Power system multi-frequency oscillation detection method, device, equipment and medium

Abstract

The invention discloses a multi-frequency oscillation detection method, device, equipment and medium for a power system, and relates to the technical field of frequency oscillation detection, and the method comprises the steps: obtaining an oscillation signal of an oscillation component through a power grid signal of the power system; processing the oscillation signal of the oscillation component by adopting a sliding window discrete Fourier transform algorithm and an abc / dq coordinate transformation matrix to obtain an oscillation characteristic parameter of the oscillation signal; and acquiring a multi-frequency oscillation detection result of the power system according to the oscillation characteristic parameter of the oscillation signal. According to the method, the oscillation signal containing multiple frequencies can be detected, the approximate frequency of the oscillation signal is firstly determined through the low-resolution SDFT algorithm, then specific directional detection is carried out through channels, and compared with an existing discrete Fourier transform algorithm, a fast Fourier transform algorithm, a Prony algorithm, wavelet transform and other full-band detection methods, calculation resources can be effectively saved, and the detection efficiency is improved. The system load is reduced.

Description

Technical Field

[0001] This invention relates to the field of frequency oscillation detection technology, and in particular to a method, apparatus, equipment and medium for detecting multi-frequency oscillations in power systems. Background Technology

[0002] With the large-scale integration of new energy sources into the power system and the continuous improvement of power electronics levels, the dynamic response speed of new power systems has increased significantly, making them more prone to oscillation problems. Therefore, to effectively prevent oscillation accidents, the primary task is to achieve accurate oscillation detection quickly and efficiently. However, existing oscillation detection methods, such as the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), Prony algorithm, wavelet transform, and frequency band detection using filters, generally suffer from long detection times and high computational costs. Specifically, while DFT and FFT algorithms can detect the frequency and amplitude of oscillating signals, they require a complete sampling period to obtain frequency and amplitude information; Prony and wavelet transform algorithms can identify the frequency and amplitude information of oscillating signals, but their algorithm complexity is high and computation time is long; frequency band detection using filters involves detection of channels in all frequency bands, leading to wasted resources and increased computational load.

[0003] Therefore, there is an urgent need for a method, device, equipment, and medium for detecting multi-frequency oscillations in power systems to address the shortcomings of existing technologies. Summary of the Invention

[0004] The purpose of this invention is to propose a method, device, equipment, and medium for detecting multi-frequency oscillations in power systems, in order to solve the problem that existing detection algorithms cannot meet the needs of power systems for rapid detection of oscillation signals, and to provide timely information on the frequency and amplitude of oscillation signals when oscillations occur in a new type of power electronic power system.

[0005] In a first aspect, to achieve the above objective, the present invention provides a method for detecting multi-frequency oscillations in a power system, comprising the following steps:

[0006] S1. Obtain the oscillation signal of the oscillation component using the power grid signal of the power system;

[0007] S2. The oscillation signal of the oscillation component is processed by the sliding window discrete Fourier transform algorithm and the abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal;

[0008] S3. Obtain the multi-frequency oscillation detection results of the power system based on the oscillation characteristic parameters of the oscillation signal.

[0009] Optionally, S1, using the power grid signal of the power system, obtain the oscillation signal of the oscillation component, including:

[0010] Signal acquisition is performed using instrument transformers to obtain power grid signals from the power system;

[0011] Based on the power grid signal of the power system, obtain the corresponding power frequency signal;

[0012] Based on the power grid signal of the power system, the oscillation signal of the oscillation component is obtained using the power frequency signal.

[0013] Optionally, S2, the oscillation signal of the oscillation component is processed using a sliding window discrete Fourier transform algorithm and an abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal, including:

[0014] The target oscillation frequency of the oscillation signal is obtained by using a sliding window discrete Fourier transform algorithm based on the oscillation component.

[0015] The oscillation signal of the oscillation component is filtered using the target oscillation frequency of the oscillation signal to obtain the filtered oscillation signal;

[0016] Based on the filtered oscillation signal and the target oscillation frequency of the oscillation signal, coordinate transformation is performed using the abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal.

[0017] Optionally, the target oscillation frequency of the oscillation signal is obtained by employing a sliding window discrete Fourier transform algorithm based on the oscillation component, including:

[0018] The oscillation signal of the oscillation component is processed using a sliding window discrete Fourier transform algorithm to obtain candidate frequencies and candidate amplitudes of the oscillation signal. The calculation formula of the sliding window discrete Fourier transform algorithm is as follows:

[0019] X k (n)=e j2πk / N [X k [(n-1)+x(n)-x(nN)]

[0020] Among them, X k (n) is the frequency domain signal at time n, e j2πk / N X is the phase shift factor, j represents the imaginary unit, k represents the k-th frequency point in the spectrum, N represents the number of spectrum calculation points, and X... k (n-1) is the frequency domain signal at time n-1, x(n) is the time domain sampled signal at time n, and x(nN) is the first sampled value in the sampled data window;

[0021] Based on the candidate amplitudes of the oscillation signal, a preset threshold is used to obtain the target amplitude of the oscillation signal;

[0022] Based on the target amplitude of the oscillation signal and the candidate frequency of the oscillation signal, the candidate frequency of the oscillation signal corresponding to the target amplitude is obtained as the target oscillation frequency of the oscillation signal.

