Method, device and equipment for evaluating power quality and power metering error

Abstract

The application discloses an electric energy quality and electric energy metering error evaluation method, device and equipment, relates to the technical field of distributed energy storage systems, and the electric energy quality and electric energy metering error evaluation method comprises the following steps: collecting electric signals of a distributed energy storage system under the operation of various charging and discharging rates; decomposing the electric signals, extracting time-frequency characteristics of the electric signals, and identifying harmonic components of the electric signals; based on the harmonic components, a comprehensive evaluation model for evaluating electric energy quality and electric energy metering error is constructed; and based on the comprehensive evaluation model, the total harmonic distortion of the distributed energy storage system under the operation of various charging and discharging rates and the corresponding electric energy metering error are fitted. The evaluation method provided by the application has stronger robustness, can be applied to various working scenes and various charging and discharging rates, has better universality, and significantly improves the modeling accuracy during evaluation and the system adaptability of the evaluation method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed energy storage systems, and more particularly, to an evaluation method, device and equipment for power quality and power metering error. BACKGROUND

[0002] With the rapid development of new power loads represented by electric vehicles, traditional centralized power supply gradually changes to a "distributed + flexible energy use" mode to adapt to the use requirements of new power loads, so the distributed energy storage system such as shared battery swapping emerges as the times require.

[0003] In the distributed energy storage system such as shared battery swapping, the charging process is the energy flow from the power grid to the energy storage end, and the discharging process is the reverse power supply from the energy storage end to the power grid. Therefore, in the frequent charging and discharging process, with the dynamic change of the charging and discharging rate, a large number of high-order harmonic components and current peaks or drops will be excited, which destroys the assumption basis of the traditional metering equipment based on the steady-state sinusoidal wave, thereby significantly affecting the accuracy and stability of the power metering result and causing power metering error. In addition, the existing power quality evaluation mainly relies on short-time Fourier transform, which can extract frequency characteristics, but has obvious defects in processing energy storage signals with mutation and non-stationary characteristics, such as spectrum leakage, fixed time-frequency resolution, etc., and it is difficult to reveal the instantaneous behavior and dynamic change process of harmonics.

[0004] Based on this, in the research on the coupling relationship between "charging and discharging rate-harmonic component-power error", it is necessary to solve the problems that the traditional method lacks a systematic modeling framework, often uses empirical curves or rough statistical analysis, and is difficult to accurately reflect the dynamic behavior under high-rate charging and discharging, resulting in that the power quality and power metering error of the distributed energy storage system cannot be accurately evaluated. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the purpose of the embodiments of the present application is to provide an evaluation method, device and equipment for power quality and power metering error, which solves the problems that in the existing distributed energy storage system, due to the dynamic change of the charging and discharging rate accompanied by frequent charging and discharging, a large number of high-order harmonic components, current peaks or drops, etc. are excited, power metering error is caused, and the power quality evaluation cannot reveal the instantaneous behavior and dynamic change process of harmonics.

[0006] To achieve the above-mentioned purpose, the first aspect of the embodiments of the present application provides an evaluation method for power quality and power metering error, comprising:

[0007] Collecting the electrical signals of the distributed energy storage system under each charging and discharging rate operation;

[0008] Decomposing the electrical signals, extracting the time-frequency characteristics of the electrical signals, and identifying the harmonic components of the electrical signals;

[0009] constructing a comprehensive evaluation model for evaluating power quality and power metering error based on the harmonic components;

[0010] fitting the total harmonic distortion and the corresponding power metering error of the distributed energy storage system under each charge-discharge rate based on the comprehensive evaluation model.

[0011] In a preferred embodiment, the acquisition of the electrical signal of the distributed energy storage system under each charge-discharge rate includes:

[0012] high-frequency sampling of the current signal and the voltage signal of the distributed energy storage system under each charge-discharge rate; and recording the acquired current signal and voltage signal to form time-domain data.

[0013] In a preferred embodiment, the decomposition of the electrical signal, the extraction of the time-frequency characteristics of the electrical signal, and the identification of the harmonic components of the electrical signal include:

[0014] selecting a wavelet function as a mother wavelet, and performing continuous wavelet transform on each charge-discharge current signal to obtain a time-frequency coefficient matrix;

[0015] calculating the energy distribution of the charge-discharge current signal in different frequency bands based on the time-frequency coefficient matrix;

[0016] identifying and extracting the harmonic frequency set of the high-frequency part of the charge-discharge current signal according to the energy distribution.

[0017] In a preferred embodiment, before calculating the energy distribution of the charge-discharge current signal in different frequency bands based on the time-frequency coefficient matrix, the method further includes:

[0018] mapping the scale axis of the time-frequency coefficient matrix to the frequency axis, and calculating the energy distribution of the current signal at different frequencies.

