A comprehensive configuration optimization method for distribution network protection adapted to distributed photovoltaic storage access

Abstract

The present invention relates to the technical field of data denoising, and specifically to a method for optimizing the comprehensive configuration of distribution network protection adapted to the access of distributed photovoltaic storage; obtaining the peak characteristic difference between any two peak points in the peak characteristic sequence of the current characteristic sequence; obtaining the noise interference probability value of the peak point according to the peak point interval and the peak characteristic difference in the peak characteristic sequence; obtaining the noise density characteristic value according to the interval characteristics and the noise interference probability value between the peak points in the third peak sequence obtained from the two peak characteristic sequences; obtaining the local noise density curve according to the noise density characteristic value; adjusting the noise covariance matrix according to the local noise density curve to obtain an adaptive covariance matrix. The present invention obtains a current characteristic denoising sequence through a Kalman filtering algorithm according to the adaptive covariance matrix to improve the denoising accuracy; and performs power grid configuration according to the current characteristic denoising sequence to make the configuration optimization more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of data denoising, and in particular to a distribution network protection comprehensive configuration optimization method adapted to distributed photovoltaic storage access. Background Art

[0002] With the development of photovoltaic power generation technology, more and more photovoltaic and energy storage systems are connected to the distribution network to improve the utilization rate of clean energy; in the process of connecting the electric energy stored in the distributed power generation system to the distribution network, it is necessary to collect and monitor the current data to deal with the impact and damage of the current change and reverse power flow caused by the distributed power generation system on the distribution network and energy storage system, and at the same time, reasonably plan and control the electric energy connected to the distribution network. Due to the access operation of distributed photovoltaic storage, the collected current data may contain instantaneous noise, which affects the judgment and configuration strategy of the monitoring system on the distributed photovoltaic storage and distribution network; therefore, it is necessary to denoise the collected current data.

[0003] The existing commonly used Kalman filter algorithm is used for denoising. However, the distribution of instantaneous noise that may exist when the distributed photovoltaic storage system is connected to the distribution network is random. Using this algorithm for denoising may result in low denoising efficiency or poor accuracy, ultimately making the monitoring system's judgment and configuration strategy for distributed photovoltaic storage and distribution networks less accurate. Summary of the invention

[0004] In order to solve the technical problem that the above-mentioned Kalman filter algorithm is inaccurate in denoising current data, which affects the accuracy of the judgment and configuration strategy of distributed photovoltaic storage and distribution network, the purpose of the present invention is to provide a distribution network protection comprehensive configuration optimization method adapted to the access of distributed photovoltaic storage, and the technical scheme adopted is as follows:

[0005] Obtain the current characteristic sequence when distributed photovoltaic storage is connected to the distribution network;

[0006] Obtain two peak characteristic sequences according to the current change characteristics of the current characteristic sequence; obtain the peak characteristic difference degree according to the amplitude difference characteristics, waveform difference characteristics and interval characteristics of any two peak points in the peak characteristic sequence; obtain the noise interference probability value of the peak point according to the difference characteristics of the peak point interval in the peak characteristic sequence and the peak characteristic difference degree;

[0007] The peak points in the two peak characteristic sequences are sorted in chronological order to obtain a third peak sequence; the noise density characteristic value of the peak point is obtained according to the interval characteristics and noise interference probability values ​​between the peak point in the third peak sequence and the preset neighboring peak point; the local noise density curve of the current characteristic sequence is obtained by fitting according to the noise density characteristic value of the peak point in the third peak sequence;

[0008] The noise covariance matrix of the current characteristic sequence is obtained through Kalman filtering; the noise covariance matrix is ​​adaptively adjusted according to the noise density eigenvalue in the local noise density curve to obtain an adaptive covariance matrix; the adaptive covariance matrix corresponding to each moment in the current characteristic sequence is filtered through the Kalman filtering algorithm to obtain a current characteristic denoising sequence; and the power grid is configured according to the current characteristic denoising sequence.

