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

Abstract

The invention relates to the technical field of circuit breakers, in particular to a semi-solid circuit breaker with an intelligent regulation and control function based on a photovoltaic system, which comprises a first acquisition module, a second acquisition module and a control module, the filtering window is used for acquiring to-be-filtered electrical data of the photovoltaic system and the to-be-filtered electrical data by using the semi-solid-state circuit breaker; the second acquisition module is used for acquiring filtering data of the to-be-filtered electrical data; and the regulation and control module is used for regulating and controlling the photovoltaic system according to the filtering data. And the filtering process of the electrical data of the photovoltaic system acquired by the semi-solid circuit breaker is optimized, so that the quality of the filtering data can be improved, and the effect of subsequently regulating and controlling the photovoltaic system based on the filtering data can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit breakers, and particularly to a semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system. Background Art

[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, etc., it is usually necessary to monitor the electrical data of the photovoltaic system based on a semi-solid circuit breaker, and then use the existing median filtering algorithm to filter the monitored electrical data, and perform intelligent regulation on the photovoltaic system based on the data obtained after filtering; however, during the operation of the photovoltaic system, there will be phenomena such as load mutation, abnormal operation or faults, and the existence of load mutation, abnormal operation or faults will lead to the problem of distortion of the data obtained by subsequent filtering, that is, it will lead to poor quality of the data obtained by filtering. When the filtered data has the problem of distortion, it will affect the subsequent regulation effect on the photovoltaic system. Therefore, how to improve the quality of the data obtained by data filtering, so as to ensure the subsequent regulation effect on the photovoltaic system based on the filtered data has become an urgent problem to be solved. Summary of the Invention

[0003] In order to solve the above problems, the present invention provides a semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system, and the specific technical solution adopted is as follows: An embodiment of the present invention provides a semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system, and the semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system includes: A first acquisition module, configured to use the semi-solid circuit breaker to acquire the electrical data to be filtered of the photovoltaic system and the filtering window of the electrical data to be filtered; A second acquisition module, configured to obtain a median replacement unreliable characterization value corresponding to the electrical data to be filtered according to the data volatility and median of the filtering window, and determine whether the median replacement unreliable characterization value is not greater than a preset unreliable threshold. If so, use the median filtering algorithm to obtain the filtered data of the electrical data to be filtered; otherwise, obtain the fitting straight line of the filtering window, and according to the distances from each window data in the filtering window to the fitting straight line, obtain the important factor and weighted average value of the electrical data to be filtered. 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, obtain the target credibility characterization value of the window data. According to the target credibility characterization value, important factor and window data, obtain the filtered data of the electrical data to be filtered; A regulation module, configured to regulate the photovoltaic system according to the filtered data.

[0004] Beneficial effects: The present invention includes a first acquisition module for acquiring electrical data to be filtered of a photovoltaic system and a filtering window of the electrical data to be filtered by using a semi-solid state circuit breaker; a second acquisition module for obtaining a median replacement unreliable characterization value corresponding to the electrical data to be filtered according to the data volatility and median of the filtering window, and determining 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 by using a median filtering algorithm. Otherwise, a fitting straight line of the filtering window is obtained, and according to the distances from each 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 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, a target credibility characterization value of the window data is obtained. According to the target credibility characterization value, the important factor, and the window data, the filtered data of the electrical data to be filtered is obtained; a regulation module for regulating the photovoltaic system according to the filtered data. Moreover, by optimizing the filtering process of the electrical data of the photovoltaic system collected by the semi-solid state circuit breaker, the present invention can improve the quality of the filtered data, thereby ensuring the effect of regulating the photovoltaic system based on the filtered data subsequently. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0006] Figure 1 It is a structural block diagram of a semi-solid state circuit breaker with an intelligent regulation function based on a photovoltaic system according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0007] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope protected by the embodiments of the present invention.

[0008] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0009] This embodiment provides a semi-solid state circuit breaker with an intelligent regulation function based on a photovoltaic system, which is described in detail as follows: As Figure 1As shown, a semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system provided in this embodiment includes: A first acquisition module 01, configured to use the semi-solid circuit breaker to acquire the electrical data to be filtered of the photovoltaic system and the filtering window of the electrical data to be filtered.

