技术领域Technical Field
本发明涉及故障风险预警技术领域，具体为一种电力设备故障预警系统。The present invention relates to the technical field of fault risk early warning, and in particular to a power equipment fault early warning system.
背景技术Background Art
电力工业的快速发展，电力设备作为电力系统的核心组成部分，其安全性、可靠性和稳定性对于保障电力供应、防止大规模停电事故具有至关重要的作用，然而，传统的电力设备监测和故障预警方法往往存在诸多不足，难以满足现代电网对智能化、高效化运维的需求。With the rapid development of the electric power industry, power equipment, as a core component of the power system, plays a vital role in ensuring power supply and preventing large-scale power outages due to its safety, reliability and stability. However, traditional power equipment monitoring and fault warning methods often have many shortcomings and cannot meet the needs of modern power grids for intelligent and efficient operation and maintenance.
随着智能电网的快速发展，对电力设备运行状态的实时监控和故障预警提出了更高要求，传统的监控系统往往依赖于云端集中处理数据，存在数据传输延迟高、响应速度慢等问题，难以满足现代电网对实时性和可靠性的需求，因此，开发一种电力设备故障预警系统显得尤为重要。With the rapid development of smart grids, higher requirements are placed on real-time monitoring and fault warning of the operating status of power equipment. Traditional monitoring systems often rely on centralized data processing in the cloud, which has problems such as high data transmission delay and slow response speed. It is difficult to meet the real-time and reliability requirements of modern power grids. Therefore, it is particularly important to develop a power equipment fault warning system.
发明内容Summary of the invention
本发明的目的为了弥补现有技术的不足，提供了一种电力设备故障预警系统，通过集成先进的物联网技术、大数据分析、人工智能算法以及动态优化机制，实现对电力设备的全面、实时、精准监测与故障预警，以显著提升电力系统的运行安全性、可靠性和维护效率。The purpose of the present invention is to make up for the shortcomings of the prior art and provide a power equipment fault warning system, which integrates advanced Internet of Things technology, big data analysis, artificial intelligence algorithms and dynamic optimization mechanisms to achieve comprehensive, real-time and accurate monitoring and fault warning of power equipment, so as to significantly improve the operation safety, reliability and maintenance efficiency of the power system.
本发明为解决上述技术问题，提供如下技术方案：一种电力设备故障预警系统，其中包括物联网传感器网络构建、边缘计算节点部署、实时数据处理与分析和故障预警与决策支持还有动态优化与自适应学习；In order to solve the above technical problems, the present invention provides the following technical solutions: a power equipment fault warning system, which includes the construction of an Internet of Things sensor network, the deployment of edge computing nodes, real-time data processing and analysis, fault warning and decision support, as well as dynamic optimization and adaptive learning;
所述物联网传感器网络构建：在电力设备的关键部位部署高灵敏度传感器，这些传感器通过物联网技术实现与云端和边缘计算节点的无线连接，形成高度实时和分布式的监测网络；The IoT sensor network construction: highly sensitive sensors are deployed at key parts of power equipment. These sensors are wirelessly connected to the cloud and edge computing nodes through IoT technology to form a highly real-time and distributed monitoring network;
所述边缘计算节点部署：在监测网络的关键节点部署边缘计算设备，负责接收来自传感器的原始数据，并进行初步的数据处理和分析，边缘计算减少了数据传输至云端的延迟，使系统能够立即响应设备的异常状态；Deployment of edge computing nodes: Edge computing devices are deployed at key nodes of the monitoring network to receive raw data from sensors and perform preliminary data processing and analysis. Edge computing reduces the delay in data transmission to the cloud, enabling the system to respond immediately to abnormal conditions of the equipment.
