Metering type intelligent reclosing circuit breaker based on Internet of Things

Abstract

The invention discloses a metering type intelligent reclosing circuit breaker based on the Internet of Things, and relates to the technical field of power systems. The method comprises the following steps of: extracting current data of each time point in each time zone, screening out the highest and lowest values, averaging to obtain a current comprehensive value, calculating a ratio of the current comprehensive value to a current comparison value of a previous set time window to obtain a current change proportion, determining an adjustment direction according to the proportion, and integrating into an increased or reduced data set. The current reference threshold value of each time zone of the next set time window is adjusted by matching the current reference threshold value with a pre-constructed proportion range set, so that the current reference threshold value can adapt to normal fluctuation of circuit current in different power consumption time periods and equipment types, and mis-tripping caused by a static threshold value of a traditional circuit breaker is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a metering intelligent reclosing circuit breaker based on the Internet of Things. Background Art

[0002] In the field of power supply, as a key device to ensure the safe and stable operation of the circuit, the performance of the circuit breaker directly affects the reliability and efficiency of power supply.

[0003] Traditional circuit breakers have many limitations in function and are difficult to meet the increasing complex requirements of modern power systems, specifically including: Usually, a fixed current threshold is used to judge circuit faults, and it cannot be dynamically adjusted according to the actual circuit operation conditions. Under different power consumption periods, this static threshold cannot adapt to the normal fluctuations of the circuit current and is prone to misjudgment. In industrial production, the instantaneous current is large when the equipment starts, which will cause the traditional circuit breaker to trip erroneously and affect the continuity of production; When the circuit trips due to a fault, it is difficult for the traditional circuit breaker to accurately distinguish between transient faults and permanent faults. For transient faults, if the power supply cannot be restored by automatic reclosing in time, it will cause unnecessary power outages and affect the normal power consumption of users; for permanent faults, if reclosing blindly, it may cause secondary damage to the equipment, expand the fault range, increase the maintenance cost and power outage time.

[0004] Therefore, a metering intelligent reclosing circuit breaker based on the Internet of Things is introduced. Summary of the Invention

[0005] In view of this, the present invention provides a metering intelligent reclosing circuit breaker based on the Internet of Things to solve the problems raised in the above background art.

[0006] The object of the present invention can be achieved by the following technical solutions: A metering intelligent reclosing circuit breaker based on the Internet of Things includes an Internet of Things platform, and the Internet of Things platform is provided with: Metering module: Real-time collect the metering data in the circuit and transmit the metering data to the protection module; the metering data includes voltage, current, active power and frequency; Protection module: Record the current data in different time zones within a set time window, evaluate it, determine the reference threshold of the current signal in the next set time window, real-time monitor the current signal in the circuit metering data, identify the time zone where the current signal is located and extract the corresponding reference threshold. When the current signal is detected to exceed the set threshold at a certain time point within the time zone, it is determined that the circuit trips and a trip signal is triggered and sent to the control module; Control module: After receiving the trip signal, analyze and evaluate the metering data that currently triggers the trip signal in combination with historical fault data, and determine the estimated fault type that currently triggers the trip signal; the estimated fault types include instantaneous faults and permanent faults, and corresponding steps are executed based on the estimated fault type.

[0007] In some embodiments, record the current data in different time zones within a set time window and evaluate it. Specifically: For the current data in different time zones within the set time window, extract the current data at each time point in different time zones; Screen out the maximum current value and the minimum current value in the current data at each time point in different time zones, and calculate the average value of the remaining current data at each time point to obtain the current comprehensive value corresponding to different time zones; Extract the current comprehensive values corresponding to different time zones in the previous set time window, denoted as current comparison values; calculate the ratio between the current comprehensive value and the current comparison value within the same time zone, with the current comprehensive value as the numerator and the current comparison value as the denominator to calculate the ratio, and obtain the current change ratio of different time zones.

[0008] In some embodiments, determine the reference threshold of the current signal in the next set time window. Specifically: Compare the current change ratios of different time zones with the integer one, and determine the adjustment direction. If the current change ratio of a certain time zone is greater than the integer one, the adjustment direction is the increasing direction, otherwise the adjustment direction is the decreasing direction; Integrate the current change ratios of different time zones into an increasing data set and a decreasing data set according to different adjustment directions. Pre-construct a ratio range set, which includes the intervals where the groups of current change ratios corresponding to the increasing direction are located and the intervals where the groups of current change ratios corresponding to the decreasing direction are located. Each interval where the current change ratio is located corresponds to an adjustment percentage; Match the increasing data set with the intervals where the groups of current change ratios corresponding to the increasing direction in the ratio range set are located, determine the adjustment percentage of each time zone in the increasing data set in the next set time window, extract the reference threshold of each time zone and increase it according to the determined adjustment percentage as the adjusted reference threshold of each time zone in the next set time window; Match the decreasing data set with the intervals where the groups of current change ratios corresponding to the decreasing direction in the ratio range set are located, determine the adjustment percentage of each time zone in the decreasing data set in the next set time window, extract the reference threshold of each time zone and decrease it according to the determined adjustment percentage as the adjusted reference threshold of each time zone in the next set time window.

