Frequency spectrum extraction method and terminal for interference source user full operation condition

Abstract

The invention discloses an interference source user full operation condition-oriented spectrum extraction method and terminal, and the method comprises the steps: monitoring the operation data of an interference source user, and dividing the operation condition of the interference source user according to the load rate of the operation data; for each operation condition, calculating the current content of each harmonic wave in the operation condition, performing probability density function estimation on the current content of each harmonic wave by using a kernel density estimation model, and extracting a typical distribution interval of the corresponding harmonic wave current content according to the estimated probability density function; and obtaining a typical frequency spectrum under the operation condition according to the maximum value of the current content of each harmonic and the extracted typical distribution interval. In this way, by counting the probability distribution of the harmonic current content, representative deviation caused by the low probability characteristic of a single index is avoided, and the actual spectrum characteristic of an interference source can be accurately reflected; harmonic probability characteristics are analyzed based on kernel density estimation, and the probability distribution condition of each harmonic current content of an interference source user can be accurately described.

Description

Technical Field

[0001] The present invention relates to the technical field of operating condition spectrum extraction, and particularly relates to a spectrum extraction method and a terminal for all operating conditions of interference source users. Background Art

[0002] Under the background of the increasingly complex power system and the large-scale access of new energy, the problem of power quality has become more prominent. The harmonics generated by interference sources have a significant impact on the stable operation of the power system and power quality. Therefore, accurate and efficient assessment of harmonic interference is crucial for ensuring the safe, reliable and economic operation of the power system.

[0003] To improve the accuracy of power quality assessment results, it is particularly necessary to carry out the work of collecting harmonic spectra of interference sources based on the online monitoring data in the PMS3.0 power quality application: on the one hand, based on the extracted typical harmonic spectra, a more accurate power quality prediction model can be constructed, and with the help of this model, the possible impact of harmonic interference on the power system can be accurately predicted in advance, and then targeted preventive measures can be taken; on the other hand, the typical harmonic spectrum data can significantly improve the accuracy of power quality assessment, make the assessment results closer to the actual situation, and provide a scientific and reasonable basis for all aspects such as the planning, design, construction and operation of the power system.

[0004] The current power quality prediction and assessment have the following deficiencies: 1. When carrying out power quality prediction and assessment, a large amount of monitoring data of interference source users will be used. Although the historical data of the same type of users can be referred to, in the face of complex and changeable interference sources, the extraction of typical spectra is extremely difficult due to the large amount of data, resulting in low assessment efficiency and poor accuracy.

[0005] Currently, the industry generally uses the maximum value of the 95% probability maximum value of harmonics within a specific period for prediction and assessment. However, this value is essentially an extreme value with low frequency and high threshold, and its occurrence probability is relatively low. Moreover, under different operating conditions, there are significant differences in the spectrum distribution of interference sources. Therefore, this single index is not representative and is difficult to comprehensively and accurately reflect the typical spectrum characteristics of interference sources, and it is difficult to effectively guide the formulation and implementation of power quality improvement measures.

[0006] 2. Traditional harmonic probability characteristic analysis methods are mostly based on parameter estimation, limited by fixed probability distributions (such as normal distribution, Rayleigh distribution, etc.), and cannot effectively deal with the complex characteristics existing in actual harmonic data, resulting in a significant difference between the analysis results and the actual situation, and it is difficult to meet the requirements of the power system for the accuracy of harmonic analysis. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a spectrum extraction method and a terminal for all operating conditions of interference source users, which can make the extracted spectrum highly reflect the actual spectrum characteristics of the interference source and effectively overcome the drawback of insufficient representativeness of the spectrum extracted by the existing methods.

[0008] To solve the above technical problem, the technical solution adopted by the present invention is as follows: A spectrum extraction method for all operating conditions of interference source users, comprising the steps of: Monitoring the operating data of the interference source user, and dividing the operating conditions of the interference source user according to the load rate of the operating data; For each operating condition, calculating the harmonic current content in the operating condition, using a kernel density estimation model to estimate the probability density function of the harmonic current content, extracting the typical distribution interval of the corresponding harmonic current content according to the estimated probability density function, and obtaining the typical spectrum under the operating condition according to the maximum value of the harmonic current content and the typical distribution interval.

[0009] To solve the above technical problem, another technical solution adopted by the present invention is as follows: A spectrum extraction terminal for all operating conditions of interference source users, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, each step of the above spectrum extraction method for all operating conditions of interference source users is implemented.

