A smart meter fault arc detection method based on intelligent perception algorithm

Abstract

The application relates to the field of intelligent electric meters, in particular to a smart electric meter fault arc detection method based on a smart sensing algorithm, which comprises the following steps: determining a circuit load state according to the dynamic characteristic value of the relevant load of the electric meter and the number of the relevant load, and determining a sampling mode as a predictive optimization sampling or sampling with a reference sampling frequency according to the circuit load state; when the predictive optimization sampling is performed, determining a primary sampling frequency based on the number of loads to be analyzed; under a primary optimization condition, performing a first optimization analysis on each load to be analyzed, and determining whether to adjust the sampling frequency of the load to be analyzed according to the average value of the node completion degree; under a secondary optimization condition, determining a sampling setting strategy according to the interval balance degree; and the application improves the sampling precision of the sampling mode, thereby improving the accuracy of the fault arc detection.

Description

Technical Field

[0001] This invention relates to the field of smart meters, and in particular to a method for detecting fault arcs in smart meters based on intelligent sensing algorithms. Background Technology

[0002] Arc faults are a common electrical fault in power systems, seriously threatening their safe operation. Currently, arc fault detection technology based on intelligent sensing algorithms collects electrical signals such as current and voltage, and combines signal processing, pattern recognition, and machine learning algorithms to achieve accurate identification of arc faults. However, in practical applications, the accuracy of arc fault detection in industrial scenarios is easily affected by the complexity of the environment, the diversity of loads, and the characteristics of the arc signal itself, making arc fault detection difficult. Therefore, how to achieve accurate arc fault detection in industrial scenarios is a technical problem that urgently needs to be solved by researchers in this field.

[0003] Chinese Patent Publication No. CN107370124B discloses an arc fault detection method, including: a data sampling step, in which data is sampled according to a predetermined sampling frequency and sampling period; a sampled data preprocessing step, in which the sampled data is preprocessed and saved; a sampled data processing step, in which the preprocessed sampled data is processed, the fault type is pre-determined based on the processing result, and a corresponding fault judgment subroutine is initiated, or a tripping instruction is given; and an action execution step, in which a tripping action is executed or not executed based on the processing result of the sampled data. If a tripping action is executed, the entire arc fault detection method ends; if a tripping action is not executed, the process returns to the data sampling step to continue data sampling and repeats the above process. It is evident that the above technical solution has the following problems: it does not consider the impact of influencing factors in industrial scenarios on arc fault detection; the predetermined sampling frequency and sampling period make it difficult for the sampled data obtained by a single sampling method to meet the actual detection efficiency requirements, thereby reducing the arc fault detection efficiency. Summary of the Invention

[0004] To address this, the present invention provides a smart meter fault arc detection method based on intelligent sensing algorithms, which overcomes the shortcomings of existing technologies that do not consider the impact of industrial scenarios on fault arc detection. The sampling data obtained by a single sampling method is difficult to meet the actual detection efficiency requirements, thereby reducing the fault arc detection efficiency.

[0005] To achieve the above objectives, the present invention provides a method for detecting fault arcs in smart meters based on intelligent sensing algorithms, comprising:

[0006] The circuit load status is determined based on the dynamic characteristic values ​​of the relevant loads of the electricity meter and the number of relevant loads, and the sampling method is determined based on the circuit load status, either predictive optimization sampling or sampling using a reference sampling frequency.

[0007] When performing predictive optimization sampling, the initial sampling frequency is determined based on the number of loads to be analyzed;

[0008] Under one optimization condition, one type of optimization analysis is performed for each load to be analyzed, and the sampling frequency of the load to be analyzed is adjusted based on the comparison results of the average node completion degree with the preset average node completion degree.

[0009] Under the condition of secondary optimization, the sampling setting strategy is determined to be uniform interval sampling or variable interval sampling based on the interval balance.