[0019] In a preferred embodiment, the construction of the comprehensive evaluation model for evaluating power quality and power metering error includes:

[0020] retaining the significant harmonic terms in the harmonic frequency set and performing Fourier series expansion;

[0021] calculating the total harmonic energy and the total harmonic distortion of the distributed energy storage system.

[0022] In a preferred embodiment, the fitting of the total harmonic distortion and the corresponding power metering error of the distributed energy storage system under each charge-discharge rate based on the comprehensive evaluation model includes:

[0023] The first fitting is performed on each charge and discharge rate and the corresponding total harmonic distortion, and a charge and discharge rate-total harmonic distortion relationship is established;

[0024] The energy measurement error is calculated based on the estimated energy and the actual energy.

[0025] The second fitting is performed on the total harmonic distortion and the corresponding energy measurement error, and a total harmonic distortion-energy measurement error relationship is established.

[0026] In a preferred embodiment, the energy measurement error is calculated based on the estimated energy and the actual energy, and includes:

[0027] The energy is estimated by integrating the instantaneous power according to the watt-hour meter data, and a measurement model is established according to the sampling frequency, integration period and filtering characteristics of the watt-hour meter;

[0028] The bandwidth compensation and phase correction are performed on the electric signal to obtain the corrected voltage signal and current signal, considering all significant harmonic terms in the harmonic frequency set.

[0029] According to the measurement model, the instantaneous power integration is performed on the corrected voltage signal and current signal according to the consistent integration window and sampling reference of the watt-hour meter, to obtain the actual energy.

[0030] The energy measurement error is obtained by comparing the estimated energy and the actual energy.

[0031] In a preferred embodiment, when the bandwidth compensation and phase correction are performed on the electric signal to obtain the corrected voltage signal and current signal, the wavelet reconstruction or Fourier reconstruction is used to perform the bandwidth compensation and phase correction on the electric signal.

[0032] The second aspect of the embodiment of the application provides an evaluation device for power quality and energy measurement error of a distributed energy storage system, including:

[0033] The acquisition module is configured to acquire the electric signal of the distributed energy storage system under each charge and discharge rate;

[0034] The feature extraction module is configured to decompose the electric signal, extract the time-frequency feature of the electric signal, and identify the harmonic component of the electric signal.

[0035] The modeling module is configured to construct a comprehensive evaluation model for evaluating the power quality and energy measurement error based on the harmonic component.

[0036] The execution and fitting module is configured to fit the total harmonic distortion and the corresponding energy measurement error of the distributed energy storage system under each charge and discharge rate based on the comprehensive evaluation model.

[0037] The third aspect of the embodiment of the application provides an electronic device, characterized by comprising:

[0038] a memory for storing a computer program;

[0039] a processor for executing the computer program to implement the method for evaluating power quality and power metering error according to the first aspect of the present application.

[0040] The present application has the following advantages:

[0041] Compared with the traditional method which only relies on fixed parameters or ignores the nonlinear characteristics of signals, the evaluation method provided by the present application has stronger robustness, is applicable to various working scenarios and various charge-discharge ratios, has strong universality, and significantly improves the modeling accuracy and system adaptability of the evaluation method. The evaluation method provided by the present application does not need to replace the existing hardware. After modeling and training on the experimental platform, it can be deployed in actual engineering applications for evaluation and use, and is especially suitable for high-frequency dynamic operation scenarios such as shared battery swap stations, distributed energy storage power grids, and integrated light storage and charging systems. The evaluation method can comprehensively reveal the influence mechanism of the change of the charge-discharge ratio on the power quality and the power metering error under different ratios, and meets the actual use requirements. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 FIG. 1 is a flowchart of the method for evaluating power quality and power metering error;

[0043] Figure 2 FIG. 2 is a schematic diagram of the principle of a harmonic generator;

[0044] Figure 3 FIG. 3 is a nonlinear relationship curve diagram of total harmonic distortion and charge-discharge ratio;

[0045] Figure 4 FIG. 4 is a relationship curve diagram of power metering error and total harmonic distortion;

[0046] Figure 5 FIG. 5 is a nonlinear relationship curve diagram of power metering error and charge-discharge ratio. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0048] Embodiment One

[0049] Please refer to Figure 1 andFigure 2 The embodiment provides an evaluation method for power quality and power metering error, and a distributed energy storage system supporting multiple charging and discharging rates is built. The distributed energy storage system is used to meet the use requirements of new power loads represented by electric vehicles, and the charging and discharging behaviors of the distributed energy storage system occur rapidly, frequently and need to be completed in a short period. The charging and discharging switching of the distributed energy storage system also needs to be managed uniformly to meet the energy supplement requirements of new power loads and good user experience at all times.

