrowband frequency bands corresponding to the broadband electromagnetic wave signals based on these interference frequency bands (equivalent to the frequency bands after removing the interference frequency bands).

[0063] Understandably, UHF partial discharge sensors typically monitor broadband electromagnetic signals in the range of 300MHz to 3GHz. The UHF partial discharge sensor first performs continuous narrowband power detection according to a certain narrowband width. Specifically, for example, the UHF sensor detects 300MHz to 310MHz, 310MHz to 320MHz, and then detects up to 3GHz in granular increments of 10MHz bandwidth (preset narrowband width), thereby obtaining a set of narrowband signal power datasets with a 10MHz bandwidth resolution.

[0064] The narrowband signal power dataset extracts multiple narrowband frequency bands of communication interference in the environment through corresponding processing methods, resulting in an interference frequency band set. This interference frequency band set includes multiple interference signal frequency bands generated by various communication signals in the current detection environment corresponding to partial discharge UHF.

[0065] Furthermore, based on the frequency band of the interference signal, the narrowband frequency band corresponding to the electromagnetic wave signal can be filtered to select the frequency band that can be used for partial discharge monitoring. In other words, in the partial discharge monitoring system, the signal power of multiple narrowband frequency bands can be calculated to achieve adaptive automatic frequency selection for multiple narrowband frequency bands. Since environmental signal interference is avoided, these multiple narrowband frequency bands have a high signal-to-noise ratio and high sensitivity to partial discharge signal monitoring, thereby improving the environmental adaptability and accuracy of the entire monitoring system.

[0066] This embodiment determines the broadband electromagnetic wave signal to be detected and performs continuous narrowband power detection on the broadband electromagnetic wave signal according to a preset narrowband width to obtain a narrowband signal power dataset. Multiple interference frequency bands are extracted from the narrowband signal power dataset to obtain an interference frequency band set, wherein the interference frequency bands are the frequency bands corresponding to various communication signals in the current detection environment that generate partial discharge monitoring interference to the broadband electromagnetic wave signal. Based on the interference frequency band set, the interference frequency bands are removed from the multiple narrowband frequency bands corresponding to the broadband electromagnetic wave signal to obtain multiple narrowband frequency bands for partial discharge monitoring. Specifically, when determining the broadband electromagnetic wave signal to be detected, its corresponding signal is first subjected to continuous narrowband power detection according to a certain narrowband width, and a narrowband signal power dataset composed of multiple narrowband signal powers is obtained. Then, the interference frequency bands are extracted from the narrowband signal power dataset... Interference frequency bands generated by various communication signals in the current detection environment that cause partial discharge (PD) monitoring interference to broadband electromagnetic wave signals are extracted and formed into an interference frequency band set. Based on this set, the corresponding interference frequency bands are removed, thereby selecting multiple narrowband frequency bands for PD monitoring. This avoids the frequency bands that interfere with PD monitoring caused by communication signals. In subsequent PD monitoring, the selected narrowband frequency bands are used as the key monitoring targets, improving the adaptability of the PD monitoring system to different environments and communication signal interference conditions. This enables the PD monitoring system to perform accurate PD monitoring on narrowband frequency bands under different environmental interference conditions. In other words, by configuring different narrowband frequency bands for PD monitoring in different measurement environments, the PD monitoring effect of the PD system on PD frequency bands in different environments is improved.

[0067] like Figure 2 As shown, based on the first embodiment, a second embodiment of the adaptive frequency selection method for partial discharge narrowband monitoring of this application is proposed. In this embodiment, the method further includes:

[0068] S110, continuously perform narrowband power detection on the broadband electromagnetic wave signal according to a preset narrowband width, and continuously execute the preset number of times to obtain a corresponding number of narrowband signal power data subsets;

[0069] S120, by using a preset narrowband power fusion calculation rule, the interference frequency bands in each subset of narrowband signal power data are extracted to obtain the interference frequency band subset corresponding to the preset number of times;

[0070] S130, perform an intersection operation on the subset of interference frequency bands corresponding to the preset number of times to obtain the interference frequency band set.

