Cable abnormity alarm system for smart power grid

Abstract

The invention relates to the technical field of alarm systems, and discloses a cable abnormity alarm system for a smart power grid, which receives and processes operation parameters, calculates operation errors, generates correction factors based on the operation errors, further accurately corrects a preset threshold value, and finally applies the corrected preset correction threshold value to an alarm trigger judgment module. The process significantly improves the alarm precision and reliability of the system. By adopting the method, not only can different working environments and condition changes be dynamically adapted, but also tiny anomalies in the operation of the cable can be effectively identified and responded, and the problem of false alarm or missing alarm possibly caused by a traditional fixed threshold value is avoided. In addition, analysis results of real-time data and historical data are fully considered in the correction process, so that the system has higher adaptive capacity and higher stability.

Description

Technical Field

[0001] The present invention relates to the technical field of alarm systems, and in particular to a cable abnormality alarm system for a smart grid. Background Art

[0002] In the operation of modern smart grids, cables are an important part of power transmission, and their health status is directly related to the safety and stability of the entire power grid. With the continuous expansion of the scale and increase in complexity of the power grid, real-time monitoring of the operating status of cables has become particularly important. However, traditional cable anomaly detection systems usually rely on fixed preset thresholds to determine whether the cable is abnormal. These fixed thresholds are set based on historical data or design standards, and fail to fully consider dynamic changing factors in actual operation, such as ambient temperature, humidity, load fluctuations, etc. Therefore, when faced with complex actual working environments, fixed thresholds often cannot accurately reflect the actual operating status of the cable, which can easily lead to false alarms (i.e., erroneously triggering alarms) or missed alarms (i.e., failing to detect the real fault in time), which not only increases maintenance costs, but may also cause serious safety accidents.

[0003] In addition, the aging and wear of the cable will further affect its electrical performance, making the traditional fixed threshold method more difficult to adapt to changing working conditions. In order to improve the accuracy and reliability of cable anomaly detection, researchers have proposed a variety of improvement schemes, such as using machine learning algorithms for data analysis and prediction, but these methods often require a lot of computing resources and still face many challenges in practical applications. In view of this, there is an urgent need for a method that can adaptively adjust the alarm threshold to achieve accurate monitoring and rapid response to the cable operating status. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a cable abnormality alarm system for smart grid, comprising the following modules:

[0005] Data acquisition module: used to collect the operating parameters of the cable operation process in real time;

[0006] Preprocessing module: connected with the data acquisition module, used to preprocess the operating parameters collected in real time in the data acquisition module;

[0007] Alarm trigger judgment module: connected to the preprocessing module, used to define a preset threshold, and judge whether to trigger an alarm according to the preprocessed operating parameters and the preset threshold. If yes, the preprocessed operating parameters are transmitted to the correction module, if not, the process ends; the preset threshold includes at least two data points;

[0008] Correction module: connected to the alarm trigger judgment module, used to receive the pre-processed operating parameters and calculate the operation error, generate a correction factor according to the operation error, correct the preset threshold according to the correction factor to obtain a preset correction threshold, and transmit the preset correction threshold to the alarm trigger judgment module, replace the preset threshold with the preset correction threshold and apply it to the alarm trigger judgment module.

[0009] Furthermore, the correction module includes the following submodules:

[0010] Difference matrix construction submodule: used to collect historical samples and construct a difference matrix by subtracting the historical samples from the preprocessed operating parameters;

[0011] Operation error calculation submodule: used to calculate the operation error according to the difference matrix;

[0012] Error threshold construction submodule: used to construct the error threshold;

[0013] Judgment submodule: used to compare the calculated operation error with the error threshold. If the operation error is greater than or equal to the error threshold, the operation error is transmitted to the correction submodule; if the operation error is less than the error threshold, the process ends.

[0014] Correction submodule: used to obtain a correction factor according to an operation error, and to correct a preset threshold according to the correction factor to obtain a preset correction threshold.