[0009] In some embodiments, analyze and evaluate the metering data that currently triggers the trip signal. Specifically: S1: Preset a data collection time window after the trigger tripping signal, and obtain the voltage, active power, frequency, and current of the circuit within the data collection time window; S2: For the voltage within the data collection time window, divide the data collection time window into a front window and a rear window based on the middle time point; extract the voltages at each time point within the rear window, and obtain the mean value of the rear window after calculating the average value respectively; Preset a voltage reference value for the circuit, perform a ratio calculation with the voltage reference value as the numerator and the mean value of the rear window as the denominator, then perform a difference calculation between the calculated ratio and the integer one, and take the absolute value to obtain the voltage confidence value of the circuit within the data collection time window; S3: For the current within the data collection time window, extract the corresponding reference threshold of the current, construct a line graph, draw the threshold line corresponding to the reference threshold within the line graph and the numerical points corresponding to the currents at each time point within the data collection time window; Identify the number of numerical points higher than the threshold line within the line graph, determine them as abnormal numerical points, count the number of abnormal numerical points and record it as the current anomaly count, calculate the proportion of the current anomaly count in the number of numerical points, and record it as the count ratio; Identify the abnormal numerical point with the highest current among each group of abnormal numerical points as the reference point, starting from the reference point, construct a vertical line segment between it and the X-axis, and obtain the length of the vertical line segment as the peak length, calculate the ratio between the peak length and the reference line length, and record it as the line length ratio; where the reference line length is the vertical distance between the threshold line and the X-axis; that is, it is calculated with the peak length as the numerator and the reference line length as the denominator; For the count ratio and line length ratio calculated within the data collection time window, after normalization processing, multiply them by the corresponding preset weight coefficients respectively, and then sum them to obtain the current confidence value of the circuit within the data collection time window; S4: Preset the reference thresholds corresponding to the active power and frequency respectively, and calculate the power confidence value and frequency confidence value of the circuit within the data collection time window in the same way as step S3.

[0010] In some embodiments, the analysis and evaluation of the measurement data for the current trigger tripping signal further include: S5: Extract the current confidence value, power confidence value, and frequency confidence value of the circuit within the data collection time window, and mark them as rea, reb, and rec respectively; After the marking is completed and normalization processing is performed, substitute into the formula Perform weighted calculation to obtain the fault judgment index of the circuit corresponding to the current trigger tripping signal; where are the weight coefficients corresponding to the current confidence value, power confidence value, and frequency confidence value respectively; is an additional coefficient, and its specific value is obtained by converting the value of the voltage confidence value.

[0011] In some embodiments, the conversion process is as follows: Preset the intervals where each group of confidence values corresponding to the voltage confidence value are located. Each interval of confidence values corresponds to an additional coefficient value. Match the voltage confidence value of the circuit within the data collection time window with the intervals where each group of confidence values are located to determine the value of the additional coefficient to be taken.

[0012] In some embodiments, the estimated fault type for determining the currently triggered tripping signal is specifically: S6: Compare the fault judgment index of the circuit corresponding to the currently triggered tripping signal with the preset index reference range. If the fault judgment index of the circuit corresponding to the currently triggered tripping signal is higher than the index reference range, it is determined that the estimated fault type is a permanent fault; if the fault judgment index of the circuit corresponding to the currently triggered tripping signal is lower than the index reference range, it is determined that the estimated fault type is a transient fault; If the fault judgment index of the circuit corresponding to the currently triggered tripping signal is within the index reference range, then step S7 is executed.

[0013] In some embodiments, the specific process of executing step S7 is: S7: Construct a historical database to record the historical cases of each triggered tripping signal, including each group of confidence values, estimated fault type, actual fault type, fault occurrence time, and fault occurrence reason; Extract the voltage confidence values corresponding to each group of cases from the historical cases of each triggered tripping signal, denoted as , where p represents the number of each group of cases, p = 1, 2,..., h, and h is the total number of historical cases; Mark the voltage confidence value of the circuit corresponding to the currently triggered tripping signal as F, and calculate the preliminary correlation values of each group of cases through ; Sort the preliminary correlation values of each group of cases from small to large, and screen the first t groups of cases from the left as the preliminary screening cases; where t > 5. For each group of preliminary screening cases, a three-dimensional space coordinate system is pre-constructed, with the current confidence value, power confidence value, and frequency confidence value as the values on the X-axis, Y-axis, and Z-axis respectively, and draw the three-dimensional coordinate points corresponding in the three-dimensional space coordinate system as reference points; Similarly, draw the three-dimensional coordinate points corresponding to the current confidence value, power confidence value, and frequency confidence value of the circuit corresponding to the currently triggered tripping signal in the three-dimensional space coordinate system as the starting point; Construct vertical line segments between the starting point and each group of reference points, screen out the top three shortest preliminary screening cases of the vertical line segments, extract the estimated fault type and the actual fault type respectively for matching. If the matching results are all consistent, select the case with the shorter vertical line segment, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the currently triggered trip signal; If the matching results are all inconsistent, select the case with the shorter vertical line segment, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the currently triggered trip signal; If there is a group of cases with consistent matching results, directly select them, and use the estimated fault type in the selected cases as the estimated fault type of the circuit corresponding to the currently triggered trip signal; If there are two groups of cases with consistent matching results, select the case with the shorter vertical line segment, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the currently triggered trip signal.

