A waveform identification and correction method and system for winding deformation diagnosis

Abstract

This invention discloses a waveform recognition and correction method and system for winding deformation diagnosis, relating to the field of transformer winding monitoring technology. The method includes: synchronously acquiring voltage and current signals and performing preprocessing; identifying waveform disturbance types in the preprocessed signals, classifying disturbance types into harmonic-dominant disturbances, voltage sag disturbances, or composite disturbances; dynamically selecting appropriate waveform processing paths based on the waveform disturbance type identification results; processing the signals using the selected waveform processing paths to correct and reconstruct the signals, obtaining compensated voltage and current waveform data; and constructing a disturbance recognition and path adaptive processing mechanism to solve the processing lag and compensation distortion problems of general algorithms under dynamic signal disturbances, improving recognition accuracy and achieving real-time error correction.

Description

Technical Field

[0001] This invention relates to the field of transformer winding monitoring technology, and in particular to a waveform identification and correction method and system for winding deformation diagnosis. Background Technology

[0002] With the widespread application of nonlinear loads such as power electronic devices, converters, and rectifiers in power systems, the power grid operating environment is becoming increasingly complex, frequently resulting in power quality disturbances such as harmonic distortion and voltage sags. These abnormal signals not only affect the stability of system operation but also pose challenges to the visualization analysis of voltage and current waveforms based on Lissajous figures, causing problems such as graph distortion and phasor feature distortion, which affect the accuracy of judging the winding deformation state.

[0003] Currently, typical solutions for dealing with current and voltage measurement disturbances are mostly based on fixed processing algorithms and centralized control systems. These solutions still suffer from problems such as path fixation, response lag, and insufficient identification accuracy under highly dynamic and nonlinear disturbance environments, making it difficult to meet the dual requirements of "accuracy + adaptability" in ultra-high voltage transmission scenarios. Summary of the Invention

[0004] The main objective of this invention is to provide a waveform recognition and correction method and system for winding deformation diagnosis, which solves the problems of processing lag and compensation distortion in general algorithms when subjected to dynamic signal disturbances, improves recognition accuracy, and achieves real-time error correction.

[0005] To achieve the above objectives, this application provides a waveform identification and correction method for winding deformation diagnosis, comprising:

[0006] Voltage and current signals are acquired synchronously and preprocessed.

[0007] The preprocessed signal is subjected to waveform disturbance type identification, and the disturbance type is classified into harmonic dominant disturbance, voltage sag disturbance or composite disturbance;

[0008] Based on the waveform disturbance type identification result, the corresponding waveform processing path is dynamically selected;

[0009] The signal is processed using the selected waveform processing path to correct and reconstruct the signal, resulting in compensated voltage and current waveform data.

[0010] This application also provides a waveform identification and correction system for winding deformation diagnosis, including:

[0011] The signal acquisition and preprocessing module is used to simultaneously acquire voltage and current signals and perform preprocessing.

[0012] The waveform disturbance type identification module is used to identify the waveform disturbance type of the preprocessed signal. 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.

[0013] The dynamic path selection module is used to dynamically select the corresponding waveform processing path based on the result of the waveform disturbance type identification.

[0014] The waveform reconstruction and compensation module is used to process the signal using the selected waveform processing path to correct and reconstruct the signal, thereby obtaining compensated voltage and current waveform data.

[0015] This application provides a waveform recognition and correction method and system for winding deformation diagnosis. The method involves synchronously acquiring voltage and current signals and performing preprocessing; identifying the waveform disturbance type of the preprocessed signal, classifying the disturbance type into harmonic-dominant disturbance, voltage sag disturbance, or composite disturbance; dynamically selecting the corresponding waveform processing path based on the waveform disturbance type identification result; and processing the signal using the selected waveform processing path to correct and reconstruct the signal, obtaining compensated voltage and current waveform data. This method, by constructing a disturbance recognition and path adaptive processing mechanism, solves the processing lag and compensation distortion problems of general algorithms when dealing with dynamic signal disturbances, improves recognition accuracy, and achieves real-time error correction. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, 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.

[0017] in:

[0018] Figure 1 A schematic diagram of the structure of a waveform identification and correction system for winding deformation diagnosis provided in an embodiment of this application;

[0019] Figure 2 This is a schematic diagram of a power system disturbance type identification process provided in an embodiment of this application;

[0020] Figure 3 This is a schematic diagram illustrating a voltage sag characteristic provided in an embodiment of this application;

[0021] Figure 4A two-layer decomposition block diagram of wavelet packets is provided for embodiments of this application;

[0022] Figure 5 A schematic diagram of an original signal waveform provided in an embodiment of this application;

[0023] Figure 6 A schematic diagram of a wavelet packet reconstruction waveform provided in an embodiment of this application;

[0024] Figure 7 This is a schematic diagram of a waveform recognition and correction system for winding deformation diagnosis provided in an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0026] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] The embodiments of this application are described below with reference to the accompanying drawings.

[0029] Please see Figure 1 This is a flowchart illustrating a waveform identification and correction method for winding deformation diagnosis provided in an embodiment of this application.