[0010] In this embodiment, the quality of the filtered data is improved by optimizing the filtering process of the electrical data of the photovoltaic system collected by the semi-solid circuit breaker, so as to ensure the effect of regulating the photovoltaic system based on the filtered data in the subsequent stage; in addition, for the convenience of understanding, in the following, 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, that is, the semi-solid circuit breaker appearing in the following of this embodiment is the semi-solid circuit breaker applied to this photovoltaic system, and the photovoltaic system appearing in the following is the same photovoltaic system; and the semi-solid circuit breaker is a composite circuit breaker that combines the advantages of mechanical circuit breakers and solid-state circuit breakers. The mechanical switch and semiconductor devices work together to achieve rapid current interruption and circuit protection. This type of circuit breaker has a fast response speed, high reliability, and long service life, and is especially suitable for occasions with strict protection performance requirements such as centralized photovoltaic power stations. The photovoltaic system usually includes multiple key devices such as photovoltaic arrays, busbar boxes, and DC cabinets. Once a short circuit, grounding, or arc fault occurs, if effective isolation cannot be completed within a very short time, it is extremely easy to cause equipment damage and even fire. Therefore, based on the semi-solid circuit breaker with an intelligent regulation function for the photovoltaic system, rapid fault identification and isolation can be achieved, thus ensuring the stable operation of the photovoltaic system.

[0011] First, during the operation of the photovoltaic system, a semi-solid state circuit breaker is used to collect the DC current and voltage output by the photovoltaic array of the photovoltaic system, and both the collected DC current and voltage are recorded as the electrical data to be filtered. Then, a sequence composed of all electrical data of the same type in the target monitoring time period is obtained and recorded as the electrical data sequence to be filtered of the photovoltaic system in the target monitoring time period, that is, the electrical data sequence to be filtered of the photovoltaic system in the target monitoring time period includes a DC current sequence and a voltage sequence. Then, the filtering window of each electrical data to be filtered in the electrical data sequence to be filtered is obtained. The filtering window is related to the subsequent process of whether to use a traditional filtering algorithm for filtering and how to determine the filtered data after determining not to use a traditional filtering algorithm for filtering. The central data in the filtering window of each electrical data to be filtered in the electrical data sequence to be filtered is generally the corresponding electrical data to be filtered. The semi-solid state circuit breaker is installed at the position between the photovoltaic busbar box and the DC power distribution cabinet. In addition, the implementer needs to determine the size of the filtering window according to the actual scenario and requirements, and usually determines it based on the noise intensity and the degree of detail retention. For example, the filtering windows in this embodiment can be set to 1×b, where b is required to be an odd number. For example, b can be set to 13, that is, the maximum number of data that can be accommodated in the filtering window is 13. That is, the filtering window of the a-th electrical data to be filtered in any electrical data sequence to be filtered is composed of all data from the -th electrical data to be filtered to the -th electrical data to be filtered in the electrical data sequence to be filtered.

[0012] In addition, it should be noted that if the electrical data to be filtered on the left or right side of a certain electrical data to be filtered in the electrical data sequence to be filtered is less than , then data supplementation is performed on the other side not less than . For example, for the b-th electrical data to be filtered in a certain electrical data sequence to be filtered, if the number of data on the left side of the b-th electrical data to be filtered in the electrical data sequence to be filtered is c and less than , then the number of data on the left side of the b-th electrical data to be filtered in the filtering window of the b-th electrical data to be filtered is c, and the number of data on the right side of the b-th electrical data to be filtered is b - 1 - c.

[0013] In this embodiment, the implementer needs to set the target monitoring time period according to the regulation frequency of the photovoltaic system and the actual situation. For example, in this embodiment, the time period between two adjacent moments of regulating the photovoltaic system can be used as the target monitoring time period. That is, if the current moment is the moment of regulating the photovoltaic system, then the time period from the previous moment of regulating the photovoltaic system before the current moment to the current moment is the target monitoring time period. In this embodiment, the implementer needs to set the acquisition frequency of the semi-solid circuit breaker for the DC current and voltage output by the photovoltaic array of the photovoltaic system under the condition of meeting the power system specifications. For example, the acquisition frequency of the semi-solid circuit breaker for the DC current and voltage output by the photovoltaic array of the photovoltaic system can be set to 100 Hz, and this embodiment requires synchronous acquisition of different types of electrical data.

[0014] And in this 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, it can automatically identify and analyze the private or standard communication protocols (such as Modbus, CAN, RS485, etc.) used for different inverter models, and complete the unification of data structures.