所述实时数据处理与分析：边缘计算节点利用内置算法对接收到的数据进行实时处理，包括数据清洗、特征提取和初步故障诊断，这一过程在本地完成，无需等待云端响应，显著提高了系统的响应速度和自主性；Real-time data processing and analysis: The edge computing node uses built-in algorithms to process the received data in real time, including data cleaning, feature extraction and preliminary fault diagnosis. This process is completed locally without waiting for cloud response, which significantly improves the response speed and autonomy of the system;
所述故障预警与决策支持：基于边缘计算节点的处理结果，系统能够实时生成故障预警信息，并通过用户界面，通信接口发送给相关人员和系统，同时，系统还能根据故障类型和严重程度，提供维护建议和自动触发应急响应机制；Fault warning and decision support: Based on the processing results of edge computing nodes, the system can generate fault warning information in real time and send it to relevant personnel and systems through the user interface and communication interface. At the same time, the system can also provide maintenance suggestions and automatically trigger emergency response mechanisms according to the type and severity of the fault;
所述动态优化与自适应学习：系统具备动态优化和自适应学习能力，能够根据历史数据和实时反馈，不断优化数据处理算法和故障诊断模型，提高预警的准确性和可靠性。The dynamic optimization and adaptive learning: The system has dynamic optimization and adaptive learning capabilities, and can continuously optimize data processing algorithms and fault diagnosis models based on historical data and real-time feedback to improve the accuracy and reliability of early warning.
进一步，所述物联网传感器网络构建需要对电力设备进行全面的分析和评估，确定可能出现故障和需要重点监测的关键部位，通过对设备的结构、工作原理、历史故障数据以及专家经验多方面的综合考虑来实现，根据确定的关键部位的监测需求，选择具有相应测量参数和精度的高灵敏度传感器，传感器的性能指标可以用以下公式表示：灵敏度(S)＝Δ输出/Δ输入其中，Δ输出表示传感器输出信号的变化量，Δ输入表示被测量的变化量，将选定的传感器安装在电力设备的关键部位，并确保安装牢固、接触良好，然后对传感器进行调试，使其能够正常工作并输出准确的测量数据，通过物联网技术，将传感器与云端和边缘计算节点进行无线连接，信号传输距离(d)与发射功率(P)、接收灵敏度(S)之间的关系可以用以下公式表示Furthermore, the construction of the IoT sensor network requires a comprehensive analysis and evaluation of the power equipment to determine the key parts that may fail and need to be monitored. This is achieved by comprehensive consideration of the equipment's structure, working principle, historical fault data, and expert experience. According to the monitoring requirements of the determined key parts, a high-sensitivity sensor with corresponding measurement parameters and accuracy is selected. The performance indicators of the sensor can be expressed by the following formula: Sensitivity (S) = Δoutput/Δinput, where Δoutput represents the change in the sensor output signal, and Δinput represents the change in the measured value. The selected sensor is installed at the key part of the power equipment, and ensures that it is firmly installed and has good contact. The sensor is then debugged so that it can work normally and output accurate measurement data. The sensor is wirelessly connected to the cloud and edge computing nodes through IoT technology. The relationship between the signal transmission distance (d) and the transmission power (P) and receiving sensitivity (S) can be expressed by the following formula
更进一步地，所述物联网传感器网络构建传感器采集到的数据通过无线连接实时传输到云端和边缘计算节点，在云端和边缘计算节点，使用相应的算法和软件对数据进行处理和分析，提取有用的信息，根据处理后的数据，对电力设备的运行状态进行实时监测，当监测数据超过设定的阈值时，及时发出预警信号，以便采取相应的维护措施，阈值(T)可以根据设备的规格和运行要求进行设定，定期对整个传感器网络系统进行性能评估和优化，包括传感器的校准、无线连接的稳定性检测、数据处理算法的改进，同时，对出现故障的传感器及时进行更换和维修，以确保系统的长期稳定运行。Furthermore, the data collected by the sensors in the IoT sensor network are transmitted to the cloud and edge computing nodes in real time through wireless connections. In the cloud and edge computing nodes, corresponding algorithms and software are used to process and analyze the data to extract useful information. Based on the processed data, the operating status of the power equipment is monitored in real time. When the monitoring data exceeds the set threshold, an early warning signal is issued in time so that corresponding maintenance measures can be taken. The threshold (T) can be set according to the specifications and operating requirements of the equipment. The performance of the entire sensor network system is regularly evaluated and optimized, including sensor calibration, stability detection of wireless connections, and improvement of data processing algorithms. At the same time, faulty sensors are replaced and repaired in time to ensure the long-term stable operation of the system.
