Inverter fast phase locking method based on cordic algorithm

Abstract

The invention discloses an inverter rapid phase locking method based on a cordic algorithm, and relates to the technical field of inverter rapid phase locking, and the method comprises the following steps: balancing the dynamic response capability and the high-frequency noise suppression capability through a preset bandwidth range, and achieving the precise initial tracking of the frequency and phase of an input signal. According to the invention, accurate initial tracking of the input signal is realized through the preset bandwidth, and the signal change state is intelligently identified in combination with real-time feature extraction and deep learning model prediction. When the signal is stable, the system keeps the preset bandwidth balance dynamic response and noise suppression; when perturbation frequency changes are detected, the bandwidth range is dynamically expanded, and frequency and phase changes are rapidly tracked. Envelope fluctuation and frequency band energy distribution characteristics are extracted; a reference value is generated; a signal change index is predicted; the bandwidth is dynamically adjusted;

Description

Technical Field

[0001] The present invention relates to the technical field of inverter fast phase locking, and in particular to an inverter fast phase locking method based on a cordic algorithm. Background Art

[0002] Inverter fast phase locking based on CORDIC (COordinate Rotation DIgital Computer) algorithm refers to the use of the efficient trigonometric function calculation capability of the CORDIC algorithm to achieve fast estimation and synchronization of the input signal phase in the inverter system. Specifically, the CORDIC algorithm can calculate the sine and cosine values ​​in real time with low hardware complexity by iteratively calculating the angle of the rotating vector, which is used to estimate the instantaneous phase and amplitude of the signal. In inverter control, this algorithm can be used to implement a fast phase-locked loop (PLL) to ensure that the output voltage or current of the inverter is synchronized with the grid signal, especially when the signal frequency changes or there is harmonic interference. Compared with the traditional phase-locked loop, this method has the advantages of faster response speed and lower computing resource usage, thereby improving the dynamic performance of the inverter and system stability.

[0003] The prior art has the following deficiencies:

[0004] The bandwidth of a phase-locked loop (PLL) refers to the frequency range that its loop filter allows to pass, usually defined as from direct current (DC) to the cutoff frequency. As a key parameter, bandwidth determines the system's ability to respond to changes in the frequency of the input signal and the suppression effect on high-frequency noise. In traditional technologies, the bandwidth range of the phase-locked loop is usually fixed to balance dynamic response capability and noise suppression performance to adapt to specific application scenarios. However, when the frequency regulation of the source of the input signal (such as a wind farm or distributed power source) is unstable and a small periodic drift occurs, the fixed bandwidth may be difficult to meet actual needs. At this time, the phase-locked loop may not be able to effectively track the frequency drift, causing the phase output to gradually deviate from the reference of the input signal. The results include an increase in the phase difference between the inverter output current and the grid signal, causing an increase in reactive power and equipment detuning; phase misalignment can also cause harmonic components to rise and reduce power quality. In addition, the accumulated phase error may trigger a frequency detuning alarm or off-grid protection, affecting system stability, and even causing abnormal equipment shutdown or grid fluctuations.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a fast phase-locking method for an inverter based on a cordic algorithm, which realizes accurate initial tracking of the input signal through a preset bandwidth, and combines real-time feature extraction with deep learning model prediction to intelligently identify the signal change state. When the signal is stable, the system maintains the preset bandwidth to balance dynamic response and noise suppression; when the perturbation frequency changes are detected, the bandwidth range is dynamically expanded to quickly track frequency and phase changes. The envelope fluctuation and frequency band energy distribution characteristics are extracted, a reference value is generated and a signal change index is predicted, the bandwidth is dynamically adjusted, phase offset accumulation is avoided, reactive power fluctuations and harmonic distortion are reduced, and power quality and system reliability are improved to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a fast phase locking method for an inverter based on a cordic algorithm, comprising the following steps:

[0008] Balance dynamic response and high-frequency noise suppression capabilities through a preset bandwidth range to achieve accurate initial tracking of input signal frequency and phase;

[0009] During the signal tracking process, parameter information of the input signal is obtained in real time to provide basic data for subsequent analysis and decision-making;

[0010] Preprocess the signal parameters acquired in real time, extract key features reflecting the unstable frequency regulation of the signal source and the trend of small periodic drift from the preprocessed signal parameters, analyze the extracted features under the detection window, and quantify the change trend and amplitude of the input signal;

[0011] The analyzed features are input into the pre-learned deep learning model, which makes intelligent predictions on the analyzed features and identifies the signal change status;

[0012] According to the prediction results of the deep learning model, the signal change state is divided into perturbation frequency change and reference frequency stability;

[0013] For reference frequency stability, the preset bandwidth range is continued to be used to maintain a balance between dynamic response capability and noise suppression performance, ensuring high-precision tracking and low noise interference under stable signals;

[0014] In response to the perturbation frequency changes, the bandwidth range is dynamically expanded, the response speed of the phase-locked loop is enhanced, the frequency and phase changes of the input signal are quickly tracked, and the accumulation of phase offset is avoided.

[0015] Preferably, key features reflecting the unstable frequency regulation of the signal source and the trend of small periodic drift are extracted from the preprocessed signal parameters, and the extracted features include the amplitude change characteristics of the input signal envelope fluctuation over time and the distribution change trend of the signal energy in the frequency band over time. Under the detection window, after analyzing the extracted amplitude change characteristics of the input signal envelope fluctuation over time and the distribution change trend of the signal energy in the frequency band over time, envelope fluctuation reference values ​​and frequency band energy distribution reference values ​​are generated respectively, and the change trend and amplitude of the input signal are quantified by the envelope fluctuation reference values ​​and the frequency band energy distribution reference values.