[0023] Optionally, the oscillation signal of the oscillation component is filtered using the target oscillation frequency of the oscillation signal to obtain the filtered oscillation signal, including:

[0024] Using the target oscillation frequency of the oscillation signal as the center frequency and the target resolution as the bandwidth, a bandpass filter is used to filter the oscillation signal of the oscillation component to obtain the filtered oscillation signal.

[0025] The bandpass filter is a first-order bandpass filter, a second-order bandpass filter, or a third-order bandpass filter.

[0026] Optionally, the oscillation characteristic parameters of the oscillation signal are obtained by performing a coordinate transformation using the abc / dq coordinate transformation matrix based on the filtered oscillation signal and the target oscillation frequency of the oscillation signal, including:

[0027] Based on the target oscillation frequency of the oscillation signal, construct the abc / dq coordinate transformation matrix;

[0028] The angles corresponding to the target oscillation frequency of the filtered oscillation signal and the oscillation signal are input into the abc / dq coordinate transformation matrix to perform coordinate transformation and obtain the oscillation signal after coordinate transformation.

[0029] Based on the oscillation signal after coordinate transformation and the target oscillation frequency of the oscillation signal, the true amplitude and frequency information of the oscillation signal are obtained as the oscillation characteristic parameters of the oscillation signal.

[0030] Secondly, to achieve the above objectives, the present invention provides a power system multi-frequency oscillation detection device, comprising: a signal detection and acquisition module, a central processing unit module, and a display and storage mechanism module;

[0031] The signal detection and acquisition module is used to acquire the oscillation signal of the oscillation component using the power grid signal of the power system;

[0032] The central processing unit module is used to process the oscillation signal of the oscillation component using a sliding window discrete Fourier transform algorithm and an abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal.

[0033] The display and storage mechanism module is used to obtain the multi-frequency oscillation detection results of the power system based on the oscillation characteristic parameters of the oscillation signal.

[0034] Optionally, the central processing unit module includes an SDFT algorithm unit, a filter unit, and a result analysis unit;

[0035] The SDFT algorithm unit is used to obtain the target oscillation frequency of the oscillation signal by using the sliding window discrete Fourier transform algorithm based on the oscillation signal of the oscillation component.

[0036] The filter unit is used to filter the oscillation signal of the oscillation component using the target oscillation frequency of the oscillation signal to obtain the filtered oscillation signal.

[0037] The result analysis unit is used to perform coordinate transformation based on the filtered oscillation signal and the target oscillation frequency of the oscillation signal to obtain the oscillation characteristic parameters of the oscillation signal.

[0038] Thirdly, embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, which, when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation of the first aspect.

[0039] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by one or more processors, implements the method as described in any implementation of the first aspect.

[0040] Compared with the closest existing technology, the present invention has the following advantages:

[0041] (1) The low-resolution SDFT algorithm used in this invention can quickly detect the approximate frequency of the oscillation signal. Thanks to the real-time calculation function and low computational load of the SDFT algorithm, especially when using low resolution, the computational load will be further reduced. Combined with the "abc / dq" coordinate transformation, the frequency and amplitude information of the oscillation signal can be detected ingeniously and accurately. The total time is less than 30ms, the detection speed is fast, and the overall computational burden is small.

[0042] (2) The present invention can detect oscillation signals containing multiple frequencies. It first determines the approximate frequency of the oscillation signal using a low-resolution SDFT algorithm, and then performs specific directional detection by channel. Compared with the existing detection methods of full-channel detection or full-band detection, it can effectively save computing resources and reduce system load.

[0043] (3) The device of the present invention has a simple structure and is easy to implement, which is conducive to portable use and can meet the requirement of "plug and play". Attached Figure Description

[0044] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart of a method for detecting multi-frequency oscillations in a power system according to an embodiment of the present invention;

[0046] Figure 2 This is a block diagram of the detection structure proposed in an embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the spectrum of the SDFT algorithm proposed in an embodiment of the present invention;

[0048] Figure 4 This is a diagram showing the amplitude relationship between the left and right sides of the approximate oscillation frequency proposed in an embodiment of the present invention.

[0049] Figure 5 This is a diagram showing the amplitude relationship between the left and right sides of the approximate oscillation frequency proposed in an embodiment of the present invention.