[0015] Furthermore, the difference matrix construction submodule includes the following units:

[0016] A historical normal operation parameter set acquisition unit is used to acquire a historical normal operation parameter set; the historical normal operation parameter set includes a number of historical samples, and each historical sample includes a number of historical operation parameters;

[0017] Euclidean distance calculation unit: used to calculate the Euclidean distance between the preprocessed operating parameters and historical samples;

[0018] Mahalanobis distance calculation unit: used to calculate the Mahalanobis distance between the pre-processed operating parameters and historical samples;

[0019] Adjustment coefficient determination unit: used to collect historical temperatures under historical operating parameters, calculate the average of the historical temperatures as the reference temperature; obtain the real-time temperature under the preprocessed operating parameters, calculate the temperature difference between the real-time temperature and the reference temperature, and calculate the adjustment coefficients of the Euclidean distance and the Mahalanobis distance based on the temperature difference;

[0020] Distance index calculation unit: used to calculate the distance index according to the Euclidean distance, Mahalanobis distance and adjustment coefficient, and take the historical sample with the smallest distance index as the historical sub-sample;

[0021] Difference matrix generation unit: used for subtracting the preprocessed operating parameter from each historical operating parameter of the historical sub-sample to obtain the operating parameter difference, and integrating all the operating parameter differences to obtain the difference matrix.

[0022] Furthermore, the operation error calculation submodule includes the following units:

[0023] A difference matrix mean calculation unit; used for adding up all the operating parameter differences in the difference matrix and calculating the mean to obtain the difference matrix mean;

[0024] Maximum operating parameter difference obtaining unit: used for obtaining the maximum operating parameter difference from the difference matrix;

[0025] An operation error calculation unit is used to calculate the operation error according to the operation parameter difference, the mean of the difference matrix and the maximum operation parameter difference.

[0026] Furthermore, the calculation formula of the operation error is: ;

[0027] Where E represents the operating error, represents the difference of the i-th running parameter in the difference matrix, represents the mean of the difference matrix, Represents the maximum operating parameter difference, represents the weight of the ith operating parameter difference, a represents the adjustment coefficient of the maximum operating parameter difference, n represents the total number of operating parameter differences in the difference matrix, and log is the logarithmic function.

[0028] Furthermore, the error threshold construction submodule includes the following units:

[0029] Error parameter acquisition unit: used to obtain the historical operation error of the historical normal operation parameter set, and determine the historical standard deviation according to the historical operation error;

[0030] Historical average temperature acquisition unit: used to calculate the historical average temperature according to the historical temperature under the historical operating parameters;

[0031] Error threshold calculation unit: used to calculate the error threshold according to the preset threshold, temperature difference, historical average temperature and historical standard deviation.

[0032] Furthermore, the calculation formula of the error threshold is: ;

[0033] Where P represents the error threshold, Represents the maximum value of the preset threshold, represents the temperature difference, represents the historical standard deviation, represents the historical average temperature, Represents the adjustment factor.

[0034] Furthermore, the correction submodule includes the following units:

[0035] Correction factor calculation unit: used to calculate the correction factor according to the operation error;

[0036] Preset threshold correction module: used to correct the preset threshold according to the correction factor to obtain the preset correction threshold.

[0037] Furthermore, the calculation formula of the correction factor is: ;

[0038] In the formula, F represents the correction factor, k represents the attenuation coefficient, Represents an exponential decay function.

[0039] Furthermore, the calculation formula of the preset correction threshold is: ;

[0040] in, represents the preset correction threshold, Represents the preset threshold.

[0041] The embodiments of the present invention have the following technical effects:

[0042] The present invention receives and processes operating parameters, calculates the operating error, and generates a correction factor based on the error, thereby accurately correcting the preset threshold, and finally applying the corrected preset correction threshold to the alarm trigger judgment module. This process significantly improves the alarm accuracy and reliability of the system. This method can not only dynamically adapt to different working environments and condition changes, but also effectively identify and respond to subtle anomalies in cable operation, avoiding the false alarm or missed alarm problems that may be caused by traditional fixed thresholds. In addition, since the correction process fully considers the analysis results of real-time data and historical data, the system has stronger adaptive ability and higher stability. This not only helps to discover potential faults in time, take maintenance measures in advance, and reduce the risk of unexpected downtime, but also provides strong support for the optimization management and intelligent monitoring of power systems. Therefore, the application of the invention greatly improves the accuracy and efficiency of cable monitoring, which is of great significance for ensuring the safe and stable operation of power systems, reducing operation and maintenance costs, and improving overall economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a structural diagram of a cable abnormality alarm system for a smart grid provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0046] Figure 1 1 is a structural diagram of a cable abnormality alarm system for a smart grid provided by an embodiment of the present invention. Figure 1 , specifically including the following modules:

[0047] Data acquisition module: used to collect the operating parameters of the cable operation process in real time.

