A method for dynamically and high-precision synchronous testing of energy storage converter efficiency

Abstract

The application provides a kind of energy storage converter efficiency dynamic high-precision synchronous test method, belongs to energy storage technical field, specifically is through fusion spectrum analysis and phase synchronous adjustment, real-time compensation current distortion and correct power factor, to obtain pure fundamental signal for energy integration, and then utilize wavelet transform decomposition harmonic energy and construct energy flow path model, again through adaptive correction algorithm optimization path parameters, finally under dynamic operation condition, realized to energy storage converter overall efficiency high-precision, synchronization test.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and more specifically to a method for dynamic high-precision synchronous testing of the efficiency of energy storage converters. Background Technology

[0002] In the field of photovoltaic and other new energy storage and conversion, the efficiency measurement of energy storage converters directly determines the reliability and energy-saving effect of the entire system. It has become a key link in ensuring grid stability and optimizing energy utilization because it can help identify energy losses and improve system performance. Existing measurement methods often ignore the interference of load changes on data accuracy, especially the signal complexity caused by the diversity of load types in actual operation, making it difficult for these methods to maintain consistent accuracy.

[0003] Traditional technologies, relying on fixed parameter settings, cannot cope with sudden fluctuations, leading to deviations in variable environments and affecting the reliability of overall assessments. The nonlinear characteristics of loads cause distortion and phase shifts in current signals, making it difficult for measurement systems to capture the true energy flow path. Nonlinear loads distort current waveforms, causing them to deviate from the standard sinusoidal shape and resulting in phase and voltage asynchrony, thus interfering with the synchronous acquisition of data at the input and output terminals. When energy storage converters are connected to industrial loads containing a large number of harmonics, current distortion amplifies measurement errors, leading to deviations in the calculation of fundamental and harmonic energy, resulting in inaccurate calculations during energy distribution and distorted system efficiency assessments.

[0004] Therefore, how to dynamically adapt to these distortions and offsets under nonlinear load conditions in order to achieve high-precision synchronous acquisition of fundamental and harmonic energy at the input and output ends has become a key issue in improving the efficiency test of energy storage converters. Summary of the Invention

[0005] This invention solves the problem in business scenarios where traditional methods struggle to simultaneously and accurately measure the distribution of fundamental and harmonic energy under the coupled influence of harmonic distortion and power factor shift, leading to inaccurate efficiency assessments.

[0006] In one aspect, this invention provides a method for dynamic high-precision synchronous testing of the efficiency of an energy storage converter, comprising: S1 collects the voltage and current signals at the input and output terminals of the energy storage converter in real time, and uses the Fourier transform algorithm to perform spectrum analysis on the collected current signal to obtain the fundamental component and harmonic components in the current signal.

[0007] S2. Based on the obtained fundamental component and harmonic components, calculate the distortion rate of the current signal. If the distortion rate exceeds the preset threshold, apply compensation filtering to the current signal to determine the compensated current signal.

[0008] S3: Obtain the phase difference between the compensated current signal and the voltage signal, calculate the power factor through the phase difference, and if the power factor is lower than the preset threshold, adjust the phase synchronization mechanism to obtain the phase difference after synchronization adjustment.

[0009] S4 uses the synchronously adjusted phase difference to perform integral calculation on the fundamental energy at the input and output terminals, and determines whether the integral result matches the preset energy model. If they do not match, the integral parameters are iteratively optimized to obtain the optimized fundamental energy value.

[0010] S5. Harmonic energy components are extracted from the optimized fundamental energy value. The harmonic energy components are decomposed using a wavelet transform algorithm to determine the distribution characteristics of each order of harmonic energy.

[0011] S6. Based on the distribution characteristics of each harmonic energy, an energy flow path model is constructed. If the path deviation in the model exceeds a preset threshold, an adaptive correction algorithm is applied to update the path parameters to obtain the corrected energy flow path.

[0012] S7 compares and calculates the overall efficiency of the energy storage converter through the corrected energy flow path, judges the consistency between the comparison result and historical data, and outputs the final efficiency measurement value if the consistency is higher than the preset threshold.

