Semi-solid circuit breaker with intelligent regulation function based on photovoltaic system

Abstract

The application relates to the technical field of circuit breakers, in particular to a semi-solid-state circuit breaker with an intelligent regulation and control function based on a photovoltaic system, which comprises a first acquisition module used for acquiring electrical data to be filtered of the photovoltaic system and a filter window of the electrical data to be filtered by using the semi-solid-state circuit breaker; a second acquisition module used for acquiring filter data of the electrical data to be filtered; and a regulation and control module used for regulating and controlling the photovoltaic system according to the filter data. The application can improve the quality of the filter data by optimizing the filter process of the electrical data of the photovoltaic system collected by the semi-solid-state circuit breaker, so as to guarantee the effect of regulating and controlling the photovoltaic system based on the filter data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of circuit breakers, in particular to a semi-solid state circuit breaker with intelligent regulation and control function based on a photovoltaic system. BACKGROUND

[0002] Currently, in order to ensure the safe operation of the photovoltaic system, optimize the power generation efficiency of the photovoltaic system, and improve the intelligent level of operation and maintenance of the photovoltaic system, the electrical data of the photovoltaic system is usually monitored based on the semi-solid state circuit breaker, and then the monitored electrical data is filtered using the existing median filtering algorithm, and the photovoltaic system is intelligently regulated and controlled based on the data obtained after filtering. However, during the operation of the photovoltaic system, there may be load mutation, abnormal operation or failure, and the existence of load mutation, abnormal operation or failure will cause the data obtained after subsequent filtering to be distorted, that is, the data quality obtained after filtering will be poor. When the filtered data is distorted, the subsequent regulation and control effect of the photovoltaic system will be affected, so how to improve the data quality obtained after data filtering and ensure the subsequent regulation and control effect of the photovoltaic system based on the filtered data has become a problem to be solved. SUMMARY

[0003] In order to solve the above problems, the present application provides a semi-solid state circuit breaker with intelligent regulation and control function based on a photovoltaic system, and the technical scheme adopted is as follows:

[0004] One embodiment of the present application provides a semi-solid state circuit breaker with intelligent regulation and control function based on a photovoltaic system, which comprises:

[0005] A first acquisition module is configured to acquire, by using the semi-solid state circuit breaker, electrical data to be filtered of the photovoltaic system and a filtering window of the electrical data to be filtered;

[0006] A second acquisition module is configured to obtain, according to data volatility and a median value of the filtering window, a median-replaced unreliable representation value corresponding to the electrical data to be filtered, and determine whether the median-replaced unreliable representation value is not greater than a preset unreliable threshold. If yes, a filtering data of the electrical data to be filtered is acquired by using a median filtering algorithm. Otherwise, a fitting straight line of the filtering window is acquired, and according to distances of window data in the filtering window to the fitting straight line, an important factor and a weighted average value of the electrical data to be filtered are obtained. According to a slope of the fitting straight line, a gradient value of the window data, a collection time interval between the window data and the electrical data to be filtered, and the weighted average value, a target credibility representation value of the window data is obtained. According to the target credibility representation value, the important factor and the window data, a filtering data of the electrical data to be filtered is obtained.

[0007] The regulation module is configured to regulate the photovoltaic system according to the filtered data.

[0008] Beneficial effects: the application comprises a first acquisition module configured to acquire electrical data to be filtered of the photovoltaic system and a filtering window of the electrical data to be filtered by using the semi-solid circuit breaker; a second acquisition module configured to obtain a median value replacement unreliable representation value corresponding to the electrical data to be filtered according to data volatility and a median value of the filtering window, and determine whether the median value replacement unreliable representation value is not greater than a preset unreliable threshold, if yes, acquire filtered data of the electrical data to be filtered by using a median filtering algorithm, otherwise, acquire a fitting straight line of the filtering window, and obtain an important factor and a weighted average value of the electrical data to be filtered according to distances of window data in the filtering window to the fitting straight line, obtain a target credibility representation value of the window data according to a slope of the fitting straight line, a gradient value of the window data, a collection time interval between the window data and the electrical data to be filtered, and the weighted average value, and obtain filtered data of the electrical data to be filtered according to the target credibility representation value, the important factor and the window data; and a regulation module configured to regulate the photovoltaic system according to the filtered data. The application optimizes the filtering process of the electrical data of the photovoltaic system collected by the semi-solid circuit breaker, improves the quality of the filtered data, and thus ensures the effect of regulating the photovoltaic system based on the filtered data. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0010] Figure 1 The structural block diagram of the semi-solid circuit breaker with the intelligent regulation function based on the photovoltaic system. DETAILED DESCRIPTION

[0011] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the embodiments of the present application.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0013] The embodiment provides a semi-solid circuit breaker with intelligent regulation and control function based on a photovoltaic system, and specifically as follows.

