Inter-harmonic measurement method, device and equipment based on power system

Abstract

The invention relates to an inter-harmonic measurement method, device and equipment based on a power system. The method comprises the following steps: acquiring parameter information of a power system by using a wavelet transform algorithm; performing fitting processing and extraction processing on the parameter information in sequence based on a pre-constructed inter-harmonic measurement model to obtain inter-harmonic frequency; and determining the inter-harmonic of the power system based on the inter-harmonic frequency. By adopting the method, the hardware cost can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, device and equipment for measuring interharmonics based on a power system. Background Art

[0002] With the widespread access of distributed energy to the power system, as well as the increasing popularity of nonlinear loads and power electronic equipment in the power system, non-sinusoidal currents and voltages are easily generated during the operation of the power system, thus causing interharmonics. Interharmonics refer to the phenomenon that periodic alternating currents contain frequency components that are not integer multiples of the fundamental frequency. Interharmonics not only affect the normal operation of equipment in the power system, but in severe cases even endanger the overall stability of the power system.

[0003] Traditional technologies usually use optimal control methods to quickly identify and measure harmonics and interharmonics. Optimal control methods require high-performance compensation devices, such as active power filters and static VAR compensators. These devices rely on expensive high-performance power electronic components and complex control systems. Especially in large-scale applications, the hardware cost is particularly prominent. Summary of the invention

[0004] Based on this, it is necessary to provide an interharmonic measurement method, device and equipment based on a power system that can reduce hardware costs in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for measuring interharmonics based on a power system, comprising:

[0006] Use wavelet transform algorithm to collect parameter information of power system;

[0007] Based on the pre-built interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency;

[0008] Based on the interharmonic frequencies, interharmonics of the power system are determined.

[0009] In one embodiment, the interharmonic measurement model includes an objective function and an extraction model. The objective function is a function with the goal of minimizing risk. Based on the pre-built interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency, including:

[0010] Fitting the parameter information based on the objective function, and determining the frequency characteristics of the parameter information according to the fitting result;

[0011] The frequency characteristics are input into the extraction model to obtain the interharmonic frequencies.

[0012] In one embodiment, the above-mentioned fitting process of the parameter information based on the objective function and determining the frequency characteristics of the parameter information according to the fitting result includes:

[0013] A regression function is constructed based on the parameter information; the regression function includes the corresponding relationship between the parameter information and the mapping of the parameter information in the high-dimensional feature space function, the bias term and the weight vector;

[0014] Fit the regression function and the pre-built objective function, and adjust the bias term and weight vector in the regression function according to the fitting result;

[0015] When the degree of fit between the regression function and the target function meets the convergence condition, the frequency characteristics of the parameter information are determined from the regression function.

[0016] In one embodiment, the step of inputting the frequency feature into the extraction model to obtain the interharmonic frequency includes:

[0017] Decomposing the frequency characteristics to obtain multiple harmonic frequencies;

[0018] An interharmonic frequency is determined from the plurality of harmonic frequencies.

[0019] In one embodiment, determining the interharmonic frequency from the plurality of harmonic frequencies comprises:

[0020] Obtain the fundamental frequency of the power system and determine the non-integer multiple frequencies of the fundamental frequency;

[0021] Non-integer multiple frequencies among the plurality of harmonic frequencies are determined as interharmonic frequencies.

[0022] In one embodiment, the method further comprises:

[0023] Optimizing the interharmonic frequency to obtain an optimized interharmonic frequency;

[0024] Correspondingly, based on the interharmonic frequency, determining the interharmonics of the power system includes:

[0025] Based on the optimized interharmonic frequencies, interharmonics of the power system are determined.

[0026] In a second aspect, the present application also provides an interharmonic measurement device based on a power system, comprising:

[0027] A collection module, used for collecting parameter information of the power system using a wavelet transform algorithm;

[0028] A processing module, used to perform fitting processing and extraction processing on parameter information in sequence based on a pre-built interharmonic measurement model to obtain interharmonic frequencies;

[0029] The determination module is used to determine the interharmonics of the power system based on the interharmonic frequencies.

[0030] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0031] Use wavelet transform algorithm to collect parameter information of power system;

[0032] Based on the pre-built interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency;

[0033] Based on the interharmonic frequencies, interharmonics of the power system are determined.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0035] Use wavelet transform algorithm to collect parameter information of power system;

[0036] Based on the pre-built interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency;

[0037] Based on the interharmonic frequencies, interharmonics of the power system are determined.

[0038] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0039] Use wavelet transform algorithm to collect parameter information of power system;

[0040] Based on the pre-built interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency;

[0041] Based on the interharmonic frequencies, interharmonics of the power system are determined.

