Dynamic waveform disturbance identification system

Abstract

The embodiment of the invention discloses a dynamic waveform disturbance identification system, and relates to the technical field of power system signal processing, and the system comprises a signal collection and preprocessing module which is used for collecting and preprocessing a voltage signal and a current signal; the waveform disturbance identification module is used for analyzing the preprocessed signal by using a multi-dimensional disturbance identification algorithm and judging whether harmonic dominant disturbance, voltage sag disturbance or compound disturbance occurs in the signal; the multi-dimensional disturbance recognition algorithm comprises effective value mutation rate calculation, harmonic energy ratio calculation and short-time energy transition coefficient calculation; the disturbance type in the signal can be accurately identified, and the signal processing precision and reliability are improved, so that the processing path can be selected according to the identification result, the self-adaptive capability and the real-time response capability of the system are enhanced, and powerful support is provided for equipment state monitoring and fault diagnosis in the power system.

Description

Technical Field

[0001] This invention relates to the field of power system signal processing technology, and in particular to a dynamic waveform disturbance identification system. Background Technology

[0002] In power systems, high-precision voltage and current waveform signals are crucial for condition monitoring and fault diagnosis of power equipment. With the widespread application of power electronic equipment, the power grid operating environment is becoming increasingly complex, frequently experiencing 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 waveform analysis-based diagnostic methods, causing problems such as waveform distortion and phasor feature distortion.

[0003] Current systems for dealing with current and voltage measurement disturbances typically cannot accurately and effectively distinguish the type of disturbance, have simplistic processing logic, and easily overlook the impact of sudden events on signal sampling, thereby affecting the accuracy of equipment condition assessment and fault diagnosis based on signal analysis. Summary of the Invention

[0004] The main objective of this invention is to provide a dynamic waveform disturbance identification system that can accurately identify the type of disturbance in a signal so as to select an appropriate processing method for correction in a timely manner.

[0005] To achieve the above objectives, this application provides a dynamic waveform disturbance identification system, comprising: The signal acquisition and preprocessing module is used to acquire voltage and current signals and perform preprocessing. The waveform disturbance identification module is used to analyze the preprocessed signal using a multi-dimensional disturbance identification algorithm to determine whether the signal is subject to harmonic-dominated disturbance, voltage sag disturbance, or composite disturbance. The multidimensional disturbance identification algorithm includes calculation of effective value mutation rate, calculation of harmonic energy ratio, and calculation of short-time energy transition coefficient.

[0006] This application also provides a dynamic waveform disturbance identification system, comprising: a signal acquisition and preprocessing module for acquiring voltage and current signals and performing preprocessing; and a waveform disturbance identification module for analyzing the preprocessed signals using a multi-dimensional disturbance identification algorithm to determine whether the signal is subject to harmonic-dominated disturbance, voltage sag disturbance, or composite disturbance. The multi-dimensional disturbance identification algorithm includes RMS mutation rate calculation, harmonic energy ratio calculation, and short-time energy transition coefficient calculation. This system can accurately identify the disturbance type in the signal, improve the accuracy and reliability of signal processing, and thus select the processing path based on the identification result, enhancing the system's adaptive capability and real-time response capability, providing strong support for equipment status monitoring and fault diagnosis in power systems. Attached Figure Description

[0007] 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.

[0008] in: Figure 1 This is a schematic diagram of the structure of a dynamic waveform disturbance identification system provided in an embodiment of this application; Figure 2 This is a schematic diagram of a power system disturbance type identification process provided in an embodiment of this application; Figure 3 A two-layer decomposition block diagram of wavelet packets is provided for embodiments of this application; Figure 4 A schematic diagram of an original signal waveform provided in an embodiment of this application; Figure 5 This is a schematic diagram of a wavelet packet reconstruction waveform provided in an embodiment of this application. Detailed Implementation

[0009] 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.

[0010] 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.

[0011] 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.

[0012] The embodiments of this application are described below with reference to the accompanying drawings.

[0013] Please see Figure 1 This is a schematic diagram of the structure of a dynamic waveform disturbance identification system provided in an embodiment of this application.

[0014] like Figure 1 As shown, the dynamic waveform disturbance identification system 100 includes: The signal acquisition and preprocessing module 110 is used to acquire voltage and current signals and perform preprocessing. The waveform disturbance identification module 120 is used to analyze the preprocessed signal using a multi-dimensional disturbance identification algorithm to determine whether the signal is subject to harmonic-dominated disturbance, voltage sag disturbance or composite disturbance. The aforementioned multidimensional disturbance identification algorithm includes calculation of effective value mutation rate, calculation of harmonic energy ratio, and calculation of short-time energy transition coefficient.