[0030] like Figure 1 As shown, the method includes:

[0031] 101. Synchronously acquire voltage and current signals and perform preprocessing.

[0032] This application proposes a waveform identification and correction method and system for winding deformation diagnosis, applicable to the requirements of high-fidelity acquisition and subsequent analysis and processing of voltage and current waveforms under various disturbance conditions in complex AC / DC transmission systems. Its core idea is to automatically switch the optimal waveform processing path based on the disturbance characteristics (such as harmonic distortion or voltage sag) in the input waveform through a pre-signal discrimination mechanism, achieving adaptive scheduling and dynamic signal correction of waveform processing, and ensuring the accurate presentation of phasor relationships required by diagnostic methods such as Lissajous figures.

[0033] The execution entity of the method in this application embodiment can be a waveform recognition and correction system for winding deformation diagnosis. The system, in terms of functional structure, may include: a signal acquisition and preprocessing module, a waveform disturbance type recognition module (event detection module), a dynamic path selection module, and a waveform reconstruction and compensation module; it may also include a data output and remote communication module.

[0034] First, step 101 can be implemented based on the signal acquisition and preprocessing module. This module, as the foundational front-end of the application, performs high-precision, synchronized data acquisition and cleaning of 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 voltage and current signals are acquired in alignment under a unified time reference, avoiding phase deviations caused by timing errors. Optionally, the sampling accuracy should be no less than 16 bits, and the sampling frequency should be better than the national standard setting to ensure accurate capture of short-term disturbance characteristics, including higher harmonics and voltage dips.

[0035] After data acquisition is completed, preprocessing can be performed, including baseline drift correction, amplitude limiting and elimination, frame division and other operations on the raw signal, and cached in a high-speed local buffer to provide high-quality, low-distortion signal input for subsequent disturbance identification and compensation calculation, laying the signal foundation for the operation of the entire system.

[0036] 102. Identify the waveform disturbance type of the preprocessed signal and classify the disturbance type into harmonic-dominated disturbance, voltage sag disturbance, or composite disturbance.

[0037] The waveform disturbance type identification in this embodiment can be implemented based on the waveform disturbance type identification module. This module is a key functional unit for implementing the "event-driven" mechanism in this application. It is mainly responsible for dynamically analyzing and identifying disturbance patterns in the acquired voltage and current signals to determine whether there are abnormal disturbances in the current signal, and further classifying them into different types of measurement error causes. These mainly include the following three disturbance types: harmonic-dominated disturbances, voltage sag disturbances, or composite disturbances.

[0038] Based on the sliding analysis window, feature extraction can be performed frame by frame to obtain feature indicators, and then the type of disturbance can be determined in real time based on the obtained feature indicators.

[0039] In one optional implementation, the waveform disturbance type identification includes:

[0040] Based on the sliding analysis window, the voltage RMS value mutation rate, short-time energy transition coefficient, and harmonic energy ratio are extracted, and the above disturbance types are determined in real time according to the built-in multidimensional threshold model.

[0041] Figure 2 This is a schematic diagram illustrating a power system disturbance type identification process provided in an embodiment of this application. Figure 2 As shown, specifically, feature extraction is performed on the acquired signal obtained in step 101. The obtained feature indicators can mainly include three types of key indicators:

[0042] First, the rate of change in the effective voltage value. (RMS change gradient) is used to quickly capture sudden drops or rises in voltage or current over a short period of time, typically manifested as the RMS drop caused by a voltage sag event. The expression for the effective voltage value change rate is:

[0043]

[0044] in This is the effective voltage value within the previous cycle; This represents the effective voltage value within the current cycle.

[0045] Secondly, the short-time energy transition coefficient, by analyzing the instantaneous energy change of the signal within a certain window, reflects the energy abrupt changes caused by system load changes, switching shocks, etc. First, the formula for calculating the instantaneous energy of the sliding window is:

[0046]

[0047] The short-time energy transition coefficient (relative rate of energy change) can be derived from the above formula:

[0048]

[0049] in This represents the original signal (voltage or current) within the k-th sliding window. Let be the instantaneous signal energy within the k-th window; The energy mutation rate between two adjacent windows; N is the window length (e.g., the number of sampling points corresponding to one power grid frequency cycle).

[0050] Third, the harmonic energy ratio is calculated by extracting the amplitudes of the 2nd to 50th harmonics using the FFT algorithm and determining their proportion to identify whether harmonic-dominated interference exists in the current signal. Before calculating the harmonic energy ratio, the total harmonic energy must first be calculated.

[0051]

[0052] Then calculate the proportion of harmonic energy:

[0053]

[0054] in H represents the frequency domain amplitude (FFT component) of the h-th harmonic; H is the highest harmonic order (e.g., up to the 50th order). Total harmonic energy; The total energy of the signal (including fundamental and harmonic frequencies); This represents the percentage of harmonic energy in the total energy.

