An ac filter-based filtered data analysis management method and system

Abstract

The application provides an alternating current filter-based filter data analysis management method and system, and belongs to the technical field of data analysis, and specifically comprises the following steps: determining an evaluation risk weight value of an aging state of the alternating current filter by an analysis deviation risk group; determining a filter control strategy of the alternating current filter based on the evaluation risk weight value of the aging state of each alternating current filter and the correlation degree between the evaluation risk weight value and distribution data of a filter processing period; determining a filter control target of the alternating current filter according to the filter control strategy; and determining a filter data analysis management method of the alternating current filter except the filter control target based on filter control target data and filter processing data of each filter control target under the filter control strategy, so that the accuracy of the evaluation processing result of the aging state of the alternating current filter is ensured.

Description

Technical Field

[0001] This invention belongs to the field of data analysis technology, and in particular relates to a filtering data analysis and management method and system based on AC filters. Background Technology

[0002] AC filters are key components in high-voltage direct current (HVDC) transmission systems, used to filter out characteristic harmonics (such as the 11th, 13th, 23rd, and 25th harmonics) generated by the converter, ensuring the power quality of the AC grid. AC filters typically consist of capacitors, reactors, and resistors, operating in harsh environments with high voltage, high current, and strong electromagnetic interference. The operating status of the AC filter directly affects the filtering effect, equipment safety, and grid stability.

[0003] To monitor and analyze the filtering status of AC filters, existing solutions involve installing power quality monitoring devices at the AC filter connection point to monitor the amplitude and phase of each harmonic voltage / current in real time and evaluate the filter's filtering effect. This approach can detect harmonic anomalies and ensure the reliability of filtering, but it suffers from the following technical problems: During filtering, prolonged operation may cause capacitors and reactors to age, leading to a deterioration in the overall filtering results. Therefore, how to implement filtering limit control on some AC filters based on the evaluation and analysis results of the aging status of AC filters, and how to use the filtering limit control data to analyze and optimize the filtering data of each AC filter, thereby avoiding excessive aging risks of AC filters, while ensuring the comprehensiveness and efficiency of the verification of the aging status evaluation and analysis results after model update, has become an urgent technical problem to be solved.

[0004] Specifically, this application provides a method and system for filtering data analysis and management based on an AC filter. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for filtering data analysis and management based on an AC filter, which includes: S1 uses the filtering monitoring data of the AC filter to determine the filtering processing period of the AC filter. Based on the similarity of the harmonic characteristics of each AC filter's filtering processing period, it determines the evaluation matching group of the AC filter. Using the evaluation matching group of each AC filter and the harmonic processing period data in the evaluation matching group, it determines the evaluation and analysis strategy of the AC filter's aging state model. S2 determines the consistency of the evaluation results of the aging state of the AC filter among the evaluation matching groups based on the evaluation analysis strategy, determines the analysis deviation risk group of the AC filter based on the consistency, determines the evaluation risk weight value of the aging state of the AC filter based on the analysis deviation risk group, and determines the filtering control strategy of the AC filter based on the evaluation risk weight value of the aging state of each AC filter and the degree of correlation between the evaluation risk weight value and the distribution data of the filtering processing period. S3 determines the filtering control target of the AC filter according to the filtering control strategy, and determines the filtering data analysis and management method of the AC filter excluding the filtering control target based on the filtering control target data and the filtering processing data of each filtering control target under the filtering control strategy.

[0006] The beneficial effects of this invention are as follows: By analyzing the risk groups of each AC filter's bias and their correlation with the distribution data of the filtering processing period, the bias risk of the model's aging state assessment and the impact of the presence of bias risk on the reliability of the AC filter's aging state assessment are determined. Based on the bias risk of the model's aging state assessment and the impact of the presence of bias risk on the reliability of the AC filter's aging state assessment, the filtering control strategy of the AC filter is determined. This not only ensures the effective control of the filtering frequency of AC filters with potential aging risks, preventing further deterioration of the aging state, but also ensures the timeliness of updating the training data for the model's aging state.

[0007] Based on the number of filtering control targets and the filtering processing limit data of each filtering control target under the filtering control strategy, a filtering data analysis and management method for AC filters excluding the filtering control targets is determined. This ensures that the filtering control targets have a high degree of restriction. Furthermore, the filtering data of AC filters excluding the filtering control targets is analyzed and processed to determine the reliability of the aging state assessment and analysis of the updated model. This also lays the foundation for targeted restriction removal processing of filtering control targets based on the reliability of the aging state assessment and analysis of the updated model, thus ensuring the reliability of the filtering process.

[0008] Furthermore, the filter monitoring data of the AC filter includes the filter processing period of the AC filter and the harmonic current content of different filter processing periods.

[0009] Furthermore, the filtering period of the AC filter is the period during which the AC filter performs harmonic filtering.

[0010] Furthermore, the method for determining the evaluation and analysis strategy of the aging state model of the AC filter is as follows: Based on the similarity of harmonic characteristics during the filtering processing period of the AC filter, filtering processing periods whose harmonic characteristics meet the similarity requirements are divided into the same group. Based on the filtering processing period data in the group of the AC filter, the evaluation matching group of the AC filter is determined. Using the filtering time period data in the evaluation matching group of the AC filter, the duration proportion of the AC filter in each evaluation matching group is determined, and the sum of the duration proportions of the AC filter in each evaluation matching group is used as the association weight value of the AC filter. The evaluation and analysis strategy for determining the aging state model of the AC filter is based on the evaluation matching group of each AC filter and the association weight value of the AC filter.

[0011] Furthermore, the method for determining the filter data analysis and management method for the AC filter excluding the filter control target is as follows: The number of filter control targets is determined based on the filter control target data; Based on the filtering processing constraint data of each filtering control target under the filtering control strategy, the filtering processing constraint time period of the filtering control target is determined; By utilizing the number of filter control targets and the filtering processing time limits for each filter control target, a method for analyzing and managing the filtering data of AC filters excluding the filter control targets is determined.