[0016] Preferably, the specific steps of analyzing the amplitude variation characteristics of the input signal envelope fluctuation over time in the detection window to generate the envelope fluctuation reference value are as follows:

[0017] First, the instantaneous envelope of the input signal is constructed using its instantaneous information. The instantaneous envelope is a key characteristic that reflects the dynamic change of the input signal amplitude. The formula is as follows:

[0018]

[0019] , where x(n) is the input signal, H[x(n)] is the Hilbert transform of the input signal, and E(n) is the instantaneous envelope;

[0020] Extract the fluctuation rate of the signal envelope, and calculate the intensity and trend of the envelope changing over time by taking the derivative of the instantaneous envelope E(n). The fluctuation rate calculation expression is as follows:

[0021]

[0022] , where is the first-order derivative of the instantaneous envelope, is the second derivative of the instantaneous envelope, γ is the adjustment factor, and R(n) is the envelope fluctuation rate;

[0023] The fluctuation intensity factor is calculated by the envelope fluctuation rate R(n) and the instantaneous envelope E(n). The calculation expression is as follows:

[0024]

[0025] , where sin(α·n) is the sinusoidal modulation term, α is the modulation frequency factor, n is the sequence index of the discrete signal, N is the total number of detection window time points, and F is the fluctuation intensity factor;

[0026] The instantaneous envelope E(n), envelope fluctuation rate R(n) and fluctuation intensity factor F are combined to generate the envelope fluctuation reference value. The generation formula is as follows:

[0027]

[0028] , where η is the weight factor, β is the adjustment coefficient, and EFI is the envelope fluctuation reference value.

[0029] Preferably, the specific steps of analyzing the distribution change trend of the signal energy in the frequency band over time under the detection window to generate the frequency band energy distribution reference value are as follows:

[0030] First, the input signal is decomposed into multiple frequency bands, and the instantaneous energy distribution of each frequency band is calculated. By accurately decomposing the frequency domain characteristics of the signal, the energy concentration or diffusion of the signal in different frequency ranges is captured. The calculation expression is as follows:

[0031]

[0032] , where E k represents the energy value of the kth frequency band, f k，1 is the starting frequency of the kth frequency band, f k，2 is the end frequency of the kth frequency band, S(f) is the power spectral density of the signal, f is the frequency, W k (f) is the weighting function of the kth frequency band;

[0033] In each frequency band, the dynamic change characteristics of signal energy over time are extracted to generate a characteristic vector for quantifying the energy fluctuation in the frequency band. The calculation expression is as follows:

[0034]

[0035] , where E k，i is the energy value of the kth frequency band at the i-th detection point, E k，i+1 is the energy value of the kth frequency band at the i+1th detection point, that is, the energy value of the previous detection point, N k is the total number of detection points in the kth frequency band, g k (i) is the nonlinear weighting factor of the kth frequency band, D k is the dynamic change characteristic of the kth frequency band;

[0036] Perform a pattern analysis on the dynamic change characteristics in each frequency band, integrate the energy distribution and change trend, and generate a pattern value that characterizes the energy distribution characteristics of the frequency band. The generation formula is as follows:

[0037]

[0038] , where P k is the distribution mode value, indicating the distribution mode value of the kth frequency band, F k (E k , D k ) is the characteristic function, which is the energy value of the combined frequency band E k and dynamic change characteristics Dk The characteristic function, H k (f) is the intra-band weighting function;

[0039] The distribution mode values ​​of all frequency bands are combined to generate the overall frequency band energy distribution reference value. The generation formula is as follows:

[0040]

[0041] , where K is the total number of frequency bands, ω k is the balance factor for the kth frequency band, is the nonlinear weight factor for the kth frequency band, γ k is the normalized weight factor of the kth frequency band, and R is the reference value of frequency band energy distribution.

[0042] Preferably, the envelope fluctuation reference value and the frequency band energy distribution reference value generated after analysis are input into a pre-learned deep learning model, and a signal change index is generated by the deep learning model, and the signal change state is identified by the signal change index.

[0043] Preferably, the analyzed features are intelligently predicted by a pre-learned deep learning model, and the signal change index generated when the signal change state is identified is compared and analyzed with a pre-set signal change index reference threshold, and the signal change state is divided, and the division steps are as follows:

[0044] If the signal change index is greater than a preset signal change index reference threshold, the signal state is classified as a perturbation frequency change;

[0045] If the signal change index is less than or equal to a preset signal change index reference threshold, the signal state is classified as reference frequency stability.

[0046] Preferably, the specific steps of dynamically expanding the bandwidth range, enhancing the response speed of the phase-locked loop, quickly tracking the frequency and phase changes of the input signal, and avoiding the accumulation of phase offset in response to the perturbation frequency change are as follows:

[0047] When the signal is in a perturbation frequency change, the phase-locked loop needs to calculate the dynamic bandwidth adjustment coefficient according to the signal change index deviation to guide the dynamic expansion of the bandwidth. The calculation expression is as follows:

[0048] α dyn =k1·ln(1+Δ sVDI )+k2

[0049] , where k1 is the proportional constant of the adjustment coefficient, k2 is the basic bandwidth adjustment offset, α dyn is the dynamic bandwidth adjustment coefficient, Δ SVDI is the signal change index deviation, and the calculation expression is as follows: ΔSVDI =SVDI-SVDI ref , where SVDI is the signal variation index, SVDI ref is the signal change index reference threshold;

[0050] According to the preset bandwidth range and dynamic adjustment coefficient α dyn , calculate the new dynamic bandwidth range to improve the dynamic response capability of the phase-locked loop. The calculation expression is as follows:

[0051] BW dyn =BW pre ·(1+α dyn )

[0052] , where BW dyn is the adjusted bandwidth range, BW pre is the preset bandwidth range;

[0053] After calculating the adjusted bandwidth range BW dyn Afterwards, the phase-locked loop enhances the input signal frequency and phase tracking capabilities through a dynamic bandwidth adjustment mechanism. At this time, the phase tracking error is recalibrated to ensure the accuracy of phase synchronization. The calculation expression is as follows:

[0054]

[0055] , where θ err，new is the adjusted phase tracking error, θ err，pre is the phase tracking error before adjustment.

[0056] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0057] The present invention achieves accurate initial tracking of the input signal through a preset bandwidth range, and combines real-time signal feature extraction and deep learning model prediction to intelligently identify the signal change state. When the signal is in a reference frequency stability state, the system maintains a preset bandwidth range to balance dynamic response capability and noise suppression performance; when a perturbation frequency change is detected, the system dynamically expands the bandwidth range to quickly track frequency and phase changes. This adaptive adjustment mechanism enables the phase-locked loop to cope with frequency regulation instability and small periodic drift problems at the signal source (such as a wind farm or distributed power source), avoiding phase offset and tracking lag caused by a fixed bandwidth design, thereby improving the tracking accuracy and response speed of the inverter.