[0050] To ensure the stable operation of the distributed energy storage system, accurately reveal and evaluate the influence of the power quality and power metering error of new power loads under each charging and discharging rate when supplementing energy, the evaluation method for power quality and power metering error is provided, which comprises the following steps: collecting the electrical signals of the distributed energy storage system under each charging and discharging rate; decomposing the electrical signals, extracting the time-frequency characteristics of the electrical signals, and identifying the harmonic components of the electrical signals; based on the harmonic components, a comprehensive evaluation model for evaluating the power quality and power metering error is constructed; based on the comprehensive evaluation model, the total harmonic distortion and the corresponding power metering error of the distributed energy storage system under each charging and discharging rate are fitted.

[0051] Based on the built distributed energy storage experimental system that can support multiple charging and discharging rates, the distributed energy storage system is connected to a controllable test environment such as a laboratory simulation power grid; then under each different charging and discharging rate, high-precision power quality analyzers or high-sampling-frequency data acquisition cards are used to collect the current signals, voltage signals and other electrical signals of the distributed energy storage system under each charging and discharging rate; the collected electrical signals are decomposed, such as through continuous wavelet transform to decompose the electrical signals, so as to filter out the noise, fundamental wave, sub-harmonic wave, inter-harmonic wave and other components mixed in the electrical signals, facilitating subsequent feature extraction; then the time-frequency characteristics of the electrical signals are extracted, such as performing Fourier transform operation on the collected electrical signals, converting the electrical signals from time domain to frequency domain, and then extracting the time-frequency characteristics of the electrical signals and identifying the harmonic components; then based on the harmonic components, a comprehensive evaluation model is established to reveal the mapping relationship between the harmonic characteristics and the power quality and power metering error, and based on the comprehensive evaluation model, the total harmonic distortion and the corresponding power metering error of the distributed energy storage system under each charging and discharging rate are fitted to evaluate the influence of the power quality and power metering error of the distributed energy storage system; the trained evaluation model is put into the operation of the actual distributed system, so as to realize the prediction and evaluation automation of the distributed system in actual use.

[0052] By comprehensively collecting the electrical signal characteristics of distributed energy storage systems under different charge and discharge rates, and decomposing these signals to extract their time-frequency characteristics and identify their harmonic components—that is, by distinguishing non-ideal components such as high-frequency harmonics and transient disturbances—a comprehensive evaluation model is constructed to assess power quality and power metering errors. Compared with traditional methods that rely solely on fixed parameters or ignore signal nonlinear characteristics, the power quality and power metering error assessment method provided by this invention has stronger robustness and is applicable to various operating scenarios and charge / discharge rates, exhibiting better universality. Therefore, it significantly improves the modeling accuracy and system adaptability of the assessment method.

[0053] In a further embodiment, the acquisition of electrical signals of the distributed energy storage system at various charge-discharge rates includes: performing high-frequency sampling of the current and voltage signals of the distributed energy storage system at various charge-discharge rates; and recording the acquired current and voltage signals to form time-domain data.

[0054] In other words, when acquiring electrical signals, the acquired signals mainly include current and voltage signals at each operating rate. High-frequency sampling is primarily used to capture high-order harmonics and spike disturbances during charging and discharging. Simultaneously, the high-frequency sampled data is preprocessed and recorded to form time-domain data, ensuring sampling accuracy and guaranteeing that the acquired electrical signals retain all characteristic components affecting power quality and the accuracy of power metering errors, especially high-frequency harmonics and pulse fluctuations.

[0055] S1. Collect electrical signals from the distributed energy storage system at various charge / discharge rates.

[0056] S11. Constructing an experimental platform for a distributed energy storage system.

[0057] Build an experimental platform for a distributed energy storage system that includes a battery pack, a bidirectional adjustable charge and discharge controller, current and / or voltage sensors, and high-precision data acquisition devices.

[0058] This distributed energy storage system experimental platform supports setting multiple charge / discharge rates, and the formula for setting these rates is as follows: ,in, Indicates the first The charge / discharge rates of the group of experiments.

[0059] S12, Acquire electrical signal data

[0060] Current signal for each rate of operation Voltage signal High-frequency sampling is performed, that is: the sampling frequency is The acquired voltage signal Current signal The time-domain data obtained after preprocessing is as follows:

[0061]

[0062] in, For the first time collected Current signal at charge / discharge rates; For the first time collected Voltage signal at charge / discharge rates; Total sampling time The total number of signal sampling points, satisfying k is an integer index from 1 to N.

[0063] By using high-precision power quality analyzers or high-sampling-frequency data acquisition cards to collect voltage and current signals of distributed energy storage systems operating at different rates, high-order harmonics and spike disturbances during charging and discharging can be accurately captured, and the data characteristics of voltage and current signals can be preserved, providing an effective source of basic data for subsequent decomposition of current signals.

[0064] In another further embodiment, the electrical signal is decomposed, its time-frequency features are extracted, and its harmonic components are identified, including: selecting a wavelet function as the mother wavelet, performing continuous wavelet transform on each charging and discharging current signal to obtain a time-frequency coefficient matrix; calculating the energy distribution of the charging and discharging current signal in different frequency bands based on the time-frequency coefficient matrix; and identifying and extracting the set of harmonic frequencies of the high-frequency part of the charging and discharging current signal according to the energy distribution.

