A dynamic harmonic synchronous phasor estimation method based on adaptive iterative quadrature demodulation

Abstract

This invention discloses a dynamic harmonic synchronization phasor estimation method based on adaptive iterative orthogonal demodulation. The method acquires discrete sampled signals of grid voltage or current and sets a target frequency. Instantaneous phasor parameters are calculated based on the target frequency and the discrete sampled signals. The corresponding harmonic components are reconstructed based on these instantaneous phasor parameters, and the residual signal is updated. From the second iteration onwards, the target frequency is updated based on the spectral characteristics of the residual signal or a preset rule. The average change trend of the instantaneous frequency from the previous iteration is used to adjust the target frequency. Instantaneous phasor parameters are calculated based on the adjusted target frequency and the residual signal, and the corresponding harmonic components are reconstructed and the residual signal is updated accordingly. If the energy of the residual signal is below a threshold or other iteration stopping conditions are met, the instantaneous phasor parameters and reconstructed harmonic components of all iterations are output. This invention significantly improves the accuracy and robustness of harmonic measurement in scenarios with dynamic frequency changes and dense spectrum.

Description

Technical Field

[0001] This invention relates to power quality management technology for distribution networks, specifically to a dynamic harmonic synchronization phasor estimation method based on adaptive iterative orthogonal demodulation. Background Technology

[0002] In power systems, voltage and current waveforms are typically time series of synchronized, GPS-timestamped synchronous phasors. Synchronous phasors are sinusoidal signals with specific amplitudes and phases. This representation assumes that voltage and current signals can be well described by a single sine curve at the nominal power system frequency defined in the IEEE / IEC standard. However, low-voltage distribution networks often operate with complex waveforms exhibiting rich harmonic content in their voltage and current signals. With the rapid growth of distributed renewable energy sources (such as photovoltaics and wind power) and electronic loads (such as electric vehicle charging stations and frequency converters) in distribution networks, waveform distortion in the power grid is becoming increasingly prominent, with voltage and current signals rich in multiple harmonics and interharmonic components. Traditional measurement methods based on power frequency synchronous phasors are insufficient to accurately describe the actual operating conditions of distribution networks with a high proportion of electronic power devices, particularly in the following aspects: Severe harmonic pollution: A large number of inverters and rectifiers cause severe distortion of the current waveform, which not only leads to the deterioration of power quality, but may also cause relay protection malfunctions, line overheating and metering errors.

[0003] Harmonic dynamics are complex: the connection of distributed power sources and fluctuating loads causes the amplitude and phase of harmonics to exhibit rapid time-varying characteristics, and traditional DFT methods based on fixed time windows cannot capture instantaneous harmonic changes.

[0004] Outdated governance methods: Existing harmonic monitoring devices have low time resolution, making it difficult to support accurate compensation from dynamic governance devices such as active power filters (APF), resulting in poor governance effects.

[0005] Harmonic responsibility allocation is difficult: In the context of multiple harmonic sources coexisting, the lack of high-precision harmonic phasor data makes it impossible to accurately locate and allocate responsibility for harmonic sources.

[0006] As harmonic distortion has become increasingly non-negligible, using a set of harmonic phasors to synchronously represent harmonic phasors has become impractical. This renders traditional phasors ineffective in providing accurate representations of voltage and current waveforms on the power grid. Previous work has used methods based on DFT, Taylor expansion of the signal, or sinc interpolation to estimate harmonic phasors. However, these methods have limitations. For example, DFT-based methods rely on the assumption that the signal is purely periodic, thus failing to estimate dynamic phasors. Using Taylor expansion to estimate dynamic phasors requires extensive iterative computations, which compromises the algorithm's fast response characteristics. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide a dynamic harmonic synchronization phasor estimation method based on adaptive iterative orthogonal demodulation, which significantly improves the accuracy and robustness of harmonic measurement in scenarios with dynamic frequency changes and dense spectrum through a closed-loop, adaptive iterative framework. This provides a reliable technical foundation for advanced power quality management applications such as accurate location of harmonic sources and real-time accurate compensation of active power filters (APF).

