A metering type intelligent reclosing circuit breaker based on the Internet of Things

Abstract

The present invention discloses a metering type intelligent reclosing circuit breaker based on the Internet of Things, which relates to the technical field of power systems; the present invention first extracts the current data at each time point in each time zone, calculates the average value after screening out the highest and lowest values to obtain the comprehensive current value, then calculates the ratio with the current comparison value in the previous set time window, obtains 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, so as to adjust the current reference threshold of each time zone in the next set time window, which can adapt to the normal fluctuations of circuit current under different electricity consumption periods and equipment types, and avoid the mis-tripping of traditional circuit breakers caused by static thresholds.

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:

[0004] 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, which is prone to misjudgment. In industrial production, the instantaneous current during equipment startup is large, which will cause traditional circuit breakers to trip falsely and affect the continuity of production;

[0005] When the circuit trips due to a fault, it is difficult for traditional circuit breakers to accurately distinguish between instantaneous faults and permanent faults. For instantaneous faults, if the power supply cannot be restored in time by automatic reclosing, 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 scope of the fault, increase the maintenance cost and power outage time.

[0006] Therefore, a metering intelligent reclosing circuit breaker based on the Internet of Things is introduced. Summary of the Invention

[0007] 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.

[0008] 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:

[0009] 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;

[0010] 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 it is detected that the current signal exceeds the set threshold at a certain time point within the time zone, it is determined that the circuit trips and a trip signal is sent to the control module;

[0011] Control module: After receiving the trip signal, it analyzes and evaluates the metering data that currently triggers the trip signal in combination with historical fault data, and determines the estimated fault type that currently triggers the trip signal; the estimated fault type includes transient faults and permanent faults, and executes corresponding steps based on the estimated fault type.

[0012] In some embodiments, the current data of different time zones within a set time window is recorded and evaluated, specifically:

[0013] For the current data in different time zones within the set time window, extract the current data at each time point in the different time zones;

[0014] The highest and lowest current values in the current data at each time point in different time zones are filtered out, and the average value of the current data at the remaining time points is calculated to obtain the comprehensive current value corresponding to different time zones;

[0015] The current comprehensive values corresponding to different time zones within the last set time window are extracted and recorded as current comparison values. The ratio between the current comprehensive value and the current comparison value within the same time zone is calculated, 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.

[0016] In some embodiments, determining the reference threshold of the current signal within the next set time window is specifically:

[0017] Compare the current change ratios in different time zones with the integer one to determine the direction of adjustment. If the current change ratio in a certain time zone is greater than the integer one, the adjustment direction is increasing; otherwise, the adjustment direction is decreasing.

[0018] The current change ratios in different time zones are integrated into increasing and decreasing data sets according to different adjustment directions. A ratio range set is pre-constructed. The ratio range set includes the intervals of each group of current change ratios corresponding to the increasing direction and the intervals of each group of current change ratios corresponding to the decreasing direction. Each interval of the current change ratio corresponds to an adjustment percentage.

[0019] Matching the incremented dataset with the intervals of the current change proportions of each group corresponding to the incrementing direction within the ratio range set, determining the adjustment percentage of each time zone in the incremented dataset in the next set time window, extracting the reference threshold of each time zone and increasing it according to the determined adjustment percentage to serve as the adjusted reference threshold of each time zone in the next set time window;

[0020] Match the reduced data set with the intervals where the current change ratios of each group corresponding to the reduction direction in the ratio range set are located, determine the adjustment percentage of each time zone in the reduced data set in the next set time window, extract the reference threshold of each time zone and perform reduction according to the determined adjustment percentage, and use it as the adjusted reference threshold of each time zone in the next set time window.

[0021] In some embodiments, the analysis and evaluation of the metering data of the current trigger trip signal are specifically as follows:

[0022] S1: Preset the data collection time window after the trigger trip signal, and obtain the voltage, active power, frequency, and current of the circuit within the data collection time window;

[0023] 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 in the rear window, and obtain the mean value of the rear window after calculating the average value respectively;

[0024] Preset the voltage reference value of the circuit, perform ratio calculation with the voltage reference value as the numerator and the mean value of the rear window as the denominator, then perform 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;

[0025] S3: For the current within the data collection time window, extract the reference threshold corresponding to 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;

[0026] 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, and calculate the proportion of the current anomaly count in the number of numerical points, and record it as the count ratio;

[0027] 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;

[0028] 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;

[0029] S4: Preset the reference thresholds corresponding to the preset 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 in step S3.

