Overvoltage and undervoltage protection method and circuit for intelligent molded case circuit breaker

Abstract

The present application relates to the technical field of circuit emergency protection, and specifically to an over-voltage and under-voltage protection method and circuit for intelligent molded case circuit breakers, the method comprising: determining the characteristic value of each type of limit voltage in each time period; determining the circuit over-voltage and under-voltage fluctuation value in each time period; in the neighborhood of each time period, based on the size distribution of the characteristic values ​​of two types of limit voltage data in any time period, determining the circuit over-voltage and under-voltage distribution value in each time period, and combining the circuit over-voltage and under-voltage fluctuation value, determining the circuit over-voltage and under-voltage degree in each time period; using an anomaly detection algorithm to determine the total abnormal score of each set before the current moment, based on the extreme distribution of the abnormal score of all time periods in each set, determining the circuit alarm threshold at the current moment, and combining the total abnormal score to protect the circuit. The present application improves the accuracy of intelligent molded case circuit breakers in judging the over-voltage and under-voltage status of circuits by analyzing the degree of voltage fluctuation.

Description

Technical Field

[0001] The present application relates to the technical field of circuit emergency protection, and in particular to an overvoltage and undervoltage protection method and circuit for an intelligent molded case circuit breaker. Background Art

[0002] With the construction and development of smart grids, the low-voltage distribution system is an important component of the smart grid terminal, and its operating status affects the stability of the entire smart grid. As one of the core components of the low-voltage distribution system of the smart grid, the protection of the circuit by the intelligent molded case circuit breaker is an important basis for ensuring the normal operation of the low-voltage distribution system. The intelligent molded case circuit breaker includes a circuit breaker body, an electric operating device and a carrier module. When the electric operating device detects that the circuit has an over-voltage or under-voltage anomaly, it sends a tripping command signal to the circuit breaker body, and the circuit breaker body triggers the tripping device through the internal moving contact to realize the circuit breaker tripping. At the same time, the electric operating device of the intelligent molded case circuit breaker realizes the information transmission between the smart grid and the circuit breaker body through the carrier module.

[0003] The microgrid system consists of multiple distributed power generation units and can operate independently without the participation of the main grid, providing continuous and reliable power supply to remote areas or residential communities. At present, when the intelligent molded case circuit breaker protects the circuit of microgrid users, it only judges whether the circuit is in an over-voltage or under-voltage state based on a fixed voltage threshold, ignoring the fluctuation characteristics of the microgrid voltage, which may cause the intelligent molded case circuit breaker to misjudge the over-voltage or under-voltage state, reducing the reliability of the intelligent molded case circuit breaker for circuit protection. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an over-voltage and under-voltage protection method and circuit for an intelligent molded case circuit breaker. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides an overvoltage and undervoltage protection method for an intelligent molded case circuit breaker, the method comprising the following steps:

[0006] Collect voltage data of all collection moments of the incoming terminal of the intelligent molded case circuit breaker within a preset time before the current moment;

[0007] The preset time length is evenly divided into multiple time periods. In each time period, based on the discreteness and extreme distribution of all voltage data, two types of limit voltage data in each time period are determined. Based on the discreteness of the time intervals between all adjacent data in each type of limit voltage data and the difference between each type of limit voltage data and the corresponding preset threshold value, the characteristic value of each type of limit voltage in each time period is determined.

[0008] The characteristic values ​​of the two types of boundary voltage data in each time period are grouped into two tuples, and the two tuples of each time period and all time periods in its neighborhood are clustered to obtain multiple clusters, and based on the number of all clusters and the size of the cluster center of each cluster, combined with the discrete degree of the time intervals between the corresponding time periods of all the two tuples of each cluster, the circuit over-voltage and under-voltage fluctuation value of each time period is determined;

[0009] In the neighborhood of each time period, based on the size distribution of the characteristic values ​​of the two types of boundary voltage data in any time period, the circuit over-voltage and under-voltage distribution value of each time period is determined, and combined with the circuit over-voltage and under-voltage fluctuation value, the circuit over-voltage and under-voltage degree of each time period is determined;

[0010] All time periods before the current moment are evenly divided into each set, and the abnormality detection algorithm is used to obtain the abnormality scores of the circuit over-voltage and under-voltage degrees of all time periods in each set. Based on the average distribution of the abnormality scores, the total abnormality score of each set before the current moment is determined. Based on the extreme distribution of the abnormality scores of all time periods in each set, the circuit alarm threshold at the current moment is determined, and the circuit is protected in combination with the total abnormality score.