[0013] As a preferred embodiment, step S1 specifically includes: The voltage and current signals at the input and output terminals of the energy storage converter are synchronously sampled by a sensor array. The sampled data are then preliminarily denoised using a digital filter to obtain the filtered voltage and current sequences. Window function weighting is applied to reduce spectral leakage, and then a fast Fourier transform algorithm is executed to calculate the amplitude spectrum and phase spectrum of the current signal. The amplitude and phase information of the fundamental component are extracted from the amplitude spectrum. If the peak value of a specific frequency in the amplitude spectrum exceeds a preset threshold, it is determined that a harmonic exists and the frequency position of each harmonic is marked. For the marked harmonic frequency positions, the harmonic component waveforms are reconstructed using inverse Fourier transform. The complete spectrum model is synthesized by combining the fundamental component information. The parameters of the fundamental component and each harmonic component in the synthesized spectrum model are obtained. The model parameters are adjusted by iterative optimization algorithm to match the original current sequence, and the final fundamental component and each harmonic component are determined.

[0014] As a preferred embodiment, step S2 specifically includes obtaining the total harmonic amplitude value by performing a square root operation based on the obtained fundamental component and each harmonic component, and calculating the ratio with the fundamental amplitude value to obtain the distortion rate value of the current signal. If the distortion rate exceeds the preset threshold, a finite impulse response filter is used to attenuate each harmonic component to generate a preliminary compensation sequence. For the initial compensation sequence, a weighted superposition operation is performed on the fundamental component to synthesize the adjusted current sequence.

[0015] As a preferred embodiment, step S3 specifically includes: The time-domain waveform data of the compensated current signal and voltage signal are obtained, and the instantaneous phase trajectory sequence of each signal is extracted by Hilbert transform. The difference operation is then performed on the trajectory sequence to obtain the initial phase difference sequence. For the initial phase difference sequence, a power factor sequence is generated by cosine function mapping, and it is determined whether the power factor sequence is lower than the threshold. If the power factor sequence is lower than the preset threshold, the phase synchronization mechanism is activated, and adaptive delay compensation is applied to the time-domain waveform data of the voltage signal to generate a delayed voltage waveform sequence. The instantaneous phase trajectory sequence is re-extracted from the delayed voltage waveform sequence and the compensated current signal, and the phase difference sequence is updated by difference operation to obtain the synchronously adjusted phase difference.

[0016] As a preferred embodiment, step S4 specifically includes: The fundamental energy data sequences of the input and output terminals are obtained, and their respective frequency domain component sequences are extracted by Fourier transform and amplitude weighting is performed to obtain the weighted frequency domain energy sequence. For the weighted frequency domain energy sequence, a time domain reconstruction operation is performed using the synchronously adjusted phase difference to generate a reconstructed time domain energy waveform, and the peak energy point sequence is extracted to obtain the peak extraction sequence. The boundary of the integration interval is calculated from the peak extraction sequence, and the initial integrated energy value is obtained by applying a cumulative summation operation to the energy waveform within the boundary of the interval using the numerical integration method. If the initial integral energy value deviates from the benchmark value of the preset energy model by more than a threshold, the iterative loop mechanism is activated to apply adaptive offset adjustment to the boundary of the integral interval, generate the offset-adjusted interval sequence, and recalculate the integral energy value to obtain the iterative integral value. Based on the iterative integral value, the coefficients of the integral parameters are updated using least squares fitting to generate an updated parameter set, which is then applied to the offset-adjusted interval sequence to obtain the parameter-optimized sequence. The fundamental wave energy at the input and output terminals is finally integrated by the parameter optimization sequence, and the calculation result is judged to match the preset energy model, thereby obtaining the optimized fundamental wave energy value.

[0017] As a preferred embodiment, step S5 specifically includes: Harmonic energy component sequences are obtained from the optimized fundamental energy value. Time-domain filtering is applied to the sequences to obtain filtered harmonic component sequences. Wavelet transform algorithm is used to perform multi-scale decomposition to generate multi-level harmonic sub-band sequences. The energy feature vectors of each sub-band are extracted from the multi-level harmonic sub-band sequence. The energy distribution ratio is calculated based on the energy feature vectors, and a normalized distribution ratio sequence is generated from the energy distribution ratio. If there is a sub-band in the normalized distribution ratio sequence with a deviation exceeding a preset threshold, then frequency domain compensation is applied to the sub-band and the energy distribution ratio is recalculated. By applying a classification mapping operation to each harmonic energy through the adjusted distribution sequence, a mapped order energy spectrum is generated, and the distribution characteristics of each harmonic energy are determined from the order energy spectrum.

