Environment analysis and closed-loop control method, system and device for converter substation partial discharge monitoring system

Abstract

This application discloses an environmental analysis and closed-loop control method, system, and device for a converter transformer partial discharge monitoring system, belonging to the field of power equipment insulation condition monitoring. The method includes: acquiring the original sampled digital stream; identifying the idle environment window that meets the idle time determination conditions; acquiring an idle environment snapshot; performing joint analysis of the snapshot in the time domain, frequency domain, and power frequency phase domain to extract environmental statistical features; generating adaptive control parameters such as trigger threshold, notch filter parameters, gain control, and phase suppression; writing the parameters back to the real-time monitoring link and / or analog front-end unit; evaluating the operating indicators within the verification window, maintaining them if the conditions are met, otherwise reverting to historical stable parameters and executing cyclically. This application can solve the problems of partial discharge detection failure and system stability under time-varying electromagnetic environments and / or strong interference scenarios with fixed parameters, reducing false alarm storms and missed detection rates, and improving the long-term operational stability and engineering deployability of converter transformer partial discharge monitoring systems.

Description

Technical Field

[0001] This application relates to the field of insulation condition monitoring of power equipment, and in particular to an environmental analysis and closed-loop control method, system and equipment for a converter transformer partial discharge monitoring system. Background Technology

[0002] Converter transformers are core equipment in ultra-high voltage direct current (UHVDC) transmission projects, and their insulation condition directly affects the safe and stable operation of the DC transmission system. Partial discharge (PD) is an important early sign of insulation degradation in converter transformers; therefore, real-time monitoring of partial discharge has significant engineering value.

[0003] However, the electromagnetic environment at the converter transformer site is extremely complex and exhibits significant time-varying and non-stationary characteristics. Specifically, this manifests as follows: (1) the background noise level fluctuates continuously with time, load, and operating conditions; (2) the frequency points of narrowband continuous wave interference introduced by communication equipment and switching power supplies in the station will drift or be added; (3) periodic broadband pulse groups related to the power frequency phase or commutation process will manifest as noise background rise or strong disturbance in a specific phase interval; (4) the analog front-end gain configuration is mismatched with the ADC dynamic range, and the utilization rate is difficult to maintain at the optimal level for a long time.

[0004] Existing partial discharge monitoring systems mostly employ fixed trigger thresholds, fixed filter parameters, and fixed gain levels, or rely on manual, periodic offline tuning. This approach has significant drawbacks: when background noise increases or new interference frequencies are added, fixed thresholds can easily lead to a surge in false triggers, invalid events consuming buffer and communication bandwidth, and even triggering a storm of false alarms; when background noise decreases, high fixed thresholds can easily miss weak partial discharge signals. Furthermore, the environmental analysis in existing systems often remains at the level of offline assessment or slow statistics, failing to directly convert the analysis results into actionable control parameters and feed them back to the monitoring link in real time. This lack of a complete closed-loop mechanism of "sensing-analysis-decision-execution-verification" makes it difficult to meet the engineering requirements of long-term unattended, highly reliable online monitoring of converter stations. Summary of the Invention

[0005] The purpose of this application is to provide an environmental analysis and closed-loop control method, system and equipment for a converter transformer partial discharge monitoring system, so as to solve the problems in the prior art such as fixed parameters being difficult to adapt to time-varying strong interference environment, disconnection between environmental analysis and real-time monitoring link, and lack of parameter update security verification mechanism.

[0006] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides an environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system, including: S1: Acquire the raw sampled digital stream of one or more partial discharge monitoring channels of the converter transformer; S2: Perform signal preprocessing on the original sampled digital stream to obtain a preprocessed digital stream; S3: Based on the preprocessed digital stream and the current operating status of the system, identify the idle time environment window that meets the preset idle time determination conditions; S4: Within the identified idle environment window, extract a data segment of a preset length as an idle environment snapshot; S5: Perform time-domain statistical analysis, frequency-domain narrowband interference identification, and power frequency phase-correlation disturbance analysis on the idle environment snapshot to extract the environmental statistical features of the current environment; S6: Based on the environmental statistical characteristics, generate adaptive control parameters for adjusting the real-time monitoring link and / or the analog front-end unit; S7: Write the adaptive control parameters back to the configuration registers corresponding to the real-time monitoring link and / or the analog front-end unit through the control interface; S8: Evaluate the system's operating indicators within the verification window after the parameter write-back takes effect. If the operating indicators meet the preset improvement conditions, keep the updated parameters. If the preset improvement conditions are not met, perform parameter rollback and restore to the historical stable parameter set. Then return to S3 for repeated execution to form adaptive closed-loop control.