[0048] Operating parameters include but are not limited to: temperature, voltage, current, vibration signals, etc.

[0049] Preprocessing module: connected to the data acquisition module, used to preprocess the operating parameters collected in real time in the data acquisition module.

[0050] Use a low-pass filter to filter the collected voltage and current signals to remove high-frequency noise;

[0051] Apply the normalization algorithm to map the filtered signal value to the interval [0,1];

[0052] Wavelet transform is used to reduce the noise of temperature and vibration signals and retain the effective signal components.

[0053] Alarm trigger judgment module: connected to the preprocessing module, used to define the preset threshold, and judge whether to trigger the alarm based on the preprocessed operating parameters and the preset threshold. If so, the preprocessed operating parameters are transmitted to the correction module, if not, the process ends; the preset threshold includes at least two data points.

[0054] The alarm trigger judgment module is an existing technology, which mainly monitors the cable operating parameters. When a certain operating parameter exceeds a preset threshold, it is considered that the current operating parameter is abnormal, which means that the cable is faulty or there is an abnormal risk in the future. The alarm trigger judgment module receives the abnormal signal and generates an alarm. After receiving the alarm, the staff inspects the cable. The abnormal monitoring and alarm process is an existing technology and will not be repeated in this embodiment.

[0055] The innovation lies in that there are operational errors in the process of abnormal operating parameter alarm, and this embodiment performs correction based on the operational errors.

[0056] Among them, the existing technology obtains a preset threshold value through the mean ±2 times the standard deviation of the historical operating parameters, and the preset threshold value is a value range, including an upper limit and a lower limit.

[0057] Correction module: connected to the alarm trigger judgment module, used to receive the pre-processed operating parameters and calculate the operation error, generate a correction factor according to the operation error, obtain a preset correction threshold according to the correction factor, and transmit the preset correction threshold to the alarm trigger judgment module, replace the preset threshold with the preset correction threshold and apply it to the alarm trigger judgment module.

[0058] Correction module, including the following sub-modules:

[0059] Difference matrix construction submodule: used to collect historical samples and construct a difference matrix by subtracting the historical samples from the preprocessed operating parameters.

[0060] The difference matrix construction submodule includes the following units:

[0061] The historical normal operation parameter set acquisition unit is used to acquire the historical normal operation parameter set; the historical normal operation parameter set includes a number of historical samples, and each historical sample includes a number of historical operation parameters.

[0062] The historical normal operating parameter set is the operating parameters generated when the distribution network cable is operating normally and no alarm occurs.

[0063] Euclidean distance calculation unit: used to calculate the Euclidean distance between the preprocessed operating parameters and historical samples.

[0064] The calculation formula of the Euclidean distance is a prior art. In the smart grid cable abnormality alarm system, this embodiment uses the prior art formula to evaluate the similarity between the preprocessed operating parameters and the historical samples. The preprocessed operating parameters and the historical samples are measured by similarity, which can significantly identify the abnormalities between the preprocessed operating parameters and the historical samples. When the Euclidean distance is smaller, the similarity between the preprocessed operating parameters and the historical samples is higher. When the Euclidean distance is larger, the similarity between the preprocessed operating parameters and the historical samples is lower, which means that the preprocessed operating parameters are abnormal.

[0065] ;

[0066] In the formula, Represents the jth running parameter after preprocessing With the i-th historical sample The Euclidean distance between represents the weight coefficient of the jth operating parameter, represents the parameter value of the jth operating parameter, represents the parameter value of the pth historical operating parameter in the ith historical sample, Represents the total number of running parameters.

[0067] Among them, the process of obtaining the weight coefficient is: taking the preprocessed operating parameters as independent variables, and taking the failure rate of the preprocessed operating parameters after abnormal monitoring (abnormal monitoring does not belong to the innovation of this embodiment, and the prior art often uses models to monitor and predict the failure rate. Therefore, the process of obtaining the failure rate is the prior art and will not be described in detail in this embodiment) as the dependent variable, to construct a multivariate linear regression analysis model and implement regression analysis, and calculate the determination coefficient of the model as the weight coefficient. The above regression analysis process is the prior art and will not be described in detail in this embodiment.

