A relay protection device status evaluation system and method based on the Internet of Things

Abstract

The present invention provides a relay protection device status evaluation system and method based on the Internet of Things (IoT), relating to the field of IoT technology. The method comprises: obtaining, in real time, operating data of the relay protection device through an IoT communication system, including operating voltage, current, temperature, switch status parameters, as well as the relay protection device's action records and abnormal alarm information; preprocessing the relay protection device's operating data to obtain processed data; establishing an initial relay protection device status evaluation model based on the processed data; and training the relay protection device status evaluation model using the processed data to obtain a final relay protection device status evaluation model. The present invention uses the IoT to obtain and process relay protection device data in real time, establish and train an evaluation model, and thus achieves accurate evaluation of the device status, improving power system stability and operation and maintenance efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a relay protection device status evaluation system and method based on the Internet of Things. Background Art

[0002] Traditional condition assessment methods rely heavily on manual inspections, which are not only time-consuming and labor-intensive but also limited by the experience and skill levels of inspectors. Human factors can lead to subjectivity and inconsistency in inspection results, compromising the accuracy of condition assessments. While regular testing is an important means of evaluating relay protection device performance, these tests are conducted on a fixed schedule and may not capture rapid changes in device status. Furthermore, the depth and scope of testing can be limited by resources and time constraints, making it difficult to fully capture all potential issues.

[0003] In traditional evaluation systems, data collection, organization, and analysis are manual and tedious processes, which may lead to delays in data processing and an inability to reflect the actual status of the device in real time. Furthermore, the lack of intelligent data analysis tools and automated monitoring systems makes it difficult to achieve continuous, real-time monitoring of the status of relay protection devices and rapid data-based decision support. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a relay protection device status evaluation system and method based on the Internet of Things, which realizes real-time monitoring and evaluation of the relay protection device status.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] In a first aspect, a method for evaluating the status of a relay protection device based on the Internet of Things comprises:

[0007] Obtain real-time operating data of relay protection devices through the IoT communication system, including operating voltage, current, temperature, switch status parameters, as well as action records and abnormal alarm information of relay protection devices;

[0008] Preprocessing the operating data of the relay protection device to obtain processed data;

[0009] Based on the processed data, an initial relay protection device status evaluation model is established, and the relay protection device status evaluation model is trained using the processed data to obtain a final relay protection device status evaluation model;

[0010] The current operating data of the relay protection device obtained in real time is input into the final relay protection device status evaluation model to evaluate the status of the relay protection device and obtain an evaluation result.

[0011] Furthermore, based on the processed data, an initial relay protection device status evaluation model is established, including:

[0012] Divide the processed data into training set and test set;

[0013] The characteristic data in the processed data is used to construct an initial relay protection device state evaluation model, and the kernel function and initial parameters of the initial relay protection device state evaluation model are determined.

[0014] Furthermore, the method further includes: training an initial relay protection device state evaluation model using a training set to obtain initial training results, and during the training process, monitoring the performance of the initial relay protection device state evaluation model in real time, wherein the performance includes accuracy and loss indicators;

[0015] According to the initial training results, the initial parameters of the initial relay protection device status evaluation model are adjusted, and the final parameters are determined through repeated experiments and verification;

[0016] The test set is used to evaluate the performance of the initial relay protection device state evaluation model after parameter adjustment, and the performance evaluation results are obtained.

[0017] Furthermore, it also includes: optimizing the initial relay protection device state evaluation model according to the performance evaluation results, and repeatedly training and verifying the optimized initial relay protection device state evaluation model to obtain the final relay protection device state evaluation model.

[0018] Furthermore, the current operating data of the relay protection device obtained in real time is input into the final relay protection device status evaluation model to evaluate the status of the relay protection device and obtain evaluation results, including:

[0019] Processing current operating data to extract key features for status evaluation, including voltage fluctuation range, current stability, temperature change trend, and switching frequency;

[0020] The key features are analyzed, and the current running data obtained each time is assigned to a specific state label to obtain the analysis results.

[0021] Furthermore, the current operating data of the relay protection device acquired in real time is input into a final relay protection device state evaluation model to evaluate the state of the relay protection device and obtain an evaluation result, including: calculating a score for each data point based on the analysis result, and judging the current state of the relay protection device according to the score of each data point to obtain a judgment result;

[0022] Based on the scoring and judgment results of each data point, the status of the relay protection device is comprehensively evaluated to obtain the evaluation results.

[0023] Furthermore, based on the scoring and judgment results of each data point, a comprehensive evaluation of the status of the relay protection device is performed to obtain the evaluation results, including:

[0024] Summarize the scores of each data point and integrate the judgment results corresponding to each data point;

[0025] Assign corresponding weights to each data point, scoring, and judgment result, and calculate the comprehensive evaluation score;

[0026] According to the comprehensive evaluation score, a threshold is set to determine the status of the relay protection device.

[0027] Furthermore, based on the scoring and judgment results of each data point, a comprehensive evaluation of the status of the relay protection device is performed to obtain the evaluation results, including:

[0028] Compare the comprehensive evaluation score with the set threshold to determine the current state of the relay protection device;

[0029] The evaluation results are obtained based on the comprehensive evaluation score and status judgment.