[0014] As shown in the figure, the embodiment provides a semi-solid circuit breaker with intelligent regulation and control function based on a photovoltaic system, which comprises: Figure 1

[0015] The first acquisition module 01 is used for acquiring electrical data to be filtered of the photovoltaic system and a filter window of the electrical data to be filtered by using the semi-solid circuit breaker.

[0016] The embodiment mainly optimizes the filtering process of the electrical data of the photovoltaic system collected by the semi-solid circuit breaker, so as to improve the quality of the filtered data, thereby guaranteeing the effect of subsequent regulation and control of the photovoltaic system based on the filtered data. In addition, in order to facilitate understanding, the filtering process of the electrical data collected by the semi-solid circuit breaker applied to any photovoltaic system will be taken as an example for analysis in the sequel, that is, the semi-solid circuit breaker appearing in the sequel is the semi-solid circuit breaker applied to the photovoltaic system, and the photovoltaic system appearing in the sequel is the same photovoltaic system. The semi-solid circuit breaker is a composite circuit breaker, which combines the advantages of mechanical circuit breakers and solid-state circuit breakers, and works cooperatively by mechanical switches and semiconductor devices to realize rapid disconnection of current and circuit protection. This type of circuit breaker has fast response speed, high reliability and long service life, and is particularly suitable for occasions with strict protection performance requirements such as centralized photovoltaic power stations. The photovoltaic system usually comprises a photovoltaic array, a current combiner box, a direct current cabinet and other key devices. Once a short circuit, grounding or arc fault occurs, if effective isolation cannot be completed in a very short time, the device is easily damaged and even a fire is caused. Therefore, the semi-solid circuit breaker with intelligent regulation and control function based on the photovoltaic system can realize rapid identification and isolation of faults, thereby guaranteeing stable operation of the photovoltaic system.

[0017] ​First, in the operation process of the photovoltaic system, the semi-solid state circuit breaker is used to collect the direct current and voltage output by the photovoltaic array of the photovoltaic system, and the collected direct current and voltage are all recorded as to-be-filtered electrical data, then all types of electrical data sequences in the target monitoring time period are obtained, and all types of electrical data sequences in the target monitoring time period are recorded as to-be-filtered electrical data sequences of the photovoltaic system in the target monitoring time period, that is, the to-be-filtered electrical data sequences of the photovoltaic system in the target monitoring time period include direct current sequences and voltage sequences, then the filter window of each to-be-filtered electrical data in the to-be-filtered electrical data sequence is obtained, the filter window is related to whether the subsequent traditional filtering algorithm is used for filtering and how to determine the filtering data after it is determined that the traditional filtering algorithm is not used for filtering, and the center data in the filter window of each to-be-filtered electrical data in the to-be-filtered electrical data sequence is generally the corresponding to-be-filtered electrical data, and the semi-solid state circuit breaker is installed at the position between the photovoltaic combiner box and the direct current power distribution cabinet. In addition, the implementer needs to determine the size of the filter window according to the actual scene and demand, and generally determines it according to the noise intensity and detail retention degree, for example, the filter window in this embodiment can be set to 1xb, wherein b is required to be an odd number, for example, b can be set to 13, that is, the maximum amount of data that can be accommodated in the filter window is 13, that is, the filter window of the a-th to-be-filtered electrical data in any to-be-filtered electrical data sequence is composed of all data from the first to the th to-be-filtered electrical data in the to-be-filtered electrical data sequence.

[0018] In addition, it needs to be explained that if the to-be-filtered electrical data on the left or right side of a to-be-filtered electrical data in the to-be-filtered electrical data sequence is less than , then data supplement is performed on the other side not less than , for example, for the b-th to-be-filtered electrical data in the to-be-filtered electrical data sequence, if the data amount on the left side of the b-th to-be-filtered electrical data in the to-be-filtered electrical data sequence is c and less than , then the number of data located on the left side of the b-th to-be-filtered electrical data in the filter window of the b-th to-be-filtered electrical data is c, and the number of data located on the right side of the b-th to-be-filtered electrical data is b-1-c.

[0019] ​​In the embodiment, the implementer needs to set the target monitoring time period according to the frequency of regulating the photovoltaic system and the actual situation. For example, the time period between two adjacent times of regulating the photovoltaic system can be set as the target monitoring time period. If the current time is the time of regulating the photovoltaic system, the time period from the last time of regulating the photovoltaic system to the current time is the target monitoring time period. In the embodiment, the implementer needs to set the frequency of collecting the direct current and voltage output by the photovoltaic array of the photovoltaic system by the semi-solid circuit breaker in the case of meeting the power system specification. For example, the frequency of collecting the direct current and voltage output by the photovoltaic array of the photovoltaic system by the semi-solid circuit breaker can be set as 100 Hz. The embodiment requires synchronous collection between different types of electrical data.