[0014] In some embodiments, the corresponding steps are performed based on the estimated fault type, specifically: If the estimated fault type is an instantaneous fault, automatically attempt to close the switch after a set delay time. If the closing fails after the set number of reclosing attempts, control the circuit breaker to lock and send a troubleshooting signal to the technician; if the estimated fault type is a permanent fault, control the circuit breaker to lock and send a troubleshooting signal to the technician.

[0015] Compared with the prior art, the beneficial effects of the present invention are: The present invention first extracts the current data at each time point in each time zone, screens out the highest and lowest values and then calculates the average value to obtain the comprehensive current value, then calculates the ratio with the current comparison value of the previous set time window to obtain the current change ratio, determines the adjustment direction according to this ratio, integrates it into an increased or decreased data set, and matches it with the pre-constructed ratio range set, thereby adjusting the current reference threshold of each time zone in the next set time window. This can adapt to the normal fluctuations of the circuit current under different electricity consumption periods and equipment types, and avoid mis-tripping of traditional circuit breakers due to static thresholds; After receiving the trip signal, the present invention obtains the voltage, active power, frequency and current data of the circuit in a preset data collection time window, calculates the confidence values of these parameters respectively, marks and normalizes these confidence values, and then substitutes them into a specific formula to calculate the fault judgment index. By comparing the fault judgment index with the preset index reference range, the fault type is preliminarily judged; When the fault judgment index is within the reference range, the present invention constructs a historical database, records the relevant information of each historical case that triggers a tripping signal, extracts the voltage confidence values of the historical cases and the current tripping signal to calculate the preliminary correlation values, sorts them, and screens the first t groups of cases as the preliminary screening cases. In a three-dimensional space coordinate system, the coordinate points of the preliminary screening cases and the current tripping signal are plotted with the current, power, and frequency confidence values as the coordinate axes. The cases are screened by comparing the lengths of the vertical line segments, and according to the matching situation between the estimated fault type and the actual fault type, the estimated fault type of the current triggered tripping signal is finally accurately determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In the following description of the exemplary embodiments in conjunction with the drawings, more details, features and advantages of the present application are disclosed. In the drawings: Figure 1 is a structural block diagram of the present invention; Figure 2 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will describe several embodiments of the present application in more detail with reference to the drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided so that the present application is comprehensive and complete, and fully conveys the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0018] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0019] Please refer to Figure 1 as shown, a metering type intelligent reclosing circuit breaker based on the Internet of Things includes an Internet of Things platform; A metering module, a protection module and a control module are arranged in the Internet of Things platform; A high-precision metering chip is arranged in the metering module, which is used to collect the metering data in the circuit in real time and transmit the metering data to the protection module; the metering data includes voltage, current, active power and frequency; The protection module is used to record the current data in different time zones within a set time window, evaluate it, and determine the reference threshold of the current signal in the next set time window; adjust and update the reference threshold for different set time windows according to the change of the recorded data; monitor the current signal in the circuit measurement data in real time, identify the time zone where the current signal is located and extract the corresponding reference threshold, and when the current signal is detected to exceed the set threshold at a certain time point in the time zone, determine that the circuit trips and trigger a trip signal to be sent to the control module; Supplementary note: The set time window can be set to one week, that is, the reference threshold for the next week is adjusted according to the current data of the previous week. Within each set time window, the protection module will accurately record the current data in different time zones. For example, if the set time window is one week, it will record the current data in different time zones (such as Monday to Sunday) within this week; Specifically: For the current data in different time zones within the set time window, extract the current data at each time point in different time zones; Screen out the highest and lowest current values in the current data at each time point in different time zones, and calculate the average value of the remaining current data at each time point to obtain the current comprehensive value corresponding to different time zones; Supplementary note: The current comprehensive value represents the current level performance in the current time zone; Extract the current comprehensive values corresponding to different time zones in the previous set time window, denoted as current comparison values; calculate the ratio between the current comprehensive value and the current comparison value in the same time zone, calculate the ratio with the current comprehensive value as the numerator and the current comparison value as the denominator to obtain the current change ratio in different time zones; Compare the current change ratio in different time zones with the integer one to determine the adjustment direction. If the current change ratio in a certain time zone is greater than the integer one, the adjustment direction is the increasing direction, otherwise the adjustment direction is the decreasing direction; Integrate the current change ratios in different time zones into an increasing data set and a decreasing data set according to different adjustment directions. Pre-construct a ratio range set, which contains the intervals where the groups of current change ratios corresponding to the increasing direction are located and the intervals where the groups of current change ratios corresponding to the decreasing direction are located, and each interval where the current change ratio is located corresponds to an adjustment percentage; Match the increasing data set with the intervals where the groups of current change ratios corresponding to the increasing direction in the ratio range set, determine the adjustment percentage of each time zone in the increasing data set in the next set time window, extract the reference threshold of each time zone and increase it according to the determined adjustment percentage as the adjusted reference threshold of each time zone in the next set time window; Match the reduced data set with the intervals where the current change ratios corresponding to the reduction direction in the ratio range set are located, determine the adjustment percentage for each time zone in the reduced data set in the next set time window, extract the reference threshold for each time zone and reduce it according to the determined adjustment percentage, as the adjusted reference threshold for each time zone in the next set time window; Supplementary note, in the first set time window, the technical staff sets the initial reference values of the current signal in different time zones as the reference threshold; Illustrated with embodiments Set time window: one week, that is, adjust the reference threshold for the next week according to the current data of the previous week; Time zone division: Divide one week into 7 time zones from Monday to Sunday; Ratio range set: Increase direction: The interval (1, 1.1] corresponds to an adjustment percentage of 5%; Interval (1.1, 1.2] corresponds to an adjustment percentage of 10%; Interval (1.2, +∞) corresponds to an adjustment percentage of 15%; Reduction direction: The interval [0.9, 1) corresponds to an adjustment percentage of -5%; Interval [0.8, 0.9) corresponds to an adjustment percentage of -10%; Interval (0, 0.8) corresponds to an adjustment percentage of -15%; Initial reference threshold: Set by the technical staff, and the initial reference thresholds from Monday to Sunday are [100, 110, 120, 130, 140, 150, 160] respectively;