[0007] Optionally, the preset idle time determination condition includes at least one of the following: No data collection events occurred within the preset time period; The system is not in a busy event upload state or a cache congestion state; The current power frequency phase is not located in the commutation strong interference dead zone, or the dead zone samples have been marked and removed.

[0008] Optionally, the method for collecting the idle environment snapshot includes at least one of the following: Directly extract segments of the original sampled digital stream; Collect snapshots of the corresponding environmental windows according to the power frequency phase interval; The snapshots of multiple idle environment windows are accumulated to form a statistical sample set.

[0009] Optionally, the extracted environmental statistical features include at least one of the following categories: Time-domain statistical results include at least one of the following: mean, variance, median, median absolute deviation (MAD), root mean square (RMS), high quantile, peak factor, skewness, kurtosis, and pruning rate. Frequency domain statistical results include at least one of the following: power spectral density, narrowband interference frequency location, interference peak amplitude, peak bandwidth, subband energy distribution, and spectral flatness. Phase domain statistical results include the correspondence between background noise energy and power frequency phase, the probability of pseudo partial discharge pulses gathering in each phase window, and at least one of the phase-correlated high interference intervals. The status assessment results include at least one of the following: false trigger rate estimate, event buffer occupancy rate, ADC dynamic range utilization rate, and analog front-end gain margin.

[0010] Optionally, the process of generating the adaptive control parameters includes: The environmental statistical characteristics are normalized, their reliability is assessed, and outliers are removed. Multiple candidate parameter values ​​are generated according to different control objectives, and the candidate parameter values ​​are filtered based on detection sensitivity, target false alarm rate, hardware security constraints and system stability constraints. The selected parameters are then subjected to parameter optimization, time smoothing, and hysteresis control to output the final adaptive control parameters.

[0011] Optionally, when the adaptive control parameters include trigger threshold parameters, the adaptive generation process includes: Perform decision domain consistency preprocessing on the idle environment snapshot, consistent with the real-time triggering link, to obtain the decision domain background sequence; Calculate the median, median absolute deviation (MAD), high quantile, skewness, and kurtosis of the background sequence in the decision domain, and construct the cumulative distribution function or histogram. Based on the target false alarm rate, the corresponding high-resolution points are obtained from the cumulative distribution function as the first candidate threshold; Long-tail compensation is performed on the robust scaling estimator based on MAD based on skewness and kurtosis to obtain the second candidate threshold; An improved Otsu inter-class separation search is performed on the histogram to obtain a third candidate threshold; The first candidate threshold, the second candidate threshold, and the third candidate threshold are fused to obtain the original threshold value; After time smoothing and hysteresis control are applied to the original threshold value, it is issued as the trigger threshold parameter.

[0012] Optionally, parameter write-back can be performed in at least one of the following ways: Online hot update, power frequency cycle boundary update, double buffer parameter switching update, safe time window update or amplitude-limited smooth update; The write-back objects include at least one of the following: the threshold register in the real-time acquisition trigger module, the notch filter parameter register in the signal preprocessing unit, the programmable gain device control register in the analog front-end unit, and the phase interval configuration register in the phase correlation suppression module.

[0013] Optionally, the operational metrics include at least one of the following: false trigger rate, idle window occupancy rate, cache congestion rate, background root mean square change rate, and effective event retention rate; the execution parameter rollback includes: immediate rollback to the previous stable parameter set, tiered rollback, or optimal recovery of multiple sets of historical parameters.

