技术领域Technical Field
本发明属于风电场领域，具体涉及一种智慧风电场综合效能评估方法。The present invention belongs to the field of wind farms, and in particular relates to a method for evaluating the comprehensive performance of a smart wind farm.
背景技术Background Art
现有的评估风电场综合效能的方法，它涵盖了风电场效能评价的多个核心维度，包括风资源和选址评估、发电性能、能耗程度以及风电机组状态，并通过一系列数学模型和算法对各项指标进行量化计算和标准化处理；现有技术存在缺点如下“风电技术快速发展，新型风电机组、控制系统、运维策略不断涌现，导致现有评估指标体系和模型参数不再完全适应新技术的特点；同时，行业标准和最佳实践的更新滞后于技术进步”，“在某些环节(如功率曲线评估采用专家打分制)，评估结果受到主观判断的影响，存在一定的主观性风险”。The existing method for evaluating the comprehensive performance of wind farms covers multiple core dimensions of wind farm performance evaluation, including wind resource and site selection evaluation, power generation performance, energy consumption level and wind turbine status, and quantifies and standardizes various indicators through a series of mathematical models and algorithms; the existing technology has the following shortcomings: "With the rapid development of wind power technology, new wind turbines, control systems, and operation and maintenance strategies are constantly emerging, resulting in the existing evaluation index system and model parameters no longer fully adapting to the characteristics of new technologies; at the same time, the update of industry standards and best practices lags behind technological progress", "In some links (such as the power curve evaluation using an expert scoring system), the evaluation results are affected by subjective judgment and there is a certain subjective risk."
发明内容Summary of the invention
本发明的目的在于提供一种智慧风电场综合效能评估方法，以解决上述背景技术中提出的问题。The purpose of the present invention is to provide a method for evaluating the comprehensive performance of a smart wind farm to solve the problems raised in the above-mentioned background technology.
为了解决上述技术问题，本发明提供如下技术方案：In order to solve the above technical problems, the present invention provides the following technical solutions:
智慧风电场综合效能评估方法，包括步骤有：The comprehensive performance evaluation method of a smart wind farm includes the following steps:
步骤1：构建动态适应性评估指标体系，实时监测与更新技术参数，引入机器学习辅助指标优化；Step 1: Build a dynamic adaptive evaluation indicator system, monitor and update technical parameters in real time, and introduce machine learning to assist indicator optimization;
步骤2：客观化与数据驱动的评估过程，标准化功率曲线评估，智能故障诊断与修复时间预测；Step 2: Objective and data-driven evaluation process, standardized power curve evaluation, intelligent fault diagnosis and repair time prediction;
步骤3：融合多源数据与智能算法的综合评估，多源数据整合，深度学习效能评估模型，模型解释与透明化评估；Step 3: Comprehensive evaluation integrating multi-source data and intelligent algorithms, multi-source data integration, deep learning performance evaluation model, model interpretation and transparent evaluation;
步骤4：智慧决策支持与持续优化，效能评估报告自动化生成，关键影响因素分析，改进建议内容，可视化展示。Step 4: Intelligent decision support and continuous optimization, automatic generation of performance evaluation reports, analysis of key influencing factors, improvement suggestions, and visual display.
进一步，实时监测与更新技术参数包括利用智慧风电场的物联网，实时采集新型风电机组、控制系统的运行参数：通信网络包括LoRa、NB-IoT、Zigbee；设置数据采集终端接收传感器数据，实时监测风电机组的各项运行参数，包括叶片角度、发电机转速、输出功率、电流电压、变流器状态、偏航角度、塔筒振动，监控风电场控制系统的关键性能指标，包括控制策略执行情况、系统响应时间、控制指令的准确度、自适应调节效果，将采集到的各类运行参数按照统一的标准进行格式化处理，将风电机组、控制系统、环境参数多源数据进行整合。Furthermore, real-time monitoring and updating of technical parameters include utilizing the Internet of Things of smart wind farms to collect real-time operating parameters of new wind turbines and control systems: communication networks include LoRa, NB-IoT, and Zigbee; setting up data acquisition terminals to receive sensor data, and real-time monitoring of various operating parameters of wind turbines, including blade angle, generator speed, output power, current and voltage, converter status, yaw angle, tower vibration, and monitoring of key performance indicators of wind farm control systems, including control strategy execution, system response time, accuracy of control instructions, and adaptive adjustment effect; formatting the various operating parameters collected according to unified standards, and integrating multi-source data of wind turbines, control systems, and environmental parameters.