[0050] Figure 6 This is a waveform diagram of the "d" axis signal proposed in an embodiment of the present invention;

[0051] Figure 7 This is a waveform diagram of the "q" axis signal proposed in an embodiment of the present invention;

[0052] Figure 8 This is a structural diagram of a power system multi-frequency oscillation detection device proposed in an embodiment of the present invention;

[0053] Figure 9 This is a diagram showing the approximate frequency detection results proposed in an embodiment of the present invention;

[0054] Figure 10 This is a diagram showing the amplitude relationship between the approximate frequencies on the left and right sides proposed in this embodiment of the invention.

[0055] Figure 11 The waveform of the 273Hz oscillation signal "d" axis proposed in this embodiment of the invention is shown.

[0056] Figure 12 The waveform of the 871Hz oscillation signal "d" axis proposed in this embodiment of the invention is shown.

[0057] Figure 13 The waveform of the 1575Hz oscillation signal "d" axis proposed in this embodiment of the invention is shown.

[0058] Figure 14 This is a schematic diagram of the frequency detection results proposed in an embodiment of the present invention;

[0059] Figure 15 This is a schematic diagram of the amplitude detection results proposed in an embodiment of the present invention;

[0060] Figure 16 This is a schematic diagram of the structure of the electronic device proposed in an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0062] The terminology used in the embodiments section of this invention is for the purpose of explaining specific embodiments of the invention only, and is not intended to limit the invention.

[0063] like Figure 1 As shown, this embodiment of the invention provides a method for detecting multi-frequency oscillations in a power system, comprising:

[0064] S1. Obtain the oscillation signal of the oscillation component using the power grid signal of the power system;

[0065] This step involves accurately acquiring the voltage and current signals of the power system using voltage transformers (VT) and current transformers (CT). The power frequency component is then subtracted from the raw signal, separating it into a signal containing only the oscillation component. On one hand, the transformers ensure the safe isolation and accurate conversion of the power grid signal, guaranteeing the reliability of the raw data. On the other hand, by eliminating power frequency signal interference, the system directly focuses on the oscillation component to be detected, laying a clean data foundation for subsequent high-frequency oscillation analysis, avoiding the consumption of computational resources by useless signals, and improving detection efficiency and accuracy.

[0066] S2. The oscillation signal of the oscillation component is processed by the sliding window discrete Fourier transform algorithm and the abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal;

[0067] This step utilizes high-performance processors such as the TMS320F28335 as its core, comprehensively applying the Sliding Discrete Fourier Transform (SDFT) algorithm and the abc / dq coordinate transformation matrix to process the oscillating component signal in real time, ultimately extracting key oscillation characteristic parameters such as frequency and amplitude. The SDFT algorithm, through its dynamic data update mechanism using a "sliding window," eliminates the need to wait for a complete sampling period, completing feature calculations for each new data point. Furthermore, each calculation involves only a small number of addition, subtraction, and multiplication operations, significantly reducing detection latency and meeting the core "real-time" requirement of power systems for signal analysis. Its lightweight computational design also reduces the consumption of processor hardware resources, enabling parallel analysis of multi-frequency oscillating signals. The abc / dq coordinate transformation matrix enables the conversion between the three-phase stationary coordinate system (abc) and the two-phase rotating coordinate system (dq), further eliminating coupling interference in the signal, optimizing the accuracy of feature parameter extraction, and ensuring that the final obtained oscillation characteristic parameters accurately reflect the oscillation state of the power system.

[0068] S3. Obtain the multi-frequency oscillation detection results of the power system based on the oscillation characteristic parameters of the oscillation signal;

[0069] This step, based on the obtained oscillation characteristic parameters (frequency, amplitude, etc.), outputs the final detection results through a display and storage module, including real-time display of oscillation frequency and amplitude information, and storage of historical data. This process intuitively presents key indicators of multi-frequency oscillations, facilitating maintenance personnel to quickly grasp the system status. The storage of historical data provides data support for oscillation incident tracing, trend analysis, and system stability optimization, achieving a closed loop of "real-time monitoring - data retention - decision support," and improving the initiative and accuracy of power system oscillation prevention and control.

[0070] In summary, steps S1 to S3 form a complete detection loop from signal acquisition to feature extraction and result output, effectively solving the problems of high delay and large resource consumption in traditional methods for multi-frequency oscillation detection, and improving the accuracy and response speed of power system oscillation monitoring.

[0071] As one possible implementation, in the above embodiments, step S1 may specifically include the following steps:

[0072] S1-1. Use current transformer devices to collect signals and obtain the power grid signals of the power system;

[0073] The voltage and current of the power grid in the power system are accurately measured and signals are acquired through VT and CT.

