Multi-modal data driven power distribution network equipment state evaluation and optimization method

Abstract

The invention discloses a multi-modal data driven power distribution network equipment state evaluation and optimization method, and relates to the technical field of power distribution network equipment, multi-modal data is collected, the data is from multiple equipment and operating environments in a power distribution network, and sensor data comprises real-time monitoring data of the equipment; and performing time sequence correction and synchronization processing on the acquired multi-modal data, and ensuring time alignment between different data sources through an advanced time sequence synchronization algorithm. According to the method, the dynamic time warping algorithm and deep learning are combined, the problem of multi-modal data synchronization is solved, the state of the power distribution network equipment is evaluated in real time, faults are recognized in advance, and early warning is provided. The reinforcement learning algorithm optimizes load distribution and self-healing scheduling, and improves the operation efficiency and fault recovery capability of the power distribution network. The system also generates intelligent early warning and maintenance decisions through the combination of deep learning and reinforcement learning, predicts faults, provides maintenance suggestions, optimizes operation and maintenance, prolongs the service life of equipment, and improves the overall stability of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network equipment, and in particular to a distribution network equipment state evaluation and optimization method driven by multimodal data. Background Art

[0002] Multimodal data-driven distribution network equipment status assessment and optimization refers to the real-time monitoring and assessment of the operating status of various types of equipment in the distribution network by integrating different types of data (such as sensor data, historical operation and maintenance data, environmental data, etc.), using data analysis and machine learning technology, and optimizing the operation and maintenance strategies of the equipment based on the assessment results. This process includes monitoring of equipment status, such as real-time collection of parameters such as voltage, current, and temperature, as well as automatic identification of fault types, and then predicting possible faults or anomalies; at the same time, the dispatching and operation of the distribution network is optimized through data-driven methods, such as load distribution, fault self-healing, energy saving and consumption reduction, so as to improve the reliability, efficiency and safety of the distribution network.

[0003] The prior art has the following deficiencies:

[0004] In the multimodal data-driven distribution network equipment status assessment and optimization method, if there are serious time delays or data synchronization problems between data sources, it will lead to lags or errors in equipment status assessment and fault diagnosis information, which will in turn affect the real-time monitoring and fault warning mechanism of the distribution network. Specifically, if the timing between sensor data, historical operation and maintenance data, and environmental data does not match or there is a large time difference, it may cause the system to misjudge the current operating status of the equipment, miss the key fault warning window, and cause the equipment failure to be discovered in time, and may even cause serious or catastrophic failures of the equipment during operation, causing large-scale power outages or equipment damage, bringing huge economic losses and social impacts to users.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a multimodal data-driven distribution network equipment status evaluation and optimization method, which combines a dynamic time warping algorithm and a deep learning model to solve the multimodal data delay and synchronization problems, accurately evaluate the distribution network equipment status in real time, identify potential fault risks in advance and provide early warnings, thereby reducing power outages and equipment damage. The load distribution, fault isolation and self-healing scheduling of the distribution network are optimized by a reinforcement learning algorithm, which improves the operating efficiency and flexibility of the distribution network and ensures that normal operation can be quickly restored when equipment fails. In addition, the system combines deep learning with reinforcement learning to generate intelligent early warnings and maintenance decision support, predict equipment failures in advance and provide maintenance suggestions, optimize the operation and maintenance of the distribution network, extend the service life of equipment, and improve overall stability to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a multimodal data-driven distribution network equipment status evaluation and optimization method, which aims to solve the influence of delay or synchronization problems between data sources on equipment status evaluation and fault diagnosis, and the method comprises the following steps:

[0008] Step 1: Collect multimodal data, including but not limited to sensor data, historical operation and maintenance data, and environmental data. The data comes from multiple devices and operating environments in the distribution network. The sensor data includes real-time monitoring data such as current, voltage, temperature, vibration, etc. of the equipment. The historical operation and maintenance data includes information such as equipment maintenance records and fault logs. The environmental data includes external factors such as weather and load demand.