[0009] Furthermore, the step of obtaining two peak characteristic sequences according to the current variation characteristics of the current characteristic sequence includes:

[0010] The peak points in the current characteristic sequence are sorted in chronological order to obtain a first peak characteristic sequence; the inverse number of each data point in the current characteristic sequence is taken to construct a new current sequence; the peak points in the new current sequence are sorted in chronological order to obtain a second peak characteristic sequence.

[0011] Furthermore, the step of obtaining the peak feature difference degree according to the amplitude difference feature, waveform difference feature and interval feature of any two peak points in the peak feature sequence includes:

[0012] Calculate the absolute value of the numerical difference between any two peak points in the peak feature sequence to obtain the data difference value of the any two peak points; construct a rectangular coordinate system of the current feature sequence, with the horizontal axis being the timestamp and the vertical axis being the current value; calculate the cosine value of the line connecting the peak point in the peak feature sequence and the data point separated by a preset time in the rectangular coordinate system to obtain the waveform characterization value of the peak point; calculate the absolute value of the difference between the waveform characterization values ​​of any two peak points to obtain the waveform difference value of the any two peak points; calculate the product of the inverse of the time interval between the any two peak points, the data difference value and the waveform difference value to obtain the peak feature difference degree of the any two peak points.

[0013] Furthermore, the step of obtaining the noise interference probability value of the peak point according to the difference characteristics of the peak point intervals in the peak feature sequence and the peak feature difference comprises:

[0014] In the peak feature sequence, the average time interval between a peak point and adjacent peak points is calculated to obtain an interval feature value of the peak point; the average value of the peak feature difference between any peak point and all other peak points is calculated to obtain the average peak feature difference of the arbitrary peak point; the absolute value of the difference between the average value of the interval feature values ​​of all peak points and the interval feature value of the arbitrary peak point is calculated to obtain the interval feature difference value of the arbitrary peak point; the product of the interval feature difference value and the peak feature average difference is calculated and normalized to obtain the noise interference probability value of the arbitrary peak point.

[0015] Furthermore, the step of obtaining the noise density characteristic value of the peak point according to the interval characteristics and the noise interference probability value between the peak point and the preset neighboring peak point in the third peak sequence includes:

[0016] In the third peak sequence, the sum of the time intervals between the peak point and all preset neighboring peak points is calculated to obtain a comprehensive time interval; the sum of the noise interference probability values ​​of the peak point and the preset neighboring peak points is calculated to obtain a comprehensive noise probability value; and the ratio of the comprehensive noise probability value to the comprehensive time interval is calculated to obtain a noise density characteristic value of the peak point.

[0017] Furthermore, the step of adaptively adjusting the noise covariance matrix according to the noise density eigenvalue in the local noise density curve to obtain an adaptive covariance matrix includes:

[0018] Calculate the average value of the noise density eigenvalues ​​at all times in the local noise density curve to obtain the average noise density value; when the average noise density value is a constant 0, use the noise covariance matrix as the adaptive covariance matrix, otherwise calculate the ratio of the noise density eigenvalue at any time to the average noise density value to obtain the adjustment coefficient at any time, calculate the product of the adjustment coefficient at any time and the noise covariance matrix to obtain the adaptive covariance matrix at any time.

[0019] Furthermore, the step of fitting according to the noise density characteristic value of the peak point in the third peak sequence to obtain the local noise density curve of the current characteristic sequence includes:

[0020] The noise density characteristic values ​​of the peak points in the third peak sequence are fitted by the least square method to obtain a local noise density curve corresponding to the current characteristic sequence.

