Waveform identification and correction method and system for winding deformation diagnosis

Abstract

The embodiment of the invention discloses a waveform identification and correction method and system for winding deformation diagnosis, and relates to the technical field of transformer winding monitoring, and the method comprises the steps: synchronously collecting voltage and current signals, and carrying out the preprocessing; performing waveform disturbance type identification on the preprocessed signal, and dividing the disturbance type into harmonic dominant disturbance, voltage sag disturbance or compound disturbance, and harmonic dominant disturbance, voltage sag disturbance or compound disturbance; dynamically selecting a corresponding waveform processing path according to a waveform disturbance type identification result; processing the signal by adopting the selected waveform processing path so as to correct and reconstruct the signal and obtain compensated voltage and current waveform data; by constructing a disturbance identification and path adaptive processing mechanism, the problems of processing lag and compensation distortion of a general algorithm during dynamic signal disturbance are solved, the identification precision is improved, and real-time error correction is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer winding monitoring, and in particular to a waveform recognition and correction method and system for winding deformation diagnosis. Background Art

[0002] With the widespread use of nonlinear loads such as power electronic devices (such as converters and rectifiers) in power systems, the grid operating environment is becoming increasingly complex, with frequent power quality disturbances such as harmonic distortion and voltage sags. These abnormal signals not only affect system stability but also pose challenges to the visualization and analysis of voltage and current waveforms based on Lissajous diagrams. These problems can cause distortion in the diagrams and phasor characteristics, affecting the accuracy of determining winding deformation status.

[0003] Typical solutions for addressing current and voltage measurement disturbances currently rely on fixed processing algorithms and centralized control systems. These waveform processing systems, in highly dynamic and nonlinear environments, suffer from rigid paths, delayed responses, and insufficient recognition accuracy, making them unable to meet the dual requirements of "accuracy and adaptability" in UHV transmission scenarios. Summary of the Invention

[0004] The main purpose of the present invention is to provide a waveform recognition and correction method and system for winding deformation diagnosis, to solve the processing lag and compensation distortion problems of general algorithms during dynamic signal disturbances, to improve recognition accuracy, and to achieve real-time error correction.

[0005] To achieve the above objectives, the present application provides a waveform recognition and correction method for winding deformation diagnosis, comprising: Synchronously collect voltage and current signals and perform preprocessing; Identify the waveform disturbance type of the pre-processed signal and classify the disturbance type into harmonic-dominated disturbance, voltage sag disturbance or composite disturbance; Dynamically selecting a corresponding waveform processing path according to the result of the waveform disturbance type identification; The signal is processed using the selected waveform processing path to correct and reconstruct the signal to obtain compensated voltage and current waveform data.

[0006] On the other hand, the present application also provides a waveform recognition and correction system for winding deformation diagnosis, including: Signal acquisition and preprocessing module, used to synchronously acquire voltage and current signals and perform preprocessing; a waveform disturbance type identification module, configured to identify the waveform disturbance type of the preprocessed signal, wherein the waveform disturbance type identification includes extracting the voltage effective value mutation rate, short-time energy transition coefficient and / or harmonic energy ratio of the signal, and identifying the disturbance type based on a preset multidimensional threshold model; A dynamic path selection module, configured to dynamically select a corresponding waveform processing path based on the result of the waveform disturbance type identification; The waveform reconstruction and compensation module is used to process the signal using the selected waveform processing path to correct and reconstruct the signal to obtain compensated voltage and current waveform data.

[0007] The present application provides a waveform recognition and correction method and system for winding deformation diagnosis, which synchronously collects voltage and current signals and performs preprocessing; identifies the waveform disturbance type of the preprocessed signal and divides the disturbance type into harmonic-dominated disturbance, voltage sag disturbance or composite disturbance; dynamically selects the corresponding waveform processing path based on the result of the waveform disturbance type identification; processes the signal using the selected waveform processing path to correct and reconstruct the signal to obtain compensated voltage and current waveform data; and solves the processing lag and compensation distortion problems of general algorithms in the event of dynamic signal disturbance by constructing a disturbance identification and path adaptive processing mechanism, thereby improving recognition accuracy and realizing real-time error correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0009] in: Figure 1 A schematic structural diagram of a waveform recognition and correction system for winding deformation diagnosis provided by an embodiment of the present application; Figure 2 A schematic diagram of a power system disturbance type identification process provided in an embodiment of the present application; Figure 3 A schematic diagram of voltage sag characteristics provided in an embodiment of the present application; Figure 4 A two-layer decomposition block diagram of a wavelet packet provided in an embodiment of the present application; Figure 5 A schematic diagram of an original signal waveform provided in an embodiment of the present application; Figure 6A schematic diagram of a wavelet packet reconstruction waveform provided in an embodiment of the present application; Figure 7 A schematic structural diagram of a waveform recognition and correction system for winding deformation diagnosis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0011] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0012] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0013] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0014] See also Figure 1 , which is a flow chart of a waveform recognition and correction method for winding deformation diagnosis provided in an embodiment of the present application.

[0015] like Figure 1 As shown, the method includes: 101. Synchronously collect voltage and current signals and perform preprocessing.

