Broadband oscillation identification method based on nine-point transformation

Abstract

The invention discloses a broadband oscillation identification method based on nine-point transformation, and relates to the technical field of power electronic converters, and the method comprises the steps: collecting a voltage signal at the output end of an inverter through a partial pressure sampler; sampling the voltage signal of the point to be measured according to a set sampling frequency to obtain a discrete signal; performing Hanning window windowing processing on the discrete signal, and then performing fast Fourier transform to obtain a transform result; improving the transformation result by using a nine-point transformation method to obtain a nine-point transformation result; and determining an oscillation region of the signal frequency by using the nine-point transformation result, and performing time-frequency wavelet transformation on the oscillation region to obtain an identification result.

Description

Technical Field

[0001] The present invention relates to the fields of power electronic converter technology and power system monitoring, and in particular to a wide-band oscillation identification method based on nine-point transformation. Background Art

[0002] In recent years, with the rapid development of new energy, the power system has undergone profound changes. The widespread integration of power electronic converters has become a prominent feature and development trend. Converter-based ultra-high voltage direct current (UHVDC) transmission and flexible transmission equipment have been widely used in power systems. The widespread integration of power electronic equipment has significantly changed the dynamic behavior of power systems, triggering new stability and oscillation issues. Overall, as the power grid gradually transitions to power electronics, broadband oscillations are becoming increasingly frequent. This not only affects the safe operation of the grid but also severely restricts the effective absorption of renewable energy sources such as wind power and photovoltaics.

[0003] To address the broadband oscillations that arise in power grids with high power electronics penetration, it is crucial to build a wide-area broadband oscillation monitoring system. This system not only provides the necessary hardware foundation for stable power system operation but also offers critical support for real-time early warning, control, and protection of system oscillations. Broadband oscillation analysis is typically performed using methods such as wavelet transforms. However, wavelet transforms rely on Fourier transforms to determine the frequency range, and fast Fourier transforms (FFTs) suffer from spectrum leakage and picket fence effects, resulting in errors in their spectrum graphs. Summary of the Invention

[0004] The purpose of the present invention is to provide a broadband oscillation identification method based on nine-point transformation, which can obtain the frequency domain characteristics of the broadband oscillation of the test point in real time, reduce the influence of spectrum leakage and fence effect, optimize the frequency selection range of wavelet transform, thereby realizing high-precision monitoring of broadband oscillation of power system, providing key support for the stable operation of power system, real-time early warning, control and protection of oscillation, and also helping to solve the problem of broadband oscillation affecting the effective absorption of renewable energy in power grids with high power electronics penetration.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions:

[0006] A method for identifying broadband oscillations based on nine-point transformation, comprising the steps of:

[0007] At the output end of the inverter, the voltage signal is collected by a voltage divider sampler;

[0008] According to the set sampling frequency, the voltage signal of the measured point is sampled to obtain a discrete signal;

[0009] Performing Hanning window processing on the discrete signal, and then performing fast Fourier transform to obtain a transformation result;

[0010] Using a nine-point transformation method to improve the transformation result, to obtain a nine-point transformation result;

[0011] The nine-point transformation result is used to determine the oscillation region of the signal frequency, and the time-frequency wavelet transformation is performed on the oscillation region to obtain an identification result.

[0012] The broadband oscillation identification method based on nine-point transformation described above further includes collecting a voltage signal at the output end of the inverter through a voltage divider sampler, specifically including:

[0013] The voltage at the output end of the inverter is divided by multiple resistors in series and then filtered by resistors and capacitors to obtain a voltage signal.