进一步，引入机器学习辅助指标优化包括利用机器学习算法对风电场运行数据进行分析，自动识别影响效能的关键因素，动态调整或新增评估指标，新增评估指标包括智能控制增益、自适应故障预测准确性、智能化运维效率，具体包括，数据准备与特征提取，机器学习模型应用与关键因素识别，动态调整与新增评估指标。Furthermore, the introduction of machine learning-assisted indicator optimization includes using machine learning algorithms to analyze wind farm operating data, automatically identifying key factors affecting performance, dynamically adjusting or adding new evaluation indicators. New evaluation indicators include intelligent control gain, adaptive fault prediction accuracy, and intelligent operation and maintenance efficiency. Specifically, they include data preparation and feature extraction, application of machine learning models and identification of key factors, and dynamic adjustment and addition of new evaluation indicators.
进一步，客观化与数据驱动的评估过程包括标准化功率曲线评估：采用数据驱动的方法替代专家打分制，通过收集实际运行数据，构建功率曲线的实际分布模型，与理想功率曲线进行对比分析；利用统计方法量化评估功率曲线的拟合程度，具体包括：数据收集与实际分布模型构建，运用统计学方法对收集到的实际功率数据进行分析，以构建功率曲线的实际分布模型，对数据进行概率密度函数估计，包括使用核密度估计、直方图方法来描绘不同风速下功率输出的概率分布情况，理想功率曲线设定与对比分析，评估过程中，将构建的实际分布模型与理想功率曲线进行细致的对比分析，对比内容不仅包括整体形状的相似度，还包括在各个风速段上实际功率与理想功率的偏差情况，以及在特定风速阈值的实际响应。Furthermore, the objective and data-driven evaluation process includes standardized power curve evaluation: using data-driven methods to replace the expert scoring system, by collecting actual operating data, constructing an actual distribution model of the power curve, and comparing and analyzing it with the ideal power curve; using statistical methods to quantitatively evaluate the degree of fit of the power curve, specifically including: data collection and actual distribution model construction, using statistical methods to analyze the collected actual power data to construct an actual distribution model of the power curve, and estimating the probability density function of the data, including using kernel density estimation and histogram methods to depict the probability distribution of power output under different wind speeds, ideal power curve setting and comparative analysis. During the evaluation process, the constructed actual distribution model is carefully compared and analyzed with the ideal power curve. The comparison content includes not only the similarity of the overall shape, but also the deviation between the actual power and the ideal power in each wind speed range, and the actual response at a specific wind speed threshold.
进一步，智能故障诊断与修复时间预测包括利用智慧风电场的故障诊断系统记录并分析故障事件，结合历史数据和机器学习算法预测平均故障修复时间，具体包括，故障事件记录与诊断，历史数据整合与分析，收集并整理智慧风电场过去一段时间内的所有故障事件记录，形成故障历史数据库；对故障历史数据进行深入统计分析，识别故障发生的规律、趋势以及各种故障之间的关联性；平均故障修复时间预测：选择机器学习算法，根据历史故障数据及其对应的修复时间，训练一个预测MTTR的模型；模型的输入特征包括故障类型、故障严重程度、故障发生时的环境条件、设备使用年限、维修资源可用性，输出则为目标变量MTTR。Furthermore, intelligent fault diagnosis and repair time prediction includes using the fault diagnosis system of the smart wind farm to record and analyze fault events, combining historical data and machine learning algorithms to predict the average fault repair time, specifically including fault event recording and diagnosis, historical data integration and analysis, collecting and organizing all fault event records of the smart wind farm in the past period of time to form a fault history database; conducting in-depth statistical analysis of fault history data to identify the patterns and trends of fault occurrence and the correlation between various faults; average fault repair time prediction: selecting a machine learning algorithm to train a model for predicting MTTR based on historical fault data and its corresponding repair time; the input features of the model include fault type, fault severity, environmental conditions when the fault occurs, equipment service life, and maintenance resource availability, and the output is the target variable MTTR.