[0021] The present invention has the following beneficial effects:

[0022] In the present invention, obtaining the peak characteristic sequence can analyze the influence of random noise on the change of current characteristic sequence data, and obtaining the peak characteristic difference can reflect the difference of peak characteristics according to the amplitude difference characteristics, waveform difference characteristics and interval characteristics of the peak points; obtaining the noise interference probability value can determine the degree of noise to which the current data is subjected at different times according to the difference in the peak characteristics of the peak points caused by noise. Obtaining the noise density characteristic value can comprehensively analyze the noise conditions at different times according to the noise interference probability values ​​of adjacent peak points, and improve the accuracy of random noise degree analysis; obtaining the local noise density curve can accurately analyze the degree of random noise to which the current data is subjected at each moment. Obtaining the adaptive covariance matrix can adaptively adjust the size according to the degree of random noise at different times, so that the Kalman filter algorithm can improve the denoising performance and denoising accuracy when filtering the current characteristic sequence; finally, the power grid is configured according to the current characteristic denoising sequence, so that the configuration optimization is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A flow chart of a method for optimizing the comprehensive configuration of distribution network protection adapted to the access of distributed photovoltaic storage provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a distribution network protection comprehensive configuration optimization method adapted to distributed photovoltaic storage access proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0026] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0027] The following is a detailed description of a specific scheme of a distribution network protection comprehensive configuration optimization method adapted to the access of distributed photovoltaic storage provided by the present invention in conjunction with the accompanying drawings.

[0028] See also Figure 1, which shows a flow chart of a method for optimizing the comprehensive configuration of distribution network protection adapted to the access of distributed photovoltaic storage provided by an embodiment of the present invention, the method comprising the following steps:

[0029] Step S1, obtaining the current characteristic sequence when the distributed photovoltaic storage is connected to the distribution network.

[0030] In an embodiment of the present invention, the implementation scenario is to denoise the current data in the process of distributed photovoltaic storage being connected to the distribution network, so as to improve the accuracy of the monitoring system's judgment and configuration strategy for distributed photovoltaic storage and distribution network; first, the current characteristic sequence in the process of distributed photovoltaic storage being connected to the distribution network is obtained. The implementer can determine the collection frequency according to the implementation scenario. In the embodiment of the present invention, the collection frequency is 50 times per second.

[0031] Step S2, obtaining two peak feature sequences according to the current change characteristics of the current feature sequence; obtaining the peak feature difference according to the amplitude difference characteristics, waveform difference characteristics and interval characteristics of any two peak points in the peak feature sequence; obtaining the noise interference probability value of the peak point according to the difference characteristics of the peak point interval in the peak feature sequence and the peak feature difference.

[0032] In the process of connecting to the distribution network, the distributed photovoltaic storage system first converts the DC power stored in the photovoltaic power generation into AC power that is consistent with the voltage and frequency of the power grid; under normal circumstances, the current in the current characteristic sequence collected presents a change in the form of a sine wave, which has a certain regularity. When random instantaneous noise occurs, the position and change law of the current will be different from the normal current change. Since the current data will suddenly change when the current noise occurs, the normal change of the current data will be changed, making it difficult for the peak point in the current characteristic sequence to present a normal sine wave feature, so firstly, two peak characteristic sequences are obtained according to the current change characteristics of the current characteristic sequence; preferably, in the embodiment of the present invention, the step of obtaining two peak characteristic sequences includes: sorting the peak points in the current characteristic sequence in time order to obtain a first peak characteristic sequence; taking the opposite number of each data point in the current characteristic sequence to construct a new current sequence; sorting the peak points in the new current sequence in time order to obtain a second peak characteristic sequence, and the second peak characteristic sequence is the trough point of the minimum value in the current characteristic sequence. Since the current characteristic sequence collects AC power, the minimum value in the sequence may also be caused by instantaneous noise; the peak point is obtained by the existing peak detection algorithm, and the specific steps are not repeated.