[0030] In some embodiments, the analyzing and evaluating the metering data of the currently triggered trip signal further includes:

[0031] 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;

[0032] After the marking is completed and normalized, 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; is an additional coefficient, and its specific value is obtained by conversion based on the value of the voltage confidence value.

[0033] In some embodiments, the conversion process is as follows:

[0034] Preset the intervals where each group of confidence values corresponding to the voltage confidence value are located. Each interval where the confidence values are located 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 value.

[0035] In some embodiments, the determining the estimated fault type of the currently triggered trip signal is specifically:

[0036] S6: Compare the fault judgment index of the circuit corresponding to the currently triggered trip signal with the preset index reference range. If the fault judgment index of the circuit corresponding to the currently triggered trip 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 trip signal is lower than the index reference range, it is determined that the estimated fault type is a transient fault;

[0037] If the fault judgment index of the circuit corresponding to the currently triggered trip signal is within the index reference range, execute step S7.

[0038] In some embodiments, the specific process of executing step S7 is:

[0039] S7: Construct a historical database to record the historical cases of each triggered trip signal, including each group of confidence values, estimated fault type, actual fault type, fault occurrence time, and fault occurrence reason;

[0040] Extract the voltage confidence value corresponding to each group of cases from each historical case that triggers the trip signal, and record it as , where p represents the number of each group of cases, p = 1, 2, ..., h, h is the total number of historical cases;

[0041] The voltage confidence value of the circuit corresponding to the current trigger trip signal is marked as F, through Calculate preliminary correlation values for each group of cases;

[0042] Sort the preliminary correlation values of each group of cases from small to large, and select the first t groups of cases from the left as preliminary screening cases; where t>5, for each group of preliminary screening cases, pre-construct a three-dimensional space coordinate system, 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 corresponding three-dimensional coordinate points in the three-dimensional space coordinate system as reference points;

[0043] Similarly, draw the current confidence value, power confidence value, and frequency confidence value of the circuit corresponding to the current trigger trip signal at the corresponding three-dimensional coordinate point in the three-dimensional space coordinate system as the starting point;

[0044] Starting from the starting point, vertical line segments are constructed between each group of reference points. The first three cases with the shortest vertical segments are selected. The predicted fault types are then extracted and matched with the actual fault types. If the matching results are consistent, the case with the shorter vertical segment is selected, and the predicted fault type in the selected case is used as the predicted fault type of the circuit corresponding to the current trigger trip signal.

[0045] If the matching results are inconsistent, the case with the shorter vertical line segment is selected, and the estimated fault type in the selected case is used as the estimated fault type of the circuit corresponding to the current trigger trip signal;

[0046] If there is a set of cases that are consistent in the matching results, they are directly selected and the estimated fault type in the selected case is used as the estimated fault type of the circuit corresponding to the current trigger trip signal;

[0047] If the matching results show that two groups of cases are consistent, the case with the shorter vertical line segment is selected, and the estimated fault type in the selected case is used as the estimated fault type of the circuit corresponding to the current triggered trip signal.

[0048] In some embodiments, the steps of performing corresponding steps based on the estimated fault type are specifically:

[0049] If the estimated fault type is a transient fault, the system will automatically attempt to close the circuit breaker after the set delay time. If closing fails after the set number of reclosing attempts, the circuit breaker will be locked and a troubleshooting signal will be sent to the technician. If the estimated fault type is a permanent fault, the circuit breaker will be locked and a troubleshooting signal will be sent to the technician.