[0014] Secondly, this application provides an environmental analysis and closed-loop control system for a converter transformer partial discharge monitoring system, comprising: The sampling digitization unit is used to acquire the raw sampling digital stream of one or more partial discharge monitoring channels of the converter transformer; A signal preprocessing unit is used to preprocess the original sampled digital stream to obtain a preprocessed digital stream. The idle window identification unit is used to identify idle environment windows that meet preset idle judgment conditions based on the preprocessed digital stream and the current operating state of the system. An environmental snapshot acquisition unit is used to extract a data segment of a preset length within the identified idle environment window as an idle environment snapshot. The environmental snapshot analysis unit is used to perform time-domain statistical analysis, frequency-domain narrowband interference identification, and power frequency phase-correlation disturbance analysis on the idle environmental snapshot, and extract the environmental statistical features of the current environment. A control parameter generation unit is used to generate adaptive control parameters for adjusting the real-time monitoring link and / or the analog front-end unit based on the environmental statistical characteristics. An adaptive closed-loop control unit is used to write back the adaptive control parameters to the configuration registers corresponding to the real-time monitoring link and / or the analog front-end unit through a control interface. The verification rollback unit is used to evaluate the system's operating indicators within the verification window after the parameter write-back takes effect. If the operating indicators meet the preset improvement conditions, the updated parameters are maintained. If the preset improvement conditions are not met, parameter rollback is performed and the system is restored to the historical stable parameter set. Then, the system returns to the idle window identification unit for cyclic execution, forming an adaptive closed-loop control.

[0015] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system as described above.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides an environmental analysis and closed-loop control method, system, and equipment for a converter transformer partial discharge monitoring system, which has the following significant advantages: (1) Enhance the ability to adapt to time-varying environments on site: By identifying idle time windows and performing multi-domain joint analysis and modeling, the trigger threshold, notch filter parameters, front-end gain, etc. are dynamically adjusted according to the distribution of noise and interference on site, thus completely solving the problem of false alarms / missed detections caused by mismatch of fixed parameters.

[0017] (2) Achieve true adaptive closed-loop control: The environmental analysis results are directly converted into executable control parameters and hot-written back to the real-time monitoring link trigger module, digital notch filter and analog front-end PGA, so that environmental perception and analysis can help optimize partial discharge monitoring decisions and effectively improve system stability.

[0018] (3) Ensure the long-term stability of the system: introduce parameter smooth update, hysteresis control, verification window evaluation and safety rollback mechanism to avoid parameter oscillation, background over-suppression or loss of effective events caused by the adaptive adjustment process itself, and significantly improve the engineering deployability in unattended scenarios.

[0019] (4) Reduce the risk of resource congestion: Dynamically suppress narrowband interference and periodic background noise rise, significantly reduce invalid trigger event writing, alleviate the pressure on event cache, internal bus and uplink communication link, and improve the overall throughput efficiency of the system.

[0020] (5) Strong compatibility and easy engineering transformation: This application can be superimposed on the existing partial discharge monitoring main link as an independent environmental modeling and closed-loop control functional unit, and the original real-time acquisition and decision logic only needs to be incrementally optimized and changed, which is convenient for upgrading existing equipment. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating an environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system, provided as an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] In one exemplary embodiment, such as Figure 1 As shown, an environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system is provided. This method is executed by computer equipment, specifically by a single computer device such as an end-side device or a server, or by both the end-side device and the server. In this embodiment, the method includes the following steps: S1: Acquire the raw sampled digital stream of one or more partial discharge monitoring channels of the converter transformer.

[0026] S2: Perform signal preprocessing on the original sampled digital stream to obtain a preprocessed digital stream.

[0027] The signal sensing unit acquires the electromagnetic induction signal output from the converter transformer partial discharge monitoring sensor (preferably HFCT), and sends it to the analog front-end unit for impedance matching, bandpass filtering, low-noise amplification, anti-aliasing processing, and programmable gain control. After sampling and digitization by a high-speed ADC, the raw sampled digital stream is obtained. This raw sampled digital stream contains ambient background noise, narrowband continuous wave interference or wireless communication modulation signal interference, periodic strong disturbances related to power frequency or commutation process, analog link noise floor and drift components, as well as possible real partial discharge pulses. Then, the raw sampled digital stream is sent to the signal preprocessing unit for digital filtering preprocessing to obtain the preprocessed digital stream.

[0028] S3: Based on the preprocessed digital stream and the current operating status of the system, identify the idle time environment window that meets the preset idle time determination conditions.

[0029] The system first identifies idle environment windows suitable for on-site environment modeling. The judgment criteria use a logical "OR" relationship; a window is determined to be an idle environment window if at least one of these conditions is met. (1) No data collection event is triggered within the preset time window; (2) The system is not in a busy event upload state or the DMA / cache usage rate is below the threshold; (3) The current power frequency phase is not located in the commutation strong interference dead zone, or the dead zone samples have been removed by phase marking; (4) The signal in the current window meets the stability conditions, such as variance, kurtosis or amplitude fluctuation is lower than the preset threshold.