[0030] In the second aspect, a relay protection device status evaluation system based on the Internet of Things includes:

[0031] The data acquisition module is used to collect the operating voltage, current, temperature, switch status operation data of the relay protection device in real time, as well as action records and abnormal alarm information, and transmit the data to the data center;

[0032] The data preprocessing module is used to clean, denoise, and normalize the data transmitted by the IoT data acquisition module to obtain processed data;

[0033] The state evaluation model module is used to establish a state evaluation model of the relay protection device based on the processed data, extract the characteristic values ​​of the state of the relay protection device, and evaluate the state of the relay protection device based on these characteristic values ​​to obtain an evaluation result;

[0034] The early warning and notification module is used to automatically trigger the early warning mechanism and notify personnel to handle the situation when the relay protection device status is abnormal or close to the fault threshold based on the evaluation results of the status evaluation model.

[0035] The above solution of the present invention includes at least the following beneficial effects:

[0036] Through the IoT communication system, this method can acquire real-time operating data of relay protection devices, including key parameters such as operating voltage, current, temperature, and switch status, as well as action records and abnormal alarm information. This enables comprehensive, real-time monitoring of relay protection devices, helping to promptly identify and address potential problems. The acquired operating data is preprocessed to ensure accuracy and consistency, providing a high-quality data foundation for model training. Based on the processed data, an initial relay protection device status evaluation model is established, and the final evaluation model is obtained through training. This improves the accuracy and reliability of the status evaluation.

[0037] The current operating data acquired in real time is input into the final status evaluation model to conduct a real-time evaluation of the relay protection device's status. This helps promptly detect abnormal device conditions or potential faults, providing timely warning information to operation and maintenance personnel. The evaluation results can serve as decision support, helping operation and maintenance personnel develop targeted maintenance strategies and improve maintenance efficiency and effectiveness. Through real-time status evaluation and early warning, this method helps reduce the occurrence of relay protection device failures, thereby reducing the risk of accidents in the power system and improving overall stability. Accurate status evaluation also helps optimize the configuration and parameter settings of relay protection devices, improving their protection performance and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flow chart of a method for evaluating the status of a relay protection device based on the Internet of Things provided by an embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram of a relay protection device status evaluation system based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0041] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the status of a relay protection device based on the Internet of Things, the method comprising the following steps:

[0042] Step 11: Obtaining the operating data of the relay protection device in real time through the Internet of Things communication system, including operating voltage, current, temperature, switch state parameters, as well as the action record and abnormal alarm information of the relay protection device;

[0043] Step 12: pre-processing the operating data of the relay protection device to obtain processed data;

[0044] Step 13: establishing an initial relay protection device state evaluation model based on the processed data, and training the relay protection device state evaluation model using the processed data to obtain a final relay protection device state evaluation model;

[0045] Step 14: input the current operating data of the relay protection device obtained in real time into the final relay protection device state evaluation model, evaluate the state of the relay protection device, and obtain an evaluation result.

[0046] In an embodiment of the present invention, real-time data monitoring and acquisition can accurately and in real time acquire key operating data such as the operating voltage, current, temperature, and switch status of the relay protection device, as well as action records and abnormal alarm information. This helps operation and maintenance personnel promptly understand the operating status of the device. By preprocessing the acquired operating data, such as data cleaning, format unification, and outlier processing, the quality and usability of the data can be improved. The initial state evaluation model established based on the processed data can be trained to obtain a more accurate and reliable final evaluation model, thereby improving the accuracy and efficiency of state evaluation.

[0047] By inputting the current operating data acquired in real time into the final status assessment model, the status of the relay protection device can be accurately evaluated in real time. This helps to promptly detect abnormal conditions or potential faults in the device and triggers early warning mechanisms, providing timely warning information to operation and maintenance personnel, thereby shortening fault handling time and reducing the impact of faults on the power system. The status assessment results can serve as decision support, helping operation and maintenance personnel develop targeted maintenance strategies, such as regular inspections and replacement of aging components. This can improve maintenance efficiency and effectiveness, reduce maintenance costs, and extend the service life of the relay protection device. Through real-time status evaluation and early warning, as well as optimized maintenance strategies, this method helps reduce the occurrence of relay protection device failures, thereby reducing the risk of accidents in the power system and improving overall stability and safety.

[0048] In a preferred embodiment of the present invention, the above step 11, in which the operating data of the relay protection device is obtained in real time through the Internet of Things communication system, including operating voltage, current, temperature, switch state parameters, and action records and abnormal alarm information of the relay protection device, may include:

[0049] In an embodiment of the present invention, an IoT communication module (such as a wireless sensor, RFID tag, etc.) is installed on the relay protection device. These modules can collect the device's operating data in real time. An IoT communication gateway is configured to upload the collected data to a cloud server or local data center. Network coverage of the IoT communication system, including wireless, wired, or hybrid networks, is ensured to ensure real-time data transmission. The IoT communication module collects data such as the operating voltage, current, temperature, and switch status parameters of the relay protection device periodically or based on preset conditions (such as event triggers). The collected data is encapsulated and encrypted by the IoT communication gateway and then transmitted over the network to the cloud server or local data center. After receiving the data, the cloud server or local data center decrypts and parses it, converting the raw data into a readable and analyzable format. The processed data is analyzed to identify features and patterns related to the status of the relay protection device. Based on the analysis results, the status of the relay protection device is evaluated. For example, if abnormal voltage fluctuations or a continuous temperature increase are detected, it is determined that the device may be in a warning or fault state.