更进一步地，所述边缘计算节点部署根据监测网络的规模和传感器的数据类型、频率，确定边缘计算节点需要处理的数据量和计算复杂度，设每个传感器每秒产生d字节的数据，网络中有n个传感器，则总数据量Dtotal＝n×d字节/秒，明确系统对数据传输和处理的延迟要求，以决定边缘计算的必要性，表达式示例：设可接受的最大延迟为Tmax秒，云端处理延迟为Tcloud秒，则需满足Tedge＜＜Tcloud且Tedge+Ttransmission≤Tmax，其中Ttransmission是数据从边缘到云端的传输延迟，边缘计算设备选型，根据处理需求选择具有足够计算能力和存储空间的边缘计算设备，考虑设备的功耗和成本，选择性价比高的设备。Furthermore, the edge computing node deployment determines the amount of data and computational complexity that the edge computing node needs to process based on the scale of the monitoring network and the data type and frequency of the sensors. Assuming that each sensor generates d bytes of data per second and there are n sensors in the network, the total data volume Dt otal = n × d bytes/second. The system's delay requirements for data transmission and processing are clarified to determine the necessity of edge computing. Expression example: Assuming the maximum acceptable delay is Tmax seconds and the cloud processing delay is Tcloud seconds, it is necessary to satisfy Tedge ＜＜Tcloud and Te dge + Ttransmission ≤Tmax , where Ttransmission is the transmission delay of data from the edge to the cloud. For edge computing device selection, select an edge computing device with sufficient computing power and storage space according to processing requirements. Considering the power consumption and cost of the device, select a cost-effective device.
更进一步地，所述边缘计算节点部署将选定的边缘计算设备安装在确定的关键节点位置，并进行硬件连接和网络配置，确保设备能够正常接入监测网络，并与传感器和其他相关设备建立通信，边缘计算设备接收来自传感器的原始数据，可以采用数据清洗、去噪预处理操作，提高数据质量，采用均值滤波进行去噪，滤波后的数据值(Dfiltered)计算方式为：其中，Di是第i个原始数据值，m是参与计算的原始数据个数。Furthermore, the edge computing node deployment installs the selected edge computing device at the determined key node position, and performs hardware connection and network configuration to ensure that the device can normally access the monitoring network and establish communication with sensors and other related devices. The edge computing device receives the raw data from the sensor, and can use data cleaning and denoising preprocessing operations to improve data quality. Mean filtering is used for denoising, and the filtered data value (Dfiltered ) is calculated as follows: Where Di is the i-th original data value, and m is the number of original data involved in the calculation.
更进一步地，所述边缘计算节点部署对预处理后的数据进行分析和处理，提取关键特征和信息，可以运用统计分析、机器学习算法方法，使用简单的线性回归模型来预测数据趋势，模型为y＝a+bx其中，y是预测值，x是输入变量，a和b是模型参数，根据分析处理结果，做出实时决策，与云端的数据交互将处理后的重要数据和无法本地处理的复杂数据上传至云端，同时接收云端下发的策略和模型更新，数据传输延迟(L)与数据量(D)、传输速度(V)的关系为：定期对边缘计算设备的性能进行监测和评估，根据实际运行情况优化算法和参数，对设备进行维护和升级。Furthermore, the edge computing node deployment analyzes and processes the pre-processed data, extracts key features and information, and can apply statistical analysis and machine learning algorithms to use a simple linear regression model to predict data trends. The model is y=a+bx, where y is the predicted value, x is the input variable, and a and b are model parameters. According to the analysis and processing results, real-time decisions are made, and data interaction with the cloud uploads processed important data and complex data that cannot be processed locally to the cloud, while receiving strategies and model updates issued by the cloud. The relationship between data transmission delay (L) and data volume (D) and transmission speed (V) is: Regularly monitor and evaluate the performance of edge computing devices, optimize algorithms and parameters based on actual operating conditions, and maintain and upgrade equipment.
