Intelligent power grid fault monitoring method and system applying artificial intelligence

Abstract

The invention discloses an intelligent power grid fault monitoring method and system applying artificial intelligence, and relates to the technical field of power system fault monitoring, and the method comprises the steps: obtaining power internet-of-things monitoring data through a sensor, carrying out the preprocessing of the data, extracting fault-related features based on the preprocessed data through a machine learning algorithm, and carrying out the fault-related features; based on historical data of the power grid, a preliminary fault monitoring threshold range is determined through statistical analysis; and comparing the fault feature vector with real-time monitoring power grid operation state data, dynamically adjusting a fault monitoring threshold value by using an adaptive algorithm based on a comparison result of historical data and real-time state data of the power grid, giving an alarm after an abnormal condition is monitored, and optimizing a fault monitoring mechanism according to feedback of a fault processing result. According to the invention, through data preprocessing, noise removal is realized, the monitoring precision is improved, and the effect of improving the fault detection accuracy is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault monitoring, and in particular to an intelligent power grid fault monitoring method and system applying artificial intelligence. Background Art

[0002] Most traditional power grid fault detection methods rely on manual inspections and simple automated devices. This method is not only inefficient but also difficult to detect hidden faults in complex power grid structures in a timely manner. In recent years, with the development of Internet of Things technology, a large number of sensors have been deployed in the power system, enabling real-time monitoring of the power grid operation status. This not only provides a rich data source for fault detection but also lays a foundation for realizing intelligent fault diagnosis.

[0003] Traditional fault detection methods often lack effective data preprocessing steps, resulting in noise data and irrelevant data affecting the accuracy of fault detection. Secondly, in the feature extraction process, many methods do not fully utilize the multi-dimensional information in the power grid operation data, resulting in incomplete feature representation and thus affecting the effect of fault recognition. Currently, after a fault monitoring system detects an anomaly, it lacks an effective feedback mechanism to continuously optimize the monitoring strategy, which limits the adaptive ability of the system. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent power grid fault monitoring method and system applying artificial intelligence to solve the problems of insufficient data preprocessing, incomplete feature extraction, poor adaptability of fixed thresholds, and lack of an effective feedback optimization mechanism in intelligent power grid fault monitoring technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an intelligent power grid fault monitoring method applying artificial intelligence, which includes:

[0008] Obtaining power Internet of Things monitoring data through sensors and preprocessing it;

[0009] Based on the preprocessed data, using machine learning algorithms to extract fault-related features and fuse them into a unified fault feature vector;

[0010] Based on the power grid historical data, determining a preliminary fault monitoring threshold range through statistical analysis; and comparing the fault feature vector with the real-time monitored power grid operation status data;

[0011] Based on the comparison result of the power grid historical data and real-time status data, using an adaptive algorithm to dynamically adjust the fault monitoring threshold;

[0012] After detecting an abnormal situation, an alarm is issued, and the fault monitoring mechanism is optimized according to the feedback of the fault handling result.

[0013] As a preferred solution of the intelligent power grid fault monitoring method applying artificial intelligence according to the present invention, wherein: the sensors include a temperature sensor, a current sensor, and a voltage sensor;

[0014] The preprocessing includes data cleaning and data denoising;

[0015] The real-time status data includes power grid load and temperature change.

[0016] As a preferred solution of the intelligent power grid fault monitoring method applying artificial intelligence according to the present invention, wherein: the power IoT monitoring data is obtained through sensors and preprocessed, and the specific steps are as follows,

[0017] Deploy temperature sensors, current sensors, and voltage sensors on power grid substations and transmission line nodes, monitor the power grid in real time, and send the data to the central data processing center in real time;

[0018] The central data processing center receives the original data, performs cleaning and denoising processing, eliminates invalid data, and uses a low-pass filter to smooth the current and voltage signals to remove noise.