[0012] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the aforementioned method for filtering data analysis and management based on an AC filter when running the computer program.

[0013] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of a filtering data analysis and management method based on an AC filter; Figure 2 This is a flowchart illustrating the method for determining the evaluation and analysis strategy of an AC filter aging state model. Figure 3 This is a flowchart illustrating the method for determining the filtering control strategy of an AC filter. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0018] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0019] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a method for filtering data analysis and management based on an AC filter is provided, specifically including: S1 uses the filtering monitoring data of the AC filter to determine the filtering processing period of the AC filter. Based on the similarity of the harmonic characteristics of each AC filter's filtering processing period, it determines the evaluation matching group of the AC filter. Using the evaluation matching group of each AC filter and the harmonic processing period data in the evaluation matching group, it determines the evaluation and analysis strategy of the AC filter's aging state model. Furthermore, the filter monitoring data of the AC filter includes the filter processing period of the AC filter and the harmonic current content of different filter processing periods.

[0020] Specifically, the filtering period of the AC filter is the period during which the AC filter performs harmonic filtering.

[0021] It should be noted that, as Figure 2 As shown, the method for determining the evaluation and analysis strategy of the aging state model of the AC filter is as follows: In this embodiment, the reliability of the evaluation and analysis of different AC filter aging states is determined by the similarity of harmonic characteristics during the filtering process of the AC filter and the filtering process data in the evaluation matching group of the AC filter. Based on the reliability of the evaluation and analysis of different AC filter aging states, the evaluation and analysis strategy of the AC filter aging state model is determined, thereby laying the foundation for verifying the reliability of the evaluation and analysis results of the model aging state.

[0022] S11 Based on the similarity of harmonic characteristics during the filtering processing period of the AC filter, the filtering processing periods of the AC filter that meet the similarity requirements of harmonic characteristics are divided into the same group, and the evaluation matching group of the AC filter is determined based on the filtering processing period data in the group of the AC filter. The filtering period of the AC filter is the period during which the AC filter performs harmonic filtering, that is, the period during which the AC filter is in operation and actively filters out harmonic currents in the power grid.

[0023] The harmonic characteristics of the filtering period refer to the characterization of the harmonic current processed by the AC filter in the multidimensional feature space during the filtering period, including characteristic parameters such as harmonic current content, amplitude distribution characteristics of each harmonic, total harmonic distortion rate, main harmonic order and its energy proportion.

[0024] The similarity of the harmonic features meets the requirements, meaning that the Euclidean distance or correlation coefficient of the harmonic features of the two filtering periods in the multidimensional feature space reaches the preset similarity judgment standard. The higher the similarity, the closer the harmonic operating conditions faced by the AC filters in the two periods are.

[0025] The evaluation matching group of the AC filter is a group in which the interval between adjacent filtering processing periods is less than a preset interval threshold. That is, the filtering processing periods in this group are close to each other on the time axis, have similar harmonic characteristics, and the time interval between adjacent periods is short, indicating that the AC filter is in a relatively dense operating state within this time interval.

[0026] Suppose that an AC filter has multiple filtering periods within a certain period of time. Some of these periods have high similarity in harmonic characteristics (such as the main harmonic orders being the 5th and 7th, and the total harmonic distortion rate being similar), and the intervals between adjacent periods are short. These periods are then grouped into the same evaluation matching group.

[0027] The filtering periods are clustered in two ways: based on the similarity of harmonic features and temporal proximity. This aggregates the scattered filtering periods into groups with temporal and feature continuity, allowing subsequent aging status assessments to be performed on a group-by-group basis. This preserves the similarity of the filtering conditions and also shows that similar filtering conditions often appear, indicating that reliable aging status assessments can be performed within the aforementioned groups. This achieves reliable identification of conditions with high reliability in aging status assessment.

[0028] S12 uses the filtering processing time period data in the evaluation matching group of the AC filter to determine the duration ratio of the AC filter in each evaluation matching group, and uses the sum of the duration ratios of the AC filter in each evaluation matching group as the association weight value of the AC filter. The percentage of the duration of the AC filter's filtering processing time in each evaluation matching group refers to the ratio of the cumulative duration of all filtering processing times of the AC filter in a certain evaluation matching group to the total duration of all filtering processing times of the AC filter. This ratio reflects the representative contribution of the group to the overall operation of the AC filter.

[0029] The correlation weight value of the AC filter refers to the value obtained by summing the duration proportions of each evaluation matching group. It reflects the overall distribution concentration of the filtering processing period of the AC filter in each evaluation matching group. The larger the correlation weight value, the more concentrated the filtering processing period is in the evaluation matching group, and therefore the higher the reliability of its aging state assessment.

[0030] Suppose an AC filter has three evaluation matching groups. The proportion of filtering time in group A is 0.15, in group B is 0.30, and in group C is 0.25. Then the association weight value of the AC filter is equal to 0.15 plus 0.30 plus 0.25, which equals 0.70.

[0031] The distribution structure of the filtering processing period is quantified into a correlation weight value. A unified numerical index is used to measure the concentration of the operating modes of different AC filters, thereby achieving an accurate assessment of the reliability of their aging status evaluation.

[0032] S13 determines the evaluation and analysis strategy for the aging state model of the AC filter based on the evaluation matching group of each AC filter and the association weight value of the AC filter.

[0033] Specifically, the above steps include the following: Case 1: If the average value of the correlation weights of each AC filter is less than the preset correlation weight threshold, then the evaluation and analysis strategy for determining the aging state of the AC filter model is to perform aging state evaluation and analysis processing on all AC filters with evaluation matching groups, that is, to perform aging state evaluation and analysis processing on the filtering processing period corresponding to the evaluation matching group in the future.

[0034] The average value of the associated weights of each AC filter refers to the arithmetic mean of the associated weights of all AC filters. It reflects the overall operational concentration level of the AC filter group. The smaller the average value, the worse the reliability of the assessment of the aging status of the AC filters.