[0020] Step 1: Record the current data of different time zones in the first week Suppose the current data recorded in each time zone from Monday to Sunday in the first week is as follows (each time zone records once per hour, 24 data points per day): Monday: [90, 92, 95, 98, 100, 102, 105, 108, 110, 112, 115, 118, 120, 122, 125, 128, 130, 132, 135, 138, 140, 142, 145, 148]; Tuesday: [100, 102, 105, 108, 110, 112, 115, 118, 120, 122, 125, 128, 130, 132, 135, 138, 140, 142, 145, 148, 150, 152, 155, 158]; Wednesday: [110, 112, 115, 118, 120, 122, 125, 128, 130, 132, 135, 138, 140, 142, 145, 148, 150, 152, 155, 158, 160, 162, 165, 168]; Thursday: [120, 122, 125, 128, 130, 132, 135, 138, 140, 142, 145, 148, 150, 152, 155, 158, 160, 162, 165, 168, 170, 172, 175, 178]; Friday: [130, 132, 135, 138, 140, 142, 145, 148, 150, 152, 155, 158, 160, 162, 165, 168, 170, 172, 175, 178, 180, 182, 185, 188]; Saturday: [140, 142, 145, 148, 150, 152, 155, 158, 160, 162, 165, 168, 170, 172, 175, 178, 180, 182, 185, 188, 190, 192, 195, 198]; Sunday: [150, 152, 155, 158, 160, 162, 165, 168, 170, 172, 175, 178, 180, 182, 185, 188, 190, 192, 195, 198, 200, 202, 205, 208];

[0021] Step 2: Calculate the comprehensive current value in different time zones for the first week For each time zone, screen out the highest and lowest current values, and then calculate the average of the remaining data: After removing 90 and 148 on Monday, the average of the remaining data is (92 + 95 +... + 145) / 22 ≈ 119; After removing 100 and 158 on Tuesday, the average of the remaining data is (102 + 105 +... + 155) / 22 ≈ 129; After removing 110 and 168 on Wednesday, the average of the remaining data is (112 + 115 +... + 165) / 22 ≈ 139; After removing 120 and 178 on Thursday, the average of the remaining data is (122 + 125 +... + 175) / 22 ≈ 149; After removing 130 and 188 on Friday, the average of the remaining data is (132 + 135 +... + 185) / 22 ≈ 159; After removing 140 and 198 on Saturday, the average value of the remaining data is (142 + 145 +... + 195) / 22 ≈ 169; After removing 150 and 208 on Sunday, the average value of the remaining data is (152 + 155 +... + 205) / 22 ≈ 179;

[0022] Step 3: Calculate the current change ratio Compare the comprehensive current value of each time zone in the first week with the initial reference threshold to obtain the current change ratio: Monday: 119 / 100 = 1.19; Tuesday: 129 / 110 ≈ 1.17; Wednesday: 139 / 120 ≈ 1.16; Thursday: 149 / 130 ≈ 1.15; Friday: 159 / 140 ≈ 1.14; Saturday: 169 / 150 ≈ 1.13; Sunday: 179 / 160 ≈ 1.12;

[0023] Step 4: Determine the adjustment direction Since the current change ratios of all time zones are greater than 1, the adjustment directions are all in the increasing direction;