[0071] As explained in the above embodiments, the process involves acquiring the corresponding narrowband signal power using a UHF sensor, extracting the interference frequency band, and determining the narrowband frequency band for partial discharge monitoring based on the interference frequency band. In this embodiment, considering the occasional errors in the system, multiple consecutive sets of narrowband datasets are used for calculation to avoid the occasional system errors caused by using only one set of datasets for calculation. By performing an intersection operation on the calculation results of multiple consecutive sets of narrowband datasets, the narrowband distribution that exists in multiple sets of narrowband datasets is obtained, enabling the method to extract the frequency band distribution of environmental interference signals more accurately.

[0072] Specifically, refer to Figure 3 Taking three preset times as an example, the UHF sensor performs three consecutive narrowband signal power acquisitions to obtain three sets of narrowband signal power datasets. Narrowband power fusion calculation and corresponding analysis are performed to obtain interference frequency band subset 1, interference frequency band subset 2 and interference frequency band subset 3 respectively. The intersection operation of the above three subsets is then performed to obtain the interference frequency band set.

[0073] The purpose of the three consecutive samplings is that UHF signals in the environment may contain not only communication interference signals but also partial discharge signals. Communication interference signals are usually relatively stable and can be collected in all three consecutive measurements. However, partial discharge signals are usually unstable and have sporadic and intermittent characteristics, so they cannot be detected in every one of the three consecutive measurements.

[0074] Therefore, by performing a data intersection operation on the three consecutive sets of narrowband signal power datasets, the signal frequency bands that exist in all three sets of data are found. The intersection frequency bands represent the distribution of environmental interference signals in each narrowband frequency band.

[0075] Furthermore, when multiple narrowband frequency bands (interference bands) where multiple environmental interference signals exist are found, the multiple interference bands determined above are removed from the multiple narrowband frequency bands in the range of 300MHz to 3GHz, leaving multiple narrowband frequency bands that can be used for partial discharge monitoring.

[0076] This embodiment continuously detects the power of the broadband electromagnetic wave signal according to a preset narrowband width and performs this detection a preset number of times to obtain a corresponding number of narrowband signal power data subsets. Using preset narrowband power fusion calculation rules, interference frequency bands are extracted from each narrowband signal power data subset to obtain the interference frequency band subsets corresponding to the preset number of detections. The interference frequency band subsets corresponding to the preset number of detections are then intersected to obtain the interference frequency band set. In other words, by increasing the number of data acquisitions during the data acquisition process and using intersection operations, the corresponding interference frequency bands in the multiple data acquisition results are identified. This avoids occasional system failures or situations where sudden partial discharge frequency bands are misjudged as interference frequency bands, thereby improving the accuracy of the partial discharge monitoring system in monitoring partial discharge frequency bands.

[0077] like Figure 4 As shown, a third embodiment of the adaptive frequency selection method for partial discharge narrowband monitoring of this application is proposed based on the first embodiment. In this embodiment, the method further includes:

[0078] The preset narrowband power fusion calculation rules include weighted average threshold judgment rules, adjustable multiquantile sequence filtering rules, and narrowband sequence MAD operation rules.

[0079] S210, by using the weighted average threshold judgment rule, each narrowband signal power data subset is judged to obtain the mean narrowband set;

[0080] S220, the adjustable multi-quantile sequence filtering rules are used to filter each set of narrowband signal power data subsets to obtain a quantile narrowband set;

[0081] S230, calculate the MAD narrowband signal power data subset for each group according to the narrowband sequence MAD operation rule to obtain the MAD narrowband set;

[0082] S240, perform a union operation on the mean narrowband set, quantile narrowband set and MAD narrowband set corresponding to each dataset to obtain the interference frequency band subset corresponding to the preset number of groups.

[0083] Understandably, when extracting interference frequency bands, this embodiment introduces a preset narrowband power fusion calculation rule. This calculation rule mainly includes the fusion application of three types of calculation rules. Specifically, the preset narrowband power fusion calculation rule includes a weighted average threshold judgment rule, an adjustable multiquantile sequence screening rule, and a narrowband sequence MAD (Median Absolute Deviation) calculation rule.