[0020] In the embodiment, the semi-solid circuit breaker used can be compatible with all mainstream photovoltaic inverter models and has the functions of adaptive matching and protocol conversion. Therefore, the private or standard communication protocols (such as Modbus, CAN, RS485, etc.) used for different inverter models can be automatically identified and analyzed to complete the unification of data structures.

[0021] Therefore, the embodiment can obtain each to-be-filtered electrical data in the to-be-filtered electrical data sequence of the photovoltaic system and the filter window of the to-be-filtered electrical data through the above process. Since the filtering process of each to-be-filtered electrical data in the embodiment is consistent, the embodiment will be described below by taking the process of obtaining the filtered data of any to-be-filtered electrical data as an example. That is, the to-be-filtered electrical data and the filter window of the to-be-filtered electrical data appearing in the embodiment below are the same to-be-filtered electrical data and the same filter window of the to-be-filtered electrical data.

[0022] The second obtaining module 02 is configured to obtain a median value replacement unreliable representation value corresponding to the to-be-filtered electrical data according to the data volatility and the median value of the filter window, determine whether the median value replacement unreliable representation value is not greater than a preset unreliable threshold, if yes, obtain the filtered data of the to-be-filtered electrical data by using a median filtering algorithm, otherwise, obtain a fitting straight line of the filter window, obtain an important factor and a weighted average value of the to-be-filtered electrical data according to the distance of each window data in the filter window to the fitting straight line, obtain a target credibility representation value of the window data according to the slope of the fitting straight line, the gradient value of the window data, the collection time interval between the window data and the to-be-filtered electrical data, and the weighted average value, and obtain the filtered data of the to-be-filtered electrical data according to the target credibility representation value, the important factor, and the window data.

[0023] Due to the complex operating environment of the photovoltaic system, multiple electromagnetic interference sources, and the high-speed switching characteristics of the semi-solid state circuit breaker, pulse-type isolated spikes, burrs and other high-frequency noise data are prone to occur in the process of electrical data collection. In order to remove such noise interference, highlight the real trend and change of the system, and ensure the reliability and accuracy of subsequent regulation of the photovoltaic system, it is usually necessary to filter the collected electrical data. However, during the operation of the photovoltaic system, load mutation, abnormal operation or failure may occur, and the existence of load mutation, abnormal operation or failure will cause distortion of the filtered data obtained by using the existing filtering algorithm, that is, it will cause poor data quality. For example, during the operation of the photovoltaic system, if local arc fault or abnormal load connection occurs, abnormal values of current or voltage will be generated at multiple consecutive time points. If the current time is at the end of the fault or the system has just recovered to steady state, that is, the current time has returned to normal level, but since most of the data in the filtering window are abnormal pulse values, the possibility of these abnormal values being in the middle position after sorting is greater, that is, the abnormal value becomes the median value. If the median value at this time is replaced by the value monitored at the current time, it will make the system mistakenly believe that the current time is still in an abnormal or fluctuating state, thereby interfering with subsequent fault judgment and control decision. In addition, repeated connection caused by poor contact, local equipment failure and other periodic load fluctuations will cause frequent impact on the system, voltage fluctuation, current peak and other abnormal phenomena, thereby causing the electrical data to present obvious periodic fluctuations. If the current time belongs to the abnormality caused by repeated connection caused by poor contact, local equipment failure and other abnormalities, and the electrical data monitored at the current time is just in the transition stage from the bottom to the peak, that is, the electrical data monitored at the current time has begun to rise obviously, while most of the data in the filtering window of the electrical data monitored at the current time are still at a low level. This will cause the median value of the filtering window of the electrical data monitored at the current time to be relatively small after sorting by the traditional median filtering algorithm, thereby causing the electrical data monitored at the current time to be replaced by smaller data, thereby causing the real abnormal characteristics to be hidden. Since the filtering data is distorted, it will affect the subsequent regulation effect of the photovoltaic system. In order to ensure the regulation effect of the photovoltaic system based on the filtered data, the data quality obtained by data filtering needs to be improved.