[0015] Therefore, through the above process, this embodiment can obtain each piece of electrical data to be filtered and the filtering window of the electrical data to be filtered in the electrical data sequence to be filtered of the photovoltaic system. Since the filtering process of each piece of electrical data to be filtered in this embodiment is the same, the subsequent description in this embodiment will take the acquisition process of the filtered data of any piece of electrical data to be filtered as an example. That is, the electrical data to be filtered and the filtering window of the electrical data to be filtered that appear subsequently in this embodiment are the same piece of electrical data to be filtered and the same filtering window of the electrical data to be filtered.

[0016] The second acquisition module 02 is used to obtain the median replacement unreliable characterization value corresponding to the electrical data to be filtered according to the data volatility and median of the filtering window, and judge 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 by using the median filtering algorithm. Otherwise, the fitting straight line of the filtering window is obtained, and according to the distances from each window data in the filtering window to the fitting straight line, the important factor and weighted average value of the electrical data to be filtered are obtained. 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, the target credibility characterization value of the window data is obtained. According to the target credibility characterization value, important factor and window data, the filtered data of the electrical data to be filtered is obtained.

[0017] Due to the complex operating environment of the photovoltaic system and the presence of multiple electromagnetic interference sources, combined with the high-speed switching characteristics inside the semi-solid circuit breaker, it will lead to the occurrence of high-frequency noise data such as pulsed isolated spikes and burrs during the electrical data acquisition process. In order to remove this noise interference, highlight the true trends and changes 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, there may be phenomena such as sudden load changes, abnormal operation, or faults, and the existence of sudden load changes, abnormal operation, or faults will cause the filtered data obtained by using existing filtering algorithms to be distorted, that is, it will lead to poor quality of the filtered data. For example, during the operation of the photovoltaic system, if local arc faults or abnormal load access occur, it will cause pulsed abnormal values of current or voltage to be generated at multiple consecutive moments. If the current moment happens to be at the end of the fault or the system has just returned to the steady state, that is, at this time the current moment has returned to the normal level, but since most of the other data within the filtering window are abnormal pulsed values, these abnormal values are more likely to be in the middle position after sorting, that is, the abnormal values become the median. Then, if the median at this time is replaced with the value monitored at the current moment, it will cause the system to mistakenly think that the current moment is still abnormal or in a fluctuating state, thus interfering with subsequent fault judgment and regulation decisions. In addition, repeated connections caused by poor contact, local equipment failures, etc. will all cause periodic load fluctuations, thereby causing abnormal phenomena such as frequent impacts on the system, voltage fluctuations, and current spikes to the system, and then leading to obvious periodic fluctuations in the electrical data. Then, if the current moment belongs to the abnormality caused by repeated connections, local equipment failures, etc. caused by poor contact, and the electrical data monitored at the current moment is exactly in the stage of transitioning from the valley to the peak, that is, the electrical data monitored at the current moment has started to rise significantly, while most of the other data within the filtering window of the electrical data monitored at the current moment are still at a relatively low level, this will cause the median of the filtering window of the electrical data monitored at the current moment to be relatively small after sorting by the traditional median filtering algorithm, thereby causing the electrical data monitored at the current moment to be replaced with smaller data, thus masking the true abnormal characteristics; also, since when there are problems with the distortion of the filtered data, it will affect the subsequent regulation effect of the photovoltaic system, and in order to ensure the regulation effect of the photovoltaic system based on the filtered data in this embodiment, it is necessary to improve the quality of the data obtained by data filtering next.