[0042] The above-mentioned interharmonic measurement method, device and equipment based on the power system first uses the wavelet transform algorithm to collect parameter information of the power system; then based on the pre-constructed interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency; finally, based on the interharmonic frequency, the interharmonics of the power system are determined. Traditional technology usually uses optimal control methods to quickly identify and measure harmonics and interharmonics, but this requires high-performance compensation devices. These devices rely on expensive power electronic components and complex control systems, especially when used on a large scale. The hardware cost is very high. However, when the method of the embodiment of the present application is adopted, these compensation devices are not required, which can effectively reduce the hardware cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0044] FIG1 is a diagram showing the internal structure of a computer device in one embodiment;

[0045] FIG2 is a schematic flow chart of an interharmonic measurement method based on a power system in one embodiment;

[0046] Figure 3 is a flow chart of an interharmonic measurement method based on a power system in another embodiment;

[0047] Figure 4 is a flow chart of an interharmonic measurement method based on a power system in another embodiment;

[0048] Figure 5 is a flow chart of an interharmonic measurement method based on a power system in another embodiment;

[0049] Figure 6 is a flow chart of an interharmonic measurement method based on a power system in another embodiment;

[0050] Figure 7 FIG. 4 is a structural block diagram of an interharmonic measurement device based on a power system in an embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] In an exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be shown in FIG1. ​​The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data in the interharmonic measurement process based on the power system. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for measuring interharmonics based on the power system is implemented.

[0053] Those skilled in the art will understand that the structure shown in FIG. 1 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0054] In an exemplary embodiment, as shown in FIG2 , a method for measuring interharmonics based on a power system is provided, which is described by taking the method applied to the computer device in FIG1 as an example, and includes the following steps 201 to 203. Among them:

[0055] Step 201: Collect parameter information of the power system using a wavelet transform algorithm.

[0056] Among them, the wavelet function corresponding to the wavelet transform algorithm can adopt Morlet complex wavelet, which has good time-frequency localization characteristics, can effectively capture the changes of different frequency components in the power system signal over time, and is helpful for the subsequent accurate extraction of relevant parameter information.

[0057] In the embodiment of the present application, first, it is necessary to select a wavelet function suitable for analyzing the characteristics of the power system signal. For the selected wavelet function, reasonably determine its relevant parameter range and step size. For example, the scale parameter and translation parameter in the wavelet transform process need to be set in combination with the actual situation of the power system signal. Among them, the scale parameter will affect the analysis precision of different frequency components. The smaller scale is used to focus on the analysis of high-frequency components, and the larger scale is conducive to the analysis of low-frequency components; the translation parameter is related to the coverage and analysis accuracy of the relevant signals in the power system in the time dimension. The appropriate value range and change step size should be determined based on the signal duration and the desired time resolution.

[0058] Next, the data acquisition equipment can be used to obtain the voltage or current signals in the power system, which carry rich frequency-related information under the operation state of the power system. The collected signals are used as raw data and processed using the wavelet transform algorithm with selected and configured parameters. The original power system signal is decomposed and transformed through the wavelet transform algorithm, and parameter information reflecting the characteristic intensity of the signal at different frequencies and time positions is extracted from it, providing basic data support for the subsequent application of the interharmonic measurement model.

[0059] In some embodiments, the operating frequency signal of the distributed energy access power system is collected using a wavelet transform method, and the continuous wavelet transform of the power system frequency signal is expressed as:

[0060] (1)

[0061] in, In order to perform scaling and translation processing on the Morlet complex wavelet, is the scale parameter, is the displacement parameter and t is the time.

[0062] The Morlet complex wavelet is selected as the mother wavelet of the adjustable window continuous wavelet transform of the power system operating frequency signal. The frequency domain expression of the Morlet complex wavelet function is:

[0063] (2)

[0064] in, represents the center frequency of the mother wavelet band; Represents the bandwidth of the mother wavelet function. The larger the bandwidth, the narrower the frequency passband of the bandpass filter, and the smaller the impact of aliasing. is the frequency.

[0065] Step 202: performing fitting processing and extraction processing on parameter information in sequence based on a pre-built interharmonic measurement model to obtain interharmonic frequencies.

[0066] In an embodiment of the present application, the parameter information is input into a pre-built interharmonic measurement model, and the interharmonic measurement model begins to match and analyze the input parameter information based on its built-in algorithms and rules. In this process, the interharmonic measurement model will try to find the correlation and matching degree between the input parameter information and different interharmonic situations based on the existing knowledge and experience patterns related to interharmonics in the power system. For example, the interharmonic measurement model will determine which interharmonic characteristic pattern the frequency change characteristics, signal strength distribution, etc. reflected in the parameter information conform to, so as to gradually determine the potential connection between the data and the interharmonics.