[0024] The embodiment next needs to judge whether the to-be-filtered electrical data is suitable for the median filtering algorithm, and only the to-be-filtered electrical data which is not suitable for the median filtering algorithm will be filtered in other ways, and the reason for the judgment is that the median filtering performs excellently in processing the current and voltage data under the normal operation state of the photovoltaic system, and is particularly good at suppressing isolated pulse interference and retaining edge characteristics, that is, the median filtering has good inhibitory capacity for burr or peak noise caused by sudden interference such as lightning strike and grounding arc, and all the embodiments need to judge whether the to-be-filtered electrical data is suitable for the median filtering algorithm in order to reduce the amount of calculation and ensure the final filtering effect. Since the embodiment needs to determine whether the to-be-filtered electrical data is suitable for the median filtering algorithm according to the median replacement unreliable representation value, the median replacement unreliable representation value corresponding to the to-be-filtered electrical data needs to be obtained before the judgment, that is, the median replacement unreliable representation value is the basis for judging whether the to-be-filtered electrical data is suitable for the median filtering algorithm, and the specific obtaining process of the median replacement unreliable representation value is as follows:

[0025] Firstly, a set composed of all window data except the maximum window data in the filtering window of the to-be-filtered electrical data is obtained, and is recorded as a first set. Then, the normalized value of the standard deviation of the first set is calculated, and is recorded as a first index value. That is, the maximum window data in the filtering window is not used when the standard deviation is calculated. Then, the median and the range of the filtering window of the to-be-filtered electrical data are obtained. The median of the filtering window refers to the data value at the middle position of the sequence obtained by arranging the window data in ascending or descending order. The range of the filtering window refers to the result of subtracting the minimum window data from the maximum window data in the filtering window. Then, the difference between the median of the filtering window of the to-be-filtered electrical data and the to-be-filtered electrical data is obtained, and is recorded as a first difference value. The sum of the range of the filtering window of the to-be-filtered electrical data and a preset first constant is obtained, and is recorded as a second difference value. The absolute value of the ratio of the first difference value to the second difference value is obtained, and is recorded as a second index value. Finally, the mean of the first index value and the second index value is obtained as the median replacement unreliable representation value corresponding to the to-be-filtered electrical data. In addition, it should be noted that when all the data in the filtering window of the to-be-filtered electrical data are equal, the normalized value of the standard deviation of all the data in the filtering window is taken as the first index value.

[0026] In specific applications, the implementer needs to set the value of the preset first constant according to the actual situation, but it is required to be a small positive number. For example, the preset first constant can be set to 0.1 in the embodiment. .

[0027] The specific calculation formula of the median replacement unreliable representation value corresponding to the to-be-filtered electrical data is as follows:

[0028]

[0029] wherein A is a median-replacement unreliable representation value corresponding to the electrical data to be filtered, is a standard deviation of the first set, tanh() is a hyperbolic tangent function, and tanh() is for normalizing is a normalized value of the standard deviation of the first set, x0 is a median value of the filter window of the electrical data to be filtered, x1 is a value of the electrical data to be filtered, d is a range of the filter window of the electrical data to be filtered, and f is a preset first constant, and the preset first constant exists for preventing the denominator from being 0.

[0030] In addition, when the photovoltaic system is in normal operation or the filter window has isolated spikes, burrs, and other high-frequency noise data, the use of the median filter algorithm for filtering is more suitable, that is, the possibility of data distortion caused by replacing the electrical data to be filtered with the median value of the filter window of the electrical data to be filtered is smaller. However, when there is a load mutation, abnormal operation, or fault, and the data in the filter window has strong oscillation, the possibility of data distortion caused by replacing the electrical data to be filtered with the median value of the filter window of the electrical data to be filtered is larger. Therefore, the degree of deviation of the electrical data to be filtered from the median value needs to be considered. That is, if there is strong oscillation in the window, but the electrical data to be filtered is more similar to the median value, the median filter algorithm can be used for filtering at this time. Therefore, when is larger and is larger, that is, A is larger, it indicates that the possibility of data distortion caused by replacing the electrical data to be filtered with the median value of the filter window of the electrical data to be filtered is larger or the possibility of distortion of the filtered data caused by using the median filter algorithm to filter the electrical data to be filtered is larger. Conversely, when is smaller and is smaller, that is, A is smaller, it indicates that the use of the median filter algorithm for filtering is more suitable, that is, the possibility of data distortion caused by replacing the electrical data to be filtered with the median value of the filter window of the electrical data to be filtered is smaller.

[0031] ​Then it is judged whether the median replacement unreliable representation value corresponding to the to-be-filtered electrical data is not greater than a preset unreliable threshold. If yes, it is determined that using the median of the filter window of the to-be-filtered electrical data to replace the to-be-filtered electrical data will not cause data distortion, so the median filter algorithm is used to filter the to-be-filtered electrical data at this time, and the data obtained by using the median filter algorithm to filter the to-be-filtered electrical data is recorded as the filtered data of the to-be-filtered electrical data, or the median of the filter window of the to-be-filtered electrical data can be directly used as the filtered data of the to-be-filtered electrical data. Otherwise, the greater the possibility that using the median filter algorithm to filter the to-be-filtered electrical data will cause distortion of the filtered data. Therefore, when it is judged that the median replacement unreliable representation value corresponding to the to-be-filtered electrical data is greater than the preset unreliable threshold, the fitting straight line of the filter window of the to-be-filtered electrical data is first obtained, and then the important factor and the weighted average value of the to-be-filtered electrical data are obtained according to the distance of each window data in the filter window of the to-be-filtered electrical data to the fitting straight line. Then, the target credibility representation value of the window data is obtained according to the slope of the fitting straight line, the gradient value of the window data, the collection time interval between the window data and the to-be-filtered electrical data, and the weighted average value. Finally, the filtered data of the to-be-filtered electrical data is obtained according to the target credibility representation value, the important factor, and the window data.