[0018] Next, this embodiment needs to first determine whether the electrical data to be filtered is suitable for the median filtering algorithm. For the electrical data to be filtered that is not suitable for the median filtering algorithm, other methods will be used for filtering. The reason for the judgment is that the median filtering performs well in processing the current and voltage data under the normal operation of the photovoltaic system, and is particularly good at suppressing isolated pulse interference and retaining edge characteristics, that is, the median filtering has good suppression ability for burrs or spike noise caused by sudden interference (such as lightning strikes, grounding arcs, etc.). Therefore, in order to reduce the amount of calculation and to ensure the final filtering effect, this embodiment needs to determine whether the electrical data to be filtered is suitable for the median filtering algorithm; and because this embodiment needs to determine whether the electrical data to be filtered is suitable for the median filtering algorithm based on the median replacement unreliable characterization value, before making a judgment, it is necessary to first 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 of the electrical data to be filtered, that is, the median replacement unreliable characterization value is the basis for judging whether the electrical data to be filtered is suitable for the median filtering algorithm, then the specific acquisition process of the median replacement unreliable characterization value is: First, a set consisting of all window data except the largest window data in the filter window of the electrical data to be filtered is obtained, and recorded as the first set. Then, the normalized value of the standard deviation of the first set is calculated, and recorded as the first index value, that is, the window data with the largest median in the filter window is not used when calculating the standard deviation. Then, the median and range of the filter window of the electrical data to be filtered are obtained, and the median of the filter window refers to the data value at the middle position of the sequence obtained by arranging the window data in order from small to large or from large to small, and the range of the filter window refers to the result of subtracting the minimum window data from the maximum window data in the filter window; Then, the difference between the median of the filtering window of the electrical data to be filtered and the electrical data to be filtered is obtained, and recorded as the first difference; the sum of the range of the wave data window of the electrical data to be filtered and the preset first constant is obtained, and recorded as the second difference; the absolute value of the ratio of the first difference to the second difference is obtained, and recorded as the second index value; finally, the mean of the first index value and the second index value is obtained, and used as the median corresponding to the electrical data to be filtered to replace the unreliable characterization value; in addition, it should be noted that when all data in the filtering window of the electrical data to be filtered are equal, the normalized value of the standard deviation of all data in the filtering window is used as the first index value.

[0019] 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, in this embodiment, the preset first constant can be set to .

[0020] The specific calculation formula for replacing the unreliable characterization value with the median value corresponding to the electrical data to be filtered is:

[0021] Wherein, A is the unreliable characterization value corresponding to the median replacement of the electrical data to be filtered, is the standard deviation of the first set, tanh() is the hyperbolic tangent function, and tanh() is used to perform normalization, that is is the normalized value of the standard deviation of the first set, x0 is the median of the filtering window of the electrical data to be filtered, x1 is the value of the electrical data to be filtered, d is the range of the filtering window of the electrical data to be filtered, f is a preset first constant, and the purpose of the existence of the preset first constant is to prevent the denominator from being 0.

[0022] In addition, when the photovoltaic system is operating normally or there are high-frequency noise data such as isolated spikes and burrs in the filtering window, it is more suitable to use the median filtering algorithm for filtering at this time, that is, the possibility of data distortion caused by replacing the electrical data to be filtered with the median of the filtering window of the electrical data to be filtered is smaller at this time. However, when there are strong oscillations in the data within the filtering window caused by load mutations, abnormal operations, or faults, etc., the possibility of data distortion caused by replacing the electrical data to be filtered with the median of the filtering window of the electrical data to be filtered will be relatively large. Then, at this time, it is necessary to consider the degree of deviation between the electrical data to be filtered and the median, that is, if there are strong oscillations in the data within the window, but the electrical data to be filtered is more similar to the median, then the median filtering algorithm can also be used for filtering at this time; thus, it can be seen that when is larger and is larger, that is, when A is larger, it indicates that the possibility of data distortion caused by replacing the electrical data to be filtered with the median of the filtering window of the electrical data to be filtered is relatively large at this time, or the possibility of data distortion occurring in the filtered data when using the median filtering algorithm to filter the electrical data to be filtered is greater. On the contrary, when is smaller and is smaller, that is, when A is smaller, it indicates that it is more suitable to use the median filtering algorithm for filtering at this time, that is, the possibility of data distortion caused by replacing the electrical data to be filtered with the median of the filtering window of the electrical data to be filtered is smaller at this time.

[0023] Next, it is judged whether the median replacement unreliable characterization value corresponding to the electrical data to be filtered is not greater than the preset unreliable threshold. If so, it is determined that using the median of the filtering window of the electrical data to be filtered to replace the electrical data to be filtered at this time will not cause data distortion. Therefore, at this time, the median filtering algorithm is used to filter the electrical data to be filtered, and the data obtained by using the median filtering algorithm to filter the electrical data to be filtered is recorded as the filtered data of the electrical data to be filtered, or directly use the median of the filtering window of the electrical data to be filtered as the filtered data of the electrical data to be filtered. Otherwise, it is determined that the possibility of data distortion in the filtered data is greater when using the median filtering algorithm to filter the electrical data to be filtered at this time. Therefore, when it is judged that the median replacement unreliable characterization value corresponding to the electrical data to be filtered is greater than the preset unreliable threshold, it is necessary to first obtain the fitting line of the filtering window of the electrical data to be filtered, and according to the distances from the respective window data in the filtering window of the electrical data to be filtered to the fitting line, obtain the important factor and weighted average value of the electrical data to be filtered. Then, according to the slope of the fitting 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, obtain the target reliability characterization value of the window data. Finally, according to the target reliability characterization value, the important factor, and the window data, obtain the filtered data of the electrical data to be filtered.