[0016] This application primarily proposes a waveform recognition and correction method and system for winding deformation diagnosis. This method is suitable for high-fidelity voltage and current waveform acquisition and subsequent analysis and processing under various disturbance conditions in complex AC and DC power transmission systems. The core concept is to automatically switch the optimal waveform processing path based on disturbance characteristics (such as harmonic distortion or voltage sag) in the input waveform through a pre-signal discrimination mechanism. This enables adaptive waveform processing scheduling and dynamic signal correction, ensuring accurate representation of the phasor relationships required by diagnostic methods such as Lissajous diagrams.

[0017] The method implemented in the embodiments of the present application can be implemented as a waveform recognition and correction system for winding deformation diagnosis. The system can include a signal acquisition and preprocessing module, a waveform disturbance type recognition module (event detection module), a dynamic path selection module, a waveform reconstruction and compensation module, and a data output and remote communication module.

[0018] First, step 101 can be implemented based on the signal acquisition and preprocessing module. The signal acquisition and preprocessing module serves as the basic front-end of the application, and performs high-precision, synchronized data acquisition and cleaning processing on electrical signals such as voltage and current. Specifically, GPS timing or a high-precision crystal oscillator clock can be used to achieve multi-channel synchronous sampling, ensuring that the voltage and current signals are aligned and collected under a unified time reference, avoiding phase deviation caused by timing errors. Optionally, the sampling accuracy is not less than 16 bits, and the sampling frequency is better than the national standard setting to ensure that short-term disturbance characteristics including high-order harmonics and voltage sags can be accurately captured.

[0019] After data acquisition is completed, preprocessing can be performed, including baseline drift correction, limit removal, frame division and other operations on the original signal, and caching it in a high-speed local buffer to provide high-quality, low-distortion signal input for subsequent disturbance identification and compensation calculations, laying the signal foundation for the operation of the entire system.

[0020] 102. Identify the waveform disturbance type of the pre-processed signal and classify the disturbance type into harmonic-dominated disturbance, voltage sag disturbance or composite disturbance.

[0021] The waveform disturbance type identification in the embodiments of this application can be implemented based on the waveform disturbance type identification module. This module is the key functional unit for implementing the "event-driven" mechanism in this application. It is primarily responsible for dynamically analyzing the collected voltage and current signals and identifying disturbance patterns to determine whether there are abnormal disturbances in the current signals and further classify them into different types of measurement error inducements. The following three main types of disturbances can be included: harmonic-dominated disturbances, voltage sag disturbances, and combined disturbances.

[0022] Based on the sliding analysis window, the signal can be extracted frame by frame to obtain characteristic indicators, and then the disturbance type can be judged in real time based on the obtained characteristic indicators.

[0023] In an optional embodiment, the waveform disturbance type identification includes: Based on the sliding analysis window, the voltage RMS mutation rate, short-time energy transition coefficient and harmonic energy ratio are extracted, and the above disturbance types are judged in real time according to the built-in multi-dimensional threshold model.

[0024] Figure 2 This is a schematic diagram of a power system disturbance type identification process provided by an embodiment of the present application. Figure 2 As shown, specifically, feature extraction is performed on the collected signal obtained in step 101, and the obtained feature indicators may mainly include three types of key indicators: First, the voltage RMS mutation rate (RMS change gradient) is used to quickly capture sudden drops or increases in voltage or current in a short period of time, typically manifested as RMS drops caused by voltage sag events. The voltage RMS mutation rate expression is:

[0025] in is the effective value of the voltage in the previous cycle; It is the effective value of voltage in the current cycle.

[0026] Secondly, the short-term energy transition coefficient reflects the energy mutation phenomenon caused by system load changes, switching shocks, etc. by analyzing the instantaneous energy changes of the signal within a certain window. First, calculate the instantaneous energy calculation formula of the sliding window:

[0027] The short-time energy transition coefficient (relative rate of energy change) can be derived from the above formula:

[0028] in is the original signal (voltage or current) in the kth sliding window. is the instantaneous signal energy in the kth window; is the energy mutation rate between two adjacent windows; N is the window length (such as the number of sampling points corresponding to one power grid frequency cycle).

[0029] Third, the harmonic energy ratio uses the FFT algorithm to extract the 2nd to 50th harmonic amplitudes and calculate the harmonic energy ratio to identify whether there is harmonic-dominated interference in the current signal. Before calculating the harmonic energy ratio, the total amount of harmonic energy must be calculated first:

[0030] Then calculate the proportion of harmonic energy:

[0031] in is the frequency domain amplitude of the hth harmonic (FFT component); H is the highest harmonic order (such as 50th); is the total harmonic energy; is the total signal energy (including fundamental wave and harmonics); is the percentage of harmonic energy in the total energy.

[0032] The system in the embodiment of the present application has a built-in configurable multi-dimensional threshold model to judge the disturbance type in real time. Optionally, the form of the multi-dimensional threshold model is not limited, and it can be a disturbance type condition trigger logic table. Based on the above-mentioned characteristic indicators, the multi-dimensional threshold judgment is performed, and the recognition results can be divided into three categories: Type A is a harmonic-dominated disturbance, indicating that the signal is dominated by abnormal periodic frequency components without obvious mutations; Type B is a voltage sag (transient) disturbance, indicating the presence of sudden amplitude changes; Type C is a composite disturbance, where two types of anomalies exist simultaneously, and a dual-path joint compensation strategy is required.

[0033] 103. Dynamically select a corresponding waveform processing path based on the result of the waveform disturbance type identification.