[0065] Based on the acquired voltage and current signals, and considering that the harmonic characteristics of the distributed energy storage system change with varying charge and discharge rates, a wavelet function is selected as the mother wavelet to perform continuous wavelet transform on the acquired current signal for precise analysis of non-ideal signals. This yields a time-frequency coefficient matrix, which is then used to decompose the current signal. Before calculating the energy distribution of the charge and discharge current signal in different frequency bands based on the time-frequency coefficient matrix, the process further includes mapping the scale axis of the time-frequency coefficient matrix to the frequency axis and calculating the energy distribution of the current signal at different frequencies. In other words, after calculating the energy distribution of the current signal at different frequencies, the calculated energy reflects the significance of the frequency components of the current signal, allowing for the identification of frequency points with significant energy in the high-frequency range, which are then considered harmonic components.

[0066] Wavelet transform decomposes current signals, providing good resolution in both time and frequency domains. This makes it suitable for analyzing transients and harmonics in distributed energy storage systems during charge / discharge rate operation. Furthermore, wavelet transform can better separate background noise, fundamental frequency, harmonics, and interharmonics, facilitating subsequent harmonic identification, frequency extraction, and the formation of the desired harmonic frequency set. Specifically:

[0067] S2. Decompose the electrical signal, extract time-frequency features, and identify harmonic components.

[0068] S21. Selecting the wavelet function

[0069] Selecting a wavelet function, the Morlet wavelet function is used. As the mother wavelet. Morlet wavelet function. The formula is:

[0070]

[0071] in, For time; The center frequency of the mother wavelet.

[0072] center frequency of mother wavelet The ratio of the number of oscillations to the width of the Gaussian window can be controlled; in signal analysis, It is a common and practical default value.

[0073] when When the frequency is larger, the wavelet oscillates more times in the time domain, resulting in higher frequency resolution, but the time resolution decreases; when... The smaller the value, but not less than 5, the more the wavelet resembles a spike in the time domain, resulting in higher time resolution but lower frequency resolution.

[0074] S22, Decompose current signal

[0075] For the first The current signal at each charge / discharge rate is subjected to continuous wavelet transform, and the wavelet transform formula is as follows:

[0076]

[0077] in, For the first time collected Current signal at charge / discharge rates; For the first The wavelet coefficients after wavelet transform of the current signal at the charge / discharge rate are the local spectral energy of the current signal at scale a and position b; a is the scale factor; b is the time shift parameter.

[0078] S23. Extract time-frequency features

[0079] The time-frequency coefficient matrix obtained based on S22 Mapping the scale axis 'a' to the frequency axis, the formula is:

[0080]

[0081] in, The center frequency of the mother wavelet used; The sampling frequency; This represents the mapped frequency, corresponding to the position of the harmonic frequency.

[0082] S24. Identify harmonic components

[0083] First, calculate the current signal at each frequency component. The energy distribution on the surface is calculated using the following formula:

[0084]

[0085] in, For the first One frequency; For frequency The corresponding scale; In frequency Total energy above; This represents the local energy density.

[0086] By calculating the energy distribution of the current signal at each frequency component. This can reflect the significance of the frequency components of the current signal.

[0087] In the high-frequency range, identify the frequency points with significant energy. These frequency points are considered harmonic components, and the identification formula is as follows:

[0088]

[0089] in, The energy threshold is used to identify the main harmonic components.

[0090] Based on the collected voltage and current signals, wavelet transform is used to quickly decompose the data and convert the signal from the time domain to the frequency domain to obtain the frequency set of the current signal. Based on the calculated frequency components of the current signal, the energy distribution of the current signal at each frequency component is calculated. The energy distribution reflects the significance of the current signal frequency, and thus the harmonic components can be identified.

[0091] Among them, the selected Morlet wavelet function has good time-frequency local characteristics and can analyze the transients and harmonics during charge and discharge rate operation, laying the foundation for subsequent harmonic characteristic modeling.

[0092] In another further embodiment, a comprehensive evaluation model for assessing power quality and power metering error is constructed, including: retaining significant harmonic terms in the harmonic frequency set and performing Fourier series expansion; calculating the total harmonic energy and total harmonic distortion of the distributed energy storage system.

[0093] Based on the set of harmonic frequencies obtained after wavelet continuous transform, the significant harmonic terms in the set are expanded using Fourier series to avoid high-order weak harmonic interference modeling accuracy; then, the total harmonic energy and total harmonic distortion are calculated to ensure that the constructed evaluation model has stronger robustness and higher accuracy. Specifically:

[0094] S3. Fourier series modeling of harmonic properties

[0095] S31, Fourier Series Expansion

[0096] Based on the high-frequency component identification of harmonic components, the main harmonic frequency set is extracted as follows: The significant harmonic terms in this set of harmonic frequencies are retained and expanded using Fourier series, as shown in the formula:

[0097]

[0098] in, Current multiplier The significant harmonic frequencies identified by wavelet transform are shown below. This is the DC component; and These are the corresponding harmonic frequencies. Fourier coefficients.

