An adaptive sampling frequency adjustment method for power equipment data acquisition system
By using an adaptive sampling frequency adjustment method, combined with multi-dimensional operating parameters and historical data of power equipment, a refined frequency adjustment of the power equipment data acquisition system was achieved, solving the problem of fixed sampling frequency and improving the accuracy of data acquisition and the economy of the system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING LIYUANXINGDA SCI & TECH CO LTD
- Filing Date
- 2026-05-26
- Publication Date
- 2026-07-31
AI Technical Summary
The sampling frequency adjustment method of existing power equipment data acquisition systems is fixed and has poor adaptability, resulting in data redundancy during periods of stable load and failure to capture fault details during periods of sudden load changes, thus affecting the operation and maintenance efficiency of power systems.
The adaptive sampling frequency adjustment method, which includes basic data acquisition, operating condition identification, dynamic threshold setting, sampling frequency adjustment, adjustment effect verification, feedback parameter optimization, and adaptive iterative update, enables dynamic adjustment of the sampling frequency and allows for fine-tuning by combining multi-dimensional operating parameters and historical data of the equipment.
It achieves precise matching between sampling frequency and equipment operating status, reduces resource consumption, captures key features during periods of sudden changes in operating conditions, and improves the accuracy of data acquisition and the economy of the system.
Abstract
Description
Technical Field
[0001] This invention relates to the field of sampling frequency adjustment technology, specifically to an adaptive sampling frequency adjustment method for a power equipment data acquisition system. Background Technology
[0002] Power equipment data acquisition systems are a core support for the safe and stable operation of power systems. They are primarily used to collect key parameters such as voltage, current, temperature, and insulation status from power equipment like transformers, switchgear, and instrument transformers, providing data support for equipment condition monitoring, fault early warning, and operation and maintenance decisions. With the rapid development of smart grids, the operating conditions of power equipment are becoming increasingly complex. Factors such as the intermittent integration of new energy power generation and dynamic load fluctuations cause power equipment parameters to exhibit nonlinear and time-varying characteristics, placing higher demands on the real-time performance, accuracy, and economy of data acquisition.
[0003] Sampling frequency, as a core parameter of a data acquisition system, directly determines the accuracy of the acquired data, system energy consumption, and storage pressure. An excessively high sampling frequency leads to data redundancy, increases system storage load and communication bandwidth consumption, and raises hardware wear and tear. Conversely, an excessively low sampling frequency fails to capture abrupt changes in equipment parameters, resulting in missed or incorrect fault diagnoses and impacting power system operation and maintenance efficiency. Therefore, achieving adaptive adjustment of the sampling frequency, balancing acquisition accuracy and system economy, has become a critical technical challenge that urgently needs to be addressed in current power equipment data acquisition systems.
[0004] Currently, the sampling frequency adjustment methods of power equipment data acquisition systems still suffer from problems such as fixed sampling frequencies and poor adaptability. Specifically, most acquisition systems use fixed sampling frequencies, which cannot be dynamically adjusted according to changes in equipment operating conditions. For example, a 110kV transformer uses a fixed 10Hz sampling frequency. During periods of stable load (such as off-peak load at night), there is a large amount of redundant sampling data, which occupies storage resources. However, during periods of sudden load changes (such as the moment of a short circuit), the sampling frequency is insufficient, making it impossible to capture fault details, resulting in increased harmonic analysis errors and delays in fault diagnosis. Summary of the Invention
[0005] To address the aforementioned technical problem of fixed sampling frequency and poor adaptability, this invention provides the following technical solution:
[0006] An adaptive sampling frequency adjustment method for a power equipment data acquisition system includes the following steps:
[0007] S1, Basic Data Acquisition: Collect raw data of power equipment operation from multiple dimensions, perform noise reduction and completion preprocessing, extract multi-dimensional feature value sets of each parameter, and output them;
[0008] S2, Operating Condition Identification: Based on the multi-dimensional feature value set output by S1, perform initial classification of operating conditions and determination of fluctuation level, and combine historical data to calibrate and output the final operating condition and fluctuation level results;
[0009] S3, Dynamic Threshold Setting:
[0010] S31, Preliminary setting of benchmark thresholds: Based on the final working conditions and fluctuation level results output by S2, preset the benchmark threshold for the sampling frequency corresponding to each working condition, and set the characteristic value fluctuation threshold, and output the benchmark threshold set.