[0084] It should be noted that, as explained above, multiple narrowband signal power datasets will be collected (multiple sets of narrowband signal power datasets) according to a preset number of times. For each set of narrowband signal power datasets, the three calculation rules mentioned above will be used simultaneously to obtain the mean narrowband set and the quantile narrowband set (MAD) narrowband set, respectively. The three narrowband sets will then be combined to obtain the interference frequency band subset corresponding to each set of narrowband signal power datasets.

[0085] The dataset judgment content for the weighted average threshold judgment rule can be found in [reference needed]. Figure 5 .

[0086] In this embodiment, the step of judging each subset of narrowband signal power data according to the weighted average threshold judgment rule to obtain the mean narrowband set includes:

[0087] Determine the power value of the broadband signal in each subset of narrowband signal power data, obtain the narrowband average bandwidth specified by the relevant user, and calculate the average narrowband power based on the power value and the narrowband average bandwidth.

[0088] The average power of multiple narrowband segments in the broadband signal of each subset of narrowband signal power data is obtained by averaging the power of multiple narrowband segments.

[0089] Calculate the average of the narrowband power mean and the average of the multiple narrowband power mean;

[0090] Signal frequency bands with power values ​​greater than the average value in each subset of narrowband signal power data are placed into the mean narrowband set.

[0091] Understandably, the application process of the weighted average threshold judgment rule mainly involves detecting the total power value of the entire 300MHz~3GHz broadband signal (determining the power value of the broadband signal in each subset of narrowband signal power data). Assuming the total power value of the 300MHz~3GHz broadband signal is A, then calculating the number of narrowbands as B based on the required equally divided narrowband bandwidth (i.e., the narrowband equally divided bandwidth specified by the user), the average narrowband power value A / B=C is obtained.

[0092] Furthermore, the measured N narrowband power values ​​are averaged (that is, the signal power of multiple narrowband segments in the broadband signal of each subset of narrowband signal power data is averaged) to obtain the mean value of N narrowband power segments (mean value of multiple narrowband power segments) D. Sometimes there is a slight difference between the mean value C and the mean value D. The mean values ​​C and D are added together and then averaged to obtain the corrected mean value of narrowband power E (the average value of the mean value of narrowband power segments and the mean value of multiple narrowband power segments).

[0093] It should be noted that the power value of the interference signal frequency band in the environment is usually greater than the average power value E of the narrowband signal. Therefore, the interference signal frequency band in the environment whose power value is greater than the average value can be extracted from each subset of narrowband signal power data.

[0094] In this embodiment, the step of placing the signal frequency bands with power values ​​greater than the average value in each subset of narrowband signal power data into the mean narrowband set includes:

[0095] Obtain the debugging coefficients specified by the relevant user;

[0096] Signal frequency bands whose power values ​​are greater than the product of the average value and the tuning coefficient in each set of narrowband signal power data subsets are placed into the mean narrowband set.

[0097] It should be noted that when the interference signal is small, the power values ​​of each frequency band are roughly the same. Directly using the average value as the threshold will dynamically extract many frequency bands as interference bands. Therefore, this embodiment also introduces a tuning coefficient. By controlling the size of the tuning coefficient, the average value can be adjusted according to user needs, thereby avoiding the situation where abnormal interference bands are extracted when the interference signal is small.

[0098] Specifically, by obtaining the adjustment coefficient F from relevant users, the average value E is multiplied by the adjustment coefficient F again. The adjustment coefficient F value is greater than 1. The adjustment coefficient F value can be used as the external adjustment coefficient F setting, making the threshold relatively larger. This can avoid filtering out too many narrowband frequency bands. However, since the judgment is based on the average value, when there are some frequency bands with high power, the average value will be pulled up relatively high. At this time, it is easy to miss the interference frequency bands with low energy values. Therefore, other algorithms are needed for fusion analysis.

[0099] In summary, the weighted average threshold judgment rule is applied to the analysis and judgment of partial discharge narrowband datasets. The adjustment coefficient F of the average value is external input data provided by relevant users, which allows different average value threshold judgment benchmarks to be obtained by setting different adjustment coefficients F, so as to meet the ability to further adapt to environmental conditions.

[0100] For details on dataset filtering for adjustable quantile sequence filtering rules, please refer to [link / reference]. Figure 6 .

