Identification and classification method and device for broadband oscillation of power system

Abstract

The embodiment of the invention provides an identification and classification method and device for broadband oscillation of a power system, and the method comprises the steps: generating target frequency spectrum data corresponding to target waveform data according to the target waveform data generated by a target power system within a preset sampling time; and according to the target spectrum data, determining peak frequency characteristics and amplitude change characteristics corresponding to each frequency component in the target spectrum data. And then, according to the peak frequency characteristic and the amplitude change characteristic corresponding to each frequency component, carrying out broadband oscillation identification on each frequency component, and determining a broadband oscillation type corresponding to each frequency component. Through the technical scheme provided by the invention, various types of broadband oscillation problems in the operation process of the electric power system can be effectively identified, the identification efficiency and accuracy of the broadband oscillation phenomenon in the operation process of the electric power system are improved, the broadband oscillation phenomenon can be found and solved in time, the normal operation of the electric power system can be ensured, and the user experience is improved. And the safety and stability of the power system are improved.

Description

Technical Field

[0001] The present application belongs to the technical field of power system monitoring, and in particular, relates to a method and device for identifying and classifying broadband oscillations in a power system. Background Art

[0002] With the rapid progress and development of technology, high-tech related to power systems is also being innovated. At this stage, the widespread application of power system technology has brought extensive and far-reaching benefits to social development and people's daily lives.

[0003] With the huge demand for electricity today, it is extremely important to ensure the normal stability of the power system during operation. Broadband oscillation is a relatively complex dynamic phenomenon in the power system with strong negative impact. If the broadband oscillation phenomenon is not discovered and handled in time, it is very likely to have a serious negative impact on the normal operation of the entire power system, such as reducing the stability of the power system operation, increasing the aging speed and damage risk of power system equipment, and affecting the quality of power generation.

[0004] Broadband oscillation is not a single oscillation phenomenon, but is divided into many different types, such as low-frequency oscillation, subsynchronous oscillation, high-frequency oscillation, etc. At present, the identification and classification of broadband oscillations in the operation of power systems are mostly based on the waveform data generated by the power system over a long period of time, and the specific broadband oscillation types of the power system are identified. This identification method has strong limitations and lacks flexibility, which will affect the efficiency of subsequent treatment of broadband oscillation problems in power systems, reduce the safety and stability of power system operation, and increase potential system operation risks. Summary of the invention

[0005] The embodiments of the present application provide a method and device for identifying and classifying broadband oscillations in an electric power system, which can efficiently identify broadband oscillation problems occurring in an electric power system and accurately classify corresponding broadband oscillation types, thereby avoiding the negative impact of broadband oscillation problems on the normal operation of the electric power system to a great extent and improving the safety and stability of the electric power system.

[0006] In a first aspect, an embodiment of the present application provides a method for identifying and classifying broadband oscillations in a power system, the method comprising:

[0007] Obtaining target waveform data generated by a target power system within a preset sampling time, the target waveform data being electrical quantity waveform data;

[0008] According to the target waveform data, target spectrum data corresponding to the target waveform data is generated, wherein the target spectrum data includes spectrum data corresponding to each frequency component;

[0009] According to the target spectrum data, determine the peak frequency characteristics and amplitude change characteristics corresponding to each frequency component;

[0010] According to the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component, broadband oscillation identification is performed on each frequency component to determine the broadband oscillation type corresponding to each frequency component.

[0011] In an optional implementation of the first aspect, generating target spectrum data corresponding to the target waveform data according to the target waveform data includes:

[0012] According to the preset sampling time and the preset sampling time threshold, determining whether the preset sampling time is not greater than the sampling time threshold;

[0013] When it is determined that the preset sampling time is not greater than the sampling time threshold, interpolation processing is performed on the target waveform data to obtain interpolated waveform data corresponding to the target waveform data;

[0014] Target spectrum data corresponding to the target waveform data is generated according to the interpolated waveform data corresponding to the target waveform data.

[0015] In an optional implementation of the first aspect, before determining the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component according to the target spectrum data, the method further includes:

[0016] According to the target spectrum data, determine the maximum peak amplitude corresponding to each frequency component;

[0017] Determine at least one target frequency component from each frequency component according to the magnitude relationship between the maximum peak amplitude corresponding to each frequency component and the fundamental peak amplitude of the fundamental frequency corresponding to the target waveform data;

[0018] According to the target spectrum data, the peak frequency characteristics and amplitude change characteristics corresponding to each frequency component are determined, including:

[0019] According to the target frequency spectrum data, a peak frequency characteristic and an amplitude variation characteristic corresponding to at least one target frequency component are determined.

[0020] In an optional implementation of the first aspect, determining, according to the target spectrum data, a peak frequency feature and an amplitude variation feature corresponding to at least one target frequency component includes:

[0021] Determine, according to the target frequency spectrum data, a maximum amplitude peak frequency corresponding to at least one target frequency component as a peak frequency feature corresponding to at least one target frequency component;

[0022] According to the maximum amplitude peak frequency corresponding to the at least one target frequency component, an invalid frequency component in the at least one target frequency component is identified, and at least one other target frequency component except the invalid frequency component is used as a valid frequency component, wherein the invalid frequency component at least includes a noise frequency component and an error frequency component;

[0023] According to the target spectrum data, the amplitude change rate corresponding to the effective frequency component is determined as the amplitude change feature corresponding to the effective frequency component.

[0024] In an optional implementation of the first aspect, identifying an invalid frequency component in at least one target frequency component according to a maximum amplitude peak frequency corresponding to at least one target frequency component includes:

[0025] For each target frequency component, determine the peak frequency difference between the maximum amplitude peak frequency corresponding to the target frequency component and the previous peak frequency of the maximum amplitude peak frequency according to the target frequency spectrum data;

[0026] When it is determined that the peak frequency difference corresponding to the target frequency is greater than a preset peak frequency difference threshold, the target frequency component is determined to be an invalid frequency component.

[0027] In an optional implementation of the first aspect, the method further includes:

[0028] For each target frequency component, determining, based on the target frequency spectrum data, whether the frequency amplitude value of the target frequency component decreases continuously by more than a preset number of decreases;

[0029] When it is determined that the frequency amplitude value of the target frequency component decreases continuously for more than a preset number of decreases, the target frequency component is determined to be an invalid frequency component.

[0030] In an optional implementation of the first aspect, performing broadband oscillation identification on each frequency component according to peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component to determine the broadband oscillation type corresponding to each frequency component includes:

[0031] For each frequency component, a preset frequency component classification model is used to determine whether the frequency component is a broadband oscillation frequency component based on the peak frequency characteristics and amplitude change characteristics corresponding to the frequency component. When the frequency component is determined to be a broadband oscillation frequency component, the broadband oscillation type corresponding to the frequency component is determined based on the peak frequency characteristics corresponding to the frequency component. The broadband oscillation type includes at least subsynchronous oscillation and high-frequency oscillation.