[0055] The system in this embodiment has a built-in configurable multi-dimensional threshold model to determine the type of disturbance in real time. Optionally, the form of the multi-dimensional threshold model is not limited; specifically, it can be a disturbance type condition triggering logic table. Based on the above feature indicators, multi-dimensional threshold judgment is performed, and the identification results can be divided into three categories:

[0056] Type A is a harmonic-dominated disturbance, indicating that the signal is dominated by periodic frequency abnormalities without obvious abrupt changes;

[0057] Type B is a voltage sag (transient) disturbance, indicating a sudden change in amplitude;

[0058] Type C is a composite disturbance, where two types of anomalies coexist, requiring a dual-path joint compensation strategy.

[0059] 103. Based on the results of the waveform disturbance type identification above, dynamically select the corresponding waveform processing path.

[0060] 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 respond quickly and handle accurately under different abnormal scenarios. This step can be implemented based on the dynamic path selection module, which is the core mechanism for the present invention to achieve intelligent scheduling and flexible switching of error processing algorithms.

[0061] Specifically, a condition-triggered logic table based on perturbation 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 perturbation types.

[0062] In an optional implementation, when the disturbance type is the harmonic-dominated disturbance, the waveform processing path is the harmonic processing path.

[0063] The above-mentioned signal processing using the selected waveform processing path includes:

[0064] The above signal was processed using a fast Fourier transform spectrum algorithm based on Nuttall window weighting and three-spectral-line interpolation.

[0065] Specifically, the disturbance type is harmonic-dominated disturbance (Type A): Trigger path 1—harmonic processing path, enabling the FFT spectrum algorithm based on Nuttall window weighting + three-spectral-line interpolation to overcome the spectral leakage and picket-fence effect problems of conventional FFT algorithms under high-order harmonic frequency drift. This path can finely extract the frequency, amplitude, and phase of the primary and secondary harmonics for sampling error correction and waveform reconstruction.

[0066] The derivation of the FFT spectrum algorithm based on Nuttall window weighting and three-line interpolation is as follows. The combined window function based on the cosine window is often used for harmonic signal analysis, and its expression is:

[0067]

[0068] Where H is the number of terms in the cosine window; a h For cosine windows h Term coefficient.

[0069] The coefficients of the cosine window should satisfy the following constraints:

[0070]

[0071] After selecting four fifth-order Nuttall windows as the window functions for the FFT, interpolation calculations are performed. Two main spectral lines (the largest and second largest amplitude lines) close to the peak frequency are chosen 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 harmonic component correction calculation, thereby significantly improving the analysis accuracy. Let the auxiliary parameters... α = k 2- k 0, where We can obtain:

[0072]

[0073] Simplify to a functional relationship β = g ( α ), to deduce parameters α Let its inverse function be F=g -1 ( β Choosing Chebyshev polynomials for F= g -1 ( β Approximation is performed to obtain parameters. α It can be represented by the following polynomial:

[0074]

[0075] Furthermore, the amplitude correction formula and the phase correction formula are derived:

[0076]

[0077]

[0078] when N When the amplitude is large, the amplitude correction formula simplifies to:

[0079]

[0080] Similarly, polynomial approximation can be used to... v ( α Approximate solution:

[0081]

[0082] Based on the three-spectral-line interpolation FFT algorithm using a four-term fifth-order Nuttall window, the polynomial fitting approximation formula is obtained by calling the polyfit function in Matlab:

[0083]

[0084] In an optional implementation, 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.

[0085] The above-mentioned signal processing using the selected waveform processing path includes:

[0086] The above signal is processed using a wavelet packet decomposition algorithm.

[0087] Specifically, for voltage sag disturbances (Type B): the system automatically switches to path 2—the transient processing path—activating the sag identification mechanism based on the Db4 wavelet packet decomposition algorithm. This algorithm can perform multi-scale, multi-band decomposition of the input signal, extract the characteristic energy in the voltage sag-related subbands, and further determine the disturbance duration, sag amplitude, and waveform disruption sections through energy mapping analysis, thereby compensating and reconstructing the waveform abrupt change intervals.

[0088] The magnitude and duration of voltage sag are the most critical indicators characterizing the voltage sag phenomenon. Figure 3 This is a schematic diagram of a voltage sag characteristic provided in an embodiment of this application.

[0089] The derivation based on the Db4 wavelet packet decomposition algorithm is as follows, for the wavelet mother function Displacement Then, at different scales a down and signal x ( t Taking the inner product, we get:

[0090]

[0091]

[0092] In multi-resolution orthogonal subspace V Wavelet packet decomposition is performed in 0, and it is represented as:

[0093]

[0094] In the signal decomposition stage, the original signal is refined into low-frequency and high-frequency components at multiple resolution levels; while in the signal synthesis stage, the components at each level are gradually reconstructed in a predetermined manner, and finally restored to the original signal.

[0095] Figure 4 A two-layer decomposition block diagram of wavelet packets is provided for an embodiment of this application.

[0096] For voltage signals u ( t ) and current signal i ( t After sampling, the wavelet packet coefficient matrices of the reconstructed fundamental and distorted signals are derived:

[0097]

[0098]

[0099] Substitute the coefficients into the voltage signal. u ( t ) and current signal i ( t The fundamental signal and the distorted signal are obtained:

[0100]

[0101]

[0102] Figure 5 This is a schematic diagram of an original signal waveform provided in an embodiment of this application.