[0058] The present invention extracts the key features of the input signal envelope fluctuation and frequency band energy distribution, and generates envelope fluctuation reference values ​​and frequency band energy distribution reference values. The phase-locked loop can accurately identify the signal change trend and predict the signal change index through a deep learning model. When the signal frequency regulation is detected to be unstable, the system can quickly and dynamically adjust the bandwidth to avoid phase offset accumulation, reduce the phase difference between the inverter output current and the grid signal, and reduce reactive power fluctuations. At the same time, this dynamic adjustment mechanism effectively reduces harmonic distortion, improves power quality, and avoids the occurrence of system protection misoperation, thereby improving the operating reliability of the inverter and the power supply stability of the power grid, and reducing the risk of large-scale power outages or equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0060] Figure 1 The present invention is a flowchart of a method for fast inverter phase locking based on a cordic algorithm. DETAILED DESCRIPTION

[0061] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0062] The present invention provides Figure 1 The inverter fast phase locking method based on cordic algorithm shown includes the following steps:

[0063] Balance dynamic response and high-frequency noise suppression capabilities through a preset bandwidth range to achieve accurate initial tracking of input signal frequency and phase;

[0064] The preset bandwidth range refers to the fixed frequency range set for the loop filter of the phase-locked loop (PLL) when it is initially configured, which is used to balance the dynamic response capability of the system and the suppression performance of high-frequency noise. This range is usually from direct current (DC) to a specific cutoff frequency, which is set based on the signal characteristics and performance requirements of the target application scenario. A wider preset bandwidth allows the phase-locked loop to respond quickly to dynamic changes in the frequency and phase of the input signal, improving the tracking speed of the system; while a narrower bandwidth helps to suppress high-frequency noise and harmonic interference, and enhance the stability and accuracy of the signal. The preset bandwidth range provides the initial working state for the phase-locked loop, enabling it to achieve accurate initial tracking of the input signal under specific conditions, and provides a basis for subsequent dynamic adjustment or optimization.

[0065] The bandwidth of the phase-locked loop is a fixed parameter set by the loop filter, which determines the system's response speed to changes in the input signal and its ability to suppress high-frequency noise. In traditional designs, the bandwidth is optimized to adapt to specific application scenarios, such as stabilizing the grid frequency or specific load conditions. By presetting the bandwidth, the phase-locked loop can effectively suppress noise when the signal is stable and improve the accuracy of phase locking; when the signal changes, the bandwidth provides sufficient response speed to maintain signal synchronization and control accuracy, thereby ensuring efficient grid-connected operation of the inverter and the grid. Continuously sample and monitor the input signal to capture possible changes in the signal.

[0066] During the signal tracking process, parameter information of the input signal is obtained in real time to provide basic data for subsequent analysis and decision-making;

[0067] In order to accurately monitor and respond to changes in input signals, the system needs to continuously collect various parameters of the input signal, such as frequency, phase, amplitude, etc. These parameters are acquired in real time through sensors and data acquisition modules and updated at a high frequency. This step ensures the system's comprehensive understanding of the input signal, provides the necessary data foundation for subsequent signal analysis and feature extraction, and ensures that the system can maintain efficient operation in a dynamically changing environment.

[0068] Preprocess the signal parameters acquired in real time, extract key features reflecting the unstable frequency regulation of the signal source and the trend of small periodic drift from the preprocessed signal parameters, analyze the extracted features under the detection window, and quantify the change trend and amplitude of the input signal;

[0069] Preprocessing is a key step in the data processing process, involving operations such as filtering, denoising, and normalization. By applying digital filters or other signal processing techniques, the system can remove high-frequency noise and random interference from the input signal and enhance the clarity and stability of the signal. In addition, preprocessing also includes synchronization and standardization of the signal to ensure that feature extraction and analysis in subsequent steps can be performed on a consistent basis, thereby improving the accuracy and reliability of the overall system.

[0070] The signal source refers to the core device or system that generates the input signal. It is the direct source of signal changes and plays a key leading role in the entire signal transmission and processing process. The state and working characteristics of the signal source directly determine the stability and change trend of the input signal's frequency, phase, amplitude and other parameters. In the power system, typical signal sources include dynamic power equipment such as wind farms, photovoltaic power stations, and distributed power sources. These devices may have unstable frequency regulation due to environmental factors (such as wind speed changes, light intensity fluctuations) or load fluctuations, resulting in a small periodic drift in the output signal of the signal source. This drift may be reflected in the phase shift, amplitude fluctuation or harmonic component change of the signal, thereby affecting the subsequent signal processing and control system. Therefore, in the phase-locked loop system, accurate monitoring and analysis of the dynamic characteristics of the signal source is the core link to achieve precise tracking and stable system operation. The instability of the signal source is the root cause of the signal change. Identifying and extracting the unstable characteristics of the frequency regulation of the signal source helps to more effectively identify and process the signal change state.

[0071] Key features reflecting the unstable frequency regulation of the signal source and the trend of small periodic drift are extracted from the preprocessed signal parameters. The extracted features include the amplitude change characteristics of the input signal envelope fluctuation over time and the distribution change trend of the signal energy in the frequency band over time. Under the detection window, after analyzing the extracted amplitude change characteristics of the input signal envelope fluctuation over time and the distribution change trend of the signal energy in the frequency band over time, envelope fluctuation reference values ​​and frequency band energy distribution reference values ​​are generated respectively. The change trend and amplitude of the input signal are quantified by the envelope fluctuation reference values ​​and the frequency band energy distribution reference values.

[0072] When the envelope fluctuation of the input signal shows periodic amplitude changes over time, it usually indicates that the current input signal is in an unstable frequency regulation and has a small periodic drift trend. This is because the periodic amplitude changes of the envelope fluctuations are usually directly related to the frequency regulation dynamics of the signal source device. When the frequency regulation is unstable, the signal frequency will produce a small periodic drift near the main frequency. This drift will cause a modulation effect in the distribution of the signal amplitude and energy, thereby causing a periodic change in the envelope fluctuation amplitude. This phenomenon is often caused by periodic disturbances of the signal source (such as wind speed changes in wind farms or dynamic regulation of distributed power loads), reflecting the comprehensive nonlinear change characteristics of the signal in frequency, phase and amplitude. The periodic fluctuation of the envelope further indicates that the modulation characteristics of the signal are consistent with the periodic disturbance of the input signal frequency, thereby revealing that the signal is in an unstable frequency regulation state, accompanied by a small periodic drift. This envelope fluctuation feature provides an important observation dimension for analyzing the dynamics of the input signal and is also one of the important indicators for detecting frequency regulation anomalies.

