Quality evaluation method for lightning protection equipment of power transmission and distribution line

Abstract

The invention relates to the technical field of power grid systems. The invention provides a power transmission and distribution line lightning protection equipment quality evaluation method, which comprises the following steps of: pre-defining and setting a unified standard for evaluating the performance of lightning protection equipment through a quality control system, and establishing a real-time monitoring system of the lightning protection equipment; historical data of the lightning protection equipment are collected, a historical data set is established, and a quality evaluation model is constructed through a quality control system; the real-time monitoring system collects related data of the lightning protection equipment to be evaluated and feeds back the related data to the quality control system to obtain a real-time data set; based on the real-time data set, performing quality evaluation on the to-be-evaluated lightning protection equipment through a quality evaluation model, and obtaining a quality evaluation result; and making corresponding operation and maintenance measures according to the quality evaluation result, and feeding back the quality evaluation result and the corresponding operation and maintenance measures to the user, thereby solving the problems of non-uniform evaluation standards, lack of real-time performance in the evaluation process, low evaluation efficiency, high error rate and low evaluation accuracy of the existing lightning protection equipment quality evaluation method.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid systems, and in particular to a method for evaluating the quality of lightning protection equipment for power transmission and distribution lines. Background Art

[0002] In the power system, transmission and distribution lines are the key link in the transmission and distribution of electric energy. Their stability and safety are directly related to the operation quality of the entire power grid and the user's power experience. However, since transmission and distribution lines are usually exposed to outdoor environments, they are easily affected by natural disasters such as lightning, resulting in equipment damage, line failures and even large-scale power outages, causing huge losses to social production and people's lives. As an important means to protect transmission and distribution lines from lightning damage, the performance and quality of lightning protection equipment are directly related to the lightning protection effect.

[0003] Traditional lightning protection equipment quality assessment methods have many shortcomings: first, the assessment standards are not unified, which makes it difficult to compare the assessment results between different devices; second, the assessment process lacks real-time performance, making it difficult to detect potential equipment problems in a timely manner; third, the assessment method is too dependent on manual labor, which is inefficient and prone to errors; fourth, there is a lack of data-driven and model optimization mechanisms, making it difficult to fully utilize the information in historical data to improve assessment accuracy. Summary of the invention

[0004] The purpose of the present invention is to provide a photovoltaic power generation energy storage grid-connected optimization system and method, aiming to solve the problems of inconsistent evaluation standards, lack of real-time evaluation process, low evaluation efficiency and prone to errors and low evaluation accuracy in existing lightning protection equipment quality evaluation methods.

[0005] The embodiments of the present invention are implemented by the following technical solutions:

[0006] A method for evaluating the quality of lightning protection equipment for power transmission and distribution lines comprises the following steps:

[0007] Predefine and set unified standards for evaluating the performance of lightning protection equipment through the quality control system, and establish a real-time monitoring system for lightning protection equipment based on the unified standards;

[0008] Collect historical data of lightning protection equipment and establish historical data sets. Based on the historical data sets, build a quality assessment model through the quality control system;

[0009] The relevant data of the lightning protection equipment to be evaluated is collected through the real-time monitoring system, fed back to the quality control system, and a real-time data set is obtained;

[0010] Based on the real-time data set, the quality assessment model is used to conduct quality assessment on the lightning protection equipment to be assessed and obtain the quality assessment results;

[0011] Formulate corresponding operation and maintenance measures based on the quality assessment results, and feedback the quality assessment results and corresponding operation and maintenance measures to users.

[0012] Optionally, the specific process of predefining and setting a unified standard for evaluating the performance of lightning protection equipment through the quality control system and establishing a real-time monitoring system for lightning protection equipment based on the unified standard is as follows:

[0013] Establish the evaluation objectives of lightning protection equipment quality assessment, formulate evaluation indicators based on the evaluation objectives, set the qualification standards or thresholds of the evaluation indicators, and obtain unified standards;

[0014] Based on unified standards, select the corresponding parameters that need to be monitored in real time for the lightning protection equipment, install corresponding data acquisition equipment on or around the lightning protection equipment, and transmit the data collected by the data acquisition equipment to the quality control system via wired or wireless means.

[0015] Optionally, alarm thresholds and rules are set in the real-time monitoring system. If the collected data is detected to be abnormal, an alarm is issued in a timely manner and relevant personnel are notified.