[0074] S1-2. Obtain the corresponding power frequency signal based on the power grid signal of the power system;

[0075] S1-3. Based on the power grid signal of the power system, the oscillation signal of the oscillation component is obtained using the power frequency signal;

[0076] like Figure 2 As shown, when the collected voltage signal V or current signal I in the power grid is processed, its power frequency voltage V0 or power frequency current I0 signal will be subtracted to obtain an oscillating voltage or oscillating current signal containing only the oscillation component.

[0077] As one possible implementation, in the above embodiments, step S2 may specifically include the following steps:

[0078] S2-1. The target oscillation frequency of the oscillation signal is obtained by using the sliding window discrete Fourier transform algorithm based on the oscillation component.

[0079] S2-2. Filter the oscillation signal of the oscillation component using the target oscillation frequency of the oscillation signal to obtain the filtered oscillation signal;

[0080] S2-3. Based on the filtered oscillation signal and the target oscillation frequency of the oscillation signal, perform coordinate transformation using the abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal.

[0081] like Figure 2 As shown, after applying the low-resolution Δf SDFT algorithm, i.e., LOW-Δf-SDFT, the approximate frequencies of the signals with larger amplitudes are obtained as f. e1 f e2 ...f en The aim is to determine the range of oscillation frequencies. Oscillation signals of different frequencies occupy an independent detection channel. The aforementioned approximate frequency is then used as the center frequency of a bandpass filter (BPF), with a bandwidth set to Δf. This filters the oscillation voltage or current signal. The filtered signal is then used as the input signal for the transformation from a three-phase stationary coordinate system to a two-phase rotating coordinate system (i.e., the "abc / dq" coordinate transformation), with the angle corresponding to the aforementioned approximate frequency as the initial phase angle. Finally, the amplitude information of the oscillation signal (A1, A2...A...) is obtained from the "dq" signal. n The true frequency (f) of the oscillation signal is obtained by combining the approximate frequency at the SDFT algorithm. e1 ±f d1 f e2 ±f d2 ...f en ±f dnIn this embodiment, the approximate frequency of a signal with a large amplitude is multiplied by 2π to represent the angular frequency. The angular frequency is then integrated to obtain the angle corresponding to the approximate frequency. Here, 1 / S represents the mathematical expression of the "integral" in the complex frequency domain, and S represents the complex frequency variable.

[0082] As one possible implementation, in the above embodiments, step S2-1 may specifically include the following steps:

[0083] S2-1-1. The sliding window discrete Fourier transform algorithm is used to process the oscillation signal of the oscillation component to obtain the candidate frequency and candidate amplitude of the oscillation signal.

[0084] The SDFT algorithm with low resolution Δf is used to process the oscillating signal and calculate multiple candidate frequencies and their amplitudes.

[0085] S2-1-2. Based on the candidate amplitudes of the oscillation signal, the target amplitude of the oscillation signal is obtained using a preset threshold.

[0086] Acquire candidate amplitude values ​​that exceed a preset threshold. The preset threshold is 10% of the power frequency signal amplitude. The specific threshold can be adjusted according to the actual application scenario.

[0087] S2-1-3. Based on the target amplitude of the oscillation signal and the candidate frequency of the oscillation signal, obtain the candidate frequency of the oscillation signal corresponding to the target amplitude as the target oscillation frequency of the oscillation signal.

[0088] Candidate frequencies whose amplitude exceeds a preset threshold are determined as the approximate oscillation frequency f. e1 f e2 ...f en .

[0089] In this embodiment, the SDFT algorithm is calculated as shown in equation (1), where X... k (n) is the frequency domain signal at time n, e j2πk / N X is the phase shift factor, j represents the imaginary unit, k represents the k-th frequency point in the spectrum, N represents the number of spectrum calculation points, and X... k (n-1) is the frequency domain signal at time n-1, x(n) is the time domain sampled signal at time n, and x(nN) is the first sampled value within the sampling data window. Since N=F s / Δf, during the calculation, the sampling frequency F sSince the value of Δf remains constant at low resolution, the number of calculation points N is relatively small. As shown in equation (1), each calculation requires only two addition operations and one multiplication operation, resulting in a small computational load. Furthermore, because the SDFT algorithm performs a calculation every time a data point is updated, it possesses real-time calculation capabilities, typically requiring only a small number of calculations to obtain the amplitude and frequency information of the oscillation signal. Finally, the frequency with an amplitude exceeding a preset threshold is considered as the approximate oscillation frequency f. e Its spectrum diagram is as follows Figure 3 As shown. Therefore, this invention performs SDFT calculations at a lower frequency resolution, enabling the rapid acquisition of the approximate oscillation frequency f in the oscillating signal. e .