进一步，融合多源数据与智能算法的综合评估包括多源数据整合：集成智慧风电场的气象数据、机组状态数据、电网交互数据、运维记录多元信息，形成全面的效能评估数据集；深度学习效能评估模型：训练深度神经网络模型，模型输入为多源数据集，输出为风电场综合效能评分；模型训练过程中，利用已有的风电场效能标签数据进行监督学习，模型解释与透明化评估：利用模型解释展示深度学习模型在做出评估决策时各因素的影响力。Furthermore, the comprehensive evaluation integrating multi-source data and intelligent algorithms includes multi-source data integration: integrating meteorological data, unit status data, grid interaction data, and operation and maintenance record information of smart wind farms to form a comprehensive performance evaluation data set; deep learning performance evaluation model: training a deep neural network model, with the model input being a multi-source data set and the output being a comprehensive wind farm performance score; during the model training process, using the existing wind farm performance label data for supervised learning, model interpretation and transparent evaluation: using model interpretation to demonstrate the influence of various factors of the deep learning model when making evaluation decisions.
有益效果:Beneficial effects:
本申请新的智慧风电场综合效能评估方法通过构建动态适应性评估指标体系、实施客观化与数据驱动的评估过程、融合多源数据与智能算法进行综合评估，以及提供智慧决策支持与持续优化机制，有效解决了现有技术存在的缺点，充分体现了智慧风电场的智能化特点。The new smart wind farm comprehensive performance evaluation method of this application effectively solves the shortcomings of the existing technology by constructing a dynamic adaptive evaluation index system, implementing an objective and data-driven evaluation process, integrating multi-source data and intelligent algorithms for comprehensive evaluation, and providing intelligent decision-making support and continuous optimization mechanisms, and fully reflects the intelligent characteristics of smart wind farms.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本申请智慧风电场综合效能评估方法流程图。FIG1 is a flow chart of the comprehensive performance evaluation method of the smart wind farm of the present application.
具体实施方式DETAILED DESCRIPTION
下面将结合本发明实施例，对本发明实施例中的技术方案进行清楚、完整地描述，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 following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.
针对现有技术存在的缺点，即评估指标体系与新技术发展存在脱节、评估过程中的主观性风险以及未能充分利用智慧风电场的智能化特点，本申请提出智慧风电场综合效能评估方法，如图1所示，包括步骤有：In view of the shortcomings of the existing technology, namely, the disconnection between the evaluation index system and the development of new technologies, the subjective risk in the evaluation process, and the failure to fully utilize the intelligent characteristics of smart wind farms, this application proposes a comprehensive performance evaluation method for smart wind farms, as shown in Figure 1, including the following steps:
步骤1：构建动态适应性评估指标体系；Step 1: Construct a dynamic adaptability evaluation indicator system;
步骤11.实时监测与更新技术参数：利用智慧风电场的物联网(IoT)，实时采集新型风电机组、控制系统的运行参数，运行参数包括功率曲线、控制策略响应、故障率；Step 11. Real-time monitoring and updating of technical parameters: Using the Internet of Things (IoT) of smart wind farms, real-time collection of operating parameters of new wind turbines and control systems, including power curves, control strategy responses, and failure rates;
利用智慧风电场的物联网(IoT)，实时采集新型风电机组、控制系统的运行参数包括：Using the Internet of Things (IoT) of smart wind farms, real-time collection of operating parameters of new wind turbines and control systems includes:
物联网基础设施建设：IoT infrastructure construction:
传感器网络：在新型风电机组及其控制系统的关键部位安装各种传感器，包括转速传感器、温度传感器、振动传感器、压力传感器等，用于实时监测机组运行状态、环境条件以及系统内部工作参数。这些传感器具备高精度、高可靠性、低功耗等特点，能够连续不断地将物理量转化为数字信号。Sensor network: Various sensors are installed at key parts of new wind turbines and their control systems, including speed sensors, temperature sensors, vibration sensors, pressure sensors, etc., to monitor the operating status of the turbines, environmental conditions, and internal working parameters of the system in real time. These sensors have the characteristics of high precision, high reliability, low power consumption, etc., and can continuously convert physical quantities into digital signals.