[0014] The broadband oscillation identification method based on nine-point transformation described above further samples the voltage signal of the test point at a set sampling frequency to obtain a discrete signal, specifically including:

[0015] Assume that the voltage signal of the test point is:

[0016]

[0017] Where A i is the amplitude, f i is the frequency, θ0 is the signal offset phase, the number of sampling times per unit cycle is N, and the set sampling frequency is f s , its discrete signal is:

[0018]

[0019] Its spectrum is:

[0020]

[0021] The broadband oscillation identification method based on nine-point transformation described above further performs Hanning windowing processing on the discrete signal, specifically including:

[0022] Perform Hanning windowing processing on the discrete signal:

[0023] Hanning window function:

[0024] Rectangular window:

[0025] X Hanning (ω)=X′(ω)·W H (ω)

[0026] When N is greater than the set value, X Hanning (n) is approximately:

[0027]

[0028] The discrete spectrum distribution after Hanning window addition is:

[0029]

[0030] Where, σ=n-n0-Δn0;

[0031] The broadband oscillation identification method based on the nine-point transformation described above further improves the transformation result using the nine-point transformation method to obtain a nine-point transformation result, specifically including:

[0032] X9(n)=aX”(n)+b[X”(n+1)+X”(n-1)]+c[X”(n+2)+X”(n-2)]

[0033] +c[X”(n+3)+X”(n-3)]+d[X”(n+4)+X”(n-4)]

[0034] According to the discrete spectrum distribution after windowing, the coefficients required for the nine-point transform result are derived.

[0035] The broadband oscillation identification method based on the nine-point transform described above further comprises: using the nine-point transform result to determine the oscillation region of the signal frequency, performing a time-frequency wavelet transform on the oscillation region, and obtaining an identification result, specifically including:

[0036] The frequency domain characteristic diagram of the voltage signal is obtained by using the nine-point transformation result, the high-frequency oscillation frequency region is determined according to the frequency domain characteristic diagram, and the time-frequency characteristic of the high-frequency oscillation frequency region is analyzed by time-frequency wavelet transformation to obtain an identification result.

[0037] Compared with the prior art, the present invention has the following advantages: ① The nine-point transform and Hanning window wavelet transform method, combined with the windowed FFT and nine-point transform algorithm, significantly improves the spectral characteristics, greatly reduces spectral leakage and the fence effect, and thus effectively improves the accuracy of frequency identification. ② The method can obtain the broadband oscillation frequency domain characteristics of the test point in real time. By using resistor voltage sampling and advanced high-speed sampling technology, the device of the present invention can accurately collect voltage waveform data at the output end of the inverter in real time, providing a solid and reliable data foundation for subsequent analysis, processing, and fault diagnosis. ③ Based on the processing results of the nine-point transform, the main area of ​​signal frequency oscillation is accurately defined, thereby optimizing the frequency selection range of the wavelet transform. This not only significantly improves the efficiency and accuracy of time-frequency analysis, but also effectively reduces computational complexity and improves overall processing performance. ④ By comprehensively utilizing multiple technical means such as resistor voltage sampling technology, Hanning window processing, nine-point transform algorithm, and wavelet transform, the present invention achieves high-precision monitoring and analysis of broadband oscillations in power systems, providing strong support for the stable operation of power systems and providing new ideas and references for research and application in similar fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 Schematic diagram of sampling at the output end of the inverter in an embodiment of the present invention.

[0040] Figure 2 Schematic diagram of a voltage divider sampler in an embodiment of the present invention.

[0041] Figure 3 4 is a flow chart of a method for identifying broadband oscillation based on nine-point transformation in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0043] Example:

[0044] It should be noted that the terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0045] Figure 1 FIG. 1 is a schematic diagram of sampling at the output end of the inverter in an embodiment of the present invention. Figure 1 As shown, Figure 1 The typical circuit of energy storage battery grid connection through inverter is shown, including energy storage battery cabinet that provides DC power, capacitor and DC / AC inverter connected in parallel at the output end of energy storage battery cabinet, inductor connected in series and capacitor connected in parallel at the AC side of DC / AC inverter, sampling points such as Figure 1 shown.