[0016] Optionally, the specific process of collecting historical data of lightning protection equipment and establishing a historical data set, and constructing a quality assessment model through a quality control system based on the historical data set is:

[0017] Collect historical data of lightning protection equipment, pre-process the historical data and obtain historical data sets;

[0018] According to the characteristics and evaluation requirements of lightning protection equipment, an initial quality evaluation model is constructed;

[0019] Based on the characteristics and evaluation requirements of lightning protection equipment, the quality feature set related to the quality evaluation of lightning protection equipment is extracted from the historical data set, and the quality feature set is divided into training set, test set and verification set;

[0020] The initial quality assessment model is trained through the training set. During the training process, the corresponding parameters and structure of the initial quality assessment model are adjusted. The initial quality assessment model after the corresponding parameters and structure are adjusted is tested and optimized through the test set, and a trained initial quality assessment model is obtained.

[0021] The trained quality assessment model is verified through the validation set to obtain the quality assessment model.

[0022] Optionally, the construction process of the initial quality assessment model is:

[0023] According to the characteristics and evaluation requirements of the lightning protection equipment, the initial target data set is set. The expression of the initial target data set is shown in the following formula (1):

[0024] D={(x1,y1),(x2,y2),…,(x n ,y n )} (1)

[0025] Among them, x i is the parameter characteristic vector of the lightning protection equipment, y i is the performance evaluation index corresponding to the parameter feature vector;

[0026] Select all y i The mean initialization model is , then the expression of the initial quality assessment model is as follows (2):

[0027]

[0028] Among them, L is the loss function and γ is the predicted value.

[0029] Optionally, the construction process of the quality assessment model is:

[0030] The initial quality assessment model is iteratively optimized through the quality feature set, and the pseudo residual of the samples in the initial target data set during the iteration process is calculated. The expression is shown in the following formula (3):

[0031]

[0032] Among them, r im is the pseudo residual of the i-th sample at the m-th iteration;

[0033] Take the pseudo residual as the target variable and train a new learner h k (x), by minimizing the loss function To train a new learner h k (x);

[0034] Through the preset optimization method, find the minimum γ k , where γ k is the learning rate of the kth new learner;

[0035] The new learner h k (x) and its corresponding learning rate γ k Add it to the current model to form a new model. The model expression is shown in the following formula (4):

[0036] F m (x) = F m-1 (x)+γ k h k (x) (4)

[0037] Among them, F m (x) is the model obtained after m iterations, Fm-1 (x) is the model obtained after m-1 iterations;

[0038] After M iterations, the quality assessment model is obtained, and the expression is shown in the following formula (5):

[0039]

[0040] Among them, F M (x) is the final quality assessment model obtained after M rounds of iterative optimization.

[0041] Optionally, the loss function L is expressed as follows:

[0042]

[0043] Where y is a real value vector, is the predicted value vector, and n is the number of samples.

[0044] Optionally, the preset optimization method is a gradient descent or a grid search method.

[0045] Optionally, the specific process of performing quality assessment on the lightning protection equipment to be assessed based on the real-time data set through the quality assessment model and obtaining the quality assessment result is:

[0046] Preprocess the real-time data set, extract the corresponding feature parameters and input them into the quality assessment model, and output the prediction results through the quality assessment model;

[0047] The operating status and failure risk rate of the lightning protection equipment are judged according to the prediction results, and the real-time data set is fused with the historical data set to obtain a fused data set, which is used to update the historical database, and the quality assessment model is optimized and trained based on the fused data set.

[0048] Optionally, the specific process of formulating corresponding operation and maintenance measures according to the quality assessment results and feeding back the quality assessment results and the corresponding operation and maintenance measures to the user is:

[0049] Determine the current operating status of the lightning protection equipment based on the quality assessment results, formulate corresponding operation and maintenance measures based on the current status of the lightning protection equipment, and feedback the quality assessment results and corresponding operation and maintenance measures to the user;

[0050] Users allocate corresponding resources according to the needs of operation and maintenance measures, break down corresponding operation and maintenance measures into specific tasks, and assign tasks to corresponding operation and maintenance personnel or teams;

[0051] Track and supervise the implementation of operation and maintenance measures. After the operation and maintenance measures are implemented, re-evaluate the operating status of the lightning protection equipment to verify the effectiveness of the operation and maintenance measures. Based on the re-evaluation results, make necessary adjustments and optimizations to the operation and maintenance measures.

