Attenuated direct current component measurement method and device, storage medium and electronic equipment

Abstract

The invention discloses an attenuation direct current component measurement method and device, a storage medium and electronic equipment. The method relates to the field of power system detection, and comprises the steps that under the condition that it is detected that a short-circuit fault occurs in a power system, a signal model of the power system is constructed, and the signal model at least comprises a fundamental wave signal, a harmonic wave signal and an attenuation direct current component; converting the attenuation direct current component into a target cosine component based on a signal model; discrete sampling is carried out on the target cosine component to obtain discrete data points; and based on the discrete data points, determining a measurement result of the attenuated direct current component, the measurement result at least comprising a frequency parameter of the attenuated direct current component. According to the invention, the technical problem that the accuracy of the attenuation direct-current component measurement result is low due to the lack of consideration for attenuation direct-current component measurement when the occurrence of the power system is accurate in the prior art, and the deviation of the abnormal analysis result of the power system is caused is solved.

Description

Technical Field

[0001] The present invention relates to the field of power system fault detection, and in particular, to a method, device, storage medium and electronic device for measuring decaying DC components. Background Art

[0002] With a large number of new energy devices and power electronic devices connected to the power system, due to the discontinuity of new energy power generation, the current power system is more prone to short-circuit faults, and at the same time, decaying DC components that interfere with phasor measurement will be generated. The decaying DC component will not only cause certain damage to equipment and devices, but also interfere with the results of phasor measurement, resulting in the inability to obtain accurate information about the phasors in the system. The current phasor estimation algorithm does not consider the influence of the decaying DC component (Decaying Direct Current Component, DDC) on the estimation result, resulting in low accuracy of the decaying DC component measurement result, and further resulting in deviation in the abnormal analysis result of the power system.

[0003] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] Embodiments of the present invention provide a method, device, storage medium and electronic device for measuring decaying DC components, so as to at least solve the technical problem in the related art that when a power system fails accurately, the consideration of measuring decaying DC components is lacking, resulting in low accuracy of the decaying DC component measurement result, and further resulting in deviation in the abnormal analysis result of the power system.

[0005] According to one aspect of the embodiments of the present invention, a method for measuring decaying DC components is provided, including: constructing a signal model of the power system when a short-circuit fault occurs in the detected power system, where the signal model at least includes: fundamental wave signal, harmonic signal, and decaying DC component; based on the signal model, converting the decaying DC component into a target cosine component; discretely sampling the target cosine component to obtain discrete data points; based on the discrete data points, determining the measurement result of the decaying DC component, where the measurement result at least includes the frequency parameter of the decaying DC component.

[0006] Optionally, the converting the decaying DC component into a target cosine component based on the signal model includes: converting the decaying DC component in the signal model into an initial cosine component, where the frequency of the initial cosine component is less than a predetermined frequency; performing frequency optimization on the initial cosine component to obtain the target cosine component.

[0007] Optionally, the frequency optimization of the initial cosine component to obtain the target cosine component includes: optimizing the frequency of the initial cosine component with the minimum power error and energy error as the optimization objectives to obtain the target cosine component; wherein, the power error is used to indicate the difference in power between the model signal and the actual signal of the power system, and the energy error is used to indicate the difference in energy between the model signal and the actual signal of the power system.

[0008] Optionally, the discretized sampling of the target cosine component to obtain discrete data points includes: discretely sampling the target cosine component based on the Shannon sampling theorem to obtain the discrete data points.

[0009] Optionally, the determination of the measurement result of the decaying DC component based on the discrete data points includes: reconstructing the discrete data points based on a sine kernel function to obtain a reconstruction model; determining the measurement result of the decaying DC component based on the reconstruction model and the initial cosine component.

[0010] Optionally, the determination of the measurement result of the decaying DC component based on the reconstruction model and the initial cosine component includes: estimating the frequency parameter of the decaying DC component by using the least squares method based on the reconstruction model and the initial cosine component; obtaining the measurement result based on the frequency parameter.

[0011] According to another aspect of the embodiments of the present invention, there is also provided a decaying DC component measurement device, including: a model construction module, configured to construct a signal model of the power system when detecting a short-circuit fault in the power system, wherein the signal model at least includes: a fundamental wave signal, a harmonic signal, and a decaying DC component; a conversion module, configured to convert the decaying DC component into a target cosine component based on the signal model; a sampling module, configured to discretely sample the target cosine component to obtain discrete data points; a measurement module, configured to determine the measurement result of the decaying DC component based on the discrete data points, wherein the measurement result at least includes the frequency parameter of the decaying DC component.