[0032] In specific applications, the implementer needs to set the preset unreliable threshold according to the value of the median replacement unreliable representation value and the actual situation. For example, the preset unreliable threshold can be set to 0.7 in this embodiment.

[0033] In this embodiment, the specific process of obtaining the fitting straight line of the filter window of the to-be-filtered electrical data is as follows: first, a mapping space is obtained, the horizontal coordinate axis of the mapping space represents time, and the vertical coordinate represents the value of the electrical data of the same type as the to-be-filtered electrical data. Then, each window data in the filter window of the to-be-filtered electrical data and the collection time of each window data are mapped into the mapping space, and the data points corresponding to each window data are obtained, that is, the horizontal coordinate value of the data point corresponding to any window data is the collection time of the window data, and the vertical coordinate value is the window data. Then, the fitting algorithm is used to fit the data points corresponding to all window data in the filter window of the to-be-filtered electrical data, and the straight line obtained by fitting is recorded as the fitting straight line of the filter window of the to-be-filtered electrical data. The fitting straight line is mainly used for subsequent obtaining of the important factor and the weighted average value. In specific applications, the implementer can select a fitting algorithm according to the actual situation. For example, a first-order linear fitting algorithm can be used to fit all window data in the filter window in this embodiment.

[0034] In the embodiment, the specific process of obtaining the importance factor of the electrical data to be filtered is as follows: first, the distance from each window data in the filter window of the electrical data to be filtered to the fitted straight line is obtained, and is recorded as the first distance of the corresponding window data; since the filter window of the electrical data to be filtered contains the electrical data to be filtered, the first distance of the electrical data to be filtered can be directly obtained, and the first distance of the electrical data to be filtered is recorded as the distance to be analyzed; then, the cumulative result of the first distances of all window data in the filter window is obtained, and is recorded as the first comprehensive distance, and then the ratio of the distance to be analyzed to the first comprehensive distance is obtained and is taken as the importance factor of the electrical data to be filtered, and the importance factor of the electrical data to be filtered is wherein N is the total number of window data in the filter window of the electrical data to be filtered, g1 is the distance to be analyzed, is the first distance of the i-th window data in the filter window of the electrical data to be filtered; in addition, when is greater, it means that the electrical data to be filtered is more likely to belong to data with obvious trend change characteristics, and also means that the electrical data to be filtered is more likely to carry a key trend mutation or key information, thereby indicating that the electrical data to be filtered is more important for subsequent fault detection or trend analysis, and also indicating that the electrical data to be filtered is more important for subsequent photovoltaic system regulation strategy, so that its original characteristics should be retained more during filtering to avoid eliminating important signals, that is, when the importance factor of the electrical data to be filtered is greater, it means that the participation or contribution of the electrical data to be filtered should be greater when determining the filtered data of the electrical data to be filtered, and vice versa, when the importance factor of the electrical data to be filtered is smaller, it means that the participation or contribution of the electrical data to be filtered should be smaller when determining the filtered data of the electrical data to be filtered.

[0035] In the embodiment, the specific process of obtaining the weighted average value of the electrical data to be filtered is as follows: obtaining the reciprocal of the first distance of each window data plus a preset first constant, and denoted as the second distance of the corresponding window data, obtaining the cumulative result of the second distances of all window data in the filtering window, and denoted as the second comprehensive distance, obtaining the ratio of the second distance of each window data to the second comprehensive distance, and denoted as the weighting factor of the corresponding window data, obtaining the product of the weighting factor of each window data and the corresponding window data, and denoted as the weighted data of the corresponding window data, and obtaining the cumulative result of the weighted data of all window data in the filtering window of the electrical data to be filtered as the weighted average value of the electrical data to be filtered, wherein the weighted average value is mainly used for subsequent analysis of the reliability of the window data and representing the overall trend information of the filtering window; and the reason for considering the weighting factor when calculating the weighted average value is that the points closer to the trend line or the points closer to the fitting straight line can better represent the trend change of the filtering window and the mainstream change of the data in the window, and are also the key to accurately reflect the overall trend information, so when obtaining the overall trend information of the filtering window, the participation of the points closer to the trend line or the points closer to the fitting straight line should be greater, and the contribution of the window data with smaller first distance to the weighted average value should be greater, so the weighting factor of the window data with smaller first distance should be greater; in addition, the purpose of adding the first distance to the preset first constant in the process of calculating the weighted average value is also to prevent the denominator from being 0.