[0010] Furthermore, the circuit load status is determined based on the dynamic characteristic values ​​of the relevant loads of the electricity meter and the number of relevant loads. The circuit load status includes:

[0011] The first circuit load state is characterized by a dynamic characteristic value greater than a preset dynamic characteristic value and a number of related loads greater than a preset number of related loads.

[0012] The second circuit load state is when the dynamic characteristic value is less than or equal to the preset dynamic characteristic value or the number of related loads is less than or equal to the preset number of related loads.

[0013] Furthermore, the sampling method is determined based on the circuit load status;

[0014] When the circuit load is in the first circuit load state, the sampling method is predictive optimization sampling;

[0015] When the circuit load is in the second circuit load state, the sampling method is to use the reference sampling frequency for sampling.

[0016] Furthermore, when using predictive optimization sampling, the initial sampling frequency is determined based on the number of loads to be analyzed;

[0017] The initial sampling frequency is positively correlated with the number of loads to be analyzed.

[0018] Furthermore, under the condition of one optimization, a type of optimization analysis is performed for each load to be analyzed to obtain the node completion degree of a type of related device corresponding to each load to be analyzed;

[0019] If the average node completion rate is greater than the preset average node completion rate, the initial sampling frequency is increased and adjusted, and the increased initial sampling frequency is recorded as the intermediate sampling frequency.

[0020] The optimization condition is that the frequency sampling method is predictive optimization sampling and the initial sampling frequency is determined.

[0021] Furthermore, under the condition of secondary optimization, a second type of optimization analysis is performed on each load to be analyzed to obtain several prediction intervals, and the sampling setting strategy is determined according to the interval balance.

[0022] If the interval balance is greater than the preset interval balance, the sampling setting strategy is equal interval sampling;

[0023] If the interval balance is less than or equal to the preset interval balance, the sampling setting strategy is variable interval sampling;

[0024] The secondary optimization condition is that the intermediate sampling frequency is confirmed or it is determined that there is no need to adjust the initial sampling frequency.

[0025] Furthermore, the second-order optimization analysis for a single load to be analyzed includes:

[0026] The route congestion coefficient and route length of each type II related device in the load to be analyzed are detected to determine the initial estimated time point of each type II related device;

[0027] The preset interval corresponding to the initial estimated time point is determined based on the degree of transportation conflict and the extent of scope change for each type of related equipment.

[0028] Furthermore, the interval balance is confirmed by extracting each preset interval corresponding to each load to be analyzed, extracting the initial estimated time point corresponding to each preset interval, and calculating the time interval between each adjacent initial estimated time point. The formula for calculating the interval balance S is:

[0029]

[0030] Where Si is the i-th time interval. i = 1, 2, 3, ..., m, where m is the total number of time intervals.

[0031] Furthermore, when the sampling strategy is set to equal-interval sampling, data is sampled using the initial sampling frequency or the intermediate sampling frequency within the preset sampling period.

[0032] Furthermore, in variable interval sampling, data is sampled at an initial sampling frequency or an intermediate sampling frequency for the first segment within a preset sampling period, and at a final sampling frequency for the second segment.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows: the circuit load state is determined based on the dynamic characteristic values ​​and the number of related loads of the electricity meter. The dynamic characteristic values ​​and the number of related loads reflect the possible influence of the related loads on fault arc detection, and different sampling methods are determined accordingly. This avoids the problem in the prior art where the data obtained by a single sampling method is affected by harmonic currents and current distortions, making it difficult to support the requirements of fault arc detection. The sampling method of the present invention can more effectively match the circuit load conditions in actual scenarios, thereby improving data sampling efficiency and data validity.

[0034] Furthermore, in the technical solution of this invention, under the condition of one optimization, when performing a type of optimization analysis on a single load to be analyzed, if the average node completion degree of each type of related device is greater than the preset average node completion degree, the sampling frequency is increased and adjusted. The average node completion degree reflects the subsequent workload of the related load corresponding to each type of related device. Taking into account the influence of the operating status of the load to be analyzed on the type of related device, it is possible to predictively adjust the primary sampling frequency, making the primary sampling frequency more consistent with the actual working scenario. Compared with the existing technology of adjusting the sampling method when the detection effect is poor, this invention avoids the delay problem of the existing technology, improves the data sampling efficiency and data validity, and further improves the fault arc detection efficiency of this invention.