[0019] As a preferred solution of the intelligent power grid fault monitoring method applying artificial intelligence according to the present invention, wherein: based on the preprocessed data, machine learning algorithms are used to extract fault-related features and fuse them into a unified fault feature vector, and the specific steps are as follows,

[0020] Use a method combining principal component analysis and convolutional neural network, and extract fault-related features from the preprocessed data. The expression is as follows,

[0021]

[0022] Wherein, F is the extracted fault feature vector, D is the preprocessed data set, PCA(D i ) is the main component obtained by applying principal component analysis to D i , D i is the data of the i-th dimension in the data set D, i is the index variable, CNN(D i ) is the feature extracted from D i by the convolutional neural network, Var(D i ) is the variance of D i ;

[0023] Feature fusion is performed according to the mutual information between features. The expression is as follows,

[0024]

[0025] Among them, V final is the fused fault feature vector, and F i , F j are the feature vectors extracted from two different dimensions respectively. MI(F i , F j ) is the mutual information between F i , F j .

[0026] As a preferred solution of the smart grid fault monitoring method applying artificial intelligence according to the present invention, wherein: based on the historical data of the power grid, the preliminary fault monitoring threshold range is determined through statistical analysis, and the fault feature vector is compared with the real-time monitored power grid operation state data. The specific steps are as follows

[0027] Summarize the feature vectors of the normal state and the fault state in the historical data of the power grid. The distribution of the feature vectors in the normal operation state follows a normal distribution, and there are significant differences in the distribution of the feature vectors in the fault state

[0028] Calculate the mean and standard deviation of the fault feature vectors in the normal state, and set the preliminary fault monitoring threshold range according to the three-sigma principle

[0029] Convert the real-time monitored power grid operation state data into a fault feature vector. If the fault feature vector obtained from the real-time monitored data is within the fault threshold range, it is considered that the power grid operation state is good; otherwise, it is considered that there is a fault in the power grid operation

[0030] As a preferred solution of the smart grid fault monitoring method applying artificial intelligence according to the present invention, wherein: based on the comparison result of the historical data and the real-time state data of the power grid, the fault monitoring threshold is dynamically adjusted by using an adaptive algorithm. The specific steps are as follows

[0031] Based on the trend change of the real-time data and the statistical characteristics of the historical data, define an adaptive adjustment factor α reflecting the deviation degree of the real-time state data relative to the historical data. The expression is as follows

[0032]

[0033] Among them, α(t) is the adaptive adjustment factor at time point t, and V final (t) is the fused fault feature vector at time point t, and μ n and σ n are the mean and standard deviation of the fault feature vectors in the normal state of the historical data respectively, and t 0Let \(t_0\) and \(t\) be the starting and ending time points for collecting real-time data, and \(dt\) refers to the integral of the absolute value of the deviation of the feature vector from the normal mean within the time interval \([t_0, t]\). 0 , t]\) for the integral of the absolute value of the deviation of the feature vector from the normal mean;

[0034] An adaptive adjustment factor \(\alpha(t)\) is used to adjust the fault monitoring threshold, and the expression is as follows:

[0035] \(T(t)=\mu\) n \(\pm3\sigma\) n +\(\alpha(t)\);

[0036] where \(T(t)\) is the dynamic fault monitoring threshold at time point \(t\).

[0037] As a preferred solution of the intelligent power grid fault monitoring method applying artificial intelligence according to the present invention, wherein: after the abnormal situation is detected, an alarm is issued, and according to the feedback of the fault handling result, the fault monitoring mechanism is optimized, and the specific steps are as follows:

[0038] When it is detected that the fault feature vector exceeds the dynamic fault monitoring threshold, the early warning mechanism is triggered, the redundant monitoring program is started, and the extraction and comparison process of the fault feature vector is repeated for secondary verification;

[0039] When the fault is confirmed, the log records the fault occurrence time, specific location, type and fault feature vector, and sends them to the control center to activate the emergency response and reallocate the power grid load;

[0040] During the fault handling process, the impact data of the handling actions and the power grid state are continuously collected, compared and analyzed with the pre-set monitoring process, the handling effect is evaluated, and the monitoring process is adjusted according to the actual situation.

