Power distribution network power transmission line intelligent control and measurement system and method based on AI algorithm

Abstract

The invention discloses a power distribution network power transmission line intelligent control and measurement system and method based on an AI algorithm, and the system comprises a data collection module, a data preprocessing module, a multi-modal data fusion module, an AI fault diagnosis module, a control execution module and a communication module. Comprehensive monitoring of the running state of the power transmission line is achieved, historical and current data are analyzed in real time based on an improved dynamic multi-mode adaptive optimization model DMAOM, and the self-learning capability is achieved. A flexible adaptive control strategy is introduced, and a control scheme is dynamically adjusted according to real-time data. Under the abnormal condition, the system can rapidly execute current limiting, load transferring or circuit breaking operation, and through a feedback mechanism, the system can continuously optimize monitoring and response strategies after each fault processing. According to the method, the safety, stability and operation efficiency of the power transmission line of the power distribution network are remarkably improved, and the method has important practical application value and wide market prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line monitoring and control in a distribution network, and in particular to an intelligent control and measurement system and method for power transmission lines in a distribution network based on an AI algorithm, which is specifically applied to the optimization control of state monitoring, fault prediction and load scheduling of transmission lines. Background Art

[0002] In modern power transmission systems, with the expansion of the scale of power grids and the growth of power demand, the reliability and safety management of transmission lines has become crucial. Traditional transmission line monitoring systems mainly rely on a single type of sensor (such as temperature and humidity sensors, current sensors, etc.) to collect data and monitor the lines based on fixed control strategies. These systems can complete basic status monitoring and control operations and have certain practicality under stable operating conditions.

[0003] However, with the increasing complexity of the operating environment of transmission lines, the existing monitoring system has exposed many deficiencies. First, the single-mode sensor acquisition method cannot fully reflect the operating status of the transmission line, especially when the environmental conditions are changeable or the load fluctuates greatly, the data acquisition is delayed and inaccurate. Secondly, the fixed control strategy lacks adaptive adjustment capabilities, resulting in the system being unable to respond in time when a fault occurs. In addition, the existing system fails to effectively integrate multi-modal data (such as temperature and humidity, traveling waves, current, etc.), and is unable to perform in-depth analysis and fault prediction, resulting in limited early warning capabilities of the system and prone to major faults.

[0004] In order to improve the operating efficiency and safety of transmission lines, there is an urgent need to be able to integrate dynamic data from multiple sensors in real time, predict faults through intelligent algorithms, and automatically adjust control strategies according to real-time conditions, thereby improving the system's ability to respond to complex operating conditions, significantly reducing the occurrence of line faults, and improving the stability and reliability of the power grid. Summary of the invention

[0005] In view of the problems of the existing distribution network transmission line monitoring system in terms of single data collection, lack of adaptive adjustment and insufficient fault prediction capabilities, the present invention proposes an intelligent control and measurement system and method for distribution network transmission lines based on AI algorithms. The system adopts multimodal data fusion technology, collects data through sensors such as temperature, humidity, current and fault detection, and combines AI algorithms such as convolutional neural networks and long short-term memory networks to dynamically adjust the control strategy and realize real-time fault prediction. The system has self-learning capabilities and can continuously optimize the control strategy based on the historical operation data and real-time monitoring data of the transmission line, significantly improving the safety and stability of the transmission line.

[0006] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0007] An intelligent control and measurement system for power distribution network transmission lines based on AI algorithm, the system comprising:

[0008] A data acquisition module is used to collect the operation status data and environmental parameter data of the power transmission line in real time through multiple sensors, the sensors include temperature and humidity sensors, traveling wave ranging sensors, current sensors and fault detection sensors, and the data acquisition module transmits data through a distributed network of a wireless local area network WLAN or a cellular network;

[0009] A data preprocessing module, which preprocesses the temperature, humidity, traveling wave, current and fault detection data sent by the data acquisition module, wherein the preprocessing includes adopting a data denoising algorithm based on wavelet transform, abnormal data filtering and preliminary fault feature extraction;

[0010] A multimodal data fusion module is used to receive the cleaned data and preliminary fault characteristics transmitted by the data preprocessing module, conduct a comprehensive analysis of the temperature, humidity, traveling wave and current data from different sensors through a multimodal data fusion algorithm, and extract the preliminary fault characteristics to generate a state assessment result and fault prediction information of the transmission line;

[0011] The AI ​​fault diagnosis module receives the state evaluation results and fault prediction information generated by the multimodal data fusion module, performs in-depth fault diagnosis on the operation state of the transmission line based on the improved dynamic multimodal adaptive optimization model DMAOM, and generates a detailed fault diagnosis report in combination with historical fault data and real-time analysis results;

[0012] A control execution module, including a circuit breaker, a switch, and a dispatching device, receives a fault diagnosis report generated by the AI ​​fault diagnosis module, and performs control operations of fault isolation, load transfer, or line protection according to information in the report;

[0013] The communication module is used to transmit data and control instructions between the data acquisition module, data preprocessing module, multimodal data fusion module, AI fault diagnosis module and control execution module, and realize real-time interaction among the modules of the system through wireless communication or wired communication.

[0014] Preferably, the temperature and humidity sensor and the traveling wave ranging sensor in the data acquisition module can adaptively adjust the sampling frequency by detecting real-time data of external temperature and humidity changes or line load status, and the data acquisition module transmits data with the data preprocessing module through a low-power Bluetooth BLE module.

[0015] Preferably, the data preprocessing module uses a convolutional neural network (CNN) model to extract fault features from the preprocessed temperature, humidity, traveling wave and current data. The CNN model is pre-trained with historical fault data and adaptively updated according to new fault data through dynamic adjustment based on online learning when the system is running.

[0016] Preferably, the multimodal data fusion module uses convolutional neural network CNN, long short-term memory network LSTM, self-attention mechanism and graph neural network GNN combined with reinforcement learning algorithm to perform multimodal data weighted fusion analysis on temperature, humidity, traveling wave and current data from different sensors. The weighting coefficient is based on the Bayesian model in multimodal data fusion and is dynamically adjusted through an online learning model according to the ambient temperature, humidity and load state of the transmission line.

[0017] Preferably, the implementation mechanism of the multimodal data weighted fusion module includes:

[0018] S1: Anomaly detection and noise removal mechanism, which removes noise data from sensors through an intelligent anomaly detection mechanism based on support vector machine (SVM), uses a dynamic interpolation algorithm based on time series to achieve automatic alignment of multimodal data, and ensures data consistency through adaptive normalization processing;

[0019] S2: Local feature and temporal dynamic relationship extraction mechanism, combining convolutional neural network (CNN) and long short-term memory (LSTM) network, extracts local features and temporal dynamic relationships of each modal data, and optimizes the correlation between data;

[0020] S3: Weight setting and adaptive adjustment mechanism, through the unsupervised learning algorithm based on K-means clustering, the initial weight of each modal data is set based on historical data, and the weight of each modal data is dynamically adjusted through the self-attention mechanism;

[0021] S4: Multi-level fusion structure, using the multi-level fusion structure of deep neural network DNN to extract and fuse the relationship between different modal data, combined with the topological information of the transmission line, and weighted processing of the fused data through graph neural network GNN;

[0022] S5: Abnormal data identification and fault tolerance mechanism, which identifies and eliminates abnormal situations in sensor data through intelligent anomaly detection mechanism, and ensures the reliability and robustness of data processing through redundant fault tolerance mechanism of multimodal data;

[0023] S6: Computation optimization mechanism, which reduces the amount of computation through model pruning and quantization, and combines distributed computing to shift some computing tasks to distributed nodes;

[0024] S7: Time series modeling and uncertainty quantification mechanism, combining long short-term memory network LSTM to enhance the time series modeling of the system, and quantifying the uncertainty in fault prediction through Bayesian deep learning technology.