[0050] Imagine a smart grid where an IoT communication module collects real-time data such as the operating voltage, current, and temperature of a relay protection device and uploads it to a cloud server. The IoT communication module collects the operating voltage, current, and temperature data of the relay protection device every five minutes and uploads it to the cloud server via a wireless network. Upon receiving the data, the cloud server decrypts and parses it, converting the raw data into a readable format and storing it in a database.

[0051] In a preferred embodiment of the present invention, the above step 12 of pre-processing the operating data of the relay protection device to obtain processed data may include:

[0052] Data quality checks identify missing data due to sensor failure or communication issues. For continuously changing data, such as voltage and current, linear interpolation can be used to fill in missing values. Specifically, the values ​​of two adjacent known data points are used to calculate the value of the missing data point through a linear equation. The formula for calculating the value of the missing data point is: Where y represents the estimated value of the missing data point; y1 and y2 represent the ordinate values ​​of two adjacent known data points; x1 and x2 represent the abscissa values ​​of two adjacent known data points; and x represents the abscissa of the missing data point. The ordinate value y of the missing data point is estimated by using the values ​​of the two adjacent known data points (x1, y1) and (x2, y2) and the abscissa x of the missing data point.

[0053] Suppose a voltage sensor records voltage values ​​v1 and v3 at time points t1 and t3, respectively, but loses data at time point t2 (where t1 < t2 < t3). Using a linear equation, we can calculate an estimated voltage value at time t2 based on the values ​​of v1, v3, t1, t3, and t2.

[0054] According to the relevant standards and specifications of the power system industry and the actual operation of the relay protection device, set the threshold range. For example, the abnormal value range of voltage and current, the high or low temperature threshold, etc. Traverse the data records, eliminate the data that exceeds the set threshold range, and record the reasons and time of elimination. Identify data from different sources and formats, such as CSV, Excel, etc. Use the minimum-maximum normalization method to convert the numerical range of the data to a specific interval (such as 0-1). Specifically, for each data point, calculate the ratio of the difference between its value and the minimum value to the difference between the maximum value and the minimum value. Traverse the data records and find the minimum and maximum values ​​of each data column. For each data point, calculate its normalized value.

[0055] Establish screening criteria related to relay protection device status evaluation, such as device number, time range, and data type. Organize the screened data and sort it by chronological order or by dimensions such as device number. Store the preprocessed data on the server where the evaluation system resides, and establish a regular backup mechanism to prevent data loss. Perform regular maintenance and management of the stored data, including operations such as data backup, data recovery, and data cleanup. Establish a data access permission mechanism to ensure that only authorized personnel can access and manipulate the data.

[0056] In a preferred embodiment of the present invention, the above step 13, establishing an initial relay protection device status evaluation model based on the processed data, may include:

[0057] Step 131, dividing the processed data into a training set and a test set;

[0058] Step 132: construct an initial relay protection device state evaluation model using the feature data in the processed data, and determine the kernel function and initial parameters of the initial relay protection device state evaluation model.

[0059] In an embodiment of the present invention, the pre-processed data set is divided according to a certain ratio (such as 70% as a training set and 30% as a test set). Such a division helps to evaluate the generalization ability of the model, that is, the performance of the model on unseen data. Based on the characteristics of the processed data, a support vector machine (SVM) is selected as a preliminary relay protection device status evaluation model. SVM is a supervised learning model that is good at handling classification and regression problems. Determine the kernel function of the SVM model, such as the radial basis function (RBF). The radial basis function is: K(xc ,x l )=exp(-γ||x c -x l || 2 )(K(x c ,x l ); where K(x c ,x l ) represents the sample x c and x l γ represents the kernel function value between [ ] and [ ]; γ represents the parameter of the radial basis function. It can handle nonlinear relationships in the data. Determine the initial parameters of the SVM model, such as the learning rate and regularization coefficient. These parameters have a significant impact on model performance and training speed and need to be optimized through experiments or cross-validation.

[0060] Assume that a set of operating data of relay protection devices has been acquired and preprocessed, including operating voltage, current, temperature and other characteristics. Here is how to build the initial state evaluation model:

[0061] Suppose a dataset contains 1000 records, each with 10 features (including operating voltage, current, temperature, etc.). The dataset is divided into a 70% training set and a 30% test set, with 700 records as the training set and 300 records as the test set. Use a machine learning library (such as scikit-learn) to build an SVM model. Select RBF as the kernel function and set initial parameters, such as a learning rate of 0.01 and a regularization coefficient of 1.0. Use the training set data to train the SVM model and adjust the parameters to optimize the model's performance. Dividing the processed data into training and test sets ensures that the model does not overfit during training—that is, it does not rely too heavily on the training data and ignore the overall distribution of the data. This ensures that the model maintains good predictive performance when faced with new, unseen data, thereby improving its generalization ability.