[0012] According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium storing multiple instructions, and the instructions are suitable for being loaded and executed by a processor to perform any one of the decaying DC component measurement methods.

[0013] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including one or more processors and a memory, where the memory is configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the attenuation DC component measurement method described in any one of the above.

[0014] According to another aspect of the embodiments of the present invention, there is also provided a computer program product, including a computer program, where when the computer program is executed by a processor, the steps of the attenuation DC component measurement method described in any one of the above are implemented.

[0015] In the embodiments of the present invention, when it is detected that a short - circuit fault occurs in the power system, a signal model of the power system is constructed. The signal model at least includes: a fundamental wave signal, a harmonic signal, and an attenuation DC component. Based on the signal model, the attenuation DC component is converted into a target cosine component. Discretized sampling is performed on the target cosine component to obtain discrete data points. Based on the discrete data points, a measurement result of the attenuation DC component is determined, where the measurement result at least includes the frequency parameter of the attenuation DC component. The purpose of accurately identifying and grasping the DDC characteristic parameters by constructing a signal model containing fundamental waves, harmonics, and DDC, converting its DDC into an analyzable target cosine component, and using discrete data points is achieved. Thus, the technical effect of improving the accuracy of attenuation DC component measurement is realized, and further, the technical problem in the related art that when a fault occurs in the power system, the measurement of the attenuation DC component is not considered, resulting in low accuracy of the attenuation DC component measurement result and deviation in the abnormal analysis result of the power system is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0017] Figure 1 is a flowchart of a method for measuring an attenuation DC component according to an embodiment of the present invention;

[0018] Figure 2 is a flowchart of an alternative method for measuring an attenuation DC component according to an embodiment of the present invention;

[0019] Figure 3 is a comparison chart of the fitting errors of two alternative methods for DDC components according to an embodiment of the present invention;

[0020] Figure 4 is a schematic diagram of a device for measuring an attenuation DC component according to an embodiment of the present invention. Detailed implementation mode

[0021] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] First, for the convenience of understanding the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below:

[0024] The decaying direct current component (DDC) refers to a direct current component that gradually decays with time and appears in an alternating current signal in a power system due to a power system fault (such as a short-circuit fault) or other abnormal operating states.

[0025] With the access of a large number of new energy devices and power electronic devices to the power system, due to the discontinuity of new energy power generation, the current power system is more prone to short-circuit faults, and at the same time, a decaying direct current component that interferes with phasor measurement will be generated. The decaying direct current component will not only cause certain damage to equipment and devices, but also interfere with the results of phasor measurement, resulting in the inability to obtain accurate information about the phasors in the system. The current phasor estimation algorithm does not consider the influence of the decaying direct current component (DDC) on the estimation result. Therefore, estimating the DDC component is a necessary process to improve the accuracy of phasor estimation under fault conditions.

[0026] At present, many phasor estimation methods have been proposed in the related art. Phasor estimation algorithms can be roughly divided into algorithms based on Fourier transform and algorithms not based on Fourier transform. For example, in algorithms based on Fourier transform, the Discrete Fourier Transform (DFT) is widely used because of its simple algorithm and fast response time. However, due to inevitable non-coherent sampling in DFT, spectral leakage will occur in the estimation result, resulting in reduced estimation accuracy. In order to reduce the influence of spectral leakage on the estimation result, it is necessary to improve the DFT algorithm process. Interpolated DFT screens three maximum frequency points in the result of Fourier transform and reduces spectral leakage based on the two-point interpolation or three-point interpolation process. However, the DFT-based method will have a large error when estimating dynamic signals. Methods not based on DFT, such as the Taylor Weighted Least Squares (TWLS) method, use the Taylor model in TFT, the least squares algorithm, and the weighted matrix of the sampling window to accurately estimate the fundamental phasor in order to suppress the interference between harmonics. Its disadvantage is that the out-of-band characteristics are poor. At non-nominal frequencies, if a higher accuracy is to be obtained using the Taylor weighted least squares method, the computational burden will be greatly increased. Since this method does not consider the influence brought by the DDC component, there may be a large error in the estimation result, resulting in low accuracy of the measurement result of the decaying DC component, and further resulting in deviation in the abnormal analysis result of the power system.

[0027] In view of the above problems, an embodiment of the present invention provides a method embodiment for measuring the decaying DC component. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0028] Figure 1 is a flowchart of the method for measuring the decaying DC component according to an embodiment of the present invention, as Figure 1 shown, the method includes the following steps:

[0029] Step S102, when it is detected that a short-circuit fault occurs in the power system, construct a signal model of the power system, where the signal model at least includes: fundamental wave signal, harmonic signal, and decaying DC component;

[0030] In step S102 of the present application, when a short-circuit fault occurs in the power system, a comprehensive signal model is constructed, including a fundamental wave signal, a harmonic signal, and a decaying DC component. Modeling in the above manner helps to comprehensively understand the complex characteristics of power signals under fault conditions and provides a mathematical basis for subsequent signal analysis and parameter estimation.