[0101] In this embodiment, the step of filtering each subset of narrowband signal power data using the adjustable quantile sequence filtering rule to obtain the quantile narrowband set includes:

[0102] The data in each narrowband signal power data subset are sorted, and the quantile coefficients adaptively set by the relevant users according to their actual needs are obtained.

[0103] Based on the quantile coefficients, the data in the sorted narrowband signal power data subset are filtered to obtain the quantile narrowband set.

[0104] In this embodiment, during the implementation of the adjustable multi-quantile sequence filtering rule, each narrowband power dataset is first sorted by data size, and then the quantile coefficient G is set (the quantile coefficient G is adaptively set by the relevant user according to actual needs). The quantile coefficient G serves as the basis for data filtering.

[0105] For example, taking a quantile coefficient G of 0.9 as an example, this quantile coefficient G means extracting data from the dataset that represents more than 90% of the data. That is, in each narrowband power dataset, the power value of the environmental interference signal frequency band will be relatively large. By setting a certain quantile coefficient G, this environmental interference signal with a large power value can be extracted.

[0106] In summary, the analysis and judgment of partial discharge narrowband datasets are carried out using adjustable multi-quantile sequence filtering rules. Among them, the quantile coefficient G is external input data provided by relevant users, which allows narrowband data at different quantile values ​​to be obtained by setting different quantile coefficients G, so as to meet the ability to further adapt to environmental conditions.

[0107] The dataset calculation content for the MAD operation rules of narrowband sequences can be found in [reference needed]. Figure 7 .

[0108] In this embodiment, the step of calculating the MAD narrowband set by using the narrowband sequence MAD operation rule for each subset of narrowband signal power data includes:

[0109] Calculate the median of the data in each subset of narrowband signal power data, calculate the deviation of each data point from the median, and calculate the median of the absolute deviations;

[0110] If the deviation value is greater than the product of the median of the absolute deviation and the coefficient of the discrete threshold point specified by the relevant user, then the data corresponding to the deviation value is determined to be an outlier, and the MAD narrowband set is obtained based on the outlier.

[0111] Understandably, in the implementation of the MAD operation rule for narrowband sequences, the median H of each narrowband power dataset is first calculated, the absolute value of the deviation between each data point Xi and the median H in each narrowband signal power data subset is calculated: |Xi-H|, and the median of the absolute deviation (MAD) is calculated. Based on the given discrete threshold coefficient K (for example, the discrete threshold coefficient specified by the user is 3), it is determined whether the data point is an outlier.

[0112] Among them, if If so, then the data point is determined to be an outlier.

[0113] It should be noted that interference signals in the environment are usually larger than most values ​​and are outliers. By setting different thresholds K, environmental interference signals with different signal strengths can be filtered out. The MAD operation rules of narrowband sequences are used for the analysis and judgment of partial discharge narrowband datasets. The discrete threshold coefficient K is external input data provided by relevant users, which allows narrowband data in different discrete states to be obtained by setting different discrete threshold coefficients K, so as to meet the ability to further adjust environmental adaptability.

[0114] In summary, referring to Figure 8 In this embodiment, the preset narrowband power fusion calculation consists of three types of calculation processes: weighted average threshold judgment rule, adjustable multiquantile sequence screening rule, and narrowband sequence MAD operation rule. Finally, the narrowband datasets output by the three different calculation processes are combined to obtain multiple interference frequency band subsets.

[0115] It should be noted that the reason for using three different computational processes is that different computational processes have certain drawbacks. By performing a union operation on the datasets after different computations, the optimal dataset can be extracted.

[0116] It should be noted that three independent and different operation processes are used to operate on the partial discharge narrowband dataset to obtain the frequency band distribution of environmental interference signals in the partial discharge monitoring scenario: weighted average threshold judgment rule, adjustable multi-quantile sequence screening rule, and narrowband sequence MAD operation rule.

[0117] It should be noted that the narrowband datasets of partial discharge interference signals obtained by the three different calculation methods are combined to obtain the complete narrowband dataset of partial discharge interference signals. Using three different calculation processes can avoid the dataset adaptation problem caused by using only one calculation method and improve the adaptability of the whole system in different environments.