[0010] The beneficial effects of the present invention are as follows: Monitoring the operating data of the interference source user, and dividing the operating conditions of the interference source user according to the load rate of the operating data; for each operating condition, calculating the harmonic current content in the operating condition, using a kernel density estimation model to estimate the probability density function of the harmonic current content, extracting the typical distribution interval of the corresponding harmonic current content according to the estimated probability density function, and obtaining the typical spectrum under the operating condition according to the maximum value of the harmonic current content and the extracted typical distribution interval. In this way, by statistically analyzing the probability distribution of the harmonic current content, the representativeness deviation caused by the low-probability characteristics of a single index is avoided, and the actual spectrum characteristics of the interference source can be accurately reflected; based on the kernel density estimation to analyze the harmonic probability characteristics, the probability distribution of the harmonic current content of each order of the interference source user can be accurately described. Description of the Drawings

[0011] Figure 1 It is a flowchart of a spectrum extraction method for all operating conditions of interference source users according to an embodiment of the present invention; Figure 2 It is a schematic diagram of a spectrum extraction terminal for all operating conditions of interference source users according to an embodiment of the present invention; Figure 3 Flow chart for extracting typical spectrum of interfering source user in an embodiment of the present invention; Figure 4 Probability density function curve graph in an embodiment of the present invention.

[0012] Label description: 1. A spectrum extraction terminal for all operating conditions of an interfering source user; 2. A memory; 3. A processor. Specific implementation manner

[0013] To describe in detail the technical content, achieved purpose and effects of the present invention, the following is described in conjunction with the implementation manners and accompanied by the drawings.

[0014] Please refer to Figure 1 , an embodiment of the present invention provides a spectrum extraction method for all operating conditions of an interfering source user, including the steps of: Monitoring the operating data of the interfering source user, and dividing the operating conditions of the interfering source user according to the load rate of the operating data; For each operating condition, calculating the content of each harmonic current in the operating condition, using a kernel density estimation model to estimate the probability density function of the content of each harmonic current, extracting the typical distribution interval corresponding to the content of the harmonic current according to the estimated probability density function, and obtaining the typical spectrum under the operating condition according to the maximum value of the content of each harmonic current and the typical distribution interval.

[0015] As can be seen from the above description, the beneficial effects of the present invention are as follows: Monitoring the operating data of the interfering source user, and dividing the operating conditions of the interfering source user according to the load rate of the operating data; for each operating condition, calculating the content of each harmonic current in the operating condition, using a kernel density estimation model to estimate the probability density function of the content of each harmonic current, extracting the typical distribution interval corresponding to the content of the harmonic current according to the estimated probability density function, and obtaining the typical spectrum under the operating condition according to the maximum value of the content of each harmonic current and the extracted typical distribution interval. In this way, by statistically analyzing the probability distribution of the harmonic current content, the representative deviation caused by the low-probability characteristics of a single index is avoided, and the actual spectrum characteristics of the interfering source can be accurately reflected; based on the kernel density estimation to analyze the harmonic probability characteristics, the probability distribution of the content of each harmonic current of the interfering source user can be accurately described.

[0016] Further, monitoring the operating data of the interfering source user, and dividing the operating conditions of the interfering source user according to the load rate of the operating data includes: Real-time monitoring of the operating data of the interfering source user at each moment, where the operating data includes real-time power and rated capacity; Calculating the load rate of the operating data moment by moment, where the load rate is the real-time power divided by the rated capacity; According to the load rate of the operating data, a load rate interval is obtained according to a preset step size, and the operating conditions of the interference source user are divided according to the load rate interval.

[0017] From the above description, it can be seen that since the harmonic characteristics of the interference source users are different under different working conditions, the spectrum distribution is different. Therefore, dividing the working conditions by load rate can ensure that the subsequently extracted spectrum characteristics are closely related to the load rate.

[0018] Furthermore, a kernel density estimation model is used to estimate the probability density function of each harmonic current content, which includes: Constructing a kernel density estimation model for each operating condition, wherein the kernel function of the kernel density estimation model is a Gaussian kernel function; According to the content of each harmonic current in the operating condition, a cross-validation method is used to determine the bandwidth of the kernel density estimation model.

[0019] From the above description, it can be seen that choosing the Gaussian kernel function as the kernel function for kernel density estimation can comprehensively consider the computational complexity and estimation accuracy; using the cross-validation method to determine the optimal bandwidth can avoid the problem that the estimated probability density function is too rough due to the selection of too small bandwidth, and the problem that the estimated probability density function is too smooth due to the selection of too large bandwidth.