[0067] In order to make the processing results of the interharmonic measurement model for the input parameter information more in line with the actual interharmonic situation, iterative optimization is required. In each iteration, the interharmonic measurement model adjusts some of its own adjustable parameters (these parameters may involve the weight allocation of different frequency characteristics, the threshold setting of signal feature judgment, etc.) according to the current matching analysis situation to optimize the processing effect of the interharmonic measurement model on the data. Repeat this iterative process until the processing of the input parameter information by the interharmonic measurement model reaches a relatively stable and expected state, that is, the model can reflect the characteristic information related to the interharmonic as accurately as possible based on the input data, and complete the fitting processing stage.

[0068] After completing the fitting process, based on the results of the interharmonic measurement model output, we begin to identify potential interharmonic features. Since interharmonics have characteristics that are different from conventional harmonics (such as integer multiple frequency components of the fundamental wave), such as their frequency being a non-integer multiple of the fundamental wave frequency, by carefully combing and analyzing the frequency-related features in the model output results, we can mark those parts that meet the interharmonic frequency characteristics. For example, if the fundamental frequency of the power system is 50Hz, then the relevant feature information of the frequency components that are not integer multiples of 50Hz, such as 75Hz and 125Hz, will be focused on and initially screened out.

[0069] In some embodiments, the potential interharmonic feature information initially marked can be further screened and verified. This may involve comparing data collected at different time points, referring to the current operating status of the power system and other factors, eliminating some misjudgments that may be caused by interference or errors, determining the frequency components that truly belong to the interharmonics, and finally obtaining the accurate interharmonic frequency.

[0070] Step 203: Determine interharmonics of the power system based on the interharmonic frequencies.

[0071] In the embodiment of the present application, a reasonable interharmonic frequency screening range is set according to the fundamental frequency of the power system. For example, for a power system with a common fundamental frequency, it is clearly stipulated that the interharmonic frequency is usually within a certain frequency range corresponding to a non-integer multiple of the fundamental frequency, which is used as a preliminary judgment basis to distinguish possible interharmonic frequencies from conventional harmonic frequencies.

[0072] For the interharmonic frequency that has been identified, further look for other key parameters corresponding to it, such as amplitude, phase, etc. By combining these related parameters with the interharmonic frequency, the specific state of the interharmonic in the power system can be determined, thereby determining the interharmonics existing in the power system.

[0073] In the above-mentioned interharmonic measurement method based on the power system, the parameter information of the power system is first collected using the wavelet transform algorithm; then, based on the pre-constructed interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency; finally, based on the interharmonic frequency, the interharmonics of the power system are determined. Traditional technologies usually use optimal control methods to quickly identify and measure harmonics and interharmonics, but this requires high-performance compensation devices, which rely on expensive power electronic components and complex control systems. Especially when used on a large scale, the hardware cost is very high. However, when the method of the embodiment of the present application is used, these compensation devices are not required, thereby effectively reducing the hardware cost.

[0074] In an exemplary embodiment, the interharmonic measurement model includes an objective function and an extraction model, on the basis of which the objective function is a function with the goal of minimizing risk, such as Figure 3 As shown, the above-mentioned "performing fitting processing and extraction processing on parameter information in sequence based on the pre-built interharmonic measurement model" includes steps 301 to 302. Among them:

[0075] Step 301 : Fitting the parameter information based on the objective function, and determining the frequency characteristics of the parameter information according to the fitting result.

[0076] In the embodiment of the present application, firstly, the objective function in the interharmonic measurement model is clarified, which aims to minimize the risk and aims to make the objective function achieve the best matching effect on the input parameter information. The power system parameter information obtained by wavelet transform is introduced into the scope of the objective function, making it the object of analysis and optimization of the objective function, and establishing data association for subsequent processing.

[0077] The objective function starts to perform feature matching operations on the input parameter information according to its preset optimization direction and rules. It analyzes the various features contained in the parameter information, such as the frequency change trend of the parameter information in different time intervals, the intensity distribution characteristics corresponding to different frequency components, etc., and then determines the fit between these features and the ideal interharmonic related features that the objective function expects to achieve in accordance with the requirements of minimizing risks. In this process, the objective function measures the closeness of the parameter information to the preset interharmonic pattern from multiple dimensions, and constantly adjusts the analysis angle to find the feature association method that best reflects the fitting effect.

[0078] Through continuous feature matching and adjustment, the objective function gradually tends to reach a state of minimizing risk. Under the optimal fitting state, the frequency characteristics presented by the parameter information are determined. These frequency characteristics are closely related to interharmonics and best reflect their existence and characteristics after being screened and optimized by the objective function. For example, the concentrated embodiment of signal characteristics in certain specific frequency ranges, the relatively stable correlation pattern between different frequency components, etc.