[0035] Assuming the average value of the association weight is low, it indicates that the overall operation is relatively dispersed and the duration of each AC filter in the time period corresponding to the matching group is low. Therefore, the reliability of the assessment of the aging state of the AC filter is poor. In this case, aging assessment is performed on all AC filters that have assessment matching groups to determine the reliability of the model's aging state identification.

[0036] Case 2: If the average value of the correlation weights of each AC filter is not less than the preset correlation weight threshold, based on the evaluation matching groups of each AC filter, AC filters whose number of evaluation matching groups is above the preset analysis group number threshold are used as evaluation matching filters. If the proportion of the evaluation matching filter among all AC filters is above the preset proportion threshold, then the evaluation and analysis strategy for the aging state model of the AC filter is to perform aging state evaluation and analysis processing in a target type of AC filter, that is, to perform aging state evaluation and analysis processing in the filtering processing period corresponding to the future evaluation matching group. If the proportion of the evaluation matching filter among all AC filters is not above the preset proportion threshold, then proceed to Case 3.

[0037] The evaluation matching filter refers to an AC filter that evaluates a large number of matching groups, reaching or exceeding a preset threshold for the number of analysis groups, and its aging status evaluation is more reliable.

[0038] The target type of AC filter is an AC filter whose evaluation matching weight value is above a preset matching weight threshold.

[0039] If the average value of the association weight is high and the proportion of the evaluation matching filter is large, it indicates that the overall operation is concentrated and the evaluation reliability of the aging state is high. Therefore, the need for verification processing of the recognition reliability of the aging state of the model is low. At this time, only the target type filter with the highest evaluation matching weight value is evaluated for aging, thereby reducing the difficulty of the evaluation processing of the aging state.

[0040] Scenario 3: Based on the correlation weight value of the AC filter and the number of evaluation matching groups, determine the evaluation matching weight value of the AC filter. Determine whether the average value of the evaluation matching weight values ​​of different AC filters meets the requirements. If yes, the average value of the evaluation matching weight values ​​is relatively large, and the evaluation analysis strategy for the aging state model of the AC filter is determined to be to perform aging state evaluation analysis processing on AC filters of one target type, that is, to perform aging state evaluation analysis processing on the filtering processing period corresponding to the future evaluation matching group. If not, the evaluation analysis strategy for the aging state model of the AC filter is determined to be to perform aging state evaluation analysis processing on AC filters of two target types, that is, to perform aging state evaluation analysis processing on the filtering processing period corresponding to the future evaluation matching group.

[0041] The evaluation matching weight value is determined by the product of the association weight value of the AC filter and the ratio of the number of evaluation matching groups to the number of preset groups. The larger the association weight value and the more evaluation matching groups there are, the larger the evaluation matching weight value, indicating that the evaluation reliability of the aging state is higher.

[0042] The two types of AC filters are AC filters that evaluate matching weight values ​​above a preset matching weight threshold and AC filters that evaluate the number of matching groups above a preset analysis group number threshold. That is, the two types of target types include all filters of the one type of target type, and additionally include filters that only meet the group number condition, thus providing a wider screening range.

[0043] If the average weight value of the evaluation does not meet the requirements, it means that the overall performance of the filter in terms of concentration and richness is average. In this case, the evaluation scope is expanded to two target types, and filters with sufficient number of groups but whose weight values ​​do not meet the requirements are also included in the evaluation, thereby ensuring effective verification of the reliability of the model's aging state identification.

[0044] A balance is established between concentration and richness by using a comprehensive index to evaluate the matching weight values. When the overall comprehensive level is good, the optimal filter is focused to maximize evaluation efficiency. When the level is average, the evaluation coverage is appropriately expanded to ensure sufficient evaluation data and avoid insufficient evaluation samples due to over-focus.

[0045] S2 determines the consistency of the evaluation results of the aging state of the AC filter among the evaluation matching groups based on the evaluation analysis strategy, determines the analysis deviation risk group of the AC filter based on the consistency, determines the evaluation risk weight value of the aging state of the AC filter based on the analysis deviation risk group, and determines the filtering control strategy of the AC filter based on the evaluation risk weight value of the aging state of each AC filter and the degree of correlation between the evaluation risk weight value and the distribution data of the filtering processing period. It is understood that the consistency of the evaluation results of the AC filter in evaluating the aging state among the matched groups is determined based on whether the evaluation results of the AC filter in evaluating the aging state among the matched groups are consistent.

[0046] Specifically, the analysis deviation risk group is the assessment matching group where the number of inconsistent processes with the baseline assessment result of the aging state does not meet the requirements, that is, the assessment matching group is greater than a certain threshold for the number of inconsistent processes. The baseline assessment result is the aging state corresponding to the assessment process with the most assessment processes that are consistent with the assessment result of the aging state during the assessment processes in which the interval between the inconsistent processes is within a preset time range.

[0047] Specifically, such as Figure 3 As shown, the method for determining the filtering control strategy of the AC filter is as follows: In this embodiment, the deviation risk of the model's aging state assessment analysis and processing, as well as the impact of the existence of deviation risk on the reliability of the AC filter's aging state assessment, are determined by the analysis deviation risk groups of each AC filter and the degree of correlation with the distribution data of the filtering processing period. Based on the deviation risk of the model's aging state assessment analysis and processing and the impact of the existence of deviation risk on the reliability of the AC filter's aging state assessment, the filtering control strategy of the AC filter is determined. This not only ensures the effective control of the filtering processing frequency of AC filters with potential aging risks, avoiding further deterioration of the aging state, but also ensures the timeliness of updating the training data of the model's aging state.

[0048] S21 determines the proportion of the analysis deviation risk group in the evaluation matching group of the AC filter based on the analysis deviation risk group of each AC filter, and uses the proportion of the analysis deviation risk group in the evaluation matching group of the AC filter as the evaluation risk weight value of the aging state of the AC filter.