[0103] Figure 6 This is a schematic diagram of a wavelet packet reconstruction waveform provided in an embodiment of this application.

[0104] Based on the above method, the initial sampling signal is first given as follows: Figure 5 As shown, it includes voltage and current signals; after decomposition and reconstruction, we can obtain... Figure 6 The signals shown include the fundamental voltage waveform, the fundamental current waveform, the distorted voltage waveform, and the distorted current waveform.

[0105] In an optional implementation, when the disturbance type is the composite disturbance, the waveform processing path is a composite cooperative path.

[0106] The above-mentioned signal processing using the selected waveform processing path includes:

[0107] The harmonic analysis path and wavelet packet analysis path are initiated in parallel to process the above signals.

[0108] Specifically, for a disturbance of type C (composite disturbance), the parallel start path 3—the composite collaborative path—is initiated, which involves the dual-channel linkage of harmonic analysis and wavelet packet analysis. Harmonic frequency domain parameters and transient energy characteristics are extracted respectively, and the two are collaboratively input into the error compensation module. Through the fusion correction model, the signal amplitude, phase, frequency, etc. are dynamically corrected, thereby achieving waveform distortion suppression under all-time, multi-source disturbances.

[0109] The harmonic analysis path employs a spectrum extraction method based on the Nuttall window and interpolation FFT algorithm, focusing on extracting the amplitude, phase, and frequency drift characteristics of the 2nd to 25th major harmonics and evaluating their contribution to the fundamental distortion. The wavelet packet analysis path uses multi-level decomposition based on the Db4 wavelet to capture the time-domain characteristics of transient events such as voltage sags, including the occurrence time, amplitude of sudden changes, local energy distribution, and duration.

[0110] The two types of extracted features are jointly input into the fusion correction model for dynamic synthesis. This model is based on a weighted decision mechanism, dynamically adjusting the weights of frequency and time domain corrections by combining disturbance source dominance indicators (such as the percentage of total harmonic energy (THD%) and the percentage of wavelet packet mutation energy). The fusion process can be represented as:

[0111]

[0112] Where α is the fusion weight coefficient, which is adaptively calculated according to the following formula:

[0113]

[0114] Where THD represents the total harmonic distortion rate (frequency domain disturbance intensity index); Et This represents the proportion of transient energy (a time-domain disturbance intensity index).

[0115] Through this mechanism, when harmonics dominate (α approaches 1), the frequency phase correction result is retained first; when transient disturbances dominate (α approaches 0), the dynamic correction output of the wavelet path is used first.

[0116] Ultimately, the fused output will comprehensively correct 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 representation and discrimination accuracy of Lissajous figures under complex disturbance conditions.

[0117] Optionally, if the aforementioned disturbance type is not identified, i.e. there is no obvious disturbance, the current compensation mechanism can be maintained and the original algorithm can be continued.

[0118] This path scheduling mechanism effectively avoids the problems of error amplification and processing failure caused by the "fixed algorithm channel" of traditional measurement systems, enabling the system to have a closed-loop response capability of "identification-matching-correction".

[0119] Table 1 is a disturbance type condition triggering logic table provided in the embodiments of this application.

[0120]

[0121] Table 1

[0122] As shown in Table 1, the feature criteria, activation paths, and processing methods for the three types of disturbances (Type A, B, and C) are set.

[0123] 104. The above signal is processed using the selected waveform processing path to correct and reconstruct the signal, and the compensated voltage and current waveform data is obtained.

[0124] The waveform reconstruction and compensation module is a key terminal module of the algorithm processing system of this application. Its main function is to incorporate the feature 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., so as to ensure the authenticity and consistency of the output results.

[0125] Optionally, the above-mentioned signal is processed using the selected waveform processing path to correct and reconstruct the signal, including:

[0126] The above signal is corrected in real time in multiple dimensions by using a dynamic power factor adjustment mechanism based on FFT results, an adaptive adjustment mechanism for the sampling window, and a fundamental amplitude reconstruction algorithm.

[0127] Specifically, to achieve multi-dimensional real-time correction, a waveform reconstruction and compensation module can be used, which mainly includes the following aspects:

[0128] First, to address the phase error caused by frequency drift and harmonic content fluctuations, the system introduces a dynamic power factor adjustment mechanism based on FFT results. 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 the decomposition accuracy without altering the original sampling structure, thereby enhancing the measurement system's ability to reproduce true signals in a harmonic context.

[0129] Specifically, the system first extracts the voltage and current components of the fundamental wave and each harmonic, assuming: V n , I n For the first n The voltage and current amplitudes of the second harmonic; for θ V , θ I Its corresponding phase angle. n The expression for the instantaneous active power of the subharmonic is:

[0130]

[0131] After combining all component power values, the total active power correction value can be defined as:

[0132]

[0133]

[0134]

[0135] in PF adj This represents the dynamically corrected power factor. N Indicates the selected harmonic order for analysis (e.g., 1st to 25th); cos( θ V - θ I () represents the phase difference between voltage and current at that frequency.