[0031] Optionally, when a short-circuit fault occurs in the power system, a mathematical model of the signal in the power system is established as the signal model. Among them, the model takes into account each harmonic and the decaying DC component, and the formula of the signal model is as follows:

[0032]

[0033] Where x(t) is the signal output when a short-circuit fault occurs in the power system, x d (t) is the DDC component, x1(t) is the fundamental wave signal, x h (t) is the h-th harmonic. λ(t) and τ(t) are the amplitude and time constant of the DDC component respectively. a1(t) and are the amplitude and phase of the fundamental wave respectively, f0 is the fundamental wave frequency. H is the maximum harmonic order, h is the harmonic order, a h (t) and are the amplitude and phase of the h-th harmonic respectively.

[0034] Step S104: Based on the signal model, convert the decaying DC component into a target cosine component;

[0035] In step S104 of this application, the target cosine component can be understood as forming a low-frequency approximation model of the DDC. Converting the decaying DC component into a target cosine component is a method based on frequency-domain analysis. It utilizes the characteristics of the DDC in the frequency spectrum, that is, the DDC can be approximated as a set of cosine signals with specific frequencies. This conversion can transform the decaying characteristics in the time domain into a parameter estimation problem in the frequency domain, enabling the frequency, amplitude, and phase parameters of the DDC to be solved by frequency-domain analysis methods, thereby improving the accuracy and efficiency of DDC parameter estimation.

[0036] In an optional embodiment, based on the signal model, converting the decaying DC component into a target cosine component includes: converting the decaying DC component in the signal model into an initial cosine component, where the frequency of the initial cosine component is less than a predetermined frequency; performing frequency optimization on the initial cosine component to obtain the target cosine component.

[0037] Optionally, converting the DDC into an initial cosine component is a preprocessing step, where the frequency of the initial cosine component is restricted to a lower range. For example, the frequency of the initial cosine component can be restricted to be lower than the frequency of the fundamental wave signal in the power system. This conversion process is essentially an approximate representation of the DDC, converting the time decay characteristic of the DDC into frequency domain parameters for subsequent analysis and processing. The selection of the initial cosine component needs to consider the actual frequency range and signal characteristics of the DDC to ensure the accuracy of the approximate representation. By optimizing the frequency of the initial pre-component, the accuracy of DDC measurement can be significantly improved. By precisely adjusting the frequency of the cosine component, the fitting error can be reduced, making the parameter estimation of the DDC more accurate. Through the frequency optimization process, the parameters of the cosine component can be dynamically adjusted to adapt to the real-time changes of the DDC, thereby improving the generality and adaptability of the measurement method.

[0038] In an optional embodiment, frequency optimization is performed on the initial cosine component to obtain a target cosine component, including: taking the minimum of the power error and the energy error as the optimization objective, and performing frequency optimization on the initial cosine component to obtain the target cosine component; wherein, the power error is used to indicate the difference in power between the model signal and the actual signal of the power system, and the energy error is used to indicate the difference in energy between the model signal and the actual signal of the power system.

[0039] Optionally, the power error refers to the difference in power between the model signal and the actual signal. In signal processing, the power of a signal can be defined as the average value of the energy of the signal over a certain period of time. When approximating the decaying DC component (DDC) with a set of cosine components, ideally, the power of the model signal should exactly match the power of the actual DDC signal. However, due to the limitations of the model and the complexity of the actual signal, there is often a deviation in power between the two. The power error is quantified by calculating the average of the differences in the instantaneous power of the model signal and the real signal at each moment. It can reflect the accuracy of the model within the dynamic range, especially when the power of the signal changes over time.

[0040] The energy error refers to the difference in energy between the model signal and the actual signal. Energy is the cumulative value of a signal over a period of time and can be regarded as the "total intensity" of the signal. When approximating the DDC with multiple low-frequency cosine components, it is expected that the model signal can accurately reproduce the energy characteristics of the actual signal. The energy error is quantified by calculating the difference in the total energy of the model signal and the real signal over the entire sampling period. It focuses on the long-term characteristics of the signal and can reflect the overall fitting effect of the model over the entire period of the signal.