[0009] Step 2: Perform timing correction and synchronization processing on the collected multimodal data. Through the advanced timing synchronization algorithm, ensure the time alignment between different data sources and eliminate the delay effect. The advanced timing synchronization algorithm is based on dynamic time warping (DTW) or phase synchronization algorithm to perform timing matching and delay correction between data sources, thereby ensuring data synchronization.

[0010] Step 3: Based on the synchronized data, a multimodal data fusion model is constructed using a deep learning algorithm, which combines a convolutional neural network (CNN) and a long short-term memory network (LSTM) to evaluate the equipment status in real time, predict potential failure risks, and generate a fault diagnosis report. The deep learning model can learn the spatiotemporal features of the equipment status data, extract key features, and perform intelligent inference.

[0011] Step 4: Optimize and adjust the equipment status evaluation results and the overall operating status of the distribution network, and use reinforcement learning algorithms (such as Q-learning) to dynamically adjust the distribution network load distribution, fault isolation and self-healing strategies according to the equipment evaluation results and network topology to achieve intelligent optimization operation of the distribution network.

[0012] Step 5: Based on the evaluation and optimization results, generate equipment maintenance and operation reports, provide specific equipment maintenance recommendations, fault warnings, and distribution network optimization plans, and use the intelligent dispatching system to provide real-time feedback of the optimization plans to the distribution network management system to guide equipment operation and maintenance decisions and ensure efficient and stable operation of the distribution network.

[0013] Preferably, the timing correction and synchronization processing in step 2 further includes the following sub-steps:

[0014] Preprocess the collected multimodal data, including denoising, standardization and normalization, to eliminate sensor errors and environmental interference and ensure data quality and consistency;

[0015] In view of the delay characteristics of different data sources, dynamic timing correction technology based on timestamp is used to match the delay between sensor data, historical operation and maintenance data, and environmental data to ensure accurate alignment of various data on the time axis;

[0016] Based on the dynamic time warping (DTW) algorithm, the weighted average method is used to optimize the data processing to improve the accuracy of timing synchronization, ensure that the delay correction results meet the real-time monitoring requirements of the distribution network, and reduce the risk of data loss or erroneous synchronization.

[0017] Through this step, the accuracy of data fusion can be significantly improved, thereby reducing the negative impact of timing mismatch on distribution network operation monitoring and fault diagnosis.

[0018] Preferably, the deep learning model in step 3 further includes:

[0019] Use convolutional neural networks (CNN) to extract features from various sensor data and identify the spatial characteristics of multi-dimensional equipment operating states such as voltage, current, and temperature;

[0020] Use the long short-term memory network (LSTM) to model the time-series changes of equipment status, capture the long-term dependencies and instantaneous anomalies during equipment operation, and ensure accurate prediction of equipment operation trends;

[0021] Combined with the autoencoder, unsupervised learning of historical operation and maintenance data is performed to identify potential failure modes of equipment, thereby providing a more accurate basis for equipment fault diagnosis and fault warning.

[0022] This deep learning model can conduct a comprehensive assessment of the health status of equipment through multi-level and multi-dimensional data analysis, and effectively improve the fault diagnosis and early warning capabilities of the distribution network.

[0023] Preferably, the Q-learning adopted by the reinforcement learning algorithm in step 4 further includes:

[0024] In the process of optimizing the dispatching of the distribution network, the state space, action space and reward function are designed. The state space includes the current load distribution, equipment status and fault information of the distribution network. The action space includes operations such as load dispatching, fault isolation and self-healing of the distribution network. The reward function is designed based on system stability, equipment health status and load balance.

[0025] The dispatching strategy of the distribution network is trained through the Q-learning algorithm, so that in different operating scenarios, the system can automatically adjust the optimal operating plan according to real-time data;

[0026] Combined with the Multi-Armed Bandit algorithm, the load allocation decision of the distribution network is optimized, and the optimization strategy is continuously updated through online learning, thereby achieving real-time optimization of the load scheduling of the distribution network.

[0027] Through this step, the reliability of the distribution network can be ensured while improving its operating efficiency and reducing energy consumption and the risk of equipment overload.