[0046] Figure 2 FIG is a schematic diagram of a voltage divider sampler in an embodiment of the present invention. Figure 2 As shown, Figure 2 The voltage divider sampler used in an embodiment of the present invention is shown. The sampler includes a left-side "A" connected to the measured voltage terminal, "N" as the reference ground, and Un representing the input AC voltage signal. Six 100KΩ resistors are connected in series, performing the primary voltage divider function, reducing the input high voltage by connecting in series. Upper branch resistors R1 and C1 are connected in parallel, with one end connected to the voltage divider resistor chain output, one end grounded, and one end connected to a signal acquisition or processing unit. Lower branch resistors R2 and C2 are connected in parallel, with one end grounded and one end connected to the signal acquisition or processing unit. R = 100KΩ, R1 = R2 = 50KΩ, and C1 = C2 = 33μF.

[0047] Figure 3 FIG. 1 is a flow chart of a method for identifying broadband oscillations based on nine-point transformation in an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides a wide-band oscillation identification method based on nine-point transformation, comprising the following steps: collecting a voltage signal at the output end of an inverter by a voltage divider sampler; sampling the voltage signal of a test point at a set sampling frequency to obtain a discrete signal; performing Hanning windowing processing on the discrete signal, and then performing fast Fourier transform to obtain a transformation result; improving the transformation result by using a nine-point transformation method to obtain a nine-point transformation result; determining the oscillation region of the signal frequency by using the nine-point transformation result, and performing time-frequency wavelet transform on the oscillation region to obtain an identification result.

[0048] In one embodiment, at the output end of the inverter, a voltage signal is collected by a voltage divider sampler, specifically including:

[0049] The voltage at the output end of the inverter is divided by multiple resistors in series and then filtered by resistors and capacitors to obtain a voltage signal.

[0050] In one embodiment, sampling the voltage signal of the test point at a set sampling frequency to obtain a discrete signal specifically includes:

[0051] Assume that the voltage signal of the test point is:

[0052]

[0053] Where A i is the amplitude, f i is the frequency, θ0 is the signal offset phase, the number of sampling times per unit cycle is N, and the set sampling frequency is f s , its discrete signal is:

[0054]

[0055] Its spectrum is:

[0056]

[0057] In one embodiment, performing Hanning windowing processing on the discrete signal specifically includes:

[0058] Perform Hanning windowing processing on the discrete signal:

[0059]

[0060] Hanning window function:

[0061]

[0062] Rectangular window:

[0063] X Hanning (ω)=X′(ω)·W H (ω)

[0064] When N is greater than the set value, X Hanning (n) is approximately:

[0065]

[0066] The discrete spectrum distribution after Hanning window addition is:

[0067]

[0068] Where, σ=n-n0-Δn0;

[0069] In one embodiment, the transformation result is improved by using a nine-point transformation method to obtain a nine-point transformation result, specifically including:

[0070] X9(n)=aX”(n)+b[X”(n+1)+X”(n-1)]+c[X”(n+2)+X”(n-2)]

[0071] +c[X”(n+3)+X”(n-3)]+d[X”(n+4)+X”(n-4)]

[0072] According to the discrete spectrum distribution after windowing, the coefficients required for the nine-point transform result are derived.

[0073] In one embodiment, the nine-point transformation result is used to determine the oscillation region of the signal frequency, and a time-frequency wavelet transform is performed on the oscillation region to obtain an identification result, which specifically includes:

[0074] The frequency domain characteristic diagram of the voltage signal is obtained by using the nine-point transformation result, the high-frequency oscillation frequency region is determined according to the frequency domain characteristic diagram, and the time-frequency characteristic of the high-frequency oscillation frequency region is analyzed by time-frequency wavelet transformation to obtain an identification result.

[0075] The following is an overall description of the broadband oscillation identification method based on the nine-point transformation.