[0020] Furthermore, a kernel density estimation model is used to estimate the probability density function of each harmonic current content, including: Set up the first m The sample data of subharmonic current content is x m1 、 x m2 、……、 x mN ; The kernel function and bandwidth of the kernel density estimation model are used to estimate the probability density function of each harmonic current content:

[0021] Where, K represents the Gaussian kernel function, h represents the bandwidth of the kernel density estimation model, x represents the observed input point of the probability density function, x mi Indicates the m The subharmonic i Sample values, N Indicates the sample size.

[0022] As can be seen from the above description, based on kernel density estimation to analyze the harmonic probability characteristics of actual interference source users, compared with traditional parameter estimation methods, it can more accurately describe the probability distribution of the harmonic current content of each order of interference source users, providing more reliable technical support for power system harmonic analysis and governance.

[0023] Furthermore, according to the estimated probability density function, extract the typical distribution intervals corresponding to the harmonic current content, including: Calculate the maximum probability density of the estimated probability density function, determine the maximum value point corresponding to the maximum probability density, and obtain the interval boundary threshold according to the product of the maximum probability density and the boundary coefficient; Use linear interpolation to determine the left boundary value point and the right boundary value point corresponding to the interval boundary threshold, and obtain the typical distribution interval according to the left boundary value point and the right boundary value point.

[0024] As can be seen from the above description, combining linear interpolation to determine the left and right boundary value points of the typical distribution interval can further improve the accuracy of the extracted typical distribution interval.

[0025] Please refer to Figure 2 , another embodiment of the present invention provides a spectrum extraction terminal for all operating conditions of interference source users, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements each step of the above spectrum extraction method for all operating conditions of interference source users.

[0026] The above spectrum extraction method and terminal for all operating conditions of interference source users of the present invention are applicable to making the extracted spectrum highly reflect the actual spectrum characteristics of the interference source, effectively overcoming the drawback of insufficient representativeness of the spectrum extracted by existing methods, which will be described below through specific embodiments: Please refer to Figure 1 and Figure 3 , the first embodiment of the present invention is: A spectrum extraction method for all operating conditions of interference source users, including the steps of: S1. Monitor the operation data of the interference source user, and divide the operation conditions of the interference source user according to the load rate of the operation data.

[0027] Among them, step S1 is specifically: monitor the operation data of the interference source user at each moment in real time, and the operation data includes real-time power and rated capacity; calculate the load rate of the operation data at each moment, and the load rate is the real-time power divided by the rated capacity; according to the size of the load rate of the operation data, obtain the load rate interval according to a preset step length, and divide the operation conditions of the interference source user according to the load rate interval.

[0028] In this embodiment, due to the differences in the harmonic characteristics and spectral distributions of user interference sources under different working conditions, among which, the spectral characteristics are closely related to the load factor. Therefore, this embodiment considers dividing the working conditions based on the load factor data, taking into account the overload situation, and dividing them into intervals with an interval of 10%, corresponding to different working conditions of the interference source load operation respectively.

[0029] First, relying on the PMS3.0 power quality application platform, synchronously collect parameters such as the real-time power data and rated capacity parameters of the interference source users, and at the same time ensure that the data covers typical operation scenarios such as the day-night load fluctuation cycle, seasonal electricity consumption peak, and sudden load events.

[0030] Based on the real-time active power and rated capacity, calculate the load factor at each moment point by point:

[0031] In the formula, P represents the real-time active power, and S n represents the rated capacity.

[0032] Thus, a time-series load factor curve with a sampling interval of 3 minutes is generated, which fully characterizes the dynamic response characteristics of the interference source users under light load, normal load, heavy load, and overload states. In this embodiment, according to the load factor size, it is discretized into 11 intervals at a step of 10% ([0, 10%), [10%, 20%),..., [90%, 100%], >100%), which correspond to different operating conditions, and the fundamental wave current and harmonic current data under different operating conditions are extracted and integrated accordingly to further analyze their harmonic probability distribution characteristics.

[0033] S2. For each operating condition, calculate the harmonic current content in the operating condition, use the kernel density estimation model to estimate the probability density function of the harmonic current content, extract the typical distribution interval corresponding to the harmonic current content according to the estimated probability density function, and obtain the typical spectrum under the operating condition according to the maximum value of the harmonic current content and the typical distribution interval.