[0136] This power factor correction value will be fed back to the signal reconstruction process as a core compensation factor, thereby guiding the voltage / current phase alignment operation and ensuring that the FFT analysis can still accurately extract the effective power component in non-ideal harmonic environments, avoiding power factor deviation caused by overestimation of apparent power.

[0137] Secondly, to address the sampling signal deviation caused by voltage sag events, an adaptive adjustment mechanism for the sampling window was designed. This mechanism dynamically revises the sampling interval based on the disturbance start and end segments identified by the wavelet packet path, avoiding the inclusion of voltage drop segments or voltage abrupt change points in the acquisition calculation, thereby effectively preventing sampling signal distortion caused by short-term disturbances.

[0138] In addition, a fundamental frequency amplitude reconstruction algorithm is introduced to extract and recover the fundamental frequency signal in scenarios with severe harmonic pollution or significant voltage deformation. Based on frequency domain filtering and time domain phase fitting techniques, this algorithm back-fits the purest fundamental frequency signal that best matches the power frequency characteristics from the extracted primary and secondary harmonic features. This signal is used for accurate reconstruction of the power factor and effective voltage value, providing reliable signal support for subsequent Lissajous signal visualization processing.

[0139] In this embodiment of the application, for frequency domain processing, the system employs a fourth-order Butterworth bandpass filter (BPF), with the following design parameters:

[0140] ① Center frequency: 50Hz (or 60Hz, depending on the region);

[0141] ② Bandwidth: +3Hz (passband range set to 47Hz~53Hz);

[0142] ③ Filter order: 4th order, to balance roll-off speed and phase response stability;

[0143] ④ Implementation method: Based on the IR digital filter structure, it is implemented after FFT spectrum processing.

[0144] This filter has a smooth passband characteristic, which can effectively suppress high-order harmonic components (such as the 3rd, 5th, and 7th harmonics) while preserving the energy of the main frequency band and reducing "signal edge ringing" caused by sharp filtering.

[0145] In this embodiment, regarding time-domain processing, the system uses a least-squares phase fitting algorithm to reconstruct the fundamental frequency of the filtered signal. The specific fitting model can be as follows:

[0146]

[0147] Where v(t) is the filtered voltage (or current) signal; Indicates power frequency; A, ε(t) represents the amplitude and initial phase to be fitted; ε(t) represents the fitting residual.

[0148] The objective function to fit is to minimize the sum of squared residuals:

[0149]

[0150] The A and obtained after fitting This refers to the equivalent fundamental amplitude and phase angle of the signal within the current period, which can be used to correct the effective voltage value, power factor, and phasor pattern. This method can stably output a smooth, non-jumping fundamental signal under complex disturbance backgrounds, and is particularly suitable for diagnostic scenarios that require high continuity of the Lissajous figure phase trajectory.

[0151] Further, optionally, the above method also includes:

[0152] The compensated data is integrated and output, and then uploaded to the main 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.

[0153] Finally, the error-compensated data is integrated and output through the measurement model, including key indicators such as real-time amplitude, phase, frequency, and power factor trend of voltage and current. This data is then uploaded to the main station system or a remote measurement platform via local storage or a communication interface. The entire module supports dynamic configuration, real-time feedback, and result traceability, ensuring high-precision and high-reliability measurement output under various disturbance scenarios. This is one of the core technologies of this invention for achieving intelligent and adaptive dynamic compensation.

[0154] 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 the sampled results of various signals after error compensation to the upper-level management platform in a structured and standardized data format, enabling remote access, centralized monitoring, and intelligent dispatch of local processing results. This module supports both field-level communication uploads and compatibility with national and industry power automation standards, ensuring seamless integration with existing power monitoring and dispatching platforms.

[0155] The data output and remote communication module first formats and encapsulates key parameters such as voltage and current amplitude, phase, and power factor after correction by the dynamic error compensation module, organizes them into a complete message according to the frame structure, and uploads it to the master station system, measurement and acquisition terminal (such as concentrator), or energy management system (EMS) through the communication control unit. Regarding 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 according to application scenarios.

[0156] The IEC 61850 interface supports data structures such as GOOSE messages, MMS services, and SV sample values, enabling millisecond-level uploading of electricity events, alarm notifications, and control command reception. The DL / T interface is suitable for data interaction with traditional meters or medium- and low-voltage distribution network equipment, offering good versatility and compatibility. Furthermore, the module reserves interfaces for extended protocols such as Modbus TCP / IP, MQTT, and RS-485 serial communication, allowing connection to industrial gateways or local control systems and enhancing system deployment flexibility.

[0157] To ensure the security and reliability of data upload, this module is equipped with a data caching mechanism, a breakpoint resume strategy, and encryption verification functions. It enables temporary data storage, recovery transmission, and tamper-proof protection in the event of network anomalies, ensuring the authenticity, integrity, and reliability of measurement data. Through this module, this application can achieve closed-loop management of the entire process of error-optimized measurement data, constructing a complete monitoring and correction system from data acquisition, event identification, error compensation to remote output. This effectively supports the engineering requirements for refined management and remote reliable auditing of voltage and current signals in AC / DC power transmission scenarios.

[0158] Optionally, in the embodiments of this application, local optimization can be performed as needed using methods such as EEMD, wavelet transform instead of FFT, small neural network instead of traditional threshold discrimination, and edge computing instead of embedded processing.