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A dynamic harmonic synchronization phasor estimation method based on adaptive iterative orthogonal demodulation includes the following steps: Acquire discrete sampled signals of grid voltage or current and set the target frequency; In the first iteration, instantaneous phasor parameters are calculated based on the target frequency and the discrete sampled signal, and the corresponding harmonic components are reconstructed and the residual signal is updated based on the instantaneous phasor parameters. Starting from the second iteration, the target frequency is updated based on the spectral characteristics of the residual signal or a preset rule. The average change trend of the instantaneous frequency in the instantaneous phasor parameters of the previous iteration is used to adjust the target frequency to compensate for the dynamic shift of the harmonic frequency. The instantaneous phasor parameters are calculated based on the adjusted target frequency and the residual signal, and the corresponding harmonic components are reconstructed and the residual signal is updated based on the instantaneous phasor parameters. If the energy of the residual signal is below the threshold, or if other iteration stopping conditions are met, output the instantaneous phasor parameters and reconstructed harmonic components of all iterations.

[0009] Furthermore, when setting the target frequency, specifically, spectral analysis is performed on the discrete sampled signal to identify spectral peaks. The spectral peaks are then sorted in descending order of energy, and their frequencies are arranged into a target frequency list. The first target frequency is then selected from this list. When updating the target frequency based on the spectral characteristics of the residual signal or a preset rule, the process includes: If the target frequency is updated based on the spectral characteristics of the residual signal, then the residual signal is subjected to spectral analysis and spectral peaks are identified, and the frequency corresponding to the spectral peak with the highest energy is taken as the new target frequency. If the target frequency is updated based on preset rules, then the target frequencies are selected sequentially from the target frequency list.

[0010] Furthermore, when calculating the instantaneous phasor parameters based on the target frequency and the discrete sampled signal, a set of digital quadrature reference signals is constructed using the target frequency as the local oscillator frequency. The discrete sampled signal is used as the input signal and multiplied by the digital quadrature reference signals to obtain the corresponding quadrature demodulation results. The quadrature demodulation results are then input into the corresponding low-pass filters for filtering. The instantaneous amplitude and phase shift are calculated based on the filtering results. The instantaneous phase is then calculated based on the phase shift, and finally, the instantaneous frequency is calculated based on the instantaneous phase.

[0011] Furthermore, when calculating the instantaneous phasor parameters based on the adjusted target frequency and the residual signal, a set of digital quadrature reference signals is constructed using the adjusted target frequency as the local oscillator frequency. The residual signal of this iteration is used as the input signal and multiplied by the digital quadrature reference signals to obtain the corresponding quadrature demodulation results. The quadrature demodulation results are then input into the corresponding low-pass filters for filtering. The instantaneous amplitude and phase shift are calculated based on the filtering results. The instantaneous phase is then calculated based on the phase shift, and finally, the instantaneous frequency is calculated based on the instantaneous phase.

[0012] Furthermore, the cutoff frequency of the low-pass filter is adaptively adjusted according to the target frequency or the adjusted target frequency value. Specifically, if the target frequency or the adjusted target frequency value is less than a specified value, the filter order is increased to obtain a narrower transition band to ensure high suppression of adjacent harmonic components. If the target frequency or the adjusted target frequency value is greater than a specified value, the filter order is decreased to obtain a wider transition band to maintain a faster dynamic response speed.

[0013] Furthermore, when reconstructing the corresponding harmonic components and updating the residual signal based on the instantaneous phasor parameters, the mathematical expression for reconstructing the corresponding harmonic components is as follows:

[0014] in, This represents the harmonic components of the current iteration and reconstruction. This represents the instantaneous amplitude in the instantaneous phasor parameters of this iteration. This represents the instantaneous phase in the instantaneous phasor parameters of this iteration, where i is the iteration number.

[0015] Furthermore, when reconstructing the corresponding harmonic components and updating the residual signal based on the instantaneous phasor parameters, the mathematical expression for updating the residual signal is as follows:

[0016] in, This represents the residual signal used in the next iteration. This represents the residual signal of this iteration, where i is the iteration number. This represents the harmonic components of the current iteration and reconstruction.

[0017] Furthermore, when adjusting the target frequency, the mathematical expression is as follows:

[0018] in, This indicates the adjusted target frequency. This indicates the target frequency updated in this iteration based on the spectral characteristics of the residual signal or a preset rule. This represents the average trend of change corresponding to the instantaneous frequency of the previous iteration. This represents the correction factor.

[0019] Furthermore, other iteration stopping conditions include: reaching a preset maximum number of iterations, or the energy decrease of the residual signal being less than a preset value during a specified number of consecutive iterations.