[0049] The analysis deviation risk group is the assessment matching group where the number of inconsistent processes with the baseline assessment result of the aging state does not meet the requirements, that is, the assessment matching group is greater than the preset threshold for the number of inconsistent processes. The baseline assessment result is the aging state corresponding to the assessment process with the most assessment processes that are consistent with the assessment result of the aging state during the assessment processes whose interval duration with the inconsistent processes is within a preset time range.

[0050] The risk weight value for assessing the aging state of the AC filter refers to the proportion of the number of groups identified as risk groups for analysis bias in all assessment matching groups of the AC filter. The higher the proportion, the more inconsistencies and biases there are in the aging state assessment results of the filter in different groups, and the worse the reliability of the model's aging assessment of the filter.

[0051] Suppose that an AC filter has 4 evaluation matching groups. If the number of times the aging state evaluation results in 2 of the groups are inconsistent with the baseline evaluation results exceeds a preset threshold, they are identified as analysis bias risk groups. Then the evaluation risk weight value of the aging state of the filter is equal to 2 divided by 4, which is 0.50.

[0052] The inconsistency of aging assessment results among different groups is quantified into assessment risk weight values. The group-level assessment consistency is used as a proxy indicator of the reliability of model assessment. The higher the assessment risk weight value of the filter, the worse the assessment stability of the model on it, and it needs to be given priority and included in the consideration of filtering control strategies.

[0053] If the sum of the assessment risk weights for the aging state of each AC filter does not meet the requirements, i.e., the sum of the assessment risk weights for the aging state of each AC filter is high (greater than or equal to the preset assessment risk weight and threshold), indicating a significant risk of assessment bias in the aging state, then in order to ensure the timeliness of updating the aging state assessment data of the model and accumulate more training data, a conservative strategy is adopted to determine the filtering control strategy for the AC filter. That is, if the number of abnormal aging state assessment results of the AC filter in history does not meet the requirements (i.e., it is greater than the preset abnormal number threshold), then the filtering control processing of the AC filter is performed.

[0054] The conservative strategy refers to using a single condition (the number of historical anomalies exceeding a threshold) as the trigger condition for filtering control. This has the highest trigger threshold and is the least likely to initiate filtering control. It is suitable for scenarios with a high risk of deviation in aging assessment and accumulates assessment data through stricter filtering control.

[0055] If the sum of the evaluation risk weights of all AC filters exceeds the threshold, it indicates that the overall evaluation deviation risk is high. In this case, a conservative strategy is adopted, and filter control processing is triggered as soon as the number of historical anomalies of the AC filters exceeds the limit.

[0056] When the overall assessment risk is high, the most conservative strategy with the strictest trigger conditions is adopted. When the overall reliability of the model aging assessment is poor, stricter filtering control is used to quickly accumulate actual data feedback of the aging assessment. The assessment risk is gradually reduced through data-driven model iteration updates.

[0057] It should also be noted that if the sum of the assessment risk weights of the aging state of each AC filter meets the requirements (i.e., the sum is less than the preset assessment risk weight and threshold), proceed to step S22.

[0058] S22, based on the correlation between the assessed risk weight value and the distribution data of the filtering processing period, determine the proportion of the assessed risk weight value of the AC filter to the average daily duration of the filtering processing period, and determine the filtering risk weight value of the AC filter based on the proportion of the assessed risk weight value of the AC filter to the average daily duration of the filtering processing period.

[0059] The average daily duration of the filtering processing period refers to the ratio of the average daily duration of the filtering processing period of the AC filter to the total daily duration within the statistical period, reflecting the average operating load intensity of the filter.

[0060] The filter risk weight value is determined by multiplying the evaluation risk weight value of the AC filter by the average daily duration of the filtering period. It also takes into account two dimensions: the degree of evaluation deviation risk and the intensity of operating load. The larger the filter risk weight value, the more unreliable the filter is in evaluation and the higher the filtering demand. The overall threat is greater when there is a deviation in aging risk.

[0061] Assuming the evaluation risk weight value of a certain AC filter is 0.40 and the daily average duration is 0.35, then the filter risk weight value is equal to 0.40 multiplied by 0.35, which is 0.14.

[0062] By multiplying and jointly considering the two dimensions of assessment deviation risk and operating load intensity, a high filtering risk weight value is assigned only when both assessment deviation risk and operating load are high (i.e., the impact is greater when the assessment is unreliable and the operating intensity is high, and the aging assessment results are abnormal).

[0063] If the average value of the filtering risk weight of each AC filter is greater than the preset risk weight threshold, a conservative strategy is adopted to determine the filtering control strategy of the AC filter. If the number of abnormalities in the aging state evaluation results of the AC filter in history does not meet the requirements (i.e., it is greater than the preset abnormality number threshold), then the filtering control processing of the AC filter is performed.

[0064] A conservative strategy is adopted when the overall weight value of the filtering risk is too high. When the multidimensional risk assessment indicates that the overall threat is large, a conservative filtering control rhythm is maintained to improve the efficiency of updating and processing training data.

[0065] Specifically, if the average value of the filtering risk weight of each AC filter is not greater than the preset risk weight threshold, proceed to step S23.

[0066] S23, using the assessment risk weight value and filtering risk weight value of the aging state of each AC filter, determine the filtering control strategy of the AC filter.

[0067] It is understandable that the above steps specifically include the following: S231: Based on the filtering risk weight values ​​of each AC filter and the number of AC filters whose filtering risk weight values ​​are greater than the preset risk weight threshold, determine the comprehensive impact value when the model has an aging state evaluation deviation. Determine whether the comprehensive impact value is greater than the preset impact threshold. If yes, proceed to step S232. If no, determine the filtering control strategy for the AC filter to be adopted using a lenient strategy. If the number of times the AC filter has abnormal aging state evaluation results in history does not meet the requirements, or if there are abnormal aging state evaluation results in a group of more than a preset number, then perform filtering control processing on the AC filter.

[0068] The comprehensive impact value refers to the overall level of the comprehensive filter risk weight value and the coverage ratio of high-risk filters, which are two factors that quantify the overall impact of the model aging assessment deviation. Specifically, it is determined by multiplying the number of AC filters with filter risk weight values ​​greater than the preset risk weight threshold by the preset scaling factor (e.g., 0.1) and the sum of the filter risk weight values ​​of each AC filter by the product of the two.