[0033] Furthermore, since random instantaneous noise may cause a sudden change in current, so that the value and interval of the peak point are different from the normal situation, the peak feature difference can be obtained according to the amplitude difference feature, waveform difference feature and interval feature of any two peak points in the peak feature sequence; preferably, in the embodiment of the present invention, the step of obtaining the peak feature difference includes: calculating the absolute value of the value difference between any two peak points in the peak feature sequence to obtain the data difference value of the any two peak points; constructing a rectangular coordinate system of the current feature sequence, with the horizontal axis being the timestamp and the vertical axis being the current value; calculating the cosine value of the line connecting the peak point in the peak feature sequence and the data point with a preset time interval in the rectangular coordinate system to obtain the waveform characterization value of the peak point; the cosine value can characterize the peak wave feature of the peak point, and in the embodiment of the present invention, the preset time is 2 seconds, that is, calculating the cosine value of the line connecting the peak point and any other peak point with a 2-second interval. Calculate the absolute value of the difference between the waveform characterization values ​​of the any two peak points to obtain the waveform difference value of the any two peak points. The product of the inverse of the time interval between any two peak points, the data difference value and the waveform difference value is calculated to obtain the peak characteristic difference between any two peak points. Under normal circumstances, the current changes in the form of a sine wave, and the value and waveform of each peak point are relatively similar, then the data difference value and the waveform difference value are both small, and the peak characteristic difference tends to 0. When the peak point is caused by instantaneous noise, the value and waveform of the peak point are greatly different from those of other peak points, then the peak characteristic difference is large. The inverse of the time interval is used as the weight. The closer the time interval between two peak points, the more similar the peak and waveform should be.

[0034] After obtaining the peak feature difference between any two peak points in the peak feature sequence, if the peak feature difference between a certain peak point and other peak points is large, then the probability that the peak point is caused by noise interference is high. In addition, under normal circumstances, the data change characteristics of AC power are in the form of a sine wave, and the time intervals between peak points are close. If the time interval between a certain peak point and other peak points is relatively rare, then the probability that the peak point is caused by noise interference is high; therefore, the noise interference probability value of the peak point can be obtained based on the difference characteristics of the peak point intervals in the peak feature sequence and the peak feature difference.

[0035] Preferably, in an embodiment of the present invention, the step of obtaining the noise interference probability value includes: calculating the average time interval between the peak point and the adjacent peak point in the peak feature sequence to obtain the interval characteristic value of the peak point; under normal circumstances, the time interval between the peak point of the alternating current and the adjacent peak point is close. Calculate the average value of the peak feature difference between any peak point and all other peak points to obtain the average difference of the peak feature of the arbitrary peak point; when the average difference of the peak feature is larger, it means that the arbitrary peak point is more likely to be a peak point caused by noise interference. Calculate the absolute value of the difference between the average value of the interval characteristic values ​​of all peak points and the interval characteristic value of the arbitrary peak point to obtain the interval characteristic difference value of the arbitrary peak point; when the interval characteristic difference value is larger, it means that the time interval and average level difference between the peak point and the adjacent peak points is larger, and the arbitrary peak point is more likely to be a peak point caused by noise interference. Calculate the product of the interval characteristic difference value and the peak characteristic average difference and normalize them to obtain the noise interference probability value of the arbitrary peak point; when the noise interference probability value is larger, it means that the peak point is more likely to be caused by noise interference. The formula for obtaining the noise interference probability value includes:

[0036]

[0037] In the formula, represents the noise interference probability value of the i-th peak point in the peak feature sequence, represents the normalization function, represents the interval characteristic value of the i-th peak point, T represents the average value of the interval characteristic values ​​of all peak points, represents the interval feature difference value, M represents the number of peak points in the peak feature sequence, Indicates the difference in peak characteristics between the i-th peak point and the m-th other peak point, Represents the average difference of peak features.

[0038] Step S3, sorting the peak points in the two peak characteristic sequences in chronological order to obtain a third peak sequence; obtaining the noise density characteristic value of the peak point according to the interval characteristics and noise interference probability value between the peak point in the third peak sequence and the preset neighboring peak point; fitting according to the noise density characteristic value of the peak point in the third peak sequence to obtain the local noise density curve of the current characteristic sequence.