[0024] And in specific applications, the implementer needs to set the preset unreliable threshold according to the value of the median replacement unreliable characterization value and the actual situation. For example, in this embodiment, the preset unreliable threshold can be set to 0.7.

[0025] In this embodiment, the specific process of obtaining the fitting line of the filtering window of the electrical data to be filtered is as follows: First, obtain the mapping space. The horizontal axis of the mapping space represents time, and the vertical axis represents the electrical data value of the same type as the electrical data to be filtered. Then, map the respective window data in the filtering window of the electrical data to be filtered and the acquisition time of each window data into the mapping space, and obtain the data points corresponding to each window data, that is, the abscissa value of the data point corresponding to any window data is the acquisition time of the window data, and the ordinate value is the window data. After that, use the fitting algorithm to fit the data points corresponding to all the window data in the filtering window of the electrical data to be filtered, and record the obtained line as the fitting line of the filtering window of the electrical data to be filtered. The fitting line is mainly used for obtaining the subsequent important factor and weighted average value. And in specific applications, the implementer can select the fitting algorithm according to the actual situation. For example, in this embodiment, the first-order linear fitting algorithm can be selected to fit the data points corresponding to all the window data in the filtering window.

[0026] In this embodiment, the specific process of obtaining the important factor of the electrical data to be filtered is as follows: First, obtain the distances from the data points corresponding to each window data in the filtering window of the electrical data to be filtered to the fitting line, and denote them as the first distances of the corresponding window data; Since the filtering window of the electrical data to be filtered contains the electrical data to be filtered, the first distance of the filtered electrical data can be directly obtained, and the first distance of the filtered electrical data is denoted as the distance to be analyzed; Then, obtain the cumulative result of the first distances of all window data in the filtering window, and denote it as the first comprehensive distance. After that, obtain the ratio of the distance to be analyzed to the first comprehensive distance, and use it as the important factor of the electrical data to be filtered. And the important factor of the electrical data to be filtered is , where N is the total number of window data in the filtering 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 filtering window of the electrical data to be filtered; In addition, when is larger, it indicates that the electrical data to be filtered is more likely to be data with obvious trend change characteristics, and it also indicates that the electrical data to be filtered is more likely to carry key trend mutations or the electrical data to be filtered carries key information. Thus, it shows that the electrical data to be filtered is more important for subsequent fault detection or trend analysis, and it also shows that the electrical data to be filtered is more important for the subsequent photovoltaic system control strategy. Then, more of its original features should be retained during filtering to avoid eliminating important signals. That is, when the important factor of the electrical data to be filtered is larger, it indicates that when determining the filtered data of the electrical data to be filtered subsequently, the participation or contribution degree of the electrical data to be filtered should be larger. On the contrary, when the important factor of the electrical data to be filtered is smaller, it indicates that when determining the filtered data of the electrical data to be filtered subsequently, the participation or contribution degree of the electrical data to be filtered should be smaller.

[0027] In this embodiment, the specific process of obtaining the weighted average of the electrical data to be filtered is as follows: obtain the reciprocal of the sum of the first distance of each window data and a preset first constant, and denote it as the second distance of the corresponding window data; obtain the accumulation result of the second distances of all window data in the filtering window, and denote it as the second comprehensive distance; obtain the ratio of the second distance of each window data to the second comprehensive distance, and denote it as the weighting factor of the corresponding window data; obtain the product of the weighting factor of each window data and the corresponding window data, and denote it as the weighted data of the corresponding window data; take the accumulation result of the weighted data of all window data in the filtering window of the electrical data to be filtered as the weighted average of the electrical data to be filtered. The weighted average is mainly used to analyze the credibility of the window data and represent the overall trend information of the filtering window in the subsequent process; the reason for considering the weighting factor when calculating the weighted average is that: since the points closer to the trend line or the points closer to the fitting line can better represent the trend change of the filtering window and the mainstream change of the data within the window, and are also the key to accurately reflecting the overall trend information, when obtaining the overall trend information of the filtering window, the points closer to the trend line or the points closer to the fitting line should have a greater participation degree, and the window data with a smaller first distance should have a greater contribution degree to the weighted average. Therefore, the weighting factor of the window data with a smaller first distance should be larger; in addition, in the process of calculating the weighted average, the purpose of adding the first distance and the preset first constant is also to prevent the denominator from being zero.