[0020] Furthermore, when calculating the average trend of change using the instantaneous frequency in the instantaneous phasor parameters of the previous iteration, the instantaneous frequency is specifically input into the linear predictor to calculate the corresponding average trend of change.

[0021] Compared with the prior art, the advantages of the present invention are as follows: This invention employs a successive estimation and separation strategy and introduces several adaptive optimization mechanisms: First, iterative initialization optimizes the order of harmonic estimation based on signal spectral energy, prioritizing the extraction of dominant components; second, in each iteration, a dynamic correction mechanism based on historical frequency estimates is introduced to actively compensate for time-varying harmonic frequency shifts; third, an intelligent iteration termination criterion is designed, incorporating multiple conditions such as residual energy thresholds and convergence judgments. By cyclically executing the process of "parameter extraction - signal reconstruction - residual update," the synchronization phasor parameters of all target harmonic components in the signal are ultimately accurately estimated. Through the aforementioned closed-loop, adaptive iterative framework, this invention significantly improves the accuracy and robustness of harmonic measurement in scenarios with dynamic frequency changes and dense spectrum, providing a reliable technical foundation for advanced power quality management applications such as precise harmonic source localization and real-time accurate compensation of active power filters (APFs). Attached Figure Description

[0022] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0023] Figure 2 The results show a comparison between the method of this invention and existing methods under frequency ramp conditions. Figure 2 (a) represents the maximum TVE result. Figure 2 (b) represents the maximum FE result. Figure 2 (c) is the result of maximum RFE. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0025] To address the issues of insufficient dynamic tracking capability and decreased estimation accuracy of traditional measurement methods in highly electronic power distribution networks due to the complexity of harmonic and interharmonic components and time-varying frequencies, this embodiment proposes an Iterative Quadrature Demodulation Estimation (IQDE) method for dynamic harmonic synchronization phasor estimation. It constructs a closed-loop iterative estimation framework that integrates adaptive optimization strategies, achieving high-precision, high-resolution dynamic measurement of each harmonic component through successive decoupling and extraction.

[0026] The technical concept of the method in this embodiment is as follows: S1. Establishment of analytical model for harmonic components based on quadrature demodulation: For the specific frequency harmonic components to be analyzed, establish their discretized in-phase-quadrature component signal model and construct the corresponding digital quadrature reference signal to provide a theoretical basis for subsequent demodulation.

[0027] Specifically, to achieve high-precision estimation of harmonic synchronization phasors, this embodiment employs and improves orthogonal demodulation technology as the core analysis framework. This framework aims to decompose power grid signals containing harmonics and dynamic characteristics into forms that can be accurately characterized by in-phase and quadrature components. Specifically, for a center frequency of... The narrowband signal component can be modeled in the time domain as follows: (1) In the digital implementation of this embodiment, a sampling frequency that satisfies Shannon's sampling theorem and is applicable to the target harmonic frequency band is used. Discretizing the signal yields: (2) To obtain from the sampling sequence Decouple and extract the time-varying coefficients and In this embodiment, a set of frequencies corresponding to the target center frequency is constructed. Synchronized digital quadrature reference signal: (3) Input signal respectively with and Multiplication then leads to the subsequent demodulation process. Demodulation results in low-frequency / DC components and high-frequency components. The DC / low-frequency baseband components include... and The term, whose spectrum is concentrated near zero frequency, carries the amplitude and phase information to be extracted. High-frequency modulation components: those containing... and The spectrum of the item is concentrated at twice the carrier frequency 2. Nearby. Therefore, to accurately separate the baseband components, a low-pass filter must be applied to the demodulated signal to suppress high-frequency modulation components. Within the iterative estimation framework of this embodiment, the design of this low-pass filter needs to comprehensively consider passband flatness, stopband attenuation, phase linearity, and computational complexity to address the varying requirements for dynamic performance and accuracy when extracting different harmonics.

[0028] S2. Extraction and parameterization of target harmonic components: Demodulate the local oscillator signal using the target frequency set in the previous step, and separate the baseband components using a low-pass filter.