[0032] In an optional implementation of the first aspect, determining the broadband oscillation type corresponding to the frequency component according to a peak frequency feature corresponding to the frequency component includes:

[0033] For each frequency component, a preset frequency component classification model is used to classify the frequency component into a broadband oscillation type according to the peak frequency characteristics corresponding to the frequency component and the broadband oscillation classification standard corresponding to the frequency component classification model, and the broadband oscillation type corresponding to the frequency component is determined.

[0034] In a second aspect, an embodiment of the present application provides a device for identifying and classifying broadband oscillations in a power system, the device comprising:

[0035] An acquisition unit, used to acquire target waveform data generated by a target power system within a preset sampling time, wherein the target waveform data is electrical quantity waveform data;

[0036] A spectrum generating unit, used for generating target spectrum data corresponding to the target waveform data according to the target waveform data, wherein the target spectrum data includes spectrum data corresponding to each frequency component;

[0037] A feature determination unit, used to determine the peak frequency feature and amplitude variation feature corresponding to each frequency component according to the target spectrum data;

[0038] The identification unit is used to identify the broadband oscillation of each frequency component according to the peak frequency characteristics and amplitude change characteristics corresponding to each frequency component, and determine the broadband oscillation type corresponding to each frequency component.

[0039] In a third aspect, an electronic device is provided, comprising: a memory for storing computer program instructions; and a processor for reading and running the computer program instructions stored in the memory to execute a method for identifying and classifying wideband oscillations in a power system provided in any optional embodiment of the first aspect.

[0040] In a fourth aspect, a computer storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, a method for identifying and classifying broadband oscillations in a power system provided by any optional implementation in the first aspect is implemented.

[0041] In a fifth aspect, a computer program product is provided. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes a method for identifying and classifying wideband oscillations in a power system provided by any optional implementation in the first aspect.

[0042] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0043] The embodiment of the present application provides a method for identifying and classifying broadband oscillations in an electric power system, including: generating target spectrum data corresponding to the target waveform data according to the target waveform data generated by the target electric power system within a preset sampling time. Then, according to the target spectrum data, determining the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component in the target spectrum data. Next, based on the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component, performing broadband oscillation identification on each frequency component, and determining the broadband oscillation type corresponding to each frequency component.

[0044] Through the technical solution provided in the embodiment of the present application, the broadband oscillation component in each frequency component can be identified according to the waveform data corresponding to the power system, and the broadband oscillation type corresponding to each broadband oscillation component can be determined. Through the technical solution provided in the embodiment of the present application, various types of broadband oscillation problems in the operation of the power system can be effectively identified, and the efficiency and accuracy of identifying broadband oscillation phenomena in the operation of the power system can be improved, so that they can be discovered and solved in time, ensuring the normal operation of the power system and improving the safety and stability of the power system.

[0045] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 A flowchart of a method for identifying and classifying broadband oscillations in a power system provided by an embodiment of the present application;

[0048] Figure 2 A schematic flow chart of an example of a process for determining a detection data set corresponding to target waveform data provided by an embodiment of the present application;

[0049] Figure 3 A flowchart of an application example of a method for identifying and classifying broadband oscillations in a power system provided by an embodiment of the present application;

[0050] Figure 4 A schematic diagram of the structure of a device for identifying and classifying broadband oscillations in a power system provided in another embodiment of the present application;

[0051] Figure 5A schematic diagram of the hardware structure of a device for identifying and classifying broadband oscillations in a power system provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0052] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0053] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0054] With the rapid advancement of technology, high-tech technologies related to power systems are also constantly innovating, which has brought great benefits to social progress and people's daily lives. In the current context of huge electricity demand, how to ensure the stable operation of the power system has become particularly important. Broadband oscillation is a frequent and negatively impacting operation problem in the power system. If it is not discovered and handled in time, it may have a serious impact on the operation of the power system.

[0055] Broadband oscillations in power systems are not a single type of oscillation phenomenon, but cover many different types of oscillations, such as low-frequency oscillations, subsynchronous oscillations, high-frequency oscillations, etc. At present, the identification and classification of broadband oscillations in power systems mainly rely on the waveform data generated by the power system over a long period of time, and target specific types of broadband oscillations for identification. This approach has strong limitations and lacks flexibility for different types of broadband oscillations.

[0056] Moreover, at the current stage, the identification of broadband oscillations can only rely on long-term waveform data, which greatly leads to a long response time from the occurrence to the discovery of broadband oscillation problems. This causes the operators responsible for solving broadband oscillation problems to respond with lags and fail to solve abnormal problems in the first place, resulting in reduced efficiency in managing broadband oscillation problems, affecting the safety and stability of power system operation, and increasing potential safety hazards.

[0057] Based on the technical problems mentioned in the above content, an embodiment of the present application provides a method and device for identifying and classifying broadband oscillations of an electric power system, which generates target spectrum data corresponding to the target waveform data according to the target waveform data generated by the target electric power system within a preset sampling time. Then, based on the target spectrum data, the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component in the target spectrum data are determined. Finally, based on the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component, broadband oscillations of each frequency component are identified to determine the type of broadband oscillation corresponding to each frequency component.

[0058] Through the technical solution provided in the embodiment of the present application, the broadband oscillation component in each frequency component can be identified according to the waveform data corresponding to the power system, and the broadband oscillation type corresponding to each broadband heavy oscillation component can be determined. Through this technical solution, various types of broadband oscillation problems in the operation of the power system can be effectively identified, and the efficiency and accuracy of identifying broadband oscillation phenomena in the operation of the power system can be improved, so that they can be discovered and solved in time, ensuring the normal operation of the power system and improving the safety and stability of the power system.

[0059] In addition, the technical solution provided by the embodiment of the present application can directly identify broadband oscillations based on the waveform data within the short sampling time corresponding to the power system, and determine the broadband oscillation type corresponding to each frequency component. It effectively overcomes the defect of the current technology that broadband oscillations can only be identified based on waveform data with a long sampling time, effectively improves the recognition efficiency of broadband oscillation identification for the power system and the subsequent handling efficiency of broadband oscillation problems by relevant operators, greatly improves the safety and stability of the power system during operation, and increases the service life of the system while ensuring the normal operation of the power system.

[0060] Among them, the execution subject adopted in the embodiment of the present application may be a terminal device for data transmission with the target power system, such as a desktop computer, a laptop computer, etc., or a similar server for remote data transmission with the target power system. In addition, the execution subject adopted in the embodiment of the present application may also be an execution subject in the form of software, such as a client, a software program, etc. installed in the terminal device. For the sake of convenience of explanation, the following embodiment of the present application only takes the terminal device as the execution subject to illustrate the provided identification and classification method for broadband oscillations of the power system.

[0061] The specific application scenarios corresponding to the method and device for identifying and classifying broadband oscillations in a power system provided in the embodiments of the present application are not strictly limited in this application and can be adjusted according to actual needs. For ease of understanding, the following examples are given for specific application scenarios corresponding to the method and device for identifying and classifying broadband oscillations in a power system provided in the embodiments of the present application.