[0041] In a second aspect, the present invention provides an intelligent power grid fault monitoring system applying artificial intelligence, including a data acquisition and preprocessing module, a feature extraction and fusion module, a fault monitoring threshold setting and comparison module, an adaptive threshold adjustment module, and an alarm and feedback optimization module;

[0042] The data acquisition and preprocessing module is used to obtain power Internet of Things monitoring data through sensors and preprocess it;

[0043] The feature extraction and fusion module is used to extract fault-related features based on the preprocessed data by using machine learning algorithms and fuse them into a unified fault feature vector;

[0044] The fault monitoring threshold setting and comparison module is used to determine the initial fault monitoring threshold range based on the power grid historical data through statistical analysis and compare it with the fault feature vector and the real-time monitoring power grid operation state data;

[0045] The adaptive threshold adjustment module is used to dynamically adjust the fault monitoring threshold by using an adaptive algorithm based on the comparison result between the historical data and the real-time status data of the power grid;

[0046] The alarm and feedback optimization module is used to send an alarm after detecting an abnormal situation, and optimize the fault monitoring mechanism according to the feedback of the fault handling result.

[0047] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the intelligent power grid fault monitoring method applying artificial intelligence as described in the first aspect of the present invention is implemented.

[0048] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the intelligent power grid fault monitoring method applying artificial intelligence as described in the first aspect of the present invention is implemented.

[0049] The beneficial effects of the present invention are as follows: By deploying a variety of sensors and performing data preprocessing, the present invention realizes the comprehensive monitoring of the power grid operation status and the improvement of data quality, reduces false alarms, improves the monitoring accuracy, extracts fault features from the preprocessed data by using machine learning algorithms and integrates them into a unified fault feature vector, realizes the high-level generalization and integration of fault features, enhances the accuracy of fault recognition and classification ability, improves the sensitivity of fault detection, determines the preliminary fault monitoring threshold by statistical analysis of historical data and compares it with real-time data, realizes the real-time monitoring of the power grid operation status and the rapid response to faults, reduces the impact of faults, dynamically adjusts the fault monitoring threshold by using an adaptive algorithm, realizes the self-optimization and adaptability enhancement of the monitoring system, and improves the system stability and the adaptability to complex environments. Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0051] Figure 1 It is a flowchart of the intelligent power grid fault monitoring method applying artificial intelligence in Embodiment 1.

[0052] Figure 2 It is a module diagram of the intelligent power grid fault monitoring system in Embodiment 1. Detailed Embodiments

[0053] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0054] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0055] Secondly, as used herein, "one embodiment" or "an embodiment" refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other.

[0056] Example 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a smart grid fault monitoring method applying artificial intelligence, including the following steps:

[0057] S1. Obtain power Internet of Things monitoring data through sensors and perform preprocessing on it.

[0058] Furthermore, deploy temperature sensors, current sensors, and voltage sensors at grid substations and transmission line nodes to monitor the grid in real time and send the data to the central data processing center in real time;

[0059] The central data processing center receives the raw data and performs cleaning and denoising processing, eliminating invalid data, and using a low-pass filter to smooth the current and voltage signals to remove noise;

[0060] S2. Based on the preprocessed data, use machine learning algorithms to extract fault-related features and fuse them into a unified fault feature vector.

[0061] Furthermore, perform standardization processing on the data after cleaning and denoising to ensure that different types of sensor data can be compared under the same dimension;

[0062] Preferably, use a method combining principal component analysis (PCA) and convolutional neural network (CNN). PCA is used to reduce the data dimension and reduce redundant information; while CNN is good at capturing local patterns in the data, which is particularly useful for detecting anomalies in the power system;

[0063] Extract fault-related features from the preprocessed data, and the expression is as follows,

[0064]

[0065] Among them, F is the extracted fault feature vector, D is the preprocessed data set, and PCA(D i ) is the main component obtained by applying principal component analysis to D i , D i is the data of the i-th dimension in the data set D, i is the index variable, and CNN(D i ) is the feature extracted from D i by the convolutional neural network, and Var(D i ) is the variance of D i , which is used to measure the importance of features;

[0066] The reciprocal square root of the variance is used as the criterion for feature selection. The larger the variance, the more stable the feature, so the contribution to the final feature vector is smaller. This adaptive mechanism makes the contribution of those features with larger fluctuations (i.e., more sensitive) greater in the final feature vector;

[0067] Further, the relationship between features is measured according to the mutual information (MI) between features, and feature fusion is performed. The expression is as follows.