[0030] S4: Within the identified idle environment window, extract a data segment of a preset length as an idle environment snapshot.

[0031] Once an idle environment window is identified, the environment snapshot acquisition unit immediately extracts a data segment of preset length (e.g., covering dozens of power frequency cycles) from the preprocessed digital stream as a snapshot. Acquisition strategies include: directly extracting continuous segments; acquiring data in segments according to power frequency phase intervals; or accumulating multiple window snapshots to form a large sample statistical set. Snapshot data is temporarily stored in on-chip RAM, on-chip cache, or external DDR for subsequent analysis.

[0032] S5: Perform time-domain statistical analysis, frequency-domain narrowband interference identification, and power frequency phase correlation disturbance analysis on the idle environmental snapshot to extract the environmental statistical features of the current environment.

[0033] Statistical feature analysis is performed on idle-time environmental snapshots to generate environmental analysis results, which preferably include at least one or more of the following categories: (1) Time-domain statistical results Including but not limited to: mean, variance, median, median absolute deviation (MAD), root mean square (RMS), high quantile, peak factor, skewness, kurtosis, and pruning rate or saturation rate.

[0034] (2) Frequency domain statistics Including but not limited to: power spectral density, narrowband interference frequency location, interference peak amplitude, peak bandwidth, subband energy distribution, and spectral flatness.

[0035] (3) Statistical results in the phase domain The system has power frequency phase synchronization capability, and further includes: The correspondence between background noise energy and power frequency phase; The probability of pseudo-partial discharge pulses converging in each power frequency phase window; High interference range due to power frequency phase correlation; Background baseline differences for each phase window.

[0036] (4) Status assessment results This includes, but is not limited to: false trigger rate estimation, event buffer occupancy, DMA or storage bandwidth pressure, ADC dynamic range utilization, ADC clipping rate, and analog front-end gain margin.

[0037] The output of step S5 is not a single statistic, but an environmental profile composed of multiple statistical results, which is used for the generation of subsequent control parameters.

[0038] S6: Based on the environmental statistical characteristics, generate adaptive control parameters for adjusting the real-time monitoring link and / or the analog front-end unit.

[0039] In S6, the parameter generation unit generates control parameters for adjusting the online monitoring link and / or the analog front end based on the environmental analysis results of S5, specifically including: S6a: Basic process of parameter generation The parameter generation process preferably includes: Receive the environmental analysis results output by S5; Normalize, assess the reliability of, and remove outliers from various statistical results; Generate multiple candidate parameter values ​​according to different control objectives; Candidate parameters were selected based on detection sensitivity, target false alarm rate, hardware security constraints, and system stability constraints. Perform joint optimization, time smoothing, and hysteresis control on the candidate parameters; Output the final control parameters.

[0040] S6b: Control parameter type The control parameters include, but are not limited to: trigger threshold parameters, notch filter parameters, analog front-end control parameters, phase correlation suppression parameters, and statistical compensation parameters; Among them, the trigger threshold parameter is used to monitor the initial threshold judgment in the link, and includes: amplitude threshold, energy threshold, high and low dual threshold, threshold update step size or threshold change rate limit parameter.

[0041] Notch filter parameters are used for narrowband interference suppression in signal preprocessing and include: notch center frequency, notch bandwidth, quality factor, FIR or IIR tap coefficients, and notch filter enable control parameters.

[0042] The analog front-end control parameters are used to simulate the dynamic adjustment of the front-end and include: programmable gain amplifier control parameters, analog attenuator settings, and channel balance parameters.

[0043] Phase-correlated suppression parameters are used to suppress phase-correlated interference and include: dead zone start phase, dead zone end phase, multi-segment dead zone parameters, and phase-correlated threshold boosting coefficient.

[0044] Statistical compensation parameters are used for slow link or graph statistical optimization and include: phase window baseline compensation value, statistical normalization coefficient, and subband weight parameters.