[0024] Step 5: Determine the adjustment percentage Match the current change ratios of each time zone with the increasing-direction intervals in the ratio range set: Monday: 1.19 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%; Tuesday: 1.17 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%; Wednesday: 1.16 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%; Thursday: 1.15 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%; Friday: 1.14 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%; Saturday: 1.13 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%; Sunday: 1.12 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%;

[0025] Step 6: Calculate the adjusted reference threshold for each time zone in the next set time window (the second week) Monday: 100 * (1 + 10%) = 110; Tuesday: 110 * (1 + 10%) = 121; Wednesday: 120 * (1 + 10%) = 132; Thursday: 130 * (1 + 10%) = 143; Friday: 140 * (1 + 10%) = 154; Saturday: 150 * (1 + 10%) = 165; Sunday: 160 * (1 + 10%) = 176;

[0026] Through the above steps, the adjustment of the reference thresholds for each time zone in the second week is completed based on the current data in the first week. In the subsequent time windows, the above process is repeated, and the reference thresholds are continuously adjusted according to the actual current data to better adapt to the operation of the circuit; The control module is used to analyze and evaluate the metering data that currently triggers the trip signal in combination with historical fault data after receiving the trip signal, and determine the estimated fault type of the currently triggered trip signal; the estimated fault types include instantaneous faults and permanent faults, and corresponding steps are executed based on the estimated fault type; The control module is the core control module of the circuit breaker and is electrically connected to the metering module and the protection module; the reclosing includes a motor interface and a trip unit interface and is electrically connected to the control module; Specifically: Determining the estimated fault type of the currently triggered trip signal specifically includes: S1: Preset a data collection time window after triggering the trip signal, and obtain the voltage, active power, frequency, and current of the circuit within the data collection time window; S2: For the voltage within the data collection time window, divide the data collection time window into a front window and a rear window based on the middle time point; extract the voltages at each time point within the rear window, and calculate the average value respectively to obtain the rear window average value; Preset a voltage reference value for the circuit, calculate the ratio with the voltage reference value as the numerator and the rear window average value as the denominator, then calculate the difference between the calculated ratio and the integer one, and take the absolute value to obtain the voltage confidence value of the circuit within the data collection time window; Supplementary note, if the voltage drops significantly at the moment of tripping and remains at a low level continuously, it is more inclined to a permanent fault; if the voltage drops briefly and then returns to normal, it is inclined to an instantaneous fault; through the calculated voltage confidence value, the higher the voltage confidence value, the higher the corresponding probability of being biased towards a permanent fault, and vice versa, the higher the probability of being biased towards an instantaneous fault; S3: For the current within the data collection time window, extract the corresponding reference threshold of the current, construct a line graph, draw the threshold line corresponding to the reference threshold within the line graph and the numerical points corresponding to the currents at each time point within the data collection time window; Identify the number of numerical points above the threshold line in the line chart, determine them as abnormal numerical points, count the number of abnormal numerical points and record it as the number of current anomalies, calculate the proportion of the number of current anomalies in the number of numerical points, and record it as the ratio of numbers; Identify the abnormal numerical point with the highest current among each group of abnormal numerical points and use it as the reference point. Starting from the reference point, construct a vertical line segment to the X-axis, and obtain the length of the vertical line segment as the peak length. Calculate the ratio between the peak length and the reference line length, and record it as the ratio of line lengths; where the reference line length is the vertical distance between the threshold line and the X-axis; that is, it is calculated with the peak length as the numerator and the reference line length as the denominator. For the ratio of numbers and the ratio of line lengths calculated within the data collection time window, after normalization, multiply them by the corresponding preset weight coefficients respectively, and then sum to obtain the current confidence value of the circuit within the data collection time window. Supplementary note, if the current amplitude at the moment of failure is very large and reaches the peak rapidly within a short time and then disappears quickly, it is more inclined to be an instantaneous fault; if the current amplitude continues to maintain at a high level without an obvious downward trend, it is more inclined to be a permanent fault; through the calculated current confidence value, the higher the current confidence value, the higher the corresponding probability of being inclined to a permanent fault, and vice versa, the higher the probability of being inclined to an instantaneous fault. S4: Preset the reference thresholds corresponding to the active power and frequency respectively, and similarly calculate the power confidence value and frequency confidence value of the circuit within the data collection time window in step S3.