[0034] Specifically, based on the waveform disturbance type identification results obtained in step 102, the most suitable error compensation path can be automatically activated to ensure that the system can quickly respond and accurately handle different abnormal scenarios. This step can be implemented based on the dynamic path selection module and is the core mechanism of the present invention for achieving intelligent error processing scheduling and flexible algorithm switching.

[0035] Specifically, a conditional trigger logic table based on the disturbance type can be constructed to support multi-branch processing decisions and dynamic algorithm loading. Different activation paths and processing methods can be set for different disturbance types.

[0036] In an optional embodiment, when the disturbance type is the harmonic-dominated disturbance, the waveform processing path is a harmonic processing path; The above-mentioned processing of the above-mentioned signal using the selected waveform processing path includes: The above signals are processed using a fast Fourier transform spectrum algorithm based on Nuttall window weighting and three-line interpolation.

[0037] Specifically, the perturbation type is harmonic-dominated (Type A): Path 1—the harmonic processing path—is triggered, enabling an FFT spectrum algorithm based on Nuttall window weighting and three-line interpolation to overcome the spectrum leakage and picket fence effects of conventional FFT algorithms when high-order harmonic frequency drift occurs. This path accurately extracts the frequency, amplitude, and phase of the primary and subharmonics for sampling error correction and waveform reconstruction.

[0038] The FFT spectrum algorithm based on Nuttall window weighting + three-line interpolation is derived as follows. The combined window function based on the cosine window is often used for harmonic signal analysis. Its expression is:

[0039] Where H is the number of terms in the cosine window; a h For cosine window h Term coefficient.

[0040] The coefficients of the cosine window should satisfy the following constraints:

[0041] After selecting the four-term fifth-order Nuttall window as the FFT window function, the interpolation calculation is performed. The two main spectral lines (maximum amplitude and sub-maximum amplitude spectral lines) close to the peak frequency are selected as the basis for calculating the actual spectral components. The three-line interpolation algorithm is an improvement on the traditional two-line interpolation method. This method introduces the third-largest amplitude spectral line to participate in the correction calculation of the harmonic components, thereby significantly improving the analysis accuracy. Let the auxiliary parameters α = k 2- k 0, where , we can get:

[0042] Simplified to a functional relationship β = g ( α ), to infer the parameters α , let its inverse function be F= g -1 ( β ). Select Chebyshev polynomials for F= g -1 ( β ) to approximate and obtain the parameters α It can be expressed as the following polynomial:

[0043] Then the amplitude correction formula and phase correction formula are derived:

[0044]

[0045] when N When it is large, the amplitude correction formula is simplified to:

[0046] The polynomial approximation method can also be used to v ( α ) approximate solution:

[0047] Based on the three-line interpolation FFT algorithm of the four-term fifth-order Nuttall window, the polyfit function is called in Matlab to obtain the polynomial fitting approximation:

[0048] In an optional embodiment, when the disturbance type is the voltage sag disturbance, the waveform processing path is a transient processing path, and the waveform processing path is a harmonic processing path; The above-mentioned processing of the above-mentioned signal using the selected waveform processing path includes: The above signals are processed using wavelet packet decomposition algorithm.

[0049] Specifically, if the disturbance type is a voltage sag (Type B), the system automatically switches to Path 2—the transient processing path—and activates the sag identification mechanism based on the Db4 wavelet packet decomposition algorithm. This algorithm performs multi-scale and multi-band decomposition of the input signal, extracting the characteristic energy in the voltage sag-related subbands. It then uses energy mapping analysis to determine the disturbance duration, sag amplitude, and waveform disruption sections, thereby compensating and reconstructing the waveform in the sudden change interval.

[0050] Voltage sag amplitude and voltage sag duration are the most critical indicators to characterize voltage sag phenomenon. Figure 3 A schematic diagram of voltage sag characteristics provided in an embodiment of the present application.

[0051] The derivation of the Db4 wavelet packet decomposition algorithm is as follows: Perform displacement Then, at different scales a Down and signal x ( t ) Taking the inner product we get:

[0052]

[0053] In multiresolution orthogonal subspace V 0, wavelet packet decomposition is performed, which is expressed as:

[0054] In the signal decomposition stage, the original signal is refined into low-frequency and high-frequency components at multiple resolution levels; in the signal synthesis stage, the components at each level are gradually reconstructed in a predetermined manner and finally restored to the original signal.

[0055] Figure 4 A two-layer decomposition block diagram of a wavelet packet provided in an embodiment of the present application.

[0056] For voltage signals u ( t ) and current signals i ( t ) is sampled, the wavelet packet coefficient matrix of the reconstructed fundamental wave and distorted signal is derived:

[0057]

[0058] Substitute the coefficients into the voltage signal u ( t ) and current signals i ( t ) to obtain the fundamental signal and the distorted signal:

[0059]

[0060] Figure 5 A schematic diagram of an original signal waveform provided in an embodiment of the present application.

[0061] Figure 6 A schematic diagram of a wavelet packet reconstruction waveform provided in an embodiment of the present application.

[0062] According to the above method, first give the initial sampling signal as Figure 5 As shown, it includes voltage signal and current signal; after decomposition and reconstruction, we can get Figure 6 The signals shown include a fundamental voltage waveform, a fundamental current waveform, a distorted voltage waveform, and a distorted current waveform.

[0063] In an optional embodiment, when the disturbance type is the composite disturbance, the waveform processing path is a composite collaborative path; The above-mentioned processing of the above-mentioned signal using the selected waveform processing path includes: The harmonic analysis path and the wavelet packet analysis path are started in parallel to process the above signal.