[0041] Optionally, the model signal is the signal representation of the power system in the signal model. The purpose of frequency optimization for the initial cosine components is to find a set of optimal frequency parameters such that the cosine components under these parameters can more accurately represent the DDC. Frequency optimization is usually based on a certain error criterion (such as power error or energy error), and an iterative algorithm (such as gradient descent, least squares method, etc.) is used to adjust the frequencies of the cosine components to minimize the difference between the DDC fitting and the actual situation. The result of this process is a set of target cosine components that can represent the DDC with high precision and maintain a good fitting effect even when the DDC amplitude changes. By taking the minimum of power error and energy error as the optimization goal, it can be ensured that the model signal is as close as possible to the actual signal. Power error focuses on the instantaneous power difference of the signal, while energy error focuses on the energy difference of the signal within the entire sampling window. The minimization of both comprehensively considers the matching of the signal in the time and frequency domains, thereby improving the accuracy of the signal model.

[0042] Optionally, the decaying DC component is approximately represented as an initial cosine component with a finite low frequency. The frequencies of the low-frequency approximation of the decaying DC component are iteratively adjusted according to power error and energy error respectively to obtain the target cosine components that satisfy the optimal frequency combination. Among them, the modeling of the improved decaying DC component (i.e., the initial cosine component) is shown as follows:

[0043]

[0044] where a dm (t), f dm and are the amplitude, frequency, and phase of the m-th cosine function in the above formula respectively, and M is the number of cosine functions. In actual situations, there will inevitably be an error between the DDC component value estimated by the above formula and the true value, so an error signal e(t) is introduced. The definition of e(t) is as follows:

[0045]

[0046] where is the DDC component signal obtained by fitting. The following gives the iterative method for determining the approximate frequency parameter range of the decaying DC component in this embodiment. Define the average power w d (t) (i.e., power error) and the total energy E d (t) (i.e., energy error) of the signal error, where N w is the total number of samples, that is, the number of data points collected from the signal.

[0047]

[0048] Step S106, discretely sample the target cosine components to obtain discrete data points;

[0049] In step S106 of the present application, the target cosine component is discretely sampled to obtain discrete data points. This step can ensure the digitization of signal data and provide a basis for digital signal processing. Based on the Shannon sampling theorem, discrete sampling can guarantee the distortionless reconstruction of the signal as long as the sampling frequency is high enough. The acquisition of discrete data points is a prerequisite for subsequent signal analysis and parameter estimation.

[0050] In an alternative embodiment, discretely sampling the target cosine component to obtain discrete data points includes: based on the Shannon sampling theorem, discretely sampling the target cosine component to obtain discrete data points.

[0051] It should be noted that the decaying DC component (i.e., DDC) is a specific signal in the power system. It usually appears in cases such as short-circuit faults and decays exponentially with time. Since the DDC is a band-limited signal, meaning its spectrum is zero above a certain frequency, the Shannon sampling theorem can be applied to sample it, converting the continuous signal into a series of discrete time-point values, i.e., sampling data. That is, discrete data points refer to the result of uniformly sampling the signal in time at a sampling frequency of at least twice the maximum frequency of the signal according to the Shannon sampling theorem during the signal sampling process.

[0052] Optionally, in signal processing, the Shannon sampling theorem states that if the sampling frequency is at least twice the highest frequency of the signal, then the original signal can be reconstructed without distortion from the discrete data points after sampling. This principle is used for sampling the target cosine component, which can ensure that the signal reconstructed from the discrete data points can accurately reflect the characteristics of the target cosine component, thereby providing a high-quality data basis for subsequent parameter estimation. Sampling in accordance with the Shannon sampling theorem can ensure that under limited computational resources, sufficient data points can be collected at an appropriate sampling frequency to meet the requirements of signal analysis. This can not only improve the efficiency of data acquisition but also ensure the computational efficiency during the data processing, which is crucial for real-time monitoring and fault response. The discrete data points obtained based on the Shannon sampling theorem can be regarded as accurate time-domain samples of the target cosine component. These sample points are used for subsequent frequency parameter estimation, which can ensure the accuracy and reliability of the estimation process. The application of the Shannon sampling theorem is directly related to the accuracy of the measurement results.

[0053] Step S108, determining the measurement result of the decaying DC component based on the discrete data points, where the measurement result includes at least the frequency parameter of the decaying DC component.

[0054] In step S108 of the present application, the frequency parameter of the decaying DC component can be determined based on discrete data points using mathematical tools (such as the least squares method). Through the above steps, especially by converting the DDC into cosine components and performing parameter estimation based on discrete data points, the accuracy of DDC measurement can be effectively improved, and the error in the measurement process can be reduced. Especially when the amplitude of the DDC changes, this method can maintain a high measurement accuracy, which helps to improve the accuracy of real-time fault monitoring and protection in the power system.