[0035] Furthermore, in the technical solution of the present invention, under the condition of secondary optimization, two types of optimization analysis are performed on each load to be analyzed to obtain several prediction intervals, and different sampling setting strategies are determined based on the comparison results of the interval balance degree and the preset interval balance degree. The interval balance degree reflects whether there is an influence between the working states of each type of related equipment, making the specific sampling settings more targeted, avoiding the problem of reduced accuracy of fault arc detection caused by a single sampling method, and thus improving the effectiveness of sampling data.

[0036] Furthermore, the technical solution of this invention detects the route congestion coefficient and route length of each type II related device of the load to be analyzed to determine the initial estimated time point of each type II related device, and determines the preset interval based on the transportation conflict degree and range change amount of each type II related device. The route congestion coefficient and route length reflect the estimated working status of the type II related devices, and the transportation conflict degree and range change amount effectively analyze and adjust the task completion time of the type II related devices, thereby effectively predicting the working status of subsequent related loads. The influence of the type II related devices on the operating status of the load to be analyzed is taken into account, thereby improving the acquisition efficiency and further improving the fault arc detection efficiency of this invention.

[0037] Furthermore, in the technical solution of this invention, the extraction effect evaluation value corresponding to each feature extraction strategy is determined according to the feature division coefficient and the feature fluctuation coefficient. The feature extraction method is determined according to the difference of the evaluation value, which is to select the feature extraction strategy with the largest extraction effect evaluation value for feature extraction or to combine feature selection. The feature division coefficient reflects the difference between different extracted features. The higher the degree of difference, the higher the effectiveness of the corresponding extracted feature. The feature fluctuation coefficient reflects whether the circuit is in a stable state. This is conducive to effectively analyzing the extraction effect evaluation value corresponding to each feature extraction strategy, avoiding the inability of a single extraction method to fully extract features, thereby improving the sufficiency of feature extraction. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the smart meter fault arc detection method based on intelligent sensing algorithm according to the present invention;

[0039] Figure 2 This is a flowchart illustrating how the circuit load state is determined based on the dynamic characteristic values ​​of the relevant loads of the electricity meter and the number of relevant loads, according to the present invention.

[0040] Figure 3 This is a flowchart illustrating how the sampling method is determined based on the circuit load state according to the present invention.

[0041] Figure 4 This is a flowchart illustrating the sampling setting strategy determined by the present invention based on interval equalization. Detailed Implementation

[0042] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0043] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0044] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0045] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0046] Please see Figures 1 to 4 As shown, this invention provides a method for detecting fault arcs in smart meters based on intelligent sensing algorithms, comprising:

[0047] The circuit load status is determined based on the dynamic characteristic values ​​of the relevant loads of the electricity meter and the number of relevant loads, and the sampling method is determined based on the circuit load status, either predictive optimization sampling or sampling using a reference sampling frequency.

[0048] When performing predictive optimization sampling, the initial sampling frequency is determined based on the number of loads to be analyzed;

[0049] Under one optimization condition, one type of optimization analysis is performed for each load to be analyzed, and the sampling frequency of the load to be analyzed is adjusted based on the comparison results of the average node completion degree with the preset average node completion degree.

[0050] Under the condition of secondary optimization, the sampling setting strategy is determined to be uniform interval sampling or variable interval sampling based on the interval balance.