[0018] As a preferred embodiment, step S6 specifically includes: Based on the distribution characteristics of each harmonic energy, the order sequence and energy amplitude sequence corresponding to the energy peak are extracted. Using graph theory, the order of the energy peak is used as the node, and the gradient of the energy amplitude sequence between adjacent orders is used as the initial weight of the edge to construct an initial directed graph of energy flow. Calculate the cumulative weight deviation of all paths in the directed graph. If the cumulative weight of a certain path deviates from the predicted value of the preset energy decay model by more than a threshold, the path is determined to be abnormal. For the abnormal path, obtain the historical energy distribution characteristics of all nodes on the path, and update the energy amplitude sequence of the nodes using the sliding window mean method. Based on the updated node energy magnitude sequence, the energy magnitude gradient between adjacent nodes is recalculated, a new set of edge weights is generated, and the corresponding edge weights in the initial energy flow directed graph are replaced with the new set of edge weights to obtain the corrected energy flow path model.

[0019] As a preferred embodiment, step S7 specifically includes: Harmonic components of each node within a specific sampling period are extracted based on the corrected energy flow path. Node losses are determined by calculating the energy difference between adjacent nodes and compared with a power reference. The local conversion efficiency of each branch is calculated using the topology of the energy storage converter. By summing up the local conversion efficiencies, the overall efficiency of the energy storage converter is obtained under the current operating conditions using a weighted summation algorithm. The overall efficiency of the energy storage converter is matched with the historical operating conditions of the stored energy, and the Euclidean distance between the two in the deviation matrix is ​​calculated to determine the consistency. If the consistency is higher than a preset threshold, the overall efficiency of the energy storage converter is output as the final efficiency measurement value.

[0020] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the method described thereon.

[0021] In another aspect, the present invention also provides a computer program product, comprising a computer program, characterized in that the computer program, when executed by a processor, implements the method described herein.

[0022] This invention integrates spectrum analysis and phase synchronization adjustment to compensate for current distortion and correct power factor in real time, thereby obtaining a pure fundamental signal for energy integration. Then, wavelet transform is used to decompose harmonic energy and construct an energy flow path model. Finally, an adaptive correction algorithm is used to optimize the path parameters, ultimately achieving high-precision and synchronized testing of the overall efficiency of the energy storage converter under dynamic operating conditions. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0025] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0026] In one aspect, the present invention provides a method for dynamic high-precision synchronous testing of the efficiency of an energy storage converter, comprising the following steps: S1 collects the voltage and current signals at the input and output terminals of the energy storage converter in real time, and uses the Fourier transform algorithm to perform spectrum analysis on the collected current signal to obtain the fundamental component and harmonic components in the current signal.

[0027] The voltage and current signals at the input and output terminals of the energy storage converter are synchronously sampled by a sensor array. High-frequency noise is removed by using a finite impulse response filter such as a low-pass filter to obtain filtered voltage and current sequences. This processing smooths signal fluctuations through convolution operations. For example, for a noisy current sequence, the filter coefficients are designed to be transparent to frequencies below 50Hz fundamental frequency while attenuating higher frequency bands, thereby obtaining a purer sequence for subsequent analysis.

[0028] The filtered current sequence is weighted using a window function to reduce spectral leakage. A Hanning window function is used to multiply the sequence by weighting coefficients, followed by a Fast Fourier Transform (FFT) algorithm to calculate the amplitude and phase spectra. After passing through the window function, a butterfly operation is performed to obtain the frequency domain representation. The amplitude spectrum displays the energy intensity at each frequency point, and the phase spectrum reflects the phase shift. The amplitude and phase information of the fundamental component are extracted from the amplitude spectrum. If a specific frequency peak exceeds a preset threshold, it is determined that a harmonic exists, and the frequency positions of each harmonic are marked. These frequencies are located through peak search; for example, the peaks of the 3rd and 5th harmonics exceeding the threshold are found in the amplitude spectrum, and their precise frequencies are recorded.