[0055] In an alternative embodiment, determining the measurement result of the decaying DC component based on discrete data points includes: reconstructing the discrete data points based on a sine kernel function to obtain a reconstruction model; and determining the measurement result of the decaying DC component based on the reconstruction model and an initial cosine component.

[0056] Optionally, signal reconstruction of discrete data points through a sine kernel function can construct a continuous model closer to the original signal based on the discrete data points obtained by the Shannon sampling theorem. The sine kernel function (i.e., the sinc function) is commonly used in signal processing for signal interpolation and reconstruction, which can provide high-fidelity signal reconstruction and help improve the accuracy of measurement results. The determination of the measurement result based on the reconstruction model and the initial cosine component is actually optimizing the estimation of the frequency parameter. The reconstruction model can more intuitively display the complete characteristics of the signal, including parameters such as frequency, amplitude, and phase, which provides a more refined signal representation for subsequent parameter estimation. By comparing with the initial cosine component, the frequency parameter can be further adjusted and optimized to improve the accuracy of its estimation. Using the sine kernel function for signal reconstruction can simplify the steps of signal processing. Compared with directly performing complex calculations in the time domain, the signal reconstructed by sinc function interpolation is easier to analyze and process in the frequency domain. The above method can not only improve the efficiency of signal processing but also reduce the consumption of computing resources, which is particularly important for real-time signal monitoring and analysis. Reconstructing the signal through the sine kernel function can better handle the noise and interference in the signal. The interpolation characteristics of the sinc function help suppress the high-frequency noise in the signal, making the main component of the signal - the decaying DC component - more clearly displayed, thereby enhancing the robustness of the measurement method in a complex signal environment.

[0057] Optionally, discrete sampling of the decaying DC component is performed to obtain discretized sampling data: Based on the Shannon sampling theorem, a band-limited signal can be represented by a set of sine kernel functions (sinc functions). Specifically, in an initial signal that does not contain frequency components higher than ω Hz, this signal is also called a band-limited signal. Then this signal can be reconstructed by its equally spaced sampling sequence values. According to the Shannon sampling theorem, its sampling frequency should be greater than or equal to 2ω. The sampling function of time-domain sampling is shown as the following formula:

[0058]

[0059] Among them, S n (t) is the sampling function, and n is the discrete sampling point.

[0060] The expression of the reconstructed signal (i.e., the reconstruction model) is as follows:

[0061]

[0062] Among them, t n is the discrete sampling time, t represents the continuous time variable, and the reconstructed signal is a continuous signal reconstructed from the discrete signal sampling values and an appropriate mathematical model. Measuring the decaying DC component, the reconstructed signal means using the sampling values of the signal and a known signal model (such as the sinc function model) to recover the continuous-time waveform of the decaying DC component. Thus, the characteristics of the signal can be analyzed more accurately, and the parameters of the signal, such as frequency, amplitude, and phase, can be extracted.

[0063] On this basis, the phasor modeling of the sinc function of the DDC component is as follows:

[0064]

[0065] Among them, f s is the sampling frequency, k = 1, 2,..., K d represents the model order, and K d is the maximum model order, and K d is set to 2. is the ceiling symbol, and p k,dm is the sample phasor of the m-th approximate cosine signal in the equivalent cosine model at the moment t = k / f s .

[0066] In an optional embodiment, based on the reconstruction model and the initial cosine component, the measurement result of the decaying DC component is determined, including: estimating the frequency parameter of the decaying DC component by using the least squares method based on the reconstruction model and the initial cosine component; obtaining the measurement result based on the frequency parameter.

[0067] Optionally, the least squares method is a mathematical tool widely used in signal processing and parameter estimation. It finds the optimal parameter estimate by minimizing the sum of squared errors. Applying the least squares method to estimate the frequency parameter of the decaying DC component based on the reconstruction model and the initial cosine component provides an effective method for solving parameters. The least squares method can provide accurate estimation of the frequency parameter, especially in the case where there are differences between the model signal and the actual signal. By minimizing the sum of squared errors between the reconstruction model and the actual signal, a set of optimal least squares methods can be found to provide a systematic optimization process that can automatically adjust the parameters to achieve the best fitting effect. This is particularly important for processing dynamically changing signals, such as the decaying DC component in a power system. The above method can not only simplify the calculation process but also ensure the efficiency and reliability of parameter estimation.

[0068] Optionally, after representing the decaying DC component by the sinc function to obtain the reconstruction model, each low-frequency component in the initial cosine component is regarded as a set of phasors to be solved, and the least squares method is used to obtain the frequency parameters of each set to be solved.