[0118] Furthermore, in this embodiment, three configurable parameter inputs are reserved, which makes the adaptive frequency selection scheme of the partial discharge monitoring frequency band in this embodiment highly customizable to meet the different field needs of different customers. The three reserved configurable parameters are: the adjustment coefficient F corresponding to the average value in the weighted average threshold judgment rule, the quantile coefficient G in the adjustable multiquantile sequence screening rule, and the discrete threshold point coefficient K in the narrowband sequence MAD operation rule.

[0119] This embodiment uses the weighted average threshold judgment rule to judge each subset of narrowband signal power data to obtain the mean narrowband set; it uses the adjustable multiquantile sequence filtering rule to filter each subset of narrowband signal power data to obtain the quantile narrowband set; it uses the narrowband sequence MAD operation rule to calculate each subset of narrowband signal power data to obtain the MAD narrowband set; and it performs a union operation on the mean narrowband set, quantile narrowband set, and MAD narrowband set corresponding to each dataset to obtain the interference frequency band subset corresponding to the preset number of sets. This achieves accurate extraction of interference frequency band subsets by performing union operations on different calculation methods, avoiding the situation where the partial discharge monitoring system has poor adaptability to different environments due to the use of a single operation or monitoring rule.

[0120] Furthermore, embodiments of this application also propose an adaptive frequency selection device for partial discharge narrowband monitoring, referring to... Figure 9 The adaptive frequency selection device for partial discharge narrowband monitoring includes:

[0121] The detection module 10 is used to determine the broadband electromagnetic wave signal to be detected, and to continuously detect the power of the broadband electromagnetic wave signal according to a preset narrowband width to obtain a narrowband signal power dataset.

[0122] Extraction module 20 is used to extract multiple interference frequency bands from the narrowband signal power dataset to obtain an interference frequency band set, wherein the interference frequency bands are the frequency bands that generate partial discharge monitoring interference to the broadband electromagnetic wave signal corresponding to various communication signals in the current detection environment;

[0123] The processing module 30 is used to remove the interfering frequency bands from the multiple narrowband frequency bands corresponding to the broadband electromagnetic wave signal according to the set of interfering frequency bands, so as to obtain multiple narrowband frequency bands for partial discharge monitoring.

[0124] This embodiment determines the broadband electromagnetic wave signal to be detected and performs continuous narrowband power detection on the broadband electromagnetic wave signal according to a preset narrowband width to obtain a narrowband signal power dataset. Multiple interference frequency bands are extracted from the narrowband signal power dataset to obtain an interference frequency band set, wherein the interference frequency bands are the frequency bands corresponding to various communication signals in the current detection environment that generate partial discharge monitoring interference to the broadband electromagnetic wave signal. Based on the interference frequency band set, the interference frequency bands are removed from the multiple narrowband frequency bands corresponding to the broadband electromagnetic wave signal to obtain multiple narrowband frequency bands for partial discharge monitoring. Specifically, when determining the broadband electromagnetic wave signal to be detected, its corresponding signal is first subjected to continuous narrowband power detection according to a certain narrowband width, and a narrowband signal power dataset composed of multiple narrowband signal powers is obtained. Then, the interference frequency bands are extracted from the narrowband signal power dataset... Interference frequency bands generated by various communication signals in the current detection environment that cause partial discharge (PD) monitoring interference to broadband electromagnetic wave signals are extracted and formed into an interference frequency band set. Based on this set, the corresponding interference frequency bands are removed, thereby selecting multiple narrowband frequency bands for PD monitoring. This avoids the frequency bands that interfere with PD monitoring caused by communication signals. In subsequent PD monitoring, the selected narrowband frequency bands are used as the key monitoring targets, improving the adaptability of the PD monitoring system to different environments and communication signal interference conditions. This enables the PD monitoring system to perform accurate PD monitoring on narrowband frequency bands under different environmental interference conditions. In other words, by configuring different narrowband frequency bands for PD monitoring in different measurement environments, the PD monitoring effect of the PD system on PD frequency bands in different environments is improved.

[0125] It should be noted that each module in the above-mentioned device can be used to implement each step in the above-mentioned method and achieve the corresponding technical effect. This embodiment will not elaborate further here.

[0126] Reference Figure 10 , Figure 10 This is a schematic diagram of the hardware operating environment of the device involved in the embodiments of this application.