[0052] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0053] Standardization and systematization: By pre-defining and setting unified evaluation standards, a systematic lightning protection equipment quality evaluation process is established to ensure the standardization and consistency of the evaluation process and improve the accuracy and reliability of the evaluation results;

[0054] Real-time monitoring and early warning: A real-time monitoring system for lightning protection equipment has been established, which can collect and analyze equipment data in real time, detect equipment abnormalities in a timely manner and issue alarms, effectively preventing power accidents caused by equipment failures and improving the safety and stability of the power grid;

[0055] Data-driven and model optimization: We build a quality assessment model based on historical data sets and continuously optimize the model through real-time data sets, thus implementing a data-driven assessment method that can fully utilize the information in historical data, improve the accuracy and generalization ability of the assessment model, and continuously optimize itself as new data is added.

[0056] Intelligence and automation: The entire assessment process is intelligent and automated, which reduces manual intervention and improves assessment efficiency. By automatically collecting data, analyzing and evaluating data, and providing operation and maintenance suggestions, it reduces labor costs and improves operation and maintenance efficiency.

[0057] Dynamic adjustment and continuous optimization: Dynamically adjust the operation and maintenance measures according to the quality assessment results, and promptly feedback the assessment results and operation and maintenance measures to users, so as to achieve dynamic adjustment and optimization of operation and maintenance work. This closed-loop feedback mechanism helps to discover and solve problems in a timely manner, ensuring that the lightning protection equipment is always in good operating condition;

[0058] Efficient use of resources: By accurately evaluating the operating status and failure risk of lightning protection equipment, users can rationally allocate resources to avoid unnecessary waste. At the same time, the operation and maintenance measures are broken down into specific tasks and assigned to the corresponding personnel or teams, thus improving resource utilization efficiency and work efficiency.

[0059] Improve user experience: Users can timely understand the operating status and operation and maintenance suggestions of lightning protection equipment, which is convenient for making decisions and arranging work. At the same time, by tracking and supervising the implementation of operation and maintenance measures, users can verify the operation and maintenance effects and adjust and optimize measures, thus improving user experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1The figure is a flow chart of the method for evaluating the quality of lightning protection equipment for power transmission and distribution lines in Example 1 of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0062] Example 1

[0063] Reference Figure 1 , a method for evaluating the quality of lightning protection equipment for power transmission and distribution lines, comprising the steps of:

[0064] Step 1: Predefine and set unified standards for evaluating the performance of lightning protection equipment through the quality control system, and establish a real-time monitoring system for lightning protection equipment based on the unified standards;

[0065] In this embodiment, a unified standard for evaluating the performance of lightning protection equipment is predefined and set by a quality control system, and a specific process of establishing a real-time monitoring system for lightning protection equipment based on the unified standard is as follows:

[0066] Establish the evaluation objectives of lightning protection equipment quality assessment, formulate evaluation indicators based on the evaluation objectives, set the qualification standards or thresholds of the evaluation indicators, and obtain unified standards;

[0067] In this embodiment, the evaluation objectives of the lightning protection equipment quality evaluation are mainly to improve the safety and reliability of lightning protection equipment, reduce the failure rate and optimize the operation and maintenance costs; widely collect relevant domestic and foreign standards, industry specifications, technical literature and practical experience, including but not limited to IEC, IEEE, GB and other standards, understand the performance requirements, test methods, failure modes, etc. of lightning protection equipment, and analyze the actual application of lightning protection equipment in power transmission and distribution lines, including environmental conditions (such as areas with frequent lightning activities, high altitudes, high temperature and high humidity environments), lightning strike frequency, the importance of protected objects (such as key substations, power supply lines for important users) and other factors; according to the survey results, determine the evaluation indicators of lightning protection equipment, including but not limited to:

[0068] Lightning resistance level: the ability of equipment to withstand lightning current without damage;

[0069] Protection range: the range or area that the equipment can effectively protect;

[0070] Residual voltage limit: The residual voltage generated at both ends of the equipment during a lightning strike should be lower than the withstand voltage of the protected equipment;

[0071] Response time: the time from when the device receives a lightning strike signal to when it starts to act;

[0072] Service life: the expected life of the equipment under normal conditions of use;

[0073] Environmental adaptability: the stability and reliability of the equipment under different environmental conditions (such as temperature, humidity, salt spray, etc.).

[0074] Establish specific qualification standards or thresholds for each evaluation indicator and clarify the evaluation method to obtain a preliminary unified standard; organize industry experts and technicians to review the preliminary unified standard and collect opinions and suggestions; based on the review results, make necessary revisions and improvements to the standard to obtain a unified standard and ensure the scientificity, rationality and operability of the standard.