[0029] For the marked harmonic frequency positions, the harmonic component waveforms are reconstructed using inverse Fourier transform. After isolating specific frequency components from the spectrum, the time-domain waveform is obtained by inverse transform. Combined with the fundamental component information, a complete spectrum model is synthesized. The model is formed by superimposing the parameters of each component, for example, the fundamental amplitude is 100A and the phase is 0 degrees, plus the 3rd harmonic amplitude is 20A and the phase is 30 degrees, etc. The parameters of the fundamental component and each harmonic component in the synthesized spectrum model are obtained. The model parameters are iteratively optimized and adjusted by the least squares method to match the original current sequence. For example, the residuals of the initial model and the original sequence are compared. If the residuals are large, the parameters are iteratively updated until convergence, thereby determining the final fundamental component and each harmonic component. This method can effectively separate interference, improve system stability, and ultimately achieve accurate harmonic analysis of the converter signal to optimize the control strategy.

[0030] S2. Based on the obtained fundamental component and harmonic components, calculate the distortion rate of the current signal. If the distortion rate exceeds the preset threshold, apply compensation filtering to the current signal to determine the compensated current signal.

[0031] Calculating the harmonic distortion rate is a crucial step in assessing power quality. The total harmonic distortion rate (THR) is obtained by summing the squares of the extracted harmonic current amplitudes and then taking the square root. This square root is then divided by the fundamental frequency amplitude to obtain the THR as a percentage. For example, when an energy storage converter is operating in grid-connected mode, if the detected 5th harmonic amplitude is 5 amps, the 7th harmonic amplitude is 3 amps, and the fundamental frequency amplitude is 100 amps, the THR is approximately 5.83%. The system's preset threshold is typically set at 5% based on grid connection standards. When the THR exceeds this threshold, a compensation sequence needs to be generated.

[0032] The process of generating the compensation sequence uses a finite impulse response filter to selectively attenuate harmonics in a specific frequency band. The core of the filter design lies in its coefficients, which determine the frequency response. For example, for the 5th harmonic at 250 Hz that needs to be suppressed, a band-stop filter with a stopband center frequency of 250 Hz can be designed. By inputting the marked harmonic component sequences into the filter, its output is the harmonic sequence with attenuated amplitude. This sequence serves as the initial compensation sequence.

[0033] Subsequently, the compensation effect needs to be reflected in the original signal by weighted superposition of the preliminary compensation sequence and the fundamental component. For example, the preliminary compensation sequence is multiplied by a negative gain coefficient and then added to the original fundamental component sequence. That is, a component with the opposite phase and proportional amplitude to the harmonics is injected into the original current signal in the opposite direction, thereby canceling the harmonics. The synthesized sequence is the adjusted current sequence.

[0034] S3: Obtain the phase difference between the compensated current signal and the voltage signal, calculate the power factor through the phase difference, and if the power factor is lower than the preset threshold, adjust the phase synchronization mechanism to obtain the phase difference after synchronization adjustment.

[0035] The time-domain waveform data of the compensated current and voltage signals are acquired. These data typically originate from real-time sampling sequences of the energy storage converter grid-connected system. The instantaneous phase trajectory sequences of each signal are extracted using Hilbert transform, converting the original time-domain signals into analytic signals to obtain the instantaneous phase. Specifically, the Hilbert transform performs convolution operations on the signals to generate orthogonal components, and then uses the arctangent function to calculate the phase trajectory sequence. For example, for a voltage signal sequence v(t), its Hilbert transform yields h(v(t)), and the instantaneous phase Φ... v (t)=atan2(h(v(t)),v(t)), and Φ is obtained by similarly processing the current signal. i (t), and then perform a difference operation on these trajectory sequences, i.e., Φ diff (t)=Φ v (t)-Φ i (t), thus obtaining the initial phase difference sequence, which reflects the phase relationship between current and voltage and is used to evaluate synchronization. A power factor sequence is generated using cosine function mapping, where pf(t) = cos(Φ). diff (t) is then compared with a preset threshold of 0.95 to determine if it is below the threshold. This mapping is based on the definition of power factor, which is the ratio of active power to apparent power. When the phase difference is zero, the power factor is 1. If the power factor sequence is below the preset threshold, the phase synchronization mechanism is activated to apply adaptive delay compensation to the time-domain waveform data of the voltage signal.

[0036] This compensation is achieved by estimating the phase difference and introducing a delay, generating a delayed voltage waveform sequence. An adaptive algorithm, such as the minimum mean square error method, is used to dynamically adjust the delay time constant, thereby aligning the voltage waveform with the current signal. The instantaneous phase trajectory sequence is then extracted again from the delayed voltage waveform sequence and the compensated current signal. The phase difference sequence is updated through interpolation, resulting in a synchronized phase difference that ensures the phase consistency of the system.