[0079] Step 302: Input the frequency characteristics into the extraction model to obtain the interharmonic frequency.

[0080] In an embodiment of the present application, the frequency characteristics of the parameter information determined in the fitting process phase are input into a pre-built extraction model.

[0081] After receiving the frequency characteristic data, the extraction model analyzes the input frequency characteristics according to the interharmonic frequency judgment rules and algorithm logic set internally, thereby obtaining the interharmonic frequency. For example, based on the characteristic that the interharmonic frequency is usually a non-integer multiple of the fundamental frequency, the frequency components that meet this condition are accurately located and screened out in the input frequency characteristics, while excluding those conventional harmonic frequency components that are integer multiples of the fundamental frequency and abnormal frequency information that may be generated due to interference and other factors, to ensure that the screened frequency information accurately points to the interharmonic.

[0082] After rigorous analysis and screening of the extraction model, the frequency information that meets the interharmonic characteristics is finally determined and output, that is, the interharmonic frequency is obtained. These interharmonic frequencies can reflect the existence of intermediate harmonics in the power system and their specific performance in the frequency dimension, providing key basic data support for further analysis of the power quality and harmonic impact of the power system.

[0083] In the above embodiment, based on the interharmonic measurement model having the objective function and the extraction model, the fitting process and the extraction process can be effectively completed from the parameter information of the power system, thereby accurately acquiring the interharmonic frequency.

[0084] In an exemplary embodiment, Figure 4As shown, the above-mentioned "performing a fitting process on the parameter information based on the objective function and determining the frequency characteristics of the parameter information according to the fitting result" includes steps 401 to 403. Among them:

[0085] Step 401, constructing a regression function based on parameter information; the regression function includes the corresponding relationship between the parameter information and the mapping of the parameter information in the high-dimensional feature space function, the bias term and the weight vector.

[0086] Among them, the regression function aims to establish the correspondence between parameter information and the mapping of parameter information in high-dimensional feature space functions, bias terms and weight vectors. The purpose is to use this correspondence to explore the characteristic laws related to interharmonics hidden behind the parameter information, so as to prepare for subsequent analysis and determination of interharmonic frequencies.

[0087] In the embodiment of the present application, the specific structure of the regression function is established according to the needs of the interharmonic analysis of the power system. The structure should reflect how to reasonably associate the parameter information with the high-dimensional feature space function, and determine how to reach the corresponding representation in the high-dimensional feature space through the conversion and mapping path starting from the input parameter information. For example, it is necessary to clearly define how the different components in the parameter information (such as frequency values ​​and signal strengths at different time points) affect the value of the corresponding function in the high-dimensional feature space, so as to build a clear correspondence relationship architecture between the two.

[0088] After determining the basic structure and mapping logic, further improve the various settings of the regression function to make it complete for practical application. This can include clearly defining the various elements involved in the function, such as determining some intermediate variables that may be needed in the mapping process, the initial settings of parameters, etc., so that the regression function can perform effective calculations and analysis based on the given parameter information, and provide a standardized and reasonable basic function framework for the subsequent fitting process with the objective function.

[0089] Step 402 , fitting the regression function and the pre-built objective function, and adjusting the bias term and weight vector in the regression function according to the fitting result.

[0090] Among them, the bias term is a fixed offset in the overall function mapping relationship, and the weight vector determines the relative importance of each component of the parameter information when constructing the corresponding relationship.

[0091] In the embodiment of the present application, the constructed regression function is connected with the pre-constructed objective function, which is a function with the goal of minimizing risk, and represents the optimal state that the regression function is expected to achieve. By establishing this association, the fitting process is ready to start, so that the regression function is adjusted and optimized in the direction of the optimal fit set by the objective function.

[0092] According to the evaluation criteria and rules set by the objective function, the performance of the regression function in processing parameter information is analyzed and compared. The objective function will examine the difference between the output of the regression function and the ideal state from multiple perspectives, such as determining whether the frequency characteristic change trend reflected by the regression function is consistent with the expectation of the objective function, whether the response under different parameter information inputs meets expectations, etc. Through these detailed evaluations, the degree of fit between the current regression function and the objective function is determined, and the direction that needs to be adjusted and optimized is found.

[0093] According to the results of the fitting analysis, the bias term and weight vector in the regression function are adjusted in a targeted manner to change the output characteristics of the regression function so that it can better match the desired fitting effect of the objective function. For example, if it is found that the output of the regression function in certain frequency intervals deviates greatly from the objective function requirements, the deviation can be gradually reduced by appropriately adjusting the weights of the components related to the frequency interval in the corresponding weight vector and appropriately changing the value of the bias term, thereby improving the degree of fitting.