[0090] X k (n)=e j2πk / N [X k [(n-1)+x(n)-x(nN)](1)

[0091] While calculating the approximate oscillation frequency, the amplitudes of the frequencies to the left and right of the approximate oscillation frequency, i.e., f, are also calculated. e The amplitude at frequency ±Δf. For example... Figure 4 As shown, if the amplitude on the left is greater than the amplitude on the right, it indicates that the spectral leakage energy is more significant on the left, and therefore the actual oscillation frequency is less than f. e Then the "±" in the previous text will be "-"; for example Figure 5 As shown, if the amplitude on the left is smaller than the amplitude on the right, it indicates that the spectral leakage energy is more pronounced on the right side, and therefore the actual oscillation frequency is greater than f. e Then the "±" in the previous text will be "+".

[0092] As one possible implementation, in the above embodiments, step S2-2 may specifically include the following steps:

[0093] Using the target oscillation frequency of the oscillation signal as the center frequency and the target resolution as the bandwidth, a bandpass filter is used to filter the oscillation signal of the oscillation component to obtain the filtered oscillation signal.

[0094] The bandpass filter is a first-order bandpass filter, a second-order bandpass filter, or a third-order bandpass filter.

[0095] This embodiment uses a second-order bandpass filter, whose transfer function is shown in equation (2). In the equation, ξ represents the damping coefficient, ω... n Let ω be the center angular frequency, where ω n =2πf n f n The center frequency is f. After the SDFT algorithm obtains the approximate oscillation frequency, the center frequency f of the BPF is... n Then it is determined that fn =f e Then, using the resolution Δf as the bandwidth BW of the BPF, the damping coefficient is determined by equation (3). At this point, all parameters of the BPF have been determined. It is worth noting that a second-order bandpass filter is used as an example here. Depending on the specific engineering environment, first-order or third-order bandpass filters of different types can also be used.

[0096] (2)

[0097] (3)

[0098] Where H(S) is the transfer function of the second-order bandpass filter, and S is the complex frequency variable.

[0099] As one possible implementation, in the above embodiments, step S2-3 may specifically include the following steps:

[0100] S2-3-1. Based on the target oscillation frequency of the oscillation signal, construct the abc / dq coordinate transformation matrix;

[0101] S2-3-2. Input the angle corresponding to the target oscillation frequency of the filtered oscillation signal into the abc / dq coordinate transformation matrix to perform coordinate transformation and obtain the oscillation signal after coordinate transformation.

[0102] S2-3-3. Based on the oscillation signal after coordinate transformation and the target oscillation frequency of the oscillation signal, obtain the true amplitude and frequency information of the oscillation signal as the oscillation characteristic parameters of the oscillation signal.

[0103] In this embodiment, the calculation matrix M of "abc / dq" is as shown in equation (4), which can convert the input three-phase oscillation voltage or current signal into a two-phase "dq" oscillation signal. The "dq" signal contains the relevant amplitude and frequency information of the oscillation signal. Here, "θ" is 2πf e1 2πf e2 ...2πf en These correspond to the approximate oscillation frequency and phase angle output by the SDFT algorithm. This embodiment takes the voltage signal (current signal can also be used) as the research object and uses the "d" axis signal as an example to explain the method. The same detection effect can be achieved if the "q" axis signal is used as the research object.

[0104] (4)

[0105] Now, assuming that there are three oscillating components in the voltage signal, the three-phase voltage signal after removing the power frequency signal is as shown in equation (5), where V a Let V be the voltage of phase a. b V is the voltage of phase b. cV is the voltage of phase c. p1 V represents the amplitude of the phase a oscillation component. p2 V represents the amplitude of the phase b oscillation component. p3 ω represents the amplitude of the c-phase oscillation component. p1 ω represents the phase angle of the oscillating component of phase a. p2 ω represents the phase angle of the b-phase oscillation component. p3 The phase angle of the c-phase oscillation component is given by t, where t is the time.

[0106] (5)

[0107] The above three-phase oscillating voltage signal is subjected to an "abc / dq" coordinate transformation. The abc / dq coordinate transformation matrix is ​​as follows:

[0108] (6)

[0109] Among them, V d V is the voltage signal along the d-axis after coordinate transformation. q This is the "q" axis voltage signal after coordinate transformation.

[0110] To make the analysis process clear, V d Write it out separately:

[0111] (7)

[0112] V d Expanding each of the three terms separately, and substituting equation (5) into equation (7), we get:

[0113] (8)

[0114] Expanding the cosine coefficients in equation (8) yields equations (9) and (10):

[0115] (9)

[0116] (10)

[0117] Multiplying equation (9)...① by equation (10)...①②③ respectively, we get:

[0118] (11)

[0119] Multiplying equations (9)...② by equations (10)...④⑤⑥ respectively, we get:

[0120] (12)

[0121] Substituting equations (8) to (12) into equation (7), then V d This can be simplified to:

[0122] (13)

[0123] Among them, f p1 f represents the frequency of the oscillation component of phase a. p2 f represents the frequency of the phase b oscillation component. p3 f represents the frequency of the c-phase oscillation component. θ This is the approximate oscillation frequency.