[0051] In this invention, the electricity meter is applied in an industrial setting. This industrial setting includes, but is not limited to, related loads, Class I related equipment, and Class II related equipment. Related loads are load devices that generate load harmonics during the fault arc detection of the electricity meter, such as arc welding machines and equipment with frequency converters. Class I related equipment refers to production equipment with a fixed location and a corresponding working relationship with the related load. Class II related equipment refers to mobile transportation equipment with a corresponding transportation relationship with the related load. It is understood that the working relationship involves the workpiece produced by Class I related equipment being transported to the related load for processing, and the transportation relationship involves Class II related equipment transporting the workpiece to the related load. For example, if the related load is an arc welding machine used for steel plate processing, then its corresponding Class I related equipment is a steel plate preparation system, and its corresponding Class II related equipment is an AGV transport vehicle. The workpiece is a steel plate. This is easily understood by those skilled in the art and will not be elaborated upon here. It is understood that in this invention, each related load and its corresponding Class I and Class II related equipment are pre-set by the user, and the brand and specifications of the electricity meter are not limited here.

[0052] This invention utilizes several historical records. Each historical record includes at least one record of fault arc detection and the corresponding detection value. The detection value includes, but is not limited to, the number of start-stop cycles, load runtime, dynamic characteristic values, the number of related loads, the average node completion rate, interval balance, loading ratio, transportation conflict degree, and range change amount. Furthermore, each historical record has a corresponding qualified mark, which records whether the fault arc detection effect of the historical record meets the user's requirements. Whether the user's requirements are met is determined by the user based on the arc detection efficiency or arc detection accuracy, which is already known to those skilled in the art and will not be elaborated here.

[0053] Specifically, the circuit load status is determined based on the dynamic characteristic values ​​of the relevant loads of the electricity meter and the number of relevant loads. The circuit load status includes:

[0054] The first circuit load state is characterized by a dynamic characteristic value greater than a preset dynamic characteristic value and a number of related loads greater than a preset number of related loads.

[0055] The second circuit load state is when the dynamic characteristic value is less than or equal to the preset dynamic characteristic value or the number of related loads is less than or equal to the preset number of related loads.

[0056] This invention employs a cyclical monitoring cycle, the duration of which is set by the user. It is understood that the greater the user's requirement for detection accuracy, the shorter the monitoring cycle. In this specific implementation, the duration of a single monitoring cycle is 5 minutes. The dynamic characteristic value is determined by performing a dynamic characteristic value assessment at the end of each monitoring cycle. The sum of the sub-dynamic values ​​of each relevant load within that monitoring cycle is recorded as the dynamic characteristic value. For a single relevant load, its corresponding sub-dynamic value = (number of starts / stops / preset number of starts / stops) × α1 + (load runtime / preset load runtime) × α2. The number of starts / stops refers to the number of times the relevant load was started and stopped in the most recent monitoring cycle, and the load runtime is the working duration of the relevant load in the most recent monitoring cycle. Here, α1 is the first dynamic weighting coefficient, and α2 is the second dynamic weighting coefficient. The values ​​of α1 and α2 can be obtained by the user using, but not limited to, historical records, human experience settings, or deep learning methods. It is understood that the user can... The system can learn from historical records that meet user needs through deep learning networks, obtain the values ​​of α1 and α2, and optimize them. Details are omitted here. In this specific implementation, α1 = 0.4, α2 = 0.6, and the preset start / stop count and preset load runtime can be set by the user according to the actual application scenario. It is understood that the start / stop count and load runtime can effectively reflect the impact of the relevant load on the accuracy of fault arc detection. The greater the user's demand for fault arc detection accuracy, the smaller the preset start / stop count and preset load runtime. A method for determining the preset start / stop count and preset load runtime is provided: extract the corresponding start / stop count and load runtime from the historical records that meet user needs, filter out outliers using machine learning methods, and record the average values ​​of the start / stop count and load runtime after removing outliers as the preset start / stop count and preset load runtime, respectively. In this specific implementation, the preset start / stop count is 3 times, and the preset load runtime is 5 minutes.