[0068] Mahalanobis distance calculation unit: used to calculate the Mahalanobis distance between the preprocessed operating parameters and historical samples.

[0069] First, the preprocessed operating parameters are subtracted from the historical operating parameters of each historical sample to obtain a difference vector. Then, a covariance matrix is ​​constructed based on the historical operating parameters in the historical normal operating parameter set. Finally, the Mahalanobis distance is obtained based on the covariance matrix and the difference vector.

[0070] The Mahalanobis distance is a measure of the difference between data points that is particularly applicable to multivariate data sets and takes into account the correlation between the variables. The main advantage of the Mahalanobis distance is that it takes into account the correlation between the variables, not just their absolute differences. This makes it very useful when dealing with multivariate data, especially when there is a correlation between these variables. In cable monitoring systems, the Mahalanobis distance can be used to detect anomalies. By comparing the Mahalanobis distance of the current operating parameters with the historical normal operating parameters, outliers can be identified.

[0071] ;

[0072] In the formula, Represents the jth running parameter after preprocessing With the i-th historical sample The Mahalanobis distance between represents the difference vector, T represents the transpose operation, represents the covariance matrix.

[0073] Represents the difference vector, which indicates the difference between the current running parameters and the historical samples. Through the subtraction operation, a vector can be obtained, each element of which represents the difference of the corresponding parameters. Represents the covariance matrix, which indicates the covariance relationship between the historical operating parameters in the historical normal operating parameter set. The covariance matrix reflects the correlation and variation between the variables. Represents the inverse matrix of the covariance matrix, which is used to standardize the difference vector. By multiplying the inverse matrix by the difference vector, the effect of different scales between variables can be eliminated and the correlation between variables can be taken into account. , represents the standardized distance between the current operating parameters and the historical samples. The historical Mahalanobis distance of the historical operating parameters in the historical normal operating parameter set is calculated, and the 95% quantile of the historical Mahalanobis distance is used as the Mahalanobis distance threshold. When the Mahalanobis distance obtained by the above calculation is greater than or equal to the Mahalanobis distance threshold, it means that the current pre-processed operating parameter is an abnormal data point.

[0074] Adjustment coefficient determination unit: used to collect historical temperatures under historical operating parameters, calculate the average of the historical temperatures as the reference temperature; obtain the real-time temperature under the preprocessed operating parameters, calculate the temperature difference between the real-time temperature and the reference temperature, and calculate the adjustment coefficients of the Euclidean distance and the Mahalanobis distance based on the temperature difference.

[0075] ;

[0076] Where f represents the adjustment factor, represents the temperature difference, Represents the adjustment factor for temperature difference.

[0077] The corresponding adjustment coefficients under different temperature differences are measured experimentally, and then these data points are used to fit the curve to obtain .

[0078] In cable monitoring systems, temperature is a key parameter because it directly affects the operating status and performance of the cable. By considering the temperature difference, it is possible to more accurately assess whether the current operating parameters are abnormal.

[0079] Distance index calculation unit: used to calculate the distance index according to the Euclidean distance, Mahalanobis distance and adjustment coefficient, and take the historical sample with the smallest distance index as the historical sub-sample.

[0080] ;

[0081] In the formula, Represents the distance index between the j-th operating parameter after preprocessing and the i-th historical sample.

[0082] Difference matrix generation unit: used for subtracting the preprocessed operating parameter from each historical operating parameter of the historical sub-sample to obtain the operating parameter difference, and integrating all the operating parameter differences to obtain the difference matrix.

[0083] Combining the Euclidean distance with the Mahalanobis distance and introducing the temperature difference as an adjustment coefficient to calculate the distance index can more accurately screen out the most suitable reference historical samples. First, by combining the two distance metrics, the local characteristics and global structure of the data can be captured at the same time - the Euclidean distance emphasizes absolute differences and is suitable for rapid preliminary screening; the Mahalanobis distance performs fine screening by considering the correlation between variables and the distribution characteristics of the data. Secondly, temperature is one of the key factors affecting the operating status of the cable, and the working performance of the cable at different temperatures may vary significantly. Therefore, introducing the temperature difference as an adjustment coefficient can dynamically adjust the weight according to the current environmental conditions to adapt to different operating scenarios. Specifically, the adjustment coefficient in the distance index formula adjusts the contribution ratio of the Euclidean distance and the Mahalanobis distance according to the temperature difference, ensuring that the most suitable historical samples can be selected under different temperature conditions. This method not only enhances the flexibility and adaptability of the model, but also improves the accuracy of the screening process, because it is based on the results of comprehensive consideration of multiple factors, rather than a single standard. In this way, the selected samples can better reflect the actual operating status, which helps to improve the quality of subsequent analysis and decision-making.