[0068]

[0069] Among them, V final is the fused fault feature vector, F i , F j are the feature vectors extracted from two different dimensions respectively, and MI(F i , F j ) is the mutual information between F i , F j , which measures the strength of the dependence relationship between the two features;

[0070] The value range of V final is (0, 1). The closer the value is to 1, the more important the feature is for fault identification, and the value close to 0 means that the feature plays a weak role in fault monitoring;

[0071] It should be noted that through the effective extraction and fusion of fault features, a feature vector that can accurately reflect the health state of the power system is also constructed, effectively improving the accuracy and efficiency of fault detection, so as to realize the real-time and accurate monitoring of the health state of the power system.

[0072] S3. Based on the historical data of the power grid, determine the preliminary fault monitoring threshold range through statistical analysis, and compare it with the real-time monitoring power grid operation state data according to the fault feature vector.

[0073] Furthermore, summarize the eigenvectors of the normal state and fault state in the historical data of the power grid. The distribution of the eigenvectors in the normal operation state follows a normal distribution, and there will be significant differences in the distribution of the eigenvectors in the fault state;

[0074] Calculate the mean and standard deviation of the fault eigenvectors in the normal state. The expressions are as follows:

[0075]

[0076]

[0077] where μ is the mean of the fault eigenvectors in the normal operation state, N is the number of samples in the normal operation state, and σ is the standard deviation of the fault eigenvectors in the normal operation state. is the fault eigenvector of the i-th sample;

[0078] Set the initial fault monitoring threshold range according to the three-sigma principle, [μ - 3σ, μ + 3σ]. The eigenvectors outside this range are considered to be in a possible fault state;

[0079] Furthermore, convert the power grid operation state data obtained from real-time monitoring into fault eigenvectors. If the fault eigenvectors obtained from the real-time monitoring data are within the fault threshold range, it is considered that the power grid operation state is good; otherwise, it is considered that there is a fault in the power grid operation.

[0080] It should be noted that by real-time monitoring and comparing with the fault detection threshold range obtained from historical data analysis, potential faults in the power system can be identified timely and accurately, effectively improving the safety and stability of the power grid operation.

[0081] S4. Dynamically adjust the fault monitoring threshold using an adaptive algorithm based on the comparison results of the historical data and real-time state data of the power grid.

[0082] Furthermore, based on the trend change of real-time data and the statistical characteristics of historical data, define an adaptive adjustment factor α that reflects the deviation degree of real-time state data relative to historical data. The expression is as follows:

[0083]

[0084] where α(t) is the adaptive adjustment factor at time point t, V final (t) is the fused fault eigenvector at time point t, μ n and σ n are the mean and standard deviation of the fault eigenvectors in the normal state of historical data respectively, t 0 and t are the start and end time points for collecting real-time data, and dt refers to the time interval [t 0Integrate the absolute value of the deviation of the eigenvector from the normal mean within

[0085] Furthermore, an adaptive adjustment factor α(t) is used to adjust the fault monitoring threshold, and the expression is as follows:

[0086] T(t) = μ n ±3σ n +α(t);

[0087] where T(t) is the dynamic fault monitoring threshold at time point t.

[0088] The increase of α(t) will lead to the increase of T(t), which means that the real-time data shows an abnormal trend, and the system will automatically relax the fault monitoring threshold to avoid false alarms; vice versa, if the data tends to be stable, the fault monitoring threshold will be more strict, thus improving the sensitivity of fault monitoring.

[0089] It should be noted that the adaptive ability of the system is enhanced, making the fault monitoring more accurate, avoiding the misjudgment risk caused by the fixed threshold, flexibly coping with the dynamic changes of the power grid operation state, and ensuring the effectiveness and timeliness of fault monitoring.