[0037] S4 uses the synchronously adjusted phase difference to perform integral calculation on the fundamental energy at the input and output terminals, and determines whether the integral result matches the preset energy model. If they do not match, the integral parameters are iteratively optimized to obtain the optimized fundamental energy value.

[0038] The fundamental energy data sequences at the input and output terminals of the energy storage converter are acquired. Fourier transform is applied to these fundamental energy data sequences to extract the frequency domain component sequences at the input terminal, such as converting the time domain signal into a frequency spectrum, where each component represents the energy contribution at a specific frequency. Then, the output terminal sequence is transformed in the same way to obtain its frequency domain component sequence. Then, amplitude weighting processing is performed, assigning greater weight to higher amplitude components according to the energy distribution characteristics. A preset weighting function is used to multiply the component amplitudes by coefficients to obtain the weighted frequency domain energy sequence, highlighting the dominant frequency components and thus avoiding secondary noise interference with the overall energy assessment.

[0039] For the weighted frequency domain energy sequence, a synchronously adjusted phase difference is introduced for time domain reconstruction. Through inverse Fourier transform combined with phase compensation, the frequency domain data is converted back to the time domain, generating a reconstructed time domain energy waveform. The waveform is scanned to identify local maxima, forming a peak extraction sequence. This peak energy point sequence is used for subsequent integration boundary determination. The integration interval boundary is calculated from the peak extraction sequence. The interval between adjacent peak points is selected as the boundary. The trapezoidal rule of numerical integration is used to apply a cumulative summation operation to the energy waveform within the interval to obtain the initial integrated energy value. If this value deviates from the benchmark value of the preset energy model by more than a threshold, an iterative loop mechanism is activated. Here, the preset energy model refers to an ideal energy distribution template constructed based on historical data, such as an energy integration standard simulating normal operation. An adaptive offset adjustment is applied to the integration interval boundary. The boundary is dynamically shifted according to the deviation direction to generate an offset-adjusted interval sequence, and the integrated energy value is recalculated to obtain the iterative integration value.

[0040] Based on the iterative integral value, the least squares fitting is used to update the integral parameters such as the weight coefficients, generating an updated parameter set, which is then applied to the offset-adjusted interval sequence to obtain the parameter optimization sequence. The fundamental energy at the input and output ends is then calculated by the parameter optimization sequence, and the result is judged to match the preset energy model, thereby obtaining the optimized fundamental energy value.

[0041] S5. Harmonic energy components are extracted from the optimized fundamental energy value. The harmonic energy components are decomposed using a wavelet transform algorithm to determine the distribution characteristics of each order of harmonic energy.

[0042] Harmonic energy component sequences are obtained from the optimized fundamental energy value. A time-domain filtering operation is then applied to these sequences to obtain a filtered harmonic component sequence. The time-domain filtering operation can employ a low-pass filter. The input harmonic sequence is fed into the filter, which removes high-frequency noise based on a preset cutoff frequency. For example, convolution is used to multiply the sequence with a filter kernel, thereby smoothing the sequence and preserving the main harmonic components. This process ensures the accuracy of subsequent analysis. The filter kernel can be a weighted sequence generated based on a Gaussian function to reduce edge effects. A wavelet transform algorithm is then used to perform multi-scale decomposition on the filtered sequence, generating a multi-level harmonic sub-band sequence. Wavelet transform algorithms, such as the Daubechies wavelet, recursively decompose the sequence into approximation and detail sub-bands. For example, in the first-level decomposition, convolution is performed on the sequence to extract the low-frequency approximation part and the high-frequency detail part. Then, the approximation part is further decomposed to generate multi-level sub-bands. The advantage of this algorithm is that it captures local features to identify transient changes in harmonics.

[0043] Energy feature vectors for each sub-band are extracted from the multi-level harmonic sub-band sequence. These feature vectors can be obtained by calculating the sum of squares for each sub-band. The distribution ratio is then calculated by dividing each vector value by the total energy value, thereby quantifying the distribution ratio. A normalization process is applied to the energy distribution ratio to generate a normalized distribution ratio sequence. If any sub-band has a deviation exceeding a preset threshold, frequency domain compensation is applied to that sub-band, and the energy distribution ratio is recalculated. For example, the normalization process maps the ratio to the interval between 0 and 1, and the compensation operation, such as multiplying by a correction factor, adjusts the sub-band energy, thereby optimizing the distribution.