[0084] Operation error calculation submodule: used to calculate the operation error based on the difference matrix.

[0085] The operation error calculation submodule includes the following units:

[0086] The difference matrix mean calculation unit is used to add up all the operating parameter differences in the difference matrix and then calculate the mean to obtain the difference matrix mean.

[0087] Maximum operating parameter difference obtaining unit: used for obtaining the maximum operating parameter difference from the difference matrix;

[0088] An operation error calculation unit is used to calculate the operation error according to the operation parameter difference, the mean of the difference matrix and the maximum operation parameter difference.

[0089] ;

[0090] Where E represents the operating error, represents the difference of the i-th running parameter in the difference matrix, represents the mean of the difference matrix, Represents the maximum operating parameter difference, represents the weight of the ith operating parameter difference, a represents the adjustment factor of the maximum operating parameter difference, , n represents the total number of running parameter differences in the difference matrix. The inverse of the variance of the running parameter differences in the difference matrix is ​​used as the weight , is a logarithmic function.

[0091] ,By considering the deviation of each operating parameter difference from its mean and assigning different weights, ,it effectively reflects the relative importance of each parameter in the overall error, so that those parameters that have a greater impact on ,system performance can get more attention in the error calculation.

[0092] , a correction mechanism for extreme values ​​(i.e., the maximum operating parameter difference of the difference matrix) is introduced, which can not only reduce the impact of individual extremely large or extremely small outliers on the total error, but also maintain sensitivity to situations that significantly deviate from the normal range, thereby avoiding misjudgment due to extreme changes in a single parameter.

[0093] The combination of these two parts not only ensures the comprehensiveness and accuracy of error assessment, but also enhances the model's adaptability to complex and changing conditions in the actual environment, providing a more reliable anomaly detection basis for application scenarios such as cable monitoring. This comprehensive consideration method helps to more accurately identify potential problem points and improve the stability and reliability of the system.

[0094] The operation error is calculated by using three parameters: the difference in operating parameters, the mean of the difference matrix, and the maximum difference in operating parameters. This can effectively capture the deviation between the current state of the system and the historical normal state. First, the difference in operating parameters reflects the degree of deviation of each specific parameter relative to its historical sample value, which allows us to evaluate the health of the system separately for each dimension. The mean of the difference matrix provides an overall perspective, summarizing the average deviation of all parameters, which helps to identify whether there are universal deviations or anomalies in the system. Considering the maximum difference in operating parameters allows us to focus on extreme deviations that may pose a major threat to the stability of the system. The advantage of combining these three parameters to calculate the operation error is that this comprehensive method can not only comprehensively cover multi-level analysis from individual to overall, ensuring that no important deviation information is missed; but also by quantifying these differences, it can more accurately reflect the gap between the actual operating status of the system and the ideal state. In addition, this method improves the ability to detect abnormal behavior, making the operation error a sensitive indicator for measuring system performance and reliability, thereby supporting more timely and effective maintenance decisions.

[0095] Error threshold construction submodule: used to construct the error threshold.

[0096] The error threshold construction submodule includes the following units:

[0097] Error parameter acquisition unit: used to obtain the historical operation error of the historical normal operation parameter set, and determine the historical standard deviation based on the historical operation error.

[0098] Historical average temperature acquisition unit: used to calculate the historical average temperature according to the historical temperature under the historical operating parameters;

[0099] Error threshold calculation unit: used to calculate the error threshold according to the preset threshold, temperature difference, historical average temperature and historical standard deviation.

[0100] ;

[0101] Where P represents the error threshold, Represents the maximum value of the preset threshold, represents the temperature difference, represents the historical standard deviation, represents the historical average temperature, Represents the adjustment factor.