[0025] Preferably, the AI ​​fault diagnosis module learns the effects of different fault handling solutions in real time through the improved dynamic multimodal adaptive optimization model DMAOM, dynamically adjusts the system response mode, combines historical fault data and the fault prediction information generated by the multimodal data fusion module to generate a detailed fault diagnosis report including the fault type, occurrence time and expected impact range, and provides targeted repair suggestions.

[0026] Preferably, the improved dynamic multi-modal adaptive optimization model DMAOM is implemented in the following steps:

[0027] Step 1: Integrate historical fault data and real-time multimodal data including temperature, current, voltage, and humidity sensor data, and use the dynamic data fusion algorithm based on the Bayesian model to perform weighted integration of real-time data and historical data. The calculation formula is as follows:

[0028] X fuse =αX real +(1-α)X hist

[0029] Among them, X fuse For real-time data; X real Sensor data for real-time multimodal data; X hist is the historical data; α is the fusion weight coefficient;

[0030] Apply self-attention mechanism to extract important global features in fault diagnosis:

[0031]

[0032] Among them, Q, K, V are feature matrices; d k is the dimension of the key matrix; softmax is a normalization function that calculates the similarity between the key and the query and converts it into a probability distribution;

[0033] Step 2: Through self-supervised learning technology, combined with unlabeled data and contrastive learning methods, the fault mode of the distribution network is dynamically learned. At each time step, the diagnostic model is optimized through the contrastive learning loss function:

[0034]

[0035] Among them, L i is the contrast loss value of the i-th sample; x i is the encoding vector representation of the current sample; With xi The relevant positive samples, With x i Related negative samples; sim(,) is the similarity between two samples; τ is the temperature parameter;

[0036] Step 3: The fault handling strategy is dynamically optimized through reinforcement learning. After each fault handling, the system t , Action performed a t To optimize the strategy:

[0037]

[0038] Among them, π new For new strategies; t is the state at time step t; Q(s t ,a t ) is the Q value of the current state and action, indicating that in state s t Next, perform action a t The expected return after α is the entropy coefficient, which controls the balance between reward and strategy entropy.

[0039] logπ(s t |a t )] is the entropy of the strategy, indicating that in state s t Next select action a t The randomness of

[0040] Step 4: The system receives real-time feedback on the effect of the processing through the feedback loop mechanism, continuously optimizes the fault diagnosis and processing strategy, and feedback information f t is introduced into the loss function:

[0041] L new =L(f t )+βL previous

[0042] Among them, L new is the new loss value used to optimize the model; L(f t ) is the current loss calculated based on the feedback information; L previous is the loss of the previous iteration; β is the feedback weight coefficient, which controls the balance between new feedback and historical loss;

[0043] Step 5: Apply the multi-modal fault handling optimization mechanism based on the diffusion model to gradually optimize the fault prediction results through the reverse diffusion process:

[0044] p θ (x t-1 ∣x t )=N(x t-1 ;μ θ (xt ,t),Σ θ (x t ,t))

[0045] Among them, p θ (x t-1 ∣x t ) is the noisy data x at time step t t The probability distribution of the data generated at time step t-1 under the condition; N(x t-1 ;μ θ (x t ,t),Σ θ (x t ,t)) is a normal distribution, which is used to model the generated data distribution in the inverse diffusion process; μ θ (x t ,t) is the mean of the neural network parameterization, which represents the generated mean estimate; Σ θ (x t ,t) is the variance of the neural network parameterization, indicating the uncertainty in the generation process; x t is the noisy data at time step t.

[0046] Preferably, the control execution module includes:

[0047] Current limiting operation unit: according to the preset current threshold and voltage threshold, when the current or voltage exceeds the preset range, the current limiting operation is performed, and the current limiting threshold is dynamically adjusted according to the load state and environmental parameters;

[0048] Load transfer and circuit breaking operation unit: when the current limiting operation fails to reduce the current or voltage to a safe range within the preset time and exceeds the specified number of times, the load transfer or circuit breaking operation is performed;

[0049] Self-check unit: In case of multiple current limiting or load transfer operation failures, the system automatically enters self-check mode, and monitors the hardware and software status in the control execution system in real time through the internal fault detection module;

[0050] Overload protection unit: When the system load exceeds the preset limit, it automatically cuts off part of the line to prevent overload damage;

[0051] Remote monitoring interface: connected to the remote monitoring terminal via the Internet, allowing operators to view system status in real time and perform remote operations.

[0052] Preferably, a method for intelligent control and measurement of power transmission lines in a distribution network based on an AI algorithm is characterized in that the method comprises the following steps:

[0053] Step 1: collect the operation status data and environmental parameter data of the power transmission line in real time through multiple sensors, the sensors include temperature and humidity sensors, traveling wave ranging sensors, current sensors and fault detection sensors, and transmit the data to the data preprocessing module through the wireless local area network WLAN or cellular network;

[0054] Step 2: In a data preprocessing module, the data is processed, and the preprocessing includes data denoising based on wavelet transform, abnormal data filtering, and preliminary fault feature extraction;

[0055] Step 3: Input the cleaned data into the multimodal data fusion module, combine the convolutional neural network CNN, long short-term memory network LSTM and graph neural network GNN algorithms to perform multimodal data fusion analysis and generate the state assessment results and fault prediction information of the transmission line;

[0056] Step 4: Apply the improved dynamic multimodal adaptive optimization model DMAOM, combine historical fault data and real-time multimodal data, use the Bayesian model to perform dynamic multimodal data weighted fusion on the data, extract global features, and dynamically optimize the fault diagnosis model through self-supervised learning and reinforcement learning;

[0057] Step 5: AI fault diagnosis, based on the fault prediction information and historical data generated by the DMAOM model, uses deep learning and reinforcement learning algorithms to generate a fault diagnosis report, which includes the fault type, occurrence time, expected impact range, and provides repair suggestions;

[0058] Step 6: Control execution. According to the fault diagnosis report, perform current limiting, load transfer or circuit breaking operations. After multiple current limiting or load transfer operations fail, it automatically enters the self-check mode to detect the hardware and software status through real-time monitoring.

[0059] Preferably, the dynamic multimodal adaptive optimization model DMAOM is implemented by the following steps:

[0060] Step S1: Inputting various sensor data of the transmission line including temperature and humidity sensor, traveling wave ranging sensor, current sensor and fault detection sensor into the DMAOM model to perform multimodal data fusion. The significant improvements of the DMAOM model include:

[0061] Step S1.1: Use the Bayesian model to dynamically adjust the fusion weights of multimodal data based on real-time monitored environmental parameters including temperature, humidity and load status. The initial weights are set through historical data sets and updated based on new real-time data.

[0062] Step S1.2: Perform global feature extraction on multimodal data through self-supervised learning technology, identify the correlation between different data modes in the transmission line, and extract important features related to fault prediction;

[0063] Step S1.3: Based on the data correlation in the multimodal data fusion process, abnormal data is detected in real time, and the abnormality is corrected through an adaptive correction mechanism;

[0064] Step S2: Adaptively optimize the fault prediction model through reinforcement learning. The significant improvements of the model optimization include:

[0065] Step S2.1: After each fault prediction, the system adjusts the model parameters according to the feedback information, which includes the accuracy of the fault occurrence, the response time and the system control effect;

[0066] Step S2.2: Combine the long short-term memory network LSTM to perform multi-step time series modeling to further optimize the correlation analysis between historical data and real-time data;

[0067] Step S3: Based on the outputs of step S1 and step S2, fault prediction information and a detailed fault diagnosis report are generated, wherein the report includes the fault type, occurrence time, impact scope, and optimized repair suggestions.