[0011] S32, Threshold Dynamic Correction: Based on the multi-dimensional feature value set output by S1 and the final working condition and fluctuation level results output by S2, the baseline threshold set output by S31 is corrected, and the corrected dynamic threshold is output.
[0012] S33, Individual Threshold Calibration: Based on the corrected dynamic threshold output by S32, combined with the individual parameters of the current power equipment, the threshold is individually calibrated, and the calibrated individual dynamic threshold is output.
[0013] S34, Threshold Range Locking: Based on the calibrated personalized dynamic threshold output by S33, the upper and lower limits of the sampling frequency adjustment are set, and the threshold range corresponding to each working condition is locked. The locked threshold range and the corresponding working condition threshold are output.
[0014] S4, Sampling frequency adjustment: Based on the locked threshold range and corresponding operating condition threshold output by S3, first adapt to the corresponding operating condition reference frequency, then dynamically fine-tune and temporarily lock the current sampling frequency, and output the temporarily locked sampling frequency and locking duration.
[0015] S5, Adjustment effect verification: Based on the temporary locking sampling frequency and locking duration output by S4, detect the data accuracy and interference adaptability, determine redundancy or insufficient sampling and evaluate the adjustment effect, and output the effect evaluation result.
[0016] S6, Feedback Parameter Optimization: Based on the effect evaluation results output by S5, analyze the causes of deviation, correct the corresponding parameters, generate an optimization scheme for adjusting the sampling frequency, and output it.
[0017] S7, Adaptive Iterative Update: Based on the effect evaluation results output by S5, determine the iteration trigger conditions, update system parameters and historical database, and synchronously adapt to the entire data acquisition system.
[0018] As a preferred embodiment of the adaptive sampling frequency adjustment method for a power equipment data acquisition system according to the present invention, the specific steps of S1 are as follows:
[0019] S11, Collect raw data: Collect the real-time operating parameters of the equipment and output the raw collected data set;
[0020] S12, Data Preprocessing: Based on the original acquired data set output from S11, the Kalman filter algorithm is used to remove noise signals, and the missing data is filled by linear interpolation to output the preprocessed data set.
[0021] S13, Feature Extraction: Based on the preprocessed data set output by S12, extract the feature values of each running parameter and output a multi-dimensional feature value set.
[0022] As a preferred embodiment of the adaptive sampling frequency adjustment method for a power equipment data acquisition system according to the present invention, the specific steps of S2 are as follows:
[0023] S21, Initial Classification of Operating Conditions: Based on the multi-dimensional feature value set output by S1, the K-means clustering algorithm is used to initially classify the equipment operating conditions into stable operating conditions, transitional operating conditions, and abnormal operating conditions, and outputs the initial operating condition classification results.
[0024] S22, Fluctuation Level Determination: Based on the preliminary working condition classification results output by S21, calculate the fluctuation coefficient of the characteristic value under each working condition, divide the stable working condition into two levels: weak fluctuation and medium fluctuation, the transition working condition into two levels: slow transition and fast transition, and the abnormal working condition into two levels: slight abnormality and severe abnormality, and output the working condition fluctuation level.
[0025] S23, Operating Condition Calibration: Based on the operating condition fluctuation level output by S22, and combined with historical operating data of power equipment, the preliminary classification results are calibrated, and the final operating condition and fluctuation level results are output.