[0036] In the embodiment, the specific process of obtaining the target credibility characteristic value of the window data according to the slope of the fitting straight line, the gradient value of the window data, the acquisition time interval between the window data and the electrical data to be filtered, and the weighted average value is as follows: for the jth window data in the filtering window of the electrical data to be filtered, first, the gradient value of the jth window data is obtained, and if there is window data on the left side of the jth window data in the filtering window, the result of subtracting the jth window data from the j-1th window data in the filtering window is taken as the gradient value of the jth window data, and if there is no window data on the left side of the jth window data in the filtering window, the result of subtracting the j+1th window data from the jth window data in the filtering window is taken as the gradient value of the jth window data; then, the negative mapping value of the absolute value of the difference between the gradient value of the jth window data and the slope of the fitting straight line of the filtering window is obtained and taken as the first characteristic value of the jth window data, the negative mapping value of the acquisition time interval between the jth window data and the electrical data to be filtered is obtained and taken as the second characteristic value of the jth window data, the negative mapping value of the absolute value of the difference between the jth window data and the weighted average value is obtained and taken as the third characteristic value of the jth window data, the result of adding the first characteristic value, the second characteristic value and the third characteristic value of the jth window data is taken as the initial credibility characteristic value of the jth window data, and then the initial credibility characteristic value of the jth window data is normalized, and the result of the normalization is taken as the target credibility characteristic value of the jth window data. In the embodiment, the initial credibility characteristic values of the filtering window are used for normalization of the initial credibility characteristic value, and the comprehensive initial credibility characteristic value of the filtering window refers to the cumulative sum of the initial credibility characteristic values of all window data in the filtering window, that is, the normalized value of the initial credibility characteristic value of the jth window data is the ratio of the initial credibility characteristic value of the jth window data to the comprehensive initial credibility characteristic value. Moreover, the target credibility characteristic value of the window data is a key parameter for determining the filtered data of the electrical data to be filtered.

[0037] In addition, the specific calculation expression of the initial credibility characteristic value of the jth window data in the embodiment is as follows:

[0038]

[0039] wherein, is the initial credibility characteristic value of the jth window data in the filtering window of the electrical data to be filtered, is the gradient value of the jth window data, and k is the slope of the fitting straight line of the filtering window of the electrical data to be filtered, is the acquisition time interval between the jth window data and the electrical data to be filtered, that is, the time interval between the acquisition time of the jth window data and the acquisition time of the electrical data to be filtered, is the weighted average value of the electrical data to be filtered, exp() is an exponential function with constant e as the base, , and The exponential function in , and is only used to obtain a negative mapping result by negatively mapping

[0040] The smaller is, the more similar the jth window data is to the overall trend of the filtering window, so the contribution of the jth window data should be greater when determining the electrical data to be filtered subsequently, thereby achieving the purpose of reducing the probability of data distortion phenomenon; because load switching and other behaviors can cause the output of the system to change to some extent within a short period of time, the window data close to the collection time of the electrical data to be filtered is more likely to be in a similar UN phase as the electrical data to be filtered, and has high temporal correlation, so the smaller is, the greater the contribution of the jth window data should be when determining the electrical data to be filtered subsequently, thereby achieving the purpose of reducing the probability of data distortion phenomenon; because the weighted average value is a concentrated reflection of the overall data in the window, the closer the jth window data is to the weighted average value, the more likely it is that the jth window data is the real response of the system under the operating condition at the collection time corresponding to the electrical data to be filtered, so the greater the contribution of the jth window data should be when determining the electrical data to be filtered subsequently, thereby achieving the purpose of reducing the probability of data distortion phenomenon; because , and are smaller, is greater, so the greater is, the greater the contribution of the jth window data in the filtering window of the electrical data to be filtered should be, that is, the greater is, the greater the initial credibility characteristic value of the jth window data in the filtering window of the electrical data to be filtered is, and vice versa, the smaller is, the smaller the initial credibility characteristic value of the jth window data in the filtering window of the electrical data to be filtered is.