[0068] Terminology explanation:

[0069] Multimodal data fusion: Multimodal data fusion refers to the integration of data from different sensors (such as temperature, humidity, current, and traveling waves) to combine information from multiple modes in order to obtain a more comprehensive and accurate assessment of the status of the transmission line.

[0070] Implementation: The system uses deep learning algorithms such as convolutional neural network (CNN) and long short-term memory (LSTM) to perform weighted fusion of data from different sensors. Specific implementation methods include:

[0071] First, feature extraction is performed on multimodal data such as temperature, humidity, and current;

[0072] Use CNN to extract local features and LSTM to capture the temporal dynamic relationship of data;

[0073] The Bayesian model is used to dynamically adjust the weights of different data sources to ensure that important data modalities are given priority in different environments.

[0074] Practical application scenario: When the load state of the line fluctuates, the weight of the current data may increase, while the weights of the temperature and humidity data are adjusted according to environmental changes. This dynamic weighted fusion improves the accuracy of data analysis.

[0075] DMAOM is an adaptive optimization model that can dynamically adjust the system response mode according to different fault handling effects. It integrates multimodal data, combines historical fault data and real-time data to achieve real-time optimization and fault diagnosis.

[0076] Implementation: DMAOM is implemented in the following steps:

[0077] Integration of historical and real-time data: Based on the Bayesian model, real-time data (such as temperature and current) is weighted and integrated with historical fault data to generate a more accurate current status assessment.

[0078] Self-supervised learning: Learn from unlabeled data to dynamically identify fault modes. At each time step, the model optimizes the accuracy of fault detection through contrastive learning.

[0079] Feedback optimization: DMAOM can optimize model parameters based on the feedback mechanism after each fault processing, making the next fault detection more accurate.

[0080] Actual application scenarios: When a transmission line fails, DMAOM can adaptively adjust and combine historical data to quickly determine the nature of the fault, and continuously optimize the fault handling plan based on system feedback to avoid similar faults from happening again.

[0081] Wavelet transform is a commonly used signal processing technology that can decompose complex signals into multiple frequency components, making it easier to remove noise and retain important signal features.

[0082] Implementation: In the present invention, wavelet transform is used to process the noise in sensor data such as current and traveling wave. The specific steps of the algorithm include:

[0083] First, the collected raw data is subjected to wavelet decomposition to separate the signal into high-frequency and low-frequency components;

[0084] Eliminate high-frequency noise components and retain low-frequency signals;

[0085] The clean signal is restored through wavelet reconstruction, thereby reducing the impact of noise on system fault detection.

[0086] Practical application scenarios: When the signal collected by the current sensor is mixed with high-frequency noise due to electromagnetic interference, wavelet transform can effectively remove the noise, allowing the system to analyze the current data more accurately and avoid false alarms or missed faults.

[0087] Convolutional neural network is a deep learning algorithm commonly used in image processing and pattern recognition. It can extract local features through convolution operations and is suitable for processing data with spatial or temporal structures.

[0088] Implementation: In the present invention, CNN is used to extract features from multimodal sensor data:

[0089] First, local features are extracted from the input data through a convolutional layer;

[0090] Use pooling layers to reduce data dimensions and retain key features;

[0091] Finally, the extracted features are integrated through the fully connected layer to output a high-dimensional feature vector for subsequent fault detection.

[0092] Practical application scenarios: When abnormalities appear in both current data and traveling wave data, CNN can automatically identify abnormal patterns in these data, extract fault-related features, and help the system determine the line status more quickly.

[0093] Reinforcement learning is a machine learning method in which an agent learns the optimal decision-making strategy through trial and error by interacting with the environment. The agent performs actions based on the state and adjusts the strategy based on the rewards obtained.

[0094] Implementation: In the present invention, reinforcement learning is used to optimize the control strategy of the transmission line. The specific implementation includes:

[0095] Status: Current status of the transmission line, such as load, current, temperature, etc.

[0096] Action: The actions that the system can take, including current limiting, load transfer, circuit breaking, etc.

[0097] Reward: The system adjusts its strategy based on the feedback after executing the action (such as whether the fault handling was successful or not). For example, the system gets a positive reward after successfully transferring the load, and a negative reward if the fault is not handled in time.

[0098] Actual application scenarios: When a line is overloaded, the reinforcement learning algorithm can adjust the current limiting or load transfer strategy based on the system's historical data, and continuously optimize the control decision through the feedback mechanism.

[0099] Self-supervised learning is a learning method that does not require labeled data. The system generates supervision signals through its own structure and learns by using the internal structure or comparison method of the data.

[0100] Implementation: In the present invention, self-supervised learning is used to identify fault modes in transmission lines:

[0101] By inputting unlabeled sensor data, the system spontaneously generates pseudo labels for constructing training samples;

[0102] Through contrastive learning, the model gradually identifies potential failure modes, thereby improving the accuracy of fault prediction.

[0103] Practical application scenarios: When the system is in the early operation stage and historical fault data is insufficient, self-supervised learning can use unlabeled sensor data to automatically generate fault modes for optimizing system fault identification.

[0104] Support vector machine is a machine learning algorithm commonly used for classification tasks. In anomaly detection, SVM is used to identify anomalies in data to ensure that the system can detect abnormal working conditions in time.

[0105] Implementation: In the present invention, SVM is used to detect anomalies in sensor data: first, normal data is trained to build a high-dimensional classification boundary; when new data is input, the system determines whether it is outside the boundary, and if it exceeds the boundary, it is determined to be abnormal data.

[0106] Practical application scenario: When the current sensor detects abnormal current fluctuations, SVM can quickly identify and determine whether these fluctuations are abnormal, helping the system to make timely adjustments.

[0107] Time series interpolation algorithms are used to fill missing values ​​or inconsistent time intervals in data collection to ensure that multimodal data can be accurately aligned and used for subsequent analysis.

[0108] Implementation: In the present invention, a time series interpolation algorithm is used to solve the problem of inconsistent sampling rates of different sensors:

[0109] When data for certain time points are missing, the system automatically generates estimates using linear interpolation or spline interpolation algorithms;

[0110] The interpolated data is then aligned with the data from other sensors to ensure data synchronization.

[0111] Actual application scenario: When the current sensor fails to collect data normally at a certain point in time, the system generates an estimated current value at that point in time through an interpolation algorithm, thereby avoiding the impact of data loss on fault detection.

[0112] The self-attention mechanism is a deep learning technique that calculates the dependencies between different data points and assigns different weights to each data point to capture global features.

[0113] Implementation: In this invention, the self-attention mechanism is used to analyze important features in multimodal data:

[0114] The system constructs the query matrix, key matrix, and value matrix and calculates the similarity between them;

[0115] The similarity is converted into weights through the softmax function, and the system decides which data is more important based on the weight distribution.

[0116] Practical application scenarios: When the system detects multimodal data, the self-attention mechanism can identify the data that is most important for the current fault diagnosis, such as current or temperature changes at certain critical moments.

[0117] Graph neural network is a deep learning algorithm used to process graph structured data. In the present invention, GNN is used to analyze the relationship and data flow between sensors in combination with the topological structure of the transmission line.

[0118] Implementation: In GNN, sensors are represented as nodes of a graph, and data transmission or physical connections between sensors are represented as edges of the graph. By propagating and aggregating node features, GNN is able to learn global information of the entire network and make predictions.

[0119] Practical application scenarios: When multiple sensors in a transmission line detect anomalies at the same time, GNN can analyze the associations between sensors based on the topological structure of the line, thereby accurately determining the location and type of the fault.