[0057] The preset dynamic characteristic value can be set by the user according to the actual application scenario. In this invention, the dynamic characteristic value can effectively reflect the degree of influence of the relevant load on the fault arc detection. The greater the user's demand for the accuracy of fault arc detection, the smaller the preset dynamic characteristic value. A method for determining the preset dynamic characteristic value is provided, which extracts the dynamic characteristic values ​​in the historical records that meet the user's needs, filters out the outliers through machine learning methods, and records the average value of the dynamic characteristic values ​​after removing the outliers as the preset dynamic characteristic value.

[0058] The value of the preset related load quantity can be set by the user according to the actual application scenario. It is understood that the current waveform of the nonlinear load is easy to be superimposed with the current characteristics generated by the fault arc, resulting in the fault arc characteristics being indistinct, increasing the complexity of detection, and thus increasing the detection difficulty. Therefore, the higher the user's expectation for the fault arc detection accuracy, the smaller the preset related load quantity. In the specific implementation of this invention, the preset related load quantity is 30%.

[0059] Specifically, the sampling method is determined based on the circuit load status;

[0060] When the circuit load is in the first circuit load state, the sampling method is predictive optimization sampling;

[0061] When the circuit load is in the second circuit load state, the sampling method is to use the reference sampling frequency for sampling.

[0062] In this invention, the larger the value of the reference sampling frequency, the greater the accuracy of the sampling data and the greater the data processing capability required. Users can set it according to the actual application scenario. One reference sampling frequency value is provided: reference sampling frequency = 500kHz.

[0063] Specifically, when using the predictive optimization sampling method, the initial sampling frequency is determined based on the number of loads to be analyzed.

[0064] The initial sampling frequency is positively correlated with the number of loads to be analyzed.

[0065] The relevant loads that correspond to at least one of the first-class and second-class related devices are denoted as the loads to be analyzed.

[0066] Specifically, under one optimization condition, a type of optimization analysis is performed for each load to be analyzed to obtain the node completion degree of a type of related device corresponding to each load to be analyzed.

[0067] If the average node completion rate is greater than the preset average node completion rate, the initial sampling frequency is increased and adjusted, and the increased initial sampling frequency is recorded as the intermediate sampling frequency.

[0068] If the average node completion rate is less than or equal to the preset average node completion rate, it is determined that no adjustment is needed for the initial sampling frequency.

[0069] The optimization condition is that the frequency sampling method is predictive optimization sampling and the initial sampling frequency is determined.

[0070] For a single type of related equipment, its corresponding node completion degree is equal to the amount of work completed in the most recent production cycle. The average node completion degree of each type of related equipment is recorded as the average node completion degree, and the amount of work completed is the number of workpieces produced by each type of related equipment in the most recent production cycle. This invention applies a production cycle value with a cyclic setting, which users can set according to the actual application scenario. The higher the production efficiency of a type of related equipment, the smaller the production cycle value. One possible value is a production cycle duration of 30 minutes.

[0071] The preset average node completion rate can be adaptively set by the user according to the actual situation. It is understood that the preset average node completion rate can effectively reflect the impact of a class of related devices on the operating status of the load to be analyzed. Furthermore, the comparison between the average node completion rate and the preset average node completion rate allows for the numerical adjustment of the primary sampling frequency in advance, improving the effectiveness of the sampling data. A method for determining the preset average node completion rate is provided, which extracts the average node completion rate from the historical records that meet the user's needs, detects outliers using the outlier method, and records the median of all average node completion rates after removing outliers as the preset average node completion rate.

[0072] When the average node completion rate is greater than the preset average node completion rate, the difference between the average node completion rate and the preset average node completion rate is denoted as V, where V = average node completion rate - preset average node completion rate. When adjusting the initial sampling frequency, V is positively correlated with the increase in the initial sampling frequency. A specific increase method is provided: intermediate sampling frequency = initial sampling frequency + k1 × V, increase = k1 × V, where k1 is the correlation coefficient, which can be obtained by the user through historical records. How to obtain training samples and learn weight coefficients through historical records is easy for those skilled in the art to understand and will not be elaborated here. This invention sets a maximum increase, which is 10kHz. When the calculated value of the increase is greater than 10kHz, the increase is taken according to the maximum increase.