[0050] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0051] In the present invention, first, the current data at each time point in each time zone is extracted. After screening out the highest and lowest values, the average value is obtained to get the comprehensive current value. Then, the ratio is calculated by comparing the comprehensive current value with the current comparison value in the previous set time window, and the current change ratio is obtained. According to this ratio, the adjustment direction is determined, and it is integrated into an increased or decreased data set, which is matched with the pre-constructed ratio range set, so as to adjust the current reference threshold of each time zone in the next set time window. In this way, it can adapt to the normal fluctuations of the circuit current under different power consumption periods and equipment types, and avoid the mis-tripping caused by the static threshold of the traditional circuit breaker;

[0052] After receiving the tripping signal, the present invention presets a data collection time window to obtain the voltage, active power, frequency, and current data of the circuit, 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 initially judged;

[0053] 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 the tripping signal, extracts the voltage confidence values of the historical case and the current tripping signal to calculate the preliminary correlation value, 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 predicted fault type and the actual fault type, the predicted fault type of the current tripping signal is finally accurately determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] 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:

[0055] Figure 1 is the structural block diagram of the present invention;

[0056] Figure 2 is the flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] 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 to make the present application comprehensive and complete, and fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0058] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this 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 expressly so defined herein.

[0059] 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;

[0060] A metering module, a protection module, and a control module are provided inside the Internet of Things platform;

[0061] A high-precision metering chip is included inside the metering module, which is used to collect 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;

[0062] 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 metering 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 within the time zone, it is determined that the circuit trips and a trip signal is sent to the control module;

[0063] Supplementary explanation, 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, the current data in different time zones (such as Monday to Sunday) within this week will be recorded;

[0064] Specifically:

[0065] 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;

[0066] 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;

[0067] Supplementary explanation, the current comprehensive value represents the current level performance within the current time zone;

[0068] Extract the integrated current values corresponding to different time zones within the previous set time window, denoted as the current comparison values; calculate the ratio between the integrated current value and the current comparison value within the same time zone, with the integrated current value as the numerator and the current comparison value as the denominator to obtain the current change ratios for different time zones.

[0069] Compare the current change ratios of different time zones with the integer one to 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.

[0070] 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 set of ratio ranges, 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.

[0071] Match the increasing data set with the intervals where the groups of current change ratios corresponding to the increasing direction in the set of ratio ranges are located, determine the adjustment percentages for each time zone in the increasing data set in the next set time window, extract the reference thresholds for each time zone and increase them according to the determined adjustment percentages as the adjusted reference thresholds for each time zone in the next set time window.

[0072] Match the decreasing data set with the intervals where the groups of current change ratios corresponding to the decreasing direction in the set of ratio ranges are located, determine the adjustment percentages for each time zone in the decreasing data set in the next set time window, extract the reference thresholds for each time zone and decrease them according to the determined adjustment percentages as the adjusted reference thresholds for each time zone in the next set time window.

[0073] Supplementary note: In the first set time window, the technical staff sets the initial reference values of the current signals in different time zones as the reference thresholds.

[0074] Illustrated with embodiments.

[0075] Set time window: one week, that is, adjust the reference thresholds for the next week based on the current data of the previous week.

[0076] Time zone division: Divide one week into 7 time zones from Monday to Sunday.

[0077] Set of ratio ranges:

[0078] Increasing direction: The interval (1, 1.1] corresponds to an adjustment percentage of 5%.

[0079] The interval (1.1, 1.2] corresponds to an adjustment percentage of 10%.

[0080] The adjustment percentage corresponding to the interval (1.2, +∞) is 15%;

[0081] Decreasing direction: The adjustment percentage corresponding to the interval [0.9, 1) is -5%;

[0082] The adjustment percentage corresponding to the interval [0.8, 0.9) is -10%;

[0083] The adjustment percentage corresponding to the interval (0, 0.8) is -15%;

[0084] Initial reference threshold: Set by the technical personnel, the initial reference thresholds from Monday to Sunday are [100, 110, 120, 130, 140, 150, 160] respectively;

[0085] Step 1: Record the current data in different time zones in the first week

[0086] Suppose the current data recorded in each time zone from Monday to Sunday in the first week is as follows (each time zone is recorded once per hour, with 24 data points in a day):

[0087] 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];

[0088] 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];

[0089] 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];