更进一步地，所述实时数据处理与分析边缘计算节点通过网络接口接收来自传感器的实时数据，去除数据中的噪声、缺失值和异常值，使数据更加准确和可靠，对于含有噪声的数据，可以使用中值滤波进行处理，中值滤波后的值Dmedian)计算方式为：将数据按升序和降序排列，Dmedian为中间的值，从清洗后的数据中提取关键特征，以便后续的分析和诊断，假设提取的特征为数据的均值(μ)和标准差(σ)，计算方式分别为：其中，xi是第i个数据值，n是数据的数量，运用内置的诊断算法对提取的特征进行分析，判断是否存在故障，可以使用阈值判断法，其中为提取的特征值，将处理和分析的结果输出，包括是否存在故障的判断以及相关的特征信息，本地决策与执行根据诊断结果，在本地做出相应的决策，如发出警报、启动备份设备，无需等待云端响应。Furthermore, the real-time data processing and analysis edge computing node receives real-time data from sensors through a network interface, removes noise, missing values and outliers in the data, and makes the data more accurate and reliable. For data containing noise, median filtering can be used for processing. The value after median filtering (Dmedian ) is calculated as follows: the data is arranged in ascending and descending order, and Dmedian is the middle value. Key features are extracted from the cleaned data for subsequent analysis and diagnosis. Assuming that the extracted features are the mean (μ) and standard deviation (σ) of the data, the calculation methods are: Among them,xi is the ith data value, n is the number of data, and the built-in diagnostic algorithm is used to analyze the extracted features to determine whether there is a fault. The threshold judgment method can be used, where xi is the extracted feature value. The results of processing and analysis are output, including the judgment of whether there is a fault and related feature information. Local decision-making and execution make corresponding decisions locally based on the diagnostic results, such as issuing an alarm and starting a backup device, without waiting for a response from the cloud.
更进一步地，所述故障预警与决策支持边缘计算节点对实时数据进行处理和分析后，得出处理结果，根据处理结果中的相关指标与预设的阈值进行比较，判断是否达到故障预警条件，假设故障预警指标为I，预警阈值为Tw，则判断公式为：若I≥Tw，则触发故障预警，生成故障预警信息当达到故障预警条件时，生成包含故障类型、发生位置、严重程度详细信息的预警消息，信息发送通过用户界面和通信接口将预警信息发送给相关人员和系统，信息发送的延迟时间Td)与网络带宽(B)和数据量(D)的关系可以表示为：Furthermore, the fault warning and decision support edge computing node processes and analyzes the real-time data to obtain a processing result, and compares the relevant indicators in the processing result with the preset threshold value to determine whether the fault warning condition is met. Assuming that the fault warning indicator is I and the warning threshold isTw , the judgment formula is: IfI≥Tw , the fault warning is triggered and the fault warning information is generated. When the fault warning condition is met, a warning message containing detailed information on the fault type, location, and severity is generated. The information is sent to the relevant personnel and system through the user interface and the communication interface. The relationship between the delay timeTd of the information transmission and the network bandwidth (B) and the data volume (D) can be expressed as:
更进一步地，所述故障预警与决策支持根据故障类型和严重程度，查询预设的维护建议数据库，生成相应的维护建议，假设严重程度用数值S表示，S的取值范围为[1，5]，1表示轻微，5表示严重，不同的S值对应不同的维护建议M(S)，如果故障严重程度极高，系统自动触发应急响应机制，如紧急停机、启动备用系统，应急响应的触发条件可以表示为：若S≥Te，其中Te为触发应急响应的严重程度阈值，则触发应急响应，记录故障预警与决策支持的整个过程和结果，以便后续的分析和改进。Furthermore, the fault warning and decision support queries a preset maintenance suggestion database according to the fault type and severity, and generates corresponding maintenance suggestions. Assuming that the severity is represented by a numerical value S, the value range of S is [1, 5], 1 represents slight, 5 represents severe, and different S values correspond to different maintenance suggestions M(S). If the fault severity is extremely high, the system automatically triggers an emergency response mechanism, such as emergency shutdown and starting the backup system. The triggering condition of the emergency response can be expressed as: if S≥Te , where Te is the severity threshold for triggering the emergency response, then the emergency response is triggered, and the entire process and results of the fault warning and decision support are recorded for subsequent analysis and improvement.
更进一步地，所述动态优化与自适应学习使用准确率评估指标，对当前的数据处理算法和故障诊断模型进行评估，准确率的计算公式为：其中，TP为真正例，TN为真反例，FP为假正例，FN为假反例，根据数据分析和模型评估的结果，对数据处理算法和故障诊断模型的参数进行调整和优化，假设模型参数为θ，优化目标函数为L(θ)，可以使用梯度下降法进行优化：其中，α是学习率，是目标函数在参数θt处的梯度，将优化后的参数应用到模型中，更新数据处理算法和故障诊断模型，使用新的测试数据对更新后的模型进行验证，监测预警的准确性和可靠性是否提高。Furthermore, the dynamic optimization and adaptive learning use the accuracy evaluation index to evaluate the current data processing algorithm and fault diagnosis model. The accuracy calculation formula is: Among them, TP is a true positive example, TN is a true negative example, FP is a false positive example, and FN is a false negative example. According to the results of data analysis and model evaluation, the parameters of the data processing algorithm and the fault diagnosis model are adjusted and optimized. Assuming that the model parameter is θ and the optimization objective function is L(θ), the gradient descent method can be used for optimization: Among them, α is the learning rate, is the gradient of the objective function at parameter θt . Apply the optimized parameters to the model, update the data processing algorithm and fault diagnosis model, verify the updated model with new test data, and monitor whether the accuracy and reliability of early warning are improved.