[0041] In the embodiment, according to the target credibility characteristic value, the important factor and the window data, the specific process of obtaining the filtered data of the to-be-filtered electrical data is as follows: first, the product of the important factor of the to-be-filtered electrical data and the to-be-filtered electrical data is obtained and recorded as a first characteristic value; then, the product of the target credibility characteristic value of each window data in the filtering window of the to-be-filtered electrical data and the corresponding window data is obtained and recorded as the participation characteristic value of the corresponding window data; then, the accumulation result of the participation characteristic values of all window data in the filtering window is obtained and recorded as a first accumulated value, the product of the complement of the important factor and the first accumulated value is obtained and recorded as a second characteristic value, and the complement of the important factor is the result of 1 minus the important factor; finally, the sum of the first characteristic value and the second characteristic value is recorded as the filtered data of the to-be-filtered electrical data.

[0042] And according to the target credibility characteristic value, the important factor and the window data, the specific expression of obtaining the filtered data of the to-be-filtered electrical data is as follows:

[0043]

[0044] Wherein, Q is the filtered data of the to-be-filtered electrical data, is the important factor of the to-be-filtered electrical data, is the target credibility characteristic value of the jth window data in the filtering window of the to-be-filtered electrical data, is the jth window data in the filtering window, N is the total number of window data in the filtering window of the to-be-filtered electrical data, x1 is the value of the to-be-filtered electrical data; and when is larger, it indicates that the to-be-filtered electrical data carries more key information, so that the contribution degree of the to-be-filtered electrical data should be larger and the contribution degree of the window data should be smaller, that is, when is larger, the contribution degree of x1 is larger than that of , thereby reducing the probability of data distortion phenomenon; on the contrary, when it is smaller, the filtered data of the to-be-filtered electrical data should be determined based on to a greater extent, so that the filtered data of the to-be-filtered electrical data tends to the trend of the filtering window, thereby reducing the probability of data distortion phenomenon.

[0045] Therefore, the embodiment takes the acquisition process of the filtered data of one to-be-filtered electrical data as an example for specific description, and the acquisition processes of the filtered data of other to-be-filtered electrical data in the to-be-filtered electrical data sequence are the same as the acquisition process of the filtered data of the to-be-filtered electrical data described above, so the embodiment will not be described further.

[0046] The control module 03 is configured to control the photovoltaic system according to the filtered data.

[0047] Since the embodiment can obtain the filtering data of all the to-be-filtered electrical data in the to-be-filtered electrical data sequence through the above process, that is, the embodiment completes the filtering of the to-be-filtered electrical data sequence through the above process and obtains the filtering data; and after obtaining the filtering data, the photovoltaic system is regulated by using the filtering data, and since the regulation of the photovoltaic system based on the filtering data is a known technology, the embodiment will not be described in detail; in addition, abnormality identification and fault detection are performed by using the filtering data obtained after filtering, which can avoid delay or false triggering of the regulation strategy caused by data misreplacement or distortion of key information, thereby providing high-quality and more reliable data support for the accurate regulation strategy, and further making the regulation strategy more accurate, timely and reliable.

[0048] Up to now, the embodiment improves the data quality obtained through data filtering, thereby ensuring the effect of subsequent regulation of the photovoltaic system based on the filtering data.

[0049] In summary, the embodiment comprises a first acquisition module for acquiring the to-be-filtered electrical data of the photovoltaic system and the filtering window of the to-be-filtered electrical data by using the semi-solid circuit breaker; a second acquisition module for obtaining the median replacement unreliable representation value corresponding to the to-be-filtered electrical data according to the data volatility and median of the filtering window, and judging whether the median replacement unreliable representation value is not greater than a preset unreliable threshold, if yes, obtaining the filtering data of the to-be-filtered electrical data by using the median filtering algorithm, otherwise, obtaining the fitting straight line of the filtering window, and obtaining the important factor and weighted average value of the to-be-filtered electrical data according to the distance of each window data in the filtering window to the fitting straight line, obtaining the target credibility representation value of the window data according to the slope of the fitting straight line, the gradient value of the window data, the collection time interval between the window data and the to-be-filtered electrical data and the weighted average value, and obtaining the filtering data of the to-be-filtered electrical data according to the target credibility representation value, the important factor and the window data; a regulation module for regulating the photovoltaic system according to the filtering data. And the embodiment optimizes the filtering process of the electrical data of the photovoltaic system collected by the semi-solid circuit breaker, which can improve the quality of the filtering data, thereby ensuring the effect of subsequent regulation of the photovoltaic system based on the filtering data.