[0099] S32. Calculate the total harmonic energy and total harmonic distortion.

[0100] The formula for calculating the total harmonic energy (THE) is:

[0101]

[0102] in, ; and These are the corresponding harmonic frequencies. Fourier coefficients.

[0103] The formula for calculating Total Harmonic Distortion (THD) is:

[0104]

[0105] in, and These are the corresponding harmonic frequencies. Fourier coefficients.

[0106] Leveraging the advantages of precise deconstruction and intuitive analysis of Fourier series, Fourier series expansion can avoid compromising the modeling accuracy of evaluation models for high-order weak harmonic interference in harmonic frequencies. During Fourier series expansion, each Fourier coefficient directly corresponds to the amplitude and phase information of a specific harmonic, thus providing complete quantitative information for each harmonic component. The Fourier coefficients obtained from Fourier series expansion, corresponding to the respective harmonic frequencies, form the basis for subsequent calculations of total harmonic energy and total harmonic distortion, ensuring the accuracy of these calculations and improving the robustness of the evaluation model.

[0107] In summary, by combining the time-frequency local analysis capabilities of wavelet transform with the harmonic modeling capabilities of Fourier series, the electrical signal characteristics of distributed energy storage systems under different charge-discharge rates can be comprehensively extracted, especially demonstrating excellent resolution of non-ideal components such as high-frequency harmonics and transient disturbances. Compared with traditional methods that rely solely on fixed parameters or ignore signal nonlinear characteristics, the power quality and metering error assessment method provided by this invention exhibits stronger robustness and is applicable to various operating scenarios and charge-discharge rates, demonstrating better universality. Therefore, it significantly improves the modeling accuracy and system adaptability of the assessment method.

[0108] In another further embodiment, based on the comprehensive evaluation model, the total harmonic distortion (THD) and corresponding energy metering error of the distributed energy storage system under various charge / discharge rates are fitted, including: performing a first fitting between each charge / discharge rate and its corresponding THD to establish a charge / discharge rate-THD relationship; calculating the energy metering error based on the estimated energy and actual energy; and performing a second fitting between the THD and its corresponding energy metering error to establish a THD-energy metering error relationship.

[0109] The energy metering error calculated based on the estimated energy and the actual energy includes: estimating energy by integrating instantaneous power based on energy meter data, and establishing a measurement model based on the energy meter's sampling frequency, integration period, and filtering characteristics; considering all significant harmonic terms in the harmonic frequency set, performing bandwidth compensation and phase correction on the electrical signal to obtain corrected voltage and current signals; according to the measurement model, performing instantaneous power integration on the corrected voltage and current signals using an integration window and sampling reference consistent with the energy meter to obtain the actual energy; and obtaining the energy metering error by comparing the estimated energy and the actual energy.

[0110] When obtaining corrected voltage and current signals by performing bandwidth compensation and phase correction on electrical signals, wavelet reconstruction or Fourier reconstruction is used to perform bandwidth compensation and phase correction on the electrical signals. That is, when calculating actual electrical energy, all significant harmonic terms in the harmonic frequency set are considered, and the corrected signals are obtained through wavelet reconstruction or Fourier reconstruction based on the decomposed electrical signals. Specifically, as follows:

[0111] S4, Deployment, Operation Evaluation Model and Fitting

[0112] S41, Electrical Energy Calculation

[0113] An energy meter was installed on the experimental platform of the distributed energy storage system. The meter readings were recorded at various charge / discharge rates, and the energy was estimated using the instantaneous power integral formula.

[0114]

[0115] Where T is the total sampling duration; For the first time collected Voltage signal at charge / discharge rates; For the first time collected Current signal at charge / discharge rates.

[0116] Considering all significant harmonic terms, the actual electrical energy is calculated using the following formula:

[0117]

[0118] Where T is the total sampling duration; Current multiplier The significant harmonic frequencies identified by wavelet transform are shown below. For significant harmonic frequencies Voltage components at time; For significant harmonic frequencies The current component at that time; It is the set of harmonic frequencies, and .

[0119] In this formula, the significant harmonic frequency Voltage components at time and significant harmonic frequencies Current component at time It can be obtained through wavelet or Fourier reconstruction.

[0120] To ensure the comparability of the energy meter integral estimation results with the reconstruction calculation, this embodiment establishes a measurement model of the energy meter before the calculation.