[0073] Specifically, under the condition of secondary optimization, secondary optimization analysis is performed on each load to be analyzed to obtain several prediction intervals, and the sampling setting strategy is determined according to the interval balance.

[0074] If the interval balance is greater than the preset interval balance, the sampling setting strategy is equal interval sampling;

[0075] If the interval balance is less than or equal to the preset interval balance, the sampling setting strategy is variable interval sampling; the secondary optimization condition is that the intermediate sampling frequency is confirmed or it is determined that there is no need to adjust the initial sampling frequency.

[0076] Specifically, the second-order optimization analysis for a single load to be analyzed includes:

[0077] The initial estimated time point for each type of related equipment is determined by detecting the route congestion coefficient and route length of each type of related equipment under the load to be analyzed.

[0078] The preset interval is determined based on the degree of transportation conflict and the extent of range changes for each type of related equipment.

[0079] For a single Class II related device with a load to be analyzed, the Class II related device corresponds to several incomplete task points. It is understood that the Class II related device may have multiple related loads with transportation relationships. When the Class II related device receives a task instruction, it obtains the task points corresponding to the task instruction, which are pick-up points and delivery points. Incomplete task points are the task points corresponding to the currently incomplete task instructions of the Class II related device. A work route is generated based on the incomplete task points, and the distance from the starting point of the work route to the load to be analyzed is recorded as the route length. How to generate a work route based on incomplete task points is a topic already understood by those skilled in the art and is not specifically limited here. A method for generating a work route based on incomplete task points is provided. The working route is determined according to the order in which the task instructions are received. If the current task instructions include Task Instruction 1, Task Instruction 2, and Task Instruction 3, the task instruction corresponding to the load to be analyzed is Task Instruction 3. The working route starts from the current location of the second-class related equipment, and is sequentially the pickup point of Task Instruction 1, the delivery point of Task Instruction 1, the pickup point of Task Instruction 2, the delivery point of Task Instruction 2, the pickup point of Task Instruction 3, and the delivery point of Task Instruction 3. The passable routes between each adjacent point (including the starting point, pickup point, and delivery point) are obtained, and the shortest passable route is recorded as a sub-route. The sum of the sub-routes is the working route, and the length of the working route is the route length of the second-class related equipment.

[0080] The method for determining the route congestion coefficient for a single Category II related device is to detect the number of Category II related devices on its working route and record it as the route congestion coefficient.

[0081] The initial estimated time point is the time point after the current moment, with an estimated duration interval. Estimated duration = (route length / estimated speed) + congestion value, where congestion value = route congestion coefficient × L. The estimated speed can be set by the user. Users can calculate the movement speed by analyzing the time required to complete different work routes in historical records, and record the average movement speed as the estimated speed. One possible value for estimated speed is 80% of the maximum operating speed of the Class II related equipment under interference-free conditions. L is the congestion baseline, a constant. It can be understood that the larger the value of L, the greater the impact of the number of Class II related equipment on the work route on the working time of the Class II related equipment. One possible value for L is L = 0.5. The units for both estimated duration and congestion value are seconds.

[0082] Each initial estimated time point corresponds to a preset interval, which is a time segment centered on the initial estimated time point, and the unit of the preset interval is seconds. For a single Class II related device, it is denoted as the target related device. Its transportation conflict degree is the number of other mobile devices on the working route of the target related device whose transportation direction is opposite to that of the target related device. Mobile devices include, but are not limited to, other Class II related devices, manual transport vehicles, and robots. The range change amount is all Class II related devices corresponding to other related loads whose loading ratio is less than the preset loading ratio within the preset range of the working route of the target related device. For a single Class II related device, its corresponding loading ratio is the weight of its currently loaded workpiece divided by its maximum loadable weight. The loaded weight can be obtained through a weight sensor. The preset loading ratio is a reference value used to determine the current loading capacity of the Class II related device. It can be understood that the stronger the loading capacity of the Class II related device, the greater the probability of it receiving task instructions and the greater the impact on the line transportation. In this invention, the preset loading ratio is 50%.