[0090] S5. After detecting an abnormal situation, an alarm is issued, and the fault monitoring mechanism is optimized according to the feedback of the fault handling result.

[0091] Furthermore, when it is detected that the fault eigenvector exceeds the dynamic fault monitoring threshold, the early warning mechanism is triggered, the redundant monitoring program is started, the extraction and comparison process of the fault eigenvector is repeated for secondary verification. If the detection results are consistent, it is confirmed as a real fault.

[0092] When the fault is confirmed, the log records the fault occurrence time, specific location, type and fault eigenvector, and sends them to the control center, activates the emergency response, reallocates the power grid load, reduces the influence range of the fault area, and ensures the power supply stability of the non-fault area.

[0093] During the fault handling process, continuously collect the impact data of the handling actions and the power grid state, including the power restoration speed, the number of affected users, and the time required for fault repair. After the handling is completed, compare and analyze with the pre-set monitoring process, evaluate the handling effect, and adjust the monitoring process according to the actual situation.

[0094] It should be noted that the entire fault monitoring system can not only detect and handle the abnormal situation in the power grid in time, but also self-learn and optimize after each fault handling, gradually improving its own fault detection ability and response strategy, and finally reaching a higher level of automation and intelligence.

[0095] This embodiment also provides an intelligent power grid fault monitoring system applying artificial intelligence, including: a data acquisition and preprocessing module, a feature extraction and fusion module, a fault monitoring threshold setting and comparison module, an adaptive threshold adjustment module, and an alarm and feedback optimization module; the data acquisition and preprocessing module is used to obtain power Internet of Things monitoring data through sensors and preprocess it; the feature extraction and fusion module is used to extract fault-related features based on the preprocessed data by using machine learning algorithms and fuse them into a unified fault feature vector; the fault monitoring threshold setting and comparison module is used to determine a preliminary fault monitoring threshold range based on the historical data of the power grid through statistical analysis and compare it with the real-time monitored power grid operation state data according to the fault feature vector; the adaptive threshold adjustment module is used to dynamically adjust the fault monitoring threshold by using an adaptive algorithm based on the comparison result of the historical data and real-time state data of the power grid; the alarm and feedback optimization module is used to issue an alarm after detecting an abnormal situation and optimize the fault monitoring mechanism according to the feedback of the fault handling result.

[0096] This embodiment also provides a computer device applicable to the situation of the intelligent power grid fault monitoring method applying artificial intelligence, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent power grid fault monitoring method applying artificial intelligence as proposed in the above embodiment.

[0097] This computer device can be a terminal, and this computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0098] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the intelligent power grid fault monitoring method applying artificial intelligence as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, magnetic disk or optical disc.

[0099] In summary, the present invention realizes comprehensive monitoring of the power grid operation state and improvement of data quality, reduces false alarms, and improves monitoring accuracy by deploying a variety of sensors and performing data preprocessing; by using machine learning algorithms to extract fault features from the preprocessed data and fuse them into a unified fault feature vector, it realizes high-level generalization and integration of fault features, enhances the accuracy of fault recognition and classification ability, and improves the sensitivity of fault detection; by statistically analyzing historical data to determine the preliminary fault monitoring threshold and comparing it with real-time data, it realizes real-time monitoring of the power grid operation state and fast fault response, reducing the impact of faults; by using an adaptive algorithm to dynamically adjust the fault monitoring threshold, it realizes self-optimization of the monitoring system and enhanced adaptability, improving system stability and adaptability to complex environments.

[0100] Embodiment 2, referring to Table 1, is the second embodiment of the present invention. To further verify the technical solution of the present invention, experimental simulation data of the intelligent power grid fault monitoring method applying artificial intelligence is given.

[0101] To verify the effectiveness and superiority of the proposed intelligent power grid fault monitoring method applying artificial intelligence, traditional artificial intelligence algorithms were used for fault detection as a control group in the experiment, and the method of the present invention was used as the experimental group for comparison.

[0102] First, temperature sensors, current sensors, and voltage sensors were deployed on the key nodes of a regional power grid to monitor the power grid state in real time. These sensors were connected to a central data processing center, which was responsible for receiving and processing the data from the sensors.