[0028] Preferably, the equipment maintenance and operation report in step 5 further includes:

[0029] Automatically generate equipment health reports based on equipment status assessment and fault diagnosis results, detailing abnormal conditions during equipment operation, fault diagnosis information, and maintenance suggestions;

[0030] Combined with the overall operation status of the distribution network, specific fault warning and emergency response plans are proposed, and equipment maintenance cycles and recommended operating procedures are provided to ensure that preventive maintenance measures can be taken in time when potential equipment failures occur;

[0031] Based on the optimization results, detailed optimization suggestions are provided for the dispatching and operation of the distribution network, including load distribution plans, energy efficiency optimization plans, equipment automation control strategies, etc., to improve the overall efficiency and stability of the distribution network.

[0032] The report provides strong data support for the operation and maintenance management of the distribution network through intelligent means, helping decision makers make accurate maintenance and adjustment decisions in a short time.

[0033] Preferably, collecting multimodal data in step 1 further includes:

[0034] Data collection from various intelligent sensors in the distribution network, including current sensors, voltage sensors, temperature sensors, vibration sensors, etc., monitors the operating status of distribution network equipment in real time and transmits data to the data center through wireless communication protocols (such as LoRa, Zigbee, 5G);

[0035] By collecting the historical operation and maintenance data of the equipment, combined with the equipment's service life, failure frequency and maintenance records, the equipment's health records are extracted to assess the equipment's life expectancy and failure probability.

[0036] Environmental data collection mainly includes external factors such as meteorological data and load demand data of the geographical area where the distribution network is located. These data help to accurately predict the impact of the external environment on the operating status of the equipment.

[0037] Through the precise collection of these multimodal data, the comprehensiveness and accuracy of the distribution network equipment status assessment is ensured, providing a rich data foundation for subsequent analysis.

[0038] Preferably, the deep learning model in step 3 further includes:

[0039] Different sub-models are used for evaluation of different equipment types (such as transformers, circuit breakers, cables, etc.) to ensure more accurate status evaluation of each equipment and reduce evaluation errors between different equipment types.

[0040] The hybrid model formed by combining multi-layer perceptron (MLP) and convolutional neural network (CNN) can further enhance the ability to identify the equipment status, especially in the case of abnormal data and a lot of noise, which can effectively suppress the risk of misdiagnosis;

[0041] Using the transfer learning method, the health data model of existing equipment is transferred to the evaluation task of new equipment or similar equipment, thereby reducing the training time for new equipment and improving the accuracy of fault diagnosis.

[0042] This deep learning model can not only handle the evaluation tasks of various devices, but also automatically adjust the learning strategy according to the specific conditions of the equipment, thereby improving the sensitivity and accuracy of distribution network fault detection.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] The present invention effectively solves the delay and synchronization problems between multimodal data by combining the timing synchronization processing based on the dynamic time warping algorithm with the deep learning model. Through precise data synchronization and multi-dimensional feature fusion, the status of each device in the distribution network can be evaluated in real time and accurately. The deep learning model can not only extract the spatial characteristics of the equipment status, but also capture its timing change trend and predict the future operating status of the equipment. This mechanism significantly improves the accuracy of equipment health assessment, especially in high-load and complex operating environments. It can identify the potential failure risks of equipment in advance and provide early warnings in time, thereby greatly reducing power outages and equipment damage caused by untimely detection of equipment failures.

[0045] The present invention adopts an adaptive scheduling algorithm based on reinforcement learning, which can realize intelligent optimization scheduling of the distribution network when equipment failure or load fluctuation occurs. In the case of load changes, equipment failures or unstable power supply in the distribution network, the Q-learning algorithm can be used to autonomously optimize load distribution, fault isolation and self-healing scheduling strategies. This intelligent scheduling capability not only improves the operating efficiency of the distribution network, but also minimizes power outages caused by equipment failures or load imbalances. The reinforcement learning algorithm continuously optimizes the strategy to ensure that the distribution network can make the best decision based on real-time data, realize the self-healing function, and quickly restore normal operation, thereby greatly improving the reliability and flexibility of the distribution network.