[0083] The preset interval is positively correlated with both the transportation conflict degree and the range change amount. In the specific implementation of this invention, the preset interval = initial interval × effective coefficient, where the effective coefficient is the larger of ratio A and ratio B. Ratio A is transportation conflict degree / preset transportation conflict degree, and ratio B is range change amount / preset range change amount. The values ​​of the initial interval, preset transportation conflict degree, and preset range change amount can be set by the user according to the actual scenario. It can be understood that the greater the user's requirement for the accuracy of the preset interval, the smaller the values ​​of the preset transportation conflict degree and preset range change amount, and the smaller the initial interval. One possible value for the initial interval, preset transportation conflict degree, and preset range change amount is provided: the initial interval is 60s, the preset transportation conflict degree is 10, and the preset range change amount is 8.

[0084] The method for confirming the interval balance is as follows: extract each preset interval corresponding to the load to be analyzed, extract the initial estimated time point corresponding to each preset interval, and calculate the time interval between each adjacent initial estimated time point. The formula for calculating the interval balance S is:

[0085]

[0086] Where Si represents the i-th time interval, and the order of i can be random without affecting the detection result. i = 1, 2, 3, ..., m, where m is the total number of time intervals. The preset interval balance value can be adaptively set by the user according to actual conditions. It is understood that the interval balance can effectively reflect the impact of the working status of two types of related equipment on the accuracy of fault arc detection. The greater the user's demand for fault arc detection accuracy, the larger the preset interval balance value. A method for determining the preset interval balance value is provided: extracting the interval balance from historical records that meet the user's needs, identifying outliers using the Z-Score method, and recording the average interval balance after removing outliers as the preset interval balance value.

[0087] Specifically, when the sampling setting strategy is equal-interval sampling, the initial sampling frequency or the intermediate sampling frequency is used for data sampling within the preset sampling period. If the average node completion rate is greater than the preset average node completion rate, the intermediate sampling frequency is used. If it is determined that there is no need to adjust the initial sampling frequency, the initial sampling frequency is used for data sampling.

[0088] Specifically, in variable interval sampling, data is sampled at the initial or intermediate sampling frequency for the first segment within the preset sampling period, and at the final sampling frequency for the second segment.

[0089] The preset sampling period is from the current time to the next sampling time. The strategy setting has been confirmed.

[0090] In this invention, the specific data sampling method is set by the user. For example, if fault arc detection is achieved through current signal analysis, the sampled data is the current waveform. After sampling, the presence of a fault arc is determined based on abnormal characteristics in the current waveform (such as high-frequency oscillation, zero-wave extension, etc.). A corresponding sampling frequency is used for sampling, and the specific waveform sampling length is set by the user. The greater the user's requirement for the validity of the sampled data, the longer the waveform sampling length will be; this is easily understood by those skilled in the art.

[0091] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for detecting fault arcs in smart meters based on intelligent sensing algorithms, characterized in that, include: The circuit load status is determined based on the dynamic characteristic values ​​of the relevant loads of the electricity meter and the number of relevant loads, and the sampling method is determined based on the circuit load status, either predictive optimization sampling or sampling using a reference sampling frequency. When performing predictive optimization sampling, the initial sampling frequency is determined based on the number of loads to be analyzed; Under one optimization condition, one type of optimization analysis is performed for each load to be analyzed, and the sampling frequency of the load to be analyzed is adjusted based on the comparison result of the average node completion degree with the preset average node completion degree. Under the condition of secondary optimization, the sampling setting strategy is determined to be uniform interval sampling or variable interval sampling based on the interval balance. Fault arc detection is achieved through current signal analysis. The sampled data is the current waveform, and after sampling, the presence of fault arc is determined based on abnormal characteristics in the current waveform, including high-frequency oscillation and zero-wave extension.