[0011] Preferably, the method for determining the two types of limit voltage data in each time period is:

[0012] Among all the voltage data in each time period, the voltage data between the minimum value and the first quartile is recorded as the lower limit voltage data, and the voltage data between the third quartile and the maximum value is recorded as the upper limit voltage data.

[0013] Preferably, the expression of the characteristic value of each type of limit voltage in each time period is: ; In the formula, Represents the characteristic value of the j-th category limit voltage in time period i; Indicates the discrete degree of the intervals between all adjacent data in the j-th category boundary voltage data within time period i; Represents the mth data in the jth category boundary voltage data in time period i; represents the preset threshold value of the j-th category limit voltage data; exp( ) represents an exponential function with a natural constant as the base; Represents the total number of the j-th category boundary voltage data in time period i.

[0014] Preferably, the expression of the circuit over-voltage and under-voltage fluctuation value in each time period is: ; In the formula, Indicates the circuit over-voltage and under-voltage fluctuation value in time period i; represents the total number of tuples in the pth cluster in time period i; Indicates the degree of discreteness of the time intervals between the corresponding time periods of all pairwise combinations of binary groups in the pth cluster within time period i; Represents the modulus value of the cluster center of the pth cluster in time period i; represents the number of all clusters in time period i; represents the normalization function; Indicates a preset constant greater than 0.

[0015] Preferably, the method for determining the circuit overvoltage and undervoltage distribution values ​​in each time period is:

[0016] For each time period and all time periods in its neighborhood, the maximum value of the characteristic values ​​of the two types of limit voltage data in any time period is counted, and the circuit overvoltage and undervoltage distribution value of each time period is the cumulative sum of the maximum values ​​of each time period and all time periods in its neighborhood.

[0017] Preferably, the circuit over-voltage and under-voltage degree in each time period is the product of the circuit over-voltage and under-voltage fluctuation value and the circuit over-voltage and under-voltage distribution value in each time period.

[0018] Preferably, the method for determining the total abnormal score of each set is:

[0019] The circuit overvoltage and undervoltage levels of all time periods in each set are used as the input of the anomaly detection algorithm to obtain the anomaly scores of the circuit overvoltage and undervoltage levels of all time periods in each set, and the average of all anomaly scores is used as the total anomaly score of each set.

[0020] Preferably, the method for determining the circuit alarm threshold at the current moment is:

[0021] The maximum value of the abnormal scores of the circuit over-voltage and under-voltage degrees in all time periods in each set before the current moment is counted, and the circuit alarm threshold at the current moment is the average of the maximum values ​​of the abnormal scores in all sets before the current moment.

[0022] Preferably, the protecting the circuit includes:

[0023] If the average of the total abnormal scores of all sets before the current moment is greater than the circuit alarm threshold, the circuit is in an overvoltage or undervoltage state, and the intelligent molded case circuit breaker is tripped; otherwise, the circuit is normal.

[0024] In a second aspect, an embodiment of the present application further provides an over-voltage and under-voltage protection circuit for an intelligent molded case circuit breaker, wherein the circuit adopts any of the steps of the over-voltage and under-voltage protection method for an intelligent molded case circuit breaker described above.

[0025] This application has at least the following beneficial effects:

[0026] This application determines two types of limit voltage data in each time period by analyzing the discreteness and extreme distribution of all voltage data in each time period, and determines the characteristic value of each type of limit voltage in each time period based on the discreteness of the time intervals between all adjacent data in each type of limit voltage data, and the difference between each type of limit voltage data and the corresponding preset threshold value. Its beneficial effect is that it can reduce the influence of pulse voltage and high-frequency noise in the microgrid on the judgment of circuit over-voltage and under-voltage status, and improve the accuracy of circuit breaking protection of intelligent molded case circuit breaker. This application determines the circuit over-voltage and under-voltage fluctuation value of each time period based on the number of all clusters and the size of the cluster center of each cluster, combined with the discreteness of the time intervals between the corresponding time periods of all pairwise combinations of binary groups in each cluster; its beneficial effect is that it can accurately distinguish the short-term voltage fluctuation caused by voltage control lag in the microgrid from the over-voltage and under-voltage status of the circuit, and prevent the intelligent molded case circuit breaker from misjudging the normal voltage fluctuation of the circuit as the over-voltage and under-voltage status of the circuit, affecting the normal power consumption of users. The present application determines the circuit over-voltage and under-voltage distribution values ​​of each time period based on the size distribution of the characteristic values ​​of two types of boundary voltage data in any time period within the neighborhood of each time period, and determines the circuit over-voltage and under-voltage degree of each time period in combination with the circuit over-voltage and under-voltage fluctuation value; and adopts an abnormality detection algorithm to obtain the total abnormality score of each set, determines the circuit alarm threshold at the current moment based on the extreme distribution of the abnormality scores of all time periods in each set, and protects the circuit in combination with the total abnormality score. The beneficial effect is to avoid misjudgment of the circuit state due to a fixed voltage threshold, and improve the accuracy of the intelligent molded case circuit breaker in judging the circuit over-voltage and under-voltage state. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A flowchart of the steps of an over-voltage and under-voltage protection method for an intelligent molded case circuit breaker provided in one embodiment of the present application;

[0029] Figure 2 A schematic diagram of a circuit voltage being disturbed provided in one embodiment of the present application;

[0030] Figure 3 A schematic diagram of an abnormal total score extraction process provided by an embodiment of the present application;

[0031] Figure 4 A diagram comparing the effects of tripping operation under a fixed voltage threshold and a circuit alarm threshold provided in one embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the over-voltage and under-voltage protection method and circuit for intelligent molded case circuit breakers proposed in the present application, its specific implementation method, structure, features and effects are described in detail as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0033] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0034] The specific scheme of the over-voltage and under-voltage protection method and circuit for the intelligent molded case circuit breaker provided by the present application is described in detail below with reference to the accompanying drawings.

[0035] See also Figure 1 , which shows a flowchart of a method for overvoltage and undervoltage protection of an intelligent molded case circuit breaker provided by an embodiment of the present application, the method comprising the following steps:

[0036] Step S1: Collecting voltage data of the incoming terminal of the intelligent molded case circuit breaker at all collection moments within a preset time period before the current moment.

[0037] The incoming line terminal of the intelligent molded case circuit breaker is generally connected to the busbar end of the low-voltage distribution system of the microgrid. Therefore, the intelligent molded case circuit breaker body is used to collect voltage data at all collection moments of the incoming line terminal within a preset time t, where the data sampling frequency is f.

[0038] It should be noted that the values ​​of the preset time length t and the data sampling frequency f are set manually. In this embodiment, the preset time length is 24h and the sampling interval is 1s. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0039] Further, the preset duration is evenly divided into multiple time periods, and each time period corresponds to the same amount of voltage data. Specifically, since the preset duration in this embodiment is 24 hours and the sampling interval frequency is 1 second, two hours is set as one time period in this embodiment, and 24 hours is evenly divided into 12 time periods. The implementer can also set the division method according to the specific situation, and this embodiment does not impose any special restrictions.

[0040] Step S2: Evenly divide the preset time length into multiple time periods. In each time period, based on the discreteness and extreme distribution of all voltage data, two types of limit voltage data in each time period are determined. Based on the discreteness of the intervals between the corresponding collection moments of all adjacent data in each type of limit voltage data, and the difference between each type of limit voltage data and the corresponding preset threshold, the characteristic value of each type of limit voltage in each time period is determined.

[0041] As one of the important functions of intelligent molded case circuit breakers, over-voltage and under-voltage protection can effectively prevent the adverse effects of grid voltage rise and fall on the system and load. When the microgrid is disconnected from the main grid and is in island mode, the microgrid loses the participation of the main grid, and its power quality parameters are mainly adjusted and controlled by the distributed power sources in the system (such as solar energy and wind energy). The output of distributed power sources is usually not very stable, which will cause frequent voltage fluctuations, resulting in a short-term decrease in the stability of the grid voltage. In a microgrid in island mode, the access of pulse power loads and the high-frequency noise generated by power electronic switching devices will affect the judgment of circuit over-voltage and under-voltage. Therefore, if the over-voltage and under-voltage protection of the intelligent molded case circuit breaker is only based on a fixed voltage threshold, it may cause the fluctuation of normal voltage or the influence of high-frequency noise, which will also cause the circuit breaker to open and close frequently, thus seriously affecting the normal power consumption of users.