[0046] The present invention generates an intelligent early warning and maintenance decision support system based on multimodal data by combining deep learning with reinforcement learning. By analyzing the real-time data, historical operation and maintenance data, and external environment data of the equipment, it is not only possible to realize real-time status monitoring of the equipment, but also to predict potential failure modes and risks of equipment operation in advance. The deep learning model identifies and analyzes the equipment failure mode based on historical data, generates accurate fault diagnosis reports, and provides targeted maintenance suggestions for operation and maintenance personnel. The scheduling strategy optimized by the reinforcement learning algorithm can also automatically adjust the load distribution and fault isolation scheme when the equipment fails, so as to achieve rapid response and self-healing functions. Through this mechanism, the operation and maintenance personnel of the distribution network can take preventive measures before the equipment fails, avoid major failures caused by long-term lack of maintenance, thereby significantly improving the service life of the equipment and the overall stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0048] Figure 1 The present invention is a flowchart of the method for evaluating and optimizing the state of distribution network equipment driven by multimodal data. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0050] The present invention provides Figure 1The multimodal data-driven distribution network equipment state assessment and optimization method shown is intended to solve the impact of time delay or synchronization problems between data sources on equipment state assessment and fault diagnosis. The method includes the following steps:

[0051] Step 1: Collect multimodal data, including but not limited to sensor data, historical operation and maintenance data, and environmental data. The data comes from multiple devices and operating environments in the distribution network. The sensor data includes real-time monitoring data such as current, voltage, temperature, vibration, etc. of the equipment. The historical operation and maintenance data includes information such as equipment maintenance records and fault logs. The environmental data includes external factors such as weather and load demand.

[0052] Step 2: Perform timing correction and synchronization processing on the collected multimodal data. Through the advanced timing synchronization algorithm, ensure the time alignment between different data sources and eliminate the delay effect. The advanced timing synchronization algorithm is based on dynamic time warping (DTW) or phase synchronization algorithm to perform timing matching and delay correction between data sources, thereby ensuring data synchronization.

[0053] Step 3: Based on the synchronized data, a multimodal data fusion model is constructed using a deep learning algorithm, which combines a convolutional neural network (CNN) and a long short-term memory network (LSTM) to evaluate the equipment status in real time, predict potential failure risks, and generate a fault diagnosis report. The deep learning model can learn the spatiotemporal features of the equipment status data, extract key features, and perform intelligent inference.

[0054] Step 4: Optimize and adjust the equipment status evaluation results and the overall operating status of the distribution network, and use reinforcement learning algorithms (such as Q-learning) to dynamically adjust the distribution network load distribution, fault isolation and self-healing strategies according to the equipment evaluation results and network topology to achieve intelligent optimization operation of the distribution network.

[0055] Step 5: Based on the evaluation and optimization results, generate equipment maintenance and operation reports, provide specific equipment maintenance recommendations, fault warnings, and distribution network optimization plans, and use the intelligent dispatching system to provide real-time feedback of the optimization plans to the distribution network management system to guide equipment operation and maintenance decisions and ensure efficient and stable operation of the distribution network.

[0056] Specific implementation method 1: Data synchronization based on dynamic time warping algorithm and deep learning model fusion application

[0057] In this implementation, multimodal data is first collected through the intelligent sensor system of the distribution network. The data types include real-time status data such as current, voltage, temperature, vibration, etc. of the equipment, as well as historical operation and maintenance data of the distribution network (such as equipment maintenance records, fault logs, operation time, etc.), and external environmental data (such as meteorological information, load demand, temperature, humidity, etc.). These data are all derived from different devices and sensors in the distribution network, and they are transmitted to the data center via wireless communication protocols (such as LoRa, 5G, etc.). Due to the different data acquisition frequencies and delays of different devices and sensors, these data have inconsistent timing, so effective data synchronization methods must be adopted.