[0127] like Figure 10As shown, the device may include: a processor 1001, such as a CPU; a communication bus 1002; a user interface 1003; a network interface 1004; and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0128] Those skilled in the art will understand that Figure 10 The structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0129] like Figure 10 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an adaptive frequency selection program for partial discharge narrowband monitoring.

[0130] exist Figure 10 In the device shown, the network interface 1004 is mainly used for data communication with an external network; the user interface 1003 is mainly used for receiving user input commands; the device calls the adaptive frequency selection program for partial discharge narrowband monitoring stored in the memory 1005 through the processor 1001, and performs the following operations:

[0131] The broadband electromagnetic wave signal to be detected is identified, and the broadband electromagnetic wave signal is continuously narrowband power detected according to a preset narrowband width to obtain a narrowband signal power dataset.

[0132] Multiple interference frequency bands are extracted from the narrowband signal power dataset to obtain an interference frequency band set, wherein the interference frequency bands are the frequency bands that generate partial discharge monitoring interference to the broadband electromagnetic wave signal corresponding to various communication signals in the current detection environment;

[0133] Based on the set of interference frequency bands, the interference frequency bands in the multiple narrowband frequency bands corresponding to the broadband electromagnetic wave signal are removed to obtain multiple narrowband frequency bands for partial discharge monitoring.

[0134] Furthermore, the processor 1001 can call the adaptive frequency selection program for partial discharge narrowband monitoring stored in the memory 1005, and also perform the following operations:

[0135] The broadband electromagnetic wave signal is continuously narrowband power detected according to a preset narrowband width, and the process is repeated a preset number of times to obtain a corresponding number of narrowband signal power data subsets.

[0136] The step of extracting multiple interference frequency bands from the narrowband signal power dataset to obtain an interference frequency band set includes:

[0137] By using a preset narrowband power fusion calculation rule, the interference frequency bands in each subset of narrowband signal power data are extracted to obtain the interference frequency band subset corresponding to the preset number of times.

[0138] The interference frequency band set is obtained by performing an intersection operation on the subset of interference frequency bands corresponding to the preset number of times.

[0139] Furthermore, the processor 1001 can call the adaptive frequency selection program for partial discharge narrowband monitoring stored in the memory 1005, and also perform the following operations:

[0140] The preset narrowband power fusion calculation rules include weighted average threshold judgment rules, adjustable multiquantile sequence filtering rules, and narrowband sequence MAD operation rules.

[0141] By using the weighted average threshold judgment rule, each subset of narrowband signal power data is judged to obtain the mean narrowband set;

[0142] The adjustable quantile sequence filtering rules are used to filter each subset of narrowband signal power data to obtain a quantile narrowband set.

[0143] The MAD operation rules for the narrowband sequence are used to calculate the power data subset of each narrowband signal to obtain the MAD narrowband set.

[0144] Perform a union operation on the mean narrowband set, quantile narrowband set, and MAD narrowband set corresponding to each dataset to obtain the interference frequency band subset corresponding to the preset number of groups.

[0145] Furthermore, the processor 1001 can call the adaptive frequency selection program for partial discharge narrowband monitoring stored in the memory 1005, and also perform the following operations:

[0146] Determine the power value of the broadband signal in each subset of narrowband signal power data, obtain the narrowband average bandwidth specified by the relevant user, and calculate the average narrowband power based on the power value and the narrowband average bandwidth.

[0147] The average power of multiple narrowband segments in the broadband signal of each subset of narrowband signal power data is obtained by averaging the power of multiple narrowband segments.

[0148] Calculate the average of the narrowband power mean and the average of the multiple narrowband power mean;

[0149] Signal frequency bands with power values ​​greater than the average value in each subset of narrowband signal power data are placed into the mean narrowband set.

[0150] Furthermore, the processor 1001 can call the adaptive frequency selection program for partial discharge narrowband monitoring stored in the memory 1005, and also perform the following operations:

[0151] Obtain the debugging coefficients specified by the relevant user;

[0152] Signal frequency bands whose power values ​​are greater than the product of the average value and the tuning coefficient in each set of narrowband signal power data subsets are placed into the mean narrowband set.