[0042] Therefore, considering that the microgrid is in island mode, the access of pulse power loads and the high-frequency noise generated by the power electronic switch device cause short-term fluctuations in its voltage data changes. In order to reduce the influence of normal pulse voltage and high-frequency noise on the intelligent molded case circuit breaker to judge whether the circuit is over-voltage or under-voltage, the fluctuation of voltage data in each time period is analyzed to accurately judge whether the circuit is in an over-voltage or under-voltage state, and timely intervention is made to avoid interference of over-voltage or under-voltage on the user's electricity consumption. Specifically:

[0043] (1) Among all the voltage data in each time period, the voltage data between the minimum value and the first quartile is recorded as the lower limit voltage data, and the voltage data between the third quartile and the maximum value is recorded as the upper limit voltage data. So far, two types of limit voltage data are obtained, namely the upper limit voltage data and the lower limit voltage data.

[0044] (2) Further, based on the discreteness of the intervals between the corresponding collection times of all adjacent data in each type of limit voltage data in each time period, and the difference between each type of limit voltage data and the corresponding preset threshold, the characteristic value of each type of limit voltage in each time period is determined, specifically:

[0045] Characteristic value of the jth class limit voltage in time period i The expression is: ; In the formula, Indicates the discrete degree of the time interval between all adjacent data in the j-th category boundary voltage data within time period i; Represents the mth data in the jth category boundary voltage data in time period i; represents the preset threshold value of the j-th category limit voltage data; exp( ) represents an exponential function with a natural constant as the base; Represents the total number of the j-th category boundary voltage data in time period i.

[0046] It should be noted that there are many methods for measuring the degree of data discreteness. In this embodiment, the degree of discreteness of the time intervals between all adjacent data is measured by calculating the coefficient of variation of the time intervals between all adjacent data in each type of limit voltage data in each time period. Implementers can also use other methods to measure the degree of data discreteness, such as variance and standard deviation. Regarding the selection of methods for measuring the degree of data discreteness, this embodiment does not impose any special restrictions.

[0047] The calculation process of the coefficient of variation is a well-known technique, and the specific calculation steps will not be repeated here.

[0048] In addition, it should be understood that the preset thresholds corresponding to the upper and lower limit voltage data are different, and the preset thresholds are set manually. In this embodiment, the preset threshold corresponding to the upper limit voltage data is 198V, and the preset threshold corresponding to the lower limit voltage data is set to 235V. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0049] Further, according to the characteristic value of each type of limit voltage in each time period, it can be understood that the greater the difference between the upper (lower) limit voltage data and the corresponding preset threshold value, the greater the characteristic value of the upper (lower) limit voltage data, and because the pulse voltage and high-frequency noise cause the circuit voltage to change suddenly in an extremely short time and the interval is relatively long, the collection time corresponding to all data in the upper (lower) limit voltage data is distributed more dispersedly, and the discrete degree of the time interval between all data in the upper (lower) limit voltage data is smaller. Therefore, if the discrete degree of the time interval between all data in the upper (lower) limit voltage data is smaller, and the difference between the upper (lower) limit voltage data and the corresponding preset threshold value is greater, the characteristic value of the limit voltage is greater;

[0050] On the contrary, the smaller the difference between the upper (lower) limit voltage data and the corresponding preset threshold value, the smaller the characteristic value of the upper (lower) limit voltage data, and because the pulse voltage and high-frequency noise cause the circuit voltage mutation time to be longer and the interval time to be shorter, the collection time corresponding to all data in the upper (lower) limit voltage data is distributed more concentratedly, and the discrete degree of the time interval between all data in the upper (lower) limit voltage data is larger. Therefore, if the discrete degree of the time interval between all data in the upper (lower) limit voltage data is greater, and the difference between the upper (lower) limit voltage data and the corresponding preset threshold value is smaller, the characteristic value of the limit voltage is smaller.

[0051] Preferably, the schematic diagram of the circuit voltage being disturbed provided in this embodiment is as follows Figure 2 As shown; in the figure, the dotted line represents the preset undervoltage threshold, the horizontal axis represents time, the unit is h, the vertical axis represents voltage data, the unit is V, and the dotted line represents the preset overvoltage threshold.