[0062] Using processed feature data to construct an initial model and determine the kernel function and initial parameters can make the model more consistent with the distribution and characteristics of the actual data. By continuously adjusting and optimizing these parameters, the model can achieve better convergence during training, thereby improving the model's prediction accuracy and stability. Selecting the kernel function and parameters can make the model more transparent and interpretable during the prediction process. For example, certain kernel functions can intuitively reflect the similarities and distances between data, helping operations and maintenance personnel better understand the model's prediction results and the basis for their decisions. By constructing an initial condition assessment model, training it, and testing it, the condition of the relay protection device under evaluation can be quickly predicted and classified. This helps shorten the condition assessment time and improve evaluation efficiency, allowing for the timely identification and resolution of potential issues, ensuring the safe and stable operation of the power system.

[0063] In a preferred embodiment of the present invention, the above step 13 further includes:

[0064] Step 134: train the initial relay protection device state evaluation model using the training set to obtain initial training results. During the training process, monitor the performance of the initial relay protection device state evaluation model in real time, including accuracy and loss indicators.

[0065] Step 135 , adjusting the initial parameters of the initial relay protection device state evaluation model based on the initial training results, and determining the final parameters through repeated experiments and verification;

[0066] Step 136 , using the test set to perform performance evaluation on the initial relay protection device state evaluation model after parameter adjustment, to obtain a performance evaluation result.

[0067] In an embodiment of the present invention, the initial relay protection device state evaluation model is trained using the divided training set data. During the training process, the performance of the initial relay protection device state evaluation model is monitored in real time, including accuracy and loss index. Accuracy refers to the proportion of correct predictions made by the model, which reflects the model's classification ability. The loss index measures the difference between the model's predicted results and the actual results and is used to optimize the training process of the initial relay protection device state evaluation model. Based on the initial training results, the initial parameters of the initial relay protection device state evaluation model are adjusted. This includes the learning rate, regularization coefficient, kernel function parameters (such as the γ value of the RBF), etc. Through repeated experiments and verification, the impact of different parameter combinations on model performance is observed, and the parameters are gradually optimized until the final parameter combination is found. The performance of the initial relay protection device state evaluation model after parameter adjustment is evaluated using the test set data. The test set data is data that the model has not seen during the training process and therefore can more objectively reflect the model's generalization ability. By calculating the accuracy and recall rate of the model on the test set, it is evaluated whether the model performance meets actual requirements.

[0068] Assume that an initial relay protection device state evaluation model based on SVM has been established, and the training set and test set data have been divided. Use the training set data (70% of the data set) to train the initial relay protection device state evaluation model of SVM. During the training process, the accuracy and loss indicators of the initial relay protection device state evaluation model of SVM are monitored in real time. For example, the accuracy and loss are calculated and recorded once each epoch of training. The initial parameters are set as: learning rate 0.01, regularization coefficient 1.0, and γ value of RBF kernel function 0.1. Through repeated experiments and verification, it is found that when the learning rate is adjusted to 0.001, the regularization coefficient is adjusted to 0.5, and the γ value is adjusted to 0.5, the performance of the initial relay protection device state evaluation model of SVM reaches the preset effect. Use the test set data (30% of the data set) to evaluate the performance of the initial relay protection device state evaluation model of SVM after parameter adjustment. Calculate the accuracy and recall rate of the initial relay protection device state evaluation model of SVM on the test set. The recall rate calculation formula is: Here, R represents the recall rate; TP represents the number of correctly classified positive samples; and FN represents the number of incorrectly classified negative samples. Assume the evaluation results are: precision 90% and recall 85%. These metrics demonstrate that the SVM-based initial relay protection device condition evaluation model performs well on the test set and meets practical requirements.

[0069] By training the model using a training set and monitoring accuracy and loss metrics in real time, model deficiencies during training, such as overfitting and underfitting, can be promptly identified. Model parameters are adjusted based on the initial training results. Through repeated trials and verification, a parameter combination suitable for the current data can be found, thereby improving the prediction accuracy and generalization ability of the SVM initial relay protection device condition assessment model. The trained model can quickly and accurately predict and classify the condition of the relay protection device being evaluated, shortening evaluation time and improving evaluation efficiency. This helps operations and maintenance personnel promptly identify and address potential problems, ensuring the safe and stable operation of the power system. Determining the final parameters through repeated trials and verification makes the model more stable across different data and scenarios, reducing performance degradation caused by data fluctuations or scenario changes. This helps improve the reliability and stability of the model in practical applications, providing strong support for the development of smart grids. Accurate performance evaluation results can provide strong decision-making support for operations and maintenance personnel. When the model predicts a potential failure risk for a relay protection device, operations and maintenance personnel can take timely measures to repair or replace it, thereby avoiding potential safety hazards. This is achieved through model training, parameter adjustment, and performance evaluation.

[0070] In a preferred embodiment of the present invention, the calculation formula of the accuracy is:

[0071]

[0072] Among them, A represents the adjusted accuracy; TP represents the number of relay protection devices correctly predicted by the model to be in abnormal state; TN represents the number of relay protection devices correctly predicted by the model to be in normal state; α and β represent weight adjustment parameters; FP represents the number of normal states incorrectly predicted by the model as abnormal states; FN represents the number of abnormal states incorrectly predicted by the model as normal states; γ represents the adjustment parameter for the total number of predictions.