[0075] Based on unified standards, select the corresponding parameters that need to be monitored in real time for lightning protection equipment, and install corresponding data acquisition equipment on or around the lightning protection equipment, such as sensors, data collectors, etc., and transmit the data collected by the data acquisition equipment to the quality control system through wired or wireless methods (such as 4G / 5G, LoRa, NB-I oT, etc.);

[0076] Set alarm thresholds and rules in the real-time monitoring system. If any abnormality is detected in the collected data, an alarm will be issued in time and relevant personnel will be notified.

[0077] Step 2: Collect historical data of lightning protection equipment and establish a historical data set. Based on the historical data set, build a quality assessment model through a quality control system;

[0078] In this embodiment, the historical data of the lightning protection equipment is collected and a historical data set is established. Based on the historical data set, the specific process of constructing a quality assessment model through a quality control system is as follows:

[0079] Collect historical data of lightning protection equipment from multiple channels such as existing lightning protection equipment monitoring systems, maintenance records, fault reports, test reports, etc., including but not limited to equipment model, installation location, environmental conditions (such as temperature, humidity, salt spray concentration), lightning strike records, maintenance records, fault records, test data, etc.;

[0080] Preprocess historical data and obtain historical data sets; preprocessing includes: removing duplicate data, processing missing values ​​(such as through interpolation, mean filling, etc.), detecting and eliminating outliers; converting non-numeric data (such as equipment model, installation location, etc.) into numeric or categorical variables for model processing; standardizing or normalizing numeric data to eliminate the impact of different dimensions on model training.

[0081] According to the characteristics and evaluation requirements of lightning protection equipment, an initial quality evaluation model is constructed;

[0082] Based on the characteristics and evaluation requirements of lightning protection equipment, the quality feature set related to the quality evaluation of lightning protection equipment is extracted from the historical data set. Features related to the quality evaluation of lightning protection equipment are extracted from the preprocessed historical data set, such as lightning resistance level test value, protection range measured value, residual voltage limit test value, response time statistical value, service life estimation value, environmental adaptability score, etc. The quality feature set is divided into training set, test set and validation set, with a ratio of 70% training set, 20% test set and 10% validation set.

[0083] The initial quality assessment model is trained through the training set. During the training process, the corresponding parameters and structure of the initial quality assessment model are adjusted. The initial quality assessment model after the corresponding parameters and structure are adjusted is tested and optimized through the test set, and a trained initial quality assessment model is obtained.

[0084] The trained quality assessment model is verified through the validation set to obtain the quality assessment model.

[0085] Step 3: Collect relevant data of the lightning protection equipment to be evaluated through the real-time monitoring system, feed it back to the quality control system, and obtain a real-time data set;

[0086] In this embodiment, according to the characteristics and monitoring requirements of the lightning protection equipment, appropriate data acquisition equipment is selected, such as temperature sensors, humidity sensors, current sensors, voltage sensors, etc.; the data acquisition equipment is installed at key locations on or around the lightning protection equipment to ensure that the operating status and environmental parameters of the lightning protection equipment can be accurately collected; the data acquisition equipment is connected to the data transmission interface of the real-time monitoring system to ensure that the data acquisition equipment can transmit data to the system in real time and stably.

[0087] In this embodiment, the data acquisition device collects various parameters of the lightning protection equipment in real time according to the preset sampling frequency and accuracy, such as current, voltage, temperature, humidity, etc.; the collected data is transmitted to the quality control system via wired (such as RS485, Ethernet, etc.) or wireless (such as Zigbee, LoRa, NB-I oT, etc.). The integrity and security of the data must be ensured during the transmission process to avoid data loss or tampering.

[0088] In this embodiment, the quality control system receives data from the data acquisition device in real time and stores it in the database; the received raw data is preprocessed, including data cleaning (removing noise, outliers, etc.), data conversion (such as unit unification, data format conversion, etc.) and data compression (reducing storage space and transmission time).

[0089] In this embodiment, the preprocessed data is organized in time series to form a real-time data set. The real-time data set should contain all monitoring parameters of the lightning protection equipment and the corresponding timestamps; as data collection continues, the real-time data set is continuously updated to reflect the latest status of the lightning protection equipment.