[0153] Furthermore, the processor 1001 can call the adaptive frequency selection program for partial discharge narrowband monitoring stored in the memory 1005, and also perform the following operations:

[0154] The data in each narrowband signal power data subset are sorted, and the quantile coefficients adaptively set by the relevant users according to their actual needs are obtained.

[0155] Based on the quantile coefficients, the data in the sorted narrowband signal power data subset are filtered to obtain the quantile narrowband set.

[0156] Furthermore, the processor 1001 can call the adaptive frequency selection program for partial discharge narrowband monitoring stored in the memory 1005, and also perform the following operations:

[0157] Calculate the median of the data in each subset of narrowband signal power data, calculate the deviation of each data point from the median, and calculate the median of the absolute deviations;

[0158] If the deviation value is greater than the product of the median of the absolute deviation and the coefficient of the discrete threshold point specified by the relevant user, then the data corresponding to the deviation value is determined to be an outlier, and the MAD narrowband set is obtained based on the outlier.

[0159] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0160] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0161] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the adaptive frequency selection method for partial discharge narrowband monitoring in the above embodiments.

[0162] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0163] The aforementioned computer-readable storage medium may be included in the adaptive frequency selective device for partial discharge narrowband monitoring; or it may exist independently and not assembled into the adaptive frequency selective device for partial discharge narrowband monitoring.

[0164] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the adaptive frequency-selective device for partial discharge narrowband monitoring, cause the adaptive frequency-selective device for partial discharge narrowband monitoring to:

[0165] The broadband electromagnetic wave signal to be detected is identified, and the broadband electromagnetic wave signal is continuously narrowband power detected according to a preset narrowband width to obtain a narrowband signal power dataset.

[0166] Multiple interference frequency bands are extracted from the narrowband signal power dataset to obtain an interference frequency band set, wherein the interference frequency bands are the frequency bands that generate partial discharge monitoring interference to the broadband electromagnetic wave signal corresponding to various communication signals in the current detection environment;

[0167] Based on the set of interference frequency bands, the interference frequency bands in the multiple narrowband frequency bands corresponding to the broadband electromagnetic wave signal are removed to obtain multiple narrowband frequency bands for partial discharge monitoring.

[0168] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0169] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0170] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0171] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the adaptive frequency selection method for partial discharge narrowband monitoring described above, thereby solving the technical problem of adaptive frequency selection in partial discharge narrowband monitoring. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the adaptive frequency selection method for partial discharge narrowband monitoring provided in the above embodiments, and will not be repeated here.

[0172] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

[0173] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0174] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0175] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0176] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An adaptive frequency selection method for partial discharge narrowband monitoring, characterized in that, The adaptive frequency selection method of partial discharge narrowband monitoring comprises the following steps: determining a wideband electromagnetic wave signal to be detected, and continuously detecting the narrowband power of the wideband electromagnetic wave signal according to a preset narrowband width, and performing the detection for a preset number of times to obtain a corresponding number of subsets of narrowband signal power data; judging each subset of narrowband signal power data by a weighted average value threshold judgment rule to obtain a mean narrowband set; including: determining the power value of the wideband signal in each subset of narrowband signal power data, obtaining a user-specified narrowband uniform bandwidth, and calculating the mean narrowband power according to the power value and the narrowband uniform bandwidth; performing signal power averaging on multiple narrowband powers in the wideband signal in each subset of narrowband signal power data to obtain multiple mean narrowband powers; calculating the average of the mean narrowband power and the multiple mean narrowband powers; obtaining a debugging coefficient specified by the user; and placing the signal frequency band in each subset of narrowband signal power data whose power value is greater than the product of the average and the debugging coefficient to the mean narrowband set; screening each subset of narrowband signal power data by an adjustable multi-quantile sequence screening rule to obtain a quantile narrowband set; including: sorting the data in each subset of narrowband signal power data, and obtaining a quantile coefficient adaptively set by the user according to actual needs; screening the sorted data in each subset of narrowband signal power data according to the quantile coefficient to obtain the quantile narrowband set; calculating each subset of narrowband signal power data by a narrowband sequence MAD operation rule to obtain a MAD narrowband set; including: calculating the median of the data in each subset of narrowband signal power data, calculating the deviation of each data from the median, and calculating the median of the absolute deviation; if the deviation is greater than the product of the median of the absolute deviation and a dispersion threshold point coefficient specified by the user, determining the data corresponding to the deviation as an outlier, and obtaining the MAD narrowband set according to the outlier; performing a union operation on the mean narrowband set, the quantile narrowband set and the MAD narrowband set corresponding to each data set to obtain a subset of interference frequency bands corresponding to a group number for a preset number of times; performing an intersection operation on the subset of interference frequency bands corresponding to the group number for the preset number of times to obtain an interference frequency band set; wherein the interference frequency band is a frequency band corresponding to various communication signals in the current detection environment that produces partial discharge monitoring interference on the wideband electromagnetic wave signal; removing the interference frequency band from a plurality of narrowband frequency bands corresponding to the wideband electromagnetic wave signal according to the interference frequency band set to obtain a plurality of narrowband frequency bands for partial discharge monitoring.