[0026] As a preferred embodiment of the adaptive sampling frequency adjustment method for a power equipment data acquisition system according to the present invention, the specific steps of S4 are as follows:
[0027] S41, Initial Frequency Adaptation: Based on the locked threshold range and corresponding operating condition threshold output by S3, the current sampling frequency of the acquisition system is adjusted to the reference threshold of the corresponding operating condition, and the initially adjusted sampling frequency is output.
[0028] S42, Frequency Dynamic Fine-tuning: Based on the multi-dimensional feature value set output by S1 and the locked threshold range and corresponding operating condition threshold output by S3, the sampling frequency initially adjusted by S41 is fine-tuned. If the feature value fluctuation exceeds the threshold, the sampling frequency is increased; if the feature value fluctuation is lower than the threshold, the sampling frequency is decreased, and the fine-tuned sampling frequency is output.
[0029] S43, Temporary Frequency Lock: Based on the fine-tuned sampling frequency output by S42, if there is no significant change in the current operating conditions, the current sampling frequency is temporarily locked, and the temporarily locked sampling frequency and lock duration are output.
[0030] As a preferred embodiment of the adaptive sampling frequency adjustment method for a power equipment data acquisition system according to the present invention, the specific steps of S5 are as follows:
[0031] S51, Accuracy Detection: Based on the sampling frequency and lock duration of the temporary lock output by S4, collect real-time operating data of the device, calculate the error value of the collected data, and output the error detection result;
[0032] S52, Interference Adaptability Verification: Based on the error detection results output by S51, simulate common interference scenarios in the power field, detect the anti-interference stability of the data under the current sampling frequency, determine whether the error still meets the requirements under interference environment, and output the interference adaptation verification results.
[0033] S53, Redundancy Detection: Based on the interference adaptation verification result output by S52, calculate the amount of data at the current sampling frequency. If the amount of data exceeds the redundancy threshold, it is determined that there is data redundancy; if the error value exceeds the accuracy threshold or the interference adaptation is unqualified, it is determined that the sampling is insufficient, and the redundancy or insufficient sampling determination result is output.
[0034] S54, Effect Evaluation: Based on the redundancy or insufficient sampling judgment result output by S53, evaluate the effect of sampling frequency adjustment, and classify it as qualified, needing optimization, or unqualified, and output the effect evaluation result.
[0035] As a preferred embodiment of the adaptive sampling frequency adjustment method for a power equipment data acquisition system according to the present invention, the specific steps of S6 are as follows:
[0036] S61, Deviation Analysis: Based on the effect evaluation results output by S5, if the results are unacceptable or require optimization, analyze the reasons for the deviation and output the reasons for the deviation and the deviation values.
[0037] S62, Parameter Correction: Based on the reasons and values of deviation output by S61, correct the corresponding parameters. If the threshold is unreasonable, adjust the dynamic threshold; if the fine-tuning amplitude is inappropriate, adjust the frequency; if the working condition is misjudged, optimize the clustering algorithm parameters and output the corrected parameter set.
[0038] S63, Optimization scheme generation: Based on the corrected parameter set output by S62, generate an optimization scheme for adjusting the sampling frequency and output the optimization scheme.
[0039] As a preferred embodiment of the adaptive sampling frequency adjustment method for a power equipment data acquisition system according to the present invention, the specific steps of S7 are as follows:
[0040] S71, Iteration Trigger Judgment: Based on the effect evaluation results output by S5, if the effect is qualified for 3 consecutive adjustments, the current adjustment parameters are maintained; if the effect is required for 2 consecutive adjustments or unqualified for 1 consecutive adjustment, the iteration update is triggered and the iteration trigger signal is output.
[0041] S72, Parameter Iterative Update: Based on the iterative trigger signal output by S71, combined with the optimization scheme output by S6, the baseline threshold, fine-tuning amplitude, and operating condition identification parameters of the sampling frequency adjustment are updated. At the same time, the historical operation database of the equipment is updated, and the updated parameters and database are output.