[0159] The method in this embodiment solves the problems of processing lag and compensation distortion in general algorithms when there are dynamic signal disturbances by constructing a disturbance recognition and path adaptive processing mechanism, thereby improving recognition accuracy and realizing real-time error correction.

[0160] Based on the description of the foregoing method embodiments, this application also provides a waveform recognition and correction system for winding deformation diagnosis.

[0161] Please see Figure 7 This is a schematic diagram of a waveform recognition and correction system for winding deformation diagnosis provided in an embodiment of this application. Figure 7 As shown, the system 700 includes:

[0162] The signal acquisition and preprocessing module 710 is used to synchronously acquire voltage and current signals and perform preprocessing.

[0163] The waveform disturbance type identification module 720 is used to identify the waveform disturbance type of the preprocessed signal. 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.

[0164] The dynamic path selection module 730 is used to dynamically select the corresponding waveform processing path based on the result of the waveform disturbance type identification.

[0165] 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, and obtain compensated voltage and current waveform data.

[0166] Optionally, the system 700 further includes a data output and remote communication module 750, which integrates and outputs the compensated data and uploads it to the main station system or remote measurement platform through local storage or a communication interface.

[0167] Among them, the functions of each module in the waveform recognition and correction system 700 for winding deformation diagnosis are as follows: Figure 1 The embodiments shown have been described in detail. For example, the waveform disturbance type identification module 720 can perform the specific content in step 102, which will not be repeated here.

[0168] The key points of the method and system proposed in this application are:

[0169] 1. Based on real-time sampling signals such as voltage and current, a high-speed signal acquisition channel is constructed at the front end. An isolated ADC sampling chip combined with a low-pass anti-aliasing filter is used to achieve high-resolution signal restoration and interference-resistant acquisition. Sampling data is stored in a FIFO buffer structure and time-aligned using Network Time Protocol (NTP) or GPS timing mechanisms, 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 under high-voltage DC environments.

[0170] 2. Utilizing a multi-dimensional disturbance identification algorithm, which integrates characteristic indicators such as RMS mutation rate, harmonic energy ratio, and wavelet energy transition coefficient, the algorithm determines whether the signal is experiencing harmonic interference, voltage sag, or a combination of disturbances. Automatic disturbance type classification is achieved based on set threshold rules. The identification results drive the path scheduling module, dynamically selecting processing strategies according to the disturbance type to avoid resource waste and improve system response sensitivity.

[0171] 3. A multi-algorithm path system driven by disturbance type is constructed. The harmonic path employs a Nuttall window-weighted windowed FFT algorithm and three-spectral-line interpolation to precisely extract harmonic features, while the transient path uses Db4 wavelet packet decomposition to reconstruct the disturbance waveform, achieving time-frequency localization and fundamental frequency recovery. In complex scenarios, the two paths operate collaboratively, forming a dual-channel data analysis structure to achieve full-spectrum error perception and compensation. Each path can be activated independently or run in parallel as needed, realizing a customizable and adaptive processing system.

[0172] 4. A dynamic compensation mechanism is designed, adjusting the identification window based on the identified disturbance duration interval, eliminating waveform input during abnormal periods to avoid error accumulation. Simultaneously, a fundamental frequency fitting and reconstruction algorithm is introduced to extract the true fundamental frequency amplitude and phase from the interference signal, 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 true sampling and results.

[0173] 5. The final measurement results and event tags are output in structured messages, supporting IEC 61850 and DL / T 645 standard communication protocols, adapting to the access requirements of the main station platform, energy management system, and intelligent operation and maintenance platform. The communication process supports encrypted transmission and authentication mechanisms to ensure the integrity and security of measurement data. The system supports local event logging and abnormal waveform tracing functions, providing data support for subsequent source analysis and optimized scheduling.

[0174] The methods and systems in the embodiments of this application will be described below in conjunction with specific application scenarios.

[0175] Case background:

[0176] In a newly constructed ±800kV UHVDC transmission project in my country, an intelligent voltage and current waveform acquisition and processing system was introduced to ensure the stability of cross-regional power operation and the reliable assessment of the status of key equipment. Considering the large number of power electronic conversion devices deployed along the point-to-point transmission path of this project, such as multi-stage rectifier stations and inverter stations, frequent disturbances such as harmonic superposition and voltage dips occur during grid operation. These complex disturbances pose significant challenges to traditional waveform acquisition and analysis equipment, easily causing waveform distortion, especially in scenarios involving winding status visualization and diagnosis using Lissajous figures, where accuracy drops significantly. Therefore, there is an urgent need to introduce a novel waveform processing solution with disturbance recognition capabilities and adaptive path processing mechanisms to improve signal fidelity and diagnostic graphic quality at critical moments.