[0073] The specific steps for analyzing the amplitude variation characteristics of the input signal envelope fluctuation over time in the detection window to generate the envelope fluctuation reference value are as follows:

[0074] First, the instantaneous envelope of the input signal is constructed using its instantaneous information. The instantaneous envelope is a key characteristic that reflects the dynamic change of the input signal amplitude. The formula is as follows:

[0075]

[0076] , where x(n) is the input signal, represents the discrete time series of the input signal, is the sampling value of the input signal at each time point, H[x(n)] is the Hilbert transform of the input signal, E(n) is the instantaneous envelope, is the instantaneous envelope of the input signal x(n), and represents the dynamic change of the signal amplitude over time;

[0077] Extract the fluctuation rate of the signal envelope, and calculate the intensity and trend of the envelope changing over time by taking the derivative of the instantaneous envelope E(n). The fluctuation rate calculation expression is as follows:

[0078]

[0079] , where is the first-order derivative of the instantaneous envelope, calculating the rate of change of the envelope at each point, is the second-order derivative of the instantaneous envelope, which indicates the rate of change of the envelope change rate, γ is the adjustment factor used to adjust the weight of the second-order derivative in the fluctuation rate calculation, and R(n) is the envelope fluctuation rate;

[0080] This step not only quantifies the changing trend of the envelope, but is also highly sensitive to nonlinear changes, providing core parameters for further analysis.

[0081] The fluctuation intensity factor is calculated by the envelope fluctuation rate R(n) and the instantaneous envelope E(n). The calculation expression is as follows:

[0082]

[0083] , where sin(α·n) is a sinusoidal modulation term used to enhance the sensitivity to periodic changes, α is the modulation frequency factor, n is the sequence index of the discrete signal, representing the discrete time points within the detection window, N is the total number of time points in the detection window, and F is the fluctuation intensity factor;

[0084] By accumulating the products of these characteristics, the fluctuation intensity factor F describes the overall intensity of the envelope fluctuation within the detection window and is an important quantitative indicator of the signal's periodic drift trend.

[0085] The instantaneous envelope E(n), envelope fluctuation rate R(n) and fluctuation intensity factor F are combined to generate the envelope fluctuation reference value. The generation formula is as follows:

[0086]

[0087] , where η is the weight factor, β is the adjustment coefficient, which is used to adjust the weight factor of the fluctuation intensity factor F, and EFI is the envelope fluctuation reference value.

[0088] The weight factor η is a factor used to adjust the signal envelope energy E(n) 2 and envelope fluctuation rate energy R(n) 2 When analyzing the dynamic characteristics of the input signal, the envelope energy E(n) 2 It reflects the overall change characteristics of the signal amplitude, while the fluctuation rate energy R(n) 2 The weight factor η is used to balance the importance of these two parts of energy in the total energy calculation. Specifically, a larger value of η will increase the fluctuation rate R(n). 2 The weight in the analysis makes the system more sensitive to the dynamic changes of the envelope; conversely, a smaller η value emphasizes the overall change characteristics of the envelope amplitude and ignores the smaller amplitude fluctuation characteristics. By flexibly adjusting η, the system can adapt to different signal environments and ensure that the generated indicators more accurately reflect the true state of the signal, especially playing a key role in the analysis of frequency regulation instability and periodic drift trends.

[0089] The larger the envelope fluctuation reference value performance value generated by analyzing the amplitude variation characteristics of the input signal envelope fluctuation over time in the detection window, the more significant the fluctuation amplitude and frequency of the signal envelope are, which usually reflects the instability of the frequency regulation of the signal source and the existence of periodic drift; the increase in this performance value is highly correlated with the amplitude and regularity of the frequency drift. On the contrary, when the envelope fluctuation reference value is small or even close to zero, it means that the signal envelope fluctuation amplitude is low and the change is stable, indicating that the input signal is in a stable frequency regulation state and has no significant periodic drift trend.

[0090] When the distribution of signal energy in the frequency band is expressed as a periodic increase in the energy percentage of the frequency band over time, this usually indicates that the current input signal is in a trend of unstable frequency regulation and a small periodic drift. The root cause of this phenomenon is that the instability of frequency regulation causes a slight shift in the main frequency of the signal, causing the energy to be periodically redistributed between the main frequency band and the adjacent frequency band. Specifically, when the frequency regulation of a signal source (such as a wind farm or distributed power source) undergoes periodic perturbations, the energy in the spectrum will be periodically transferred with the regulation, for example, the energy concentrated in the main frequency band will diffuse upward or downward to the adjacent frequency bands and return to the main frequency within a certain period. This periodic increase or decrease in the energy percentage of the frequency band reflects that the signal spectrum is significantly affected by the regulation disturbance and is one of the typical characteristics of frequency instability. At the same time, the dynamic fluctuations in the energy distribution of the frequency band also indicate that the regulation process of the signal source has a certain regularity and periodicity, which is consistent with the trend of small periodic drifts.

[0091] The specific steps of analyzing the distribution trend of signal energy in the frequency band over time under the detection window to generate the reference value of frequency band energy distribution are as follows:

[0092] First, the input signal is decomposed into multiple frequency bands, and the instantaneous energy distribution of each frequency band is calculated. By accurately decomposing the frequency domain characteristics of the signal, the energy concentration or diffusion of the signal in different frequency ranges is captured, laying the foundation for the subsequent dynamic change characteristics analysis. The calculation expression is as follows:

[0093]

[0094] , where E k represents the energy value of the kth frequency band, which represents the sum of the signal power in the frequency band, f k，1 is the starting frequency of the kth frequency band, f k，2 is the end frequency of the kth frequency band, S(f) is the power spectral density of the signal, which is the power spectral density function of the signal in the frequency domain, describing the energy distribution of the signal at different frequencies f, where f is the frequency, and W k (f) is a weighting function of the kth frequency band, which is a weighting function set for the frequency components in the kth frequency band and is used to adjust the contribution of the components of different frequencies f in the frequency band;

[0095] By calculating E k , the energy distribution characteristics of the signal in each frequency band can be quantified, and basic data can be provided for the next step of analyzing its dynamic change trend.