[0044] By applying a classification mapping operation to each order of harmonic energy through the adjusted distribution sequence, the classification mapping maps the proportions to a specific order, thereby revealing the distribution pattern and generating a mapped order energy spectrum. The distribution characteristics of each order of harmonic energy are then determined from the order energy spectrum.

[0045] S6. Based on the distribution characteristics of each harmonic energy, an energy flow path model is constructed. If the path deviation in the model exceeds a preset threshold, an adaptive correction algorithm is applied to update the path parameters to obtain the corrected energy flow path.

[0046] For harmonic analysis in energy storage converters, the order sequence and energy amplitude sequence corresponding to the energy peak are first extracted based on the distribution characteristics of each order of harmonic energy. The initial directed energy flow graph is constructed using graph theory, where the order of the energy peak is used as the node, for example, the 3rd, 5th and 7th order are set as nodes. The gradient of the energy amplitude sequence between adjacent orders is used as the initial weight of the edge. The directed edge is generated by calculating the amplitude difference as the gradient, thus forming a directed flow graph from low order to high order.

[0047] The pre-defined energy decay model predicts path weights based on an exponential decay function. It calculates the cumulative weight deviation for all paths, for example, summing the weights for paths from order 3 to 7 and comparing this sum with the model's prediction. If the deviation exceeds a threshold, the path is considered an anomaly. For anomaly-prone paths, the historical energy distribution characteristics of all nodes on that path are obtained. For example, the multi-time period distribution of order 3 nodes is extracted from historical data. Then, a sliding window mean algorithm is used to update the node energy amplitude sequence. A smoothed sequence is calculated using a window, and the energy amplitude gradient between adjacent nodes is recalculated based on the updated sequence. A difference operation generates a new set of edge weights, which replaces the corresponding edge weights in the initial graph, resulting in a corrected energy flow path model. This correction mechanism improves the model's robustness through iterative optimization, thereby reducing misjudgments in practical applications.

[0048] S7 compares and calculates the overall efficiency of the energy storage converter through the corrected energy flow path, judges the consistency between the comparison result and historical data, and outputs the final efficiency measurement value if the consistency is higher than the preset threshold.

[0049] Based on the corrected energy flow path, harmonic components within a specific sampling period are extracted from the path nodes. For example, the voltage and current components of the 3rd and 5th harmonics are acquired within a 1-second sampling period. Node losses are determined by calculating the energy difference between adjacent nodes, such as between the 3rd and 5th harmonics. This difference reflects energy dissipation and is calculated using a simple subtraction method. The node loss is expressed as the absolute value of the difference, thus providing fundamental data for subsequent efficiency assessment. Node losses are compared with a power benchmark, and the local conversion efficiency of each branch is calculated using the topology of the energy storage converter. Specifically, the topology includes multiple parallel branches, and the efficiency of each branch is derived as the ratio of loss to input power.

[0050] The local conversion efficiencies of all branches are summarized, and a weighted summation algorithm is applied under the current operating conditions to obtain the overall efficiency. For example, the weights are based on the branch power ratio, and the weighted overall efficiency is the system average. The overall efficiency is matched with the stored historical operating conditions. Consistency is determined by calculating the Euclidean distance in the deviation matrix. For example, historical features include the efficiency vectors of the past N operating conditions. If the distance is less than a certain threshold, it is considered consistent, and thus the final efficiency measurement value is output.

[0051] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0052] In another aspect, the present invention also proposes a computer program product, comprising a computer program, characterized in that the computer program implements the above-described method when executed by a processor.

[0053] In particular, according to some embodiments of this disclosure, the processes described above can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, it performs the functions defined above in the methods of some embodiments of this disclosure.

[0054] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a task data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated task data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0055] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can interconnect with digital task data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0056] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: determine the network connection status of the switch production line management application in response to detecting a query operation on a production collaboration document in the switch production line management application; replace the webpage entry information corresponding to the production collaboration document with target entry file information and load target webpage resource information in response to determining that the network connection status of the switch production line management application indicates an offline state, so as to display the webpage of the production collaboration document offline in the switch production line management application, wherein the target entry file information is the file information of the entry file corresponding to the webpage of the production collaboration document downloaded in advance, and the target webpage resource information is the resource information corresponding to the webpage stored locally; in response to determining that the network connection status of the switch production line management application indicates an online state and that the webpage resource information corresponding to the production collaboration document is not stored locally, download the webpage resource information of the webpage from the production line document server, wherein the webpage resource information includes an entry file and resource information; display the webpage of the production collaboration document in the switch production line management application according to the webpage resource information, and store the webpage resource information in a local database.