[0120] Model pruning and quantization are optimization techniques for deep learning models that aim to reduce computational effort and storage space, making the model more efficient, especially for embedded systems or edge computing devices.

[0121] Implementation method: Model pruning: Reduce the complexity of the model by removing unimportant neurons or connections in the network;

[0122] Quantization: Convert floating-point operations in the model into fixed-point operations to further reduce computational overhead and memory usage.

[0123] Practical application scenarios: In the present invention, pruning and quantization techniques are applied to edge computing devices of distributed nodes, reducing power consumption and computing delays, ensuring that the system can process multimodal data in real time and respond quickly to faults.

[0124] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention can fully reflect the operating status of the transmission line by integrating real-time data from multiple sensors and adopting a multimodal data weighted fusion module. Through the weighting mechanism based on the Bayesian model, the system can dynamically adjust the fusion weights of different modal data, so that the accuracy of fault detection is significantly improved, thereby more effectively monitoring and analyzing the health status of the line in a complex environment. Based on the improved dynamic multimodal adaptive optimization model DMAOM, the fault diagnosis module can analyze historical data and current data in real time and has good self-learning ability. Through the deep learning model, the system can not only identify and predict potential faults, but also continuously optimize model parameters during the processing process, take preventive measures in advance, reduce the risk of faults, and ensure the stability of the power grid. The present invention has flexible adaptive control capabilities and can dynamically adjust the control strategy according to real-time monitoring data. This capability enables the system to respond to and optimize the operation of the power grid in a timely manner when the load changes and the external environment changes, significantly improving the safety and efficiency of the power grid. In the event of an abnormal situation, the system can quickly formulate and execute response strategies, such as current limiting, load transfer or circuit breaker operations. Through an efficient dynamic response mechanism, the reliability of the power grid in emergencies is ensured, and the risk of large-scale power outages caused by faults is reduced. The feedback mechanism introduced in this invention enables the system to continuously optimize the monitoring and response strategies based on the actual situation after each fault handling. This mechanism, combined with the dynamic learning ability of DMAOM, improves the intelligence level of the system, enabling it to cope with more unknown challenges in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0125] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0126] in:

[0127] Figure 1 A schematic diagram of the overall structure of a system according to an embodiment of the present invention;

[0128] Figure 2 The following is a detailed processing flow of multimodal data fusion in an embodiment of the present invention;

[0129] Figure 3 This is a flowchart of the control execution module in an embodiment of the present invention;

[0130] Figure 4 The figure is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0131] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0132] Example 1

[0133] like Figure 1 As shown in FIG. 1 , an embodiment of the present invention is provided, which provides an intelligent control and measurement system for power transmission lines in a distribution network based on an AI algorithm, including:

[0134] A data acquisition module is used to collect the operation status data and environmental parameter data of the power transmission line in real time through multiple sensors, the sensors include temperature and humidity sensors, traveling wave ranging sensors, current sensors and fault detection sensors, and the data acquisition module transmits data through a distributed network of a wireless local area network WLAN or a cellular network;

[0135] Specifically, the temperature and humidity sensor and the traveling wave ranging sensor in the data acquisition module can adaptively adjust the sampling frequency by detecting real-time data of external temperature and humidity changes or line load status. The data acquisition module transmits data with the data preprocessing module through the low-power Bluetooth BLE module.

[0136] This module is responsible for collecting the operation status data and environmental parameter data of the transmission line in real time through multiple sensors. It is the basic data source of the entire system and directly affects the accuracy of subsequent processing and decision-making.

[0137] Sensor Type:

[0138] The data acquisition module integrates a variety of sensors to comprehensively monitor the operating status of the transmission line. Including:

[0139] Temperature and humidity sensor: used to collect temperature and humidity data around the transmission line. Changes in temperature and humidity may directly affect the load capacity and failure rate of the line.

[0140] Traveling wave ranging sensor: It is used to detect the traveling wave phenomenon in the transmission line, so as to accurately measure the electrical fluctuations in the line. This information is very important for fault location.

[0141] Current sensor: used to monitor the current of the transmission line in real time to detect the load status of the line and identify whether there is an overload or short circuit risk.

[0142] Fault detection sensors: used to monitor and identify potential fault points in transmission lines, and provide early fault warnings in conjunction with other sensor data.

[0143] Data transmission method:

[0144] The data acquisition module transmits data through a wireless local area network (WLAN) or a cellular network. The use of a distributed network architecture ensures that the data collected at different sensor locations is transmitted to the central processing unit in a timely manner, ensuring the real-time and reliability of the data.

[0145] Application scenarios: In actual applications, temperature and humidity sensors and current sensors can be deployed at key nodes of power transmission lines to monitor changes in the environment and load in real time. When the line load exceeds a certain threshold, the current sensor will trigger an alarm and transmit the data to the preprocessing module for further analysis.

[0146] Furthermore, a data preprocessing module preprocesses the temperature, humidity, traveling wave, current and fault detection data sent by the data acquisition module, wherein the preprocessing includes adopting a data denoising algorithm based on wavelet transform, abnormal data filtering and preliminary fault feature extraction;

[0147] Specifically, the data preprocessing module uses a convolutional neural network (CNN) model to extract fault features from the preprocessed temperature, humidity, traveling wave and current data. The CNN model is pre-trained with historical fault data and adaptively updated according to new fault data through dynamic adjustment based on online learning when the system is running.

[0148] The data preprocessing module cleans, denoises and extracts preliminary features from the raw data from the data acquisition module. This step ensures that the data input to the subsequent modules is accurate and reliable, which helps to improve the overall stability of the system and the accuracy of fault detection.

[0149] Preprocessing algorithm:

[0150] Wavelet transform denoising algorithm: It is mainly used to remove high-frequency noise in the sensor acquisition process. Wavelet transform can effectively separate the noise components in the data, thereby retaining key signal features. For example, in the current sensor signal, there may be high-frequency interference. Wavelet transform can remove this interference and retain the real current fluctuation information.

[0151] Abnormal data filtering: Based on the set rules or anomaly detection algorithms, invalid or abnormal values ​​in the data are eliminated. For example, if a sensor suddenly has extreme data, it may be a sensor failure or an environmental emergency. This module can filter out this data.

[0152] Preliminary fault feature extraction: Through preliminary processing of temperature, humidity, traveling wave and current data, some possible fault features are extracted. These features can be used to preliminarily determine whether there are hidden faults in the transmission line and provide a basis for subsequent data fusion.

[0153] Application scenario: When the current sensor detects a rapid increase in current, the system will automatically call the wavelet transform denoising algorithm to ensure that the acquired current waveform data is clear and reliable. The preprocessing module will then determine whether there is an abnormality based on the preset threshold and extract the corresponding fault characteristics.

[0154] Further, such as Figure 2 As shown, the multimodal data fusion module is used to receive the cleaned data and preliminary fault characteristics transmitted by the data preprocessing module, conduct a comprehensive analysis of the temperature, humidity, traveling wave and current data from different sensors through a multimodal data fusion algorithm, and extract the preliminary fault characteristics to generate a state assessment result and fault prediction information of the transmission line;

[0155] Specifically, the multimodal data fusion module uses convolutional neural network CNN, long short-term memory network LSTM, self-attention mechanism and graph neural network GNN combined with reinforcement learning algorithm to perform multimodal data weighted fusion analysis on temperature, humidity, traveling wave and current data from different sensors. The weighting coefficient is based on the Bayesian model in multimodal data fusion and is dynamically adjusted through an online learning model according to the ambient temperature, humidity and load status of the transmission line.