[0096] In each frequency band, the dynamic change characteristics of signal energy over time are extracted to generate a characteristic vector for quantifying the energy fluctuation in the frequency band, which reflects the periodic change of energy distribution that may be caused by unstable signal frequency regulation. The calculation expression is as follows:

[0097]

[0098] , where E k，i is the energy value of the kth frequency band at the i-th detection point, indicating the energy distribution of the frequency band over time, E k，i+1 is the energy value of the kth frequency band at the i+1th detection point, that is, the energy value of the previous detection point, N k is the total number of detection points in the kth frequency band, g k (i) is the nonlinear weighting factor of the kth frequency band, which is used to emphasize the contribution of the energy change at a specific time point in the frequency band to the overall dynamic characteristics. D is the dynamic change characteristic of the kth frequency band, which describes the energy change trend and fluctuation amplitude in the kth frequency band;

[0099] By calculating D k , the energy variation trend of the signal within the frequency band can be quantified, and the dynamic instability of the signal during the regulation process can be identified.

[0100] The dynamic change characteristics in each frequency band are analyzed in mode, and the energy distribution and change trend are integrated to generate the mode value that characterizes the energy distribution characteristics of the frequency band, aiming to capture the overall law of energy transfer between frequency bands. The generation formula is as follows:

[0101]

[0102] , where P k is the distribution mode value, which indicates the distribution mode value of the kth frequency band and is used to quantify the distribution characteristics of energy within the frequency band and its relationship with dynamic changes. k (E k , D k ) is the characteristic function, which is the energy value of the combined frequency band E k and dynamic change characteristics D k The characteristic function is used to integrate the static and dynamic characteristics within the frequency band, H k (f) is the intra-band weighting function used to emphasize the contribution of specific frequencies within the band;

[0103] By P kThe calculation can quantify the energy distribution pattern of each frequency band and its correlation with the dynamic change characteristics, providing a basis for generating an overall reference value.

[0104] The distribution mode values ​​of all frequency bands are combined to generate the overall frequency band energy distribution reference value, which is used to characterize the global dynamic characteristics of the signal energy distribution. This step provides an important quantitative indicator of the signal frequency domain stability. The generation formula is as follows:

[0105]

[0106] , where K is the total number of frequency bands, ω k is the balance factor of the kth frequency band and is the positive correction parameter used to adjust the offset of the reference value of the kth frequency band. is the nonlinear weight factor of the kth frequency band, which is an exponential factor that controls the nonlinear amplification of the contribution of the kth frequency band, γ k is the normalized weight factor of the kth frequency band, which is the proportional factor that controls the contribution of the kth frequency band in the denominator and is used to normalize the contribution of different frequency bands. R is the reference value of the frequency band energy distribution.

[0107] By calculating the reference value R, the energy distribution pattern of each frequency band and its importance can be integrated to form an integrated indicator that characterizes the frequency domain characteristics of the signal, providing a reliable benchmark for dynamic analysis and state judgment of the signal.

[0108] The larger the performance value of the frequency band energy distribution reference value generated by analyzing the distribution change trend of the signal energy in the frequency band over time under the detection window, the more likely the current input signal is in unstable frequency regulation and has a small periodic drift trend. Conversely, it indicates that the signal is relatively stable and does not show obvious frequency regulation instability characteristics. The size of the performance value reflects the intensity and regularity of the distribution change of signal energy in the frequency band over time. When the input signal frequency regulation is unstable, its main frequency energy will spread to the adjacent frequency bands and periodically redistribute between different frequency bands, resulting in an increase in the reference value. When the signal is running stably, the energy is mainly concentrated in the main frequency band and is evenly distributed in time, and the performance value is small.

[0109] The analyzed features are input into the pre-learned deep learning model, which makes intelligent predictions on the analyzed features and identifies the signal change status;

[0110] The envelope fluctuation reference value and the frequency band energy distribution reference value generated after analysis are input into a pre-learned deep learning model, and a signal change index is generated through the deep learning model. The signal change state is identified through the signal change index.

[0111] A pre-learned deep learning model refers to a model that has been fully trained and verified with historical data and has the ability to extract signal change patterns from input features and perform state classification or prediction. The model usually undergoes multiple rounds of data processing, including data cleaning, feature engineering, model training, and tuning, in order to accurately identify the dynamic change state of the input signal. The model training process usually involves large-scale historical signal data that covers different signal change scenarios (such as frequency stability, perturbation frequency drift, unstable frequency fluctuations, etc.) and contains relevant envelope fluctuation reference values ​​and frequency band energy distribution reference values. Through supervised learning or semi-supervised learning, the model learns the mapping relationship between the characteristics of signal changes and their corresponding states. For example, the model is trained to understand the correlation between abnormal increases in envelope fluctuation values ​​and periodic drift of signals, as well as the impact of dynamic changes in frequency band energy distribution on signal instability.

[0112] These deep learning models can use architectures such as convolutional neural networks (CNN), recurrent neural networks (RNN), or hybrid models (such as LSTM-CNN) to extract the spatiotemporal information and pattern changes of signal features. The pre-learned models are also cross-validated, tested, and evaluated for model accuracy to ensure good generalization capabilities for unseen data. Therefore, when the envelope fluctuation reference values ​​and band energy distribution reference values ​​generated after analysis are input into the model, the model can efficiently process these features and generate a signal change index associated with the input pattern for further identification of the signal change state.

[0113] The pre-learned deep learning model provides a reliable means for identifying the signal change state by automatically and intelligently processing complex input features. In practical applications, the input envelope fluctuation reference value and the frequency band energy distribution reference value can reflect the dynamic change trend of the signal within a specific time window. The deep learning model uses these reference values ​​as feature inputs, and identifies the nonlinear change pattern and potential periodicity of the signal through its deep network structure, thereby generating a quantitative signal change index. The signal change index is the result of the model's comprehensive analysis of the input features, reflecting key attributes such as the amplitude, frequency, and stability of the signal change, and has strong descriptive and discriminative capabilities.