[0052] Step S3: The characteristic values ​​of the two types of limit voltage data in each time period are grouped into tuples, and the tuples of each time period and all time periods in its neighborhood are clustered to obtain multiple clusters. Based on the number of all clusters and the size of the cluster center of each cluster, combined with the discrete degree of the time intervals between the corresponding time periods of all pairwise combinations of tuples in each cluster, the circuit over- and under-voltage fluctuation value of each time period is determined.

[0053] When the circuit is in an overvoltage state, the electronic equipment and household appliances at the user end may heat up the wires of the electronic equipment and household appliances at the user end due to the overvoltage, thereby causing damage to the equipment. When the circuit is in an undervoltage state, the electrical appliances and equipment at the user end may not work in the best state. Long-term undervoltage will also cause excessive wear of the internal circuits and components of the equipment, shortening the service life of the equipment. Therefore, by analyzing the size distribution of the characteristic values ​​of the two types of boundary voltage data in any period of time, the circuit over-voltage and under-voltage distribution values ​​of each period of time are determined, and combined with the circuit over-voltage and under-voltage fluctuation values, the circuit over-voltage and under-voltage degree of each period of time is determined, so as to intervene in the circuit in time and reduce the loss at the user end. Specifically:

[0054] (1) The characteristic values ​​of the two types of boundary voltage data in each time period are grouped into two tuples, and the neighborhood W is divided with each time period as the center. The two tuples of each time period and all the time periods in its neighborhood are clustered to obtain multiple clusters.

[0055] It should be noted that the value of the neighborhood W is artificially set. In this embodiment, the value of the neighborhood W is 2. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0056] (2) Further, based on the number of all clusters in each time period and the size of the cluster center of each cluster, combined with the discrete degree of the time period length corresponding to all binary groups in each cluster, the circuit overvoltage and undervoltage fluctuation value of each time period is determined, specifically:

[0057] Circuit over-voltage and under-voltage fluctuation value in period i The expression is: ; In the formula, represents the total number of tuples in the pth cluster in time period i; Indicates the degree of discreteness of the time intervals between the corresponding time periods of all pairwise combinations of binary groups in the pth cluster within time period i; Represents the modulus value of the cluster center of the pth cluster in time period i; represents the number of all clusters in time period i; represents the normalization function; Indicates a constant greater than 0 to prevent the denominator from being 0. The value of is set manually. The value of is 0.01. Under the premise of ensuring that the denominator is not 0 and does not excessively affect the calculation result, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0058] It should be noted that there are many methods for measuring the degree of data dispersion. In this embodiment, the degree of dispersion of the time intervals between the time periods corresponding to different tuples is measured by calculating the variance of the time intervals between the corresponding time periods of all two-tuple combinations in each cluster. The implementer may also use other methods for measuring the degree of data dispersion, such as standard deviation or dispersion coefficient. This embodiment does not impose any special restrictions on the selection of dispersion method.

[0059] Furthermore, according to the circuit over-voltage and under-voltage fluctuation values ​​in each time period, it can be understood that in the island mode, the microgrid loses the participation of the main grid, and its power quality parameters are mainly adjusted and controlled by the power generation units and energy storage units in the system. The stability of the grid voltage will drop temporarily, and the fluctuation degree of the grid control voltage maintained within the normal range will increase. On the one hand, the greater the amplitude of the circuit voltage fluctuation, the greater the possibility of over-voltage and under-voltage, the greater the modulus value of the cluster center of the cluster cluster, and the greater the calculated circuit over-voltage and under-voltage fluctuation value. On the other hand, the more frequent the voltage fluctuation in the voltage data window and the longer the fluctuation duration, the greater the probability of the microgrid causing circuit over-voltage and under-voltage due to abnormal control, the more concentrated the distribution of the corresponding time periods of all binary groups in the cluster cluster, the smaller the discrete degree of the time intervals between the corresponding time periods of all binary groups in the cluster cluster, and the greater the circuit over-voltage and under-voltage fluctuation value; therefore, when the modulus value of the cluster center of the cluster cluster is larger, the smaller the discrete degree of the time intervals between the corresponding time periods of all binary groups in the cluster cluster, the greater the circuit over-voltage and under-voltage fluctuation value;