[0042] S73, System Synchronization Adaptation: Based on the updated parameters and database output by S72, the optimized adjustment method is synchronized to the entire data acquisition system.
[0043] Compared with existing technologies:
[0044] 1. By constructing a dynamic adjustment mechanism for sampling frequency based on the real-time operating conditions of the equipment, it can achieve precise matching between sampling frequency and equipment operating status. During the stable operation phase of the equipment, the sampling frequency is reduced to reduce resource consumption, and during the sudden change phase of the operating conditions, the sampling frequency is automatically increased to capture key features, thus fully adapting to the complex and ever-changing operating conditions of power equipment.
[0045] 2. By integrating multi-dimensional operating parameters of the equipment and subdividing the operating conditions, combined with a dynamic threshold correction strategy, it can achieve fine-grained adjustment of the sampling frequency, avoid the limitations of single parameter adjustment, and improve the accuracy of frequency adjustment. Detailed Implementation
[0046] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.
[0047] This invention provides an adaptive sampling frequency adjustment method for a power equipment data acquisition system, comprising the following steps:
[0048] S1, Basic Data Acquisition: Collect raw data of power equipment operation from multiple dimensions, perform noise reduction and completion preprocessing, extract multi-dimensional feature value sets of each parameter, and output them;
[0049] The specific steps of S1 are as follows:
[0050] S11, Acquire raw data: Collect real-time operating parameters of the equipment through the sensors (voltage sensor, current sensor, temperature sensor, etc.) of the power equipment data acquisition terminal, including voltage amplitude, current amplitude, winding temperature, insulation resistance, and partial discharge signal. Set the sampling frequency to the initial reference frequency (5-20Hz, which can be preset according to the equipment type) and output the raw data collection set.
[0051] S12, Data Preprocessing: Based on the raw data set output from S11, the Kalman filter algorithm is used to remove noise signals such as electromagnetic interference and communication interference. At the same time, missing data is filled by linear interpolation to avoid interference signals and missing data affecting subsequent analysis, and the preprocessed data set is output.
[0052] S13, Feature Value Extraction: Based on the preprocessed data set output by S12, feature values of each operating parameter are extracted, including voltage fluctuation value, current change rate, temperature difference value, insulation resistance attenuation rate, and partial discharge pulse frequency, and a multi-dimensional feature value set is output.
[0053] S2, Operating Condition Identification: Based on the multi-dimensional feature value set output by S1, perform initial classification of operating conditions and determination of fluctuation level, and combine historical data to calibrate and output the final operating condition and fluctuation level results;
[0054] The specific steps of S2 are as follows:
[0055] S21, Initial Classification of Operating Conditions: Based on the multi-dimensional feature value set output by S1, the K-means clustering algorithm is used to initially classify the operating conditions of the equipment into stable operating conditions (small parameter fluctuations, no abnormalities), transitional operating conditions (slow parameter changes, no faults), and abnormal operating conditions (sudden parameter changes, potential faults), and outputs the initial classification results of operating conditions.
[0056] S22, Fluctuation Level Determination: Based on the preliminary working condition classification results output by S21, calculate the fluctuation coefficient (the ratio of fluctuation value to the reference value) of the characteristic value under each working condition, divide the stable working condition into two levels: weak fluctuation and medium fluctuation, the transition working condition into two levels: slow transition and fast transition, and the abnormal working condition into two levels: slight abnormality and severe abnormality, and output the working condition fluctuation level.
[0057] S23, Operating Condition Calibration: Based on the operating condition fluctuation level output by S22, and combined with the historical operating data of the power equipment (the corresponding data of operating conditions and characteristic values for the past 3 months), the preliminary classification results are calibrated to eliminate misclassification (such as misjudging temporary fluctuations caused by interference as abnormal operating conditions), and the final operating condition and fluctuation level results are output.