[0069] Specifically, the total number of samples N w is set to an odd number so that the t = 0 moment is at the center of the observation window. The discrete form of the approximate DDC component (i.e., the initial cosine component) can be expressed by the following formula:

[0070]

[0071] where * is the conjugate operator, p d is the matrix representation of p d (t); is the conjugate matrix of p d . Φ d is the sampling matrix, is the conjugate matrix of Φ d . x d is the column vector of N w sample points of the cosine signal in the right expression of (3). The elements of the sampling matrix Φ d are Φ k,dm , and the definition of Φ k,dm is shown in the following formula:

[0072]

[0073] The estimated value of the decaying DC component phasor matrix is obtained by the least squares method and the calculation process is shown in the following formula.

[0074] In the matrix , is the element of this matrix, representing the phasor estimate of the DDC component at different moments.

[0075]

[0076] where Ψ d H is the conjugate transpose of Ψ, and the derivative of the DDC phasor form with respect to time can be estimated from its phasor d and is given by the expression below.

[0077]

[0078] where is the estimated frequency of f dm and is defined as follows.

[0079]

[0080] Substituting the conditions n = 0 and t = t0, we can obtain which is the phasor estimate of the DDC component at t = t0. Furthermore, the amplitude and phase of the approximate cosine signal of the DDC component can be obtained. Combining and reconstructs the DDC component. The upper and lower bounds of power and energy are determined using the given boundary conditions of power and energy respectively. By fitting the range of the frequency f d through the upper and lower bounds, the range of the low-frequency f d in this paper is taken as [0.75, 1.25] Hz. That is, when the frequency is between 0.75 Hz and 1.25 Hz, the fitting accuracy of this method can be optimized.

[0081] Through the above steps S102 to S108, the purpose of accurately identifying and grasping the DDC characteristic parameters can be achieved by constructing a signal model including the fundamental wave, harmonics, and DDC, converting its DDC into an analyzable target cosine component, and using discrete data points, thereby realizing the technical effect of improving the accuracy of measuring the decaying DC component, and further solving the technical problem that in the related art, when a fault occurs in the power system, the consideration of measuring the decaying DC component is lacking, resulting in low accuracy of the measurement result of the decaying DC component and thus deviation in the abnormal analysis result of the power system.

[0082] Based on the above embodiments and alternative embodiments, the present invention proposes an alternative implementation manner Figure 2 which is a flowchart of an alternative method for measuring the decaying DC component according to an embodiment of the present invention, as Figure 2 shown, and the method includes:

[0083] S1. Fault signal modeling: When a short-circuit fault occurs in the power system, establish a mathematical model of the signal in the power system as the signal model. ​

[0084] S2. Decay DC component approximation considering power and energy errors: The decay DC component is approximated as an initial cosine component with a finite low frequency. The frequency iteration of the low frequency approximation of the decay DC component is performed according to power error and energy error respectively to obtain the target cosine component that satisfies the optimal frequency combination.

[0085] S4. Discretized sampling of the decay DC component to obtain discretized sampling data: Based on the Shannon sampling theorem, a band-limited signal can be represented by a set of sine kernel functions (sinc functions).

[0086] S5. Low frequency parameter estimation of the decay DC component: After representing the decay DC component by the sinc function to obtain the reconstruction model, each low frequency component in the initial cosine component is regarded as a set of phasors to be solved, and the least squares method is used to obtain the frequency parameters to be solved for each group.

[0087] In the above way, the decay DC component can be estimated with high precision, and at the same time, when the amplitude of the decay DC component changes, it can be measured without reducing the error.

[0088] As an optional embodiment, further verify the effect based on Figure 2 the method shown. Set the number Q of DDC components in the test signal to 1. When fitting the DDC component under the test conditions in this embodiment, the frequencies f d1 , f d2 and f d3 are set to 0.85 Hz, 1.05 Hz and 1.25 Hz respectively, because at these frequencies, the error obtained by iteration is the smallest. In the comparison method, the DDC component in the test signal is Taylor-expanded and the expansion order is set to the second order, so as to fit the DDC component. In each test, the sampling frequency is 10 kHz. In each test scenario, the test results are obtained after 1000 repeated runs.

[0089] The signal model is given by the following formula.