[0090] In this embodiment, a data monitoring interface is set in the real-time monitoring system to display the real-time data curves, statistical charts, etc. of the lightning protection equipment, so that the operation and maintenance personnel can intuitively understand the equipment status; according to the alarm thresholds and rules set in the unified standards, corresponding alarm conditions are set in the real-time monitoring system. When abnormal data is monitored (such as excessive current, excessive temperature, excessive humidity, etc.), the system automatically triggers an alarm and notifies relevant personnel through sound, email, text message, etc.

[0091] In this embodiment, the real-time data set is verified regularly to ensure the accuracy and integrity of the data. The verification method includes data comparison, logic check, statistical analysis, etc. According to the verification results, the data collection and transmission process is optimized to improve the data quality.

[0092] Step 4: Based on the real-time data set, the quality assessment model is used to perform quality assessment on the lightning protection equipment to be assessed and obtain the quality assessment result;

[0093] In this embodiment, based on the real-time data set, the specific process of performing quality assessment on the lightning protection equipment to be assessed through the quality assessment model and obtaining the quality assessment result is as follows:

[0094] The real-time data set is preprocessed to extract corresponding feature parameters. In this embodiment, the preprocessing includes:

[0095] Data cleaning: cleaning the data in the real-time data set to remove noise, outliers or missing values, including checking the integrity, consistency and rationality of the data to ensure the data quality of the input model;

[0096] Data conversion: according to the requirements of the quality assessment model, necessary conversions are performed on the cleaned data, such as data normalization, standardization or encoding, so that the model can correctly understand and process the data;

[0097] Feature extraction, extracting feature parameters related to the quality assessment of lightning protection equipment from the preprocessed data. These feature parameters should be consistent with the feature set used in the previous construction of the quality assessment model.

[0098] The quality assessment model is input, and the prediction result is outputted through the quality assessment model. In this embodiment, it includes:

[0099] Model loading: Load the trained and verified quality assessment model into the quality control system;

[0100] Data input: The extracted feature parameters are passed as input to the quality assessment model;

[0101] Model prediction: The quality assessment model performs internal calculations based on the input feature parameters and outputs prediction results. The prediction results usually include key indicators such as the operating status of the lightning protection equipment (such as normal, warning, fault, etc.) and the failure risk rate.

[0102] The operating status and failure risk rate of the lightning protection equipment are determined based on the prediction results. In this embodiment, the output results of the quality assessment model are analyzed to clarify the current operating status and failure risk rate of the lightning protection equipment. Based on the assessment results, it is determined whether the lightning protection equipment is in a normal state or whether there is a potential failure risk. If the assessment results show that the lightning protection equipment has a failure risk, corresponding early warning measures are formulated based on the risk level, and a corresponding operation and maintenance plan is prepared.

[0103] The real-time data set is fused with the historical data set to obtain a fused data set. In this embodiment, the real-time data set is fused with the historical data set to form a fused data set. The fused data set not only contains the latest real-time data, but also contains useful information in the historical data, which helps to more comprehensively understand the performance changes of the lightning protection equipment; based on the fused data set, the quality assessment model is regularly optimized and trained. By introducing new data samples, adjusting the parameters and structure of the model, the prediction accuracy and generalization ability of the model are improved; the fused data set is updated to the historical database to provide richer and more comprehensive data support for subsequent quality assessment and model optimization.

[0104] The fused data set is used to update the historical database, and the quality assessment model is optimized and trained based on the fused data set. In this embodiment, the quality assessment results and corresponding operation and maintenance suggestions are fed back to the user in a timely manner. The user can formulate corresponding operation and maintenance measures based on the assessment results and operation and maintenance suggestions to ensure the normal operation of the lightning protection equipment; the results of each assessment, the assessment process, the operation and maintenance measures and their implementation effects are recorded and archived to form the operation and maintenance archives of the lightning protection equipment. These archives are of great reference value for subsequent operation and maintenance management, fault analysis and performance improvement.

[0105] Step 5: Formulate corresponding operation and maintenance measures based on the quality assessment results, and feedback the quality assessment results and corresponding operation and maintenance measures to users;

[0106] In this embodiment, the specific process of formulating corresponding operation and maintenance measures according to the quality assessment results and feeding back the quality assessment results and the corresponding operation and maintenance measures to the user is as follows:

[0107] The current operating state of the lightning protection equipment is determined based on the quality assessment results, and corresponding operation and maintenance measures are formulated based on the current state of the lightning protection equipment, and the quality assessment results and corresponding operation and maintenance measures are fed back to the user; in this embodiment, the quality assessment results are analyzed in detail to clarify the current operating state of the lightning protection equipment (such as normal, warning, fault, etc.) and the specific fault risk rate; based on the assessment results, targeted operation and maintenance measures are formulated. For example, if the lightning protection equipment is in a warning state, it may be necessary to increase the inspection frequency or perform preventive maintenance; if the equipment has failed, it is necessary to arrange for repair or replacement immediately.