[0060] On the contrary, the smaller the amplitude of the circuit voltage fluctuation, the smaller the possibility of over-voltage and under-voltage, the smaller the modulus value of the cluster center of the clustering cluster, and the smaller the calculated circuit over-voltage and under-voltage fluctuation value; the voltage in the voltage data window hardly fluctuates, the smaller the probability of circuit over-voltage and under-voltage caused by abnormal control of the microgrid, the more dispersed the distribution of the corresponding time periods of all binary groups in the clustering cluster, the greater the discrete degree of the time intervals between the corresponding time periods of all pairwise combinations of binary groups in the clustering cluster, and the smaller the circuit over-voltage and under-voltage fluctuation value; therefore, when the modulus value of the cluster center of the clustering cluster is smaller, the greater the discrete degree of the time intervals between the corresponding time periods of all pairwise combinations of binary groups in the clustering cluster, the smaller the circuit over-voltage and under-voltage fluctuation value.

[0061] Step S4: in the neighborhood of each time period, based on the size distribution of the characteristic values ​​of the two types of boundary voltage data in any time period and in combination with the circuit over-voltage or under-voltage fluctuation value, determine the circuit over-voltage or under-voltage degree of each time period.

[0062] In the island mode of the microgrid, due to the decrease in voltage control ability, the circuit voltage is in a higher or lower state in some periods of time. When the circuit voltage exceeds the overvoltage value or falls below the undervoltage value by a large margin, the larger the corresponding overvoltage characteristic value or undervoltage characteristic value, the greater the possibility that the circuit is in an overvoltage or undervoltage state.

[0063] Therefore, in order to improve the accuracy of the judgment of the two abnormal states of overvoltage and undervoltage, the circuit overvoltage and undervoltage degree of each time period is determined according to the circuit overvoltage and undervoltage fluctuation value of each time period and the characteristic value reflecting the circuit overvoltage and undervoltage degree of voltage data, specifically:

[0064] For each time period and all time periods in its neighborhood, the maximum value of the characteristic values ​​of the two types of limit voltage data in any time period is counted, and the cumulative sum of the maximum values ​​of each time period and all time periods in its neighborhood is calculated, which is recorded as the circuit overvoltage and undervoltage distribution value of each time period;

[0065] The circuit over-voltage and under-voltage degree in each time period is the product of the circuit over-voltage and under-voltage fluctuation value and the circuit over-voltage and under-voltage distribution value in each time period.

[0066] Furthermore, according to the degree of over-voltage and under-voltage of the circuit in each time period, it can be understood that the degree of over-voltage and under-voltage of the circuit reflects the abnormal degree of overvoltage or undervoltage in each time period and its neighborhood. When the characteristic value reflecting the over-voltage and under-voltage of the voltage data in each time period and its neighborhood is larger, the distribution value of the over-voltage and under-voltage of the circuit is larger, and the fluctuation value of the over-voltage and under-voltage of the circuit is larger, the degree of over-voltage and under-voltage of the circuit is larger, and the circuit voltage control is abnormal. The more it is necessary to break the circuit in time when the voltage control of the microgrid is abnormal, so as to improve the protection of the circuit by the intelligent molded case circuit breaker; on the contrary, when the characteristic value reflecting the over-voltage and under-voltage of the voltage data in each time period and its neighborhood is smaller, the distribution value of the over-voltage and under-voltage of the circuit is smaller, and the fluctuation value of the over-voltage and under-voltage of the circuit is smaller, and the degree of over-voltage and under-voltage of the circuit is smaller, indicating that the possibility of the circuit being in an over-voltage and under-voltage state is smaller, and the possibility of abnormal voltage control of the microgrid is smaller.

[0067] Step S5: Divide all time periods before the current moment evenly into each set, determine the total abnormal score of each set before the current moment based on the abnormal distribution of circuit overvoltage and undervoltage levels in all time periods in each set, and complete the overvoltage protection of the intelligent molded case circuit breaker.

[0068] Therefore, in order to improve the accuracy of circuit over-voltage and under-voltage judgment and improve the reliability of overvoltage protection of intelligent molded case circuit breakers, the overvoltage protection of intelligent molded case circuit breakers is realized based on the abnormal distribution of circuit over-voltage and under-voltage levels, specifically:

[0069] Firstly, the over-voltage and under-voltage degree of the circuit in all time periods in each set is used as the input of the anomaly detection algorithm to obtain the anomaly scores of the over-voltage and under-voltage degree of the circuit in all time periods in each set, and the mean of all anomaly scores is used as the total anomaly score of each set;

[0070] It should be noted that there are many commonly used anomaly detection algorithms. In this embodiment, the isolation forest algorithm is used to obtain the abnormal score of the circuit over-voltage and under-voltage degree. The implementer can also use other anomaly detection algorithms such as the LOF anomaly detection algorithm. This embodiment does not impose any special restrictions on the selection of the anomaly detection algorithm.