[0090]

[0091] Among them, the fundamental frequency f0 is set to 50 Hz, the maximum harmonic order is H = 13, and the phases of the fundamental wave and each harmonic are uniformly distributed random numbers. Set τ(t) and λ(t) of the DDC signal in the above formula to 0.1 s and 0.1 respectively. In the complete sampling time, the fitting errors (i.e., DDC error) of the two methods for the DDC component are as Figure 3 shown, Figure 3It is a comparison chart of the fitting errors of the DDC component by two alternative methods according to an embodiment of the present invention. The abscissa represents time (in seconds), and the ordinate represents the fitting error (in %). The blue line A represents the DDC error obtained based on the method of this embodiment, and the red line represents the DDC error obtained based on the Taylor weighted least squares method (hereinafter referred to as the Taylor method). It can be seen that the DDC error obtained based on this embodiment is less than the DDC error obtained based on the Taylor method, which further indicates that, compared with the decaying DC component measurement method based on the Taylor method, this embodiment has better effects in measuring the decaying DC component.

[0092] In this embodiment, a decaying DC component measurement device is also provided. This device is used to implement the above-mentioned embodiment and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the terms "module" and "device" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0093] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-mentioned decaying DC component measurement method is also provided. Figure 4 It is a schematic structural diagram of a decaying DC component measurement device according to an embodiment of the present invention. As Figure 4 shown, the above-mentioned decaying DC component measurement device includes: a model construction module 400, a conversion module 402, a sampling module 404, and a measurement module 406, where:

[0094] The model construction module 400 is configured to construct a signal model of the power system when it is detected that a short-circuit fault occurs in the power system. The signal model at least includes: a fundamental wave signal, a harmonic signal, and a decaying DC component.

[0095] The conversion module 402 is configured to convert the decaying DC component into a target cosine component based on the signal model.

[0096] The sampling module 404 is configured to discretely sample the target cosine component to obtain discrete data points.

[0097] The measurement module 406 is configured to determine a measurement result of the decaying DC component based on the discrete data points. The measurement result at least includes a frequency parameter of the decaying DC component.

[0098] In an embodiment of the present invention, by providing a model construction module 400, which is configured to construct a signal model of a power system when detecting a short - circuit fault in the power system, where the signal model at least includes: a fundamental wave signal, a harmonic signal, and a decaying DC component; a conversion module 402, which is configured to convert the decaying DC component into a target cosine component based on the signal model; a sampling module 404, which is configured to discretely sample the target cosine component to obtain discrete data points; and a measurement module 406, which is configured to determine a measurement result of the decaying DC component based on the discrete data points, where the measurement result at least includes a frequency parameter of the decaying DC component, the object of accurately identifying and grasping the characteristic parameters of the DDC by constructing a signal model including fundamental waves, harmonics, and DDC, converting its DDC into an analyzable target cosine component, and using discrete data points is achieved, thereby realizing the technical effect of improving the measurement accuracy of the decaying DC component, and further solving the technical problem that in the related art, when a short - circuit occurs in the power system, the measurement of the decaying DC component is not considered, resulting in low accuracy of the measurement result of the decaying DC component and further causing deviation in the abnormal analysis result of the power system.

[0099] It should be noted that the above - mentioned respective modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following manner: the above - mentioned respective modules can be located in the same processor; or, the above - mentioned respective modules can be located in different processors in any combination.

[0100] It should be noted here that the above - mentioned model construction module 400, conversion module 402, sampling module 404, and measurement module 406 correspond to steps S102 to S108 in the embodiment. The examples and application scenarios implemented by the above - mentioned modules and the corresponding steps are the same, but are not limited to the content disclosed in the above - mentioned embodiment. It should be noted that the above - mentioned modules, as part of the device, can run in a computer terminal.

[0101] It should be noted that the optional or preferred implementation manners of this embodiment can refer to the relevant descriptions in the embodiment, and will not be elaborated here.

[0102] The above - mentioned decaying DC component measurement device may further include a processor and a memory. The above - mentioned model construction module 400, conversion module 402, sampling module 404, measurement module 406, etc. are all stored in the memory as program modules, and the processor executes the above - mentioned program modules stored in the memory to implement corresponding functions.

[0103] The processor contains a kernel which retrieves corresponding program modules from the memory. One or more kernels can be set. The memory may include non-permanent memory in computer-readable media, in the form of random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory includes at least one memory chip.

[0104] According to an embodiment of the present application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, and when the program runs, it controls the device where the non-volatile storage medium is located to execute any of the above attenuation DC component measurement methods.

[0105] Optionally, in this embodiment, the non-volatile storage medium can be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group. The non-volatile storage medium includes a stored program.

[0106] Optionally, when the program runs, it controls the device where the non-volatile storage medium is located to perform the following functions: in the case of detecting a short-circuit fault in the power system, constructing a signal model of the power system, where the signal model at least includes: fundamental wave signal, harmonic signal, and attenuation DC component; based on the signal model, converting the attenuation DC component into a target cosine component; discretely sampling the target cosine component to obtain discrete data points; based on the discrete data points, determining the measurement result of the attenuation DC component, where the measurement result at least includes the frequency parameter of the attenuation DC component.