[0058] S3, Dynamic Threshold Setting: Based on the final operating conditions and fluctuation level results output by S2, a baseline threshold is initially set and dynamically corrected. After calibration with individual equipment parameters, the threshold range is locked, and the locked threshold range and corresponding operating condition threshold are output.
[0059] The specific steps of S3 are as follows:
[0060] S31, Preliminary setting of reference threshold: Based on the final operating conditions and fluctuation level results output by S2, preset the sampling frequency reference threshold corresponding to each operating condition (stable operating condition: 2-5Hz, transitional operating condition: 5-15Hz, abnormal operating condition: 15-30Hz), and set the characteristic value fluctuation threshold (such as voltage fluctuation threshold ±5%), and output the reference threshold set.
[0061] S32, Threshold Dynamic Correction: Based on the multi-dimensional feature value set output by S1 and the final operating condition and fluctuation level results output by S2, the baseline threshold set output by S31 is corrected. For example, when the operating condition is stable with weak fluctuations, the sampling frequency threshold is reduced; when the abnormal operating condition is severely abnormal, the sampling frequency threshold is increased. At the same time, the feature value fluctuation threshold is corrected to adapt to the current operating condition, and the corrected dynamic threshold is output.
[0062] S33, Individual Threshold Calibration: Based on the corrected dynamic threshold output by S32, combined with the individual parameters of the current power equipment (such as the service life of the equipment and historical fault records), the threshold is calibrated in a personalized manner to avoid the limitation of uniform thresholds adapting to different equipment, and outputs the calibrated personalized dynamic threshold.
[0063] S34, Threshold Range Locking: Based on the calibrated personalized dynamic threshold output by S33, set the upper and lower limits of the sampling frequency adjustment range (lower limit: 1Hz, to avoid insufficient sampling; upper limit: 35Hz, to avoid redundancy), and lock the threshold range corresponding to each operating condition to prevent excessive threshold fluctuations from causing frequent frequency adjustments. Output the locked threshold range and the corresponding operating condition threshold.
[0064] S4, Sampling frequency adjustment: Based on the locked threshold range and corresponding operating condition threshold output by S3, first adapt to the corresponding operating condition reference frequency, then dynamically fine-tune and temporarily lock the current sampling frequency, and output the temporarily locked sampling frequency and locking duration.
[0065] The specific steps of S4 are as follows:
[0066] S41, Initial Frequency Adaptation: Based on the locked threshold range and corresponding operating condition threshold output by S3, the current sampling frequency of the acquisition system is adjusted to the reference threshold of the corresponding operating condition. For example, when the operating condition is stable with slight fluctuations, it is adjusted to 2Hz; when the operating condition is abnormal and severe, it is adjusted to 30Hz, and the initially adjusted sampling frequency is output.
[0067] S42, Dynamic Frequency Fine-tuning: Based on the multi-dimensional feature value set output by S1 and the locked threshold range and corresponding operating condition threshold output by S3, the sampling frequency initially adjusted by S41 is fine-tuned. If the feature value fluctuation exceeds the threshold, the sampling frequency is increased (by 1-2 Hz each time); if the feature value fluctuation is lower than the threshold, the sampling frequency is decreased (by 0.5-1 Hz each time) to ensure that the frequency matches the parameter fluctuation, and the fine-tuned sampling frequency is output.
[0068] S43, Temporary Frequency Lock: Based on the fine-tuned sampling frequency output by S42, if the current operating conditions do not change significantly (characteristic value fluctuations remain within the threshold for more than 5 minutes), the current sampling frequency is temporarily locked to avoid frequent adjustments. The temporarily locked sampling frequency and lock duration are output.
[0069] S5, Adjustment effect verification: Based on the temporary locking sampling frequency and locking duration output by S4, detect the data accuracy and interference adaptability, determine redundancy or insufficient sampling and evaluate the adjustment effect, and output the effect evaluation result.