[0064] Specifically, if the disturbance type is a composite disturbance (Type C), path 3—the composite collaborative path—is started in parallel, i.e., the dual-channel linkage of the harmonic analysis and wavelet packet analysis paths is used to extract the harmonic frequency domain parameters and transient energy characteristics respectively. The two are input into the error compensation module in a coordinated manner, and the signal amplitude, phase, frequency, etc. are dynamically corrected through the fusion correction model, thereby realizing waveform distortion suppression under full-time and multi-source disturbances.

[0065] Among them, the harmonic analysis path adopts a spectrum extraction method based on the Nuttall window + interpolation FFT algorithm, focusing on extracting the amplitude, phase and frequency drift characteristics of the 2nd to 25th main harmonics, and evaluating their contribution to the degree of fundamental distortion; while the wavelet packet analysis path adopts multi-layer decomposition based on Db4 wavelet to capture the time domain characteristics of transient events such as voltage sag, such as the occurrence time, sudden change amplitude, local energy distribution and duration.

[0066] The two extracted features are fed into a fusion correction model for dynamic integration. This model uses a weighted decision-making mechanism to dynamically adjust the weights of frequency and time domain corrections, combining dominant indicators of disturbance sources (such as total harmonic energy percentage (THD%) and wavelet packet mutation energy percentage). The fusion process can be expressed as:

[0067] Among them, α is the fusion weight coefficient, which is adaptively calculated according to the following formula:

[0068] Among them, THD represents the total harmonic distortion rate (frequency domain disturbance intensity index); E t Indicates the transient energy ratio (time domain disturbance intensity index).

[0069] Through this mechanism, when harmonics dominate (α approaches 1), the frequency and phase correction results are retained first; when transient disturbances dominate (α approaches 0), the dynamic correction output of the wavelet path is used first.

[0070] Ultimately, the fusion output will perform comprehensive corrections on the signal in three dimensions: amplitude, phase, and frequency, ensuring that the generated voltage / current waveform has high fidelity and good continuity, which is suitable for the graphical presentation and discrimination accuracy of Lissajous figures under complex disturbance conditions.

[0071] Optionally, if the aforementioned disturbance type is not identified, that is, if there is no obvious disturbance, the current compensation mechanism can be maintained and the original algorithm processing can be maintained.

[0072] This path scheduling mechanism effectively avoids the problems of error amplification and processing failure caused by the "fixed algorithm channel" of the traditional measurement system, enabling the system to have a closed-loop response capability of "identification-matching-correction".

[0073] Table 1 is a disturbance type condition trigger logic table provided in an embodiment of the present application.

[0074]

[0075] Table 1 As shown in Table 1, the characteristic criteria, activation paths, and processing methods of the above three disturbance types (types A, B, and C) are set.

[0076] 104. Process the signal using the selected waveform processing path to correct and reconstruct the signal to obtain compensated voltage and current waveform data.

[0077] The waveform reconstruction and compensation module is the key terminal module of the algorithm processing system of this application. Its main function is to incorporate the characteristic quantities output by the preceding path analysis (harmonic path, wavelet path or composite path) into the vector relationship modeling process, and dynamically correct the amplitude and phase deviations caused by harmonic distortion, transient disturbances, etc. to ensure the authenticity and consistency of the output results.

[0078] Optionally, the processing of the signal using the selected waveform processing path to correct and reconstruct the signal includes: The above signals are corrected in real time in multiple dimensions using a dynamic power factor adjustment mechanism based on FFT result calculation, a sampling window adaptive adjustment mechanism, and a fundamental amplitude reconstruction algorithm.

[0079] Specifically, in order to realize the multi-dimensional real-time correction function, a waveform reconstruction and compensation module can be used, which mainly includes the following aspects: First, to address phase errors caused by frequency drift and fluctuations in harmonic content, the system introduces a dynamic power factor adjustment mechanism based on FFT calculations. This mechanism optimizes the separation accuracy of voltage and current signals by utilizing the phase difference and amplitude ratio of primary and secondary harmonic components. This improves decomposition accuracy without changing the original sampling structure, thereby enhancing the measurement system's ability to restore true signals in a harmonic environment.

[0080] Specifically, the system first extracts the voltage and current components of the fundamental wave and each harmonic, assuming: V n 、 I n For the n The voltage and current amplitudes of the subharmonics are θ V 、 θ I Its corresponding phase angle. n The expression of the instantaneous active power of subharmonics is:

[0081] After combining all component powers, the total active power correction value can be defined as:

[0082]

[0083]

[0084] in PF adj Indicates the power factor after dynamic correction; N Indicates the harmonic order selected for analysis (such as 1st to 25th); cos( θ V - θ I ) represents the phase difference between voltage and current at that frequency.

[0085] This power factor correction value is fed back into the signal reconstruction process as a core compensation factor, guiding the voltage / current phase alignment operation. This ensures that FFT analysis can still accurately extract the effective power component in a non-ideal harmonic environment, avoiding power factor deviation caused by overestimation of apparent power.

[0086] Secondly, to address sampling signal deviations caused by voltage sag events, an adaptive sampling window adjustment mechanism was designed. This mechanism dynamically revises the sampling interval based on the start and end sections of the disturbance identified by the wavelet packet path, avoiding including voltage dips or sudden voltage changes in the acquisition calculation, thereby effectively avoiding sampling signal distortion caused by short-term disturbances.