与现有技术相比，一种电力设备故障预警系统具备如下有益效果：Compared with the existing technology, a power equipment fault early warning system has the following beneficial effects:
一、本发明通过集成先进的物联网技术和智能分析算法，系统能够实时监测电力设备的运行状态，一旦检测到异常，系统会立即生成预警信息，并通知相关人员，这种即时响应机制大大缩短了故障发现到处理的时间间隔，有效避免了故障扩大对电网运行造成的不良影响，同时，智能分析算法的应用提高了故障诊断的准确性，减少了误报和漏报的情况，确保了运维人员能够精准定位故障点，采取有效措施进行修复。1. By integrating advanced Internet of Things technology and intelligent analysis algorithms, the present invention enables the system to monitor the operating status of power equipment in real time. Once an abnormality is detected, the system will immediately generate early warning information and notify relevant personnel. This instant response mechanism greatly shortens the time interval from fault discovery to processing, and effectively avoids the adverse effects of fault expansion on power grid operation. At the same time, the application of intelligent analysis algorithms improves the accuracy of fault diagnosis, reduces false alarms and missed alarms, and ensures that operation and maintenance personnel can accurately locate the fault point and take effective measures to repair it.
二、本发明通过自动化、智能化的监测与预警，大幅减少了人工干预的需求，运维人员可以根据系统提供的预警信息，有针对性地开展维护工作，避免了盲目巡检和无效劳动，此外，系统还能提供故障分析、维护建议等辅助功能，帮助运维人员更加高效地完成维护工作，进一步降低了运维成本，这种智能化的运维方式不仅提高了工作效率，还减轻了运维人员的劳动强度，提升了整体的工作满意度和幸福感。2. The present invention greatly reduces the need for manual intervention through automated and intelligent monitoring and early warning. Operation and maintenance personnel can carry out maintenance work in a targeted manner based on the early warning information provided by the system, avoiding blind inspections and ineffective work. In addition, the system can also provide auxiliary functions such as fault analysis and maintenance suggestions to help operation and maintenance personnel complete maintenance work more efficiently, further reducing operation and maintenance costs. This intelligent operation and maintenance method not only improves work efficiency, but also reduces the labor intensity of operation and maintenance personnel, and improves overall job satisfaction and happiness.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍。显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
图1一种电力设备故障预警系统操作流程图。Figure 1 is an operation flow chart of a power equipment fault early warning system.
具体实施方式DETAILED DESCRIPTION
下面将对本发明实施例中的技术方案进行清楚、完整的描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
实施例一Embodiment 1
本实施例详细描述了一种电力设备故障预警系统的工作流程，首先，在变压器的关键部位精准部署高灵敏度传感器，温度传感器、油位与油质传感器、振动与声学传感器、绝缘监测传感器和气体传感器，传感器的性能指标可以用以下公式表示：灵敏度(S)＝Δ输出/Δ输入其中，Δ输出表示传感器输出信号的变化量，Δ输入表示被测量的变化量，以全面监测变压器的电气参数，采用低功耗、高可靠性的无线通信技术，传感器数据的实时、稳定传输，同时，设计合理的网络拓扑结构，以提高数据传输效率和覆盖范围。This embodiment describes in detail the working process of a power equipment fault warning system. First, high-sensitivity sensors, temperature sensors, oil level and oil quality sensors, vibration and acoustic sensors, insulation monitoring sensors and gas sensors are accurately deployed at key parts of the transformer. The performance indicators of the sensors can be expressed by the following formula: Sensitivity (S) = Δoutput/Δinput, where Δoutput represents the change in the sensor output signal, and Δinput represents the change in the measured value, so as to comprehensively monitor the electrical parameters of the transformer, adopt low-power and high-reliability wireless communication technology, and transmit sensor data in real time and stably. At the same time, a reasonable network topology is designed to improve data transmission efficiency and coverage.