[0015] The signal acquisition and preprocessing module 110 serves as the basic front-end of the application, performing high-precision, synchronized data acquisition and cleaning processing on electrical signals such as voltage and current.

[0016] 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 collected in alignment under a unified time reference, avoiding phase deviations 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, ensuring accurate capture of short-term disturbance characteristics, including higher harmonics and voltage dips.

[0017] After data acquisition is completed, the signal acquisition and preprocessing module 110 can perform preprocessing, including baseline drift correction, amplitude limiting and elimination, frame division and other operations on the original signal, and cache it 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.

[0018] In one optional implementation, the signal acquisition and preprocessing module 110 includes: An ADC sampling chip is used to perform analog-to-digital conversion on voltage and current signals. A low-pass anti-aliasing filter is used to filter the acquired signal to remove high-frequency noise. A FIFO buffer structure is used to store preprocessed signals.

[0019] In practical applications, the signal acquisition and preprocessing module is implemented as follows: An 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 uses a high-resolution Σ-Δ ADC for analog-to-digital conversion, and a built-in isolation driver ensures 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 removal, 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.

[0020] The waveform disturbance identification module 120 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. The waveform disturbance identification module 120 can use a multi-dimensional disturbance identification algorithm to determine the disturbance type in real time, which mainly includes the following three disturbance types: harmonic-dominated disturbance, voltage sag disturbance, and composite disturbance.

[0021] In one optional implementation, the waveform disturbance identification module 120 includes: The effective value mutation rate calculation unit is used to calculate the effective value mutation rate of voltage to determine whether there is a voltage dip or surge. The harmonic energy ratio calculation unit is used to calculate the harmonic energy ratio and determine whether the dominant harmonic disturbance exists. The short-time energy transition coefficient calculation unit is used to calculate the short-time energy transition coefficient and determine whether there is a load change or switching impact.

[0022] 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 signal obtained by the signal acquisition and preprocessing module 110. The obtained feature indicators mainly include three types of key indicators: 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:

[0023] in, This is the effective voltage value within the previous cycle; This represents the effective voltage value within the current cycle.

[0024] 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 instantaneous energy of the sliding window is calculated using the following formula:

[0025] The short-time energy transition coefficient (relative rate of energy change) can be derived from the above formula. :

[0026] in For the first k The original signal (voltage or current) within a sliding window. For the first k Instantaneous signal energy within a window; The energy mutation rate between two adjacent windows; N This is the window length (e.g., the number of sampling points corresponding to one power grid frequency cycle).

[0027] 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.

[0028] Then calculate the proportion of harmonic energy:

[0029] in, For the first h The frequency domain amplitude (FFT component) of the second 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.

[0030] Optionally, based on the aforementioned characteristic indicators, the system incorporates a configurable multidimensional threshold model to determine the disturbance type in real time. The form of the multidimensional threshold model is not limited; it can specifically be a mapping table. The identification results can be categorized into three types: Type A is a harmonic-dominated disturbance, indicating that the signal is dominated by periodic frequency abnormalities without obvious abrupt changes; Type B is a voltage sag (transient) disturbance, indicating a sudden change in amplitude; Type C is a composite disturbance, where two types of anomalies coexist, requiring a dual-path joint compensation strategy.

[0031] The dynamic waveform disturbance recognition system 100 in this application embodiment can be used for waveform recognition in winding deformation diagnosis to realize waveform disturbance recognition; further combined with the corresponding processing path and compensation process, it can improve the image stability and interpretation accuracy of Lissajous figures in transformer winding condition assessment.

[0032] The following is an example of a practical application: During system operation, a grid connection start-up 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. The waveform disturbance identification module 120 automatically classified the signal as Type B: voltage sag event based on the feature extraction results and issued an interrupt signal, notifying the dynamic path scheduling module to skip harmonic paths and prioritize the activation of the transient path algorithm, avoiding resource waste and analysis path conflicts.

[0033] The dynamic waveform disturbance identification system in this embodiment constructs a high-speed signal acquisition channel at the front end based on real-time sampled signals such as voltage and current. It employs an isolated ADC sampling chip combined with a low-pass anti-aliasing filter to achieve high-resolution signal restoration and anti-interference acquisition. The sampled data is stored using 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 protection capability and long-term operational safety under high-voltage DC environments.

[0034] Furthermore, this system utilizes a multi-dimensional disturbance identification algorithm, integrating characteristic indicators such as RMS mutation rate, harmonic energy ratio, and wavelet energy transition coefficient to determine whether a signal is experiencing harmonic interference, voltage sag, or a combination of disturbances. It automatically classifies disturbance types based on predefined threshold rules. The identification results can be used to drive the path scheduling module, dynamically selecting processing strategies according to the disturbance type, avoiding resource waste, and improving system response sensitivity.