[0087] Furthermore, a fundamental amplitude reconstruction algorithm has been introduced to extract and recover the fundamental signal in scenarios with severe harmonic pollution or significant voltage deformation. Based on frequency-domain filtering and time-domain phase fitting techniques, this algorithm reverse-fits the extracted primary and secondary harmonic characteristics to produce a pure fundamental signal that best matches the power frequency characteristics. This algorithm is used to accurately reconstruct the power factor and RMS voltage, providing reliable signal support for subsequent Lissajous signal visualization.

[0088] In the embodiment of the present application, in terms of frequency domain processing, the system adopts a fourth-order Butterworth bandpass filter (Butterworth BPF), and its design parameters are as follows: ① Center frequency: 50Hz (or 60HZ, depending on the region); ② Bandwidth: +3Hz (passband range is set to 47HZ~53Hz); ③ Filter order: 4th order, to balance roll-off speed and phase response stability; ④ Implementation method: Based on the IR digital filter structure, implemented after FFT spectrum processing.

[0089] The filter has a smooth passband characteristic and can effectively suppress high-order harmonic components (such as 3rd, 5th, 7th, etc.) while retaining the main frequency band energy and reducing the "signal edge ringing" caused by sharp filtering.

[0090] In the embodiment of the present application, in terms of time domain processing, the system uses the least squares phase fitting algorithm to reconstruct the fundamental wave of the filtered signal. The specific fitting model can be as follows:

[0091] Where v(t) is the voltage (or current) signal after filtering; Indicates power frequency; A, represents the amplitude and initial phase to be fitted; ε(t) represents the fitting residual.

[0092] The fitting objective function is to minimize the sum of squared residuals:

[0093] After the fitting is completed, the A and This represents the equivalent fundamental amplitude and phase angle of the signal within the current cycle, which can be used to correct the voltage RMS value, power factor, and phasor diagram. This method can stably output a smooth, non-jumpy fundamental signal even in complex disturbance environments, making it particularly suitable for diagnostic scenarios requiring high continuity of the Lissajous figure phase trajectory.

[0094] Optionally, the above method further includes: The above compensated data are integrated and output, and uploaded to the master station system or remote measurement platform through local storage or communication interface. The output data includes but is not limited to the real-time amplitude, phase, frequency, and power factor trend of voltage and current.

[0095] Ultimately, the error-compensated data is integrated and output through the measurement model, including key indicators such as real-time voltage and current amplitude, phase, frequency, and power factor trends. This data is then uploaded to a master system or remote measurement platform via local storage or a communication interface. The entire module supports dynamic configuration, real-time feedback, and traceability, ensuring high-precision and reliable measurement output in various disturbance scenarios. This is one of the core technologies of the present invention, which enables intelligent and adaptive dynamic compensation.

[0096] Specifically, the data output and remote communication module serves as the system's final data publishing and system integration interface. It is responsible for outputting error-compensated signal sampling results to the higher-level management platform in a structured, standardized data format, enabling remote access, centralized monitoring, and intelligent dispatch of local processing results. This module supports both on-site communication uploads and compatibility with national and industry power automation standard interfaces, ensuring seamless integration with existing power monitoring and dispatch platforms.

[0097] The data output and remote communication module first formats and encapsulates key parameters such as voltage and current amplitude, phase, and power factor corrected by the dynamic error compensation module, organizes them into a complete message according to the frame structure, and uploads them to the master station system, measurement and acquisition terminals (such as concentrators), or energy management systems (EMS) through the communication control unit. In terms of interface protocols, the system supports multiple mainstream power communication standards, including IEC 61850 (substation automation communication protocol) and DL / T 645 (electricity meter communication protocol), and can automatically adapt to different communication requirements based on the application scenario.

[0098] The IEC 61850 interface supports data structures such as GOOSE messages, MMS services, and SV sampling values, enabling millisecond-level power event uploads, alarm notifications, and control command reception. The DL / T interface is suitable for data exchange with traditional electricity meters or medium and low voltage distribution network equipment, demonstrating excellent versatility and compatibility. Furthermore, the module also includes reserved interfaces for extended protocols such as Modbus TCP / IP, MQTT, and RS-485 serial communication, enabling access to industrial gateways or local control systems, enhancing system deployment flexibility.

[0099] To ensure the security and reliability of data uploads, this module is equipped with a data caching mechanism, a breakpoint resume strategy, and encryption verification functions. This allows for temporary storage, transmission recovery, and tamper-proof protection of data in the event of network anomalies, ensuring that the measured data is authentic, complete, and reliable. Through this module, this application can implement full-process closed-loop management of error-optimized measurement data, building a complete monitoring and correction system from data acquisition, event identification, error compensation, to remote output, effectively supporting the engineering needs of refined voltage and current signal management and remote trusted auditing in AC and DC transmission scenarios.

[0100] Optionally, in the embodiments of the present application, local optimization can be performed as needed by using methods such as EEMD, wavelet transform instead of FFT, small neural network instead of traditional threshold judgment, edge computing instead of embedded processing, etc.

[0101] The method in the embodiment of the present application solves the processing lag and compensation distortion problems of general algorithms when dynamic signals are disturbed by constructing a disturbance recognition and path adaptive processing mechanism, thereby improving recognition accuracy and realizing real-time error correction.