[0102] In this embodiment, the error threshold is dynamically adjusted by combining temperature changes and historical standard deviations, so that the system can automatically adapt and set a reasonable error range under different temperature conditions. This dynamic adjustment mechanism helps to improve the robustness and reliability of the system, ensuring that operational errors can be effectively monitored and controlled in various operating environments, thereby promptly discovering and handling abnormal situations and ensuring stable operation of the system.

[0103] Judgment submodule: used to compare the calculated operation error with the error threshold. If the operation error is greater than or equal to the error threshold, the operation error is transmitted to the correction submodule; if the operation error is less than the error threshold, the process ends.

[0104] Correction submodule: used to obtain a correction factor according to an operation error, and to correct a preset threshold according to the correction factor to obtain a preset correction threshold.

[0105] The correction submodule includes the following units:

[0106] Correction factor calculation unit: used to calculate the correction factor according to the operation error;

[0107] Preset threshold correction module: used to correct the preset threshold according to the correction factor to obtain the preset correction threshold.

[0108] ; ;

[0109] In the formula, F represents the correction factor, k represents the attenuation coefficient, and the attenuation coefficient is obtained through experimental measurement, k=2.197, represents the preset correction threshold, represents the preset threshold, Represents an exponential decay function.

[0110] pass The operation error is mapped to a value between 0 and 1. When the operation error is small (0.1 or less) or almost 0, no correction is required; when E is close to 1, , the actual correction factor calculated based on the experimentally determined value of k is 0.902, that is, 0.902 is used to correct the preset threshold.

[0111] When the operation error is greater than 0.1 (this embodiment considers that the current operation error is large), it means that the system has deviated from the normal operating state and entered a larger error range. In this case, using a larger correction factor to adjust the preset threshold can effectively amplify the system's response to these significant deviations. This not only helps to identify potential problems or abnormal conditions in a timely manner, but also improves the system's early warning capability and stability. For example, in a cable monitoring system, if the difference between the operating parameters monitored in real time and historical samples increases significantly, the system can use a higher correction factor to adjust its judgment criteria in time, thereby issuing an alarm or taking preventive measures earlier to avoid the occurrence or expansion of faults, ensuring the sensitivity of the system while enhancing its ability to adapt to changes in actual operating conditions, thereby improving the reliability and safety of the overall system.

[0112] When the calibrated preset correction threshold is applied to the alarm trigger judgment module, the monitoring capability of abnormal operating parameters is significantly enhanced. By dynamically adjusting the preset threshold to reflect the changes in the current system status and environmental conditions, it is possible to more accurately determine whether the cable is abnormal. This adaptive mechanism not only improves the accuracy of detection and reduces the possibility of false alarms and missed alarms, but also optimizes for different working environments and operating conditions to ensure that potential problems can be identified in time under any circumstances. In addition, since the correction process takes into account the actual error distribution and historical data, the system has stronger robustness and higher reliability, which provides a scientific basis for cable maintenance, helps to detect and solve possible faults in advance, avoid major losses, and extend the service life of the cable. Therefore, the use of the calibrated preset threshold can not only improve the system's immediate response capability and stability, but also lay a solid foundation for the realization of intelligent monitoring and management. Ultimately, these improvement measures work together to effectively ensure the safe and stable operation of the power system.

[0113] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A cable abnormality alarm system for smart grid, characterized in that: Includes the following modules: Data acquisition module: used to collect the operating parameters of the cable operation process in real time; Preprocessing module: connected with the data acquisition module, used to preprocess the operating parameters collected in real time in the data acquisition module; Alarm trigger judgment module: connected to the preprocessing module, used to define a preset threshold, and judge whether to trigger an alarm according to the preprocessed operating parameters and the preset threshold. If yes, the preprocessed operating parameters are transmitted to the correction module, if not, the process ends; the preset threshold includes at least two data points; Correction module: connected to the alarm trigger judgment module, used to receive the pre-processed operating parameters and calculate the operation error, generate a correction factor according to the operation error, correct the preset threshold according to the correction factor to obtain a preset correction threshold, and transmit the preset correction threshold to the alarm trigger judgment module, replace the preset threshold with the preset correction threshold and apply it to the alarm trigger judgment module.