[0114] Based on the signal change index, the model can further classify the current signal status into categories such as stable frequency or unstable frequency regulation. This classification process relies on the classification boundaries and prediction rules established by the model during the training phase. Combined with real-time input features, the signal status can be identified quickly and efficiently. This approach not only reduces the need for manual intervention, but also significantly improves the accuracy and real-time performance of state identification in complex signal environments. It is particularly suitable for monitoring and control scenarios of dynamic signal source equipment such as wind farms and distributed power sources. Through pre-learned deep learning models, the characteristics of signal changes can be quickly quantified and interpreted, providing key support for the dynamic adjustment and optimization decisions of the system.

[0115] The deep learning model is not limited here, and any deep learning model that can perform comprehensive analysis on the envelope fluctuation reference value EFI and the frequency band energy distribution reference value R to generate the signal change index SVDI is acceptable. To realize the technical solution of the present invention, the present invention provides a specific implementation method;

[0116] The signal variation index SVDI generation formula is as follows:

[0117]

[0118] , where s a 、s b are the preset proportional coefficients of the envelope fluctuation reference value EFI and the frequency band energy distribution reference value R, and s a 、s b Both are greater than 0.

[0119] It can be seen from the signal change index that the larger the envelope fluctuation reference value performance value generated after analyzing the amplitude change characteristics of the input signal envelope fluctuation over time in the detection window, the larger the frequency band energy distribution reference value performance value generated after analyzing the distribution change trend of the signal energy in the frequency band over time in the detection window. This indicates that the larger the signal change index performance value generated when the signal change state is identified by intelligently predicting the analyzed features through the pre-learned deep learning model, the greater the probability that the signal source is developing towards unstable frequency regulation and small periodic drift. Otherwise, it indicates that the signal source is in a stable state.

[0120] According to the prediction results of the deep learning model, the signal change state is divided into perturbation frequency change and reference frequency stability;

[0121] The pre-learned deep learning model is used to make intelligent predictions on the analyzed features. The signal change index generated when identifying the signal change state is compared with the pre-set signal change index reference threshold to divide the signal change state. The division steps are as follows:

[0122] If the signal change index is greater than a preset signal change index reference threshold, the signal state is classified as a perturbation frequency change;

[0123] If the signal change index is less than or equal to a preset signal change index reference threshold, the signal state is classified as reference frequency stability.

[0124] Perturbation frequency variation refers to the phenomenon that small-amplitude, periodic and nonlinear fluctuations occur in the frequency adjustment process of the input signal; reference frequency stability refers to the phenomenon that the frequency of the input signal remains near a relatively constant value during the adjustment process, and the signal variation amplitude is extremely small, showing a steady-state characteristic.

[0125] For reference frequency stability, the preset bandwidth range is continued to be used to maintain a balance between dynamic response capability and noise suppression performance, ensuring high-precision tracking and low noise interference under stable signals;

[0126] The purpose of continuing to use the preset bandwidth range for reference frequency stability is to enable the phase-locked loop (PLL) to maintain high-precision tracking of the input signal frequency and phase without introducing additional complexity when the signal remains stable, while effectively suppressing high-frequency noise and interference. The application of this fixed bandwidth range ensures that the system has sufficient dynamic response capability under steady-state signal conditions to quickly adapt to small normal fluctuations and minimize the impact of high-frequency harmonics and random noise when the signal does not change significantly. Through this strategy, unnecessary bandwidth adjustments can be reduced, reducing the system's computational burden and stability risks, thereby improving the long-term tracking accuracy of the phase-locked loop and the reliability and efficiency of system operation under stable input signal environments.

[0127] In response to the perturbation frequency changes, the bandwidth range is dynamically expanded, the response speed of the phase-locked loop is enhanced, the frequency and phase changes of the input signal are quickly tracked, and the accumulation of phase offset is avoided;

[0128] The specific steps to dynamically expand the bandwidth range, enhance the response speed of the phase-locked loop, quickly track the frequency and phase changes of the input signal, and avoid the accumulation of phase offset in response to the perturbation frequency change are as follows:

[0129] When the signal is in a perturbation frequency change, the phase-locked loop needs to calculate the dynamic bandwidth adjustment coefficient according to the signal change exponential deviation to guide the dynamic expansion of the bandwidth. The adjustment coefficient is proportional to the amplitude of the signal change, that is, the more drastic the signal change, the greater the bandwidth expansion amplitude. The calculation expression is as follows:

[0130] α dyn =k1·ln(1+Δ sVDI )+k2

[0131] , where k1 is the proportional constant of the adjustment coefficient, which is used to control the sensitivity of bandwidth expansion, k2 is the basic bandwidth adjustment offset, which ensures fine-tuning even at low deviations, and α dyn is the dynamic bandwidth adjustment coefficient, which represents the proportional factor of bandwidth expansion, Δ SVDI is the signal change index deviation, which indicates the deviation degree of the current signal change relative to the reference threshold. The calculation expression is as follows: Δ SVDI =SVDI-SVDI ref , where SVDI is the signal variation index, SVDI ref is the signal change index reference threshold;

[0132] The dynamic bandwidth adjustment coefficient is processed by a logarithmic function to avoid overly drastic bandwidth adjustments while responding quickly to changes in the signal amplitude. This coefficient is used to calculate the new bandwidth range to ensure that the phase-locked loop can follow the frequency and phase changes of the input signal.

[0133] According to the preset bandwidth range and dynamic adjustment coefficient α dyn , calculate the new dynamic bandwidth range, the new bandwidth range is expanded on the basis of the original bandwidth to improve the dynamic response capability of the phase-locked loop, the calculation expression is as follows:

[0134] BW dyn =BW pre ·(1+α dyn )

[0135] , where BW dyn is the adjusted bandwidth range, BW pre is the preset bandwidth range;

[0136] When the signal variation index exceeds the reference threshold, the bandwidth is expanded in proportion to the dynamic adjustment factor. This dynamic adjustment of the bandwidth ensures that the phase-locked loop can quickly track small periodic drifts of the input signal and avoid the accumulation of phase errors.