[0094] Step 403: when the degree of fit between the regression function and the target function meets the convergence condition, the frequency characteristics of the parameter information are determined from the regression function.

[0095] The convergence condition is a pre-set standard to measure whether the fitting process has reached an ideal stable state. For example, it can be set to the situation that the change range of the fitting degree is very small for multiple consecutive times, or the fitting degree reaches a certain threshold close to the optimal value. By constantly comparing the current fitting degree with the convergence condition, it is judged whether the convergence requirement is met.

[0096] In the embodiment of the present application, in the process of continuously adjusting the regression function and fitting it with the objective function, it is necessary to closely monitor the degree of fit between the two.

[0097] When the degree of fit between the regression function and the objective function meets the convergence condition, it means that the regression function has been optimized to a relatively stable and expected state. At this time, based on this optimized regression function, the frequency characteristics of the parameter information are extracted. These frequency characteristics are the key frequency performances that best reflect the inter-harmonic correlation of the power system after the regression function is optimized based on the fit with the objective function, such as the frequency components with significant characteristics in a specific frequency range, and the stable correlation patterns between different frequencies.

[0098] The above embodiment can complete the construction of the regression function, the fitting process with the target function and finally determine the frequency characteristics of the parameter information based on the parameter information, laying a foundation for the smooth progress of the entire interharmonic measurement process.

[0099] In an exemplary embodiment, Figure 5 As shown, the above “inputting the frequency characteristics into the extraction model to obtain the interharmonic frequency” includes steps 501 to 502. Among them:

[0100] Step 501, decomposing the frequency characteristics to obtain multiple harmonic frequencies.

[0101] In the embodiment of the present application, the computer device decomposes the frequency characteristics, marks out the frequencies that meet the definition of harmonics from the complex frequency characteristics one by one, gradually separates out multiple harmonic frequencies, and completes the initial decomposition work.

[0102] Then, sort out the identified harmonic frequencies, record the specific value of each harmonic frequency and some of its corresponding related features (such as performance in different time intervals, if relevant data is available), and summarize these harmonic frequencies to form a relatively complete set of harmonic frequencies, clearly showing all the harmonic frequencies decomposed from the original frequency characteristics, so as to facilitate the subsequent screening of interharmonic frequencies.

[0103] Step 502: determine interharmonic frequencies from a plurality of harmonic frequencies.

[0104] In the embodiment of the present application, based on the interharmonic frequency determination criteria, each frequency in the aggregated harmonic frequency set is checked one by one. Conventional harmonic frequencies that are integer multiples of the fundamental frequency are excluded from the set, leaving only frequency components that do not meet the condition of integer multiples of the fundamental frequency, and these remaining frequency components are potential interharmonic frequencies.

[0105] The potential interharmonic frequencies screened out are verified, and possible interference factors, data errors, etc. are considered. Combined with the current actual operation status of the power system and past relevant experience, it is confirmed again that these frequencies do meet the characteristics of interharmonics, rather than misjudgments caused by abnormal conditions. After the verification process, the interharmonic frequencies are finally determined.

[0106] Through the above embodiments, the frequency characteristics can be decomposed and processed in an orderly manner, and the interharmonic frequency can be accurately determined from multiple harmonic frequencies, providing strong support for relevant analysis and research of the power system.

[0107] In an exemplary embodiment, Figure 6 As shown, the above “determining the interharmonic frequency from multiple harmonic frequencies” includes steps 601 to 602. Among them:

[0108] Step 601, obtaining the fundamental frequency of the power system and determining a non-integer multiple frequency of the fundamental frequency.

[0109] Step 602: determine non-integer multiple frequencies among a plurality of harmonic frequencies as interharmonic frequencies.

[0110] In the embodiment of the present application, the voltage or current signal in the power system is collected using a power signal acquisition device. The collected signal is processed using a selected spectrum analysis method to obtain corresponding spectrum information. In the spectrum information, multiple frequency peaks are usually presented, among which the frequency component with the lowest frequency, relatively large amplitude, and obvious periodic characteristics is the fundamental frequency.

[0111] After obtaining the fundamental frequency, sort out all the frequency components obtained by spectrum analysis. Classify and sort these frequency components according to their multiples of the fundamental frequency, and clarify which frequencies are integer multiples of the fundamental frequency (conventional harmonic frequencies) and which frequencies are not integer multiples of the fundamental frequency, so as to prepare for the subsequent accurate screening of interharmonic frequencies.

[0112] According to the definition of interharmonics, that is, the characteristic that they are non-integer multiples of the fundamental frequency, all the sorted frequency components are screened. The frequency components that do not meet the integer multiple relationship of the fundamental frequency are extracted separately. These extracted frequencies are potential interharmonic frequencies, and the screening work based on the multiple relationship is preliminarily completed.