[0124] Equation (13) shows that the "d" axis signal contains the relationship between three oscillation frequencies and the approximate oscillation frequencies of the SDFT algorithm. Furthermore, because the input signal of the "abc / dq" coordinate transformation is filtered, the "d" axis signal consists of only one amplitude (V). p1 V p2 V p3 …V pn The frequency and amplitude information can be quickly obtained from the waveform of the signal. Therefore, the true frequency of the oscillating signal can be obtained simply by performing a sum-difference operation between the frequency of the "d" axis signal and the approximate frequency.

[0125] like Figure 6 As shown, to obtain amplitude and frequency information from the "d" axis signal, it is only necessary to obtain the extreme value A of the signal, which is the amplitude of the oscillation signal. The time difference t between the first zero-crossing time t1 and the first extreme value t2 after the zero-crossing time is taken. d =t2-t1, then perform simple calculations, i.e., f d =1 / 4t d This yields the frequency information of the "d" axis signal. Finally, performing a sum-difference operation with the approximate frequency gives the true frequency of the oscillation signal, where f d The frequency of the "d" axis signal.

[0126] Similarly, by performing the above-mentioned analysis on the "q" axis signal, we can obtain V. q The simplified form is:

[0127] (14)

[0128] As can be seen from equation (14), the "q" axis signal also contains the relationship between three oscillation frequencies and the approximate oscillation frequencies of the SDFT algorithm. Furthermore, because the input signal of the "abc / dq" coordinate transformation is filtered, the "q" axis signal consists of only one amplitude (V). p1 V p2 V p3 …V pn Therefore, the frequency and amplitude information can be quickly obtained from the waveform of the signal. The true frequency of the oscillating signal can be obtained by simply performing a sum-difference operation between the frequency of the "q" axis signal and the approximate frequency.

[0129] like Figure 7 As shown, to obtain amplitude and frequency information from the "q" axis signal, it is only necessary to obtain the extreme value A of the signal, which is the amplitude of the oscillation signal. The time t1 corresponding to the first extreme value is obtained, and then the time t2 corresponding to the first zero-crossing point after the extreme value is derived. The time difference is t. q =t2-t1, then perform simple calculations, i.e., f q =1 / 4t q This yields the frequency information of the "q" axis signal. Finally, performing a sum-difference operation with the approximate frequency gives the true frequency of the oscillation signal, where f... q The frequency of the "q" axis signal.

[0130] Further reference Figure 8 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a power system multi-frequency oscillation detection device, which is similar to... Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0131] like Figure 8 As shown, a power system multi-frequency oscillation detection device according to this embodiment includes: a signal detection and acquisition module, a central processing unit module, and a display and storage mechanism module;

[0132] The signal detection and acquisition module is used to acquire the oscillation signal of the oscillation component using the power grid signal of the power system;

[0133] This module performs precise measurement and signal acquisition of voltage and current in the power system using VT and CT. The acquired voltage or current signal is processed by subtracting the power frequency voltage or current signal, resulting in an oscillating voltage or current signal containing only the oscillating component. These oscillating signals are then transmitted to the central processing unit module for further processing.

[0134] The central processing unit module is used to process the oscillation signal of the oscillation component using a sliding window discrete Fourier transform algorithm and an abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal.

[0135] This module uses the TMS320F28335 digital signal processor as its core processing unit. However, it is not limited to this model; a suitable processor can be flexibly selected according to actual needs. With its excellent computing power and rich peripheral interfaces, this processor provides strong support for the detection and analysis of oscillation signals.

[0136] The display and storage mechanism module is used to obtain the multi-frequency oscillation detection results of the power system based on the oscillation characteristic parameters of the oscillation signal.

[0137] This module is responsible for displaying the final test results and storing recent test data to ensure the integrity and traceability of key test data, providing a solid data foundation for subsequent troubleshooting, performance evaluation and system optimization.

[0138] In this embodiment, the specific processing of a power system multi-frequency oscillation detection device and its resulting technical effects can be referred to separately. Figure 1 The relevant descriptions of steps S1, S2 and S3 in the corresponding embodiments will not be repeated here.

[0139] In some optional implementations, the central processing unit module includes: an SDFT algorithm unit, a filter unit, and a result analysis unit;

[0140] The SDFT algorithm unit is used to obtain the target oscillation frequency of the oscillation signal by using the sliding window discrete Fourier transform algorithm based on the oscillation signal of the oscillation component.