[0062] For example, when performing broadband oscillation identification on a power system that has already experienced broadband oscillation problems, the terminal device using the technical solution provided in the embodiment of the present application can determine the spectrum data corresponding to the target waveform data, as well as the peak frequency characteristics and amplitude change characteristics corresponding to each frequency component in the spectrum data, based on the target waveform data of the target power system within a preset sampling time. Then, the terminal device can accurately identify each broadband oscillation frequency component in the target waveform data based on the peak frequency characteristics and amplitude change characteristics corresponding to each frequency component through an artificial intelligence classifier (e.g., Random Forest (RF), Gradient Boosting Decision Trees (GBDT), etc.), and determine the broadband oscillation type corresponding to each broadband oscillation frequency component, so that the relevant personnel responsible for the maintenance of the target power system can immediately discover the broadband oscillation problem, and take corresponding maintenance operations according to the corresponding broadband oscillation problem, to ensure the normal operation of the target power system and improve safety and stability.

[0063] For another example, in the regular problem detection for the daily operation of the power system, the terminal device using the technical solution provided by the embodiment of the present application can regularly obtain the target waveform data corresponding to the target power system, and after determining the spectrum data according to the target waveform data, perform broadband oscillation identification on each frequency component in the spectrum data to determine the broadband oscillation type corresponding to each frequency component. The regular detection method can effectively ensure the long-term normal operation of the target power system, and when the broadband oscillation problem occurs, it can be discovered and solved in time, thereby improving the safety and stability of the power system operation process.

[0064] It should be noted that the application scenarios described in the above embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. A person of ordinary skill in the art will appreciate that, with the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems. The identification and classification method for broadband oscillations in power systems provided in the embodiments of the present application can be applied to various application scenarios where broadband oscillations in power systems need to be identified.

[0065] The following describes the method and device for identifying and classifying broadband oscillations in a power system provided by the present application.

[0066] Figure 1 A flowchart of a method for identifying and classifying broadband oscillations in a power system is provided in accordance with an embodiment of the present application.

[0067] like Figure 1 As shown, the method for identifying and classifying broadband oscillations in a power system provided in the embodiment of the present application includes steps S101 to S104.

[0068] S101: Acquire target waveform data generated by a target power system at a preset sampling time.

[0069] In step S101, the terminal device using the technical solution provided in the embodiment of the present application can obtain the target waveform data generated by the target power system within the preset sampling time. The target waveform data can be the electrical quantity waveform data corresponding to the target power system, which can specifically include but not be limited to: voltage waveform data, current waveform data, power waveform data, various frequency waveform data, etc.

[0070] It should be noted that in the embodiments provided in the present application, the specific implementation method for the terminal device to obtain the target waveform data generated by the target power system within the preset sampling time is not strictly limited. In one embodiment, the terminal device can monitor the real-time data of the target power system through the power monitoring system preset for the target power system, thereby obtaining the target waveform data generated by the target power system within the preset sampling time.

[0071] In another embodiment, the terminal device may also collect data from the target power system in real time through a synchronized phasor measurement unit (PMU) preset for the target power system, thereby obtaining the target waveform data generated by the target power system at the preset sampling time. In other embodiments, the terminal device may also use other feasible data collection methods to obtain the target waveform data, and the specific implementation method of obtaining the target waveform data in this application can be flexibly set according to the actual application scenario and requirements.

[0072] S102: generating target spectrum data corresponding to the target waveform data according to the target waveform data, wherein the target spectrum data includes spectrum data corresponding to each frequency component.

[0073] In step S102, the terminal device may generate target spectrum data corresponding to the target waveform data according to the target waveform data acquired through the above steps, wherein the generated target spectrum data may include spectrum data corresponding to each frequency component in the target waveform data.

[0074] It should be noted that the current technology for identifying broadband oscillations mainly analyzes the waveform data generated by the power system within a longer sampling time, which has a strong delay. When it is determined that the power system has broadband oscillations, the broadband oscillations have occurred for some time and are likely to have had a certain degree of negative impact on the power system.

[0075] Taking into account the timeliness of discovering and solving problems when broadband oscillations occur in the power system, in the embodiments provided in the present application, the acquired target waveform data is not strictly limited to the waveform data of the target power system within a longer sampling time, but can also be the waveform data within a shorter sampling time. For the waveform data corresponding to the shorter sampling time, the terminal device can expand the data volume of the waveform data corresponding to the shorter sampling time through interpolation processing, so that subsequent broadband oscillation identification can be performed.

[0076] Specifically, the terminal device can determine whether the preset sampling time corresponding to the target waveform data is not greater than the sampling time threshold according to the preset sampling time corresponding to the target waveform data and the preset sampling time threshold. When it is determined that the preset sampling time is not greater than the sampling time threshold, the terminal device can determine that the target waveform data is the waveform data of the target power system within a shorter sampling time. At this time, if the target waveform data is directly used for subsequent broadband oscillation identification, the problem of small data volume will arise, which will in turn affect the recognition accuracy of the subsequent broadband oscillation identification process, so interpolation processing is required to ensure that the data volume for broadband oscillation identification is sufficient.

[0077] Furthermore, the terminal device can perform interpolation processing on the target waveform data whose preset sampling time is not greater than the sampling time threshold, so as to obtain the interpolated waveform data corresponding to the target waveform data. Based on the interpolated waveform data after the interpolation processing, the terminal device can determine the spectrum data corresponding to the target waveform data.

[0078] Regarding the specific implementation methods and approaches of interpolation processing, in one embodiment provided in the present application, the terminal device can use a cubic spline interpolation function to interpolate target waveform data whose preset sampling time is not greater than a sampling time threshold. For example, the target waveform data whose preset sampling time is not greater than the sampling time threshold and whose waveform data length is not greater than 4000 is interpolated through a cubic spline interpolation function to generate interpolated waveform data with a waveform length of 10000.

[0079] The specific process of interpolation processing by cubic spline interpolation function can refer to the following formula (1) and formula (2):

[0080] x i (m) = a i +b i (nn i )+c i (nn i ) 2 +d i (nn i ) 3 (1)

[0081]

[0082] Among them, x i (m) is used to represent n in the target waveform data i With n i+1 The interpolated data size between i , b i 、c i and i They are used to represent the polynomial coefficients corresponding to the cubic spline interpolation function, n and m are the index values ​​of each data point in the target waveform data and the interpolated data, L is the waveform length corresponding to the target waveform data, N i The number of data points generated after the target waveform data is interpolated.

[0083] Through the calculation of the above formulas (1) and (2), the terminal device can interpolate the target waveform data whose preset sampling time is not greater than the sampling time threshold, increase the data length of the target waveform data, so that even the waveform data of the target power system in a short period of time can be used as data for identification and analysis of broadband oscillations.

[0084] It should be noted that the embodiments provided in the present application do not strictly limit the specific implementation method of the above-mentioned terminal device to interpolate the target waveform data. It can be the above-mentioned interpolation of the target waveform data through the cubic spline interpolation function, or other feasible methods. In another embodiment, the terminal device can also interpolate the target waveform data by polynomial interpolation. In other embodiments, the terminal device can also implement the interpolation of the target waveform data whose preset sampling time is not greater than the sampling time threshold by other feasible methods to obtain the interpolated waveform data corresponding to the target waveform data. The specific implementation method of the interpolation process can be determined according to the actual application scenario and actual needs.