然后，根据变电站的实际情况和数据处理需求，选择高性能、低功耗的边缘计算设备，确定边缘计算节点需要处理的数据量和计算复杂度，设每个传感器每秒产生d字节的数据，网络中有n个传感器，则总数据量Dtotal＝n×d字节/秒，明确系统对数据传输和处理的延迟要求，以决定边缘计算的必要性，表达式示例：设可接受的最大延迟为Tmax秒，云端处理延迟为Tcloud秒，则需满足Tedge＜＜Tcloud且Tedge+Ttransmission≤Tmax，其中Ttransmission是数据从边缘到云端的传输延迟，考虑设备的扩展性、可靠性和可维护性，确保长期稳定运行，在边缘计算节点上部署轻量级的操作系统和数据处理软件，实现数据的快速采集、处理和分析，同时，集成机器学习和深度学习算法库，提升故障识别的准确性和效率，利用边缘计算节点的计算能力，实现初步的数据处理和故障识别，通过本地化处理，减少数据传输延迟和带宽消耗，提高系统的实时性和响应速度。Then, according to the actual situation of the substation and the data processing requirements, high-performance, low-power edge computing devices are selected, and the amount of data and computational complexity that the edge computing nodes need to process are determined. Assuming that each sensor generates d bytes of data per second and there are n sensors in the network, the total data volumeDtotal = n×d bytes/second. The system's delay requirements for data transmission and processing are clarified to determine the necessity of edge computing. Expression example: Assuming that the maximum acceptable delay isTmax seconds and the cloud processing delay isTcloud seconds, it is necessary to satisfyTedge ＜＜Tcloud andTedge +Ttransmission≤Tmax , whereTtransmission is the transmission delay of data from the edge to the cloud. Considering the scalability, reliability, and maintainability of the equipment to ensure long-term stable operation, lightweight operating systems and data processing software are deployed on edge computing nodes to achieve rapid data collection, processing, and analysis. At the same time, machine learning and deep learning algorithm libraries are integrated to improve the accuracy and efficiency of fault identification. The computing power of edge computing nodes is used to achieve preliminary data processing and fault identification. Through localized processing, data transmission delay and bandwidth consumption are reduced, and the real-time and response speed of the system are improved.
接着，对接收到的原始数据进行清洗、去噪、滤波预处理操作，提高数据质量，同时，对数据进行压缩和编码，减少存储空间占用和传输成本，假设原始数据为Doriginal，去噪后的数据为Ddenoised，去噪操作可以表示为Ddenooised＝f(Doriginal)，其中f表示去噪函数，利用机器学习和深度学习算法，从预处理后的数据中提取关键特征，并进行分类和识别，根据历史故障数据和专家知识库，建立故障模式库和诊断规则库，提高故障识别的准确性和可靠性，对变压器状态进行实时诊断，生成初步诊断结果和故障预警信息，根据诊断结果和故障类型，提供具体的维护建议和触发相应的应急响应机制，同时，将诊断结果和预警信息实时反馈给运维人员和远程监控中心。Next, the received raw data is cleaned, denoised, and filtered to improve data quality. At the same time, the data is compressed and encoded to reduce storage space and transmission costs. Assuming that the original data is Doriginal and the denoised data is Ddenoised , the denoising operation can be expressed as Ddenooised = f(Doriginal ), where f represents the denoising function. Machine learning and deep learning algorithms are used to extract key features from the preprocessed data, and classify and identify them. Based on historical fault data and expert knowledge base, a fault mode library and a diagnostic rule library are established to improve the accuracy and reliability of fault identification. The transformer status is diagnosed in real time, and preliminary diagnostic results and fault warning information are generated. Based on the diagnostic results and fault types, specific maintenance suggestions are provided and the corresponding emergency response mechanism is triggered. At the same time, the diagnostic results and warning information are fed back to the operation and maintenance personnel and the remote monitoring center in real time.