[0050] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A semi-solid-state circuit breaker with intelligent control function based on photovoltaic system, characterized in that, The semi-solid-state circuit breaker with intelligent control function based on photovoltaic system includes: The first acquisition module is used to acquire the electrical data to be filtered from the photovoltaic system and the filtering window of the electrical data to be filtered using a semi-solid circuit breaker. The second acquisition module is used to obtain the median replacement unreliable characterization value corresponding to the electrical data to be filtered based on the data volatility and median of the filtering window, and to determine whether the median replacement unreliable characterization value is not greater than a preset unreliable threshold. If so, the filtered data of the electrical data to be filtered is obtained using the median filtering algorithm; otherwise, the fitted line of the filtering window is obtained, and the importance factor and weighted average value of the electrical data to be filtered are obtained based on the distance of each window data in the filtering window to the fitted line. The target confidence characterization value of the window data is obtained based on the slope of the fitted line, the gradient value of the window data, the acquisition time interval between the window data and the electrical data to be filtered, and the weighted average value. The filtered data of the electrical data to be filtered is obtained based on the target confidence characterization value, the importance factor, and the window data. The control module is used to regulate the photovoltaic system based on the filtered data; A method for obtaining unreliable median replacement values ​​includes: taking the normalized standard deviation of all window data remaining in the filtering window except for the largest window data as a first index value; recording the difference between the electrical data to be filtered and the median of the filtering window as a first difference value; recording the sum of the range of the filtering window and a preset first constant as a second difference value; recording the absolute value of the ratio of the first difference value to the second difference value as a second index value; and taking the mean of the first index value and the second index value as the unreliable median replacement value corresponding to the electrical data to be filtered. The method for obtaining the important factors of the electrical data to be filtered includes: recording the distance from the data point corresponding to each window data in the filtering window to the fitted line as the first distance of the corresponding window data; recording the distance from the data point corresponding to the electrical data to be filtered in the filtering window to the fitted line as the distance to be analyzed; recording the sum of the first distances of all window data in the filtering window as the first comprehensive distance; and using the ratio of the distance to be analyzed to the first comprehensive distance as the important factor of the electrical data to be filtered.

2. The semi-solid-state circuit breaker with intelligent control function based on photovoltaic system as described in claim 1, characterized in that, The method for obtaining the weighted average of the electrical data to be filtered includes: The reciprocal of the sum of the first distance of the window data and the preset first constant is recorded as the second distance of the corresponding window data. The sum of the second distances of all window data in the filtering window is recorded as the second comprehensive distance. The ratio of the second distance of the window data to the second comprehensive distance is recorded as the weighting factor of the corresponding window data. The product of the weighting factor of the window data and the corresponding window data is recorded as the weighted data of the corresponding window data. The sum of the weighted data of all window data in the filtering window is used as the weighted average value of the electrical data to be filtered.

3. The semi-solid-state circuit breaker with intelligent control function based on photovoltaic system as described in claim 1, characterized in that, The gradient value of the window data refers to the difference between the window data and the adjacent window data of the corresponding window data.

4. The semi-solid-state circuit breaker with intelligent control function based on photovoltaic system as described in claim 1, characterized in that, The method for obtaining the target credibility representation value of the window data includes: For any window data in the filtering window, an initial confidence characterization value of the window data is obtained based on the difference between the slope of the fitted line and the gradient value of the window data, the acquisition time interval between the window data and the electrical data to be filtered, and the difference between the weighted average value and the window data. The normalized result of the initial confidence characterization value is then used as the target confidence characterization value of the window data.

5. The semi-solid-state circuit breaker with intelligent control function based on photovoltaic system as described in claim 4, characterized in that, The method for obtaining the initial credibility representation value of the window data includes: The negative mapping value of the absolute value of the difference between the gradient value of the window data and the slope of the fitted line is used as the first feature value of the window data. The negative mapping value of the acquisition time interval between the window data and the electrical data to be filtered is used as the second feature value of the window data. The negative mapping value of the absolute value of the difference between the window data and the weighted average is used as the third feature value of the window data. The sum of the first feature value, the second feature value and the third feature value of the window data is recorded as the initial confidence characterization value of the window data.

6. The semi-solid-state circuit breaker with intelligent control function based on photovoltaic system as described in claim 1, characterized in that, A method for obtaining filtered data of the electrical data to be filtered based on the target confidence characterization value, importance factors, and window data includes: The product of the important factor and the electrical data to be filtered is recorded as the first characterization value. The product of the target confidence characterization value of the window data and the corresponding window data is recorded as the participation characterization value of the corresponding window data. The sum of the participation characterization values ​​of all window data in the filtering window is recorded as the first accumulated value. The product of the complement of the important factor and the first accumulated value is recorded as the second characterization value. The sum of the first characterization value and the second characterization value is recorded as the filtered data of the electrical data to be filtered.

7. The semi-solid-state circuit breaker with intelligent control function based on photovoltaic system as described in claim 1, characterized in that, The horizontal axis of the data point corresponding to the window data represents the acquisition time of the window data, and the vertical axis represents the window data itself.

8. The semi-solid-state circuit breaker with intelligent control function based on photovoltaic system as described in claim 1, characterized in that, The fitted line of the filtering window is obtained by performing a first-order linear fit on the window data in the filtering window.

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