[0069] The relaxed strategy refers to triggering filtering control processing when either of two conditions (the number of historical anomalies exceeds the limit or anomalies exist in a sufficient number of groups) is met. Compared with the conservative strategy, it adds a group-level triggering path and the triggering conditions are more diversified, making it suitable for scenarios where the impact of evaluation deviations is controllable.

[0070] Assuming the overall impact value is not higher than the preset impact threshold, it indicates that the overall assessment deviation is controllable. A lenient strategy is adopted, and the filter can be triggered as long as one of the following conditions is met (the number of historical anomalies exceeds the limit or the coverage of group anomalies is sufficient).

[0071] The overall impact value is used as the general evaluation index to assess the severity of the deviation. When the deviation affects the overall controllable situation, more flexible triggering conditions are used to screen out filters that have both abnormal performance and require filtering control, thereby ensuring the timeliness and reliability of filter management.

[0072] S232, based on the average of the assessment risk weight values ​​of the aging state of each AC filter, determine whether the average of the assessment risk weight values ​​of the aging state of each AC filter is less than a preset risk weight threshold. If so, the assessment deviation risk of the aging state is low, and a conservative strategy is adopted to determine the filtering control strategy of the AC filter. That is, if the number of times the assessment result of the aging state of the AC filter in history is abnormal does not meet the requirements (i.e., it is greater than the preset abnormal number threshold), then the filtering control processing of the AC filter is performed. If not, a second relaxed strategy is adopted to determine the filtering control strategy of the AC filter. If the number of times the assessment result of the aging state of the AC filter in history is abnormal does not meet the requirements, or if there is a group with a preset number or more of abnormal assessment results of the aging state that has a number of times exceeding the preset abnormal number threshold, then the filtering control processing of the AC filter is performed.

[0073] The difference between the second lenient strategy and the lenient strategy lies in the different thresholds for group anomaly conditions: the second lenient strategy requires the number of anomalies in the group to reach a preset anomaly threshold before it is triggered, while the lenient strategy only requires the existence of anomalies in the group to be triggered. The triggering conditions of the second lenient strategy are more stringent.

[0074] Assuming that the average risk weight value is not lower than the threshold, it indicates that there is still a certain degree of overall assessment bias. The second lenient strategy is adopted, which has an additional group-level triggering path (the number of anomalies in the group exceeds the limit) compared to the conservative strategy. However, the conditions of this path are more stringent than those of the lenient strategy (requiring the number of anomalies rather than just the existence of anomalies).

[0075] The average risk weight value is used as the final refined judgment basis. Under the condition that the comprehensive impact value has reached the standard, the strategy is further finely adjusted from the perspective of evaluation deviation. When the deviation risk is low, the conservative strategy is reverted to ensure the timeliness of training data updates. When the deviation risk still exists, the second relaxed strategy is adopted to achieve an appropriate balance between conservatism and relaxation.

[0076] It should be noted that when performing filtering control processing of the AC filter, if filtering processing has not been performed within the most recent preset time period, or if the leakage current monitoring data at monitoring times that exceed the target content threshold or the target proportion belongs to the evaluation matching group and the leakage current monitoring data at monitoring times that exceed the target number belong to the evaluation matching group, then filtering control processing of the AC filter will be performed to avoid frequent input.

[0077] The leakage current refers to the small current generated by the AC filter in its non-operating state due to insulation aging and other reasons. The fact that its monitoring data belongs to the evaluation matching group indicates that the characteristics of the leakage current are similar to the operating conditions of the filter during its historical intensive operating periods (time periods within the evaluation matching group). The evaluation reliability of the evaluation model can be effectively identified by combining the evaluation processing results in the evaluation matching group.

[0078] This additional condition sets up multiple safeguards for the actual execution of filter control processing. Even if the filter control requirement is logically triggered, filter wave processing must not be performed for a sufficiently long period of time recently (to avoid frequent operation), and filter control will only be actually executed when the harmonic content or leakage current monitoring data reaches a certain severity and characteristic matching degree. This prevents the frequent switching of AC filters due to excessive triggering at the strategy level from affecting grid stability and equipment lifespan.

[0079] S3 determines the filtering control target of the AC filter according to the filtering control strategy, and determines the filtering data analysis and management method of the AC filter excluding the filtering control target based on the filtering control target data and the filtering processing data of each filtering control target under the filtering control strategy.

[0080] It is understood that the method for determining the filter data analysis and management method for AC filters other than the filter control target is as follows: In this embodiment, based on the number of filter control targets and the filtering processing restriction data of each filter control target under the filter control strategy, a filtering data analysis and management method for AC filters excluding the filter control targets is determined. This ensures that the restriction degree of the filter control targets is high. Furthermore, the filtering data of AC filters excluding the filter control targets is analyzed and processed to determine the reliability of the aging state assessment and analysis of the updated model. This also lays the foundation for targeted restriction removal processing of filter control targets based on the reliability of the aging state assessment and analysis of the updated model, thus ensuring the reliability of the filtering process.

[0081] S31 uses the filter control target data to determine the number of filter control targets.

[0082] The filter control target refers to the AC filters that are determined to require filter control processing according to the filter control strategy determined in S2. These filters are included in the key focus and management scope of filter control.

[0083] The filtering control target data includes the identifier of each filtering control target, the type of filtering control strategy to which it belongs, and the basis for triggering filtering control (such as exceeding the limit for the number of abnormal events or meeting the limit for group abnormal coverage).

[0084] Assuming that, according to the lenient strategy, three filters (F2, F5, and F8) out of six Class I target filters need to be filtered and controlled, then the number of filter control targets is three.

[0085] The total number of filtering control targets in the current system is counted. The scale of the filtering control targets is used as a macro-indicator to evaluate the execution strength of the filtering control strategy, providing a benchmark for subsequent analysis and determination of what management methods should be adopted for filters other than filtering control targets.