[0108] According to the needs of operation and maintenance measures, users allocate corresponding resources, break down corresponding operation and maintenance measures into specific tasks, and assign tasks to corresponding operation and maintenance personnel or teams. In this embodiment, the operation and maintenance measures are broken down into specific task lists, including task content, execution time, responsible persons, etc. Ensure that each task is clear and easy to execute and track; allocate corresponding resources, such as human, material, and financial resources, according to the needs of operation and maintenance measures. Ensure that the operation and maintenance work can proceed smoothly and achieve the expected results; assign the refined tasks to the corresponding operation and maintenance personnel or teams. Clarify everyone's responsibilities and tasks to ensure that the tasks can be effectively executed; track and supervise the implementation of operation and maintenance measures. Through regular inspections and records, understand the progress and effects of task execution, and discover and solve problems in a timely manner.

[0109] Track and supervise the implementation of operation and maintenance measures. After the operation and maintenance measures are implemented, re-evaluate the operating status of the lightning protection equipment to verify the effectiveness of the operation and maintenance measures. According to the re-evaluation results, make necessary adjustments and optimizations to the operation and maintenance measures. In this embodiment, after the operation and maintenance measures are implemented, re-evaluate the operating status of the lightning protection equipment. Verify the effectiveness of the operation and maintenance measures by comparing the evaluation results before and after; and promptly feedback the operation and maintenance effect evaluation results to the user. Report to the user the current status of the lightning protection equipment, the operation and maintenance measures that have been taken and their effects, as well as subsequent suggestions and plans; make necessary adjustments and optimizations to the operation and maintenance measures according to the operation and maintenance effect evaluation results. If some measures are not effective or have problems, they should be improved or replaced in a timely manner; establish a continuous optimization mechanism for operation and maintenance measures. By continuously collecting and analyzing operation and maintenance data, summarizing experience and lessons, and continuously improving and optimizing operation and maintenance measures, the operating efficiency and reliability of lightning protection equipment can be improved; maintain close communication with users and promptly understand their needs and feedback. Answer users' questions, provide technical support and consulting services; and establish a close collaborative relationship with users. During the operation and maintenance process, we work with users to formulate operation and maintenance plans, allocate resources, execute tasks, etc. to ensure the smooth progress of operation and maintenance work.

[0110] Example 2

[0111] Based on Example 1, in this embodiment, the construction process of the initial quality assessment model is as follows:

[0112] According to the characteristics and evaluation requirements of the lightning protection equipment, the initial target data set is set. The expression of the initial target data set is shown in the following formula (1):

[0113] D={(x1,y1),(x2,y2),…,(x n ,y n )} (1)

[0114] Among them, x i is the parameter characteristic vector of the lightning protection equipment, y i is the performance evaluation index corresponding to the parameter feature vector, such as lightning protection effect, equipment life, etc.;

[0115] Select all y i The mean initialization model is , then the expression of the initial quality assessment model is as follows (2):

[0116]

[0117] Among them, L is the loss function and γ is the predicted value.

[0118] In this embodiment, the construction process of the quality assessment model is as follows:

[0119] The initial quality assessment model is iteratively optimized through the quality feature set, and the pseudo residual of the samples in the initial target data set during the iteration process is calculated. The expression is shown in the following formula (3):

[0120]

[0121] Among them, r im is the pseudo residual of the i-th sample at the m-th iteration;

[0122] Take the pseudo residual as the target variable and train a new learner g k (x), by minimizing the loss function To train a new learner h k (x);

[0123] Through the preset optimization method, find the minimum γ k , where γ k is the learning rate of the kth new learner;

[0124] The new learner h k (x) and its corresponding learning rate γ k Add it to the current model to form a new model. The model expression is shown in the following formula (4):

[0125] F m (x) = Fm-1 (x)+γ k h k (x) (4)

[0126] Among them, F m (x) is the model obtained after m iterations, F m-1 (x) is the model obtained after m-1 iterations;

[0127] After M iterations, the quality assessment model is obtained, and the expression is shown in the following formula (5):

[0128]

[0129] Among them, F M (x) is the final quality assessment model obtained after M rounds of iterative optimization.