[0073] In an embodiment of the present invention, (TP+TN)·(1+α) is the weighting of the correct prediction (TP and TN), where α is used to increase the weight of the correct prediction. If α>0, the weight of the correct prediction will increase; if α<0, the weight will decrease. β·(FP+FN) is a penalty term for incorrect predictions. If β>0, it will actually reduce the accuracy (because this is a subtraction operation), but if it is regarded as a negative number (that is, β is actually a penalty coefficient, and it is a negative value or a positive number less than 1 multiplied by a positive number), it increases the penalty for incorrect predictions. Then, consider the adjustment parameter γ of the total number of predictions, 1+γ, which is an adjustment to the total number of predictions (denominator). If γ>0, the total number of predictions will increase; if γ<0, the total number of predictions will decrease (but in actual applications, the total number of predictions will not be reduced, so γ is non-negative). The adjusted accuracy A is obtained by dividing the adjusted numerator (RP+TN)·(1+α)-β·(FP+FN) by the adjusted denominator (TP+TN+FP+FN)·(1+γ).

[0074] Assume that there is a relay protection device prediction model and the following prediction results are obtained:

[0075] TP (number of relay protection devices correctly predicted to be in abnormal state by the model): 80 units;

[0076] TN (number of relay protection devices correctly predicted by the model to be in normal state): 60 units;

[0077] FP (number of times the model incorrectly predicted a normal state as an abnormal state): 10 units;

[0078] FN (number of abnormal states that the model incorrectly predicted as normal): 5 units;

[0079] In addition, assume that the weight adjustment parameters and the total prediction quantity adjustment parameters are as follows:

[0080] α = 0.2 (increase the weight of correct predictions);

[0081] β = -0.3 (penalizes incorrect predictions, note that this is a negative number);

[0082] γ=0.1 (adjust the total number of predictions). Substitute these values ​​into the formula to calculate the adjusted accuracy Substitute specific values: (Keep four decimal places).

[0083] By introducing the adjustment parameter γ, adaptive adjustments can be made to different data sets or model performance, so that the calculated accuracy can reflect the actual performance of the model in different scenarios. By analyzing the adjusted accuracy A, it is easier to identify the performance deficiencies of the model in specific categories (abnormal and normal), and then optimize the model in a targeted manner. For example, if the A value is found to be low, the number of FP and FN can be checked to find areas for improvement. Adjusting the accuracy can help decision makers understand the reliability of the model. For example, in power systems, understanding the accuracy of relay protection devices can directly influence the formulation of safety strategies.

[0084] In another preferred embodiment of the present invention, the above step 13 further includes: optimizing the initial relay protection device state evaluation model based on the performance evaluation results, and repeatedly training and verifying the optimized initial relay protection device state evaluation model to obtain a final relay protection device state evaluation model, which may include:

[0085] Carefully review the performance evaluation report, especially key metrics such as accuracy and recall. Develop specific optimization strategies for issues identified during the performance evaluation. For example, if the initial relay protection device status evaluation model has low accuracy in predicting abnormal conditions, consider increasing the proportion of abnormal condition samples or adjusting the feature selection of the initial relay protection device status evaluation model. Further preprocess the training data, such as data cleaning, feature selection, and sample balancing. Adjust the parameters of the initial relay protection device status evaluation model (such as the learning rate and regularization coefficient) based on the optimization strategy. Use the adjusted data and parameters to repeatedly train and validate the initial relay protection device status evaluation model. After each training session, perform a performance evaluation on the model to observe the optimization effect. After multiple iterations and optimizations, when the performance of the initial relay protection device status evaluation model meets the preset standards, determine the final relay protection device status evaluation model. Conduct a comprehensive performance evaluation of the final model, including its performance under different data sets and scenarios. Deploy the final model in real-world applications for relay protection device status evaluation. During use, continuously monitor the model's performance to promptly identify and address potential issues.

[0086] In a preferred embodiment of the present invention, the above step 14, inputting the current operating data of the relay protection device obtained in real time into the final relay protection device state evaluation model to evaluate the state of the relay protection device and obtain an evaluation result, may include:

[0087] Step 114: Process the current operating data to extract key features for status evaluation, including voltage fluctuation range, current stability, temperature change trend, and switching frequency.

[0088] Step 115 , analyzing the key features, assigning each acquired current operation data to a specific state tag, and obtaining an analysis result.