[0121] The measurement model considers the sampling frequency, integration period, and filtering characteristics of the electricity meter, specifically:

[0122] The overall frequency response of the energy meter was measured using the frequency scanning method. Standard sinusoidal voltage and current signals generated by a signal generator were input to the energy meter, with a frequency range covering 0–2 kHz, in stepped intervals. Incrementing the frequency, the measured power output of the energy meter and the standard power value are recorded at each frequency point. The amplitude ratio and phase difference are calculated to obtain the amplitude-frequency response. Phase frequency response .

[0123] To incorporate the integral period factor into the measurement model, the instantaneous power sampling frequency of the electricity meter is recorded. and integration cycle The integration period reflects the time-averaged window of the energy meter for instantaneous power, and can be represented by a rectangular window function. This indicates that its amplitude-frequency response in the frequency domain is:

[0124]

[0125] This response reflects the high-frequency decay characteristics caused by the integral period.

[0126] To describe the impact of the digital filtering unit inside the energy meter, such as the anti-aliasing filter or the moving average filter, the filter transfer function is extracted by testing with an input step signal or broadband white noise. When the energy meter has a typical finite impulse response (FIR) structure, it can be expressed as:

[0127]

[0128] in, These are the filter coefficients. This represents the filter order.

[0129] Ultimately, the comprehensive measurement model of the electricity meter can be expressed as the following transfer function:

[0130]

[0131] in, The amplitude and phase response of the electricity meter is obtained by frequency scanning. Characterizing the average effect of the integral period, This function reflects the internal filtering characteristics of the energy meter. It reflects the bandwidth limitations, integral period averaging effect, and digital filter delay characteristics within the meter.

[0132] After decomposition, each significant harmonic component During reconstruction, the transfer function is applied to each frequency. Bandwidth compensation and phase correction are performed to obtain the corrected harmonic components. The specific formula is as follows:

[0133]

[0134] in for The frequency domain representation of the signal can be represented in the time domain as either deconvolution or an implementation based on a finite impulse response (FIR) inverse filter, thus yielding the time-domain corrected signal. By synthesizing all significant harmonic components, the corrected overall voltage and current signals are obtained:

[0135]

[0136] To match the meter's sampling frequency, the reconstructed signal (v'_i(t), i'_i(t)) is set to the same frequency as the meter, and the same integration period and window function are used during reconstruction. The corrected actual electrical energy is then calculated under this sampling and integration parameters. :

[0137]

[0138] By and By comparing, the deviation in power metering caused by factors such as harmonic and filter bandwidth limitations, inconsistent sampling frequencies, differences in integration windows, and phase delays can be directly assessed.

[0139] S42. Calculation and Fitting of Electricity Metering Error

[0140] The formula for calculating electricity metering error is:

[0141]

[0142] in, To estimate electrical energy; This refers to actual electrical energy.

[0143] Please see Figure 3 Total Harmonic Distortion With charge / discharge rate The nonlinear relationship curve is plotted, and the total harmonic distortion (THD) is calculated based on S32; the charge / discharge rates in each set of experimental data are then analyzed. Total Harmonic Distortion under Operation The fitting process is performed, and the fitting formula is as follows:

[0144]

[0145] in, , , The fitting coefficients are denoted as .

[0146] Please see Figure 4 Electricity metering error With total harmonic distortion The relationship curve is then used to calculate the electricity metering error. Used at different total harmonic distortion levels Electrical energy measurement error measured under certain conditions The fitting model is performed, and the fitting model formula is:

[0147]

[0148] in, , , The fitting coefficients are denoted as .

[0149] Please see Figure 5 Electricity metering error With charge / discharge rate The nonlinear relationship curve is based on the various charge / discharge rates. Total Harmonic Distortion under Operation Fitting and at different total harmonic distortion levels Electrical energy measurement error measured under certain conditions By performing a fitting test, the electrical energy metering error is obtained. With charge / discharge rate The nonlinear relationship.

[0150] During operation, the electricity consumption of a distributed energy storage system can be estimated through two methods: firstly, by using the electricity meters installed on the system; and secondly, by calculating the actual electricity consumption by considering all significant harmonic terms. The percentage difference between the estimated and actual electricity consumption is then calculated to determine the percentage of electricity metering error. This percentage can, to some extent, assess the influencing factors of electricity metering error. Furthermore, based on the charge / discharge rate during operation, the total harmonic distortion (THD) is fitted. Fitting the electricity metering error based on the THD reflects the direct impact mechanism of harmonic distortion on electricity metering error, enabling predictable and traceable analysis of electricity metering error.

[0151] In summary, by leveraging the time-frequency local analysis capabilities of wavelet transform and the harmonic modeling capabilities of Fourier series, the characteristics of current and voltage signals at various charge / discharge rates can be comprehensively extracted. In particular, it has a good ability to distinguish non-ideal components such as high-frequency harmonics and transient disturbances, providing an accurate and comprehensive data foundation for subsequent modeling. By constructing a mathematical modeling framework of "rate-harmonic-error", the contribution and impact of harmonics on electricity metering errors at various rates can be quantitatively calculated, and the dynamic estimation and source analysis of electricity meter output errors can be supported. It can serve as an auxiliary diagnostic and compensation tool for electricity metering errors.