[0141] When running the SDFT algorithm, the digital signal processor (DSP) can quickly and accurately analyze the input oscillation signal and obtain relevant data information, namely the approximate frequency of the signal with a large amplitude (taking 10% of the amplitude of the power frequency signal as an example, the specific threshold can be adjusted according to the actual application scenario).

[0142] The filter unit is used to filter the oscillation signal of the oscillation component using the target oscillation frequency of the oscillation signal to obtain the filtered oscillation signal.

[0143] After completing the parameter design of the filter, this unit uses the approximate frequency processed by the above unit as the center frequency of the BPF to filter the oscillating voltage or oscillating current signal.

[0144] The result analysis unit is used to perform coordinate transformation based on the filtered oscillation signal and the target oscillation frequency of the oscillation signal to obtain the oscillation characteristic parameters of the oscillation signal.

[0145] This unit processes the filtered oscillation signal by performing an "abc / dq" coordinate transformation, and finally processes the result to obtain the detection result.

[0146] Simulation analysis and verification:

[0147] The invention is analyzed and verified using a specific actual signal, with the three-phase voltage signal and the "d" axis signal as the objects of analysis.

[0148] After subtracting the power frequency signal, the voltage signal containing three oscillation frequency signals is as shown in Equation (15), with the amplitude and frequency of the oscillation signals being 12V, 10V, 6V and 273Hz, 871Hz, 1575Hz, respectively.

[0149] (15)

[0150] Starting from time 0, after detecting the above signals using the SDFT algorithm with a resolution of Δf = 100Hz, the approximate frequencies of the three oscillation signals are obtained as follows: Figure 9 As shown in the figure, the approximate frequencies of the oscillation signal were determined to be 300Hz, 900Hz, and 1600Hz after approximately 2ms. Simultaneously, the amplitude relationship between the frequencies on the left and right sides of the approximate frequencies was also detected; if the amplitude on the left side was greater than that on the right side, it was represented by "1", and vice versa, "-1". Figure 10 It can be seen that for all three oscillation signals, the amplitude on the left side is smaller than that on the right side.

[0151] After obtaining the approximate frequency, the filter parameters are determined according to equation (3). The oscillating voltage signal is filtered in three channels (for this case only), and the processed signal is transformed by the coordinate system of "abc / dq" to obtain the "d" axis signal of each channel, as shown in the figure. Figures 11-13 As shown in the figure, the first zero-crossing point and the first extreme point thereafter are marked. The frequency of the "d"-axis signal can then be obtained through simple mathematical calculations. Figure 11 For example, the frequency of the "d" signal of the 273Hz oscillation signal is 1 / 4×(0.0224-0.0115)=22.94Hz. Since the amplitude relationship between the left and right sides of the known approximate frequency is as follows, the frequency detection result of the 273Hz oscillation signal is 300-22.94≈277Hz.

[0152] The frequency and amplitude of the oscillation signal were finally detected in approximately 25ms, and the detection results are as follows: Figures 14-15 As shown, the detection results for the 273Hz oscillation signal are 277Hz and 11.7V, with errors of 1.5% and 2.5%, respectively; the detection results for the 871Hz oscillation signal are 873Hz and 9.63V, with errors of 0.2% and 3.7%, respectively; and the detection results for the 1575Hz oscillation signal are 1575Hz and 5.87V, with errors of 0% and 2.2%, respectively.

[0153] It should be noted that the implementation details and technical effects of each module and unit in the device provided in the embodiments of this disclosure can be referred to the description of other embodiments in this disclosure, and will not be repeated here.

[0154] The following is for reference. Figure 16It shows a schematic diagram of the structure of a computer system 500 suitable for implementing the electronic device of the present disclosure. Figure 16 The computer system 500 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0155] like Figure 16 As shown, the computer system 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the computer system 500. The processing device 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0156] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows computer system 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 16 A computer system 500 with various electronic devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0157] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0158] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0159] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0160] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the following functions: Figure 1 The embodiments shown and their alternative implementations illustrate a large-model-based energy-saving method for pet devices.

[0161] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0163] The units or modules described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units or modules do not necessarily limit the unit itself; for example, an acquisition module can also be described as "acquiring preset prompts, including modality fusion prompts, attention mechanism prompts, and / or time-related prompts."

[0164] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

Claims

1. A method for detecting multi-frequency oscillations in a power system, characterized in that, include: S1. Obtain the oscillation signal of the oscillation component using the power grid signal of the power system; S2. The oscillation signal of the oscillation component is processed by the sliding window discrete Fourier transform algorithm and the abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal; S3. Obtain the multi-frequency oscillation detection results of the power system based on the oscillation characteristic parameters of the oscillation signal.