[0089] In an embodiment of the present invention, sensor types, such as voltage sensors, current sensors, temperature sensors, and switch status sensors, are selected to ensure that they can accurately measure the key parameters of the relay protection device. Configure the data acquisition system and set the data acquisition frequency to ensure that the operating data of the relay protection device can be obtained in real time. Receive the raw data transmitted from the sensor network, perform preliminary data cleaning, and remove obvious outliers and noise, such as sudden interference signals or data transmission errors. Format the data to ensure that the data format is unified, perform time series analysis on continuous data, and identify trends and periodic changes in the data. Calculate the voltage fluctuation range and fluctuation frequency to evaluate the voltage stability. The voltage fluctuation range is obtained by calculating the difference between the maximum and minimum voltage values ​​over a period of time. The formula is: Δv = V max -V min ; Among them, Δv represents the voltage fluctuation range; V max Indicates the maximum voltage within a time period; V min Represents the minimum voltage value within a time period. The voltage fluctuation frequency is obtained by analyzing voltage time series data and counting the number of voltage fluctuations per unit time. This requires filtering and denoising the data to more accurately identify fluctuations. The formula for fluctuation frequency can be expressed as: Where, f represents the voltage fluctuation frequency; N f Represents the number of voltage fluctuations within a time period T, where T represents time. Analyze the smoothness of the current curve, calculate the standard deviation of the current rate of change, and identify abnormal current fluctuations, such as overcurrent or undercurrent.

[0090] The smoothness of the current curve is evaluated by calculating the standard deviation of the current change rate. A smooth current curve means that the current change rate is small and the standard deviation is also small. The formula is: Where δ represents the standard deviation of the current change rate; represents the current change rate at the i-th time point; Represents the average value of the current change rate; μ represents the number of data points in a time period. Use time series analysis technology to predict temperature trends and identify possible overheating risks, such as continuous temperature increases or exceeding preset thresholds. Count the number of switch actions per unit time, analyze the frequency of switch operations, and evaluate mechanical wear, such as switch contact wear or mechanism jamming. Perform state recognition on the extracted features and establish a mapping relationship between features and states. Set thresholds, for example, the "normal" state score is 0-10 points, the "warning" is 11-50 points, and the "abnormal" is 51-100 points. Assign real-time data to specific state labels such as "normal", "warning", and "abnormal" to facilitate operation and maintenance personnel to quickly understand the status of the device.

[0091] By analyzing key characteristics such as the voltage fluctuation range and current stability in real time, potential fault signs, such as abnormal voltage fluctuations and unstable current, can be promptly detected, thereby issuing early warnings and reducing the occurrence of sudden failures. The status evaluation results can provide strong decision-making support for operation and maintenance personnel. Based on the real-time status of the device, operation and maintenance personnel can formulate more reasonable and efficient operation and maintenance plans, such as prioritizing devices with warnings or abnormal conditions, and reducing unnecessary inspections and maintenance work. By continuously monitoring and analyzing the operating status of relay protection devices, problems that may affect the life of the equipment, such as overheating and mechanical wear, can be promptly discovered and addressed. This helps to extend the service life of the equipment and reduce the cost and frequency of equipment replacement.

[0092] Relay protection devices are a crucial component of power systems, and their stability and reliability directly impact the operation of the entire system. Real-time status evaluation ensures the proper functioning of relay protection devices at critical moments, thereby improving the overall stability of the power system. Real-time status evaluation models analyze and predict large amounts of historical and real-time data, providing a more scientific and accurate basis for operation and maintenance decisions. This helps reduce the subjectivity and uncertainty inherent in human judgment and improves decision-making efficiency and accuracy.

[0093] In a preferred embodiment of the present invention, the above step 14, inputting the current operating status information of the relay protection device obtained in real time into the final relay protection device status evaluation model to evaluate the status of the relay protection device and obtain an evaluation result, further includes:

[0094] Step 144, based on the analysis results, calculating a score for each data point, and judging the current state of the relay protection device according to the score for each data point to obtain a judgment result;

[0095] Step 145 : Based on the score and judgment result of each data point, comprehensively evaluate the status of the relay protection device to obtain an evaluation result.

[0096] In the embodiment of the present invention, a score is calculated for each data point (such as voltage V, current I, temperature T, switching frequency F, etc.). This score is determined based on the degree of deviation or abnormality of the data point relative to the normal state. V =f(V); where S V Indicates the voltage rating; V represents voltage. I =f(I); where S I Indicates the current rating; I represents the current. T =f(T); where S T The temperature score is represented by T, which represents the temperature. The current status of the relay protection device can be determined based on the score of each data point.

[0097] Assume that the real-time operating status information of a relay protection device includes three key characteristics: voltage, current, and temperature. Follow the steps below to perform status evaluation:

[0098] Real-time acquisition of voltage V, current I, and temperature T data. Preprocess the data, such as removing noise and normalizing. Based on historical data, set scoring rules for each key feature. For example:

[0099] Voltage rating: If V exceeds the normal range (such as ±5%), a corresponding rating will be given based on the degree of excess.

[0100] Current score: If I exceeds the stable range or reaches the maximum fluctuation range, a score is given.

[0101] Temperature score: A score is given if T is outside the normal range or reaches the overheating threshold.

[0102] Use the scoring formula to calculate the score of each data point, and judge the current status of the relay protection device based on the score of each data point and the preset threshold range. For example:

[0103] If S V ≥Threshold range or S I ≥Threshold range or S T If the score is greater than or equal to the threshold, the device status is "abnormal". Otherwise, it is judged as "warning" or "normal" based on the proximity of the score.

[0104] In a real-time data acquisition, the voltage V = 225V (exceeding the normal range of 220V), the current I = 10A (within the normal range), and the temperature T = 55°C (close to the overheating threshold of 60°C). V =2 (because the voltage exceeds the normal range), S I =0 (because the current is within the normal range), S T =1 (because the temperature is close to the overheat threshold).