[0137] After calculating the adjusted bandwidth range BW dyn Afterwards, the phase-locked loop enhances the input signal frequency and phase tracking capabilities through a dynamic bandwidth adjustment mechanism. At this time, the phase tracking error is recalibrated to ensure the accuracy of phase synchronization. The calculation expression is as follows:

[0138]

[0139] , where θ err，new is the adjusted phase tracking error, θ err，pre is the phase tracking error before adjustment.

[0140] The expansion of the dynamic bandwidth range reduces the phase tracking error of the phase-locked loop, ensuring that accurate frequency and phase synchronization can be maintained even when the perturbation frequency changes. The exponential decay model of the phase tracking error shows that the larger the bandwidth expansion, the faster the phase error decays, thus effectively avoiding the accumulation of phase offset.

[0141] When the perturbation frequency changes, the bandwidth range of the phase-locked loop (PLL) is dynamically expanded. The main function is to improve the system's response speed to the frequency and phase changes of the input signal, and to ensure that when the signal has a small periodic drift, the phase-locked loop can quickly track the dynamic changes of the input signal to avoid the accumulation of phase offset, thereby maintaining the stable operation of the system. In practical applications, perturbation frequency changes are usually caused by load fluctuations and environmental changes in wind farms, photovoltaic power stations or distributed power sources. This change is small but periodic. If the bandwidth range of the phase-locked loop is fixed, it may not be able to adapt to these frequency changes in time, causing the output phase to gradually deviate from the reference of the input signal, which in turn causes problems such as increased reactive power, increased harmonic distortion, and system protection malfunctions. Therefore, the purpose of dynamically expanding the bandwidth range is to provide faster dynamic response capabilities when the signal is unstable, so that the phase-locked loop can adjust the frequency and phase of the output signal in time to maintain synchronization with the input signal.

[0142] The core of dynamic bandwidth adjustment is to flexibly adjust the filtering range of the phase-locked loop according to the degree of change of the input signal. When the signal changes rapidly and fluctuates greatly, by expanding the bandwidth range, the phase-locked loop can capture more details of the input signal changes, thereby quickly locking the new phase reference and avoiding cumulative phase errors. In addition, dynamic bandwidth adjustment can also effectively reduce phase tracking lag, reduce system risks caused by frequency offset, and improve the robustness and adaptability of the phase-locked loop in complex dynamic environments.

[0143] Through this step, the phase-locked loop can not only quickly respond to the perturbation changes of the signal, but also achieve high-precision frequency tracking and low-noise interference suppression in a dynamically changing environment, thereby improving the overall performance and system stability of the grid-connected equipment. This intelligent dynamic bandwidth adjustment mechanism can better meet the high dynamic requirements of distributed power and renewable energy systems for frequency tracking, effectively avoid the limitations of traditional fixed bandwidth design, and enable the system to maintain reliable operation when the frequency fluctuates greatly.

[0144] The present invention achieves accurate initial tracking of the input signal through a preset bandwidth range, and combines real-time signal feature extraction and deep learning model prediction to intelligently identify the signal change state. When the signal is in a reference frequency stability state, the system maintains a preset bandwidth range to balance dynamic response capability and noise suppression performance; when a perturbation frequency change is detected, the system dynamically expands the bandwidth range to quickly track frequency and phase changes. This adaptive adjustment mechanism enables the phase-locked loop to cope with frequency regulation instability and small periodic drift problems at the signal source (such as a wind farm or distributed power source), avoiding phase offset and tracking lag caused by a fixed bandwidth design, thereby improving the tracking accuracy and response speed of the inverter.

[0145] The present invention extracts the key features of the input signal envelope fluctuation and frequency band energy distribution, and generates envelope fluctuation reference values ​​and frequency band energy distribution reference values. The phase-locked loop can accurately identify the signal change trend and predict the signal change index through a deep learning model. When the signal frequency regulation is detected to be unstable, the system can quickly and dynamically adjust the bandwidth to avoid phase offset accumulation, reduce the phase difference between the inverter output current and the grid signal, and reduce reactive power fluctuations. At the same time, this dynamic adjustment mechanism effectively reduces harmonic distortion, improves power quality, and avoids the occurrence of system protection misoperation, thereby improving the operating reliability of the inverter and the power supply stability of the power grid, and reducing the risk of large-scale power outages or equipment failures.

[0146] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0147] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0148] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0149] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0150] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0154] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0155] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. The inverter fast phase locking method based on cordic algorithm is characterized by: The following steps are involved: Balance dynamic response and high-frequency noise suppression capabilities through a preset bandwidth range to achieve accurate initial tracking of input signal frequency and phase; During the signal tracking process, parameter information of the input signal is obtained in real time to provide basic data for subsequent analysis and decision-making; Preprocess the signal parameters acquired in real time, extract key features reflecting the unstable frequency regulation of the signal source and the trend of small periodic drift from the preprocessed signal parameters, analyze the extracted features under the detection window, and quantify the change trend and amplitude of the input signal; The analyzed features are input into the pre-learned deep learning model, which makes intelligent predictions on the analyzed features and identifies the signal change status; According to the prediction results of the deep learning model, the signal change state is divided into perturbation frequency change and reference frequency stability; For reference frequency stability, the preset bandwidth range is continued to be used to maintain a balance between dynamic response capability and noise suppression performance, ensuring high-precision tracking and low noise interference under stable signals; In response to the perturbation frequency changes, the bandwidth range is dynamically expanded, the response speed of the phase-locked loop is enhanced, the frequency and phase changes of the input signal are quickly tracked, and the accumulation of phase offset is avoided.

2. The inverter fast phase locking method based on cordic algorithm according to claim 1, characterized in that: Key features reflecting the unstable frequency regulation of the signal source and the trend of small periodic drift are extracted from the preprocessed signal parameters. The extracted features include the amplitude change characteristics of the input signal envelope fluctuation over time and the distribution change trend of the signal energy in the frequency band over time. Under the detection window, after analyzing the extracted amplitude change characteristics of the input signal envelope fluctuation over time and the distribution change trend of the signal energy in the frequency band over time, envelope fluctuation reference values ​​and frequency band energy distribution reference values ​​are generated respectively. The change trend and amplitude of the input signal are quantified by the envelope fluctuation reference values ​​and the frequency band energy distribution reference values.