[0113] Finally, the potential interharmonic frequencies screened out are further verified, taking into account various factors that may exist in the actual operating environment of the power system, such as signal interference, equipment characteristics, etc., which affect the frequency measurement, to avoid misjudgment caused by these factors. By combining the current actual operating status of the power system, referring to the experience data of similar situations in the past, and other comprehensive judgments, it is confirmed that these screened frequencies are indeed frequencies that meet the true characteristics of interharmonics, and finally they are determined to be interharmonic frequencies.

[0114] Through the above embodiments, the fundamental frequency of the power system can be obtained in an orderly and accurate manner, and the non-integer multiple frequencies of the fundamental frequency can be determined, and the non-integer multiple frequencies among multiple harmonic frequencies can be determined as interharmonic frequencies, thereby laying the foundation for fully understanding the harmonic conditions of the power system.

[0115] In an exemplary embodiment, the method further includes:

[0116] The interharmonic frequency is optimized to obtain an optimized interharmonic frequency.

[0117] Correspondingly, based on the interharmonic frequency, determining the interharmonics of the power system includes:

[0118] Based on the optimized interharmonic frequencies, interharmonics of the power system are determined.

[0119] In the embodiment of the present application, firstly, a comprehensive data quality assessment is performed on the obtained interharmonic frequencies to check whether there are obvious abnormal values ​​in the interharmonic frequencies, such as certain frequency values ​​that differ too much from the overall data distribution, or the frequency range that does not conform to the normal operation logic of the power system.

[0120] Next, based on the results of the data quality assessment, identify and remove those obviously abnormal interharmonic frequency data. Among them, abnormal data may be caused by interference in the signal acquisition process, temporary failure of the measuring equipment, etc. If these abnormal values ​​are retained, it will affect the subsequent accurate analysis of the interharmonic situation. By setting reasonable abnormal judgment criteria, such as based on factors such as the historical normal frequency range and the common interharmonic frequency range of the same type of power system, the frequency values ​​that exceed the reasonable range are removed from the data set.

[0121] Through the above embodiments, the interharmonic frequency can be effectively optimized, and the interharmonic situation in the power system can be accurately determined based on the optimized interharmonic frequency, providing strong support for subsequent work such as power system operation analysis and power quality assessment.

[0122] According to some embodiments of the present application, a method for measuring interharmonics based on a power system is provided. The method is described by taking the application of the method to a computer device as an example, and the method may include the following steps:

[0123] Step 1: Use wavelet transform algorithm to collect parameter information of power system.

[0124] Step 2: construct a regression function based on the parameter information. The regression function includes the corresponding relationship between the parameter information and the mapping of the parameter information in the high-dimensional feature space function, the bias term and the weight vector.

[0125] Step 3: Fit the regression function and the pre-built objective function, and adjust the bias term and weight vector in the regression function according to the fitting result.

[0126] Step 4: When the degree of fit between the regression function and the target function meets the convergence condition, the frequency characteristics of the parameter information are determined from the regression function.

[0127] Step 5: Decompose the frequency characteristics to obtain multiple harmonic frequencies.

[0128] Step 6: Obtain the fundamental frequency of the power system and determine the non-integer multiple frequency of the fundamental frequency.

[0129] Step 7: determine non-integer multiple frequencies among the multiple harmonic frequencies as interharmonic frequencies.

[0130] Step 8: Optimize the interharmonic frequency to obtain an optimized interharmonic frequency.

[0131] Step 9: Determine the interharmonics of the power system based on the optimized interharmonic frequencies.

[0132] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0133] Based on the same inventive concept, the embodiment of the present application also provides an interharmonic measurement device based on a power system for implementing the interharmonic measurement method based on a power system involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the interharmonic measurement device based on a power system provided below can refer to the limitations of the interharmonic measurement method based on a power system above, and will not be repeated here.

[0134] In an exemplary embodiment, Figure 7 As shown, an interharmonic measurement device based on a power system is provided, comprising: a collection module 701, a processing module 702 and a determination module 703, wherein:

[0135] The acquisition module 701 is used to acquire parameter information of the power system using a wavelet transform algorithm;

[0136] The processing module 702 is used to perform fitting processing and extraction processing on the parameter information in sequence based on the pre-built interharmonic measurement model to obtain the interharmonic frequency;

[0137] The determination module 703 is used to determine the interharmonics of the power system based on the interharmonic frequencies.

[0138] In one embodiment, the interharmonic measurement model includes an objective function and an extraction model. The objective function is a function that aims to minimize risk. The processing module 702 is specifically used to fit the parameter information based on the objective function, and determine the frequency characteristics of the parameter information based on the fitting results; the frequency characteristics are input into the extraction model to obtain the interharmonic frequency.