[0085] Furthermore, after determining that the interpolated waveform data corresponds to the target waveform data whose preset sampling time is not greater than the sampling time threshold, or determining that the preset sampling time of the target waveform data is greater than the sampling time threshold, the terminal device can generate target spectrum data corresponding to the target waveform data.

[0086] Specifically, taking the interpolated waveform data corresponding to the target waveform data whose preset sampling time is not greater than the sampling time threshold as an example, the terminal device can first perform windowing processing on the interpolated waveform data corresponding to the target waveform data through a Hanning window, and then perform frequency domain conversion on the interpolated waveform data corresponding to the target waveform data through a fast Fourier transform (FFT) to determine the frequency spectrum data corresponding to the target waveform data.

[0087] Among them, the spectral data corresponding to the target waveform data may include spectral data corresponding to each frequency component in the target waveform data. The above-mentioned windowing processing process performed by the Hanning window can reduce the spectral leakage of the waveform data after interpolation in the subsequent frequency domain conversion process, and improve the sidelobe suppression and other beneficial effects. In other embodiments, the same purpose can also be achieved through other feasible windowing methods and approaches, which can be flexibly adjusted according to actual application scenarios and actual needs.

[0088] It should also be noted that the specific method for determining the spectral data corresponding to the target waveform data is not strictly limited in the present application, and it can be the spectral data of the frequency domain data converted from the interpolated waveform data of the time domain data or the target waveform data by the fast Fourier transform. In another embodiment, the terminal device can also determine the spectral data corresponding to the target waveform data by other frequency domain conversion methods, such as Discrete Fourier Transform (DFT), Short-Time Fourier Transform (STFT), etc., which can be flexibly selected and set according to the actual application scenario and needs.

[0089] S103: Determine the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component according to the target spectrum data.

[0090] In step S103, the terminal device can determine the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component in the target waveform data according to the target frequency spectrum data determined through the above steps.

[0091] Among them, for each frequency component, the peak frequency feature corresponding to the frequency component can be used to represent the change of the peak frequency of the frequency component within the preset sampling time, and specifically can be the maximum amplitude peak frequency corresponding to the frequency component. As for the amplitude change feature, the amplitude change feature corresponding to the frequency component can be used to represent the change of the amplitude of the frequency component within the preset sampling time, and specifically can be the amplitude change rate corresponding to the frequency component within the preset sampling time.

[0092] It should be noted in advance that the power system may be affected by noise or errors during operation, resulting in the presence of some frequency components generated by noise or errors in the collected waveform data. Such noise or error frequency components do not belong to broadband oscillations.

[0093] Therefore, in order to avoid the influence of frequency components generated by noise or errors in the subsequent wide-band oscillation identification process on the identification accuracy, the terminal device can filter each frequency component according to the spectrum data corresponding to each frequency component and the fundamental frequency corresponding to the target waveform data to determine the target frequency component before determining the peak frequency characteristics and amplitude change characteristics corresponding to each frequency component based on the target spectrum data.

[0094] Specifically, in the embodiment provided by the present application, the terminal device can determine the maximum peak amplitude corresponding to each frequency component according to the target spectrum data determined in the above steps. Then, for each frequency component, the terminal device can determine whether the frequency component is a target frequency component according to the magnitude relationship between the maximum peak amplitude corresponding to the frequency component and the fundamental peak amplitude of the fundamental frequency corresponding to the target waveform data, thereby determining at least one target frequency component from each frequency component.

[0095] Among them, the size relationship between the maximum peak amplitude corresponding to the frequency component and the fundamental peak amplitude of the fundamental frequency corresponding to the target waveform data can be specifically judged based on whether the maximum peak amplitude corresponding to the frequency component is less than 0.5% of the fundamental peak amplitude of the fundamental frequency corresponding to the target waveform data. For each frequency component, when the maximum peak amplitude corresponding to the frequency is less than 0.5% of the fundamental peak amplitude of the fundamental frequency, the terminal device can determine that the frequency component is caused by noise or error, and directly filter it, and it will not be used as the target frequency component; when the maximum peak amplitude corresponding to the frequency is not less than 0.5% of the fundamental peak amplitude of the fundamental frequency, the terminal device can determine that the frequency component is not caused by noise or error, and can be used as one of the target frequency components. The above specific judgment values ​​are only for ease of understanding, not strictly limited, and can be flexibly adjusted according to actual application scenarios and needs.

[0096] Then, after the above-mentioned filtering process based on the fundamental frequency for the spectrum book corresponding to the target waveform data, the terminal device can determine at least one target frequency component among each frequency component, and determine the corresponding peak frequency characteristics and amplitude change characteristics for the at least one determined target frequency component. For the sake of ease of description, the at least one target frequency component will be referred to as each target frequency component.

[0097] Specifically, in the embodiment provided in the present application, the terminal device can determine the maximum amplitude peak frequency corresponding to each target frequency component according to the target spectrum data corresponding to the target waveform data, and use it as the peak frequency feature corresponding to each target frequency component. The specific determination process of the maximum amplitude peak frequency can refer to the following formula (3):

[0098] f OSC =K max Δf(3)

[0099] Among them, for each target frequency component, f OSC Used to indicate the maximum amplitude peak frequency corresponding to the target frequency component, K maxIt is used to indicate the index number corresponding to the maximum amplitude peak value in the spectrum data of the target frequency component, and Δf is used to indicate the frequency resolution corresponding to the frequency domain conversion method in the process of determining the spectrum data corresponding to the target waveform data (for example, the frequency resolution corresponding to the fast Fourier transform). Through the above formula (3), the terminal device can determine the maximum amplitude peak frequency f corresponding to each target frequency component. OSC .

[0100] It should be noted that after determining the maximum amplitude peak frequency corresponding to each target frequency component, the terminal device does not immediately determine the amplitude change rate corresponding to each target frequency component, but performs another filtering process.

[0101] The reason for filtering again is that the previous process of filtering each frequency component based on the fundamental frequency only considers the size relationship between the error or noise frequency component and the amplitude peak of the fundamental frequency. Taking the above filtering process as an example, when the amplitude peak of the frequency component is greater than 0.5% of the amplitude peak of the fundamental frequency, there may also be an error or noise frequency component. This type of error or noise frequency component can no longer be determined based on the size relationship between the amplitude peak and the amplitude peak of the fundamental frequency.

[0102] Therefore, considering the above reasons, in the embodiment provided by the present application, the terminal device can identify invalid frequency components from each target frequency component according to the maximum amplitude peak frequency corresponding to each target frequency component determined above, and use other target frequency components except the invalid frequency components as valid frequency components. Wherein, the invalid frequency components at least include noise frequency components and error frequency components.

[0103] Specifically, for each target frequency component, the terminal device can determine the peak frequency difference between the maximum amplitude peak frequency and the previous amplitude peak frequency of the maximum amplitude peak frequency in the spectrum data corresponding to the target frequency component based on the maximum amplitude peak frequency corresponding to the target frequency component, and determine whether the peak frequency difference of the target frequency component is greater than the preset peak difference threshold based on the peak frequency difference of the target frequency component and the preset peak difference threshold.