2. A cable abnormality alarm system for smart grid according to claim 1, characterized in that: Correction module, including the following sub-modules: Difference matrix construction submodule: used to collect historical samples and construct a difference matrix by subtracting the historical samples from the preprocessed operating parameters; Operation error calculation submodule: used to calculate the operation error according to the difference matrix; Error threshold construction submodule: used to construct the error threshold; Judgment submodule: used to compare the calculated operation error with the error threshold. If the operation error is greater than or equal to the error threshold, the operation error is transmitted to the correction submodule; if the operation error is less than the error threshold, the process ends. Correction submodule: used to obtain a correction factor according to an operation error, and to correct a preset threshold according to the correction factor to obtain a preset correction threshold.

3. A cable abnormality alarm system for smart grid according to claim 2, characterized in that: The difference matrix construction submodule, The following units are included: A historical normal operation parameter set acquisition unit is used to acquire a historical normal operation parameter set; the historical normal operation parameter set includes a number of historical samples, and each historical sample includes a number of historical operation parameters; Euclidean distance calculation unit: used to calculate the Euclidean distance between the pre-processed operating parameters and historical samples; Mahalanobis distance calculation unit: used to calculate the Mahalanobis distance between the pre-processed operating parameters and historical samples; Adjustment coefficient determination unit: used to collect historical temperatures under historical operating parameters and calculate the average of the historical temperatures as the reference temperature; Obtain the real-time temperature under the preprocessed operating parameters, calculate the temperature difference between the real-time temperature and the reference temperature, and calculate the adjustment coefficient of the Euclidean distance and the Mahalanobis distance according to the temperature difference; Distance index calculation unit: used to calculate the distance index according to the Euclidean distance, Mahalanobis distance and adjustment coefficient, and take the historical sample with the smallest distance index as the historical sub-sample; Difference matrix generation unit: used for subtracting the preprocessed operating parameter from each historical operating parameter of the historical sub-sample to obtain the operating parameter difference, and integrating all the operating parameter differences to obtain the difference matrix.

4. A cable abnormality alarm system for smart grid according to claim 3, characterized in that: The operation error calculation submodule includes the following units: A difference matrix mean calculation unit; used for adding up all the operating parameter differences in the difference matrix and calculating the mean to obtain the difference matrix mean; Maximum operating parameter difference obtaining unit: used for obtaining the maximum operating parameter difference from the difference matrix; An operation error calculation unit is used to calculate the operation error according to the operation parameter difference, the mean of the difference matrix and the maximum operation parameter difference.

5. A cable abnormality alarm system for smart grid according to claim 4, characterized in that: The calculation formula of operating error is: ； Where E represents the operating error, represents the difference of the i-th running parameter in the difference matrix, represents the mean of the difference matrix, Represents the maximum operating parameter difference, represents the weight of the ith operating parameter difference, a represents the adjustment coefficient of the maximum operating parameter difference, n represents the total number of operating parameter differences in the difference matrix, is a logarithmic function.

6. A cable abnormality alarm system for smart grid according to claim 5, characterized in that: The error threshold construction submodule includes the following units: Error parameter acquisition unit: used to obtain the historical operation error of the historical normal operation parameter set, and determine the historical standard deviation according to the historical operation error; Historical average temperature acquisition unit: used to calculate the historical average temperature according to the historical temperature under the historical operating parameters; Error threshold calculation unit: used to calculate the error threshold according to the preset threshold, temperature difference, historical average temperature and historical standard deviation.

7. A cable abnormality alarm system for smart grid according to claim 6, characterized in that: The calculation formula of the error threshold is: ； Where P represents the error threshold, Represents the maximum value of the preset threshold, represents the temperature difference, represents the historical standard deviation, represents the historical average temperature, Represents the adjustment factor.

8. The cable abnormality alarm system for smart grid according to claim 7, characterized in that: The correction submodule includes the following units: Correction factor calculation unit: used to calculate the correction factor according to the operation error; Preset threshold correction module: used to correct the preset threshold according to the correction factor to obtain the preset correction threshold.

9. A cable abnormality alarm system for smart grid according to claim 8, characterized in that: The correction factor is calculated as: ； In the formula, F represents the correction factor, k represents the attenuation coefficient, Represents an exponential decay function.

10. A cable abnormality alarm system for smart grid according to claim 9, characterized in that: The calculation formula for the preset correction threshold is: ； in, represents the preset correction threshold, Represents the preset threshold.

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