[0130] In this embodiment, the expression of the loss function L is shown in the following formula (6):

[0131]

[0132] Where y is a real value vector, is the predicted value vector, and n is the number of samples.

[0133] Example 3

[0134] Based on Example 1 and Example 2, in this embodiment, the preset optimization method is the gradient descent method, and the parameters in the initialization model, including the learning rate γ k and the new learner h k (x); for each iteration, the loss function is calculated with respect to the learning rate γ k and the new learner h k The gradient of the parameters of (x) represents the trend of the loss function as the parameters change, that is, the slope of the loss function in the parameter space. k The gradient calculation expression of is shown in the following formula (7):

[0135]

[0136] Among them, F m (x i ) is the predicted value of the current model for the i-th sample, L(y i ,F m (x i )) is the loss value of the i-th sample, n is the number of samples;

[0137] Assume h k (x) is a parameterized model, whose parameter set is θ k ,θ kIt can include weight ω and bias b, etc., then h k (x;θ k ) represents the given parameter θ k When the model h k Output for input x; loss function L with respect to h k (x;θ k The gradient of the parameters of ) can be expressed as shown in the following formula (8):

[0138]

[0139] Where d is the parameter set θ k Dimensions, is θ k The i-th parameter in ;

[0140] For the loss function L(y,F m (x)), where F m (x) is the number containing h k (x;θ k ), these gradients are calculated by the chain rule, as shown in equation (9):

[0141]

[0142] in, is the loss function with respect to F m The gradient of (x) can be directly calculated through the definition of the loss function; Depends on how you combine h k (x;θ k ) and other parts, for example, if F m (x) = F m-1 (x)+γ k h k (x), then the other part is γ k ; Yes k (x;θ k ) with respect to its parameters, depends on h k (x;θ k ) in specific form, for example, if h k (x;θ k ) is a linear model, that is, h k (x,ω,b)=ωx+b, then the gradients of weight and bias are as follows (10) and (11), respectively:

[0143]

[0144] in, is the loss function with respect to h kThe gradient of (x,ω,b) can be obtained by the chain rule from Derived; in the above formula It is actually a vector with a corresponding value for each sample, and x and 1 are treated as That is, for batch processing, each sample needs to be calculated and summed or averaged separately. In actual implementation, these calculations can be completed by the automatic differentiation library.

[0145] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the quality of lightning protection equipment for power transmission and distribution lines, characterized in that: Includes steps: Predefine and set unified standards for evaluating the performance of lightning protection equipment through the quality control system, and establish a real-time monitoring system for lightning protection equipment based on the unified standards; Collect historical data of lightning protection equipment and establish historical data sets. Based on the historical data sets, build a quality assessment model through the quality control system; The relevant data of the lightning protection equipment to be evaluated is collected through the real-time monitoring system, fed back to the quality control system, and a real-time data set is obtained; Based on the real-time data set, the quality assessment model is used to conduct quality assessment on the lightning protection equipment to be assessed and obtain the quality assessment results; Formulate corresponding operation and maintenance measures based on the quality assessment results, and feedback the quality assessment results and corresponding operation and maintenance measures to users.

2. The method for evaluating the quality of lightning protection equipment for power transmission and distribution lines according to claim 1, characterized in that: The specific process of predefining and setting a unified standard for evaluating the performance of lightning protection equipment through the quality control system and establishing a real-time monitoring system for lightning protection equipment based on the unified standard is as follows: Establish the evaluation objectives of lightning protection equipment quality assessment, formulate evaluation indicators based on the evaluation objectives, set the qualification standards or thresholds of the evaluation indicators, and obtain unified standards; Based on unified standards, select the corresponding parameters that need to be monitored in real time for the lightning protection equipment, install corresponding data acquisition equipment on or around the lightning protection equipment, and transmit the data collected by the data acquisition equipment to the quality control system via wired or wireless means.

3. The method for evaluating the quality of lightning protection equipment for power transmission and distribution lines according to claim 2, characterized in that: Set alarm thresholds and rules in the real-time monitoring system. If any abnormality is detected in the collected data, an alarm will be issued in time and relevant personnel will be notified.