接着，根据故障紧急程度和潜在影响，设置不同级别的预警阈值和响应策略，采用颜色编码和声光报警方式，直观展示变压器健康状态和预警信息，判断是否达到故障预警条件，故障预警指标为I，预警阈值为Tw，则判断公式为：若I≥Tw，则触发故障预警，生成故障预警信息当达到故障预警条件，信息发送通过用户界面和通信接口将预警信息发送给相关人员和系统，设计直观、易用的用户界面和交互方式，方便运维人员查看变压器实时状态、历史趋势、故障预警信息，提供灵活的报警通知方式，确保运维人员能够及时接收预警信息，建立完善的应急响应机制，包括应急预案制定、应急演练、应急资源调配，一旦接收到故障预警信息，立即启动应急预案，采取相应措施减少故障对电网运行的影响，同时，与远程监控中心建立联动机制，实现故障信息的快速上报和远程技术支持。Next, according to the urgency and potential impact of the fault, different levels of warning thresholds and response strategies are set. Color coding and sound and light alarm methods are used to intuitively display the health status and warning information of the transformer to determine whether the fault warning conditions are met. The fault warning index is I, and the warning threshold isTw . The judgment formula is: IfI≥Tw , the fault warning is triggered and the fault warning information is generated. When the fault warning conditions are met, the information is sent to the relevant personnel and systems through the user interface and communication interface. An intuitive and easy-to-use user interface and interaction method are designed to facilitate operation and maintenance personnel to view the real-time status, historical trends, and fault warning information of the transformer. Flexible alarm notification methods are provided to ensure that operation and maintenance personnel can receive warning information in a timely manner. A complete emergency response mechanism is established, including emergency plan formulation, emergency drills, and emergency resource allocation. Once the fault warning information is received, the emergency plan is immediately activated and corresponding measures are taken to reduce the impact of the fault on the power grid operation. At the same time, a linkage mechanism is established with the remote monitoring center to achieve rapid reporting of fault information and remote technical support.
最后，加强系统安全防护措施，确保系统免受网络攻击和数据泄露安全威胁，定期对系统数据进行备份和恢复测试，确保数据的安全性和可恢复性，在发生故障和意外情况时，能够迅速恢复系统正常运行和数据完整性，建立完善的运维管理制度和流程，包括设备巡检、维护记录、故障排查，定期对系统进行巡检和维护保养工作，确保系统长期稳定运行和高效运行，同时，加强对运维人员的培训和管理，提高其专业技能和应急处理能力。Finally, strengthen system security protection measures to ensure that the system is protected from security threats such as cyber attacks and data leaks. Regularly back up and restore system data to ensure data security and recoverability. In the event of failures and accidents, the system can be quickly restored to normal operation and data integrity. Establish a sound operation and maintenance management system and process, including equipment inspections, maintenance records, and troubleshooting. Regularly inspect and maintain the system to ensure long-term stable and efficient operation of the system. At the same time, strengthen the training and management of operation and maintenance personnel to improve their professional skills and emergency response capabilities.
实施例二Embodiment 2
本实施例在实施例一的基础上进一步描述了一种电力设备故障预警系统的工作流程，首先，选择高灵敏度的振动传感器和温度传感器，振动传感器能够捕捉到轴承运行过程中微小的振动变化，而温度传感器则用于监测轴承工作温度，两者结合能够全面反映轴承的健康状态，将振动传感器安装在发电机轴承座的适当位置，确保能够准确捕捉到轴承的振动信号，温度传感器则紧贴轴承外壳安装，以获取最准确的温度数据，利用LoRa、NB-IoT低功耗广域网技术，实现传感器与云端及边缘计算节点的无线连接，确保数据传输的稳定性和实时性。This embodiment further describes the working process of a power equipment fault warning system based on the first embodiment. First, a highly sensitive vibration sensor and temperature sensor are selected. The vibration sensor can capture tiny vibration changes during the operation of the bearing, and the temperature sensor is used to monitor the operating temperature of the bearing. The combination of the two can fully reflect the health status of the bearing. The vibration sensor is installed at an appropriate position on the generator bearing seat to ensure that the vibration signal of the bearing can be accurately captured. The temperature sensor is installed close to the bearing housing to obtain the most accurate temperature data. The LoRa and NB-IoT low-power wide area network technologies are used to realize wireless connection between the sensor and the cloud and edge computing nodes to ensure the stability and real-time performance of data transmission.
然后，在风电场控制室部署高性能的边缘计算服务器，该服务器具备强大的数据处理能力和存储空间，能够满足实时数据处理和故障预警的需求，边缘计算服务器配备高速网络接口和多核处理器，以及足够的存储空间，确保数据处理的高效性和稳定性，在边缘计算服务器上安装定制化的数据处理和分析软件，包括信号处理算法、故障诊断模型和应急响应机制。Then, a high-performance edge computing server is deployed in the wind farm control room. The server has powerful data processing capabilities and storage space, which can meet the needs of real-time data processing and fault warning. The edge computing server is equipped with a high-speed network interface and a multi-core processor, as well as sufficient storage space to ensure the efficiency and stability of data processing. Customized data processing and analysis software is installed on the edge computing server, including signal processing algorithms, fault diagnosis models and emergency response mechanisms.