[0103] After receiving the original data, the central data processing center immediately carried out data cleaning and denoising. For current and voltage signals, a low-pass filter was used for smoothing to reduce noise interference. The preprocessed data was used for subsequent fault feature extraction.

[0104] Then, a method combining principal component analysis (PCA) and convolutional neural network (CNN) was used to extract fault features and fuse them into a unified fault feature vector.

[0105] Next, by statistically analyzing the feature vectors of normal and fault states in the power grid historical data over the past year, the mean and standard deviation of the fault feature vectors in the normal operating state were calculated, and a preliminary fault monitoring threshold range was set according to the three-standard-deviation principle.

[0106] Finally, the power grid operation state data obtained from real-time monitoring was converted into a fault feature vector and compared with the set threshold range. In addition, an adaptive adjustment factor was defined based on the trend change of real-time data and the statistical characteristics of historical data to dynamically adjust the fault monitoring threshold.

[0107] Specifically, it is shown in Table 1 below:

[0108] Table 1 Performance comparison table of smart grid fault monitoring methods

[0109]

[0110] As can be seen from the above table, the method of the present invention has smaller mean and standard deviation of the fault feature vector than the traditional method, which means that the fault features extracted by the method of the present invention are more concentrated, reducing the possibility of false alarms. In terms of the fault monitoring threshold, the method of the present invention is also lower, indicating that under the same fault conditions, the method of the present invention can detect potential faults earlier, thus improving the safety of the system.

[0111] More importantly, in terms of the key indicator of fault detection accuracy, the method of the present invention reaches 93%, while the traditional method is only 85%. This shows that by introducing an adaptive algorithm to dynamically adjust the fault monitoring threshold and combining a redundant monitoring program for secondary verification, the method of the present invention can significantly improve the accuracy and reliability of fault detection.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A smart grid fault monitoring method using artificial intelligence, characterized in that: include, Obtain power IoT monitoring data through sensors and pre-process it; Based on the preprocessed data, machine learning algorithms are used to extract fault-related features and fuse them into a unified fault feature vector; Based on the historical data of the power grid, the preliminary fault monitoring threshold range is determined through statistical analysis; And compare the fault feature vector with the real-time monitoring grid operation status data; Based on the comparison results of historical data and real-time status data of the power grid, the fault monitoring threshold is dynamically adjusted using an adaptive algorithm; An alarm is issued after an abnormal situation is detected, and the fault monitoring mechanism is optimized based on the feedback of the fault handling results.

2. The method for monitoring faults in a smart grid using artificial intelligence as claimed in claim 1, characterized in that: The sensors include a temperature sensor, a current sensor, and a voltage sensor; The preprocessing includes data cleaning and data denoising; The real-time status data includes grid load and temperature changes.

3. The method for monitoring faults in a smart grid using artificial intelligence as claimed in claim 2, characterized in that: The specific steps of obtaining the power IoT monitoring data through sensors and preprocessing it are as follows: Deploy temperature sensors, current sensors, and voltage sensors at power grid substations and transmission line nodes to monitor the power grid in real time and send data to the central data processing center in real time; The central data processing center receives the raw data, cleans it, denoises it, removes invalid data, and uses a low-pass filter to smooth the current and voltage signals and remove noise.

4. The method for monitoring faults in a smart power grid using artificial intelligence as claimed in claim 3, characterized in that: Based on the preprocessed data, the machine learning algorithm is used to extract fault-related features and fuse them into a unified fault feature vector. The specific steps are as follows: A method combining principal component analysis and convolutional neural network is used to extract fault-related features from the preprocessed data. The expression is as follows: Among them, F is the extracted fault feature vector, D is the preprocessed data set, PCA (D i ) is through D i The main components obtained by principal component analysis, D i is the data of the i-th dimension in the dataset D, i is the index variable, CNN(D i ) is obtained from D by convolutional neural network i The features extracted from Var(D i ) is D i The variance of Feature fusion is performed based on the mutual information between features. The expression is as follows: Among them, V final is the fused fault feature vector, F i ,F j are the feature vectors extracted from two different dimensions, MI(F i ,F j ) is F i ,F j The mutual information between them.