[0039] After obtaining the noise interference probability value of the peak point in the peak characteristic sequence, the random noise characteristics at each moment in the current characteristic sequence can be analyzed, so the peak points in the two peak characteristic sequences are sorted in chronological order to obtain a third peak sequence, and the peak points in the third peak sequence are the peak points and trough points in the current characteristic sequence. When the noise interference probability values ​​of a certain moment and its adjacent moments are both large, it means that the current data of this period is more affected by noise interference; therefore, in order to improve the accuracy of denoising, it is necessary to analyze the accurate noise density at each moment, so the noise density characteristic value of the peak point is obtained according to the interval characteristics and noise interference probability value between the peak point and the preset neighboring peak point in the third peak sequence.

[0040] Preferably, in an embodiment of the present invention, the step of obtaining a noise density characteristic value includes: calculating the sum of the time intervals between the peak point and all preset neighboring peak points in the third peak sequence to obtain a comprehensive time interval; in an embodiment of the present invention, the preset neighboring peak point is the four other peak points in the third peak sequence that are closest to the peak point, and the implementer can determine the number of preset neighboring peak points according to the implementation scenario. The comprehensive time interval characterizes the interval between the peak point and the preset neighboring peak point. The smaller the comprehensive time interval, the more likely it is that there will be a peak point with noise interference. Calculate the sum of the noise interference probability values ​​of the peak point and the preset neighboring peak point to obtain a comprehensive noise probability value; the larger the comprehensive noise probability value, the greater the degree of noise influence in this period. Calculate the ratio of the comprehensive noise probability value to the comprehensive time interval to obtain the noise density characteristic value of the peak point; the larger the noise density characteristic value of the peak point, the more obvious the noise of the current at this moment.

[0041] Furthermore, after obtaining the noise density characteristic values ​​of different peak points in the third peak sequence, the noise density characteristic values ​​at different moments in the current characteristic sequence can be fitted, so the local noise density curve of the current characteristic sequence is obtained by fitting according to the noise density characteristic values ​​of the peak points in the third peak sequence; preferably, in an embodiment of the present invention, the noise density characteristic values ​​of the peak points in the third peak sequence are fitted by the least squares method to obtain the local noise density curve corresponding to the current characteristic sequence. The local noise density curve characterizes the degree of noise at each moment in the current characteristic sequence. It should be noted that fitting by the least squares method belongs to the prior art, and the specific steps will not be repeated.

[0042] Step S4, obtaining the noise covariance matrix of the current characteristic sequence through Kalman filtering; adaptively adjusting the noise covariance matrix according to the noise density eigenvalue in the local noise density curve to obtain an adaptive covariance matrix; filtering the adaptive covariance matrix corresponding to each moment in the current characteristic sequence through the Kalman filtering algorithm to obtain a current characteristic denoising sequence; and configuring the power grid according to the current characteristic denoising sequence.

[0043] The existing commonly used Kalman filter algorithm denoises the data, but the noise impact at different times in the current characteristic sequence is different. Improper parameter setting may lead to low denoising efficiency or poor accuracy, which affects the accuracy of the configuration optimization strategy. In the Kalman filter algorithm, the noise covariance matrix reflects the size of the observation noise. When the noise level increases, increasing the noise covariance matrix will rely more on the predicted value and reduce the impact of noise on the estimated value; when the noise level decreases, reducing the noise covariance matrix will rely more on the observed value and improve the accuracy of the estimated value. Therefore, adaptively adjusting the noise covariance matrix according to the noise level can reduce the denoising error, improve the denoising performance and accuracy of the algorithm, and ultimately enable the distribution network and distributed photovoltaic storage to rely on more accurate current data for configuration optimization, making the power grid operation more stable and efficient. Therefore, the noise covariance matrix of the current characteristic sequence is first obtained by Kalman filtering. It should be noted that the Kalman filter algorithm belongs to the prior art, and the specific acquisition steps will not be repeated.