[0104] At the same time, the terminal device can also determine whether the frequency amplitude value of the target frequency component has been continuously reduced by more than a preset number of reductions (for example, 3 times) based on the spectrum data corresponding to the target frequency component. When it is determined that the peak frequency difference of the target frequency component is greater than the preset peak difference threshold, or when it is determined that the frequency amplitude value of the target frequency component has been continuously reduced by more than a preset number of reductions, the terminal device can determine that the target frequency component is an invalid frequency component, that is, a noise frequency component or an error frequency component.

[0105] The above process of determining invalid frequency components in the target frequency components can be simply understood as follows: if the peak frequency difference of the target frequency component is greater than the preset peak difference threshold, or the frequency amplitude value continuously decreases by more than the preset number of reductions, it will be determined as an invalid frequency component. The spectrum data of such frequency components show a sudden increase in vibration amplitude, and the trend of the amplitude increase is not long-lasting. The amplitude tends to show a continuous downward trend after the increase. Only when the peak frequency difference of the target frequency component is not greater than the preset peak difference threshold, and the frequency amplitude value continuously decreases by less than the preset number of reductions, it will be determined as an invalid frequency component. The terminal device will determine the corresponding target frequency component as a valid frequency component.

[0106] Further, in the embodiment provided by the present application, after determining at least one effective frequency component in each target frequency component through the above steps, the terminal device can determine the amplitude change rate corresponding to the effective frequency component according to the spectrum data corresponding to the effective frequency component. The specific determination process corresponding to the amplitude change rate can refer to the following formula (4):

[0107]

[0108] Among them, for each effective frequency component, It is used to indicate the amplitude change rate of the effective frequency component at the moment M0. It is used to indicate the amplitude of the effective frequency component at the moment M0. It is used to indicate the amplitude magnitude of the effective frequency component at the time M0-1. Through the above formula (4), the terminal device can determine the amplitude change rate of each effective frequency component at each time point. Then, the average value of the amplitude change rate at each time point is determined as the amplitude change rate corresponding to the effective frequency component, and as the amplitude change feature corresponding to the effective frequency component.

[0109] It should be noted that the above-mentioned determination process of the amplitude variation characteristics can be used to distinguish the harmonic frequency components in the effective frequency components. Harmonics refer to sine waves whose frequencies are integer multiples of the fundamental frequency. The corresponding amplitude variations are relatively stable as a whole, and the amplitude variation rate is approximately zero. The amplitude variation characteristics (i.e., amplitude variation rate) corresponding to each effective frequency component are determined by the above formula, which can be used to distinguish the harmonic frequency components in each effective frequency component during the subsequent broadband oscillation identification, thereby improving the accuracy of the broadband oscillation identification process.

[0110] Through the above steps, the terminal device can construct a detection data set for the subsequent broadband oscillation identification process based on the peak frequency characteristics (such as the maximum amplitude peak frequency) and amplitude change characteristics (such as the amplitude change rate) corresponding to the effective frequency component. In order to facilitate the overall understanding of the above peak frequency characteristics and amplitude change characteristics, as well as the determination process of the detection data set, the following is a flow chart of the determination process of the detection data set corresponding to a target waveform data for explanation, as shown in the figure. Figure 2 as shown in .

[0111] Figure 2 A schematic flow chart of an example of a process for determining a detection data set corresponding to target waveform data provided in one embodiment of the present application.

[0112] like Figure 2 As shown, an example of a process for determining a detection data set corresponding to target waveform data provided in an embodiment of the present application includes steps S201 to S204.

[0113] In the embodiments provided in this application, execution Figure 2 The execution entity of steps S201 to S204 shown in the figure can be the terminal device mentioned in the above steps, or can be a server, etc., or can also be an execution entity in the form of software. The specific type and form of the execution entity for determining the detection data set corresponding to the target waveform data are not strictly limited in this application.

[0114] S201: Acquire target waveform data.

[0115] S202: performing interpolation processing on the target waveform data based on the cubic spline difference function, and performing frequency domain conversion on the interpolated waveform data through Hanning windowing and fast Fourier transform to obtain target spectrum data.

[0116] S203: Determine the effective frequency component in the target spectrum data, and determine the maximum amplitude peak frequency corresponding to the effective frequency component as the peak frequency feature, and determine the amplitude change rate as the amplitude change feature.

[0117] S204: Constructing a detection data set for broadband oscillation recognition according to the peak frequency characteristics and amplitude variation characteristics corresponding to the effective frequency components.

[0118] The detailed contents corresponding to the above steps S201 to S204 have been correspondingly explained and introduced in the above steps S101 to S103, which can be directly referred to, and will not be repeated here.

[0119] S104: performing broadband oscillation identification on each frequency component according to the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component, and determining the broadband oscillation type corresponding to each frequency component.

[0120] Specifically, in the embodiments provided in the present application, the terminal device can form a detection data set with the peak frequency characteristics and amplitude change characteristics corresponding to each effective frequency component, and input the detection data set into a preset frequency component classification model, so that the frequency component classification model can determine whether each effective frequency component is a broadband oscillation frequency component or a harmonic frequency component based on the received detection data set. While determining that the effective frequency component is a broadband oscillation component, the terminal device can also determine the broadband oscillation type corresponding to the effective frequency component that is a broadband oscillation component. The specific expression of the detection data set can refer to the following formula (5):

[0121]

[0122] Among them, D f A data set used to represent the peak frequency characteristics corresponding to each effective frequency component, f OSCn It is used to represent the peak frequency characteristics corresponding to the nth effective frequency component, and specifically can be the maximum amplitude peak frequency corresponding to the nth effective frequency component. s The data set used to represent the amplitude change characteristics corresponding to each effective frequency component, D n It is used to represent the amplitude change characteristic corresponding to the nth effective frequency component, and specifically can be the amplitude change rate corresponding to the nth effective frequency component.

[0123] Based on the detection data set in the above formula (5), for each effective frequency component, the terminal device can determine whether the frequency component is a harmonic frequency component or a broadband oscillation frequency component according to the peak frequency characteristics and amplitude change characteristics corresponding to the effective frequency component through the frequency component classification model. The specific judgment criteria can be based on whether the peak frequency characteristics of the effective frequency component indicate that the frequency of the frequency component is an integer multiple of the fundamental frequency, and whether the amplitude change characteristics of the effective frequency component indicate that the amplitude change of the frequency component is almost unchanged.

[0124] When it is determined that the effective frequency component is not a harmonic frequency component, the terminal device can determine that the effective frequency component is a broadband oscillation frequency component through the frequency component classification model, and classify the broadband oscillation type of the effective frequency component according to the peak frequency characteristics corresponding to the effective frequency component and the broadband oscillation classification standard corresponding to the frequency component classification model, and determine the broadband oscillation type corresponding to the effective frequency component. The broadband oscillation type can at least include subsynchronous oscillation, high-frequency oscillation, etc., and the broadband oscillation classification standard can be predetermined according to different peak frequency characteristics corresponding to different broadband oscillation types.