4. The method for evaluating the quality of lightning protection equipment for power transmission and distribution lines according to claim 1, characterized in that: The specific process of collecting historical data of lightning protection equipment and establishing a historical data set, and building a quality assessment model through a quality control system based on the historical data set is as follows: Collect historical data of lightning protection equipment, pre-process the historical data and obtain historical data sets; According to the characteristics and evaluation requirements of lightning protection equipment, an initial quality evaluation model is constructed; Based on the characteristics and evaluation requirements of lightning protection equipment, the quality feature set related to the quality evaluation of lightning protection equipment is extracted from the historical data set, and the quality feature set is divided into training set, test set and verification set; The initial quality assessment model is trained through the training set. During the training process, the corresponding parameters and structure of the initial quality assessment model are adjusted. The initial quality assessment model after the corresponding parameters and structure are adjusted is tested and optimized through the test set, and a trained initial quality assessment model is obtained. The trained quality assessment model is verified through the validation set to obtain the quality assessment model.

5. The method for evaluating the quality of lightning protection equipment for power transmission and distribution lines according to claim 4, characterized in that: The construction process of the initial quality assessment model is as follows: According to the characteristics and evaluation requirements of the lightning protection equipment, the initial target data set is set. The expression of the initial target data set is shown in the following formula (1): D={(x1,y1),(x2,y2),…,(x n ,y n )}(1) Among them, x i is the parameter characteristic vector of the lightning protection equipment, y i is the performance evaluation index corresponding to the parameter feature vector; Select all y i The mean initialization model is , then the expression of the initial quality assessment model is as follows (2): Among them, L is the loss function and γ is the predicted value.

6. The method for evaluating the quality of lightning protection equipment for power transmission and distribution lines according to claim 5, characterized in that: The construction process of the quality assessment model is as follows: The initial quality assessment model is iteratively optimized through the quality feature set, and the pseudo residual of the samples in the initial target data set during the iteration process is calculated. The expression is shown in the following formula (3): Among them, r im is the pseudo residual of the i-th sample at the m-th iteration; Take the pseudo residual as the target variable and train a new learner h k (x), by minimizing the loss function To train a new learner h k (x); Through the preset optimization method, find the minimum γ k , where γ k is the learning rate of the kth new learner; The new learner h k (x) and its corresponding learning rate γ k Add it to the current model to form a new model. The model expression is shown in the following formula (4): F m (x)=F m-1 (x)+γ k h k (x)(4) Among them, F m (x) is the model obtained after m iterations, F m-1 (x) is the model obtained after m-1 iterations; After M iterations, the quality assessment model is obtained, and the expression is shown in the following formula (5): Among them, F M (x) is the final quality assessment model obtained after M rounds of iterative optimization.

7. The method for evaluating the quality of lightning protection equipment for power transmission and distribution lines according to claim 5 or 6, characterized in that: The loss function L is expressed as follows: Where y is a real value vector, is the predicted value vector, and n is the number of samples.

8. The method for evaluating the quality of lightning protection equipment for power transmission and distribution lines according to claim 6, characterized in that: The preset optimization method is gradient descent or grid search method.

9. The method for evaluating the quality of lightning protection equipment for power transmission and distribution lines according to claim 1, characterized in that: The specific process of performing quality assessment on the lightning protection equipment to be assessed and obtaining the quality assessment result through the quality assessment model based on the real-time data set is as follows: Preprocess the real-time data set, extract the corresponding feature parameters and input them into the quality assessment model, and output the prediction results through the quality assessment model; The operating status and failure risk rate of the lightning protection equipment are judged according to the prediction results, and the real-time data set is fused with the historical data set to obtain a fused data set, which is used to update the historical database, and the quality assessment model is optimized and trained based on the fused data set.

10. The method for evaluating the quality of lightning protection equipment for power transmission and distribution lines according to claim 1, characterized in that: The specific process of formulating corresponding operation and maintenance measures according to the quality assessment results and feeding back the quality assessment results and corresponding operation and maintenance measures to the user is as follows: Determine the current operating status of the lightning protection equipment based on the quality assessment results, formulate corresponding operation and maintenance measures based on the current status of the lightning protection equipment, and feedback the quality assessment results and corresponding operation and maintenance measures to the user; Users allocate corresponding resources according to the needs of operation and maintenance measures, break down corresponding operation and maintenance measures into specific tasks, and assign tasks to corresponding operation and maintenance personnel or teams; Track and supervise the implementation of operation and maintenance measures. After the operation and maintenance measures are implemented, re-evaluate the operating status of the lightning protection equipment to verify the effectiveness of the operation and maintenance measures. Based on the re-evaluation results, make necessary adjustments and optimizations to the operation and maintenance measures.

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