[0105] By analyzing the scores of each data point in real time, it is possible to more accurately predict possible failures of relay protection devices. This precise fault warning helps reduce sudden failures and ensure the stable operation of the power system. Through continuous monitoring and scoring, problems that may affect the life of the device, such as overheating and voltage fluctuations, can be discovered and addressed in a timely manner. This helps to extend the service life of relay protection devices and reduce equipment replacement costs in Shanghai. Relay protection devices are an important part of the power system. By accurately evaluating their status, it is possible to ensure that the devices can function normally at critical moments, thereby improving the reliability of the entire power system. The application of real-time status evaluation models is an important part of the intelligent management of power systems. By evaluating the status of relay protection devices in real time, potential problems can be discovered and addressed in a timely manner, ensuring the stable operation of the power system and thus improving user satisfaction.

[0106] In a preferred embodiment of the present invention, the above step 145, based on the score and judgment result of each data point, comprehensively evaluates the status of the relay protection device to obtain an evaluation result, which may include:

[0107] Step 1455 , summarizing the scores of each data point and integrating the judgment results corresponding to each data point;

[0108] Step 1456 , assign corresponding weights to each data point, scoring, and judgment result, and calculate a comprehensive evaluation score;

[0109] Step 1457: Set a threshold value based on the comprehensive evaluation score to determine the status of the relay protection device.

[0110] In this embodiment of the present invention, scores for each data point are summarized: voltage fluctuation is scored 30; current stability is scored 50; and temperature variation is scored 80. According to the scoring rules, voltage fluctuation is within the normal range, so the result is judged as "normal." However, due to the impact of its score on the overall evaluation, its status is not directly judged for now. Similarly, according to the scoring rules, current stability is also within the normal range, but its score needs to be considered. Temperature variation is close to the overheating threshold, so its score is higher and requires special attention.

[0111] Assign corresponding weights to each data point, scoring, and judgment result:

[0112] Voltage fluctuation: weight = 0.2 (after normalization);

[0113] Current stability: weight = 0.3 (normalized);

[0114] Temperature change: weight = 0.5 (after normalization).

[0115] According to the formula S=∑(R h×W h ) calculates the comprehensive evaluation score; where S represents the comprehensive evaluation score; R h represents the score of the hth data point; W h = represents the weight of the hth data point. Substitute the value S = 30 × 0.2 + 50 × 0.3 + 80 × 0.5 = 6 + 15 + 40 = 61. Based on the comprehensive evaluation score, set a threshold to determine the status of the relay protection device: Set the threshold range: normal state (0-40), warning state (41-70), abnormal state (71-100). Based on the comprehensive evaluation score of 61, the status of the relay protection device is determined to be "warning state".

[0116] Real-time monitoring and scoring can promptly identify potential faults and improve the accuracy of early warnings. Based on the comprehensive evaluation score, more rational operation and maintenance plans can be formulated, prioritizing high-scoring data points and improving operation and maintenance efficiency. By promptly identifying and addressing issues that may affect equipment life, such as overheating and voltage fluctuations, the service life of relay protection devices can be extended. Relay protection devices are a vital component of the power system, and comprehensive evaluation can ensure their optimal condition, thereby improving the stability of the entire power system.

[0117] In a preferred embodiment of the present invention, the above step 145, which comprehensively evaluates the status of the relay protection device based on the score and judgment result of each data point to obtain an evaluation result, further includes:

[0118] Step 1458 , comparing the comprehensive evaluation score with a set threshold to determine the current state of the relay protection device;

[0119] Step 1459, determine the evaluation result based on the comprehensive evaluation score and status.

[0120] In an embodiment of the present invention, the threshold range is set: normal state (0-40), warning state (41-70), abnormal state (71-100). The comprehensive evaluation score 61 is compared with the threshold to determine that the current state of the relay protection device is the "warning state". According to the comprehensive evaluation score and state judgment, the evaluation result is obtained: the relay protection device is currently in the "warning state", and it is necessary to strengthen the monitoring of temperature changes, regularly check the current stability, and pay attention to voltage fluctuations.

[0121] Through real-time monitoring and scoring, as well as the calculation of comprehensive evaluation scores, potential faults can be detected promptly, improving the accuracy and timeliness of early warnings. This helps operations and maintenance personnel take swift action to prevent further escalation of faults and ensure the stable operation of the power system. The comprehensive evaluation score reflects the overall status of the relay protection device. Based on the score, operations and maintenance personnel can prioritize data points or devices with higher scores and optimize the allocation of operations and maintenance resources. By promptly identifying and addressing issues that may affect the device's lifespan, such as overheating and voltage fluctuations, the service life of the relay protection device can be extended.