[0139] In one embodiment, the processing module 702 is specifically used to construct a regression function based on parameter information; the regression function includes the corresponding relationship between the parameter information and the mapping of the parameter information in the high-dimensional feature space function, the bias term and the weight vector; the regression function and the pre-constructed objective function are fitted, and the bias term and the weight vector in the regression function are adjusted according to the fitting result; when the degree of fitting between the regression function and the objective function meets the convergence condition, the frequency characteristics of the parameter information are determined from the regression function.

[0140] In one embodiment, the processing module 702 is specifically used to input the frequency feature into the extraction model to obtain the interharmonic frequency, including: decomposing the frequency feature to obtain multiple harmonic frequencies; and determining the interharmonic frequency from the multiple harmonic frequencies.

[0141] In one embodiment, the processing module 702 is specifically used to obtain the fundamental frequency of the power system and determine the non-integer multiple frequencies of the fundamental frequency; and determine the non-integer multiple frequencies among the multiple harmonic frequencies as interharmonic frequencies.

[0142] In one embodiment, the above device further comprises:

[0143] The optimization module 704 is used to optimize the interharmonic frequency to obtain the optimized interharmonic frequency; correspondingly, determining the interharmonics of the power system based on the interharmonic frequency includes: determining the interharmonics of the power system based on the optimized interharmonic frequency.

[0144] Each module in the above-mentioned interharmonic measurement device based on the power system can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0145] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0146] Use wavelet transform algorithm to collect parameter information of power system;

[0147] Based on the pre-built interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency;

[0148] Based on the interharmonic frequencies, interharmonics of the power system are determined.

[0149] In one embodiment, the interharmonic measurement model includes an objective function and an extraction model, wherein the objective function is a function with the goal of minimizing risk, and when the processor executes the computer program, the following steps are also implemented:

[0150] Fitting the parameter information based on the objective function, and determining the frequency characteristics of the parameter information according to the fitting result;

[0151] The frequency characteristics are input into the extraction model to obtain the interharmonic frequencies.

[0152] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0153] A regression function is constructed based on the parameter information; the regression function includes the corresponding relationship between the parameter information and the mapping of the parameter information in the high-dimensional feature space function, the bias term and the weight vector;

[0154] Fit the regression function and the pre-built objective function, and adjust the bias term and weight vector in the regression function according to the fitting result;

[0155] When the degree of fit between the regression function and the target function meets the convergence condition, the frequency characteristics of the parameter information are determined from the regression function.

[0156] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0157] Decomposing the frequency characteristics to obtain multiple harmonic frequencies;

[0158] An interharmonic frequency is determined from the plurality of harmonic frequencies.

[0159] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0160] Obtain the fundamental frequency of the power system and determine the non-integer multiple frequencies of the fundamental frequency;

[0161] Non-integer multiple frequencies among the plurality of harmonic frequencies are determined as interharmonic frequencies.

[0162] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0163] Optimizing the interharmonic frequency to obtain an optimized interharmonic frequency;

[0164] Correspondingly, based on the interharmonic frequency, determining the interharmonics of the power system includes:

[0165] Based on the optimized interharmonic frequencies, interharmonics of the power system are determined.

[0166] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0167] Use wavelet transform algorithm to collect parameter information of power system;

[0168] Based on the pre-built interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency;

[0169] Based on the interharmonic frequencies, interharmonics of the power system are determined.

[0170] In one embodiment, the interharmonic measurement model includes an objective function and an extraction model, wherein the objective function is a function with the goal of minimizing risk, and when the computer program is executed by a processor, the following steps are also implemented:

[0171] Fitting the parameter information based on the objective function, and determining the frequency characteristics of the parameter information according to the fitting result;

[0172] The frequency characteristics are input into the extraction model to obtain the interharmonic frequencies.

[0173] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0174] A regression function is constructed based on the parameter information; the regression function includes the corresponding relationship between the parameter information and the mapping of the parameter information in the high-dimensional feature space function, the bias term and the weight vector;

[0175] Fit the regression function and the pre-built objective function, and adjust the bias term and weight vector in the regression function according to the fitting result;

[0176] When the degree of fit between the regression function and the target function meets the convergence condition, the frequency characteristics of the parameter information are determined from the regression function.

[0177] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0178] Decomposing the frequency characteristics to obtain multiple harmonic frequencies;

[0179] An interharmonic frequency is determined from the plurality of harmonic frequencies.

[0180] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0181] Obtain the fundamental frequency of the power system and determine the non-integer multiple frequencies of the fundamental frequency;

[0182] Non-integer multiple frequencies among the plurality of harmonic frequencies are determined as interharmonic frequencies.