[0057] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including product-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic high-precision synchronous testing of the efficiency of an energy storage converter, characterized in that, include, S1 collects the voltage and current signals at the input and output terminals of the energy storage converter in real time, and uses the Fourier transform algorithm to perform spectrum analysis on the collected current signal to obtain the fundamental component and harmonic components in the current signal. S2. Based on the obtained fundamental component and harmonic components, calculate the distortion rate of the current signal. If the distortion rate exceeds the preset threshold, apply compensation filtering to the current signal to determine the compensated current signal. S3: Obtain the phase difference between the compensated current signal and the voltage signal, calculate the power factor through the phase difference, and if the power factor is lower than the preset threshold, adjust the phase synchronization mechanism to obtain the phase difference after synchronization adjustment. S4 uses the synchronously adjusted phase difference to perform integral calculation on the fundamental energy at the input and output terminals, and determines whether the integral result matches the preset energy model. If they do not match, the integral parameters are iteratively optimized to obtain the optimized fundamental energy value. S5. Harmonic energy components are extracted from the optimized fundamental energy value. The harmonic energy components are decomposed using a wavelet transform algorithm to determine the distribution characteristics of each order of harmonic energy. S6. Based on the distribution characteristics of each harmonic energy, an energy flow path model is constructed. If the path deviation in the model exceeds a preset threshold, an adaptive correction algorithm is applied to update the path parameters to obtain the corrected energy flow path. S7 compares and calculates the overall efficiency of the energy storage converter through the corrected energy flow path, judges the consistency between the comparison result and historical data, and outputs the final efficiency measurement value if the consistency is higher than the preset threshold.

2. The method for dynamic high-precision synchronous testing of energy storage converter efficiency as described in claim 1, characterized in that, Step S1 specifically includes: The voltage and current signals at the input and output terminals of the energy storage converter are synchronously sampled by a sensor array. The sampled data are then preliminarily denoised using a digital filter to obtain the filtered voltage and current sequences. Window function weighting is applied to reduce spectral leakage, and then a fast Fourier transform algorithm is executed to calculate the amplitude spectrum and phase spectrum of the current signal. The amplitude and phase information of the fundamental component are extracted from the amplitude spectrum. If the peak value of a specific frequency in the amplitude spectrum exceeds a preset threshold, it is determined that a harmonic exists and the frequency position of each harmonic is marked. For the marked harmonic frequency positions, the harmonic component waveforms are reconstructed using inverse Fourier transform. The complete spectrum model is synthesized by combining the fundamental component information. The parameters of the fundamental component and each harmonic component in the synthesized spectrum model are obtained. The model parameters are adjusted by iterative optimization algorithm to match the original current sequence, and the final fundamental component and each harmonic component are determined.

3. The method for dynamic high-precision synchronous testing of energy storage converter efficiency as described in claim 2, characterized in that, Specifically, step S2 includes obtaining the total harmonic amplitude value by performing a square root operation based on the obtained fundamental component and each harmonic component, and calculating the ratio with the fundamental amplitude value to obtain the distortion rate value of the current signal. If the distortion rate exceeds the preset threshold, a finite impulse response filter is used to attenuate each harmonic component to generate a preliminary compensation sequence. For the initial compensation sequence, a weighted superposition operation is performed on the fundamental component to synthesize the adjusted current sequence.

4. The method for dynamic high-precision synchronous testing of energy storage converter efficiency as described in claim 3, characterized in that, Step S3 specifically includes: The time-domain waveform data of the compensated current signal and voltage signal are obtained, and the instantaneous phase trajectory sequence of each signal is extracted by Hilbert transform. The difference operation is then performed on the trajectory sequence to obtain the initial phase difference sequence. For the initial phase difference sequence, a power factor sequence is generated by cosine function mapping, and it is determined whether the power factor sequence is lower than the threshold. If the power factor sequence is lower than the preset threshold, the phase synchronization mechanism is activated, and adaptive delay compensation is applied to the time-domain waveform data of the voltage signal to generate a delayed voltage waveform sequence. The instantaneous phase trajectory sequence is re-extracted from the delayed voltage waveform sequence and the compensated current signal, and the phase difference sequence is updated by difference operation to obtain the synchronously adjusted phase difference.