[0152] Compared to traditional evaluation methods, which often rely on fixed parameters or ignore signal nonlinear characteristics during data acquisition, processing, and modeling, the evaluation method provided by this invention comprehensively considers data characteristics and accurately identifies harmonic components during data acquisition and processing. This results in stronger robustness and universality, making it applicable to various operating scenarios and different power rates. Furthermore, this evaluation method requires no replacement of existing hardware. After modeling and training on an experimental platform, it can be deployed to actual engineering applications for evaluation. It is particularly suitable for high-frequency, dynamic operating scenarios such as shared battery swapping stations, distributed energy storage grids, and integrated photovoltaic-storage-charging systems, further enhancing its universality. It comprehensively reveals the impact mechanism of power rate changes on power quality and metering errors under various operating rates. Moreover, after deployment and application in actual engineering scenarios, the data collected from the specific engineering scenario is processed, fitted, and fed back to the evaluation model itself for self-optimization and upgrading to better meet practical needs.

[0153] Example 2

[0154] This embodiment provides an evaluation device for power quality and power metering error in a distributed energy storage system, including:

[0155] The acquisition module is used to acquire electrical signals of the distributed energy storage system at various charge and discharge rates.

[0156] The feature extraction module is used to decompose the electrical signal, extract the time-frequency features of the electrical signal, and identify the harmonic components of the electrical signal;

[0157] The modeling module is used to construct a comprehensive evaluation model for assessing power quality and power metering error based on the harmonic components.

[0158] The execution and fitting module is used to fit the total harmonic distortion and corresponding energy metering error of the distributed energy storage system under various charge and discharge rates based on the comprehensive evaluation model.

[0159] The evaluation device includes an acquisition module, a feature extraction module, a modeling module, and an execution and fitting module, which can accurately execute the entire evaluation method process of data acquisition, signal processing, model building, and error evaluation in Example 1.

[0160] Specifically, the acquisition module includes a bidirectional adjustable charge / discharge controller, a high-precision current / voltage sensor, a high-sampling-frequency data acquisition card, a synchronous triggering unit, and a data buffer unit. The bidirectional adjustable charge / discharge controller can support multiple charge / discharge rates from 0.5C to 5C, and the high-sampling-frequency data acquisition card should have a sampling frequency of no less than 10kHz. During operation, the acquisition module synchronously acquires voltage and current signals from the constructed distributed energy storage system at different charge / discharge rates. It uses a high-frequency sampling frequency of no less than 10kHz to capture high-order harmonics and spike disturbance signals contained in the charging and discharging process of the distributed energy storage system, ensuring comprehensive acquisition of electrical signal information and avoiding the loss of high-frequency characteristics.

[0161] The feature extraction module includes an FPGA signal processing unit, a wavelet transform calculation unit, a Fourier transform calculation unit, and a harmonic feature storage unit. In use, the acquired electrical signal is first preprocessed, including denoising, filtering, and amplitude calibration. This preprocessed signal is then saved for subsequent feature extraction. Next, the Morlet wavelet function is selected as the mother wavelet, and a continuous wavelet transform is performed on the preprocessed current signal to calculate the time-frequency coefficient matrix. The scale axis is then mapped to the frequency axis, converting the signal from the time domain to the frequency domain. Based on the time-frequency coefficient matrix, the energy distribution of the current signal at each frequency component is calculated. Then, a fast Fourier transform is performed on the voltage and current signals to extract the amplitude and phase information corresponding to each harmonic frequency, providing supplementary features for subsequent modeling. Finally, the time-frequency features obtained from the wavelet transform and the amplitude and phase information obtained from the Fourier series are uploaded to the modeling module.

[0162] The modeling module includes a central processing unit, a model training unit, a data fitting unit, a human-computer interaction unit, and a communication interface unit. Based on the harmonic frequency set and Fourier series modeling of harmonic characteristics in the feature extraction module, a comprehensive evaluation model for power quality and power metering error is constructed.

[0163] The execution and fitting module can fit the total harmonic distortion (THD) based on the charge and discharge rate during operation, and then fit the power metering error based on the THD. By constructing and fitting, it can directly reflect the impact mechanism of harmonic distortion on power metering error, thereby realizing the predictability and source analysis of power metering error.

[0164] After deployment and operation, the evaluation device can retrain the fitting model based on the received field operation data in actual engineering scenarios, so that the comprehensive evaluation model can continuously optimize itself to ensure its evaluation accuracy and better meet the usage needs of application scenarios such as frequent charging and discharging of distributed energy storage systems.

[0165] Example 3

[0166] This embodiment provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the power quality and power metering error evaluation method described in Embodiment 1.