[0125] In the embodiments provided in the present application, the specific model type and structure corresponding to the above-mentioned frequency component classification model are not strictly limited in the present application, and may be a random forest classifier model (Random forest) composed of multiple decision data.

[0126] Specifically, the terminal device can input the detection data set corresponding to the above target waveform data into the frequency component classification model of the random forest classifier model. The random forest classifier model can first normalize the received detection data set and generate multiple decision tree models by random sampling. Then, the terminal device can select the optimal features of the classification nodes of each decision tree based on the Gini index through the random forest classifier model, thereby achieving effective segmentation of different broadband oscillation recognition types in the detection data set. The Gini index can be specifically referred to in the following formula (6):

[0127]

[0128] Among them, Gini (D s / D f ) is used to represent the Gini index determined for the test data set, C is used to represent the number of classification categories, and p c It is used to indicate the proportion of the c-th class samples in all classified samples. Through the above formula, the terminal device can determine the classification nodes of each decision tree through the random forest classifier model, and output a classification result T corresponding to each effective frequency component through each decision tree. The classification result T contains the classification result corresponding to the decision tree for each effective frequency component, that is, whether each effective frequency component classified by the decision tree is a harmonic frequency component or a broadband oscillation frequency component, and the broadband oscillation type corresponding to the broadband oscillation frequency component.

[0129] Then, the terminal device can use the majority voting mechanism to finally integrate the classification results of each decision tree and determine the final classification results corresponding to each valid frequency component. The majority voting mechanism can be specifically referred to in the following formula (7):

[0130]

[0131] Among them, Y is used to represent the final classification result determined by the majority voting mechanism for each valid frequency component, and h s (·) is used to represent the classification function corresponding to the random forest classifier model, h s (T q ) is used to represent the number of samples T in the sth decision tree. q (ie, each effective frequency component in this application) is the result of the division, and MV is used to represent the logarithmic voting function.

[0132] Through the above formula (7), the terminal device can determine whether each effective frequency component is a harmonic frequency component or a broadband oscillation frequency component, and the broadband oscillation type corresponding to the broadband oscillation frequency component according to the classification results corresponding to each decision tree in the random forest classifier model.

[0133] It should be noted that the above application example using the random forest classifier model as the frequency component classification model is only used to understand the specific process of broadband oscillation identification of each effective frequency component, and the specific model type and structure of the frequency component classification model are not strictly limited in this application. In other embodiments, the frequency component classification model can also be a computing model such as a support vector machine (Support Vector Machine, SVM), a multilayer perceptron (Multilayer Perceptron, MLP), etc., which can be flexibly selected and applied according to actual application scenarios and actual needs.

[0134] In addition, the above process of determining the detection data set corresponding to the waveform data can also be applied to the pre-training process of the frequency component classification model, and the classification ability and accuracy of the frequency component classification model for broadband oscillation types can be improved through, for example, supervised training. The trained frequency component classification model can determine the broadband oscillation type corresponding to each frequency component according to the detection data set corresponding to the target waveform data in the actual process of identifying broadband oscillations in the power system.

[0135] In order to facilitate understanding of the overall process content corresponding to the above-mentioned method for identifying and classifying broadband oscillations in power systems, the following is an overall process diagram of the method for identifying and classifying broadband oscillations in power systems for explanation and introduction, as shown in FIG. Figure 3 as shown in .

[0136] Figure 3 A flowchart of an application example of a method for identifying and classifying broadband oscillations in a power system provided in one embodiment of the present application.

[0137] like Figure 3As shown, the application example of the method for identifying and classifying broadband oscillations in a power system provided in the embodiment of the present application includes steps S301 to S306.

[0138] It should also be noted that in the embodiments provided in this application, the execution Figure 3 The execution subject of steps S301 to S306 shown in the figure can be the terminal device mentioned in the above steps, or can be a server, etc., or can also be an execution subject in the form of software. The specific type and expression form of the execution subject used in the application example of the method for identifying and classifying broadband oscillations in power systems are not strictly limited in this application.

[0139] In addition, Figure 3 In the illustrated embodiment, broadband oscillation simulation may be performed based on the target power system to obtain target waveform data containing known broadband oscillation frequency components, which may be used to detect the accuracy and reliability of the broadband oscillation identification process.

[0140] S301: Acquire target waveform data of a target power system in a wide-band oscillation simulation state.

[0141] In step S301, the target waveform data of the target power system in the wide-band oscillation simulation state within the preset sampling time can be obtained through the power monitoring system or the synchronous phasor measurement unit, and the target waveform data can include waveform data corresponding to the voltage, current, etc. of the target power system. Step S301 can be regarded as a data collection process for the target power system.

[0142] S302: Determine target spectrum data corresponding to the target waveform data through a cubic spline interpolation function, a Hanning window, and a fast Fourier transform.

[0143] In step S302, the target waveform data can be interpolated based on the cubic spline interpolation function to obtain interpolated data corresponding to the target waveform data. For the interpolated waveform data, Hanning windowing and fast Fourier transform can be performed to achieve frequency domain conversion of the target waveform data and determine the target spectrum data corresponding to the target waveform data.

[0144] S303: Filter the error frequency components and noise frequency components in the target spectrum data to determine each target frequency component.

[0145] In step S303, the error frequency components and noise frequency components of each frequency component in the target spectrum data can be filtered according to the spectrum data corresponding to each frequency component in the target spectrum data determined in the above steps, and the fundamental frequency corresponding to the target waveform data, so as to determine each target frequency component in the target spectrum data.

[0146] S304: Determine the maximum amplitude peak frequency corresponding to the target frequency component as a peak frequency feature.

[0147] In step 304, for each target frequency component, the peak frequency feature corresponding to the target frequency component can be determined according to the spectrum data corresponding to the target frequency component. The peak frequency feature can be, for example, Figure 3 The frequency of the maximum amplitude peak shown in .

[0148] S305: Determine the effective frequency components and invalid frequency components in each target frequency component according to the maximum amplitude peak frequency

[0149] In step S305, based on the maximum amplitude peak frequency corresponding to each target frequency component determined in the above steps and the spectrum data corresponding to each target frequency component, it can be identified whether there are invalid frequency components in each target frequency component, that is, whether they are noise frequency components or error frequency components, and then the valid frequency components and invalid frequency components in each target frequency component can be determined.

[0150] S306: Determine the amplitude change rate corresponding to the effective frequency component as the amplitude change feature.

[0151] In step S306, for each effective frequency component determined by the above steps, the amplitude variation feature corresponding to the frequency component can be determined according to the spectrum data corresponding to the effective frequency component. The amplitude variation feature can be specifically as follows: Figure 3 The rate of change of amplitude shown in .

[0152] S307: The data set of the peak frequency characteristics and amplitude variation characteristics corresponding to each effective frequency component is used as a detection data set.