[0028] In this embodiment, the specific process of obtaining the target credibility characterization value of the window data based on the slope of the fitting 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 is as follows: For the j-th window data in the filter window of the electrical data to be filtered: First, obtain the gradient value of the j-th window data. If there is window data on the left side of the j-th window data in the filter window, then use the result of subtracting the j-th window data from the (j - 1)-th window data in the filter window as the gradient value of the j-th window data. If there is no window data on the left side of the j-th window data in the filter window, then use the result of subtracting the (j + 1)-th window data from the j-th window data in the filter window as the gradient value of the j-th window data. Then, obtain the negative mapping value of the absolute value of the difference between the gradient value of the j-th window data and the slope of the fitting line of the filter window, and use it as the first eigenvalue of the j-th window data. Obtain the negative mapping value of the acquisition time interval between the j-th window data and the electrical data to be filtered, and use it as the second eigenvalue of the j-th window data. Obtain the negative mapping value of the absolute value of the difference between the j-th window data and the weighted average, and use it as the third eigenvalue of the j-th window data. Obtain the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue of the j-th window data, and denote it as the initial credibility characterization value of the j-th window data. After that, perform normalization processing on the initial credibility characterization value of the j-th window data, and use the result of the normalization processing as the target credibility characterization value of the j-th window data. And in this embodiment, the comprehensive initial credibility characterization value of the filter window is used to normalize the initial credibility characterization value. The comprehensive initial credibility characterization value of the filter window refers to the sum of the initial credibility characterization values of all window data in the filter window, that is, the normalization value of the initial credibility characterization value of the j-th window data is the ratio of the initial credibility characterization value of the j-th window data to the comprehensive initial credibility characterization value. Moreover, the target credibility characterization value of the window data is a key parameter for subsequently determining the filtered data of the electrical data to be filtered.

[0029] In addition, the specific calculation expression of the initial credibility characterization value of the j-th window data in this embodiment is:

[0030] Wherein, is the initial credibility characterization value of the j-th window data in the filter window of the electrical data to be filtered, is the gradient value of the j-th window data, and k is the slope of the fitting line of the filter window of the electrical data to be filtered, is the acquisition time interval between the j-th window data and the electrical data to be filtered, that is, the time interval between the acquisition time of the j-th window data and the acquisition time of the electrical data to be filtered, is the data of the j-th window, X is the weighted average of the electrical data to be filtered, exp() is the exponential function with the constant e as the base, and as well as The exponential functions in are mainly used to and as well as perform negative mapping to obtain the negative mapping result.

[0031] And when is smaller, it indicates that the data of the j-th window is more similar to the overall change trend of the filtering window. Then, when determining the electrical data to be filtered later, the contribution degree of the data of the j-th window should be made larger, so as to achieve the purpose of reducing the probability of data distortion; because some behaviors such as load access switching will cause the output of the system to change to a certain extent in a short time, so the window data closer to the acquisition time of the electrical data to be filtered is more likely to be in the same similar UN stage as the electrical data to be filtered and has a high time series correlation. Therefore, when is smaller, then when determining the electrical data to be filtered later, the contribution degree of the data of the j-th window should be made larger, so as to achieve the purpose of reducing the probability of data distortion; because the weighted average value is a concentrated reflection of the overall data within the window, so when the data of the j-th window is closer to the weighted average value, it indicates that the data of the j-th window is more likely to be the true response of the system's operating conditions at the acquisition moment corresponding to the electrical data to be filtered. Then, when determining the electrical data to be filtered later, the contribution degree of the data of the j-th window should be made larger, so as to achieve the purpose of reducing the probability of data distortion; because and as well as is smaller, is larger. Therefore, when is larger, the contribution degree of the data of the j-th window in the filtering window of the electrical data to be filtered should be larger. That is to say, when is larger, the initial credibility characterization value of the data of the j-th window in the filtering window of the electrical data to be filtered is larger. On the contrary, when is smaller, the initial credibility characterization value of the data of the j-th window in the filtering window of the electrical data to be filtered is smaller.