[0035] Further optionally, the dynamic waveform disturbance identification system 100 also includes: The dynamic path selection module 130 is used to dynamically select the corresponding waveform processing path based on the waveform disturbance type identification results mentioned above. The waveform reconstruction and compensation module 140 is used to process the above signal using the selected waveform processing path to correct and reconstruct the above signal and obtain the compensated voltage and current waveform data.

[0036] Specifically, the dynamic path selection module 130 can automatically activate the most suitable error compensation path based on the recognition results obtained by the waveform disturbance recognition module 120, ensuring that the system can respond quickly and handle accurately under different abnormal scenarios.

[0037] In one implementation, 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 their processing methods can be set for different perturbation types.

[0038] In one optional implementation, the waveform processing path includes: The harmonic processing path is used to select an improved FFT algorithm based on Nuttall window weighting for spectrum analysis and correction when the above-mentioned harmonic dominant disturbances are identified. The transient processing path is used to select the wavelet packet decomposition algorithm for transient signal analysis and compensation when the above-mentioned voltage sag disturbance is identified; The composite collaborative path is used to simultaneously activate the harmonic processing path and the transient processing path when the above-mentioned composite disturbance is detected, so as to perform comprehensive processing on the signal.

[0039] Specifically, the disturbance type is harmonic-dominated disturbance (Type A): the dynamic path selection module 130 triggers path 1—the 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 precisely extract the frequency, amplitude, and phase of the primary and secondary harmonics for sampling error correction and waveform reconstruction.

[0040] 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:

[0041] Where H is the number of terms in the cosine window; a h For cosine windows h Term coefficient.

[0042] The coefficients of the cosine window should satisfy the following constraints:

[0043] 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:

[0044] 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 pairing g -1 ( β Approximation is performed to obtain parameters. α It can be represented by the following polynomial:

[0045] Furthermore, the amplitude correction formula and the phase correction formula are derived:

[0046]

[0047] when N When the amplitude is large, the amplitude correction formula simplifies to:

[0048] Similarly, polynomial approximation can be used to... v ( α Approximate solution:

[0049] 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:

[0050] Specifically, the disturbance type is voltage sag disturbance (Type B): the dynamic path selection module 130 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 sub-bands, 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.

[0051] 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:

[0052]

[0053] In multi-resolution orthogonal subspace V Wavelet packet decomposition is performed in 0, and it is represented as:

[0054] 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.

[0055] Figure 3 A two-layer decomposition block diagram of wavelet packets is provided for an embodiment of this application.

[0056] 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:

[0057]

[0058] Substitute the coefficients into the voltage signal. u ( t ) and current signal i ( t The fundamental signal and the distorted signal are obtained:

[0059]

[0060] Figure 4This is a schematic diagram of an original signal waveform provided in an embodiment of this application.

[0061] Figure 5 This is a schematic diagram of a wavelet packet reconstruction waveform provided in an embodiment of this application.

[0062] Based on the above method, the initial sampling signal is first given as follows: Figure 4 As shown, it includes voltage and current signals; after decomposition and reconstruction, we can obtain... Figure 5 The signals shown include the fundamental voltage waveform, the fundamental current waveform, the distorted voltage waveform, and the distorted current waveform.

[0063] Specifically, the disturbance type is a composite disturbance (Type C): the dynamic path selection module 130 starts path 3 in parallel—the composite collaborative path, that is, the harmonic analysis and wavelet packet analysis paths are linked in a dual-channel manner, extracting harmonic frequency domain parameters and transient energy characteristics respectively. The two are collaboratively input into the error compensation module, and the signal amplitude, phase, frequency, etc. are dynamically corrected by the fusion correction model, thereby realizing waveform distortion suppression under all time periods and multi-source disturbances.

[0064] 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.

[0065] 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:

[0066] Where α is the fusion weight coefficient, which is adaptively calculated according to the following formula:

[0067] Where THD represents the total harmonic distortion rate (frequency domain disturbance intensity index); E t This represents the proportion of transient energy (a time-domain disturbance intensity index).

[0068] 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.

[0069] 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.

[0070] 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.

[0071] The dynamic path selection module 130 adopts the above-mentioned path scheduling mechanism to effectively avoid the problems of error amplification and processing failure caused by the "fixed algorithm channel" of traditional measurement system, and enables the system to have a closed-loop response capability of "identification-matching-correction".

[0072] The main function of the waveform reconstruction and compensation module 140 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.

[0073] Optionally, the waveform reconstruction and compensation module 140 described above is specifically used for: 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.

[0074] 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.

[0075] 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:

[0076] After combining all component power values, the total active power correction value can be defined as:

[0077]

[0078]

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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 pure 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.

[0083] 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: ① Center frequency: 50Hz (or 60Hz, depending on the region); ② Bandwidth: +3Hz (passband range 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, it is implemented after FFT spectrum processing.