[0070] The specific steps of S5 are as follows:
[0071] S51, Accuracy Detection: Based on the temporary locking sampling frequency and locking duration output by S4, collect real-time operating data of the device, calculate the error value of the collected data (the difference between the collected data and the standard detection data), and output the error detection result;
[0072] S52, Interference Adaptability Verification: Based on the error detection results output by S51, simulate common interference scenarios (electromagnetic interference, communication interference) in the power field, detect the anti-interference stability of the data under the current sampling frequency, determine whether the error still meets the requirements under the interference environment, and output the interference adaptation verification results.
[0073] S53, Redundancy Detection: Based on the interference adaptation verification results output by S52, calculate the amount of data at the current sampling frequency (number of data points collected per hour). If the amount of data exceeds the redundancy threshold (preset according to the device type), it is determined that there is data redundancy; if the error value exceeds the accuracy threshold (e.g., ±3%) or the interference adaptation is unqualified, it is determined that the sampling is insufficient, and the redundancy or insufficient sampling determination result is output.
[0074] S54, Effect Evaluation: Based on the redundancy or insufficient sampling judgment result output by S53, evaluate the effect of sampling frequency adjustment, and classify it into qualified (error ≤ accuracy threshold, no redundancy and interference adaptation qualified), need optimization (error ≤ accuracy threshold, no redundancy but interference adaptation slightly poor, or error slightly exceeds threshold, no redundancy and interference adaptation qualified), and unqualified (error far exceeds accuracy threshold or interference adaptation unqualified), and output the effect evaluation result.
[0075] S6, Feedback Parameter Optimization: Based on the effect evaluation results output by S5, analyze the causes of deviation, correct the corresponding parameters, generate an optimization scheme for adjusting the sampling frequency, and output it.
[0076] The specific steps of S6 are as follows:
[0077] S61, Deviation Analysis: Based on the effect evaluation results output by S5, if the results are in need of optimization or are unqualified, analyze the reasons for the deviation (such as unreasonable threshold setting, improper fine-tuning range, misjudgment of working condition identification), and output the reasons for the deviation and the deviation value.
[0078] S62, Parameter Correction: Based on the reasons and values of deviation output by S61, correct the corresponding parameters. If the threshold is unreasonable, adjust the dynamic threshold; if the fine-tuning amplitude is inappropriate, adjust the frequency and fine-tuning amplitude; if the working condition is misjudged, optimize the clustering algorithm parameters and output the corrected parameter set.
[0079] S63, Optimization scheme generation: Based on the corrected parameter set output by S62, generate an optimization scheme for adjusting the sampling frequency, clarify the adjustment strategy, threshold range and fine-tuning amplitude under each working condition, and output the optimization scheme.
[0080] S7, Adaptive Iterative Update: Based on the effect evaluation results output by S5, determine the iteration trigger conditions, update system parameters and historical database, and synchronously adapt to the entire data acquisition system;
[0081] The specific steps of S7 are as follows:
[0082] S71, Iteration Trigger Judgment: Based on the effect evaluation results output by S5, if the effect is qualified for 3 consecutive adjustments, the current adjustment parameters are maintained; if the effect is required for 2 consecutive adjustments or unqualified for 1 consecutive adjustment, the iteration update is triggered and the iteration trigger signal is output.
[0083] S72, Parameter Iterative Update: Based on the iterative trigger signal output by S71, combined with the optimization scheme output by S6, the baseline threshold, fine-tuning amplitude, and operating condition identification parameters of the sampling frequency adjustment are updated. At the same time, the historical operation database of the equipment is updated, and the updated parameters and database are output.
[0084] S73, System Synchronization Adaptation: Based on the updated parameters and database output by S72, the optimized adjustment method is synchronized to the entire data acquisition system to ensure the uniformity of parameters of acquisition terminals, transmission networks, and data centers, realize long-term adaptive adjustment of sampling frequency, and output synchronization adaptation results.
[0085] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.