3. The inverter fast phase locking method based on cordic algorithm according to claim 2 is characterized in that: The specific steps for analyzing the amplitude variation characteristics of the input signal envelope fluctuation over time in the detection window to generate the envelope fluctuation reference value are as follows: First, the instantaneous envelope of the input signal is constructed using its instantaneous information. The instantaneous envelope is a key characteristic that reflects the dynamic change of the input signal amplitude. The formula is as follows: Where x(n) is the input signal, H[x(n)] is the Hilbert transform of the input signal, and E(n) is the instantaneous envelope; Extract the fluctuation rate of the signal envelope, and calculate the intensity and trend of the envelope changing over time by taking the derivative of the instantaneous envelope E(n). The fluctuation rate calculation expression is as follows: In the formula, is the first-order derivative of the instantaneous envelope, is the second derivative of the instantaneous envelope, γ is the adjustment factor, and R(n) is the envelope fluctuation rate; The fluctuation intensity factor is calculated by the envelope fluctuation rate R(n) and the instantaneous envelope E(n). The calculation expression is as follows: Where sin(α·n) is the sinusoidal modulation term, α is the modulation frequency factor, n is the sequence index of the discrete signal, N is the total number of detection window time points, and F is the fluctuation intensity factor; The instantaneous envelope E(n), envelope fluctuation rate R(n) and fluctuation intensity factor F are combined to generate the envelope fluctuation reference value. The generation formula is as follows: Where η is the weight factor, β is the adjustment coefficient, and EFI is the envelope fluctuation reference value.

4. The inverter fast phase locking method based on cordic algorithm according to claim 2, characterized in that: The specific steps of analyzing the distribution trend of signal energy in the frequency band over time under the detection window to generate the reference value of frequency band energy distribution are as follows: First, the input signal is decomposed into multiple frequency bands, and the instantaneous energy distribution of each frequency band is calculated. By accurately decomposing the frequency domain characteristics of the signal, the energy concentration or diffusion of the signal in different frequency ranges is captured. The calculation expression is as follows: In the formula, E k represents the energy value of the kth frequency band, f k，1 is the starting frequency of the kth frequency band, f k，2 is the end frequency of the kth frequency band, S(f) is the power spectral density of the signal, f is the frequency, W k (f) is the weighting function of the kth frequency band; In each frequency band, the dynamic change characteristics of signal energy over time are extracted to generate a characteristic vector for quantifying the energy fluctuation in the frequency band. The calculation expression is as follows: In the formula, E k，i is the energy value of the kth frequency band at the i-th detection point, E k，i+1 is the energy value of the kth frequency band at the i+1th detection point, that is, the energy value of the previous detection point, N k is the total number of detection points in the kth frequency band, g k (i) is the nonlinear weighting factor of the kth frequency band, D k is the dynamic change characteristic of the kth frequency band; Perform a pattern analysis on the dynamic change characteristics in each frequency band, integrate the energy distribution and change trend, and generate a pattern value that characterizes the energy distribution characteristics of the frequency band. The generation formula is as follows: Where P k is the distribution mode value, indicating the distribution mode value of the kth frequency band, F k (E k , D k ) is the characteristic function, which is the energy value of the combined frequency band E k and dynamic change characteristics D k The characteristic function, H k (f) is the intra-band weighting function; The distribution mode values ​​of all frequency bands are combined to generate the overall frequency band energy distribution reference value. The generation formula is as follows: Where K is the total number of frequency bands, ω k is the balance factor for the kth frequency band, is the nonlinear weight factor for the kth frequency band, γ k is the normalized weight factor of the kth frequency band, and R is the reference value of frequency band energy distribution.

5. The inverter fast phase locking method based on cordic algorithm according to claim 2, characterized in that: The envelope fluctuation reference value and the frequency band energy distribution reference value generated after analysis are input into a pre-learned deep learning model, and a signal change index is generated through the deep learning model. The signal change state is identified through the signal change index.

6. The inverter fast phase locking method based on cordic algorithm according to claim 5, characterized in that: The pre-learned deep learning model is used to make intelligent predictions on the analyzed features. The signal change index generated when identifying the signal change state is compared with the pre-set signal change index reference threshold to divide the signal change state. The division steps are as follows: If the signal change index is greater than a preset signal change index reference threshold, the signal state is classified as a perturbation frequency change; If the signal change index is less than or equal to a preset signal change index reference threshold, the signal state is classified as reference frequency stability.

7. The inverter fast phase locking method based on cordic algorithm according to claim 6, characterized in that: The specific steps to dynamically expand the bandwidth range, enhance the response speed of the phase-locked loop, quickly track the frequency and phase changes of the input signal, and avoid the accumulation of phase offset in response to the perturbation frequency change are as follows: When the signal is in a perturbation frequency change, the phase-locked loop needs to calculate the dynamic bandwidth adjustment coefficient according to the signal change index deviation to guide the dynamic expansion of the bandwidth. The calculation expression is as follows: a dyn =k1·ln(1+Δ SVDI )+k2 Where k1 is the proportional constant of the adjustment coefficient, k2 is the basic bandwidth adjustment offset, and α dyn is the dynamic bandwidth adjustment coefficient, Δ SVDI is the signal change index deviation, and the calculation expression is as follows: Δ SVDI =SVDI-SVDI ref , where SVDI is the signal variation index, SVDI ref is the signal change index reference threshold; According to the preset bandwidth range and dynamic adjustment coefficient α dyn , calculate the new dynamic bandwidth range to improve the dynamic response capability of the phase-locked loop. The calculation expression is as follows: BW dyn =BW pre ·(1+a dyn ) Where, BW dyn is the adjusted bandwidth range, BW pre is the preset bandwidth range; After calculating the adjusted bandwidth range BW dyn Afterwards, the phase-locked loop enhances the input signal frequency and phase tracking capabilities through a dynamic bandwidth adjustment mechanism. At this time, the phase tracking error is recalibrated to ensure the accuracy of phase synchronization. The calculation expression is as follows: In the formula, θ err，new is the adjusted phase tracking error, θ err，pre is the phase tracking error before adjustment.

Images