接着，使用边缘计算节点采用FFT先进信号处理技术，对振动信号进行频谱分析，对于振动信号x(n)，其离散傅里叶变换(DFT)可以表示为：快速傅里叶变换(FFT)是一种快速计算DFT的算法，提取出反映轴承状态的关键特征值，同时监测轴承温度数据，与振动信号进行综合分析，判断轴承是否存在过热、磨损异常情况，基于提取的特征值和预设的健康评估模型，对轴承的健康状态进行实时评估，识别出潜在的故障模式。Next, the edge computing node is used to adopt FFT advanced signal processing technology to perform spectrum analysis on the vibration signal. For the vibration signal x(n), its discrete Fourier transform (DFT) can be expressed as: Fast Fourier Transform (FFT) is an algorithm that quickly calculates DFT, extracts key eigenvalues that reflect the bearing status, monitors the bearing temperature data at the same time, and conducts a comprehensive analysis with the vibration signal to determine whether the bearing is overheating or abnormally worn. Based on the extracted eigenvalues and the preset health assessment model, the health status of the bearing is evaluated in real time to identify potential failure modes.
当轴承健康状态评估结果异常时，系统自动生成故障预警信息，包括故障类型、位置、严重程度及初步诊断建议，通过用户界面和通信接口，将预警信息及时发送给风电场的运维团队和相关管理人员，根据故障类型和严重程度，系统提供详细的维护建议和自动触发应急响应机制，远程调整发电机负荷、启动备用发电机和发送停机检修指令。When the bearing health status assessment results are abnormal, the system automatically generates fault warning information, including fault type, location, severity and preliminary diagnostic suggestions, and sends the warning information to the wind farm's operation and maintenance team and relevant managers through the user interface and communication interface. According to the fault type and severity, the system provides detailed maintenance suggestions and automatically triggers the emergency response mechanism to remotely adjust the generator load, start the backup generator and send shutdown maintenance instructions.
最后，系统利用机器学习算法对历史故障数据进行持续学习和优化，假设使用机器学习算法来训练轴承故障诊断模型，损失函数为L(θ)，其中θ是模型的参数，通过梯度下降，优化算法来更新参数，公式为：其中，α是学习率，是损失函数在参数θt处的梯度，不断提升轴承故障诊断模型的准确性和可靠性，定期对边缘计算节点的性能和数据处理算法进行评估，根据评估结果对系统进行调优和升级，根据风电场实际运行条件和环境变化，自动调整传感器参数、数据处理算法和故障诊断模型，以适应不同条件下的故障预警需求。Finally, the system uses machine learning algorithms to continuously learn and optimize historical fault data. Assuming that a machine learning algorithm is used to train the bearing fault diagnosis model, the loss function is L(θ), where θ is the parameter of the model. The parameters are updated through the gradient descent optimization algorithm. The formula is: Among them, α is the learning rate, It is the gradient of the loss function at the parameterθt . It continuously improves the accuracy and reliability of the bearing fault diagnosis model, regularly evaluates the performance of edge computing nodes and data processing algorithms, optimizes and upgrades the system according to the evaluation results, and automatically adjusts sensor parameters, data processing algorithms and fault diagnosis models according to the actual operating conditions and environmental changes of the wind farm to adapt to the fault warning needs under different conditions.
对于本领域技术人员而言，显然本发明不限于上述示范性实施例的细节，而且在不背离本发明的精神或基本特征的情况下，能够以其他的具体形式实现本发明。因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本发明的范围由所附权利要求而不是上述说明限定，因此旨在将落在权利要求的等同要件的含义和范围内的所有变化囊括在本发明内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.
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| CN202411032791.6ACN118917834A (en) | 2024-07-30 | 2024-07-30 | Power equipment fault early warning system |
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| CN202411032791.6ACN118917834A (en) | 2024-07-30 | 2024-07-30 | Power equipment fault early warning system |
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| Application Number | Title | Priority Date | Filing Date |
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| CN202411032791.6APendingCN118917834A (en) | 2024-07-30 | 2024-07-30 | Power equipment fault early warning system |
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