[0058] In order to eliminate the delay differences between data, this implementation method first uses the dynamic time warping (DTW) algorithm to perform time series synchronization processing on these multimodal data. The DTW algorithm is an alignment method based on distance measurement, which can effectively solve the problems of time delay and different acquisition frequencies between time series data. Specifically, DTW finds the best alignment method by minimizing the cumulative distance between two sets of data sequences, making the correspondence between different data sources in the time series more accurate. In the application of distribution networks, the DTW algorithm can achieve high-precision synchronization of sensor data with historical operation and maintenance data and external environment data, ensuring that various types of data are accurately aligned on the time axis and avoiding data deviations caused by time asynchrony.

[0059] After completing the timing correction, the system inputs the synchronized multimodal data into the deep learning model for equipment status assessment and fault prediction. The deep learning model uses a combination of a convolutional neural network (CNN) and a long short-term memory network (LSTM). In this combined model, CNN is first used to extract features from multi-dimensional equipment status data such as current, voltage, and temperature collected by sensors. Through multiple convolution and pooling operations of the convolution layer, CNN can identify the spatial characteristics of the equipment status and extract feature information that is critical for fault prediction. LSTM is used to analyze the temporal changes of equipment status, especially the dependencies in long-term time series data, and can capture long-term trends and periodic fluctuations during equipment operation. Through its memory unit, the LSTM network can effectively avoid the gradient vanishing problem of traditional RNN (recurrent neural network) in long-sequence data learning, thereby achieving accurate prediction of equipment status.

[0060] The deep learning model that combines CNN and LSTM can not only realize the joint analysis of spatial features and temporal features, but also accurately diagnose equipment failures. Specifically, by conducting comprehensive feature learning on the operating status of the equipment, the model can identify abnormal performance of the equipment during operation and issue fault warnings in a timely manner. For example, if the model detects a sudden increase in the current change of a certain device, or an abnormal increase in temperature, the model can combine historical data analysis to determine whether the anomaly may cause equipment failure, and then provide a fault warning. In addition, by learning from historical data, the deep learning model can also identify potential failure modes, such as equipment aging, sensor failure, etc., and predict the type of failure that may occur in the equipment in advance, providing a basis for subsequent maintenance and optimization.

[0061] Finally, based on the fault diagnosis report generated by the model, the operation and management system of the distribution network can monitor the operating status of the equipment in real time and take preventive maintenance measures based on the equipment evaluation results. For example, when an abnormality occurs in the equipment, the system can automatically generate maintenance suggestions and maintenance plans based on the predicted fault type, and pass these suggestions to the operation and maintenance personnel through the intelligent scheduling system. This process not only improves the accuracy of fault prediction, but also ensures the timely maintenance of distribution network equipment, thereby avoiding power outages caused by equipment failures.

[0062] This implementation method effectively solves the timing inconsistency problem in the status assessment of distribution network equipment by combining efficient data synchronization algorithms and deep learning technology, improves the detection rate of equipment failures, and optimizes the maintenance and operation strategies of equipment through intelligent scheduling, thereby significantly improving the stability and operation efficiency of the distribution network.

[0063] Specific implementation 2: Real-time optimization and self-healing scheduling of distribution networks based on reinforcement learning

[0064] In this implementation, the core task of the system is to achieve intelligent optimization and dispatching of the distribution network through reinforcement learning algorithms based on real-time equipment status evaluation and fault diagnosis results. In order to ensure that the distribution network can automatically perform self-healing dispatching when facing equipment failures or load fluctuations, the system first obtains the current operating status of each device in the distribution network through multimodal data collection, including sensor data, equipment historical operation and maintenance records, and external environmental data. The system not only monitors key parameters such as current, voltage, and temperature of the equipment in real time, but also predicts the probability of equipment failure through historical data analysis.

[0065] After the data collection is completed, the system inputs the real-time collected device status data together with the historical data into the reinforcement learning framework. **Reinforcement Learning (RL)** is a learning method based on the interaction between the agent and the environment. Its basic idea is that the agent obtains reward signals by continuously interacting with the environment, thereby adjusting the behavior strategy and finally finding the optimal decision. In this embodiment, the input of the reinforcement learning algorithm is the current operating status of the distribution network, including load demand, equipment health, environmental factors, etc., and the output is the load scheduling, fault isolation, self-healing strategy and other operational decisions of the distribution network.