[0029] Specifically, this involves separating the signal from the composite signal by a preset target frequency. For specific harmonic components, demodulation and filtering must be performed. This step is fundamental to the subsequent iterative estimation process. The low-pass filter... H The design of (z) requires a trade-off between dynamic response speed and high-frequency suppression capability to accommodate the time-varying characteristics of power grid harmonics. Its cutoff frequency... Should meet < < - The basic constraints. In this embodiment, a Butterworth filter with maximally flat passband characteristics can be used, and its order is... N The appropriate filter can be selected based on requirements for stopband attenuation and computational complexity. The filter's transfer function and implementation design are provided. N The transfer function of the Butterworth low-pass filter is: (4) Where the order N The higher the value, the steeper the stopband attenuation and the better the filtering effect, but the computational complexity also increases accordingly.

[0030] The two filtered output signals are denoted as follows: and They contain the target frequency. The amplitude and phase information of the harmonic component at time [time value missing]. Based on this, the amplitude and phase information of the harmonic at time [time value missing] can be calculated. n Instantaneous phasor parameters: (5) (6) At the same time, its instantaneous frequency It can be estimated through phase difference: (7) Where parameters This is the sampling interval. The frequency estimate here. This can be used in subsequent steps to determine whether the harmonic frequency deviates from the preset value. And provide feedback for possible frequency tracking.

[0031] S3. Iterative estimation framework for dynamic harmonic separation: To accurately estimate multi-component, time-varying harmonic synchronization phasors in power electronic distribution networks, this embodiment proposes an improved iterative orthogonal demodulation framework. This framework is based on the following signal model considering harmonic components: (8) Wherein, parameter k represents the highest order of the harmonics in the signal model, parameter and Indicates time as t The first time i The amplitude and phase of the subharmonic components.

[0032] The core of this framework lies in optimizing key aspects of the traditional iterative QD process by introducing an adaptive strategy to address dynamic frequency shifts and dense spectral interference. The specific iterative steps are as follows: 1. Initialization and Preprocessing Input discrete sampled signal Set the fundamental nominal frequency. (e.g., 50Hz). Initialize a dynamic target frequency list; traditional methods typically set it sequentially. ,3 5 As one of the optimization points, it can also be based on the signal... Preliminary spectral analysis (such as calculating FFT and identifying spectral peaks) is performed, and the list is initialized in descending order of energy to prioritize the estimation of dominant harmonics, thereby improving convergence efficiency and noise robustness. A residual energy threshold is then set. .

[0033] 2. Adaptive Iterative Estimation Process (i-th Iteration) Step 1: Dynamic setting and correction of the target frequency Read the initial value of the target frequency for the current iteration. (From the initialization list or the prediction of the previous iteration). Instead of using the value directly, a frequency correction mechanism is introduced: if the current iteration is the second round or later, the instantaneous frequency sequence estimated in the previous round can be used. Its average trend of change is calculated using a simplified linear predictor. And fine-tune the actual target frequency for this iteration to (9) Where λ is the correction coefficient (0≤λ≤1). This step aims to actively compensate for the dynamic shift of harmonic frequencies and reduce demodulation errors caused by inaccurate preset frequencies.

[0034] Step 2: Component Extraction and Parameter Calculation by The local oscillator frequency is used for the current input signal (the first round is...). The following is the residual signal. Perform the demodulation and filtering operations described in S2 to obtain the instantaneous amplitude of the current target component. With phase shift As another optimization point, the cutoff frequency of the low-pass filter... Based on the current situation The value is adaptively configured. For example, for lower harmonics (such as...) For frequencies <250Hz, a narrower transition band is used to ensure high suppression of adjacent components; for higher harmonics, the transition band requirement is moderately relaxed to maintain a faster dynamic response speed, thereby balancing estimation accuracy and tracking capability overall.

[0035] Step 3: Instantaneous frequency feedback calculation Calculate the instantaneous phase and instantaneous frequency of this component. (10) (11) This calculation Not only is it used to characterize the dynamic properties of the harmonic component, but its statistical characteristics will also be fed back to step 1 to evaluate frequency stability and guide the frequency preset for the next iteration.

[0036] Step 4: Signal Reconstruction and Residual Update Reconstruct the harmonic component using the estimated parameters: (12) Subtract the reconstructed signal from the current input signal to obtain the residual signal for the next iteration: (13) Step 5: Intelligent determination of iteration termination Calculate the residual signal The energy. If it is below the threshold. If the preset maximum number of iterations has been reached, or the residual energy no longer decreases significantly in several consecutive iterations, the iteration terminates, and the parameters of all estimated harmonic components are output. Otherwise, based on the spectral characteristics of the current residual signal or a preset rule (such as the next frequency in the turn list), the initial value of the target frequency is updated, and the process returns to step 1 for the next round of iteration.