[0044] Furthermore, the noise density eigenvalues ​​at different moments can characterize the degree of noise received, so the noise covariance matrix can be adaptively adjusted according to the noise density eigenvalues ​​in the local noise density curve to obtain an adaptive covariance matrix; preferably, in an embodiment of the present invention, the step of obtaining the adaptive covariance matrix includes: calculating the average value of the noise density eigenvalues ​​at all moments in the local noise density curve to obtain an average noise density value; when the average noise density value is a constant 0, it means that there is no random noise in the current characteristic sequence, so the noise covariance matrix is ​​used as an adaptive covariance matrix; otherwise, the ratio of the noise density eigenvalue to the average noise density value at any moment is calculated to obtain the adjustment coefficient at any moment, when the noise density eigenvalue is larger than the average noise density value, it means that the noise degree at any moment is larger, and the noise covariance matrix needs to be increased; when the noise density eigenvalue is smaller than the average noise density value, it means that the noise degree at any moment is smaller, and the noise covariance matrix needs to be reduced; the product of the adjustment coefficient at any moment and the noise covariance matrix is ​​calculated to obtain the adaptive covariance matrix at any moment. The adaptive covariance matrix at each moment can be adaptively adjusted according to the noise level at that moment, so that the denoising performance and accuracy of the Kalman filter are higher.

[0045] According to the adaptive covariance matrix corresponding to each moment in the current characteristic sequence, the Kalman filter algorithm is used for filtering to obtain the current characteristic denoising sequence; it should be noted that the Kalman filter algorithm belongs to the prior art, and the specific denoising steps are not repeated. The current characteristic denoising sequence can more accurately reflect the current characteristics of the distributed photovoltaic storage connected to the distribution network, and then the grid configuration can be performed according to the current characteristic denoising sequence, improving the accuracy of configuration optimization and making the grid operation more accurate and efficient.

[0046] In summary, the embodiment of the present invention provides a comprehensive configuration optimization method for distribution network protection adapted to the access of distributed photovoltaic storage; obtain the peak characteristic difference between any two peak points in the peak characteristic sequence of the current characteristic sequence; obtain the noise interference probability value of the peak point according to the peak point interval and the peak characteristic difference in the peak characteristic sequence; obtain a third peak sequence from the two peak characteristic sequences; obtain the noise density characteristic value according to the interval characteristics and the noise interference probability value between the peak points in the third peak sequence; obtain the local noise density curve of the current characteristic sequence according to the noise density characteristic value; adaptively adjust the noise covariance matrix according to the local noise density curve to obtain an adaptive covariance matrix. The present invention performs filtering through the Kalman filtering algorithm according to the adaptive covariance matrix to obtain a current characteristic denoising sequence, thereby improving the denoising accuracy; and performs power grid configuration according to the current characteristic denoising sequence, thereby making the configuration optimization more accurate.