[0045] S6c: Parameter Generation Logic Increase the trigger threshold when the background noise RMS, high quantiles, or long tail increase. When a stable narrowband interference frequency is identified, the corresponding notch filter parameters are generated. When the ADC clipping rate increases or the input approaches full scale, reduce the analog front-end gain or increase the attenuation. When the background noise energy is consistently high in certain power frequency phase intervals, update the phase dead zone boundary or adjust the phase correlation threshold. When statistical results show that there is a phase window baseline imbalance in the slow link, statistical compensation parameters are generated.

[0046] S6d: Parameter Stability Handling To avoid frequent fluctuations in parameters due to single snapshots, it is preferable to further perform the following on the generated candidate parameters: upper and lower limit pruning, exponential smoothing, hysteresis update, and hierarchical update frequency control.

[0047] The update frequency of the digital threshold parameters can be higher than that of the analog front-end gain parameters.

[0048] S7: Write the adaptive control parameters back to the configuration registers corresponding to the real-time monitoring link and / or the analog front-end unit through the control interface.

[0049] The trigger threshold parameters are adaptively generated based on the statistical distribution of the environmental snapshot. The process includes: 1) Take an environmental snapshot A background signal is captured as an environmental snapshot during system idle time.

[0050] 2) Consistency processing of the decision domain The environmental snapshot undergoes preprocessing consistent with the logic of the real-time trigger acquisition module to obtain the background sequence of the decision domain. If the threshold comparison object is the preprocessed time-domain amplitude, the absolute value of the preprocessed background sequence is taken; if the threshold comparison object is an energy statistic, the corresponding energy sequence is calculated for the background sequence.

[0051] 3) Statistical distribution estimation Calculate the background sequence for the decision domain: median ; Median absolute deviation (MAD); High quantiles; Skewness; cliff; And construct the corresponding probability distribution, histogram or cumulative distribution function.

[0052] 4) Generation of the first candidate threshold Based on the target false alarm rate ( The threshold for the corresponding high quantile is obtained from the background cumulative distribution function: ; in, Indicates the first candidate threshold. This represents the background cumulative distribution function.

[0053] 5) Generation of the second candidate threshold Long-tail compensation is applied to the robust scaling threshold based on skewness and kurtosis to obtain: ; in, This indicates the second candidate threshold. This represents the median. Indicates the basic scale magnification factor. This represents the robust scaling estimate based on MAD. This represents the skewness compensation weighting coefficient. Indicates the skewness of the background distribution. Indicates the baseline value of skewness. This represents the kurtosis compensation weighting coefficient. Indicates the kurtosis of the background distribution. This represents the baseline value for kurtosis.

[0054] 6) Generation of the third candidate threshold An improved Otsu search is performed on the histogram corresponding to the environment snapshot to obtain the optimal inter-class separation candidate threshold. .

[0055] 7) Candidate threshold fusion Multiple candidate thresholds are fused to form the original threshold value. The preferred method is to use the maximum value or a weighted fusion method.

[0056] 8) Time smoothing and hysteresis control The original threshold value is updated smoothly, and a hysteresis condition is set to avoid frequent changes in the threshold due to short-term fluctuations.

[0057] 9) Threshold issuance The final trigger threshold parameter is written into the monitoring link trigger register for subsequent partial discharge event determination.

[0058] S8: Evaluate the system's operating indicators within the verification window after the parameter write-back takes effect. If the operating indicators meet the preset improvement conditions, keep the updated parameters. If the preset improvement conditions are not met, perform parameter rollback and restore to the historical stable parameter set. Then return to S3 for repeated execution to form adaptive closed-loop control.

[0059] In S8, the control parameters generated in S6 are fed back to the real-time monitoring link and / or the analog front end.

[0060] The feedback objects include: fast link threshold decision module, preprocessing digital filtering module, phase gating module, analog front-end gain or attenuation control module, and slow link statistical compensation module.

[0061] After the parameters are fed back, the system continues to collect statistics on: false trigger rate, actual event pass rate, event buffer usage, interference frequency suppression effect, and ADC dynamic range utilization.

[0062] Based on the above results, environmental sampling and parameter updates will be performed again in the next idle period, thus forming a closed-loop mechanism of "environmental sampling - environmental analysis - parameter generation - parameter feedback - effect evaluation".