[0183] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0184] Optimizing the interharmonic frequency to obtain an optimized interharmonic frequency;

[0185] Correspondingly, based on the interharmonic frequency, determining the interharmonics of the power system includes:

[0186] Based on the optimized interharmonic frequencies, interharmonics of the power system are determined.

[0187] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0188] Use wavelet transform algorithm to collect parameter information of power system;

[0189] Based on the pre-built interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency;

[0190] Based on the interharmonic frequencies, interharmonics of the power system are determined.

[0191] In one embodiment, the interharmonic measurement model includes an objective function and an extraction model, wherein the objective function is a function with the goal of minimizing risk, and when the computer program is executed by a processor, the following steps are also implemented:

[0192] Fitting the parameter information based on the objective function, and determining the frequency characteristics of the parameter information according to the fitting result;

[0193] The frequency characteristics are input into the extraction model to obtain the interharmonic frequencies.

[0194] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0195] A regression function is constructed based on the parameter information; the regression function includes the corresponding relationship between the parameter information and the mapping of the parameter information in the high-dimensional feature space function, the bias term and the weight vector;

[0196] Fit the regression function and the pre-built objective function, and adjust the bias term and weight vector in the regression function according to the fitting result;

[0197] When the degree of fit between the regression function and the target function meets the convergence condition, the frequency characteristics of the parameter information are determined from the regression function.

[0198] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0199] Decomposing the frequency characteristics to obtain multiple harmonic frequencies;

[0200] An interharmonic frequency is determined from the plurality of harmonic frequencies.

[0201] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0202] Obtain the fundamental frequency of the power system and determine the non-integer multiple frequencies of the fundamental frequency;

[0203] Non-integer multiple frequencies among the plurality of harmonic frequencies are determined as interharmonic frequencies.

[0204] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0205] Optimizing the interharmonic frequency to obtain an optimized interharmonic frequency;

[0206] Correspondingly, based on the interharmonic frequency, determining the interharmonics of the power system includes:

[0207] Based on the optimized interharmonic frequencies, interharmonics of the power system are determined.

[0208] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0209] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0210] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for measuring interharmonics based on a power system, characterized in that: The method comprises: Use wavelet transform algorithm to collect parameter information of power system; Based on a pre-built interharmonic measurement model, the parameter information is sequentially fitted and extracted to obtain the interharmonic frequency; Based on the interharmonic frequencies, interharmonics of the power system are determined.

2. The method according to claim 1, characterized in that The interharmonic measurement model includes an objective function and an extraction model, wherein the objective function is a function with the goal of minimizing risk, and the interharmonic measurement model based on the pre-constructed parameter information is sequentially fitted and extracted to obtain the interharmonic frequency, including: Performing fitting processing on the parameter information based on the objective function, and determining the frequency characteristics of the parameter information according to the fitting result; The frequency characteristics are input into the extraction model to obtain the interharmonic frequency.

3. The method according to claim 2, characterized in that The fitting process is performed on the parameter information based on the objective function, and the frequency characteristics of the parameter information are determined according to the fitting result, including: Constructing a regression function based on the parameter information; the regression function includes the corresponding relationship between the parameter information and the mapping of the parameter information in the high-dimensional feature space function, the bias term and the weight vector; Fitting the regression function to a pre-constructed objective function, and adjusting the bias term and the weight vector in the regression function according to the fitting result; In a case where the degree of fit between the regression function and the target function satisfies a convergence condition, the frequency characteristics of the parameter information are determined from the regression function.

4. The method according to claim 2, characterized in that: The step of inputting the frequency feature into the extraction model to obtain the interharmonic frequency comprises: Decomposing the frequency characteristics to obtain multiple harmonic frequencies; The interharmonic frequency is determined from among the plurality of harmonic frequencies.

5. The method according to claim 4, characterized in that Determining the interharmonic frequency from the plurality of harmonic frequencies comprises: Acquire the fundamental frequency of the power system and determine a non-integer multiple frequency of the fundamental frequency; The non-integer multiple frequency among the plurality of harmonic frequencies is determined as the interharmonic frequency.

6. The method according to claim 4, characterized in that The method further comprises: Optimizing the interharmonic frequency to obtain an optimized interharmonic frequency; Correspondingly, determining the interharmonics of the power system based on the interharmonic frequency includes: Based on the optimized interharmonic frequencies, interharmonics of the power system are determined.

7. An interharmonic measurement device based on a power system, characterized in that: The device comprises: A collection module, used for collecting parameter information of the power system using a wavelet transform algorithm; A processing module, used for performing fitting processing and extraction processing on the parameter information in sequence based on a pre-built interharmonic measurement model to obtain an interharmonic frequency; A determination module is used to determine the interharmonics of the power system based on the interharmonic frequency.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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