Claims
1. An adaptive sampling frequency adjustment method for a power equipment data acquisition system, characterized in that, Includes the following steps: S1, Basic Data Acquisition: Collect raw data of power equipment operation from multiple dimensions, perform noise reduction and completion preprocessing, extract multi-dimensional feature value sets of each parameter, and output them; S2, Operating Condition Identification: Based on the multi-dimensional feature value set output by S1, perform initial classification of operating conditions and determination of fluctuation level, and combine historical data to calibrate and output the final operating condition and fluctuation level results; S3, Dynamic Threshold Setting: S31, Preliminary setting of benchmark thresholds: Based on the final working conditions and fluctuation level results output by S2, preset the benchmark threshold for the sampling frequency corresponding to each working condition, and set the characteristic value fluctuation threshold, and output the benchmark threshold set. S32, Threshold Dynamic Correction: Based on the multi-dimensional feature value set output by S1 and the final working condition and fluctuation level results output by S2, the baseline threshold set output by S31 is corrected, and the corrected dynamic threshold is output. S33, Individual Threshold Calibration: Based on the corrected dynamic threshold output by S32, combined with the individual parameters of the current power equipment, the threshold is individually calibrated, and the calibrated individual dynamic threshold is output. S34, Threshold Range Locking: Based on the calibrated personalized dynamic threshold output by S33, the upper and lower limits of the sampling frequency adjustment are set, and the threshold range corresponding to each working condition is locked. The locked threshold range and the corresponding working condition threshold are output. S4, Sampling frequency adjustment: Based on the locked threshold range and corresponding operating condition threshold output by S3, first adapt to the corresponding operating condition reference frequency, then dynamically fine-tune and temporarily lock the current sampling frequency, and output the temporarily locked sampling frequency and locking duration. S5, Adjustment effect verification: Based on the temporary locking sampling frequency and locking duration output by S4, detect the data accuracy and interference adaptability, determine redundancy or insufficient sampling and evaluate the adjustment effect, and output the effect evaluation result. S6, Feedback Parameter Optimization: Based on the effect evaluation results output by S5, analyze the causes of deviation, correct the corresponding parameters, generate an optimization scheme for adjusting the sampling frequency, and output it. S7, Adaptive Iterative Update: Based on the effect evaluation results output by S5, determine the iteration trigger conditions, update system parameters and historical database, and synchronously adapt to the entire data acquisition system.
2. The adaptive sampling frequency adjustment method for a power equipment data acquisition system according to claim 1, characterized in that, The specific steps of S1 are as follows: S11, Collect raw data: Collect the real-time operating parameters of the equipment and output the raw collected data set; S12, Data Preprocessing: Based on the original acquired data set output from S11, the Kalman filter algorithm is used to remove noise signals, and the missing data is filled by linear interpolation to output the preprocessed data set. S13, Feature Extraction: Based on the preprocessed data set output by S12, extract the feature values of each running parameter and output a multi-dimensional feature value set.
3. The adaptive sampling frequency adjustment method for a power equipment data acquisition system according to claim 1, characterized in that, The specific steps of S2 are as follows: S21, Initial Classification of Operating Conditions: Based on the multi-dimensional feature value set output by S1, the K-means clustering algorithm is used to initially classify the equipment operating conditions into stable operating conditions, transitional operating conditions, and abnormal operating conditions, and outputs the initial operating condition classification results. S22, Fluctuation Level Determination: Based on the preliminary working condition classification results output by S21, calculate the fluctuation coefficient of the characteristic value under each working condition, divide the stable working condition into two levels: weak fluctuation and medium fluctuation, the transition working condition into two levels: slow transition and fast transition, and the abnormal working condition into two levels: slight abnormality and severe abnormality, and output the working condition fluctuation level. S23, Operating Condition Calibration: Based on the operating condition fluctuation level output by S22, and combined with the historical operating data of the power equipment, the preliminary classification results are calibrated, and the final operating condition and fluctuation level results are output.