[0047] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0048] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A distribution network protection comprehensive configuration optimization method adapted to the access of distributed photovoltaic storage, characterized in that: The method comprises the following steps: Obtain the current characteristic sequence when distributed photovoltaic storage is connected to the distribution network; Obtain two peak characteristic sequences according to the current change characteristics of the current characteristic sequence; obtain the peak characteristic difference degree according to the amplitude difference characteristics, waveform difference characteristics and interval characteristics of any two peak points in the peak characteristic sequence; obtain the noise interference probability value of the peak point according to the difference characteristics of the peak point interval in the peak characteristic sequence and the peak characteristic difference degree; The peak points in the two peak characteristic sequences are sorted in chronological order to obtain a third peak sequence; the noise density characteristic value of the peak point is obtained according to the interval characteristics and noise interference probability values ​​between the peak point in the third peak sequence and the preset neighboring peak point; the local noise density curve of the current characteristic sequence is obtained by fitting according to the noise density characteristic value of the peak point in the third peak sequence; Obtaining the noise covariance matrix of the current characteristic sequence through Kalman filtering; adaptively adjusting the noise covariance matrix according to the noise density characteristic value in the local noise density curve to obtain an adaptive covariance matrix; filtering the adaptive covariance matrix corresponding to each moment in the current characteristic sequence through the Kalman filtering algorithm to obtain a current characteristic denoising sequence; and configuring the power grid according to the current characteristic denoising sequence; The step of obtaining the peak feature difference degree according to the amplitude difference feature, waveform difference feature and interval feature of any two peak points in the peak feature sequence comprises: Calculate the absolute value of the numerical difference between any two peak points in the peak feature sequence to obtain the data difference value of the any two peak points; construct a rectangular coordinate system of the current feature sequence, with the horizontal axis being the timestamp and the vertical axis being the current value; calculate the cosine value of the line connecting the peak point in the peak feature sequence and the data point separated by a preset time in the rectangular coordinate system to obtain the waveform characterization value of the peak point; calculate the absolute value of the difference between the waveform characterization values ​​of any two peak points to obtain the waveform difference value of the any two peak points; calculate the product of the inverse of the time interval between any two peak points, the data difference value and the waveform difference value to obtain the peak feature difference degree of the any two peak points; The step of obtaining the noise interference probability value of the peak point according to the difference characteristics of the peak point intervals in the peak feature sequence and the peak feature difference comprises: In the peak feature sequence, the average time interval between a peak point and adjacent peak points is calculated to obtain an interval feature value of the peak point; the average value of the peak feature difference between any peak point and all other peak points is calculated to obtain the average peak feature difference of the arbitrary peak point; the absolute value of the difference between the average value of the interval feature values ​​of all peak points and the interval feature value of the arbitrary peak point is calculated to obtain the interval feature difference value of the arbitrary peak point; the product of the interval feature difference value and the peak feature average difference is calculated and normalized to obtain the noise interference probability value of the arbitrary peak point.

2. According to claim 1, a distribution network protection comprehensive configuration optimization method adapted to the access of distributed photovoltaic storage is characterized in that: The step of obtaining two peak characteristic sequences according to the current variation characteristics of the current characteristic sequence comprises: The peak points in the current characteristic sequence are sorted in chronological order to obtain a first peak characteristic sequence; the inverse number of each data point in the current characteristic sequence is taken to construct a new current sequence; the peak points in the new current sequence are sorted in chronological order to obtain a second peak characteristic sequence.

3. According to claim 1, a distribution network protection comprehensive configuration optimization method adapted to the access of distributed photovoltaic storage is characterized in that: The step of obtaining the noise density characteristic value of the peak point according to the interval characteristics and noise interference probability value between the peak point and the preset neighboring peak point in the third peak sequence comprises: In the third peak sequence, the sum of the time intervals between the peak point and all preset neighboring peak points is calculated to obtain a comprehensive time interval; the sum of the noise interference probability values ​​of the peak point and the preset neighboring peak points is calculated to obtain a comprehensive noise probability value; and the ratio of the comprehensive noise probability value to the comprehensive time interval is calculated to obtain a noise density characteristic value of the peak point.

4. According to claim 1, a distribution network protection comprehensive configuration optimization method adapted to the access of distributed photovoltaic storage is characterized in that: The step of adaptively adjusting the noise covariance matrix according to the noise density eigenvalue in the local noise density curve to obtain an adaptive covariance matrix comprises: Calculate the average value of the noise density eigenvalues ​​at all times in the local noise density curve to obtain the average noise density value; when the average noise density value is a constant 0, use the noise covariance matrix as the adaptive covariance matrix, otherwise calculate the ratio of the noise density eigenvalue at any time to the average noise density value to obtain the adjustment coefficient at any time, calculate the product of the adjustment coefficient at any time and the noise covariance matrix to obtain the adaptive covariance matrix at any time.

5. According to claim 1, a distribution network protection comprehensive configuration optimization method adapted to the access of distributed photovoltaic storage is characterized in that: The step of fitting according to the noise density characteristic value of the peak point in the third peak sequence to obtain the local noise density curve of the current characteristic sequence comprises: The noise density characteristic values ​​of the peak points in the third peak sequence are fitted by the least square method to obtain a local noise density curve corresponding to the current characteristic sequence.

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