[0027] Supplementary note, if the active power returns to normal quickly after the trip, it is more inclined to be an instantaneous fault; if the active power has been in an abnormally high state, it is more inclined to be a permanent fault; through the calculated current confidence value, the higher the current confidence value, the higher the corresponding probability of being inclined to a permanent fault, and vice versa, the higher the probability of being inclined to an instantaneous fault. If the frequency returns to normal quickly after the trip, it is more inclined to be an instantaneous fault; if the frequency continues to rise abnormally, it is more inclined to be a permanent fault; through the calculated current confidence value, the higher the current confidence value, the higher the corresponding probability of being inclined to a permanent fault, and vice versa, the higher the probability of being inclined to an instantaneous fault. S5: Extract the current confidence value, power confidence value, and frequency confidence value of the circuit within the data collection time window, and mark them as rea, reb, and rec respectively. After the marking is completed and normalization is performed, substitute into the formula Perform weighted calculation to obtain the fault judgment index of the circuit corresponding to the currently triggered trip signal; where are the weight coefficients corresponding to the current confidence value, power confidence value, and frequency confidence value respectively, and ; is an additional coefficient, and its specific value is obtained by converting according to the value of the voltage confidence value; For each interval where the confidence values corresponding to the preset voltage confidence values are located, each interval where the confidence values are located corresponds to an additional coefficient value. The value range of the additional coefficient is set between 0.937 - 1.128. The higher the voltage confidence value, the higher the corresponding additional coefficient value obtained by matching, and vice versa; Match the voltage confidence value of the circuit within the data collection time window with the intervals where each group of confidence values are located to determine the value of the additional coefficient ; For example, if an interval where a group of confidence values is located is set as (0 - 0.01), then the additional coefficient value corresponding to this interval is 0.937 at this time; S6: Compare the fault judgment index of the circuit corresponding to the current trigger tripping signal with the preset index reference range. If the fault judgment index of the circuit corresponding to the current trigger tripping signal is higher than the index reference range, it is determined that the predicted fault type is a permanent fault. If the fault judgment index of the circuit corresponding to the current trigger tripping signal is lower than the index reference range, it is determined that the predicted fault type is a transient fault; If the fault judgment index of the circuit corresponding to the current trigger tripping signal is within the index reference range, execute step S7; S7: Build a historical database to record the historical cases of each trigger tripping signal, including each group of confidence values, predicted fault type, actual fault type, fault occurrence time, and fault occurrence reason; Extract the voltage confidence values corresponding to each group of cases from the historical cases of each trigger tripping signal, denoted as , where p represents the number of each group of cases, p = 1, 2,..., h, and h is the total number of historical cases; Mark the voltage confidence value of the circuit corresponding to the current trigger tripping signal as F, and calculate the preliminary correlation values of each group of cases through ; Sort the preliminary correlation values of each group of cases from small to large, and screen the first t groups of cases from the left as the preliminary screening cases; where t > 5, which is specifically set by the technical personnel; For each group of preliminary screening cases, pre - construct a three - dimensional space coordinate system, use the current confidence value, power confidence value, and frequency confidence value as the values on the X - axis, Y - axis, and Z - axis respectively, and draw the three - dimensional coordinate points corresponding in the three - dimensional space coordinate system as reference points; Similarly, draw the three - dimensional coordinate points corresponding to the current confidence value, power confidence value, and frequency confidence value of the circuit corresponding to the current trigger tripping signal in the three - dimensional space coordinate system as the starting point; Construct vertical line segments between the starting point and each group of reference points, screen out the top three shortest preliminary screening cases of the vertical line segments, and extract the estimated fault type and the actual fault type respectively for matching. If the matching results are all consistent, select the case with the shorter vertical line segment, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the currently triggered tripping signal; If the matching results are all inconsistent, select the case with the shorter vertical line segment, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the currently triggered tripping signal; If there is a group of cases with consistent matching results, directly select them, and use the estimated fault type in the selected cases as the estimated fault type of the circuit corresponding to the currently triggered tripping signal; If there are two groups of cases with consistent matching results, select the case with the shorter vertical line segment, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the currently triggered tripping signal; Supplementary note, through the weighted fusion of multi-parameter confidence values and the deep association with historical data, the one-sidedness of single-parameter determination is solved. Especially in the fuzzy range, by mining historical fault patterns, the accuracy of fault type recognition is significantly improved, providing a more reliable decision-making basis for the intelligent reclosing strategy; Execute corresponding steps based on the estimated fault type, specifically: If the estimated fault type is an instantaneous fault, automatically attempt to close the switch after a set delay time. If the closing fails after the set number of reclosing attempts, control the circuit breaker to lock and send a troubleshooting signal to the technician; If the estimated fault type is a permanent fault, control the circuit breaker to lock and send a troubleshooting signal to the technician to avoid unnecessary closing operations; Supplementary note, Figure 2 The specific processes of each step in Data acquisition and threshold adjustment: Dynamically adjust the current reference threshold for each time zone according to historical current data; corresponding to the content of the metering module and the protection module; Specifically: Real-time collect the voltage, current, active power, and frequency of the circuit, and record the current data in different time zones within a set time window; for the current data in each time zone, screen out the maximum and minimum values and then calculate the average value to obtain the current comprehensive value; Calculate the ratio of the current comprehensive value of the current time window to the current comparison value of the previous window to obtain the current change ratio.