[0156] Specifically, the implementation mechanism of the multimodal data weighted fusion module includes:

[0157] S1: Anomaly detection and noise removal mechanism, which removes noise data from sensors through an intelligent anomaly detection mechanism based on support vector machine (SVM), uses a dynamic interpolation algorithm based on time series to achieve automatic alignment of multimodal data, and ensures data consistency through adaptive normalization processing;

[0158] S2: Local feature and temporal dynamic relationship extraction mechanism, combining convolutional neural network (CNN) and long short-term memory (LSTM) network, extracts local features and temporal dynamic relationships of each modal data, and optimizes the correlation between data;

[0159] S3: Weight setting and adaptive adjustment mechanism, through the unsupervised learning algorithm based on K-means clustering, the initial weight of each modal data is set based on historical data, and the weight of each modal data is dynamically adjusted through the self-attention mechanism;

[0160] S4: Multi-level fusion structure, using the multi-level fusion structure of deep neural network DNN to extract and fuse the relationship between different modal data, combined with the topological information of the transmission line, and weighted processing of the fused data through graph neural network GNN;

[0161] S5: Abnormal data identification and fault tolerance mechanism, which identifies and eliminates abnormal situations in sensor data through intelligent anomaly detection mechanism, and ensures the reliability and robustness of data processing through redundant fault tolerance mechanism of multimodal data;

[0162] S6: Computation optimization mechanism, which reduces the amount of computation through model pruning and quantization, and combines distributed computing to shift some computing tasks to distributed nodes;

[0163] S7: Time series modeling and uncertainty quantification mechanism, combining long short-term memory network LSTM to enhance the time series modeling of the system, and quantifying the uncertainty in fault prediction through Bayesian deep learning technology.

[0164] The multimodal data fusion module receives data from the data preprocessing module and generates a comprehensive evaluation result of the transmission line status and fault prediction information by fusing the data from different sensors.

[0165] Multimodal data fusion algorithm:

[0166] The system combines data from multiple modes such as temperature, humidity, traveling waves and current, and uses multi-modal weighted fusion technology. This technology dynamically adjusts the fusion method based on the weights of different data sources to ensure the effectiveness and accuracy of data fusion. For example, when the temperature or humidity is abnormal, the system will appropriately increase the weight of the temperature and humidity data to enhance the fault prediction of this factor.

[0167] During the fusion process, the algorithm will extract the correlation features between different sensors. By analyzing the interactions between these features, more accurate transmission line status assessment results can be obtained.

[0168] Status assessment and fault prediction: The results generated by the fused data include the current operating status assessment of the transmission line and potential fault prediction information. For example, when the temperature and current data show abnormal changes, the system may predict that the line is at risk of overload or temperature-induced short circuit.

[0169] Furthermore, the AI ​​fault diagnosis module receives the state assessment results and fault prediction information generated by the multimodal data fusion module, performs in-depth fault diagnosis on the operation state of the transmission line based on the improved dynamic multimodal adaptive optimization model DMAOM, and generates a detailed fault diagnosis report in combination with historical fault data and real-time analysis results;

[0170] Specifically, the AI ​​fault diagnosis module learns the effects of different fault handling solutions in real time through the improved dynamic multimodal adaptive optimization model DMAOM, dynamically adjusts the system response mode, combines historical fault data and the fault prediction information generated by the multimodal data fusion module to generate a detailed fault diagnosis report containing fault type, occurrence time and expected impact range, and provides targeted repair suggestions.

[0171] The improved dynamic multi-modal adaptive optimization model DMAOM is implemented in the following steps:

[0172] Step 1: Integrate historical fault data and real-time multimodal data including temperature, current, voltage, and humidity sensor data, and use the dynamic data fusion algorithm based on the Bayesian model to perform weighted integration of real-time data and historical data. The calculation formula is as follows:

[0173] X fuse =αX real +(1-α)X hist

[0174] Among them, X fuse For real-time data; X real Sensor data for real-time multimodal data; X hist is the historical data; α is the fusion weight coefficient;

[0175] Apply self-attention mechanism to extract important global features in fault diagnosis:

[0176]

[0177] Among them, Q, K, V are feature matrices; d k is the dimension of the key matrix; softmax is a normalization function that calculates the similarity between the key and the query and converts it into a probability distribution;

[0178] Step 2: Through self-supervised learning technology, combined with unlabeled data and contrastive learning methods, the fault mode of the distribution network is dynamically learned. At each time step, the diagnostic model is optimized through the contrastive learning loss function:

[0179]

[0180] Among them, L i is the contrast loss value of the i-th sample; x i is the encoding vector representation of the current sample; With x i The relevant positive samples, With x i Related negative samples; sim(,) is the similarity between two samples; τ is the temperature parameter;

[0181] Step 3: The fault handling strategy is dynamically optimized through reinforcement learning. After each fault handling, the system t , Action performed a t To optimize the strategy:

[0182] π new = argmaxE(s t ,a t )~π[Q(s t ,a t )-αlogπ(s t |a t )]

[0183] π

[0184] Among them, π new For new strategies; t is the state at time step t; Q(s t ,a t ) is the Q value of the current state and action, indicating that in state s t Next, perform action a t The expected return after α is the entropy coefficient, which controls the balance between reward and strategy entropy.

[0185] logπ(s t |a t )] is the entropy of the strategy, indicating that in state s t Next select action a t The randomness of

[0186] Step 4: The system receives real-time feedback on the effect of the processing through the feedback loop mechanism, continuously optimizes the fault diagnosis and processing strategy, and feedback information f t is introduced into the loss function:

[0187] L new =L(f t )+βL previous

[0188] Among them, L new is the new loss value used to optimize the model; L(f t ) is the current loss calculated based on the feedback information; L previous is the loss of the previous iteration; β is the feedback weight coefficient, which controls the balance between new feedback and historical loss;

[0189] Step 5: Apply the multi-modal fault handling optimization mechanism based on the diffusion model to gradually optimize the fault prediction results through the reverse diffusion process:

[0190] p θ (x t-1 ∣x t )=N(xt-1 ;μ θ (x t ,t),Σ θ (x t ,t))

[0191] Among them, p θ (x t-1 ∣x t ) is the noisy data x at time step t t The probability distribution of the data generated at time step t-1 under the condition; N(x t-1 ;μ θ (x t ,t),Σ θ (x t ,t)) is a normal distribution, which is used to model the generated data distribution in the inverse diffusion process; μ θ (x t ,t) is the mean of the neural network parameterization, which represents the generated mean estimate; Σ θ (x t ,t) is the variance of the neural network parameterization, indicating the uncertainty in the generation process; x t is the noisy data at time step t.

[0192] The AI ​​fault diagnosis module receives the status assessment and fault prediction information provided by the multimodal data fusion module, and generates a detailed fault diagnosis report through further AI analysis.

[0193] Dynamic Multimodal Adaptive Optimization Model (DMAOM):

[0194] This module is based on the improved DMAOM model, combining historical fault data and real-time data to further diagnose transmission line faults. The DMAOM model can generate detailed fault reports by comparing historical data and analyzing the abnormal performance of the current line.

[0195] Self-learning capability: The AI ​​fault diagnosis module has self-learning capability and can continuously optimize the fault identification model and improve the accuracy of diagnosis based on the fault history records of the transmission line.

[0196] Fault diagnosis report: The report includes information such as the type of fault, the time of occurrence, and the expected scope of impact. For example, if the current of a certain line fluctuates abnormally, the system can determine based on historical fault data that it may be due to insulation damage caused by overload, and provide recommended repair methods.