[0102] Based on the description of the aforementioned method embodiment, the embodiment of the present application also provides a waveform recognition and correction system for winding deformation diagnosis.

[0103] See also Figure 7 , is a schematic diagram of the structure of a waveform recognition and correction system for winding deformation diagnosis provided by an embodiment of the present application. Figure 7 As shown, the system 700 includes: The signal acquisition and preprocessing module 710 is used to synchronously acquire voltage and current signals and perform preprocessing; A waveform disturbance type identification module 720 is configured to identify the waveform disturbance type of the preprocessed signal, wherein the waveform disturbance type identification includes extracting the voltage effective value mutation rate, short-time energy transition coefficient, and / or harmonic energy ratio of the signal, and identifying the disturbance type based on a preset multi-dimensional threshold model; A dynamic path selection module 730 is configured to dynamically select a corresponding waveform processing path based on the result of the waveform disturbance type identification; The waveform reconstruction and compensation module 740 is used to process the signal using the selected waveform processing path to correct and reconstruct the signal to obtain compensated voltage and current waveform data.

[0104] Optionally, the system 700 further includes a data output and remote communication module 750 for integrating and outputting the compensated data and uploading the data to a master station system or a remote measurement platform via a local storage or communication interface.

[0105] The functions of each module in the waveform recognition and correction system 700 for winding deformation diagnosis are as follows: Figure 1 The detailed description has been made in the illustrated embodiment. For example, the waveform disturbance type identification module 720 may execute the specific content in step 102, which will not be described in detail here.

[0106] The key points of the method and system proposed in this application are: 1. Based on real-time sampling signals such as voltage and current, a front-end high-speed signal acquisition channel is constructed. An isolated ADC sampling chip is combined with a low-pass anti-aliasing filter to achieve high-resolution signal restoration and anti-interference acquisition. Sampled data is stored in a FIFO buffer structure and time alignment is achieved using the Network Time Protocol (NTP) or GPS timing mechanism, providing a unified reference timeline for subsequent disturbance identification. Electrical isolation and TVS protection circuits enhance the system's surge resistance and long-term operational safety in high-voltage DC environments.

[0107] 2. Utilizing a multidimensional disturbance identification algorithm, this algorithm integrates characteristic indicators such as the effective value mutation rate, harmonic energy ratio, and wavelet energy transition coefficient to determine whether the signal is experiencing harmonic interference, voltage sag, or a combination of these disturbances. Disturbance types are automatically classified based on predefined threshold rules. The identification results drive the path scheduling module, which dynamically selects a processing strategy based on the disturbance type, minimizing resource waste and improving system response sensitivity.

[0108] 3. Build a multi-algorithm path system driven by disturbance type. The harmonic path uses a Nuttall-windowed weighted windowed FFT algorithm and three-line interpolation to precisely extract harmonic features. The transient path uses Db4 wavelet packet decomposition to reconstruct the disturbance waveform, enabling time-frequency location and fundamental wave recovery. In complex scenarios, the two paths operate collaboratively, forming a dual-channel data analysis structure that enables full-spectrum error detection and compensation. Each path can be enabled independently or run in parallel as needed, creating a customizable adaptive processing system.

[0109] 4. A dynamic compensation mechanism is designed to adjust the recognition window based on the duration of the identified disturbance, eliminating waveform input during abnormal periods to prevent error accumulation. A fundamental wave fitting and reconstruction algorithm is also introduced to extract the true fundamental wave amplitude and phase from the interference signal, which is used to correct the signal amplitude, phase, and load characteristic data. The compensation results are dynamically updated through the measurement model to ensure that the voltage and current signal output data reflect the actual sampling results.

[0110] 5. Final measurement results and event tags are output as structured messages, supporting IEC 61850 and DL / T 645 standard communication protocols, adapting to the access requirements of master stations, energy management systems, and intelligent operations and maintenance platforms. The communication process supports encrypted transmission and identity authentication mechanisms to ensure the integrity and security of measurement data. The system supports local event recording and abnormal waveform tracing, providing data for subsequent traceability analysis and optimized scheduling.

[0111] The following describes the method and system in the embodiments of the present application in conjunction with specific application scenarios.

[0112] Case background: To ensure the stability of cross-regional power transmission and reliable assessment of key equipment status, a new ±800kV ultra-high voltage direct current (UHVDC) transmission project in my country introduced an intelligent voltage and current waveform acquisition and processing system. Considering the large number of power electronic conversion devices deployed in the project's point-to-point transmission paths, such as multi-stage rectifiers and inverters, disturbances such as harmonic superposition and voltage sags frequently occur during grid operation. These complex disturbances pose significant challenges to traditional waveform acquisition and analysis equipment, easily causing waveform distortion. This is particularly true in winding status visualization diagnostic scenarios, such as the Lissajous diagram method, where accuracy is significantly reduced. Therefore, a new waveform processing solution with disturbance identification capabilities and adaptive path processing mechanisms is urgently needed to improve signal fidelity and diagnostic image quality at critical moments.