[0037] Based on the above ideas, the method in this embodiment is as follows: Figure 1 As shown, it includes the following steps: S101) Acquire discrete sampled signals of grid voltage or current and set the target frequency; Specifically, the initialization and preprocessing steps described above are performed to obtain discrete sampled signals of the grid voltage or current. Set residual energy threshold When setting the target frequency, it specifically refers to the discrete sampled signal. Spectral analysis is performed to identify spectral peaks. The peaks are then sorted in descending order of energy, and their frequencies are arranged into a target frequency list. The first target frequency is then selected from this list. In addition, discrete sampled signals as the initial residual .

[0038] S102) Adaptive iterative estimation, including: In the first iteration (round number i=1): S1021) Calculate the instantaneous phasor parameters based on the target frequency and the discrete sampled signal, specifically according to formula (3) above, with the target frequency... Construct a set of digital quadrature reference signals for the local oscillator frequency and The discrete sampled signal As the input signal and the digital orthogonal reference signal and Multiply each product to obtain the corresponding quadrature demodulation result. Input the quadrature demodulation result into the corresponding low-pass filter for filtering. Then, according to the formulas (5) and (6) above, based on the filtering result... and Calculate instantaneous amplitude With phase shift Then, according to formula (10) mentioned above, based on the phase shift... Calculate instantaneous phase Then, according to the formula (11) mentioned above, based on the instantaneous phase... Calculate instantaneous frequency ; S1022) Reconstruct the corresponding harmonic components and update the residual signal according to the instantaneous phasor parameters, specifically according to the formula (12) above, based on the instantaneous amplitude. With instantaneous phase Calculate the harmonic components of this round of iterative reconstruction. Then, according to the formula (13) above, based on the reconstructed harmonic components... Compared with the initial residual Calculate the residual signal for the second iteration. .

[0039] Starting from the second iteration (round number i≥2): S1023) Update the target frequency based on the spectral characteristics of the residual signal or a preset rule, including: If the target frequency is updated based on the spectral characteristics of the residual signal, then the residual signal in this iteration... Perform spectral analysis and identify spectral peaks, then use the frequency corresponding to the peak with the highest energy as the new target frequency and the initial value of the target frequency for the current iteration. ; If the target frequency is updated based on a preset rule, then the target frequencies are selected sequentially from the target frequency list to obtain the initial value of the target frequency for the current iteration. ; S1024) According to formula (9) above, the instantaneous frequency in the instantaneous phasor parameters of the previous iteration is used. Calculate the average trend of change To adjust the target frequency, i.e., the initial value of the target frequency in the current iteration. To compensate for the dynamic shift of harmonic frequencies, the adjusted target frequency is obtained. ; S1025) Calculate the instantaneous phasor parameters based on the adjusted target frequency and the residual signal, specifically according to formula (3) above, using the adjusted target frequency. Construct a set of digital quadrature reference signals for the local oscillator frequency and The residual signal of this iteration As the input signal and the digital orthogonal reference signal and Multiply each product to obtain the corresponding quadrature demodulation result. Input the quadrature demodulation result into the corresponding low-pass filter for filtering. Then, according to the formulas (5) and (6) above, based on the filtering result... and Calculate instantaneous amplitude With phase shift Then, according to formula (10) mentioned above, based on the phase shift... Calculate instantaneous phase Then, according to the formula (11) mentioned above, based on the instantaneous phase... Calculate instantaneous frequency ; S1026) Reconstruct the corresponding harmonic components and update the residual signal according to the instantaneous phasor parameters, specifically according to the formula (12) above, based on the instantaneous amplitude. With instantaneous phase Calculate the harmonic components of this round of iterative reconstruction. Then, according to the formula (13) above, based on the reconstructed harmonic components... The residual signal of this iteration Calculate the residual signal for the next iteration. ; After each iteration, a decision is made to stop the iteration. If the residual signal is used for the next iteration... If the energy is below the threshold, or other iteration stopping conditions are met (reaching the preset maximum number of iterations, or the energy decrease of the residual signal is less than the preset value in a specified number of consecutive iterations), output the instantaneous phasor parameters (instantaneous amplitude, instantaneous phase, instantaneous frequency) and reconstructed harmonic components of all i iterations.