[0153] In step S307, the data set of the peak frequency characteristics and amplitude change characteristics corresponding to each effective frequency component determined in the above steps, that is, the data set of the maximum amplitude peak frequency and amplitude change rate corresponding to each effective frequency component, can be used as a detection data set for the subsequent broadband oscillation identification process. Steps S302 to S307 can be regarded as a data preprocessing process for the target waveform data.

[0154] S308: Input the detection data set into the random forest classifier model, and perform broadband oscillation recognition through the random forest classifier model.

[0155] In step S308, the detection data set consisting of the peak frequency characteristics and amplitude change characteristics corresponding to each effective frequency component can be input into a preset frequency component classification model. The frequency component classification model can be specifically Figure 3The random forest classifier model shown in .

[0156] The effective frequency components are classified by the random forest classifier model to determine the harmonic frequency components and broadband oscillation frequency components in each effective frequency component, as well as the broadband oscillation type corresponding to the broadband oscillation frequency component. The model construction process of the random forest classifier model can be constructed in advance based on each preset broadband oscillation type. Step S308 can be regarded as a process of identifying and classifying broadband oscillations for the target power system.

[0157] Figure 3 The contents shown in can be used to understand the overall process of the embodiment of the present application and the relationship between the specific operations corresponding to each step. The specific content introduction corresponding to each step S301 to S308 and the detailed embodiments have been introduced in detail in the corresponding contents of the above steps S101 to S104, and will not be repeated here. You can directly refer to the corresponding contents in the above steps S101 to S104.

[0158] The above is a specific implementation of the method for identifying and classifying broadband oscillations in a power system provided in the embodiment of the present application. Through the technical solution provided in the embodiment of the present application, the broadband oscillation components in each frequency component can be identified according to the waveform data corresponding to the power system, and the broadband oscillation type corresponding to each broadband heavy oscillation component can be accurately determined. Through the above method, various types of broadband oscillation problems in the operation of the power system can be effectively identified, and the efficiency and accuracy of identifying broadband oscillation phenomena in the operation of the power system can be improved, so that they can be discovered and solved in a timely manner, ensuring the normal operation of the power system and improving the safety and stability of the power system.

[0159] The above method can also directly identify broadband oscillations based on the waveform data within the short sampling time corresponding to the power system, and determine the broadband oscillation type corresponding to each frequency component. It effectively overcomes the defect of the current technology that broadband oscillations can only be identified based on waveform data with a long sampling time, effectively improves the recognition efficiency of broadband oscillations for power systems and the efficiency of subsequent related operators in handling broadband oscillation problems, greatly improves the safety and stability of the power system during operation, and increases the service life of the system while also ensuring the normal operation of the power system.

[0160] Based on the method for identifying and classifying broadband oscillations in a power system provided in the above-mentioned embodiment, accordingly, the present application also provides a specific implementation method of an apparatus for identifying and classifying broadband oscillations in a power system, please refer to the following embodiment.

[0161] Figure 4A schematic diagram of the structure of a device for identifying and classifying broadband oscillations in a power system provided in another embodiment of the present application.

[0162] like Figure 4 As shown, the identification and classification device for broadband oscillation of the power system provided by the present application includes the following units:

[0163] An acquisition unit 401 is used to acquire target waveform data generated by a target power system within a preset sampling time, where the target waveform data is electrical quantity waveform data;

[0164] The spectrum generating unit 402 is used to generate target spectrum data corresponding to the target waveform data according to the target waveform data, wherein the target spectrum data includes spectrum data corresponding to each frequency component;

[0165] A feature determination unit 403 is used to determine the peak frequency feature and amplitude variation feature corresponding to each frequency component according to the target spectrum data;

[0166] The identification unit 404 is used to identify the broadband oscillation of each frequency component according to the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component, and determine the broadband oscillation type corresponding to each frequency component.

[0167] In the embodiment provided by the present application, the device can identify the broadband oscillation component in each frequency component according to the waveform data corresponding to the power system, and accurately determine the broadband oscillation type corresponding to each broadband heavy oscillation component. Thus, various types of broadband oscillation problems in the operation of the power system can be effectively identified, and the efficiency and accuracy of identifying broadband oscillation phenomena in the operation of the power system can be improved, so that they can be discovered and solved in time, ensuring the normal operation of the power system and improving the safety and stability of the power system.

[0168] In an embodiment, the spectrum generating unit 402 is specifically configured to determine whether the preset sampling time is not greater than the sampling time threshold according to the preset sampling time and the preset sampling time threshold.

[0169] When it is determined that the preset sampling time is not greater than the sampling time threshold, interpolation processing is performed on the target waveform data to obtain interpolated waveform data corresponding to the target waveform data.

[0170] Target spectrum data corresponding to the target waveform data is generated according to the interpolated waveform data corresponding to the target waveform data.

[0171] In one embodiment, the feature determination unit 403 is specifically configured to determine the maximum peak amplitude corresponding to each frequency component according to the target spectrum data.

[0172] At least one target frequency component is determined from each frequency component according to the magnitude relationship between the maximum peak amplitude corresponding to each frequency component and the fundamental peak amplitude of the fundamental frequency corresponding to the target waveform data.

[0173] The feature determination unit 403 is specifically configured to determine, according to the target frequency spectrum data, a peak frequency feature and an amplitude variation feature corresponding to at least one target frequency component.

[0174] In one embodiment, the feature determination unit 403 is specifically configured to determine, based on the target spectrum data, a maximum amplitude peak frequency corresponding to at least one target frequency component as a peak frequency feature corresponding to at least one target frequency component.

[0175] According to the maximum amplitude peak frequency corresponding to at least one target frequency component, an invalid frequency component in at least one target frequency component is identified, and at least one other target frequency component except the invalid frequency component is used as a valid frequency component, wherein the invalid frequency component at least includes a noise frequency component and an error frequency component.

[0176] According to the target spectrum data, the amplitude change rate corresponding to the effective frequency component is determined as the amplitude change feature corresponding to the effective frequency component.

[0177] In one embodiment, the feature determination unit 403 is specifically used to determine, for each target frequency component, according to the target spectrum data, a peak difference between a maximum amplitude peak frequency corresponding to the target frequency component and a previous peak frequency of the maximum amplitude peak frequency.

[0178] When it is determined that the peak difference corresponding to the target frequency is greater than a preset peak difference threshold, the target frequency component is determined to be an invalid frequency component.

[0179] In one embodiment, the feature determination unit 403 is specifically configured to determine, for each target frequency component, whether the frequency amplitude value of the target frequency component decreases continuously for more than a preset number of decreases according to the target spectrum data.

[0180] When it is determined that the frequency amplitude value of the target frequency component decreases continuously for more than a preset number of decreases, the target frequency component is determined to be an invalid frequency component.

[0181] In one embodiment, the identification unit 404 is specifically used to determine, for each frequency component, whether the frequency component is a broadband oscillation frequency component according to the peak frequency characteristics and amplitude variation characteristics corresponding to the frequency component through a preset frequency component classification model; when the frequency component is determined to be a broadband oscillation frequency component, the broadband oscillation type corresponding to the frequency component is determined according to the peak frequency characteristics corresponding to the frequency component, and the broadband oscillation type includes at least subsynchronous oscillation and high-frequency oscillation.