[0107] According to an embodiment of the present application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, and when the program runs, it executes any of the above attenuation DC component measurement methods.

[0108] According to an embodiment of the present application, an embodiment of a computer program product is also provided. When executed on a data processing device, it is adapted to execute a program initialized with the steps of any of the above attenuation DC component measurement methods.

[0109] Optionally, when the above computer program product is executed on a data processing device, it is adapted to execute a program initialized with the following method steps: in the case of detecting a short - circuit fault in the power system, construct a signal model of the power system, where the signal model at least includes: fundamental wave signal, harmonic signal, and decaying DC component; based on the signal model, convert the decaying DC component into a target cosine component; perform discretized sampling on the target cosine component to obtain discrete data points; based on the discrete data points, determine the measurement result of the decaying DC component, where the measurement result at least includes the frequency parameter of the decaying DC component.

[0110] An embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, the following steps are implemented: in the case of detecting a short - circuit fault in the power system, construct a signal model of the power system, where the signal model at least includes: fundamental wave signal, harmonic signal, and decaying DC component; based on the signal model, convert the decaying DC component into a target cosine component; perform discretized sampling on the target cosine component to obtain discrete data points; based on the discrete data points, determine the measurement result of the decaying DC component, where the measurement result at least includes the frequency parameter of the decaying DC component.

[0111] The order of the above - mentioned embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments.

[0112] In the above - mentioned embodiments of the present invention, the descriptions of the respective embodiments have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the above - mentioned module division can be a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of modules or modules can be in an electrical or other form.

[0114] The above - mentioned modules described as separate components may or may not be physically separated. The components displayed as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, in each embodiment of the present invention, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0116] If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present invention. The aforementioned non-volatile storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs.

[0117] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for measuring the decaying DC component, characterized in that Including: When a short - circuit fault occurs in the power system is detected, a signal model of the power system is constructed, where the signal model at least includes: fundamental wave signal, harmonic signal, and decaying DC component; Based on the signal model, the decaying DC component is converted into a target cosine component; The target cosine component is discretely sampled to obtain discrete data points; Based on the discrete data points, a measurement result of the decaying DC component is determined, where the measurement result at least includes a frequency parameter of the decaying DC component.

2. The method according to claim 1, wherein The converting the decaying DC component into a target cosine component based on the signal model includes: Converting the decaying DC component in the signal model into an initial cosine component, where the frequency of the initial cosine component is less than a predetermined frequency; Performing frequency optimization on the initial cosine component to obtain the target cosine component.

3. The method according to claim 2, wherein The performing frequency optimization on the initial cosine component to obtain the target cosine component includes: Taking the minimum of power error and energy error as the optimization objective, performing frequency optimization on the initial cosine component to obtain the target cosine component; Wherein, the power error is used to indicate the difference in power between the model signal and the actual signal of the power system, and the energy error is used to indicate the difference in energy between the model signal and the actual signal of the power system.

4. The method according to claim 1, wherein The discretely sampling the target cosine component to obtain discrete data points includes: Based on the Shannon sampling theorem, the target cosine component is discretely sampled to obtain the discrete data points.

5. The method according to claim 2, wherein The determining the measurement result of the decaying DC component based on the discrete data points includes: Reconstructing the discrete data points based on a sine kernel function to obtain a reconstruction model; Based on the reconstruction model and the initial cosine component, the measurement result of the decaying DC component is determined.

6. The method according to claim 5, wherein The determining the measurement result of the decaying DC component based on the reconstruction model and the initial cosine component includes: Based on the reconstruction model and the initial cosine component, the least - squares method is used to estimate the frequency parameter of the decaying DC component; Based on the frequency parameter, the measurement result is obtained.

7. A DC component attenuation measurement device, characterized in that, Including: A model construction module, configured to construct a signal model of the power system when a short - circuit fault occurs in the power system is detected, where the signal model at least includes: fundamental wave signal, harmonic signal, and decaying DC component; A conversion module, configured to convert the decaying DC component into a target cosine component based on the signal model; A sampling module, configured to discretely sample the target cosine component to obtain discrete data points; 8. A non-volatile storage medium, characterized in that, A measurement module, configured to determine a measurement result of the decaying DC component based on the discrete data points, where the measurement result at least includes a frequency parameter of the decaying DC component. The non - volatile storage medium stores multiple instructions, and the instructions are adapted to be loaded and executed by a processor to perform the decaying DC component measurement method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, Comprising one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the decaying DC component measurement method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the decaying DC component measurement method according to any one of claims 1 to 6 are implemented.

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