5. The method for monitoring faults in a smart grid using artificial intelligence as claimed in claim 4, characterized in that: Based on the historical data of the power grid, the preliminary fault monitoring threshold range is determined through statistical analysis, and the fault feature vector is compared with the real-time monitoring power grid operation status data. The specific steps are as follows: Summarize the feature vectors of normal state and fault state in the historical data of the power grid. The distribution of feature vectors in normal operation state follows normal distribution, while the distribution of feature vectors in fault state will be significantly different. Calculate the mean and standard deviation of the fault feature vector under normal conditions, and set the initial fault monitoring threshold range based on the principle of three times the standard deviation; The grid operation status data obtained by real-time monitoring is converted into a fault feature vector. If the fault feature vector obtained by real-time monitoring data is within the fault threshold range, it is considered that the grid operation status is good, otherwise it is considered that there is a fault in the grid operation.

6. The method for monitoring faults in a smart power grid using artificial intelligence as claimed in claim 5, characterized in that: The comparison result based on the historical data of the power grid and the real-time status data is used to dynamically adjust the fault monitoring threshold using an adaptive algorithm. The specific steps are as follows: Based on the trend change of real-time data and the statistical characteristics of historical data, an adaptive adjustment factor α is defined to reflect the degree of deviation of real-time status data relative to historical data. The expression is as follows: Among them, α(t) is the adaptive adjustment factor at time point t, V final (t) is the fault feature vector after fusion at time point t, μ n and σ n are the mean and standard deviation of the fault feature vector under normal conditions in the historical data, t0 and t are the time points for starting and ending the real-time data collection, and dt refers to the integration of the absolute value of the feature vector’s deviation from the normal mean in the time interval [t0, t]; The adaptive adjustment factor α(t) is used to adjust the fault detection threshold. The expression is as follows: T(t)=μ n ±3σ n +α(t); Where T(t) is the dynamic fault monitoring threshold at time point t.

7. The method for monitoring faults in a smart power grid using artificial intelligence according to claim 6, characterized in that: After the abnormal situation is detected, an alarm is issued, and the fault monitoring mechanism is optimized according to the feedback of the fault handling result. The specific steps are as follows: When the fault feature vector is detected to exceed the dynamic fault monitoring threshold, the early warning mechanism is triggered, the redundant monitoring program is started, and the extraction and comparison process of the fault feature vector is repeated for secondary verification; When a fault is confirmed, the log records the time, location, type and fault feature vector of the fault and sends it to the control center to activate the emergency response and redistribute the grid load; During the fault handling process, the impact data of the handling actions and power grid status are continuously collected and compared with the pre-set monitoring process, the handling effect is evaluated, and the monitoring process is adjusted according to the actual situation.

8. A smart grid fault monitoring system using artificial intelligence, based on the smart grid fault monitoring method using artificial intelligence according to any one of claims 1 to 7, characterized in that: Including data acquisition and preprocessing module, feature extraction and fusion module, fault monitoring threshold setting and comparison module, adaptive threshold adjustment module and alarm and feedback optimization module; The data acquisition and preprocessing module is used to obtain power IoT monitoring data through sensors and preprocess it; The feature extraction and fusion module is used to extract fault-related features based on the preprocessed data using a machine learning algorithm and fuse them into a unified fault feature vector; The fault monitoring threshold setting and comparison module is used to determine the preliminary fault monitoring threshold range through statistical analysis based on the historical data of the power grid, and compare it with the real-time monitoring power grid operation status data according to the fault feature vector; The adaptive threshold adjustment module is used to dynamically adjust the fault monitoring threshold using an adaptive algorithm based on the comparison result between the historical data and the real-time status data of the power grid; The alarm and feedback optimization module is used to issue an alarm after detecting an abnormal situation and optimize the fault monitoring mechanism according to the feedback of the fault handling result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the smart grid fault monitoring method using artificial intelligence described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the smart grid fault monitoring method using artificial intelligence described in any one of claims 1 to 7 are implemented.

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