[0182] In one embodiment, the identification unit 404 is specifically used to classify each frequency component into a broadband oscillation type by using a preset frequency component classification model, according to the peak frequency characteristics corresponding to the frequency component and the broadband oscillation classification standard corresponding to the frequency component classification model, and determine the broadband oscillation type corresponding to the frequency component.

[0183] Each module in the image processing device provided in the embodiment of the present application can be implemented Figure 1 , Figure 2 or Figure 3 The method steps of the illustrated embodiment can achieve the corresponding technical effects, and for the sake of brevity, they will not be repeated here.

[0184] Figure 5 A schematic diagram of the hardware structure of a device for identifying and classifying broadband oscillations in a power system provided in yet another embodiment of the present application.

[0185] The electronic device may include a processor 501 and a memory 502 storing computer program instructions.

[0186] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0187] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 502 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 502 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory.

[0188] The memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method for identifying and classifying broadband oscillations in a power system according to the present disclosure.

[0189] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement any one of the methods for identifying and classifying broadband oscillations in the power system in the above embodiments.

[0190] In one example, the electronic device may further include a communication interface 503 and a bus 510. Figure 5 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 and communicate with each other.

[0191] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0192] Bus 510 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 510 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0193] In addition, in combination with the method for identifying and classifying broadband oscillations in the above-mentioned embodiment, the embodiment of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the method for identifying and classifying broadband oscillations in the power system provided in the embodiment of the present application is implemented.

[0194] The embodiment of the present application also provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the method for identifying and classifying broadband oscillations of a power system as provided in the embodiment of the present application.

[0195] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0196] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0197] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0198] Aspects of the present disclosure are described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable image processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable image processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0199] The above are only specific implementation methods of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for identifying and classifying broadband oscillations in a power system, characterized in that: The method comprises: Acquire target waveform data generated by a target power system within a preset sampling time, wherein the target waveform data is electrical quantity waveform data; Generating target spectrum data corresponding to the target waveform data according to the target waveform data, wherein the target spectrum data includes spectrum data corresponding to each frequency component; Determining peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component according to the target spectrum data; According to the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component, broadband oscillation identification is performed on each frequency component to determine the broadband oscillation type corresponding to each frequency component.

2. The method according to claim 1, characterized in that Generating target spectrum data corresponding to the target waveform data according to the target waveform data, including: According to the preset sampling time and the preset sampling time threshold, determining whether the preset sampling time is not greater than the sampling time threshold; When it is determined that the preset sampling time is not greater than the sampling time threshold, interpolation processing is performed on the target waveform data to obtain interpolated waveform data corresponding to the target waveform data; Target spectrum data corresponding to the target waveform data are generated according to the interpolated waveform data corresponding to the target waveform data.

3. The method according to claim 1, characterized in that Before determining the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component according to the target spectrum data, the method further includes: Determining the maximum peak amplitude corresponding to each frequency component according to the target spectrum data; Determine at least one target frequency component from the frequency components according to the magnitude relationship between the maximum peak amplitude corresponding to the frequency components and the fundamental peak amplitude of the fundamental frequency corresponding to the target waveform data; Determining the peak frequency characteristics and amplitude variation characteristics corresponding to each frequency component according to the target spectrum data includes: According to the target frequency spectrum data, a peak frequency characteristic and an amplitude variation characteristic corresponding to the at least one target frequency component are determined.

4. The method according to claim 3, characterized in that Determining, according to the target spectrum data, a peak frequency characteristic and an amplitude variation characteristic corresponding to the at least one target frequency component, comprising: Determining, according to the target frequency spectrum data, a maximum amplitude peak frequency corresponding to the at least one target frequency component as a peak frequency feature corresponding to the at least one target frequency component; According to the maximum amplitude peak frequency corresponding to the at least one target frequency component, an invalid frequency component in the at least one target frequency component is identified, and at least one other target frequency component except the invalid frequency component is used as a valid frequency component, wherein the invalid frequency component at least includes a noise frequency component and an error frequency component; According to the target frequency spectrum data, the amplitude change rate corresponding to the effective frequency component is determined as the amplitude change feature corresponding to the effective frequency component.

5. The method according to claim 4, characterized in that Identifying an invalid frequency component in the at least one target frequency component according to a maximum amplitude peak frequency corresponding to the at least one target frequency component includes: For each target frequency component, determining, according to the target frequency spectrum data, a peak frequency difference between a maximum amplitude peak frequency corresponding to the target frequency component and a peak frequency preceding the maximum amplitude peak frequency; When it is determined that the peak frequency difference corresponding to the target frequency is greater than a preset peak frequency difference threshold, the target frequency component is determined to be an invalid frequency component.

6. The method according to claim 5, characterized in that The method further comprises: For each target frequency component, determining, based on the target frequency spectrum data, whether the frequency amplitude value of the target frequency component decreases continuously by more than a preset number of decreases; When it is determined that the frequency amplitude value of the target frequency component decreases continuously for more than a preset number of decreases, the target frequency component is determined to be an invalid frequency component.

7. The method according to claim 1, characterized in that According to the peak frequency characteristics and amplitude variation characteristics corresponding to the frequency components, broadband oscillation identification is performed on the frequency components to determine the broadband oscillation type corresponding to the frequency components, including: For each frequency component, a preset frequency component classification model is used to determine whether the frequency component is a broadband oscillation frequency component based on the peak frequency characteristics and amplitude change characteristics corresponding to the frequency component. When the frequency component is determined to be a broadband oscillation frequency component, the broadband oscillation type corresponding to the frequency component is determined based on the peak frequency characteristics corresponding to the frequency component. The broadband oscillation type includes at least subsynchronous oscillation and high-frequency oscillation.

8. The method according to claim 7, characterized in that According to the peak frequency characteristics corresponding to the frequency component, the broadband oscillation type corresponding to the frequency component is determined, including: For each frequency component, a preset frequency component classification model is used to classify the frequency component into a broadband oscillation type according to the peak frequency characteristics corresponding to the frequency component and the broadband oscillation classification standard corresponding to the frequency component classification model, and the broadband oscillation type corresponding to the frequency component is determined.

9. A device for identifying and classifying broadband oscillations in a power system, characterized in that: The device comprises: An acquisition unit, used to acquire target waveform data generated by a target power system within a preset sampling time, wherein the target waveform data is electrical quantity waveform data; A spectrum generating unit, configured to generate target spectrum data corresponding to the target waveform data according to the target waveform data, wherein the target spectrum data includes spectrum data corresponding to each frequency component; A feature determination unit, used to determine the peak frequency feature and amplitude variation feature corresponding to each frequency component according to the target spectrum data; The identification unit is used to identify the broadband oscillation of each frequency component according to the peak frequency characteristics and amplitude change characteristics corresponding to each frequency component, and determine the broadband oscillation type corresponding to each frequency component.

10. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the method for identifying and classifying broadband oscillations in a power system according to any one of claims 1 to 8.

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