[0086] Specifically, in the above steps, the influence coefficient is determined based on the proportion of the number of filter control targets in the AC filter. It is then determined whether the influence coefficient is greater than the preset influence coefficient threshold. If so, the filter data analysis and management method for the AC filter excluding the filter control targets is to perform aging state analysis and processing in all groups. If not, the process proceeds to step S32.

[0087] The influence coefficient is determined based on the proportion of the number of filter control targets. The influence coefficient is equal to the proportion of the number of filter control targets to the total number of AC filters. The larger the proportion, the wider the scope of filter control, and the management of non-control target filters also needs to be expanded accordingly.

[0088] Assume that 3 out of the 6 Class I target filters are filtering control targets, accounting for 0.30 of the total 10 filters, with an influence coefficient of 0.30. If the influence coefficient is large (exceeding the threshold), then a comprehensive aging analysis is performed on all groups of non-control targets to ensure that the model can effectively identify aging states while improving its reliability in recognizing aging conditions.

[0089] Using the coverage of filter control as the primary decision factor in non-control target management methods, when filter control involves a large proportion of filters, non-control target filters cannot be relaxed in monitoring. Comprehensive analysis is needed in all groups to ensure that the accuracy of model aging state identification can be improved in a timely manner, thereby lifting control.

[0090] S32, Based on the filtering processing restriction data of each filtering control target under the filtering control strategy, determine the filtering processing restriction period of the filtering control target.

[0091] The filtering processing limitation data refers to the recorded data of the filtering processing period that is limited or postponed during the execution of filtering control processing by the filtering control target due to the limitations of the filtering control strategy (such as recent processing interval, harmonic content limitation, leakage current matching requirements, etc.).

[0092] The filtering control target's filtering processing restriction period refers to the time period between when the filtering control target is determined to require filtering control processing and when the filtering control processing is actually executed, which is limited by various control conditions and is the time period during which filtering control is required but not performed.

[0093] If F2 is determined to be the target of filtering control, and cannot be put into use during the period when filtering is needed because it does not meet the requirements, then the above-mentioned period will be regarded as the filtering processing restriction period.

[0094] It should be noted that the above steps include the following: The limiting influence value of the filter control target is determined by the ratio of the total duration of the filtering processing restriction period to the total duration of the filtering processing period. It is then determined whether the mean value of the limiting influence value of the filter control target is less than a preset influence threshold. If so, the filtering data analysis and management method for AC filters other than the filter control target is determined to be to perform aging state analysis and processing in all groups of AC filters of the target type other than the filter control target. If not, proceed to step S33.

[0095] The limiting impact value refers to the ratio of the total duration of the filtering and processing limitation period to the total duration of the filtering and processing period. It reflects the severity of the limitation period relative to the total running time. The larger the limiting impact value, the more severe the impact of the policy limitation on the filtering and control target during operation.

[0096] It is understood that the method for determining the AC filter type other than the filtering control target is as follows: The AC filter, excluding the filtering restriction target, is taken as the analysis target. If the evaluation risk weight value of the analysis target is not less than the preset risk weight threshold, then the analysis target is determined to be an analysis target of the target type.

[0097] The target type, excluding the filtering control target, refers to the AC filter that is selected as the analysis target after removing the filtering restriction target, and whose evaluation risk weight value is not less than the preset risk weight threshold.

[0098] Assuming the average value of the limiting impact is low, it indicates that the overall limiting burden of the filtering control target is controllable. In this case, aging analysis can be performed on the target type filters with higher risk weight values ​​among the non-control targets in all groups. This ensures both the efficiency of the model in identifying improvements and the reliability of identifying the aging state of the AC filters.

[0099] S33 uses the number of filter control targets and the filter processing time limit of each filter control target to determine the filter data analysis and management method for AC filters other than the filter control targets.

[0100] It should be noted that the above steps specifically include the following content.

[0101] Based on the filtering processing restriction period of each filtering control target, S331 determines the restriction influence value of the filtering control target, and judges whether the number of filtering control targets with restriction influence values ​​not less than the preset risk weight threshold meets the requirements. If yes, proceed to step S332. If no, the number of filtering control targets with greater restriction influence is large. Therefore, the filtering data analysis and management method for AC filters other than filtering control targets is determined to be to perform aging state analysis and processing in all groups.

[0102] The filter control targets whose restriction impact value is not less than the preset risk weight threshold refer to filter control targets with a large degree of restriction impact (the proportion of restriction period exceeds the preset threshold). A large number of such targets indicates that the policy restriction has a relatively widespread negative impact on the operation of the overall filter control targets.

[0103] If the number of filter control targets with high limiting impact values ​​is small (meets the requirements), it means that the number of heavily restricted filters is controllable, and further refined judgment can be made in S332; if the number is large (does not meet the requirements), it means that filters with a high degree of restriction are more common, and non-control targets also need to be fully analyzed in all groups to maintain overall aging monitoring coverage.

[0104] The number of filters with high limiting impact values ​​is used as a key criterion for whether to expand the management scope of non-controlled targets. When the limiting impact is generally heavy, the most comprehensive management method is adopted to ensure the efficiency of identification and processing when the model has room for improvement.

[0105] S332 determines the comprehensive influence coefficient based on the limiting influence value of each filtering constraint target and the proportion of the number of filtering control targets in the AC filter. It then determines whether the comprehensive influence coefficient is greater than a preset influence coefficient threshold. If so, the filtering data analysis and management method for AC filters excluding filtering control targets is determined to be to perform aging state analysis and processing in all groups of AC filters of the second target type excluding filtering control targets. If not, the filtering data analysis and management method for AC filters excluding filtering control targets is determined to be to perform aging state analysis and processing in all groups of AC filters of the target type excluding filtering control targets.

[0106] The comprehensive influence coefficient is determined by multiplying the average of the limiting influence values ​​of each filtering control target by the proportion of the number of filtering control targets, while also taking into account the overall severity of the limiting burden and the coverage of the controlled filter.