[0071] Preferably, the schematic diagram of the abnormal total score extraction process provided in this embodiment is as follows: Figure 3 shown.

[0072] Among them, the isolation forest algorithm is a well-known technology in the field of anomaly detection algorithms, and the specific process of obtaining the anomaly score is not repeated here.

[0073] Further, the maximum value of the abnormal scores of the circuit over-voltage and under-voltage degree in all time periods in each set is counted, and the average of the maximum values ​​of the abnormal scores in all sets before the current moment is recorded as the circuit alarm threshold at the current moment;

[0074] If the average of the total abnormal scores of all sets before the current moment is greater than the circuit alarm threshold, the circuit is in an overvoltage or undervoltage state, and the electric operating device of the intelligent molded case circuit breaker sends a trip command signal to the circuit breaker. The circuit breaker body triggers the release through the internal moving contact to open the circuit breaker; otherwise, the circuit is normal.

[0075] At this point, this embodiment determines the characteristic value of each type of limit voltage data by analyzing the distribution of the upper and lower limit voltage data serial numbers, reduces the interference of the pulse voltage and high-frequency noise in the microgrid on the judgment of the over-voltage and under-voltage state of the circuit, and accurately distinguishes the short-term voltage fluctuation caused by the voltage control lag of the microgrid and the over-voltage and under-voltage state of the circuit by clustering the binary groups composed of the characteristic values, prevents the intelligent molded case circuit breaker from misjudging the circuit state and affecting the normal power consumption of the user, and adopts the isolation forest algorithm to obtain the abnormal score of the over-voltage and under-voltage degree of the circuit, determines the circuit alarm threshold of the circuit based on the abnormal score, and improves the accuracy of the intelligent molded case circuit breaker in judging the over-voltage and under-voltage state of the circuit.

[0076] Preferably, the comparison effect diagram of the opening operation under the fixed voltage threshold and the circuit alarm threshold provided in this embodiment is as follows: Figure 4As shown in the figure, the horizontal axis represents time, the unit is h, the vertical axis represents voltage data, the unit is V, the dotted line represents the preset overvoltage threshold, the dotted line represents the preset undervoltage threshold, the dotted line represents the opening operation based on the fixed voltage threshold at the corresponding moment, and the solid line represents the opening operation based on the voltage alarm threshold at the corresponding moment.

[0077] Based on the same inventive concept as the above method, an embodiment of the present application also provides an over-voltage and under-voltage protection circuit for an intelligent molded case circuit breaker, and the circuit adopts any of the steps of the over-voltage and under-voltage protection method for an intelligent molded case circuit breaker described above.

[0078] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0080] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. An overvoltage and undervoltage protection method for an intelligent molded case circuit breaker, characterized in that: The method comprises the following steps: Collect voltage data of all collection moments of the incoming terminal of the intelligent molded case circuit breaker within a preset time before the current moment; The preset time length is evenly divided into multiple time periods. In each time period, based on the discreteness and extreme distribution of all voltage data, two types of limit voltage data in each time period are determined. Based on the discreteness of the time intervals between all adjacent data in each type of limit voltage data and the difference between each type of limit voltage data and the corresponding preset threshold value, the characteristic value of each type of limit voltage in each time period is determined. The characteristic values ​​of the two types of boundary voltage data in each time period are grouped into two tuples, and the two tuples of each time period and all time periods in its neighborhood are clustered to obtain multiple clusters, and based on the number of all clusters and the size of the cluster center of each cluster, combined with the discrete degree of the time intervals between the corresponding time periods of all the two tuples of each cluster, the circuit over-voltage and under-voltage fluctuation value of each time period is determined; In the neighborhood of each time period, based on the size distribution of the characteristic values ​​of the two types of boundary voltage data in any time period, the circuit over-voltage and under-voltage distribution value of each time period is determined, and combined with the circuit over-voltage and under-voltage fluctuation value, the circuit over-voltage and under-voltage degree of each time period is determined; All time periods before the current moment are evenly divided into each set, and the abnormality detection algorithm is used to obtain the abnormality scores of the circuit over-voltage and under-voltage degrees of all time periods in each set. Based on the average distribution of the abnormality scores, the total abnormality score of each set before the current moment is determined. Based on the extreme distribution of the abnormality scores of all time periods in each set, the circuit alarm threshold at the current moment is determined, and the circuit is protected in combination with the total abnormality score.