[0197] Further, such as Figure 3As shown, the control execution module, including a circuit breaker, a switch and a dispatching device, receives the fault diagnosis report generated by the AI ​​fault diagnosis module and performs control operations of fault isolation, load transfer or line protection according to the information in the report;

[0198] Specifically, the control execution module includes:

[0199] Current limiting operation unit: according to the preset current threshold and voltage threshold, when the current or voltage exceeds the preset range, the current limiting operation is performed, and the current limiting threshold is dynamically adjusted according to the load state and environmental parameters;

[0200] Load transfer and circuit breaking operation unit: when the current limiting operation fails to reduce the current or voltage to a safe range within the preset time and exceeds the specified number of times, the load transfer or circuit breaking operation is performed;

[0201] Self-check unit: In case of multiple current limiting or load transfer operation failures, the system automatically enters self-check mode, and monitors the hardware and software status in the control execution system in real time through the internal fault detection module;

[0202] Overload protection unit: When the system load exceeds the preset limit, it automatically cuts off part of the line to prevent overload damage;

[0203] Remote monitoring interface: connected to the remote monitoring terminal via the Internet, allowing operators to view system status in real time and perform remote operations.

[0204] Overview:

[0205] The control execution module receives the fault diagnosis report generated by the AI ​​fault diagnosis module and performs corresponding control operations to prevent further fault expansion.

[0206] Control Operation:

[0207] Fault isolation: When the system detects a fault in a certain line section, the control execution module can quickly cut off the faulty line through a circuit breaker or switch device to prevent the accident from expanding.

[0208] Load transfer: If a line overload is detected, the system can automatically transfer the load to other lines to reduce the load on the faulty line.

[0209] Line protection: In some cases, the system will perform protection measures, such as adjusting the line current to prevent current overload and damage to equipment.

[0210] Application scenario: When a line detects an overload and the temperature rises, the system will immediately start the current limiting operation. If the current limiting is ineffective, the control execution module will quickly cut off the line and transfer the load to the backup line.

[0211] Furthermore, the communication module is responsible for transmitting data and control instructions between various modules of the system. It ensures that data acquisition, preprocessing, fusion, fault diagnosis and control operations can interact in real time.

[0212] Communication mode: supports wireless and wired communication. For example, data can be transmitted in real time via wireless LAN or cellular network. The communication module ensures that different parts of the system can transmit information efficiently and with low latency to ensure real-time performance.

[0213] Real-time interaction: The real-time interaction capability between system modules ensures that when a fault occurs, each module can respond quickly and work together, reducing accident handling time and improving fault response efficiency.

[0214] Example 2

[0215] like Figure 4 As shown in FIG. 1 , another embodiment of the present invention provides an intelligent control and measurement method for power transmission lines in a distribution network based on an AI algorithm, the method comprising the following steps:

[0216] Step 1: collect the operation status data and environmental parameter data of the power transmission line in real time through multiple sensors, the sensors include temperature and humidity sensors, traveling wave ranging sensors, current sensors and fault detection sensors, and transmit the data to the data preprocessing module through the wireless local area network WLAN or cellular network;

[0217] Step 2: In a data preprocessing module, the data is processed, and the preprocessing includes data denoising based on wavelet transform, abnormal data filtering, and preliminary fault feature extraction;

[0218] Step 3: Input the cleaned data into the multimodal data fusion module, combine the convolutional neural network CNN, long short-term memory network LSTM and graph neural network GNN algorithms to perform multimodal data fusion analysis and generate the state assessment results and fault prediction information of the transmission line;

[0219] Step 4: Apply the improved dynamic multimodal adaptive optimization model DMAOM, combine historical fault data and real-time multimodal data, use the Bayesian model to perform dynamic multimodal data weighted fusion on the data, extract global features, and dynamically optimize the fault diagnosis model through self-supervised learning and reinforcement learning;

[0220] Step 5: AI fault diagnosis, based on the fault prediction information and historical data generated by the DMAOM model, uses deep learning and reinforcement learning algorithms to generate a fault diagnosis report, which includes the fault type, occurrence time, expected impact range, and provides repair suggestions;

[0221] Step 6: Control execution. According to the fault diagnosis report, perform current limiting, load transfer or circuit breaking operations. After multiple current limiting or load transfer operations fail, it automatically enters the self-check mode to detect the hardware and software status through real-time monitoring.

[0222] Furthermore, the dynamic multimodal adaptive optimization model DMAOM is implemented by the following steps:

[0223] Step S1: Inputting various sensor data of the transmission line including temperature and humidity sensor, traveling wave ranging sensor, current sensor and fault detection sensor into the DMAOM model to perform multimodal data fusion. The significant improvements of the DMAOM model include:

[0224] Step S1.1: Use the Bayesian model to dynamically adjust the fusion weights of multimodal data based on real-time monitored environmental parameters including temperature, humidity and load status. The initial weights are set through historical data sets and updated based on new real-time data.

[0225] Step S1.2: Perform global feature extraction on multimodal data through self-supervised learning technology, identify the correlation between different data modes in the transmission line, and extract important features related to fault prediction;

[0226] Step S1.3: Based on the data correlation in the multimodal data fusion process, abnormal data is detected in real time, and the abnormality is corrected through an adaptive correction mechanism;

[0227] Step S2: Adaptively optimize the fault prediction model through reinforcement learning. The significant improvements of the model optimization include:

[0228] Step S2.1: After each fault prediction, the system adjusts the model parameters according to the feedback information, which includes the accuracy of the fault occurrence, the response time and the system control effect;

[0229] Step S2.2: Combine the long short-term memory network LSTM to perform multi-step time series modeling to further optimize the correlation analysis between historical data and real-time data;

[0230] Step S3: Based on the outputs of step S1 and step S2, fault prediction information and a detailed fault diagnosis report are generated, wherein the report includes the fault type, occurrence time, impact scope, and optimized repair suggestions.

[0231] Example 3

[0232] Application of multimodal data fusion and AI fault diagnosis in transmission line fault prediction

[0233] In this embodiment, the system collects the operation status data and environmental parameter data of the line in real time through multiple sensors deployed on the transmission line, including temperature and humidity sensors, traveling wave ranging sensors, and current sensors. These data are transmitted to the data preprocessing module of the system through a wireless local area network WLAN or a cellular network.

[0234] The data preprocessing module first denoises the raw data collected by the sensor and filters out abnormal data. The wavelet transform algorithm is used to denoise the current and traveling wave data to eliminate the interference of high-frequency noise on data analysis. At the same time, the abnormal data detected by the system will be eliminated through a rule-based filtering algorithm to ensure that the data sent to the downstream module is valid data.

[0235] The preprocessed data is transmitted to the multimodal data fusion module. This module performs weighted fusion of multimodal data such as temperature and humidity, traveling waves, and current through convolutional neural networks (CNN), long short-term memory networks (LSTM), and graph neural networks (GNN). The weighting coefficient is dynamically adjusted by the Bayesian model, and is adaptively optimized based on current environmental parameters such as temperature, humidity, and load status.

[0236] During the data fusion process, the system correlates and analyzes the current changes and traveling wave data at different times with the temperature and humidity data to extract the real-time status characteristics of the transmission line. At the same time, the system combines LSTM to model the temporal dynamic relationship of the data to capture the fluctuation characteristics of the line in a short time, especially to predict the abnormal state of the line under sudden load increase or drastic environmental change.

[0237] The fused data enters the AI ​​fault diagnosis module, and the system uses the improved dynamic multimodal adaptive optimization model DMAOM to perform in-depth diagnosis of the operating status of the transmission line. The DMAOM model not only considers the current real-time data, but also combines historical fault data, extracts fault modes from it using a deep learning model, and predicts possible fault types and occurrence times.