[0032] In this embodiment, the specific process of obtaining the filtered data of the electrical data to be filtered according to the target credibility characterization value, the important factor, and the window data is as follows: First, obtain the product of the important factor of the electrical data to be filtered and the electrical data to be filtered, and denote it as the first characterization value. Then, obtain the product of the target credibility characterization value of each window data in the filtering window of the electrical data to be filtered and the corresponding window data, and denote it as the participation characterization value of the corresponding window data. Then, obtain the cumulative result of the participation characterization values of all window data in the filtering window, and denote it as the first cumulative value. Obtain the product of the complement of the important factor and the first cumulative value, and denote it as the second characterization value. The complement of the important factor is the result of 1 minus the important factor. Finally, denote the sum of the first characterization value and the second characterization value as the filtered data of the electrical data to be filtered.

[0033] And the specific expression for obtaining the filtered data of the electrical data to be filtered according to the target credibility characterization value, the important factor, and the window data is:

[0034] Where Q is the filtered data of the electrical data to be filtered, is the important factor of the electrical data to be filtered, is the target credibility characterization value of the j-th window data in the filtering window of the electrical data to be filtered, is the j-th window data in the filtering window, N is the total number of window data in the filtering window of the electrical data to be filtered, and x1 is the value of the electrical data to be filtered. And when is larger, it indicates that the possibility of the electrical data to be filtered carrying key information is greater. Then, when determining the filtered data of the electrical data to be filtered, the contribution degree of the electrical data to be filtered should be made larger, and the contribution degree of the window data should be made smaller. That is to say, when is larger, the contribution degree of x1 is greater than 's contribution degree, thereby reducing the probability of data distortion. On the contrary, when it is smaller, the filtered data of the electrical data to be filtered should be determined more based on , so that the determined filtered data of the electrical data to be filtered approaches the trend of the filtering window, thereby reducing the probability of data distortion.

[0035] Therefore, this embodiment has specifically described the process of obtaining the filtered data of an electrical data to be filtered. The process of obtaining the filtered data of other electrical data to be filtered in the electrical data to be filtered sequence is the same as the process of obtaining the filtered data of the electrical data to be filtered exemplified above. Therefore, this embodiment will not be further expanded.

[0036] The regulation module 03 is used to regulate the photovoltaic system according to the filtered data.

[0037] Since all the filtered electrical data of the electrical data sequence to be filtered can be obtained through the above process in this embodiment, that is, the filtering of the electrical data sequence to be filtered is completed through the above process in this embodiment, and the filtered data is obtained; after the filtered data is obtained, the photovoltaic system is regulated by using the filtered data. Since regulating the photovoltaic system based on the data obtained by filtering is a well-known technology, this embodiment will not be described in detail; in addition, using the filtered data obtained after filtering for anomaly recognition and fault detection can avoid delays or mis-triggered regulation strategies caused by data misreplacement or distortion of key information, thereby providing high-quality and more reliable data support for accurate regulation strategies, and further making the regulation strategies more accurate, timely and reliable.

[0038] So far, this embodiment has improved the data quality obtained by data filtering, thus ensuring the effect of subsequent regulation of the photovoltaic system based on the filtered data.

[0039] In summary, this embodiment includes a first acquisition module for using a semi-solid circuit breaker to acquire the electrical data to be filtered of the photovoltaic system and the filtering window of the electrical data to be filtered; a second acquisition module for obtaining an unreliable median replacement characterization value corresponding to the electrical data to be filtered according to the data volatility and median of the filtering window, and determining whether the unreliable median replacement characterization value is not greater than a preset unreliable threshold. If so, the filtered data of the electrical data to be filtered is obtained by using the median filtering algorithm. Otherwise, the fitting line of the filtering window is obtained, and according to the distances from each window data in the filtering window to the fitting line, the important factor and weighted average value of the electrical data to be filtered are obtained. According to the slope of the fitting 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 target reliability characterization value of the window data is obtained. According to the target reliability characterization value, the important factor and the window data, the filtered data of the electrical data to be filtered are obtained; a regulation module for regulating the photovoltaic system according to the filtered data. And this embodiment can improve the quality of the filtered data by optimizing the filtering process of the electrical data of the photovoltaic system collected by the semi-solid circuit breaker, thereby ensuring the effect of subsequent regulation of the photovoltaic system based on the filtered data.

[0040] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the 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 all be included in the protection scope of the present application.