[0177] 1. Traditional methods:

[0178] Traditional waveform processing systems deployed in previous projects primarily relied on fixed-parameter models and single-path FFT algorithms to analyze voltage and current signals, making them ill-suited for dynamic changes under nonlinear disturbance conditions. These methods have significant limitations: First, under harmonic frequency drift or non-integer-cycle sampling conditions, FFT analysis is prone to spectral leakage, leading to distortion of extracted waveform features and consequently affecting the accuracy of Lissajous figures. Second, traditional processing methods struggle to identify transient disturbances; phenomena such as voltage drops and instantaneous waveform shifts are often overlooked or misjudged, causing critical analysis windows to cover abnormal waveform regions, disrupting the continuity and symmetry of the graph. Third, all system calculations are concentrated at the main station, resulting in slow response times, a lack of real-time path switching mechanisms, and low resource utilization. In a practical test, the original system's Lissajous figure output under voltage drops exhibited severe distortion, with an image error rate exceeding 12%, significantly reducing the accuracy and visibility of winding state identification.

[0179] 2. The method in this application:

[0180] By employing the waveform processing strategy proposed in this application, the system achieves full-process closed-loop control, from high-fidelity voltage and current signal acquisition and intelligent disturbance event identification at the front end, to dynamic switching of processing paths, real-time reconstruction of waveform characteristics, and remote signal output. The specific implementation steps are as follows:

[0181] (1) The signal acquisition and preprocessing module plays its role:

[0182] The intelligent measurement device deployed on the converter station bus side performs high-frequency synchronous sampling of the voltage signal at 10 kHz using a three-phase sampling module to obtain an information stream with rich frequency and time domain characteristics. This module employs a high-resolution Σ-Δ ADC for analog-to-digital conversion, with a built-in isolation driver ensuring electrical safety isolation at the input. An active low-pass anti-aliasing filter is integrated at the sampling front end to effectively shield high-frequency interference noise above the power frequency, improving signal purity. After sampling, the system performs preprocessing operations such as baseline drift elimination (removal of DC bias) and soft-limiting anomaly rejection, and divides the signal into frames 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.

[0183] (2) Voltage and current disturbance identification module identifies disturbance types:

[0184] During system operation, a grid connection startup event caused the bus voltage to suddenly drop by more than 20% within two grid cycles (approximately 40ms), while the power frequency component remained within the range of 50Hz ± 0.1Hz. The system used a sliding window RMS rate of change calculation model to determine that the effective voltage value change rate exceeded the threshold. Simultaneously, it further analyzed the signal change energy distribution using wavelet packet energy coefficient variation spectra, confirming the presence of transient disturbance characteristics. Based on the feature extraction results, the disturbance identification module automatically classified the signal as Type B: voltage sag event and issued an interrupt signal, notifying the dynamic path scheduling module to skip harmonic paths and prioritize the activation of the transient path algorithm to avoid resource waste and analysis path conflicts.

[0185] (3) Dynamic path selection module switching strategy:

[0186] The identification result triggers the automatic activation of "Path 2: Transient Processing Path". The system calls 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, analyzes the time-series distribution of sub-band energy coefficients, and accurately locates the start and recovery points of voltage sags. During the reconstruction phase, the system generates an approximately ideal fundamental band for reference by comparing the sub-band waveforms before and after the disturbance, ensuring that the sampled signal is not affected by the disturbance. At the same time, the system disables the FFT harmonic analysis algorithm of "Path 1" to release computing resources and improve processing efficiency and real-time performance.

[0187] (4) The compensation module performs fine correction:

[0188] After acquiring the persistent disturbance segment (e.g., frames 16 to 21), the compensation module performs interval exclusion reconstruction on the waveform reconstruction model, dynamically removing the integration results for that interval and performing a re-integration operation after the signal recovery segment. Simultaneously, this module activates an amplitude fitting fundamental wave recovery algorithm to remove harmonic interference from the disturbance phase on the desired waveform, recalculates the amplitude and phase of the fundamental voltage and current, and corrects the compensated sampling waveform accordingly to ensure that the output voltage and current signals reflect the true values.

[0189] (5) The data output module transmits the correction results:

[0190] After compensation and correction are completed, the system encapsulates data including the current time period's amplitude, phase, frequency, disturbance identification tag, and processing status code into a structured communication message, which is then uploaded to the backend master station system via a network communication module supporting the IEC 61850 MMS protocol. The message uses the MMS service encapsulation format and includes complete timestamps, event logs, and processing path marking information, ensuring that the backend scheduling system can accurately reproduce the event background and perform decision analysis.

[0191] Compared to the limitations of traditional waveform processing and correction methods, 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 UHV transmission systems, and has higher accuracy, adaptability, and engineering applicability.

[0192] Compared with current mainstream FFT dynamic waveform detection and correction systems based on fixed paths, this application has significant advantages in algorithm structure, adaptability, accuracy control, and system intelligence:

[0193] Enhanced identification capability: Existing technologies typically cannot effectively distinguish between harmonics and transient disturbances, have simplistic processing logic, and easily overlook the impact of sudden events on signal sampling. This application introduces a multi-dimensional disturbance identification mechanism, which can accurately classify and identify harmonic-dominated disturbances, voltage sags, and composite disturbances, resulting in faster identification response and more accurate judgment.