[0090] 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];

[0091] 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];

[0092] 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];

[0093] 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];

[0094] Step 2: Calculate the comprehensive current value in different time zones for the first week

[0095] For each time zone, screen out the highest and lowest current values, and then calculate the average of the remaining data:

[0096] After removing 90 and 148 on Monday, the average of the remaining data is (92 + 95 +... + 145) / 22 ≈ 119;

[0097] After removing 100 and 158 on Tuesday, the average of the remaining data is (102 + 105 +... + 155) / 22 ≈ 129;

[0098] After removing 110 and 168 on Wednesday, the average of the remaining data is (112 + 115 +... + 165) / 22 ≈ 139;

[0099] After removing 120 and 178 on Thursday, the average of the remaining data is (122 + 125 +... + 175) / 22 ≈ 149;

[0100] After removing 130 and 188 on Friday, the average of the remaining data is (132 + 135 +... + 185) / 22 ≈ 159;

[0101] After removing 140 and 198 on Saturday, the average of the remaining data is (142 + 145 +... + 195) / 22 ≈ 169;

[0102] After removing 150 and 208 on Sunday, the average of the remaining data is (152 + 155 +... + 205) / 22 ≈ 179;

[0103] Step 3: Calculate the current change ratio

[0104] Compare the comprehensive current value of each time zone in the first week with the initial reference threshold to obtain the current change ratio:

[0105] Monday: 119 / 100 = 1.19;

[0106] Tuesday: 129 / 110 ≈ 1.17;

[0107] Wednesday: 139 / 120 ≈ 1.16;

[0108] Thursday: 149 / 130 ≈ 1.15;

[0109] Friday: 159 / 140 ≈ 1.14;

[0110] Saturday: 169 / 150 ≈ 1.13;

[0111] Sunday: 179 / 160 ≈ 1.12;

[0112] Step 4: Determine the adjustment direction

[0113] Since the current change ratios of all time zones are greater than 1, the adjustment directions are all in the increasing direction;

[0114] Step 5: Determine the adjustment percentage

[0115] Match the current change ratios of each time zone with the increasing-direction intervals in the ratio range set:

[0116] Monday: 1.19 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%;

[0117] Tuesday: 1.17 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%;

[0118] Wednesday: 1.16 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%;

[0119] Thursday: 1.15 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%;

[0120] Friday: 1.14 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%;

[0121] Saturday: 1.13 falls within the interval (1.1, 1.2], and the adjustment percentage is 10%;

[0122] Sunday: 1.12 falls within the interval (1.1, 1.2], and the adjustment percentage is

[0123] Step 6: Calculate the adjusted reference thresholds for each time zone in the next set time window (the second week).

[0124] Monday: 100 * (1 + 10%) = 110;

[0125] Tuesday: 110 * (1 + 10%) = 121;

[0126] Wednesday: 120 * (1 + 10%) = 132;

[0127] Thursday: 130 * (1 + 10%) = 143;

[0128] Friday: 140 * (1 + 10%) = 154;

[0129] Saturday: 150 * (1 + 10%) = 165;

[0130] Sunday: 160 * (1 + 10%) = 176;

[0131] 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 subsequent time windows, repeat the above process and continuously adjust the reference thresholds according to the actual current data to better adapt to the operation of the circuit.

[0132] The control module is used to receive 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 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.

[0133] 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 interface and is electrically connected to the control module.

[0134] Specifically:

[0135] To determine the estimated fault type of the currently triggered trip signal, specifically:

[0136] S1: Preset the data collection time window after the trip signal is triggered, and obtain the voltage, active power, frequency, and current of the circuit within the data collection time window.

[0137] S2: For the voltage within the data collection time window, divide the data collection time window into a front window and a back window based on the middle time point; extract the voltages at each time point in the back window, and obtain the mean value of the back window after calculating the average value respectively.