[0063] Based on the same inventive concept, this application also provides an environmental analysis and closed-loop control system for a converter transformer partial discharge monitoring system, which implements the aforementioned environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more embodiments of the environmental analysis and closed-loop control system for a converter transformer partial discharge monitoring system provided below can be found in the limitations of the environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system described above, and will not be repeated here.

[0064] In one exemplary embodiment, an environmental analysis and closed-loop control system for a converter transformer partial discharge monitoring system is provided, comprising: The sampling digitization unit is used to acquire the raw sampling digital stream of one or more partial discharge monitoring channels of the converter transformer; A signal preprocessing unit is used to preprocess the original sampled digital stream to obtain a preprocessed digital stream. The idle window identification unit is used to identify idle environment windows that meet preset idle judgment conditions based on the preprocessed digital stream and the current operating state of the system. An environmental snapshot acquisition unit is used to extract a data segment of a preset length within the identified idle environment window as an idle environment snapshot. The environmental snapshot analysis unit is used to perform time-domain statistical analysis, frequency-domain narrowband interference identification, and power frequency phase-correlation disturbance analysis on the idle environmental snapshot, and extract the environmental statistical features of the current environment. A control parameter generation unit is used to generate adaptive control parameters for adjusting the real-time monitoring link and / or the analog front-end unit based on the environmental statistical characteristics. An adaptive closed-loop control unit is used to write back the adaptive control parameters to the configuration registers corresponding to the real-time monitoring link and / or the analog front-end unit through a control interface. The verification rollback unit is used to evaluate the system's operating indicators within the verification window after the parameter write-back takes effect. If the operating indicators meet the preset improvement conditions, the updated parameters are maintained. If the preset improvement conditions are not met, parameter rollback is performed and the system is restored to the historical stable parameter set. Then, the system returns to the idle window identification unit for cyclic execution, forming an adaptive closed-loop control.

[0065] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 2 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores environmental analysis and closed-loop control data for the converter transformer partial discharge monitoring system. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an environmental analysis and closed-loop control method for the converter transformer partial discharge monitoring system.

[0066] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0067] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0068] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0069] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0070] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0071] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0072] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system, characterized in that, The environmental analysis and closed-loop control method for the converter transformer partial discharge monitoring system includes: S1: Acquire the raw sampled digital stream of one or more partial discharge monitoring channels of the converter transformer; S2: Perform signal preprocessing on the original sampled digital stream to obtain a preprocessed digital stream; S3: Based on the preprocessed digital stream and the current operating status of the system, identify the idle time environment window that meets the preset idle time determination conditions; S4: Within the identified idle environment window, extract a data segment of a preset length as an idle environment snapshot; S5: Perform time-domain statistical analysis, frequency-domain narrowband interference identification, and power frequency phase-correlation disturbance analysis on the idle environment snapshot to extract the environmental statistical features of the current environment; S6: Based on the environmental statistical characteristics, generate adaptive control parameters for adjusting the real-time monitoring link and / or the analog front-end unit; S7: Write the adaptive control parameters back to the configuration registers corresponding to the real-time monitoring link and / or the analog front-end unit through the control interface; S8: Evaluate the system's operating indicators within the verification window after the parameter write-back takes effect. If the operating indicators meet the preset improvement conditions, keep the updated parameters. If the preset improvement conditions are not met, perform parameter rollback and restore to the historical stable parameter set. Then return to S3 for repeated execution to form adaptive closed-loop control.

2. The environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system according to claim 1, characterized in that, The preset idle time determination condition includes at least one of the following: No data collection events occurred within the preset time period; The system is not in a busy event upload state or a cache congestion state; The current power frequency phase is not located in the commutation strong interference dead zone, or the dead zone samples have been marked and removed.

3. The environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system according to claim 1, characterized in that, The methods for collecting the idle environment snapshots include at least one of the following: Directly extract segments of the original sampled digital stream; Collect snapshots of the corresponding environmental windows according to the power frequency phase interval; The snapshots of multiple idle environment windows are accumulated to form a statistical sample set.

4. The environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system according to claim 1, characterized in that, The extracted environmental statistical features include at least one of the following categories: Time-domain statistical results include at least one of the following: mean, variance, median, median absolute deviation (MAD), root mean square (RMS), high quantile, peak factor, skewness, kurtosis, and pruning rate. Frequency domain statistical results include at least one of the following: power spectral density, narrowband interference frequency location, interference peak amplitude, peak bandwidth, subband energy distribution, and spectral flatness. Phase domain statistical results include the correspondence between background noise energy and power frequency phase, the probability of pseudo partial discharge pulses gathering in each phase window, and at least one of the phase-correlated high interference intervals. The status assessment results include at least one of the following: false trigger rate estimate, event buffer occupancy rate, ADC dynamic range utilization rate, and analog front-end gain margin.