2. An adaptive frequency selection device for partial discharge narrowband monitoring, characterized by The adaptive frequency selection device of partial discharge narrowband monitoring comprises: a detection module configured to determine a wideband electromagnetic wave signal to be detected, and continuously detect the narrowband power of the wideband electromagnetic wave signal according to a preset narrowband width, and perform the detection for a preset number of times to obtain a corresponding number of subsets of narrowband signal power data; The extraction module is configured to determine the power value of the wideband signal of each narrowband signal power data subset, obtain the relevant user-specified narrowband uniform bandwidth, and calculate the narrowband power mean value according to the power value and the narrowband uniform bandwidth; perform signal power averaging on multiple narrowband powers in the wideband signal of each narrowband signal power data subset to obtain multiple narrowband power mean values; calculate the mean value of the narrowband power mean value and the multiple narrowband power mean values; obtain the debugging coefficient specified by the relevant user; and place the signal frequency band in each narrowband signal power data subset whose power value is greater than the product of the mean value and the debugging coefficient to the mean narrowband set. The extraction module is configured to determine the power value of the wideband signal of each narrowband signal power data subset, obtain the relevant user-specified narrowband uniform bandwidth, and calculate the narrowband power mean value according to the power value and the narrowband uniform bandwidth; perform signal power averaging on multiple narrowband powers in the wideband signal of each narrowband signal power data subset to obtain multiple narrowband power mean values; calculate the mean value of the narrowband power mean value and the multiple narrowband power mean values; obtain the debugging coefficient specified by the relevant user; and place the signal frequency band in each narrowband signal power data subset whose power value is greater than the product of the mean value and the debugging coefficient to the mean narrowband set. The extraction module is configured to determine the power value of the wideband signal of each narrowband signal power data subset, obtain the relevant user-specified narrowband uniform bandwidth, and calculate the narrowband power mean value according to the power value and the narrowband uniform bandwidth; perform signal power averaging on multiple narrowband powers in the wideband signal of each narrowband signal power data subset to obtain multiple narrowband power mean values; calculate the mean value of the narrowband power mean value and the multiple narrowband power mean values; obtain the debugging coefficient specified by the relevant user; and place the signal frequency band in each narrowband signal power data subset whose power value is greater than the product of the mean value and the debugging coefficient to the mean narrowband set. The processing module is configured to remove the interference frequency bands in the multiple narrowband frequency bands corresponding to the wideband electromagnetic wave signal according to the interference frequency band set, and obtain the multiple narrowband frequency bands for partial discharge monitoring. The adaptive frequency selection device for partial discharge narrowband monitoring comprises a memory, a processor, and an adaptive frequency selection program for partial discharge narrowband monitoring stored on the memory and executable on the processor, and the adaptive frequency selection program for partial discharge narrowband monitoring is configured to implement the steps of the adaptive frequency selection method for partial discharge narrowband monitoring.

3. An adaptive frequency selection device for partial discharge narrowband monitoring, characterized by A storage medium stores a program for implementing the adaptive frequency selection method for partial discharge narrowband monitoring, and the program for implementing the adaptive frequency selection method for partial discharge narrowband monitoring is executed by a processor to implement the steps of the adaptive frequency selection method for partial discharge narrowband monitoring.

4. A storage medium, characterized by ​

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