[0028] If the change ratio > 1, it is determined as the "increasing direction", otherwise it is determined as the "decreasing direction", and match the adjustment amplitude according to the preset ratio range set (such as the adjustment percentages corresponding to the increasing direction intervals of 5%, 10%, etc.), and update the current reference threshold for the next window; Fault detection and data collection: After a trip is triggered, multi-parameter data is collected and the confidence value is calculated to provide a basis for fault type determination; A preset data collection time window (such as 10 seconds after the trip) is set, and voltage, current, active power, and frequency data within this window are collected, and voltage confidence value, current confidence value, power confidence value, and frequency confidence value are calculated through analysis; Fault type determination: Combining multi-parameter confidence values and historical data to determine the fault type; Specifically: Combining the current, power, and frequency confidence values, substituting them into the formula to calculate the fault judgment index, and comparing it with the preset range. If the index is greater than the range, it is determined as a permanent fault; if the index is lower than the range, it is determined as a transient fault; if the index is within the range, it enters the historical data matching process; Build a historical database, extract the voltage confidence value of the current fault and the voltage confidence value of historical cases to calculate the preliminary correlation value, screen the top t similar cases, and calculate the coordinate point distance between the current fault and historical cases in a three-dimensional coordinate system (with current, power, and frequency confidence values as axes), and select the 3 cases with the shortest distance; Finally determine the current fault type according to the matching situation between the estimated fault type and the actual type of historical cases; Execution strategy: Perform differential operations according to the fault type to avoid misclosing or expanding the fault; Specifically: If the estimated fault type is a transient fault, automatically attempt to close the switch after the set delay time. If the closing fails after the set number of reclosing attempts, control the circuit breaker to lock and send a troubleshooting signal to the technician; If the estimated fault type is a permanent fault, control the circuit breaker to lock and send a troubleshooting signal to the technician to avoid unnecessary closing operations; The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific implementation manners. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the art in the relevant technical field can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A metering type intelligent reclosing circuit breaker based on the Internet of Things, characterized in that, It includes an Internet of Things platform, in which the following are set up in the Internet of Things platform: Measurement module: It collects the measurement data in the circuit in real time and transmits the measurement data to the protection module; the measurement data includes voltage, current, active power, and frequency. Protection module: It records the current data in different time zones within a set time window, evaluates it, determines the reference threshold of the current signal in the next set time window, monitors the current signal in the circuit measurement data in real time, identifies the time zone where the current signal is located and extracts the corresponding reference threshold. When the current signal is detected to exceed the set threshold at a certain time point within the time zone, it determines that the circuit trips and triggers a trip signal to be sent to the control module. Control module: After receiving the trip signal, it analyzes and evaluates the measurement data that triggers the current trip signal in combination with historical fault data, and determines the estimated fault type of the current trip signal. Among them, the estimated fault types include instantaneous faults and permanent faults, and corresponding steps are executed based on the estimated fault type.

2. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 1, characterized in that, The recording of the current data in different time zones within the set time window and the evaluation are specifically as follows: For the current data in different time zones within the set time window, the current data at each time point in different time zones is extracted. The highest current value and the lowest current value in the current data at each time point in different time zones are screened out, and the average value of the remaining current data at each time point is calculated to obtain the current comprehensive value corresponding to different time zones. The current comprehensive values corresponding to different time zones in the previous set time window are extracted and recorded as current comparison values; the ratio between the current comprehensive value and the current comparison value in the same time zone is calculated, with the current comprehensive value as the numerator and the current comparison value as the denominator to calculate the ratio, and the current change ratio in different time zones is obtained.

3. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 2, wherein, The determination of the reference threshold of the current signal in the next set time window is specifically as follows: The current change ratios in different time zones are compared with the integer one to determine the adjustment direction. If the current change ratio in a certain time zone is greater than the integer one, the adjustment direction is the increasing direction, otherwise the adjustment direction is the decreasing direction. The current change ratios in different time zones are integrated into an increasing data set and a decreasing data set according to different adjustment directions. A ratio range set is pre-constructed, and the ratio range set includes the intervals where the groups of current change ratios corresponding to the increasing direction are located and the intervals where the groups of current change ratios corresponding to the decreasing direction are located. Each interval where the current change ratio is located corresponds to an adjustment percentage. The increasing data set is matched with the intervals where the groups of current change ratios corresponding to the increasing direction in the ratio range set to determine the adjustment percentage of each time zone in the increasing data set in the next set time window, extract the reference threshold of each time zone and increase it according to the determined adjustment percentage as the adjusted reference threshold of each time zone in the next set time window. The decreasing data set is matched with the intervals where the groups of current change ratios corresponding to the decreasing direction in the ratio range set to determine the adjustment percentage of each time zone in the decreasing data set in the next set time window, extract the reference threshold of each time zone and decrease it according to the determined adjustment percentage as the adjusted reference threshold of each time zone in the next set time window.

4. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 3, characterized in that, Analyze and evaluate the metering data of the currently triggered tripping signal, specifically as follows: S1: Preset a data collection time window after the triggered tripping signal, and obtain the voltage, active power, frequency, and current of the circuit within the data collection time window; S2: For the voltage within the data collection time window, divide the data collection time window into a front window and a rear window based on the middle time point; extract the voltages at each time point within the rear window, and calculate the average value respectively to obtain the rear window average value; Preset a voltage reference value for the circuit, calculate the ratio with the voltage reference value as the numerator and the rear window average value as the denominator, then calculate the difference between the calculated ratio and the integer one, and take the absolute value to obtain the voltage confidence value of the circuit within the data collection time window; S3: For the current within the data collection time window, extract the corresponding reference threshold of the current, construct a line graph, draw the threshold line corresponding to the reference threshold within the line graph and the numerical points corresponding to the currents at each time point within the data collection time window; Identify the number of numerical points higher than the threshold line within the line graph, determine them as abnormal numerical points, count the number of abnormal numerical points and record it as the current anomaly count, calculate the proportion of the current anomaly count in the number of numerical points, and record it as the count ratio; Identify the abnormal numerical point with the highest current among each group of abnormal numerical points as the reference point, starting from the reference point, construct a vertical line segment to the X-axis, and obtain the length of the vertical line segment as the peak length, calculate the ratio between the peak length and the reference line length, and record it as the line length ratio; where the reference line length is the vertical distance between the threshold line and the X-axis; that is, calculated with the peak length as the numerator and the reference line length as the denominator; For the count ratio and line length ratio calculated within the data collection time window, after normalization processing, multiply them by the corresponding preset weight coefficients respectively, and then sum to obtain the current confidence value of the circuit within the data collection time window; S4: Preset the reference thresholds corresponding to the active power and frequency respectively, and calculate the power confidence value and frequency confidence value of the circuit within the data collection time window in the same way as step S3.

5. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 4, characterized in that, The analysis and evaluation of the metering data of the currently triggered tripping signal also include: S5: Extract the current confidence value, power confidence value, and frequency confidence value of the circuit within the data collection time window, and mark them as rea, reb, and rec respectively; After the marking is completed and normalized, substitute it into the formula Perform weighted calculation to obtain the fault judgment index corresponding to the current trigger trip signal of the circuit; where are the weight coefficients corresponding to the current confidence value, power confidence value, and frequency confidence value respectively; is an additional coefficient, and the specific value is obtained by converting the value of the voltage confidence value.

6. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 5, characterized in that, The said The conversion process is as follows: The intervals where each group of confidence values corresponding to the preset voltage confidence value are located, and each interval where the confidence value is located corresponds to an additional coefficient value. The voltage confidence value of the circuit within the data collection time window is matched with the intervals where each group of confidence values are located to determine the value of the additional coefficient value.

7. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 6, characterized in that, The determination of the estimated fault type of the currently triggered tripping signal is specifically as follows: S6: Compare the fault judgment index of the circuit corresponding to the currently triggered tripping signal with the preset index reference range. If the fault judgment index of the circuit corresponding to the currently triggered tripping signal is higher than the index reference range, then determine that the estimated fault type is a permanent fault; if the fault judgment index of the circuit corresponding to the currently triggered tripping signal is lower than the index reference range, then determine that the estimated fault type is a transient fault; If the fault judgment index of the circuit corresponding to the currently triggered tripping signal is within the index reference range, then execute step S7.

8. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 7, characterized in that, The specific process of executing step S7 is as follows: S7: Construct a historical database, record the historical cases of each triggered tripping signal, including each group of confidence values, estimated fault types, actual fault types, fault occurrence times, and fault occurrence reasons; Extract the voltage confidence values corresponding to each group of cases from the historical cases of each triggered tripping signal, denoted as , where p represents the number of each group of cases, p = 1, 2,......, h, and h is the total number of historical cases; Mark the voltage confidence value corresponding to the current trigger trip signal of the circuit as F, and through calculate the preliminary correlation values of each group of cases; Sort the preliminary correlation values of each group of cases from smallest to largest, and screen the first t groups of cases from the left as the preliminary screening cases; where t > 5. For each group of preliminary screening cases, a three-dimensional space coordinate system is pre-constructed, with the current confidence value, power confidence value, and frequency confidence value as the values on the X-axis, Y-axis, and Z-axis respectively, and the three-dimensional coordinate points corresponding in the three-dimensional space coordinate system are plotted as reference points. Similarly, plot the three-dimensional coordinate points corresponding to the current confidence value, power confidence value, and frequency confidence value of the circuit corresponding to the current trip signal in the three-dimensional space coordinate system as the starting point. Construct vertical line segments between the starting point and each group of reference points, screen the first three shortest preliminary screening cases of the vertical line segments, and extract and match the estimated fault type and the actual fault type respectively. If the matching results are all consistent, select the case with the shorter vertical line segment, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the current trip signal. If the matching results are all inconsistent, select the case with the shorter vertical line segment, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the current trip signal. If there is a group of cases with consistent matching results, directly select them, and use the estimated fault type in the selected cases as the estimated fault type of the circuit corresponding to the current trip signal. If there are two groups of cases with consistent matching results, select the case with the shorter vertical line segment, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the current trip signal.

9. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 8, characterized in that, Execute the corresponding steps based on the estimated fault type, specifically: If the estimated fault type is an instantaneous fault, automatically attempt to close the switch after the set delay time. If the closing fails after the set number of reclosing attempts, control the circuit breaker to lock and send a troubleshooting signal to the technician; if the estimated fault type is a permanent fault, control the circuit breaker to lock and send a troubleshooting signal to the technician.

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