2. The method for detecting multi-frequency oscillations in a power system according to claim 1, characterized in that, S1. Obtain the oscillation signal of the oscillation component using the power grid signal of the power system, including: Signal acquisition is performed using instrument transformers to obtain power grid signals from the power system; Based on the power grid signal of the power system, obtain the corresponding power frequency signal; Based on the power grid signal of the power system, the oscillation signal of the oscillation component is obtained using the power frequency signal.

3. The method for detecting multi-frequency oscillations in a power system according to claim 1, characterized in that, S2. The oscillation signal of the oscillation component is processed using the sliding window discrete Fourier transform algorithm and the abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal, including: The target oscillation frequency of the oscillation signal is obtained by using a sliding window discrete Fourier transform algorithm based on the oscillation component. The oscillation signal of the oscillation component is filtered using the target oscillation frequency of the oscillation signal to obtain the filtered oscillation signal; Based on the filtered oscillation signal and the target oscillation frequency of the oscillation signal, coordinate transformation is performed using the abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal.

4. The method for detecting multi-frequency oscillations in a power system according to claim 3, characterized in that, The target oscillation frequency of the oscillation signal is obtained by using a sliding window discrete Fourier transform algorithm based on the oscillation component, including: The oscillation signal of the oscillation component is processed using a sliding window discrete Fourier transform algorithm to obtain candidate frequencies and candidate amplitudes of the oscillation signal. The calculation formula of the sliding window discrete Fourier transform algorithm is as follows: X k (n)=e j2πk / N [X k (n-1)+x(n)-x(n-N)] Among them, X k (n) is the frequency domain signal at time n, e j2πk / N X is the phase shift factor, j represents the imaginary unit, k represents the k-th frequency point in the spectrum, N represents the number of spectrum calculation points, and X... k (n-1) is the frequency domain signal at time n-1, x(n) is the time domain sampled signal at time n, and x(nN) is the first sampled value in the sampled data window; Based on the candidate amplitudes of the oscillation signal, a preset threshold is used to obtain the target amplitude of the oscillation signal; Based on the target amplitude of the oscillation signal and the candidate frequency of the oscillation signal, the candidate frequency of the oscillation signal corresponding to the target amplitude is obtained as the target oscillation frequency of the oscillation signal.

5. The method for detecting multi-frequency oscillations in a power system according to claim 3, characterized in that, The oscillation signal of the oscillation component is filtered using the target oscillation frequency of the oscillation signal to obtain the filtered oscillation signal, including: Using the target oscillation frequency of the oscillation signal as the center frequency and the target resolution as the bandwidth, a bandpass filter is used to filter the oscillation signal of the oscillation component to obtain the filtered oscillation signal. The bandpass filter is a first-order bandpass filter, a second-order bandpass filter, or a third-order bandpass filter.

6. The method for detecting multi-frequency oscillations in a power system according to claim 3, characterized in that, Based on the filtered oscillation signal and the target oscillation frequency of the oscillation signal, coordinate transformation is performed using the abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal, including: Based on the target oscillation frequency of the oscillation signal, construct the abc / dq coordinate transformation matrix; The angles corresponding to the target oscillation frequency of the filtered oscillation signal and the oscillation signal are input into the abc / dq coordinate transformation matrix to perform coordinate transformation and obtain the oscillation signal after coordinate transformation. Based on the oscillation signal after coordinate transformation and the target oscillation frequency of the oscillation signal, the true amplitude and frequency information of the oscillation signal are obtained as the oscillation characteristic parameters of the oscillation signal.

7. A power system multi-frequency oscillation detection device, implementing the method as described in any one of claims 1-6, characterized in that, include: Signal detection and acquisition module, central processing unit module and display and storage mechanism module; The signal detection and acquisition module is used to acquire the oscillation signal of the oscillation component using the power grid signal of the power system; The central processing unit module is used to process the oscillation signal of the oscillation component using a sliding window discrete Fourier transform algorithm and an abc / dq coordinate transformation matrix to obtain the oscillation characteristic parameters of the oscillation signal. The display and storage mechanism module is used to obtain the multi-frequency oscillation detection results of the power system based on the oscillation characteristic parameters of the oscillation signal.

8. A power system multi-frequency oscillation detection device according to claim 7, characterized in that, The central processing unit module includes an SDFT algorithm unit, a filter unit, and a result analysis unit; The SDFT algorithm unit is used to obtain the target oscillation frequency of the oscillation signal by using the sliding window discrete Fourier transform algorithm based on the oscillation signal of the oscillation component. The filter unit is used to filter the oscillation signal of the oscillation component using the target oscillation frequency of the oscillation signal to obtain the filtered oscillation signal. The result analysis unit is used to perform coordinate transformation based on the filtered oscillation signal and the target oscillation frequency of the oscillation signal to obtain the oscillation characteristic parameters of the oscillation signal.

9. An electronic device, characterized in that, include: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by one or more processors, implements the method as described in any one of claims 1 to 6.

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