[0066] Specifically, the system achieves optimal dispatching of the distribution network by defining the state space, action space, and reward function. The state space contains information such as the health status, power load, and operating status of each device in the distribution network; the action space includes dispatching operations such as load distribution, equipment isolation, and fault self-healing of the distribution network; and the reward function is designed based on factors such as the stability of the distribution network, the balance of power supply, and the health status of the equipment. By continuously interacting with the environment, the intelligent agent can dynamically adjust the load distribution and equipment management strategies of the distribution network in different scenarios, thereby optimizing the operating efficiency and reliability of the distribution network.

[0067] Q-learning is a reinforcement learning algorithm used in this implementation. It is based on the value iteration method and evaluates the expected return of taking a certain action under different states by updating the Q value (state-action value function). The system continuously trains according to the Q-learning algorithm to optimize the decision-making strategy for load scheduling and fault isolation. Specifically, when a failure or load fluctuation occurs in the distribution network, the system automatically selects the optimal load distribution plan or fault isolation strategy based on information such as equipment health status and load distribution, thereby realizing the self-healing function of the distribution network. For example, if a device fails, the system can switch to the backup line in the shortest time, adjust the load distribution, and isolate the faulty device from the network to ensure the stable operation of the distribution network.

[0068] In addition, this implementation also combines the Multi-Armed Bandit algorithm to further optimize the load dispatching strategy of the distribution network through online learning and multiple attempts. In this process, the system continuously adjusts the strategy based on feedback information and gradually improves the effect of the dispatching plan. Ultimately, the system can autonomously optimize the load distribution and fault isolation strategy according to the real-time operating status of the distribution network, thereby greatly improving the operating efficiency and fault self-healing ability of the distribution network.

[0069] Through this implementation method, the distribution network can not only realize intelligent real-time optimization and scheduling, but also respond quickly and take self-healing measures when a fault occurs, thereby improving the reliability of the distribution network and reducing the power outage time and equipment damage risk caused by faults.

[0070] Specific implementation method 3: Intelligent early warning and maintenance decision support system based on multimodal data fusion

[0071] The core goal of this implementation method is to build an intelligent early warning and maintenance decision support system based on multimodal data fusion. The system can provide intelligent fault warning, maintenance decision-making and optimization solutions for the operation of the distribution network by collecting multimodal data of the distribution network in real time and combining deep learning and reinforcement learning technologies. During the implementation process, the system first collects real-time operating data of the distribution network equipment through the sensor network. The data includes status information such as current, voltage, temperature, vibration, etc. At the same time, the health records of the equipment are obtained by analyzing the historical operation and maintenance data of the equipment, including equipment fault records, maintenance records, operating hours, etc. In addition, the system also obtains external environmental data (such as meteorological conditions, load demand, etc.) in real time. These data provide more dimensional information for the equipment status evaluation and optimization of the distribution network.

[0072] To ensure that different types of data can be effectively integrated, the system first synchronizes the time series of these multimodal data to eliminate data inconsistencies caused by delays. The dynamic time warping (DTW) algorithm and timestamp correction method are used to accurately align data from different sources. Then, deep learning models (such as CNN and LSTM) are used to extract features and recognize patterns on the synchronized data to evaluate the real-time health status of the equipment and predict potential failures. By learning from historical operation and maintenance data, the deep learning model can identify the potential failure mode of the equipment and provide a scientific basis for future maintenance of the equipment.

[0073] Based on the equipment status assessment results generated by the deep learning model, the system further generates equipment health reports, fault warnings, and maintenance recommendations. The report content includes not only the current status of the equipment, but also potential fault risk assessments and specific maintenance recommendations. In addition, combined with the operating status of the distribution network, the system can generate optimized load distribution plans, equipment scheduling plans, and fault self-healing strategies. These plans will be transmitted to the distribution network management system to guide operation and maintenance personnel to make scientific decisions and ensure that equipment can be repaired in a timely manner when potential faults occur, thereby avoiding the expansion of faults and ensuring the stable operation of the distribution network.