[0040] As mentioned above, in steps S1021 and S1025, the cutoff frequency of the low-pass filter is adaptively adjusted according to the target frequency or the adjusted target frequency value. Specifically, if the target frequency... Or the adjusted target frequency If the value is less than the specified value (250Hz in this embodiment), the filter order is increased to obtain a narrower transition band to ensure high suppression of adjacent harmonic components. If the target frequency or the adjusted target frequency is greater than the specified value, the filter order is decreased to obtain a wider transition band to maintain a faster dynamic response speed.

[0041] The method in this embodiment executes the above steps in a loop of "parameter extraction - signal reconstruction - residual update" to accurately estimate the synchronization phasor parameters of all target harmonic components in the signal.

[0042] The performance of the method (Iterative Quadrature Demodulation Estimation, IQDE) proposed in this embodiment will be evaluated below with specific experiments.

[0043] To analyze the performance of the proposed method under frequency ramp conditions, the test signal shown in Equation (14) was used: (14) in, ,parameter This represents the rate of change of the fundamental frequency, and is taken as 1.2 Hz / s. Similarly, the harmonic frequencies are expressed as 1.2 Hz / s. h The ramp rate is 49.4 Hz / s. h Linear change to 50.6 h Hz. The sampling window length is 3 nominal periods. Existing methods SIFE, MPME, and CSTM are selected as comparative methods. The estimation results of the method in this embodiment and the comparative methods are as follows: Figure 2 As shown.

[0044] exist Figure 2 In this context, scatter plots are used to represent the estimation results at different harmonic frequencies, while box plots are used to statistically represent these estimation results. From... Figure 2 As can be seen, the maximum TVE, FE, and RFE of the method in this embodiment are 0.48%, 0.059 Hz, and 2.14 Hz / s, respectively. The maximum TVE, FE, and RFE of SIFE are 0.76%, 0.107 Hz, and 5.13 Hz / s, respectively. The maximum TVE, FE, and RFE of MPME are 1.93%, 0.697 Hz, and 6.41 Hz / s, respectively. The maximum TVE, FE, and RFE of CSTM are 2.37%, 0.834 Hz, and 7.81 Hz / s, respectively. Analysis of the above data shows that the method in this embodiment has the highest estimation accuracy and excellent dynamic characteristics.

[0045] In summary, this invention proposes a dynamic harmonic synchronization phasor estimation method and system based on adaptive iterative orthogonal demodulation. Through step-by-step orthogonal demodulation technology, it directly extracts the amplitude and phase information of each harmonic at the sampling point level, achieving sample-level updates of harmonic phasors. This significantly improves the ability to capture transient harmonics and interharmonics, and its time resolution is superior to traditional time-window-based methods. This method does not require assumptions about signal periodicity and can adapt to rapid fluctuations in harmonic frequency and phase, making it particularly suitable for distribution network scenarios with inverter power supplies. It also supports dynamic behavior analysis and source tracing of harmonic sources.

[0046] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0047] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic harmonic synchrophasor estimation method based on adaptive iterative quadrature demodulation, characterized in that, Includes the following steps: Acquire discrete sampled signals of grid voltage or current and set the target frequency; In the first iteration, instantaneous phasor parameters are calculated based on the target frequency and the discrete sampled signal, and the corresponding harmonic components are reconstructed and the residual signal is updated based on the instantaneous phasor parameters. Starting from the second iteration, the target frequency is updated based on the spectral characteristics of the residual signal or a preset rule. The average change trend of the instantaneous frequency in the instantaneous phasor parameters of the previous iteration is used to adjust the target frequency to compensate for the dynamic shift of the harmonic frequency. The instantaneous phasor parameters are calculated based on the adjusted target frequency and the residual signal, and the corresponding harmonic components are reconstructed and the residual signal is updated based on the instantaneous phasor parameters. If the energy of the residual signal is below the threshold, or if other iteration stopping conditions are met, output the instantaneous phasor parameters and reconstructed harmonic components of all iterations.