[0034] S21. Calculate the harmonic current content in the operating condition.

[0035] Specifically, in the case of knowing the 95th percentile value of the harmonic current of each order and the fundamental wave current I 1 of each operating condition of the interference source users, calculate the harmonic current content HRI m :

[0036] In the formula, I m represents the mth harmonic current (95th percentile value); I1 represents the fundamental wave current (95th percentile value).

[0037] S22. Construct a kernel density estimation model for each operating condition.

[0038] In this embodiment, the kernel function of the kernel density estimation model is a Gaussian kernel function; according to the harmonic current content in the operating condition, the bandwidth of the kernel density estimation model is determined by the cross-validation method.

[0039] Specifically, the kernel function is the core component of kernel density estimation, which determines the shape of the estimated probability density function. Commonly used kernel functions include Gaussian kernel function, Epanechnikov kernel function, rectangular kernel function, etc. In this embodiment, considering the computational complexity and estimation accuracy comprehensively, the Gaussian kernel function is selected as the kernel function of kernel density estimation. The expression of the Gaussian kernel function is:

[0040] where u represents the independent variable.

[0041] Bandwidth h is another key parameter in kernel density estimation, which controls the smoothness of the probability density function. If the bandwidth is selected too small, the estimated probability density function will be too rough, with many local fluctuations; if the bandwidth is selected too large, the estimated probability density function will be too smooth, losing the detailed information of the data. Therefore, the cross-validation method is adopted in this embodiment to determine the optimal bandwidth. The specific steps are as follows: (1) Randomly divide the m-th harmonic current content data under a single operating condition into K non-overlapping subsets. In this embodiment, K takes the value of 5.

[0042] (2) For each subset k ( k = 1, 2, …, K ), use it as the test set, and at the same time use the remaining K - 1 subsets as the training set.

[0043] (3) On the training set, perform kernel density estimation using different bandwidth values h to obtain the estimated values of the probability density function for each sample in the training set:

[0044] where x i represents the i-th harmonic current content data in the training set.

[0045] (4) According to the estimated values of the probability density function on the training set, calculate the log-likelihood function value on the test set Lk ( h ):

[0046] In the formula, S k represents the sample set of the test set.

[0047] (5) Calculate the log-likelihood values at different bandwidths according to the log-likelihood function, and draw a curve with the bandwidth on the horizontal axis and the likelihood value on the vertical axis based on the calculation results. The point where the peak of the curve is located is the optimal bandwidth.

[0048] S23. Use the kernel density estimation model to estimate the probability density function of each harmonic current content.

[0049] Specifically, for each harmonic in the power system (such as the 2nd harmonic, 3rd harmonic,..., nth harmonic), use the kernel density estimation model constructed in step S22 to estimate the probability density function respectively.

[0050] Let the sample data of the mth harmonic current content be x m1 、 x m2 、……、 x mN ( N represents the number of samples), then the estimated value of the probability density function of the mth harmonic current content is:

[0051] In the formula, K represents the Gaussian kernel function, h represents the bandwidth of the kernel density estimation model, x represents the observed input point of the probability density function, x mi represents the m th i sample value of the mth harmonic.

[0052] S24. Extract the typical distribution interval of the corresponding harmonic current content according to the estimated probability density function.

[0053] Among them, calculate the maximum value of the probability density of the estimated probability density function, determine the maximum value point corresponding to the maximum value of the probability density, and obtain the interval boundary threshold according to the product of the maximum value of the probability density and the boundary coefficient; use linear interpolation to determine the left boundary value point and the right boundary value point corresponding to the interval boundary threshold, and obtain the typical distribution interval according to the left boundary value point and the right boundary value point.

[0054] Specifically, for the obtained probability density function, within its domain, the probability density values corresponding to each value point are calculated sequentially at a certain step size of 0.1. The maximum value is found from all the calculated probability density values, and the value point corresponding to this maximum value is recorded as x peak , and the maximum probability density value is denoted as f max . 70% of the maximum probability density value is used as the boundary threshold for the left and right intervals, that is, the threshold T = 0.7 × f max .