[0122] like Figure 2 As shown, an embodiment of the present invention further provides a relay protection device status evaluation system 20 based on the Internet of Things, comprising:

[0123] The data acquisition module 21 is used to collect the operating voltage, current, temperature, switch status operation data of the relay protection device, as well as action records and abnormal alarm information in real time, and transmit the data to the data center;

[0124] The data preprocessing module 22 is used to clean, remove noise, and normalize the data transmitted by the IoT data acquisition module to obtain processed data;

[0125] The state evaluation model module 23 is used to establish a relay protection device state evaluation model based on the processed data, extract characteristic values ​​of the relay protection device state, and evaluate the state of the relay protection device based on these characteristic values ​​to obtain an evaluation result;

[0126] The early warning and notification module 24 is used to automatically trigger the early warning mechanism and notify personnel to handle the situation when the relay protection device is in an abnormal state or close to the fault threshold according to the evaluation results of the state evaluation model.

[0127] It should be noted that this system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0128] An embodiment of the present invention further provides a computing device comprising: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the above-described method. All implementations in the above-described method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0129] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the above-described method. All implementations in the above-described method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0130] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for evaluating the status of a relay protection device based on the Internet of Things, characterized in that: The method comprises: S11. Obtain the operating data of the relay protection device in real time through the Internet of Things communication system, including operating voltage, current, temperature, switch status parameters, as well as the action record and abnormal alarm information of the relay protection device; S12. Preprocessing the operating data of the relay protection device to obtain processed data; S13. Establishing an initial relay protection device state evaluation model based on the processed data, and training the relay protection device state evaluation model using the processed data to obtain a final relay protection device state evaluation model; S14, inputting the current operating data of the relay protection device obtained in real time into the final relay protection device state evaluation model, evaluating the state of the relay protection device, and obtaining an evaluation result; S13 further includes training the initial relay protection device state evaluation model using the training set to obtain initial training results. During the training process, the performance of the initial relay protection device state evaluation model is monitored in real time, wherein the performance includes accuracy and loss index. The accuracy is calculated as follows: Where A represents the adjusted accuracy; TP represents the number of relay protection devices correctly predicted by the model to be in abnormal state; TN represents the number of relay protection devices correctly predicted by the model to be in normal state; α and β represent weight adjustment parameters; FP represents the number of normal states incorrectly predicted by the model as abnormal states; FN represents the number of abnormal states incorrectly predicted by the model as normal states; γ represents the adjustment parameter for the total number of predictions; According to the initial training results, the initial parameters of the initial relay protection device status evaluation model are adjusted, and the final parameters are determined through repeated experiments and verification; The performance of the initial relay protection device status evaluation model after parameter adjustment is evaluated using the test set to obtain the performance evaluation results; S14 also includes: processing the current operating data to extract key features for status evaluation, wherein the key features include voltage fluctuation range, current stability, temperature change trend, and switching action frequency; Analyze key features and assign each acquired current operation data to a specific state label to obtain analysis results; Based on the analysis results, a score for each data point is calculated, and according to the score for each data point, the current state of the relay protection device is judged to obtain a judgment result; Based on the scoring and judgment results of each data point, the status of the relay protection device is comprehensively evaluated to obtain the evaluation results.

2. The method for evaluating the state of a relay protection device based on the Internet of Things according to claim 1, characterized in that: Based on the processed data, an initial relay protection device status evaluation model is established, including: Divide the processed data into training set and test set; The characteristic data in the processed data is used to construct an initial relay protection device state evaluation model, and the kernel function and initial parameters of the initial relay protection device state evaluation model are determined.

3. The method for evaluating the state of a relay protection device based on the Internet of Things according to claim 1, characterized in that: Also includes: According to the performance evaluation results, the initial relay protection device state evaluation model is optimized, and the optimized initial relay protection device state evaluation model is repeatedly trained and verified to obtain the final relay protection device state evaluation model.

4. The method for evaluating the state of a relay protection device based on the Internet of Things according to claim 1, wherein: Based on the scoring and judgment results of each data point, the status of the relay protection device is comprehensively evaluated to obtain the evaluation results, including: Summarize the scores of each data point and integrate the judgment results corresponding to each data point; Assign corresponding weights to each data point, scoring, and judgment result, and calculate the comprehensive evaluation score; According to the comprehensive evaluation score, a threshold is set to determine the status of the relay protection device.

5. The method for evaluating the state of a relay protection device based on the Internet of Things according to claim 4, characterized in that: Based on the scoring and judgment results of each data point, the status of the relay protection device is comprehensively evaluated to obtain the evaluation results, including: Compare the comprehensive evaluation score with the set threshold to determine the current state of the relay protection device; The evaluation results are obtained based on the comprehensive evaluation score and status judgment.

6. A relay protection device status evaluation system based on the Internet of Things, the system implementing the method according to any one of claims 1 to 5, characterized in that: include: The data acquisition module is used to collect the operating voltage, current, temperature, switch status operation data of the relay protection device in real time, as well as action records and abnormal alarm information, and transmit the data to the data center; The data preprocessing module is used to clean, denoise, and normalize the data transmitted by the IoT data acquisition module to obtain processed data; The state evaluation model module is used to establish a state evaluation model of the relay protection device based on the processed data, extract the characteristic values ​​of the state of the relay protection device, and evaluate the state of the relay protection device based on these characteristic values ​​to obtain an evaluation result; The early warning and notification module is used to automatically trigger the early warning mechanism and notify personnel to handle the situation when the relay protection device status is abnormal or close to the fault threshold based on the evaluation results of the status evaluation model.

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