[0113] 1. Traditional method: Traditional waveform processing systems deployed in previous projects primarily rely on fixed-parameter models and single-path FFT algorithms to analyze voltage and current signals, making them difficult to adapt to dynamic changes under nonlinear disturbance conditions. These methods have significant limitations: First, under conditions of harmonic frequency drift or non-integer-cycle sampling, FFT analysis is prone to spectral leakage, resulting in distortion of extracted waveform features and, in turn, affecting the accuracy of Lissajous diagrams. Second, traditional processing methods struggle to identify transient disturbances. Phenomena such as voltage dips and instantaneous waveform offsets are often overlooked or misinterpreted, causing critical analysis windows to overlap with abnormal waveform regions, disrupting the continuity and symmetry of the diagrams. Third, all system computations are centralized at the master station, resulting in slow response, a lack of real-time path switching mechanisms, and low resource utilization. In a field test, the Lissajous diagrams output by the original system exhibited severe distortion under voltage dip conditions, with an image error rate exceeding 12%, significantly reducing the accuracy and visibility of winding status identification.

[0114] 2. Methods in this application: By adopting the waveform processing strategy proposed in this application, the system as a whole realizes the closed-loop control of the entire process, from front-end high-fidelity voltage and current signal acquisition, intelligent identification of disturbance events, dynamic switching of processing paths, real-time reconstruction of waveform characteristics, and remote signal output. The specific implementation steps are as follows: (1) Signal acquisition and preprocessing module plays a role: The intelligent measurement device deployed on the bus side of the converter station uses a three-phase sampling module to synchronously sample the voltage signal at a high frequency of 10 kHz to obtain an information stream with rich frequency and time domain characteristics. This module uses a high-resolution Σ-Δ ADC for analog-to-digital conversion, and a built-in isolation driver ensures safe electrical isolation at the input. The sampling front end integrates an active low-pass anti-aliasing filter to effectively shield high-frequency interference noise above the power frequency and improve signal purity. After sampling, the system performs pre-processing operations on the signal, such as baseline drift elimination (DC offset removal) and soft limit anomaly rejection, and then frames the signal according to a set sampling window width (e.g., 20ms). Finally, the structured frame data is cached in a local high-speed FIFO queue, providing a real-time readable data source for subsequent identification and processing.

[0115] (2) The voltage and current disturbance identification module identifies the disturbance type: During system operation, a grid-connected startup event caused the bus voltage to drop by more than 20% within two grid cycles (approximately 40ms), while the power frequency component remained within 50Hz±0.1Hz. The system used a sliding window RMS rate-of-change calculation model to determine that the voltage RMS mutation rate exceeded the threshold. It also used a wavelet packet energy coefficient variation map to further analyze the signal's mutation energy distribution, confirming the presence of transient disturbance characteristics. Based on the feature extraction results, the disturbance identification module automatically classified the signal as a Type B voltage sag event and issued an interrupt signal, notifying the dynamic path scheduling module to skip the harmonic path and prioritize the transient path algorithm, avoiding resource waste and analysis path conflicts.

[0116] (3) Dynamic path selection module switching processing strategy: The identification result triggers "Path 2": The transient processing path automatically starts. The system invokes a multi-layer wavelet packet decomposition algorithm based on the Db4 mother wavelet to decompose the current data frame signal into sub-signals of different frequency bands. It then analyzes the temporal distribution of the sub-band energy coefficients to accurately locate the starting and recovery points of the voltage sag. During the reconstruction phase, the system compares the sub-band waveforms before and after the disturbance to generate an approximate ideal fundamental band for reference, ensuring that the sampled signal is not affected by the disturbance. Simultaneously, the system disables the FFT harmonic analysis algorithm of "Path 1," freeing up computing resources and improving processing efficiency and real-time performance.

[0117] (4) The compensation module performs fine correction: After acquiring the duration of the disturbance (e.g., frames 16 to 21), the compensation module performs interval exclusion reconstruction on the waveform reconstruction model, dynamically eliminating the integration results in that interval and performing a reintegration operation after the signal recovery period. The module also activates an amplitude-fitting fundamental recovery algorithm to remove the interference of harmonics on the desired waveform during the disturbance phase. It recalculates the amplitude and phase of the fundamental voltage and current, and uses this to correct and compensate the sampled waveforms, ensuring that the output voltage and current signals reflect their true values.

[0118] (5) Data output module transmits correction results: After completing compensation and correction, the system encapsulates the data, including the current period's amplitude, phase, frequency, disturbance identification tag, and processing status code, into a structured communication message and uploads it to the backend master system via a network communication module supporting the IEC 61850 MMS protocol. The message uses the MMS service encapsulation format and includes complete time stamp, event record, and processing path information, ensuring that the backend dispatch system can accurately reproduce the event context and conduct decision analysis.

[0119] Compared with the limitations of traditional waveform processing and correction, such as fixed algorithms, slow response, and weak recognition capabilities, the method in this application achieves accurate classification of harmonics and transient disturbances through an event-driven mechanism. Combined with dynamic path selection and error compensation strategies, it significantly improves the ability to handle waveform distortion caused by nonlinear loads in ultra-high voltage transmission systems, and has higher accuracy, adaptability, and engineering practicality.