5. The method for dynamic high-precision synchronous testing of energy storage converter efficiency as described in claim 4, characterized in that, Step S4 specifically includes: The fundamental energy data sequences of the input and output terminals are obtained, and their respective frequency domain component sequences are extracted by Fourier transform and amplitude weighting is performed to obtain the weighted frequency domain energy sequence. For the weighted frequency domain energy sequence, a time domain reconstruction operation is performed using the synchronously adjusted phase difference to generate a reconstructed time domain energy waveform, and the peak energy point sequence is extracted to obtain the peak extraction sequence. The boundary of the integration interval is calculated from the peak extraction sequence, and the initial integrated energy value is obtained by applying a cumulative summation operation to the energy waveform within the boundary of the interval using the numerical integration method. If the initial integral energy value deviates from the benchmark value of the preset energy model by more than a threshold, the iterative loop mechanism is activated to apply adaptive offset adjustment to the boundary of the integral interval, generate the offset-adjusted interval sequence, and recalculate the integral energy value to obtain the iterative integral value. Based on the iterative integral value, the coefficients of the integral parameters are updated using least squares fitting to generate an updated parameter set, which is then applied to the offset-adjusted interval sequence to obtain the parameter-optimized sequence. The fundamental wave energy at the input and output terminals is finally integrated by the parameter optimization sequence, and the calculation result is judged to match the preset energy model, thereby obtaining the optimized fundamental wave energy value.

6. The method for dynamic high-precision synchronous testing of energy storage converter efficiency as described in claim 5, characterized in that, Step S5 specifically includes: Harmonic energy component sequences are obtained from the optimized fundamental energy value. Time-domain filtering is applied to the sequences to obtain filtered harmonic component sequences. Wavelet transform algorithm is used to perform multi-scale decomposition to generate multi-level harmonic sub-band sequences. The energy feature vectors of each sub-band are extracted from the multi-level harmonic sub-band sequence. The energy distribution ratio is calculated based on the energy feature vectors, and a normalized distribution ratio sequence is generated from the energy distribution ratio. If there is a sub-band in the normalized distribution ratio sequence with a deviation exceeding a preset threshold, then frequency domain compensation is applied to the sub-band and the energy distribution ratio is recalculated. By applying a classification mapping operation to each harmonic energy through the adjusted distribution sequence, a mapped order energy spectrum is generated, and the distribution characteristics of each harmonic energy are determined from the order energy spectrum.

7. The method for dynamic high-precision synchronous testing of energy storage converter efficiency as described in claim 6, characterized in that, Step S6 specifically includes: Based on the distribution characteristics of each harmonic energy, the order sequence and energy amplitude sequence corresponding to the energy peak are extracted. Using graph theory, the order of the energy peak is used as the node, and the gradient of the energy amplitude sequence between adjacent orders is used as the initial weight of the edge to construct an initial directed graph of energy flow. Calculate the cumulative weight deviation of all paths in the directed graph. If the cumulative weight of a certain path deviates from the predicted value of the preset energy decay model by more than a threshold, the path is determined to be abnormal. For the abnormal path, obtain the historical energy distribution characteristics of all nodes on the path, and update the energy amplitude sequence of the nodes using the sliding window mean method. Based on the updated node energy magnitude sequence, the energy magnitude gradient between adjacent nodes is recalculated, a new set of edge weights is generated, and the corresponding edge weights in the initial energy flow directed graph are replaced with the new set of edge weights to obtain the corrected energy flow path model.

8. The method for dynamic high-precision synchronous testing of energy storage converter efficiency as described in claim 7, characterized in that, Step S7 specifically includes: Harmonic components of each node within a specific sampling period are extracted based on the corrected energy flow path. Node losses are determined by calculating the energy difference between adjacent nodes and compared with a power reference. The local conversion efficiency of each branch is calculated using the topology of the energy storage converter. By summing up the local conversion efficiencies, the overall efficiency of the energy storage converter is obtained under the current operating conditions using a weighted summation algorithm. The overall efficiency of the energy storage converter is matched with the historical operating conditions of the stored energy, and the Euclidean distance between the two in the deviation matrix is ​​calculated to determine the consistency. If the consistency is higher than a preset threshold, the overall efficiency of the energy storage converter is output as the final efficiency measurement value.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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