[0055] Starting from x peak , check the probability density values of each value point sequentially to the left (i.e., in the direction of decreasing values) at the set step size. When the probability density value x l of a certain value point f ( x l ) ≤ T is first encountered, if the probability density value x l-1 of the previous value point f ( x l-1 ) > T , then calculate x left accurately by linear interpolation:

[0056] Similarly, calculate x right , and its linear interpolation formula is:

[0057] In the formula, x r-1 , x r are the value points encountered sequentially to the right starting from x peak , x left represents walking in the left direction starting from x peak until the position of x l corresponding to the probability density value f ( x l ) ≤ T is first encountered, x of x right represents starting fromx peak Start walking in the right direction until the first encounter with a certain value point x r The probability density value of f ( x r ) ≤ T The corresponding x Position; Indicates x l - x l-1 , Indicates x r - x r-1 .

[0058] Finally, it is obtained that the typical distribution intervals of the harmonic current contents of each order are centered on x peak and bounded by 70% of the maximum values on the left and right, that is x left , x right .

[0059] Visualize the kernel density estimation results of the harmonic current contents of each order in the form of a probability density function curve, as Figure 4 shown, where the abscissa represents the harmonic current content and the ordinate represents the probability density.

[0060] S25. Obtain the typical spectrum under the operating conditions according to the maximum value of the harmonic current content of each order and the said typical distribution interval.

[0061] For the operating conditions divided in step S1, respectively extract the probability distribution characteristic parameters of the harmonic current content of each order, including the maximum value of the harmonic current content of each order and the typical distribution interval of the harmonic current content of each order, that is, the typical spectrum of the interfering source user includes the typical spectra of the harmonic currents of each order, and the typical spectrum of each harmonic current contains the maximum value of the harmonic current content of that order and the typical distribution interval of the harmonic current content of that order. According to the previous steps, the maximum value of the harmonic current content and the typical distribution interval can be obtained. Finally, the typical spectrum of the interfering source user under a certain operating condition as shown in Table 1 can be obtained.

[0062] Table 1 Harmonic current content when the load factor ≤ 10% (schematic table)

[0063] In summary, in this embodiment, by using the kernel density estimation technology to analyze the harmonic probability characteristics of the harmonic current content of each order, the probability distribution of harmonics can be described more accurately, providing effective technical support for the harmonic analysis and governance of the power system.

[0064] Please refer to Figure 2 , Embodiment 2 of the present invention is as follows: A spectrum extraction terminal 1 for all operating conditions of interference source users includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, each step of a spectrum extraction method for all operating conditions of interference source users in Embodiment 1 is implemented.

[0065] In summary, a spectrum extraction method and terminal for all operating conditions of interference source users provided by the present invention monitor the operating data of interference source users, and divide the operating conditions of interference source users according to the load rate of the operating data; for each operating condition, calculate the harmonic current content of each order in the operating condition, use a kernel density estimation model to estimate the probability density function of the harmonic current content of each order, extract the typical distribution interval corresponding to the harmonic current content according to the estimated probability density function, and obtain the typical spectrum under the operating condition according to the maximum value of the harmonic current content of each order and the extracted typical distribution interval. In this way, by statistically analyzing the probability distribution of the harmonic current content, the representative deviation caused by the low probability characteristics of a single index is avoided, and the actual spectrum characteristics of the interference source can be reflected more accurately; and by analyzing the harmonic probability characteristics of interference source users based on kernel density estimation, the probability distribution of the harmonic current content of each order of interference source users can be described more accurately compared with traditional parameter estimation methods, thus effectively overcoming the drawback of insufficient representativeness of the spectrum extracted by existing methods.

[0066] The above are only embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in the relevant technical field, shall be equally included in the patent protection scope of the present invention.

Claims

1. A spectrum extraction method for all operating conditions of interference source users, characterized in that, Including the steps: Monitoring the operating data of interfering source users, and dividing the operating conditions of the interfering source users according to the load rate of the operating data; For each operating condition, calculating the harmonic current content of each order in the operating condition, using a kernel density estimation model to estimate the probability density function of the harmonic current content of each order, extracting the typical distribution interval corresponding to the harmonic current content according to the estimated probability density function, and obtaining the typical spectrum under the operating condition according to the maximum value of the harmonic current content of each order and the typical distribution interval.

2. The spectrum extraction method for all operating conditions of interference source users according to claim 1, characterized in that Monitoring the operating data of interfering source users, and dividing the operating conditions of the interfering source users according to the load rate of the operating data, including: Real-time monitoring of the operating data of interfering source users at each moment, where the operating data includes real-time power and rated capacity; Calculating the load rate of the operating data moment by moment, where the load rate is the real-time power divided by the rated capacity; According to the magnitude of the load rate of the operating data, obtaining a load rate interval at a preset step size, and dividing the operating conditions of the interfering source users according to the load rate interval.