[0238] The fault diagnosis module further optimizes the accuracy and response speed of the fault prediction model through self-supervised learning algorithms. While predicting faults, the system also generates a diagnostic report containing the fault type, impact range, fault probability, and possible fault recovery solutions. This report can help power companies adjust their dispatch strategies in a timely manner to avoid potential grid failures.

[0239] Based on the report generated by the fault diagnosis module, the control execution module will issue corresponding control instructions through the dispatch center. If the diagnosis report clearly indicates that a certain line may fail due to overload or abnormal temperature, the system will first perform current limiting operations. When the current limiting operation fails to alleviate the abnormal state, the system will automatically perform load transfer operations or circuit breaking operations to ensure stable operation of the power grid.

[0240] The system is equipped with a feedback mechanism to further optimize the fault handling strategy by continuously monitoring the effects of current limiting, circuit breaking or load transfer operations. If the current operation does not achieve the expected effect, the system will adjust the model parameters again based on the newly acquired data, continuously improving the accuracy and response speed of fault handling through the feedback loop.

[0241] The advantage of this embodiment is that it can quickly and accurately predict potential faults of transmission lines and take timely fault handling measures through multimodal data fusion technology, combined with real-time monitoring and historical fault data. This AI-based fault diagnosis system not only greatly improves the fault prediction capability of transmission lines, but also dynamically adjusts the control strategy through the optimized model to ensure the stability and safety of the power grid.

[0242] The difference from the prior art is that the present invention utilizes a multimodal data fusion algorithm and a dynamic adaptive optimization model DMAOM to effectively integrate data from multiple sensors, and continuously improves the system's diagnostic and predictive capabilities through self-supervised learning and reinforcement learning, significantly improving the early warning and processing capabilities of transmission line faults in complex environments.

[0243] In summary, the present invention significantly improves the monitoring, prediction and control capabilities of distribution network transmission lines by integrating multimodal data fusion technology and AI-based intelligent fault diagnosis system. Through the multimodal data weighted fusion module, the system can dynamically integrate data from different sensors and make full use of information from various data modes, thereby improving the accuracy of fault detection and the overall performance of the system. Based on the improved dynamic multimodal adaptive optimization model DMAOM, the present invention can analyze historical and current data in real time and has the ability of self-learning and adaptive adjustment. This enables the system to identify potential faults, take preventive measures in time, and reduce the risk of faults. The system can automatically adjust the control strategy based on real-time monitoring data to ensure that the power grid remains efficient and safe in the case of load changes or external environmental fluctuations. In the event of an abnormal situation, the present invention can quickly formulate and execute response strategies such as current limiting, load transfer or circuit breaking, reducing the risk of large-scale power outages caused by faults and ensuring the stability and reliability of the power grid. By introducing feedback mechanisms and dynamic learning capabilities, the system can continuously optimize monitoring and response strategies after each fault processing, improve its intelligence level, and adapt to the ever-changing power grid environment in the future. The present invention not only provides strong technical support for the safe operation of the distribution network, but also has important practical application value in improving the efficiency and reliability of the power system, and has broad market prospects and promotion potential.

[0244] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0245] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0246] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent control and measurement system for power distribution network transmission lines based on AI algorithm, characterized in that: The system comprises: A data acquisition module is used to collect the operation status data and environmental parameter data of the power transmission line in real time through multiple sensors, the sensors include temperature and humidity sensors, traveling wave ranging sensors, current sensors and fault detection sensors, and the data acquisition module transmits data through a distributed network of a wireless local area network WLAN or a cellular network; A data preprocessing module, which preprocesses the temperature, humidity, traveling wave, current and fault detection data sent by the data acquisition module, wherein the preprocessing includes adopting a data denoising algorithm based on wavelet transform, abnormal data filtering and preliminary fault feature extraction; A multimodal data fusion module is used to receive the cleaned data and preliminary fault characteristics transmitted by the data preprocessing module, conduct a comprehensive analysis of the temperature, humidity, traveling wave and current data from different sensors through a multimodal data fusion algorithm, and extract the preliminary fault characteristics to generate a state assessment result and fault prediction information of the transmission line; The AI ​​fault diagnosis module receives the state evaluation results and fault prediction information generated by the multimodal data fusion module, performs in-depth fault diagnosis on the operation state of the transmission line based on the improved dynamic multimodal adaptive optimization model DMAOM, and generates a detailed fault diagnosis report in combination with historical fault data and real-time analysis results; A control execution module, including a circuit breaker, a switch, and a dispatching device, receives a fault diagnosis report generated by the AI ​​fault diagnosis module, and performs control operations of fault isolation, load transfer, or line protection according to information in the report; The communication module is used to transmit data and control instructions between the data acquisition module, data preprocessing module, multimodal data fusion module, AI fault diagnosis module and control execution module, and realize real-time interaction among the modules of the system through wireless communication or wired communication.

2. According to claim 1, the intelligent control and measurement system for power distribution network transmission lines based on AI algorithm is characterized in that: The temperature and humidity sensor and the traveling wave ranging sensor in the data acquisition module can detect real-time data of external temperature and humidity changes or line load status and adaptively adjust the sampling frequency. The data acquisition module transmits data with the data preprocessing module through a low-power Bluetooth BLE module.

3. According to claim 1, the intelligent control and measurement system for power distribution network transmission lines based on AI algorithm is characterized in that: The data preprocessing module uses a convolutional neural network (CNN) model to extract fault features from the preprocessed temperature, humidity, traveling wave and current data. The CNN model is pre-trained with historical fault data and adaptively updated according to new fault data through dynamic adjustment based on online learning when the system is running.

4. The intelligent control and measurement system for power distribution network transmission lines based on AI algorithm according to claim 1 is characterized in that: The multimodal data fusion module uses convolutional neural network CNN, long short-term memory network LSTM, self-attention mechanism and graph neural network GNN combined with reinforcement learning algorithm to perform multimodal data weighted fusion analysis on temperature, humidity, traveling wave and current data from different sensors. The weighting coefficient is based on the Bayesian model in multimodal data fusion and is dynamically adjusted through an online learning model according to the ambient temperature, humidity and load status of the transmission line.

5. The intelligent control and measurement system for power distribution network transmission lines based on AI algorithm according to claim 4 is characterized in that: Implementation mechanism of the multimodal data weighted fusion module include: S1: Anomaly detection and noise removal mechanism, which removes noise data from sensors through an intelligent anomaly detection mechanism based on support vector machine (SVM), uses a dynamic interpolation algorithm based on time series to achieve automatic alignment of multimodal data, and ensures data consistency through adaptive normalization processing; S2: Local feature and temporal dynamic relationship extraction mechanism, combining convolutional neural network (CNN) and long short-term memory (LSTM) network to extract local features and temporal dynamic relationship of each modal data; S3: Weight setting and adaptive adjustment mechanism, through the unsupervised learning algorithm based on K-means clustering, the initial weight of each modal data is set based on historical data, and the weight of each modal data is dynamically adjusted through the self-attention mechanism; S4: Multi-level fusion structure, using the multi-level fusion structure of deep neural network DNN to extract and fuse the relationship between different modal data, combined with the topological information of the transmission line, and weighted processing of the fused data through graph neural network GNN; S5: Abnormal data identification and fault tolerance mechanism, which identifies and eliminates abnormal situations in sensor data through intelligent anomaly detection mechanism, and ensures the reliability and robustness of data processing through redundant fault tolerance mechanism of multimodal data; S6: Computation optimization mechanism, which reduces the amount of computation through model pruning and quantization and combines distributed computing to shift some computing tasks to distributed nodes; S7: Time series modeling and uncertainty quantification mechanism, combining long short-term memory network LSTM to enhance the time series modeling of the system, and quantifying the uncertainty in fault prediction through Bayesian deep learning technology.