2. The dynamic harmonic synchrophasor estimation method based on adaptive iterative quadrature demodulation according to claim 1, characterized in that, When setting the target frequency, the specific steps are to perform spectral analysis on the discrete sampled signal and identify the spectral peaks, sort the spectral peaks in descending order of energy, and form a target frequency list by sorting the frequencies of the spectral peaks. Then, the first target frequency is selected from the target frequency list. When updating the target frequency based on the spectral characteristics of the residual signal or a preset rule, the following are included: If the target frequency is updated based on the spectral characteristics of the residual signal, then the residual signal is subjected to spectral analysis and spectral peaks are identified, and the frequency corresponding to the spectral peak with the highest energy is taken as the new target frequency. If the target frequency is updated based on preset rules, then the target frequencies are selected sequentially from the target frequency list.

3. The dynamic harmonic synchrophasor estimation method based on adaptive iterative quadrature demodulation according to claim 1, characterized in that, When calculating the instantaneous phasor parameters based on the target frequency and the discrete sampled signal, a set of digital quadrature reference signals is constructed using the target frequency as the local oscillator frequency. The discrete sampled signal is used as the input signal and multiplied by the digital quadrature reference signals to obtain the corresponding quadrature demodulation results. The quadrature demodulation results are then input into the corresponding low-pass filters for filtering. The instantaneous amplitude and phase shift are calculated based on the filtering results. The instantaneous phase is then calculated based on the phase shift, and finally, the instantaneous frequency is calculated based on the instantaneous phase.

4. The method of claim 1, wherein the method further comprises: When calculating the instantaneous phasor parameters based on the adjusted target frequency and the residual signal, a set of digital quadrature reference signals is constructed using the adjusted target frequency as the local oscillator frequency. The residual signal of this iteration is used as the input signal and multiplied by the digital quadrature reference signals to obtain the corresponding quadrature demodulation results. The quadrature demodulation results are then input into the corresponding low-pass filters for filtering. The instantaneous amplitude and phase shift are calculated based on the filtering results. The instantaneous phase is then calculated based on the phase shift, and finally, the instantaneous frequency is calculated based on the instantaneous phase.

5. The dynamic harmonic synchrophasor estimation method based on adaptive iterative quadrature demodulation according to claim 3 or 4, characterized in that, The cutoff frequency of the low-pass filter is adaptively adjusted according to the target frequency or the adjusted target frequency value. Specifically, if the target frequency or the adjusted target frequency value is less than a specified value, the filter order is increased to obtain a narrower transition band to ensure high suppression of adjacent harmonic components. If the target frequency or the adjusted target frequency value is greater than a specified value, the filter order is decreased to obtain a wider transition band to maintain a faster dynamic response speed.

6. The dynamic harmonic synchronization phasor estimation method based on adaptive iterative orthogonal demodulation according to claim 1, characterized in that, When reconstructing the corresponding harmonic components and updating the residual signal based on the instantaneous phasor parameters, the mathematical expression for reconstructing the corresponding harmonic components is as follows: in, This represents the harmonic components of the current iteration and reconstruction. This represents the instantaneous amplitude in the instantaneous phasor parameters of this iteration. This represents the instantaneous phase in the instantaneous phasor parameters of this iteration, where i is the iteration number.

7. The dynamic harmonic synchronization phasor estimation method based on adaptive iterative orthogonal demodulation according to claim 6, characterized in that, When reconstructing the corresponding harmonic components and updating the residual signal based on the instantaneous phasor parameters, the mathematical expression for updating the residual signal is as follows: in, This represents the residual signal used in the next iteration. This represents the residual signal of this iteration, where i is the iteration number. This represents the harmonic components of the current iteration and reconstruction.

8. The dynamic harmonic synchronization phasor estimation method based on adaptive iterative orthogonal demodulation according to claim 1, characterized in that, When adjusting the target frequency, the mathematical expression is as follows: in, This indicates the adjusted target frequency. This indicates the target frequency updated in this iteration based on the spectral characteristics of the residual signal or a preset rule. This represents the average trend of change corresponding to the instantaneous frequency of the previous iteration. This represents the correction factor.

9. The dynamic harmonic synchronization phasor estimation method based on adaptive iterative orthogonal demodulation according to claim 1, characterized in that, Other iteration stopping conditions include: reaching the preset maximum number of iterations, or the energy decrease of the residual signal being less than a preset value during a specified number of consecutive iterations.

10. A dynamic harmonic synchronization phasor estimation method based on adaptive iterative orthogonal demodulation, characterized in that, When calculating the average trend of change using the instantaneous frequency in the instantaneous phasor parameters of the previous iteration, the instantaneous frequency is specifically input into the linear predictor to calculate the corresponding average trend of change.

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