[0138] The voltage reference value of the preset circuit is used as the numerator, and the mean value of the rear window is used as the denominator for ratio calculation. After that, the difference between the calculated ratio and the integer one is calculated, and the absolute value is taken to obtain the voltage confidence value of the circuit within the data collection time window;

[0139] 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 a transient fault; through the calculated voltage confidence value, the higher the voltage confidence value, the higher the probability of corresponding to a permanent fault, and vice versa, the higher the probability of corresponding to a transient fault;

[0140] S3: For the current within the data collection time window, extract the reference threshold corresponding to 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 current at each time point within the data collection time window within the line graph;

[0141] 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, and calculate the proportion of the current anomaly count in the number of numerical points, which is recorded as the count ratio;

[0142] 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, which is recorded 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 by taking the peak length as the numerator and the reference line length as the denominator;

[0143] 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;

[0144] Supplementary note: If the current amplitude is very large at the moment of fault and reaches the peak quickly within a short time and then disappears quickly, it is more inclined to a transient 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 probability of corresponding to a permanent fault, and vice versa, the higher the probability of corresponding to a transient fault;

[0145] 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 in step S3;

[0146] Supplementary note: If the active power returns to normal quickly after the trip, it is more likely to be an instantaneous fault; if the active power remains abnormally high, it is more likely to be a permanent fault; through the calculated current confidence value, the higher the current confidence value, the higher the corresponding likelihood of a permanent fault, and vice versa, the higher the likelihood of an instantaneous fault;

[0147] If the frequency returns to normal quickly after the trip, it is more likely to be an instantaneous fault; if the frequency continues to rise abnormally, it is more likely to be a permanent fault; through the calculated current confidence value, the higher the current confidence value, the higher the corresponding likelihood of a permanent fault, and vice versa, the higher the likelihood of an instantaneous fault;

[0148] 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;

[0149] After the marking is completed and normalized, substitute it 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 the value of the voltage confidence value;

[0150] For the preset voltage confidence value, each group of confidence value intervals 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, and vice versa; match the voltage confidence value of the circuit within the data collection time window with each group of confidence value intervals to determine the value of the additional coefficient value;

[0151] For example, if a group of confidence value intervals is set to (0 - 0.01), the additional coefficient value corresponding to this interval is 0.937 at this time;

[0152] S6: Compare the fault judgment index of the circuit corresponding to the currently triggered trip signal with the preset index reference range. If the fault judgment index of the circuit corresponding to the currently triggered trip 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 trip signal is lower than the index reference range, it is determined that the estimated fault type is an instantaneous fault;

[0153] If the fault judgment index of the circuit corresponding to the currently triggered trip signal is within the index reference range, execute step S7;

[0154] S7: Construct a historical database to record historical cases of each triggered trip signal, including confidence values of each group, estimated fault types, actual fault types, fault occurrence times, and fault occurrence reasons;

[0155] Extract the voltage confidence values corresponding to each group of cases from the historical cases of each triggered trip 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;

[0156] Mark the voltage confidence value of the circuit corresponding to the current triggered trip signal as F, and calculate the preliminary correlation values of each group of cases through ;

[0157] 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 technical personnel;

[0158] 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 plot the three - dimensional coordinate points in the three - dimensional space coordinate system as reference points;

[0159] Similarly, plot the three - dimensional coordinate points of the current confidence value, power confidence value, and frequency confidence value of the circuit corresponding to the current triggered trip signal in the three - dimensional space coordinate system as the starting point;

[0160] Construct vertical line segments between the starting point and each group of reference points, screen out the first three shortest vertical line segments of the preliminary screening cases, 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 triggered trip signal;

[0161] 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 triggered trip signal;

[0162] If there is a group of cases with consistent matching results, directly select it, and use the estimated fault type in the selected case as the estimated fault type of the circuit corresponding to the current triggered trip signal;

[0163] 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 triggered trip signal;

[0164] Supplementary note: By 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 modes, the accuracy of fault type recognition is significantly improved, providing a more reliable decision-making basis for the intelligent reclosing strategy;

[0165] Execute corresponding steps based on the predicted fault type, specifically:

[0166] If the predicted fault type is a transient 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;

[0167] If the predicted 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;

[0168] Supplementary note, Figure 2 The specific processes of each step in

[0169] 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;

[0170] Specifically:

[0171] 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;

[0172] 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.