[0107] The second target type, excluding the filter control target, includes AC filters whose average risk weight value for aging state assessment is not less than a preset risk weight threshold and whose number of assessment matching groups is above a preset analysis group number threshold. In other words, the screening range of the second target type is wider than that of the target type, and filters that only meet the group number standard are additionally included.

[0108] If the overall impact coefficient is high, it indicates a heavy and broad constraint burden. In this case, the second target type (with broader screening criteria) is used to analyze more non-control targets. If the overall impact coefficient is low, it indicates that the overall impact is controllable. In this case, only the filter with the target type (high risk weight value) is analyzed.

[0109] The comprehensive impact coefficient is used as the final basis for determining the management method. Under the premise that the number of filters with high impact values ​​is controllable, the distinction is further refined. When the comprehensive impact is serious, the analysis scope of non-control targets is expanded to ensure the identification and processing efficiency when the model can be improved. When the comprehensive impact is controllable, the focus is on high-risk filters to improve the analysis efficiency.

[0110] It should be noted that when using the filter data analysis and management method of AC filters other than the filter control target to analyze and process the filter data, the AC filters that have undergone aging state analysis and processing in all groups will be used as screening targets. If the number of screening targets in groups without analysis bias risk meets the requirements (i.e., is greater than the preset number threshold), then the restriction scheme will be determined based on the evaluation result of the aging state with the largest number of groups with consistent aging state evaluation results. Filter restriction processing is only required when the evaluation result of the aging state with the largest number of groups with consistent aging state evaluation results is abnormal. Existing filter control targets do not need to continue to undergo filter restriction processing if the above situation does not exist.

[0111] The screening targets for groups without analytical bias risk refer to those whose aging assessments in each group are consistent with the baseline assessment results and are not identified as groups with analytical bias risk. The assessment results of these filters are highly consistent and stable in each group.

[0112] If a sufficient number of screening targets do not have bias risk groups, it indicates that the model's evaluation reliability for the screening targets has met the standard. In this case, filtering restriction is only required when the mainstream evaluation result of the AC filter (the result with the most consistent groups) is abnormal. If the mainstream result is normal, no restriction is required.

[0113] This final rule provides the final convergence condition for the analysis and management of all AC filter filtering data. After comprehensive group analysis, the number of filters in groups without bias risk and the mainstream evaluation results are used as dual judgment criteria. Filter restriction processing is only triggered when the number of screening targets is sufficient (the analysis sample is sufficient) and the mainstream results are abnormal. This ensures the sufficiency of the analysis while avoiding unnecessary filter restriction operations due to occasional evaluation inconsistencies.

[0114] In one possible embodiment, it is assumed that there are 10 AC filters (F1 to F10) in the system. The number of evaluation matching groups for each AC filter and the duration of filtering processing in each group are as follows (total runtime in hours): Calculate the correlation weight value of each AC filter (the sum of the duration percentages of each group): F1: 0.05. F2: 0.06. F3: 0.04. F4: 0.05. F5: 0.03. F6: 0.07, etc., a total of 10 AC filters. The average correlation weight value is 0.05. The preset correlation weight threshold is 0.03. If the average correlation weight value of 0.05 is not less than 0.03, proceed to case 2.

[0115] In scenario 2, the preset threshold for the number of analysis groups is 3. The filters evaluated for matching groups with a number ≥ 3 are: F1(3), F2(5), F4(4), F5(6), F7(3), F8(7), and F10(4), for a total of 7 evaluation matching filters.

[0116] The evaluation matched filter ratio is 7 ÷ 10 = 0.70. The preset ratio threshold is 0.60. If the evaluation matched filter ratio is 0.70 or higher than 0.60, the evaluation and analysis strategy for determining the aging state of the model is to perform aging state evaluation and analysis on a target type of AC filter.

[0117] Following the specific embodiment of S1, the aging assessment is performed on six filters (F1 / F2 / F4 / F5 / F8 / F10) of a target type. After assessing the aging state of each filter in each assessment matching group, the number of inconsistencies with the benchmark assessment result (the aging state result with the most inconsistencies within a preset time interval) is analyzed.

[0118] The preset threshold for the number of inconsistencies is 2. The evaluation matching group data and analysis of the risk group determination for each filter are as follows: F1 (3 groups): G1 inconsistency 2 times (≤2, non-bias risk), G2 inconsistency 1 time (non-bias risk), G3 inconsistency 4 times (>2, bias risk group). Analyze 1 bias risk group, and assess the risk weight value = 1 ÷ 3 ≈ 0.333.

[0119] Calculate the sum of the risk weights for assessment: 0.333 + 0.40 + 0.25 + 0.333 + 0.429 + 0.25 = 1.995. The preset threshold for the sum of the risk weights is 2.00. The sum of the risk weights, 1.995, is less than 2.00 (meets the requirement, i.e., the risk is still controllable), proceed to step S22.

[0120] In S22, the average daily duration of each filter is as follows: for example, F1=0.08, F2=0.05.

[0121] Calculate the filter risk weight value (assessment risk weight value × average daily duration percentage): F1: 0.333 × 0.02 ≈ 0.007, mean filter risk weight value ≈ 0.008, preset risk weight threshold is 0.005. The mean filter risk weight value of 0.008 is greater than 0.005, so a lenient strategy is adopted.

[0122] The specific conditions for the lenient strategy are: a preset threshold of 3 anomalies and a preset number of groups of 2. If the filter has more than 10 anomalies in the aging state evaluation results in the history (not meeting the requirements), or if there are anomalies in the aging state evaluation results in more than 2 groups, then the filter will be subject to filtering control processing.

[0123] When the filtering control process is actually executed, if F2 has not performed filtering processing in the last 5 hours (the preset duration is 4 hours), then the filtering processing will actually be executed.

[0124] Following the specific implementation of S2, the filtering control strategy adopts a relaxed strategy. From the six target filters, the filters that need filtering control processing are determined according to the triggering conditions of the relaxed strategy: F2: 4 historical anomalies (>3 times, meeting the condition), determined as a filter control target.