[0084] 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.

[0085] 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:

[0086] 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.

[0087] The objective function to fit is to minimize the sum of squared residuals:

[0088] 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.

[0089] Alternatively, the system 100 may also include a data output and remote communication module 150, which integrates and outputs the error-compensated data through a measurement model, including key indicators such as the real-time amplitude, phase, frequency, and power factor trend of voltage and current, and uploads them to the main station system or remote measurement platform through local storage or a communication interface.

[0090] Specifically, the data output and remote communication module 150 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 scheduling of local processing results. This module supports both field-level communication uploads and compatibility with national and industry power automation standards, ensuring seamless integration of this system with existing power monitoring and dispatching platforms.

[0091] The data output and remote communication module 150 first formats and encapsulates key parameters such as voltage and current amplitude, phase, and power factor after correction by the dynamic error compensation module 130, organizes them into a complete message according to a frame structure, and uploads it to the master station system, measurement and acquisition terminal (such as a concentrator), or energy management system (EMS) through the communication control unit. Regarding interface protocols, the system 100 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.

[0092] 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, module 150 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.

[0093] The dynamic waveform disturbance identification system proposed in this application, compared with the limitations of traditional waveform processing and correction methods such as fixed algorithms, slow response, and weak identification ability, 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 practicality.

[0094] 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. The identification response is faster, the judgment is more accurate, and thus better error compensation can be achieved.

[0095] 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.

[0096] 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 dynamic waveform disturbance identification system, characterized in that, The system includes: The signal acquisition and preprocessing module is used to acquire voltage and current signals and perform preprocessing. The waveform disturbance identification module is used to analyze the preprocessed signal using a multi-dimensional disturbance identification algorithm to determine whether the signal is subject to harmonic-dominated disturbance, voltage sag disturbance, or composite disturbance. The multidimensional disturbance identification algorithm includes calculation of effective value mutation rate, calculation of harmonic energy ratio, and calculation of short-time energy transition coefficient.

2. The dynamic waveform disturbance identification system according to claim 1, characterized in that, The signal acquisition and preprocessing module includes: An ADC sampling chip is used to perform analog-to-digital conversion on the voltage signal and the current signal; A low-pass anti-aliasing filter is used to filter the acquired signal to remove high-frequency noise. A FIFO buffer structure is used to store the preprocessed signal.

3. The dynamic waveform disturbance identification system according to claim 1, characterized in that, The waveform disturbance identification module includes: The effective value mutation rate calculation unit is used to calculate the effective value mutation rate of voltage to determine whether there is a voltage dip or surge. The harmonic energy ratio calculation unit is used to calculate the harmonic energy ratio and determine whether the dominant harmonic disturbance exists. The short-time energy transition coefficient calculation unit is used to calculate the short-time energy transition coefficient and determine whether there is a load change or switching impact.

4. The dynamic waveform disturbance identification system according to claim 3, characterized in that, The formula for calculating the effective voltage value mutation rate is: in, This is the effective voltage value within the previous cycle; This represents the effective voltage value within the current cycle.

5. The dynamic waveform disturbance identification system according to claim 3, characterized in that, The formula for calculating the harmonic energy ratio is as follows: in, For the first h The frequency domain amplitude of the second harmonic; H is the highest harmonic order; Total harmonic energy; This represents the total energy of the signal.

6. The dynamic waveform disturbance identification system according to claim 3, characterized in that, The formula for calculating the short-time energy transition coefficient is as follows: in, For the first k Instantaneous signal energy within a sliding window; For the first k The original signal within a sliding window N The length of the window; This represents the energy mutation rate between two adjacent windows.

7. The dynamic waveform disturbance identification system according to claim 1, characterized in that, The signal acquisition and preprocessing module also includes: A GPS timing module or a high-precision crystal clock is used to achieve multi-channel synchronous sampling, ensuring that the voltage signal and the current signal are collected in alignment under a unified time reference.

8. The dynamic waveform disturbance identification system according to claim 1, characterized in that, Also includes: 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, thereby obtaining compensated voltage and current waveform data.

9. The dynamic waveform disturbance identification system according to claim 8, characterized in that, The waveform processing path includes: The harmonic processing path is used to select an improved FFT algorithm based on Nuttall window weighting for spectrum analysis and correction when the dominant harmonic disturbance is identified. The transient processing path is used to select the wavelet packet decomposition algorithm for transient signal analysis and compensation when the voltage sag disturbance is detected. A composite collaborative path is used to simultaneously activate the harmonic processing path and the transient processing path to perform comprehensive signal processing when the composite disturbance is detected.

10. The dynamic waveform disturbance identification system according to claim 8, characterized in that, The waveform reconstruction and compensation module is specifically used for: 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.

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