[0173] 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 set of ratio ranges (such as the adjustment percentages corresponding to the increasing direction intervals of 5%, 10%, etc.) to update the current reference threshold for the next window;

[0174] Fault detection and data collection: After triggering a trip, collect multi-parameter data and calculate the confidence value to provide a basis for fault type determination;

[0175] Preset a data collection time window (such as 10 seconds after the trip), collect the voltage, current, active power, and frequency data within this window, and analyze and calculate to obtain the voltage confidence value, current confidence value, power confidence value, and frequency confidence value;

[0176] Fault type determination: Combine multi-parameter confidence values and historical data to judge the fault type;

[0177] Specifically:

[0178] Integrate the confidence values of current, power, and frequency, substitute them into the formula to calculate the fault judgment index, and compare 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, enter the historical data matching process;

[0179] Construct a historical database, extract the voltage confidence value of the current fault and the voltage confidence value of the historical cases to calculate the preliminary correlation value, screen the first t groups of similar cases, and calculate the coordinate point distance between the current fault and the historical cases in a three-dimensional coordinate system (with the confidence values of current, power, and frequency as axes), and select the 3 cases with the shortest distance;

[0180] Finally determine the current fault type according to the matching situation between the predicted fault type and the actual type of the historical cases;

[0181] Execution strategy: Perform differential operations according to the fault type to avoid misclosing or expanding the fault;

[0182] Specifically:

[0183] If the predicted 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;

[0184] If the predicted 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;

[0185] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not elaborate on all details, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can well understand and utilize the present invention. 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 provided: 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 it is detected that the current signal exceeds 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. 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 denoted 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. 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, and the adjustment direction is determined. If the current change ratio in a certain time zone is greater than the integer one, the adjustment direction is changed to the increasing direction, otherwise the adjustment direction is changed to 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 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. 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. Control module: After receiving the trip signal, it analyzes and evaluates the measurement data that currently triggers the trip signal in combination with historical fault data, and determines the estimated fault type of the currently triggered trip signal. Among them, the estimated fault types include instantaneous faults and permanent faults, and corresponding steps are executed based on the estimated fault types.

2. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 1, wherein, Analyze and evaluate the metering data of the currently triggered trip signal, specifically as follows: S1: Preset a data collection time window after the triggered 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; 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 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.

3. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 2, wherein, The analysis and evaluation of the metering data of the currently triggered trip 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.

4. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 3, 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. 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.

5. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 4, characterized in that, The determination of the estimated fault type of the currently triggered trip signal is specifically as follows: S6: Compare the fault judgment index of the circuit corresponding to the currently triggered trip signal with the preset index reference range. If the fault judgment index of the circuit corresponding to the currently triggered trip signal is higher than the index reference range, then determine the estimated fault type as a permanent fault; if the fault judgment index of the circuit corresponding to the currently triggered trip signal is lower than the index reference range, then determine the estimated fault type as a transient fault; If the fault judgment index of the circuit corresponding to the currently triggered trip signal is within the index reference range, then execute step S7; S7: Construct a historical database to record the historical cases of each triggered trip 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 trigger trip 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 value 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, and the current confidence value, power confidence value, and frequency confidence value are used 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 triggered 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 out the first three shortest vertical line segments of the preliminary screening cases, and extract and match the predicted 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 predicted fault type in the selected case as the predicted fault type of the circuit corresponding to the current triggered trip signal. If the matching results are all inconsistent, select the case with the shorter vertical line segment, and use the predicted fault type in the selected case as the predicted fault type of the circuit corresponding to the current triggered trip signal. If there is a group of cases with consistent matching results, directly select them, and use the predicted fault type in the selected case as the predicted fault type of the circuit corresponding to the current 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 predicted fault type in the selected case as the predicted fault type of the circuit corresponding to the current triggered trip signal.

6. The metering type intelligent reclosing circuit breaker based on the Internet of Things according to claim 5, characterized in that, Execute the corresponding steps based on the predicted fault type, specifically: If the predicted 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 predicted fault type is a permanent fault, control the circuit breaker to lock and send a troubleshooting signal to the technician.

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