[0125] F5: If there are 2 (≤3) historical anomalies, but there are anomalies in 3 groups (≥2 groups, meeting the condition), it is determined to be a filter control target.

[0126] F8: 5 historical anomalies (>3 times) indicate a target for filtering control.

[0127] There are 3 target units for filtering and control (F2 / F5 / F8) and 7 target units for non-control (F1 / F3 / F4 / F6 / F7 / F9 / F10).

[0128] In step S31, the percentage of the number of filter control targets among all 10 filters is 3 ÷ 10 = 0.30. The influence coefficient is calculated as the percentage of the number of filters = 0.30. The preset threshold for the influence coefficient is 0.50. If the influence coefficient of 0.30 is not greater than 0.50, proceed to step S32.

[0129] In S32, the filtering processing limitation time periods for each filtering control target are statistically analyzed: F2: Limitation effect value = 0.267.

[0130] F5: Limitation effect value = 0.187.

[0131] F8: Limitation effect value = 0.25.

[0132] The mean influence value is calculated as (0.267 + 0.187 + 0.25) ÷ 3 ≈ 0.235. The preset influence threshold is 0.30. Since the mean influence value of 0.235 is less than 0.30, the method for analyzing and managing filter data, excluding the filter control target, is to perform aging analysis on all groups within the target type filter.

[0133] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the aforementioned method for filtering data analysis and management based on an AC filter when running the computer program.

[0134] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0135] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0136] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for analyzing and managing filtered data based on an AC filter, characterized in that, Specifically, it includes: Using the filtering monitoring data of the AC filter, the filtering processing period of the AC filter is determined. Based on the similarity of the harmonic characteristics of each AC filter during the filtering processing period, the evaluation matching group of the AC filter is determined. Using the evaluation matching group of each AC filter and the harmonic processing period data in the evaluation matching group, the evaluation and analysis strategy of the AC filter aging state model is determined. Based on the evaluation and analysis strategy, the consistency of the evaluation results of the aging state of the AC filter among the evaluation matching groups is determined. Based on the consistency, the analysis deviation risk group of the AC filter is determined. The evaluation risk weight value of the aging state of the AC filter is determined based on the analysis deviation risk group. Based on the evaluation risk weight value of the aging state of each AC filter and the correlation between the evaluation risk weight value and the distribution data of the filtering processing period, the filtering control strategy of the AC filter is determined. Based on the filtering control strategy, the filtering control target of the AC filter is determined. Using the filtering control target data and the filtering processing data of each filtering control target under the filtering control strategy, a filtering data analysis and management method for the AC filter excluding the filtering control target is determined.

2. The method for analyzing and managing filtered data based on an AC filter as described in claim 1, characterized in that, The filtering monitoring data of the AC filter includes the filtering processing time period of the AC filter and the harmonic current content of different filtering processing time periods.

3. The method for analyzing and managing filtered data based on an AC filter as described in claim 1, characterized in that, The filtering period of the AC filter is the period during which the AC filter performs harmonic filtering.

4. The method for filtering data analysis and management based on an AC filter as described in claim 1, characterized in that, The method for determining the evaluation and analysis strategy of the aging state model of the AC filter is as follows: Based on the similarity of harmonic characteristics during the filtering processing period of the AC filter, filtering processing periods whose harmonic characteristics meet the similarity requirements are divided into the same group. Based on the filtering processing period data in the group of the AC filter, the evaluation matching group of the AC filter is determined. Using the filtering time period data in the evaluation matching group of the AC filter, determine the duration proportion of the AC filter in each evaluation matching group, and use the sum of the duration proportions of the AC filter in each evaluation matching group as the association weight value of the AC filter. The evaluation and analysis strategy for determining the aging state model of the AC filter is based on the evaluation matching group of each AC filter and the association weight value of the AC filter.

5. The method for filtering data analysis and management based on an AC filter as described in claim 4, characterized in that, The evaluation matching group of the AC filter is a group whose interval between adjacent filtering processing periods is less than a preset interval threshold.

6. The method for filtering data analysis and management based on an AC filter as described in claim 4, characterized in that, Based on the evaluation matching groups of each AC filter and the association weight values ​​of the AC filters, an evaluation and analysis strategy for the aging state model of the AC filters is determined, specifically including: If the average value of the correlation weights of each AC filter is less than the preset correlation weight threshold, then the evaluation and analysis strategy for determining the aging state of the AC filter model is to perform aging state evaluation and analysis processing on all AC filters that have evaluation matching groups. If the average value of the correlation weight of each AC filter is not less than the preset correlation weight threshold, based on the evaluation matching group of each AC filter, the AC filter whose number of evaluation matching groups is above the preset analysis group number threshold is used as the evaluation matching filter. If the proportion of the evaluation matching filter in all AC filters is above the preset proportion threshold, then the evaluation and analysis strategy for determining the aging state model of the AC filter is to perform aging state evaluation and analysis processing in a target type of AC filter.

7. The method for filtering data analysis and management based on an AC filter as described in claim 6, characterized in that, The target type of AC filter is an AC filter whose evaluation matching weight value is above a preset matching weight threshold.

8. The method for filtering data analysis and management based on an AC filter as described in claim 1, characterized in that, The consistency of the evaluation results of the AC filter in assessing the aging state among the matching groups is determined based on whether the evaluation results of the AC filter in assessing the aging state among the matching groups are consistent.

9. The method for filtering data analysis and management based on an AC filter as described in claim 1, characterized in that, The method for determining the filtering data analysis and management method for AC filters other than the filtering control target is as follows: The number of filter control targets is determined based on the filter control target data; Based on the filtering processing constraint data of each filtering control target under the filtering control strategy, the filtering processing constraint time period of the filtering control target is determined; By utilizing the number of filter control targets and the filtering processing time limits for each filter control target, a method for analyzing and managing the filtering data of AC filters excluding the filter control targets is determined.

10. A computer system, comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a filtered data analysis and management method based on an AC filter as described in any one of claims 1-9.

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