Claims

1. A semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system, characterized in that, The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system includes: A first acquisition module, configured to use the semi-solid circuit breaker to acquire the electrical data to be filtered of the photovoltaic system and the filtering window of the electrical data to be filtered; A second acquisition module, configured to obtain a median replacement unreliable characterization value corresponding to the electrical data to be filtered according to the data volatility and median of the filtering window, and determine whether the median replacement unreliable characterization value is not greater than a preset unreliable threshold. If so, use a median filtering algorithm to obtain the filtered data of the electrical data to be filtered. Otherwise, obtain the fitting straight line of the filtering window, and obtain the important factor and weighted average value of the electrical data to be filtered according to the distances from each window data in the filtering window to the fitting straight line. 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, obtain the target reliability characterization value of the window data. According to the target reliability characterization value, important factor, and window data, obtain the filtered data of the electrical data to be filtered; A regulation module, configured to regulate the photovoltaic system according to the filtered data.

2. The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system according to claim 1, characterized in that, A method for obtaining the median replacement unreliable characterization value includes: Taking the normalized value of the standard deviation of all window data except the maximum window data in the filtering window as the first index value, recording the difference between the electrical data to be filtered and the median of the wave data window as the first difference, recording the sum of the range of the wave data window and a preset first constant as the second difference, taking the absolute value of the ratio of the first difference to the second difference as the second index value, and taking the mean of the first index value and the second index value as the median replacement unreliable characterization value corresponding to the electrical data to be filtered.

3. The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system as claimed in claim 1, wherein A method for obtaining the important factor 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 fitting straight 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 fitting straight line as the distance to be analyzed, recording the cumulative result of the first distances of all window data in the filtering window as the first comprehensive distance, and taking the ratio of the distance to be analyzed to the first comprehensive distance as the important factor of the electrical data to be filtered.

4. The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system according to claim 3, characterized in that, A method for obtaining the weighted average value of the electrical data to be filtered includes: Taking the reciprocal of the sum of the first distance of the window data and a preset first constant as the second distance of the corresponding window data, recording the cumulative result of the second distances of all window data in the filtering window as the second comprehensive distance, taking the ratio of the second distance of the window data to the second comprehensive distance as the weighted factor of the corresponding window data, taking the product of the weighted factor of the window data and the corresponding window data as the weighted data of the corresponding window data, and taking the cumulative result of the weighted data of all window data in the filtering window as the weighted average value of the electrical data to be filtered.

5. The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system as claimed in claim 1, wherein, The gradient value of the window data refers to the difference between the window data and the adjacent window data corresponding to the window data.

6. The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system according to claim 1, characterized in that, The method for obtaining the target credibility characterization value of the window data includes: For any window data in the filtering window, based on the difference between the slope of the fitting 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 and the window data, obtain the initial credibility characterization value of the window data, and use the normalization result of the initial credibility characterization value as the target credibility characterization value of the window data.

7. The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system according to claim 6, characterized in that, The method for obtaining the initial credibility characterization value of the window data includes: Take the negative mapping value of the absolute value of the difference between the gradient value of the window data and the slope of the fitting line as the first eigenvalue of the window data, take the negative mapping value of the acquisition time interval between the window data and the electrical data to be filtered as the second eigenvalue of the window data, take the negative mapping value of the absolute value of the difference between the window data and the weighted average as the third eigenvalue of the window data, and record the sum of the first eigenvalue, the second eigenvalue, and the third eigenvalue of the window data as the initial credibility characterization value of the window data.

8. The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system as claimed in claim 1, wherein, The method for obtaining the filtered data of the electrical data to be filtered according to the target credibility characterization value, the important factor, and the window data includes: Record the product of the important factor and the electrical data to be filtered as the first characterization value, record the product of the target credibility characterization value of the window data and the corresponding window data as the participation characterization value of the corresponding window data, record the cumulative result of the participation characterization values of all window data in the filtering window as the first cumulative value, record the product of the complement of the important factor and the first cumulative value as the second characterization value, and record the sum of the first characterization value and the second characterization value as the filtered data of the electrical data to be filtered.

9. The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system according to claim 3, characterized in that, The abscissa of the data point corresponding to the window data is the acquisition time of the window data, and the ordinate is the window data.

10. The semi-solid circuit breaker with an intelligent regulation function based on a photovoltaic system as claimed in claim 1, wherein, The fitting line of the filtering window is obtained by performing first-order linear fitting on the window data in the filtering window.

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