6. The intelligent control and measurement system for power distribution network transmission lines based on AI algorithm according to claim 1 is characterized in that: The AI ​​fault diagnosis module learns the effects of different fault handling solutions in real time through the improved dynamic multimodal adaptive optimization model DMAOM, dynamically adjusts the system response mode, combines historical fault data and the fault prediction information generated by the multimodal data fusion module to generate a detailed fault diagnosis report including fault type, occurrence time and expected impact range, and provides targeted repair suggestions.

7. The intelligent control and measurement system for power distribution network transmission lines based on AI algorithm according to claim 6 is characterized in that: The improved dynamic multi-modal adaptive optimization model DMAOM is implemented in the following steps: Step 1: Integrate historical fault data and real-time multimodal data including temperature, current, voltage, and humidity sensor data, and use the dynamic data fusion algorithm based on the Bayesian model to perform weighted integration of real-time data and historical data. The calculation formula is as follows: X fuse =αX real +(1-a)X hist Among them, X fuse For real-time data; X real Sensor data for real-time multimodal data; X hist is the historical data; α is the fusion weight coefficient; Apply self-attention mechanism to extract important global features in fault diagnosis: Among them, Q, K, V are feature matrices; d k is the dimension of the key matrix; softmax is a normalization function that calculates the similarity between the key and the query and converts it into a probability distribution; Step 2: Through self-supervised learning technology, combined with unlabeled data and contrastive learning methods, the fault mode of the distribution network is dynamically learned. At each time step, the diagnostic model is optimized through the contrastive learning loss function: Among them, L i is the contrast loss value of the i-th sample; x i is the encoding vector representation of the current sample; With x i The relevant positive samples, With x i Related negative samples; sim(,) is the similarity between two samples; τ is the temperature parameter; Step 3: The fault handling strategy is dynamically optimized through reinforcement learning. After each fault handling, the system t , Action performed a t To optimize the strategy: Among them, π new For new strategies; t is the state at time step t; Q(s t ,a t ) is the Q value of the current state and action, indicating that in state s t Next, perform action a t The expected return after the policy is implemented; α is the entropy coefficient, which controls the balance between reward and policy entropy; logπ(s t |a t )] is the entropy of the strategy, indicating that in state s t Next select action a t The randomness of Step 4: The system receives real-time feedback on the effect of the processing through the feedback loop mechanism, continuously optimizes the fault diagnosis and processing strategy, and feedback information f t is introduced into the loss function: L new =L(f t )+βL previous Among them, L new is the new loss value used to optimize the model; L(f t ) is the current loss calculated based on the feedback information; L previous is the loss of the previous iteration; β is the feedback weight coefficient, which controls the balance between new feedback and historical loss; Step 5: Apply the multi-modal fault handling optimization mechanism based on the diffusion model to gradually optimize the fault prediction results through the reverse diffusion process: p θ (x t-1 ∣x t )=N(x t-1 ;μ θ (x t ,t),Σ θ (x t ,t)) Among them, p θ (x t-1 ∣x t ) is the noisy data x at time step t t The probability distribution of the data generated at time step t-1 under the condition; N(x t-1 ;μ θ (x t ,t),Σ θ (x t ,t)) is a normal distribution, which is used to model the generated data distribution in the inverse diffusion process; μ θ (x t ,t) is the mean of the neural network parameterization, which represents the generated mean estimate; Σ θ (x t ,t) is the variance of the neural network parameterization, indicating the uncertainty in the generation process; x t is the noisy data at time step t.

8. The intelligent control and measurement system for power distribution network transmission lines based on AI algorithm according to claim 1 is characterized in that: The control execution module includes: Current limiting operation unit: according to the preset current threshold and voltage threshold, when the current or voltage exceeds the preset range, the current limiting operation is performed, and the current limiting threshold is dynamically adjusted according to the load state and environmental parameters; Load transfer and circuit breaking operation unit: when the current limiting operation fails to reduce the current or voltage to a safe range within the preset time and exceeds the specified number of times, the load transfer or circuit breaking operation is performed; Self-check unit: In case of multiple current limiting or load transfer operation failures, the system automatically enters self-check mode, and monitors the hardware and software status in the control execution system in real time through the internal fault detection module; Overload protection unit: When the system load exceeds the preset limit, it automatically cuts off part of the line to prevent overload damage; Remote monitoring interface: connected to the remote monitoring terminal via the Internet, allowing operators to view system status in real time and perform remote operations.

9. A distribution network transmission line intelligent control and measurement method based on an AI algorithm according to any one of claims 1 to 8, characterized in that: The method comprises the following steps: Step 1: collect the operation status data and environmental parameter data of the power transmission line in real time through multiple sensors, the sensors include temperature and humidity sensors, traveling wave ranging sensors, current sensors and fault detection sensors, and transmit the data to the data preprocessing module through the wireless local area network WLAN or cellular network; Step 2: In a data preprocessing module, the data is processed, and the preprocessing includes data denoising based on wavelet transform, abnormal data filtering, and preliminary fault feature extraction; Step 3: Input the cleaned data into the multimodal data fusion module, combine the convolutional neural network CNN, long short-term memory network LSTM and graph neural network GNN algorithms to perform multimodal data fusion analysis and generate the state assessment results and fault prediction information of the transmission line; Step 4: Apply the improved dynamic multimodal adaptive optimization model DMAOM, combine historical fault data and real-time multimodal data, use the Bayesian model to perform dynamic multimodal data weighted fusion on the data, extract global features, and dynamically optimize the fault diagnosis model through self-supervised learning and reinforcement learning; Step 5: AI fault diagnosis, based on the fault prediction information and historical data generated by the DMAOM model, uses deep learning and reinforcement learning algorithms to generate a fault diagnosis report, which includes the fault type, occurrence time, expected impact range, and provides repair suggestions; Step 6: Control execution. According to the fault diagnosis report, perform current limiting, load transfer or circuit breaking operations. After multiple current limiting or load transfer operations fail, it automatically enters the self-check mode to detect the hardware and software status through real-time monitoring.

10. The intelligent control and measurement method for power transmission lines in a distribution network based on an AI algorithm according to claim 9, characterized in that: The dynamic multi-modal adaptive optimization model DMAOM is implemented by the following steps: Step S1: Inputting various sensor data of the transmission line including temperature and humidity sensor, traveling wave ranging sensor, current sensor and fault detection sensor into the DMAOM model to perform multimodal data fusion. The significant improvements of the DMAOM model include: Step S1.1: Use the Bayesian model to dynamically adjust the fusion weights of multimodal data based on real-time monitored environmental parameters including temperature, humidity and load status. The initial weights are set through historical data sets and updated based on new real-time data. Step S1.2: Perform global feature extraction on multimodal data through self-supervised learning technology, identify the correlation between different data modes in the transmission line, and extract important features related to fault prediction; Step S1.3: Based on the data correlation in the multimodal data fusion process, abnormal data is detected in real time, and the abnormality is corrected through an adaptive correction mechanism; Step S2: Adaptively optimize the fault prediction model through reinforcement learning. The significant improvements of the model optimization include: Step S2.1: After each fault prediction, the system adjusts the model parameters according to the feedback information, which includes the accuracy of the fault occurrence, the response time and the system control effect; Step S2.2: Combine the long short-term memory network LSTM to perform multi-step time series modeling to further optimize the correlation analysis between historical data and real-time data; Step S3: Based on the outputs of step S1 and step S2, fault prediction information and a detailed fault diagnosis report are generated, wherein the report includes the fault type, occurrence time, impact scope and optimized repair suggestions.

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