3. A spectrum extraction method for all operating conditions of interference source users according to claim 1, characterized in that Before using a kernel density estimation model to estimate the probability density function of the harmonic current content of each order, it includes: Constructing a kernel density estimation model for each operating condition, where the kernel function of the kernel density estimation model is a Gaussian kernel function; Determining the bandwidth of the kernel density estimation model according to the harmonic current content of each order in the operating condition by using the cross-validation method.

4. A spectrum extraction method for all operating conditions of interference source users according to claim 3, characterized in that Using a kernel density estimation model to estimate the probability density function of the harmonic current content of each order, including: Let the sample data of the m harmonic current content be x m1 , x m2 , ……, x mN ; Using the kernel function and bandwidth of the kernel density estimation model to estimate the probability density function of the harmonic current content of each order: In the formula, K represents the Gaussian kernel function, h represents the bandwidth of the kernel density estimation model, x represents the observed input point of the probability density function, x mi represents the m th harmonic i th sample value, N represents the number of samples.

5. A spectrum extraction method for all operating conditions of interfering source users according to claim 1, characterized in that Extracting the typical distribution interval corresponding to the harmonic current content according to the estimated probability density function, including: Calculating the maximum value of the probability density of the estimated probability density function, determining the maximum value sampling point corresponding to the maximum value of the probability density, and obtaining the interval boundary threshold according to the product of the maximum value of the probability density and the boundary coefficient; Using linear interpolation to determine the left boundary sampling point and the right boundary sampling point corresponding to the interval boundary threshold, and obtaining the typical distribution interval according to the left boundary sampling point and the right boundary sampling point.

6. A spectrum extraction terminal for all operating conditions of interference source users, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the following steps are implemented: Monitoring the operating data of interfering source users, and dividing the operating conditions of the interfering source users according to the load rate of the operating data; For each operating condition, calculating the harmonic current content of each order in the operating condition, using a kernel density estimation model to estimate the probability density function of the harmonic current content of each order, extracting the typical distribution interval corresponding to the harmonic current content according to the estimated probability density function, and obtaining the typical spectrum under the operating condition according to the maximum value of the harmonic current content of each order and the typical distribution interval.

7. A spectrum extraction terminal for all operating conditions of interfering source users according to claim 6, characterized in that Monitoring the operating data of interfering source users, and dividing the operating conditions of the interfering source users according to the load rate of the operating data, including: Real-time monitoring of the operating data of interfering source users at each moment, where the operating data includes real-time power and rated capacity; Calculating the load rate of the operating data moment by moment, where the load rate is the real-time power divided by the rated capacity; According to the load rate of the operating data, obtain a load rate interval at a preset step size, and divide the operating conditions of the interfering source user according to the load rate interval.

8. A spectrum extraction terminal for all operating conditions of interference source users according to claim 6, characterized in that Using a kernel density estimation model to estimate the probability density function of the harmonic current content in each time, including: Construct a kernel density estimation model for each operating condition, and the kernel function of the kernel density estimation model is a Gaussian kernel function; According to the harmonic current content in the operating condition, use the cross-validation method to determine the bandwidth of the kernel density estimation model.

9. A spectrum extraction terminal for all operating conditions of interfering source users according to claim 8, characterized in that, Using a kernel density estimation model to estimate the probability density function of the harmonic current content in each time, including: Let the sample data of the m -th harmonic current content be x m1 , x m2 , ……, x mN ; Use the kernel function and bandwidth of the kernel density estimation model to estimate the probability density function of the harmonic current content in each time: In the formula, K represents the Gaussian kernel function, h represents the bandwidth of the kernel density estimation model, x represents the observed input point of the probability density function, x mi represents the m th harmonic i th sample value, N represents the number of samples.

10. A spectrum extraction terminal for all operating conditions of interfering source users according to claim 6, characterized in that, Extract the typical distribution interval of the corresponding harmonic current content according to the estimated probability density function, including: Calculate the maximum value of the probability density of the estimated probability density function, determine the maximum value point corresponding to the maximum value of the probability density, and obtain the interval boundary threshold according to the product of the maximum value of the probability density and the boundary coefficient; Use linear interpolation to determine the left boundary value point and the right boundary value point corresponding to the interval boundary threshold, and obtain the typical distribution interval according to the left boundary value point and the right boundary value point.

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