5. The environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system according to claim 1, characterized in that, The process of generating the adaptive control parameters includes: The environmental statistical characteristics are normalized, their reliability is assessed, and outliers are removed. Multiple candidate parameter values ​​are generated according to different control objectives, and the candidate parameter values ​​are filtered based on detection sensitivity, target false alarm rate, hardware security constraints and system stability constraints. The selected parameters are then subjected to parameter optimization, time smoothing, and hysteresis control to output the final adaptive control parameters.

6. The environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system according to claim 1, characterized in that, When the adaptive control parameters include trigger threshold parameters, the adaptive generation process includes: Perform decision domain consistency preprocessing on the idle environment snapshot, consistent with the real-time triggering link, to obtain the decision domain background sequence; Calculate the median, median absolute deviation (MAD), high quantile, skewness, and kurtosis of the background sequence in the decision domain, and construct the cumulative distribution function or histogram. Based on the target false alarm rate, the corresponding high-resolution points are obtained from the cumulative distribution function as the first candidate threshold; Long-tail compensation is performed on the robust scaling estimator based on MAD based on skewness and kurtosis to obtain the second candidate threshold; An improved Otsu inter-class separation search is performed on the histogram to obtain a third candidate threshold; The first candidate threshold, the second candidate threshold, and the third candidate threshold are fused to obtain the original threshold value; After time smoothing and hysteresis control are applied to the original threshold value, it is issued as the trigger threshold parameter.

7. The environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system according to claim 1, characterized in that, Parameter write-back can be performed using at least one of the following methods: Online hot update, power frequency cycle boundary update, double buffer parameter switching update, safe time window update or amplitude-limited smooth update; The write-back objects include at least one of the following: the threshold register in the real-time acquisition trigger module, the notch filter parameter register in the signal preprocessing unit, the programmable gain device control register in the analog front-end unit, and the phase interval configuration register in the phase correlation suppression module.

8. The environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system according to claim 1, characterized in that, The operational metrics include at least one of the following: false trigger rate, number of events per unit time, idle window occupancy rate, cache congestion rate, root mean square change rate of background, and effective event retention rate; the execution parameter rollback includes: immediate rollback to the previous stable parameter set, tiered rollback, limited smooth rollback, or optimal recovery of multiple sets of historical parameters.

9. An environmental analysis and closed-loop control system for a converter transformer partial discharge monitoring system, characterized in that, include: The sampling digitization unit is used to acquire the raw sampling digital stream of one or more partial discharge monitoring channels of the converter transformer; A signal preprocessing unit is used to preprocess the original sampled digital stream to obtain a preprocessed digital stream. The idle window identification unit is used to identify idle environment windows that meet preset idle judgment conditions based on the preprocessed digital stream and the current operating state of the system. An environmental snapshot acquisition unit is used to extract a data segment of a preset length within the identified idle environment window as an idle environment snapshot. The environmental snapshot analysis unit is used to perform time-domain statistical analysis, frequency-domain narrowband interference identification, and power frequency phase-correlation disturbance analysis on the idle environmental snapshot, and extract the environmental statistical features of the current environment. A control parameter generation unit is used to generate adaptive control parameters for adjusting the real-time monitoring link and / or the analog front-end unit based on the environmental statistical characteristics. An adaptive closed-loop control unit is used to write back the adaptive control parameters to the configuration registers corresponding to the real-time monitoring link and / or the analog front-end unit through a control interface. The verification rollback unit is used to evaluate the system's operating indicators within the verification window after the parameter write-back takes effect. If the operating indicators meet the preset improvement conditions, the updated parameters are maintained. If the preset improvement conditions are not met, parameter rollback is performed and the system is restored to the historical stable parameter set. Then, the system returns to the idle window identification unit for cyclic execution, forming an adaptive closed-loop control.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the environmental analysis and closed-loop control method for a converter transformer partial discharge monitoring system as described in any one of claims 1-8.

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