2. The over-voltage and under-voltage protection method for intelligent molded case circuit breaker according to claim 1, characterized in that: The method for determining the two types of limit voltage data in each time period is as follows: Among all the voltage data in each time period, the voltage data between the minimum value and the first quartile is recorded as the lower limit voltage data, and the voltage data between the third quartile and the maximum value is recorded as the upper limit voltage data.

3. The over-voltage and under-voltage protection method for intelligent molded case circuit breaker according to claim 1, characterized in that: The expression of the characteristic value of each type of limit voltage in each time period is: ; In the formula, Represents the characteristic value of the j-th category limit voltage in time period i; Indicates the discrete degree of the intervals between all adjacent data in the j-th category boundary voltage data within time period i; Represents the mth data in the jth category boundary voltage data in time period i; represents the preset threshold value of the j-th category limit voltage data; exp( ) represents an exponential function with a natural constant as the base; Represents the total number of the j-th category boundary voltage data in time period i.

4. The over-voltage and under-voltage protection method for intelligent molded case circuit breaker according to claim 1, characterized in that: The expression of the circuit over-voltage and under-voltage fluctuation value in each period is: ; In the formula, Indicates the circuit over-voltage and under-voltage fluctuation value in time period i; represents the total number of tuples in the pth cluster in time period i; Indicates the degree of discreteness of the time intervals between the corresponding time periods of all pairwise combinations of binary groups in the pth cluster within time period i; Represents the modulus value of the cluster center of the pth cluster in time period i; represents the number of all clusters in time period i; represents the normalization function; Indicates a preset constant greater than 0.

5. The over-voltage and under-voltage protection method for intelligent molded case circuit breaker according to claim 1, characterized in that: The method for determining the circuit overvoltage and undervoltage distribution values ​​in each time period is as follows: For each time period and all time periods in its neighborhood, the maximum value of the characteristic values ​​of the two types of limit voltage data in any time period is counted, and the circuit overvoltage and undervoltage distribution value of each time period is the cumulative sum of the maximum values ​​of each time period and all time periods in its neighborhood.

6. The over-voltage and under-voltage protection method for intelligent molded case circuit breaker according to claim 1, characterized in that: The circuit over-voltage and under-voltage degree in each time period is the product of the circuit over-voltage and under-voltage fluctuation value and the circuit over-voltage and under-voltage distribution value in each time period.

7. The over-voltage and under-voltage protection method for intelligent molded case circuit breaker according to claim 1, characterized in that: The method for determining the total abnormal score of each set is: The circuit overvoltage and undervoltage levels of all time periods in each set are used as the input of the anomaly detection algorithm to obtain the anomaly scores of the circuit overvoltage and undervoltage levels of all time periods in each set, and the average of all anomaly scores is used as the total anomaly score of each set.

8. The over-voltage and under-voltage protection method for intelligent molded case circuit breaker according to claim 1, characterized in that: The method for determining the circuit alarm threshold at the current moment is: The maximum value of the abnormal scores of the circuit over-voltage and under-voltage degrees in all time periods in each set before the current moment is counted, and the circuit alarm threshold at the current moment is the average of the maximum values ​​of the abnormal scores in all sets before the current moment.

9. The over-voltage and under-voltage protection method for intelligent molded case circuit breaker according to claim 1, characterized in that: The protecting the circuit comprises: If the average of the total abnormal scores of all sets before the current moment is greater than the circuit alarm threshold, the circuit is in an overvoltage or undervoltage state, and the intelligent molded case circuit breaker is tripped; otherwise, the circuit is normal.

10. An overvoltage and undervoltage protection circuit for an intelligent molded case circuit breaker, characterized in that: The circuit adopts the steps of the over-voltage and under-voltage protection method for an intelligent molded case circuit breaker as described in any one of claims 1-9.

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