[0074] Ultimately, through intelligent early warning and maintenance decision support, the system can automatically detect equipment failures during the operation of the distribution network and propose targeted maintenance and optimization plans, thereby improving the operating efficiency of the distribution network, the level of equipment health management and the speed of fault response, and significantly reducing the risk of power outages and equipment damage caused by equipment failures.

[0075] The present invention effectively solves the delay and synchronization problems between multimodal data by combining the timing synchronization processing based on the dynamic time warping (DTW) algorithm with the deep learning model. Through precise data synchronization and multi-dimensional feature fusion, the system can evaluate the status of each device in the distribution network in real time and accurately. Deep learning models (such as the combination of CNN and LSTM) can not only extract the spatial characteristics of the device status, but also capture its timing change trend and predict the future operating status of the device. This mechanism significantly improves the accuracy of equipment health assessment, especially in high-load and complex operating environments. It can identify the potential failure risks of equipment in advance and provide early warnings in time, thereby greatly reducing power outages and equipment damage caused by untimely detection of equipment failures.

[0076] The present invention adopts an adaptive scheduling algorithm based on reinforcement learning (Q-learning), which can realize intelligent optimization scheduling of the distribution network when equipment failure or load fluctuation occurs. In the case of load changes, equipment failures or unstable power supply in the distribution network, the system can autonomously optimize load distribution, fault isolation and self-healing scheduling strategies through the Q-learning algorithm. This intelligent scheduling capability not only improves the operating efficiency of the distribution network, but also minimizes power outages caused by equipment failures or load imbalances. The reinforcement learning algorithm continuously optimizes the strategy to ensure that the distribution network can make the best decision based on real-time data, realize the self-healing function, and quickly restore normal operation, thereby greatly improving the reliability and flexibility of the distribution network.

[0077] The present invention combines deep learning with reinforcement learning to generate an intelligent early warning and maintenance decision support system based on multimodal data. By analyzing the real-time data, historical operation and maintenance data, and external environment data of the equipment, the system can not only realize real-time status monitoring of the equipment, but also predict potential failure modes and risks of equipment operation in advance. The deep learning model identifies and analyzes the equipment failure mode based on historical data, generates accurate fault diagnosis reports, and provides targeted maintenance suggestions for operation and maintenance personnel. The scheduling strategy optimized by the reinforcement learning algorithm can also automatically adjust the load distribution and fault isolation scheme when the equipment fails, so as to achieve rapid response and self-healing functions. Through this mechanism, the operation and maintenance personnel of the distribution network can take preventive measures before the equipment fails, avoiding major failures caused by long-term lack of maintenance, thereby significantly improving the service life of the equipment and the overall stability of the distribution network.

[0078] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0079] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0080] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0081] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0083] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0084] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0085] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of 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. A multimodal data-driven distribution network equipment status assessment and optimization method, characterized in that: The following steps are involved: Step 1: Collect multimodal data from multiple devices and operating environments in the distribution network. Sensor data includes real-time monitoring data of the equipment. Step 2: Perform timing correction and synchronization processing on the collected multimodal data. Through the advanced timing synchronization algorithm, ensure the time alignment between different data sources and eliminate the delay effect. The advanced timing synchronization algorithm is based on dynamic time warping or phase synchronization algorithm to perform timing matching and delay correction between data sources to ensure data synchronization. Step 3: Based on the synchronized data, a multimodal data fusion model is constructed using a deep learning algorithm. The convolutional neural network and the long short-term memory network are combined to evaluate the equipment status in real time, predict potential failure risks, and generate a fault diagnosis report. The deep learning model learns the spatiotemporal features of the equipment status data, extracts key features, and performs intelligent inference. Step 4: Optimize and adjust the equipment status evaluation results and the overall operation status of the distribution network. Use the reinforcement learning algorithm to dynamically adjust the distribution network load distribution, fault isolation and self-healing strategy according to the equipment evaluation results and the network topology structure to achieve intelligent optimization operation of the distribution network. Step 5: Generate equipment maintenance and operation reports based on the evaluation and optimization results, provide specific equipment maintenance recommendations, fault warnings, and distribution network optimization plans, and provide real-time feedback of the optimization plans to the distribution network management system through the intelligent dispatching system to guide equipment operation and maintenance decisions.