[0194] Adaptive processing path: Traditional techniques use a single algorithm to handle all working conditions, which cannot flexibly adjust the processing logic for complex environments. This application dynamically selects the FFT or wavelet packet algorithm path through an event-driven strategy and supports dual-path collaborative analysis, significantly improving the targeting and effectiveness of the algorithm processing;

[0195] More accurate error compensation: Existing systems mostly rely on static correction models, which are 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 recover the true fundamental wave characteristics based on waveform fitting, effectively avoiding misintegration and miscalculation, and outputting sampled waveform data that is closer to the real load;

[0196] The system has stronger real-time performance and robustness: Compared with the traditional centralized processing mode of the main station, this application completes all identification, processing and compensation processes locally on the edge device, which has the advantages of low response latency and high operational stability, and is particularly suitable for UHV power transmission scenarios with high frequency disturbances and high reliability requirements.

[0197] Superior Standard Communication and Integration Capabilities: This application supports power industry standard protocol interfaces such as IEC 61850 and DL / T 645, facilitating integration into various signal acquisition platforms, energy efficiency management systems, and dispatch backends, and possessing excellent interconnectivity and deployment adaptability.

[0198] In summary, this application not only overcomes the problems of processing lag and compensation distortion when dealing with dynamic signal disturbances due to fixed algorithm path structures, but also significantly enhances the intelligence level and engineering application value of the system through algorithm path adaptation, improved recognition accuracy 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 a decrease in the accuracy of subsequent winding diagnosis.

[0199] 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.

[0200] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A waveform identification and correction method for winding deformation diagnosis, characterized in that, The method includes: Voltage and current signals are acquired synchronously and preprocessed. The preprocessed signal is subjected to waveform disturbance type identification, and the disturbance type is classified into harmonic-dominated disturbance, voltage sag disturbance, or composite disturbance. The waveform disturbance type identification includes: extracting the voltage effective value mutation rate, short-time energy transition coefficient and harmonic energy ratio based on the sliding analysis window, and judging the disturbance type in real time according to the built-in multidimensional threshold model. Based on the waveform disturbance type identification result, the corresponding waveform processing path is dynamically selected; The selected waveform processing path is used to process the signal to correct and reconstruct the signal, thereby obtaining compensated voltage and current waveform data; When the disturbance type is the harmonic-dominant disturbance, the waveform processing path is the harmonic processing path; the process of processing the signal using the selected waveform processing path includes: processing the signal using a fast Fourier transform spectrum algorithm based on Nuttall window weighting and three-spectral-line interpolation; When the disturbance type is the voltage sag disturbance, the waveform processing path is a transient processing path; the step of processing the signal using the selected waveform processing path includes: processing the signal using a wavelet packet decomposition algorithm; When the disturbance type is the composite disturbance, the waveform processing path is a composite cooperative path; the process of processing the signal using the selected waveform processing path includes: parallel activation of the harmonic analysis path and the wavelet packet analysis path to process the signal.

2. The waveform identification and correction method for winding deformation diagnosis according to claim 1, characterized in that, The step of processing the signal using the selected waveform processing path to correct and reconstruct the signal includes: The signal is corrected in real time in multiple dimensions by using a dynamic power factor adjustment mechanism based on FFT results, an adaptive adjustment mechanism for the sampling window, and a fundamental amplitude reconstruction algorithm.

3. The waveform identification and correction method for winding deformation diagnosis according to claim 1, characterized in that, The preprocessing includes baseline drift correction, amplitude limiting, and frame division of the original signal.

4. The waveform identification and correction method for winding deformation diagnosis according to claim 1, characterized in that, The method further includes: The compensated data is integrated and output, and uploaded to the main 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.

5. A waveform identification and correction system for winding deformation diagnosis, characterized in that, include: The signal acquisition and preprocessing module is used to simultaneously acquire voltage and current signals and perform preprocessing. The waveform disturbance type identification module is used to identify the waveform disturbance type of the preprocessed signal and classify the disturbance type into harmonic-dominated disturbance, voltage sag disturbance or composite disturbance. The waveform disturbance type identification includes: extracting the voltage effective value mutation rate, short-time energy transition coefficient and harmonic energy ratio based on a sliding analysis window, and judging the disturbance type in real time according to the built-in multidimensional threshold model; The dynamic path selection module is used to dynamically select the 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, and obtain compensated voltage and current waveform data. When the disturbance type is the harmonic-dominant disturbance, the waveform processing path is the harmonic processing path; the waveform reconstruction and compensation module is specifically used to process the signal using a fast Fourier transform spectrum algorithm based on Nuttall window weighting and three-spectral-line interpolation; When the disturbance type is the voltage sag disturbance, the waveform processing path is the transient processing path; the waveform reconstruction and compensation module is specifically used to process the signal using a wavelet packet decomposition algorithm; When the disturbance type is the composite disturbance, the waveform processing path is a composite collaborative path; the waveform reconstruction and compensation module is specifically used to: initiate the harmonic analysis path and the wavelet packet analysis path in parallel to process the signal.

6. The waveform identification and correction system for winding deformation diagnosis according to claim 5, characterized in that, The system also includes a data output and remote communication module, which integrates and outputs the compensated data and uploads it to the main station system or remote measurement platform through local storage or communication interface.

Images