[0076] Get the voltage signal (or current signal) of the power system and set the measured voltage signal to Among them A i is the amplitude, f i is the frequency, θ0 is the signal offset phase, and the number of sampling times per unit cycle is N. Assume that the sampling frequency is f s . Its sampling signal is:

[0077]

[0078] Its spectrum is:

[0079]

[0080] Apply Hanning window processing to the system voltage signal:

[0081]

[0082] Window the signal using the Hanning window:

[0083] X Hanning (ω)=X′(ω)·W H (ω) (5)

[0084] To X Hanning (n), when N is large, it can be approximated as:

[0085]

[0086] A new spectrum sequence can be generated by performing polynomial transformation on nine adjacent points in the spectrum sequence obtained by the windowed FFT algorithm.

[0087] X9(n)=a

[0088] The discrete spectrum distribution of the signal after Hanning window addition is:

[0089]

[0090] in

[0091] σ=n-n0-Δn0 (9)

[0092]

[0093] According to (7)(8), we can get:

[0094] a=1 / 25920, b=-1 / 32400, c=1 / 64800, d=-1 / 226800, e=1 / 1814400.

[0095] The above calculation method can obtain the frequency domain characteristic diagram of the electrical signal and determine the high-frequency oscillation frequency region of the system [x p ,x q ], where x p ,x q is the starting frequency and ending frequency of the oscillation, and then the time-frequency characteristics of the region are analyzed through time-frequency wavelet transform.

[0096] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0097] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made based on the essence of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. A broadband oscillation identification method based on nine-point transformation, characterized in that: Including steps: At the output end of the inverter, the voltage signal is collected by a voltage divider sampler; According to the set sampling frequency, the voltage signal of the measured point is sampled to obtain a discrete signal; Performing Hanning window processing on the discrete signal, and then performing fast Fourier transform to obtain a transformation result; Using a nine-point transformation method to improve the transformation result, to obtain a nine-point transformation result; The nine-point transformation result is used to determine the oscillation region of the signal frequency, and the time-frequency wavelet transformation is performed on the oscillation region to obtain an identification result.

2. The broadband oscillation identification method based on nine-point transformation according to claim 1, characterized in that: At the output end of the inverter, the voltage signal is collected by a voltage divider sampler, specifically including: The voltage at the output end of the inverter is divided by multiple resistors in series and then filtered by resistors and capacitors to obtain a voltage signal.

3. The broadband oscillation identification method based on nine-point transformation according to claim 1, characterized in that: According to the set sampling frequency, the voltage signal of the test point is sampled to obtain a discrete signal, which includes: Assume that the voltage signal of the point to be measured is: Where A i is the amplitude, f i is the frequency, θ0 is the signal offset phase, the number of sampling times per unit cycle is N, and the set sampling frequency is f s , its discrete signal is: Its spectrum is:

4. The broadband oscillation identification method based on nine-point transformation according to claim 3, characterized in that: Performing Hanning windowing processing on the discrete signal specifically includes: Perform Hanning windowing processing on the discrete signal: Hanning window function: Rectangular window: When N is greater than the set value, X Hanning (n) is approximately: The discrete spectrum distribution after Hanning window addition is: In the formula, σ=n-n0-Δn0; 5. The broadband oscillation identification method based on nine-point transformation according to claim 4, characterized in that: The transformation result is improved by using the nine-point transformation method to obtain a nine-point transformation result, specifically including: X9(n)=aX”(n)+b[X”(n+1)+X”(n-1)]+c[X”(n+2)+X”(n-2)] +c[X”(n+3)+X”(n-3)]+d[X”(n+4)+X”(n-4)] According to the discrete spectrum distribution after windowing, the coefficients required for the nine-point transform result are derived.

6. The broadband oscillation identification method based on nine-point transformation according to claim 5, characterized in that: The oscillation region of the signal frequency is determined by using the nine-point transformation result, and the oscillation region is subjected to time-frequency wavelet transformation to obtain an identification result, which specifically includes: The frequency domain characteristic diagram of the voltage signal is obtained by using the nine-point transformation result, the high-frequency oscillation frequency region is determined according to the frequency domain characteristic diagram, and the time-frequency characteristic of the high-frequency oscillation frequency region is analyzed by time-frequency wavelet transformation to obtain an identification result.

Images