2. The method for detecting fault arcs in smart meters based on intelligent sensing algorithms according to claim 1, characterized in that, The circuit load status is determined based on the dynamic characteristic values ​​of the relevant loads of the electricity meter and the number of relevant loads. The circuit load status includes: The first circuit load state is characterized by a dynamic characteristic value greater than a preset dynamic characteristic value and a number of related loads greater than a preset number of related loads. The second circuit load state is when the dynamic characteristic value is less than or equal to the preset dynamic characteristic value or the number of related loads is less than or equal to the preset number of related loads.

3. The method for detecting fault arcs in smart meters based on intelligent sensing algorithms according to claim 2, characterized in that, The sampling method is determined based on the circuit load status; When the circuit load is in the first circuit load state, the sampling method is predictive optimization sampling; When the circuit load is in the second circuit load state, the sampling method is to use the reference sampling frequency for sampling.

4. The method for detecting fault arcs in smart meters based on intelligent sensing algorithms according to claim 3, characterized in that, When the sampling method is predictive optimization sampling, the initial sampling frequency is determined based on the number of loads to be analyzed; The initial sampling frequency is positively correlated with the number of loads to be analyzed.

5. The method for detecting fault arcs in smart meters based on intelligent sensing algorithms according to claim 4, characterized in that, Under a single optimization condition, a type of optimization analysis is performed for each load to be analyzed to obtain the node completion degree of a type of related device corresponding to each load to be analyzed. If the average node completion rate is greater than the preset average node completion rate, the initial sampling frequency is increased and adjusted, and the increased initial sampling frequency is recorded as the intermediate sampling frequency. The optimization condition is that the frequency sampling method is predictive optimization sampling and the initial sampling frequency is determined.

6. The method for detecting fault arcs in smart meters based on intelligent sensing algorithms according to claim 5, characterized in that, Under the condition of secondary optimization, a second type of optimization analysis is performed on each load to be analyzed to obtain several prediction intervals, and the sampling setting strategy is determined according to the interval balance. If the interval balance is greater than the preset interval balance, the sampling setting strategy is equal interval sampling; If the interval balance is less than or equal to the preset interval balance, the sampling setting strategy is variable interval sampling; The secondary optimization condition is that the intermediate sampling frequency is confirmed or it is determined that there is no need to adjust the initial sampling frequency.

7. The method for detecting fault arcs in smart meters based on intelligent sensing algorithms according to claim 6, characterized in that, Type II optimization analysis for a single load to be analyzed includes: The route congestion coefficient and route length of each type II related device in the load to be analyzed are detected to determine the initial estimated time point of each type II related device; The preset interval corresponding to the initial estimated time point is determined based on the degree of transportation conflict and the extent of scope change for each type of related equipment.

8. The method for detecting fault arcs in smart meters based on intelligent sensing algorithms according to claim 7, characterized in that, The interval balance is determined by extracting each preset interval corresponding to each load to be analyzed, extracting the initial estimated time point corresponding to each preset interval, and calculating the time interval between each adjacent initial estimated time point. The formula for calculating the interval balance S is as follows: ， Where Si is the i-th time interval. , i = 1, 2, 3, ..., m, where m is the total number of time intervals.

9. The method for detecting fault arcs in smart meters based on intelligent sensing algorithms according to claim 8, characterized in that, When the sampling strategy is set to equal interval sampling, data is sampled using the initial sampling frequency or the intermediate sampling frequency within the preset sampling period.

10. The method for detecting fault arcs in smart meters based on intelligent sensing algorithms according to claim 8, characterized in that, In variable interval sampling, data is sampled at the initial or intermediate sampling frequency for the first segment within the preset sampling period, and at the final sampling frequency for the second segment.

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