4. The adaptive sampling frequency adjustment method for a power equipment data acquisition system according to claim 1, characterized in that, The specific steps of S4 are as follows: S41, Initial Frequency Adaptation: Based on the locked threshold range and corresponding operating condition threshold output by S3, the current sampling frequency of the acquisition system is adjusted to the reference threshold of the corresponding operating condition, and the initially adjusted sampling frequency is output. S42, Frequency Dynamic Fine-tuning: Based on the multi-dimensional feature value set output by S1 and the locked threshold range and corresponding operating condition threshold output by S3, the sampling frequency initially adjusted by S41 is fine-tuned. If the feature value fluctuation exceeds the threshold, the sampling frequency is increased. If the feature value fluctuation is below the threshold, reduce the sampling frequency and output the fine-tuned sampling frequency. S43, Temporary Frequency Lock: Based on the fine-tuned sampling frequency output by S42, if there is no significant change in the current operating conditions, the current sampling frequency is temporarily locked, and the temporarily locked sampling frequency and lock duration are output.
5. The adaptive sampling frequency adjustment method for a power equipment data acquisition system according to claim 1, characterized in that, The specific steps of S5 are as follows: S51, Accuracy Detection: Based on the sampling frequency and lock duration of the temporary lock output by S4, collect real-time operating data of the device, calculate the error value of the collected data, and output the error detection result; S52, Interference Adaptability Verification: Based on the error detection results output by S51, simulate common interference scenarios in the power field, detect the anti-interference stability of the data under the current sampling frequency, determine whether the error still meets the requirements under interference environment, and output the interference adaptation verification results. S53, Redundancy Detection: Based on the interference adaptation verification result output by S52, calculate the amount of data at the current sampling frequency. If the amount of data exceeds the redundancy threshold, it is determined that there is data redundancy; if the error value exceeds the accuracy threshold or the interference adaptation is unqualified, it is determined that the sampling is insufficient, and the redundancy or insufficient sampling determination result is output. S54, Effect Evaluation: Based on the redundancy or insufficient sampling judgment result output by S53, evaluate the effect of sampling frequency adjustment, and classify it as qualified, needing optimization, or unqualified, and output the effect evaluation result.
6. The adaptive sampling frequency adjustment method for a power equipment data acquisition system according to claim 1, characterized in that, The specific steps of S6 are as follows: S61, Deviation Analysis: Based on the effect evaluation results output by S5, if the results are unacceptable or require optimization, analyze the reasons for the deviation and output the reasons for the deviation and the deviation values. S62, Parameter Correction: Based on the cause and value of the deviation output by S61, correct the corresponding parameters. If the threshold is unreasonable, adjust the dynamic threshold. If the fine-tuning amplitude is inappropriate, adjust the frequency; if the working condition is misidentified, optimize the clustering algorithm parameters and output the corrected parameter set. S63, Optimization scheme generation: Based on the corrected parameter set output by S62, generate an optimization scheme for adjusting the sampling frequency and output the optimization scheme.
7. The adaptive sampling frequency adjustment method for a power equipment data acquisition system according to claim 1, characterized in that, The specific steps of S7 are as follows: S71, Iteration Trigger Judgment: Based on the effect evaluation result output by S5, if the effect of 3 consecutive adjustments is qualified, the current adjustment parameters are maintained; If two consecutive results indicate a need for optimization or one result indicates a failure, an iterative update is triggered, and an iterative trigger signal is output. S72, Parameter Iterative Update: Based on the iterative trigger signal output by S71, combined with the optimization scheme output by S6, the baseline threshold, fine-tuning amplitude, and operating condition identification parameters of the sampling frequency adjustment are updated. At the same time, the historical operation database of the equipment is updated, and the updated parameters and database are output. S73, System Synchronization Adaptation: Based on the updated parameters and database output by S72, the optimized adjustment method is synchronized to the entire data acquisition system.