[0120] Compared with the current mainstream fixed-path-based FFT dynamic waveform detection and correction system, this application has significant advantages in algorithm structure, adaptability, precision control, and system intelligence: Enhanced recognition capability: Existing technologies are generally unable to effectively distinguish between harmonics and transient disturbances, and their processing logic is simple, which easily ignores the impact of sudden events on signal sampling. This application introduces a multi-dimensional disturbance recognition mechanism that can accurately classify and identify harmonic-dominated disturbances, voltage sags, and complex disturbances, with faster recognition response and more accurate judgment; Processing path adaptation: Traditional technologies use a single algorithm to handle all working conditions, which makes it difficult to flexibly adjust the processing logic for complex environments. This application uses an event-driven strategy to dynamically select the FFT or wavelet packet algorithm path and supports dual-path collaborative analysis, significantly improving the pertinence and effectiveness of algorithm processing. More accurate error compensation: Existing systems often rely on static correction models, making it difficult to cope with measurement deviations in dynamic disturbance environments. This application can adjust the integration interval in real time after a disturbance occurs, and restore the true fundamental wave characteristics based on waveform fitting, effectively avoiding misintegration and miscalculation, and outputting sampled waveform data that is closer to the actual load; The system is more real-time and robust: Compared with the traditional master station centralized processing mode, this application completes the entire identification, processing and compensation process locally on the edge device, with the advantages of low response delay and high operational stability. It is particularly suitable for ultra-high voltage transmission scenarios with high-frequency disturbances and high reliability requirements. Better standard communication and integration capabilities: This application supports power industry standard protocol interfaces such as IEC 61850 and DL / T 645, making it easy to integrate into various signal acquisition platforms, energy efficiency management systems, and dispatching backends, and has good interoperability and deployment adaptability.

[0121] In summary, this application not only overcomes the processing lag and compensation distortion problems of the fixed algorithm path structure when dealing with dynamic signal disturbances, but also significantly enhances the intelligence level and engineering application value of the system through algorithm path adaptation, recognition accuracy improvement and real-time error correction. It is especially suitable for scenarios such as ultra-high voltage, new energy grid connection, and complex AC / DC hybrid systems where signal distortion leads to reduced accuracy of subsequent winding diagnosis.

[0122] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0123] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A waveform recognition and correction method for winding deformation diagnosis, characterized in that: The method comprises: Synchronously collect voltage and current signals and perform preprocessing; Identify the waveform disturbance type of the pre-processed signal and classify the disturbance type into harmonic-dominated disturbance, voltage sag disturbance or composite disturbance; Dynamically selecting a corresponding waveform processing path according to the result of the waveform disturbance type identification; The signal is processed using the selected waveform processing path to correct and reconstruct the signal to obtain compensated voltage and current waveform data.

2. The waveform recognition and correction method for winding deformation diagnosis according to claim 1 is characterized in that: The waveform disturbance type identification includes: Based on the sliding analysis window, the voltage effective value mutation rate, short-time energy transition coefficient and harmonic energy ratio are extracted, and the disturbance type is judged in real time according to the built-in multi-dimensional threshold model.

3. The waveform recognition and correction method for winding deformation diagnosis according to claim 1, characterized in that: When the disturbance type is the harmonic-dominated disturbance, the waveform processing path is a harmonic processing path; The adopting the selected waveform processing path to process the signal includes: The signal is processed using a fast Fourier transform spectrum algorithm based on Nuttall window weighting and three-line interpolation.

4. The waveform recognition and correction method for winding deformation diagnosis according to claim 1, characterized in that: When the disturbance type is the voltage sag disturbance, the waveform processing path is a transient processing path, and the waveform processing path is a harmonic processing path; The adopting the selected waveform processing path to process the signal includes: The signal is processed using a wavelet packet decomposition algorithm.

5. The waveform recognition and correction method for winding deformation diagnosis according to claim 1, characterized in that: When the disturbance type is the composite disturbance, the waveform processing path is a composite collaborative path; The adopting the selected waveform processing path to process the signal includes: A harmonic analysis path and a wavelet packet analysis path are started in parallel to process the signal.

6. The waveform recognition and correction method for winding deformation diagnosis according to claim 1, characterized in that: The adopting the selected waveform processing path to process the signal to correct and reconstruct the signal includes: The signal is corrected in real time in multiple dimensions using a dynamic power factor adjustment mechanism based on FFT result calculation, a sampling window adaptive adjustment mechanism, and a fundamental amplitude reconstruction algorithm.

7. The waveform recognition and correction method for winding deformation diagnosis according to claim 1, characterized in that: The preprocessing includes performing baseline drift correction, clipping and frame division on the original signal.

8. The waveform recognition and correction method for winding deformation diagnosis according to claim 1, characterized in that: The method further comprises: The compensated data is integrated and output, and uploaded to the master station system or remote measurement platform through local storage or communication interface. The output data includes but is not limited to the real-time amplitude, phase, frequency, and power factor trend of voltage and current.

9. A waveform recognition and correction system for winding deformation diagnosis, characterized in that: include: Signal acquisition and preprocessing module, used to synchronously acquire voltage and current signals and perform preprocessing; a waveform disturbance type identification module, configured to identify the waveform disturbance type of the preprocessed signal, wherein the waveform disturbance type identification includes extracting the voltage effective value mutation rate, short-time energy transition coefficient and / or harmonic energy ratio of the signal, and identifying the disturbance type based on a preset multidimensional threshold model; A dynamic path selection module, configured to dynamically select a corresponding waveform processing path based on the result of the waveform disturbance type identification; The waveform reconstruction and compensation module is used to process the signal using the selected waveform processing path to correct and reconstruct the signal to obtain compensated voltage and current waveform data.

10. The waveform recognition and correction system for winding deformation diagnosis according to claim 9, characterized in that: The system further comprises a data output and remote communication module for integrating and outputting the compensated data and uploading the data to a master station system or a remote measurement platform via a local storage or communication interface.

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