2. The multimodal data driven distribution network equipment status assessment and optimization method according to claim 1, characterized in that: The timing correction and synchronization process in step 2 further includes the following sub-steps: Preprocess the collected multimodal data, including denoising, standardization and normalization, to eliminate sensor errors and environmental interference and ensure data quality and consistency; In view of the delay characteristics of different data sources, dynamic timing correction technology based on timestamp is used to match the delay between sensor data, historical operation and maintenance data, and environmental data to ensure accurate alignment of various data on the time axis; Based on the dynamic time warping algorithm, the weighted average method is used to optimize the data processing to improve the accuracy of timing synchronization and ensure that the delay correction results meet the real-time monitoring requirements of the distribution network.

3. The multimodal data driven distribution network equipment status assessment and optimization method according to claim 1, characterized in that: The deep learning model in step 3 further includes: Use convolutional neural networks to extract features from various sensor data and identify the spatial characteristics of multi-dimensional equipment operation status; Use long short-term memory networks to model the time-series changes of equipment status, capture long-term dependencies and instantaneous anomalies during equipment operation, and ensure accurate prediction of equipment operation trends; Combined with autoencoders, unsupervised learning of historical operation and maintenance data is performed to identify potential failure modes of equipment, thereby providing a more accurate basis for equipment fault diagnosis and fault warning.

4. The multimodal data driven distribution network equipment status assessment and optimization method according to claim 1, characterized in that: The Q-learning used in the reinforcement learning algorithm in step 4 further includes: In the process of optimizing the dispatching of the distribution network, the state space, action space and reward function are designed. The state space includes the current load distribution, equipment status and fault information of the distribution network. The action space includes the load dispatching, fault isolation and self-healing operation of the distribution network. The reward function is designed based on system stability, equipment health status and load balance. The dispatching strategy of the distribution network is trained through the Q-learning algorithm, so that the optimal operation plan can be automatically adjusted according to real-time data in different operation scenarios; Combined with the multi-armed bandit algorithm, the load allocation decision of the distribution network is optimized, and the optimization strategy is continuously updated through online learning to achieve real-time optimization of the load scheduling of the distribution network.

5. The multimodal data driven distribution network equipment status assessment and optimization method according to claim 1, characterized in that: The equipment maintenance and operation report in step 5 further includes: Automatically generate equipment health reports based on equipment status assessment and fault diagnosis results, detailing abnormal conditions during equipment operation, fault diagnosis information, and maintenance suggestions; Combined with the overall operation status of the distribution network, specific fault warning and emergency response plans are proposed, and equipment maintenance cycles and recommended operating procedures are provided to ensure that preventive maintenance measures can be taken in time when potential equipment failures occur; Based on the optimization results, detailed optimization suggestions are provided for the dispatching and operation of the distribution network to improve the overall efficiency and stability of the distribution network.

6. The multimodal data driven distribution network equipment status assessment and optimization method according to claim 1, characterized in that: Collecting multimodal data in step 1 further includes: Data collection from various intelligent sensors in the distribution network, real-time monitoring of the operating status of distribution network equipment, and transmission of data to the data center through wireless communication protocols; By collecting the historical operation and maintenance data of the equipment, combined with the equipment's service life, failure frequency and maintenance records, the equipment's health profile is extracted to assess the equipment's life expectancy and failure probability.

7. The multimodal data driven distribution network equipment status assessment and optimization method according to claim 1, characterized in that: The deep learning model in step 3 also includes: Different sub-models are used for evaluation of different equipment types to ensure more accurate status evaluation of each equipment and reduce evaluation errors between different equipment types. A hybrid model formed by combining a multi-layer perceptron and a convolutional neural network can further enhance the ability to identify device status; Use transfer learning methods to migrate the health data model of existing equipment to the evaluation tasks of new or similar equipment, reduce the training time for new equipment, and improve the accuracy of fault diagnosis.

Images