[0167] This electronic device also includes a data acquisition interface, a communication interface, and a power supply. During use, the memory ensures real-time caching of high-frequency sampled data, guaranteeing comprehensive and reliable acquired electrical signal data. Simultaneously, the memory can long-term store experimental data from the constructed distributed energy storage system and training data for the comprehensive evaluation model, supporting subsequent data analysis and model self-updating and iteration. The processor, based on wavelet transform and Fourier transform, performs calculations for the comprehensive evaluation model and fits the power metering error based on total harmonic distortion. This electronic device is suitable for high-frequency dynamic scenarios such as shared battery swapping stations, distributed energy storage grids, and integrated photovoltaic-storage-charging systems. It can be directly used in laboratories or field projects without replacing existing energy storage hardware; evaluation functions can be achieved simply through interface integration.

[0168] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0169] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0170] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0171] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0172] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0173] In conclusion, 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 evaluating power quality and power metering error, characterized in that, include: Collect electrical signals from the distributed energy storage system at various charge / discharge rates; The electrical signal is decomposed, its time-frequency characteristics are extracted, and its harmonic components are identified. Based on the harmonic components, a comprehensive evaluation model for assessing power quality and power metering error is constructed. Based on the comprehensive evaluation model, each charge / discharge rate is fitted to its corresponding total harmonic distortion (THD) for the first time to establish the relationship between charge / discharge rate and total harmonic distortion. Based on the energy metering error calculated from the estimated energy and the actual energy, the total harmonic distortion (THD) is fitted a second time with its corresponding energy metering error to establish the relationship between THD and energy metering error, thus obtaining the THD and corresponding energy metering error of the distributed energy storage system under various charge and discharge rates.

2. The method for evaluating power quality and power metering error according to claim 1, characterized in that, The acquisition of electrical signals from the distributed energy storage system at various charge / discharge rates includes: The current and voltage signals of the distributed energy storage system are sampled at high frequency under various charge and discharge rates; and the collected current and voltage signals are recorded to form time-domain data.

3. The method for evaluating power quality and power metering error according to claim 1 or 2, characterized in that, Decomposing the electrical signal, extracting its time-frequency characteristics, and identifying its harmonic components includes: A wavelet function is selected as the mother wavelet, and continuous wavelet transform is performed on each charging and discharging current signal to obtain the time-frequency coefficient matrix. Based on the time-frequency coefficient matrix, the energy distribution of the charging and discharging current signal in different frequency bands is calculated; Based on the energy distribution, the set of harmonic frequencies of the high-frequency part of the charging and discharging current signal is identified and extracted.

4. The method for evaluating power quality and power metering error according to claim 3, characterized in that, Based on the aforementioned time-frequency coefficient matrix, before calculating the energy distribution of the charging and discharging current signal in different frequency bands, the method further includes: The scale axis of the time-frequency coefficient matrix is ​​mapped to the frequency axis, and the energy distribution of the current signal at different frequencies is calculated.

5. The method for evaluating power quality and power metering error according to claim 4, characterized in that, A comprehensive evaluation model for assessing power quality and power metering errors is constructed, including: Preserve the significant harmonic terms in the harmonic frequency set and perform a Fourier series expansion; Calculate the total harmonic energy and total harmonic distortion of the distributed energy storage system.

6. The method for evaluating power quality and power metering error according to claim 1, characterized in that, The electricity metering error calculated based on the estimated electricity energy and the actual electricity energy includes: Based on the data from the electricity meter, the electrical energy is estimated by integrating the instantaneous power, and a measurement model is established based on the sampling frequency, integration period, and filtering characteristics of the electricity meter. Considering all significant harmonic terms in the harmonic frequency set, bandwidth compensation and phase correction are performed on the electrical signal to obtain the corrected voltage and current signals; Based on the measurement model, the corrected voltage and current signals are instantaneously integrated using an integration window consistent with that of the energy meter and a sampling reference to obtain the actual electrical energy. The energy metering error is obtained by comparing the estimated energy with the actual energy.

7. The method for evaluating power quality and power metering error according to claim 6, characterized in that, When bandwidth compensation and phase correction are performed on electrical signals to obtain corrected voltage and current signals, bandwidth compensation and phase correction are performed on the electrical signals through wavelet reconstruction or Fourier reconstruction.

8. A device for evaluating power quality and metering error in a distributed energy storage system, characterized in that, include: The acquisition module is used to acquire electrical signals of the distributed energy storage system at various charge and discharge rates. The feature extraction module is used to decompose the electrical signal, extract the time-frequency features of the electrical signal, and identify the harmonic components of the electrical signal; The modeling module is used to construct a comprehensive evaluation model for assessing power quality and power metering error based on the harmonic components. The execution and fitting module is used to fit the total harmonic distortion and corresponding energy metering error of the distributed energy storage system under various charge and discharge rates based on the comprehensive evaluation model.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the power quality and power metering error assessment method as described in any one of claims 1 to 7.

Images