通信网络：构建覆盖整个风电场的无线或有线通信网络，确保传感器采集的数据能够及时、准确地传输到数据中心或边缘计算节点。采用的技术包括LoRa、NB-IoT、Zigbee等低功耗广域网(LPWAN)技术，或者工业以太网、光纤通信等高速有线通信方式。Communication network: Build a wireless or wired communication network covering the entire wind farm to ensure that the data collected by the sensors can be transmitted to the data center or edge computing node in a timely and accurate manner. The technologies used include low-power wide area network (LPWAN) technologies such as LoRa, NB-IoT, Zigbee, or high-speed wired communication methods such as industrial Ethernet and optical fiber communication.
数据采集与边缘处理：设置数据采集终端(如网关、RTU等)接收传感器数据，并进行初步的数据清洗、预处理、聚合或本地分析。边缘处理可以减轻云端数据处理的压力，实现快速响应和降低网络带宽需求。部分异常检测、预警功能在边缘层完成。Data collection and edge processing: Set up data collection terminals (such as gateways, RTUs, etc.) to receive sensor data and perform preliminary data cleaning, preprocessing, aggregation or local analysis. Edge processing can reduce the pressure of cloud data processing, achieve rapid response and reduce network bandwidth requirements. Some anomaly detection and early warning functions are completed at the edge layer.
实时数据监测：Real-time data monitoring:
风电机组参数监测：实时监测风电机组的各项运行参数，包括叶片角度、发电机转速、输出功率、电流电压、变流器状态、偏航角度、塔筒振动。这些参数反映了机组的工作状态、能源转换效率以及对风资源的利用情况，是评估风电场效能的基础数据。Wind turbine parameter monitoring: Real-time monitoring of various operating parameters of wind turbines, including blade angle, generator speed, output power, current and voltage, converter status, yaw angle, and tower vibration. These parameters reflect the working status of the unit, energy conversion efficiency, and utilization of wind resources, and are basic data for evaluating the performance of wind farms.
控制系统参数监测：监控风电场控制系统的关键性能指标，包括控制策略执行情况、系统响应时间、控制指令的准确度、自适应调节效果。控制系统参数反映了风电场整体调度与管理的智能化水平，以及在不同工况下对风电机组的优化控制能力。Control system parameter monitoring: monitor the key performance indicators of the wind farm control system, including control strategy execution, system response time, control command accuracy, and adaptive adjustment effect. The control system parameters reflect the intelligent level of the overall dispatch and management of the wind farm, as well as the ability to optimize the control of wind turbines under different working conditions.
环境参数监测：借助气象站、风速风向仪等设备，实时获取风电场周边的风速、风向、气温、湿度、气压、太阳辐射等环境数据，这些信息对评估风电场的实际发电潜力及预测未来发电性能至关重要。Environmental parameter monitoring: With the help of weather stations, anemometers and other equipment, real-time environmental data such as wind speed, wind direction, temperature, humidity, air pressure, solar radiation, etc. around the wind farm can be obtained. This information is crucial for evaluating the actual power generation potential of the wind farm and predicting future power generation performance.
数据整合与传输：Data integration and transmission:
数据标准化与融合：将采集到的各类运行参数按照统一的标准进行格式化处理，确保数据的一致性和互操作性。将风电机组、控制系统、环境参数多源数据进行整合，形成完整的风电场实时运行数据集。Data standardization and integration: Format the collected operating parameters according to unified standards to ensure data consistency and interoperability. Integrate multi-source data of wind turbines, control systems, and environmental parameters to form a complete wind farm real-time operation data set.
云平台对接：将经过整合的实时数据通过安全通道上传至智慧风电场的云平台或数据中心，供后续的大数据分析、机器学习模型训练、效能评估等高级应用使用。数据传输过程中应遵循相关数据安全与隐私保护规范，确保数据完整性、保密性和可用性。Cloud platform connection: Upload the integrated real-time data to the cloud platform or data center of the smart wind farm through a secure channel for subsequent advanced applications such as big data analysis, machine learning model training, and performance evaluation. Relevant data security and privacy protection specifications should be followed during data transmission to ensure data integrity, confidentiality, and availability.
步骤12.引入机器学习辅助指标优化：利用机器学习算法(机器学习算法包括决策树、随机森林、深度学习)对风电场运行数据进行分析，自动识别影响效能的关键因素，动态调整或新增评估指标，新增评估指标包括智能控制增益、自适应故障预测准确性、智能化运维效率，以适应智慧风电场的特性；利用机器学习算法对风电场运行数据进行分析，自动识别影响效能的关键因素，动态调整或新增评估指标，新增评估指标包括智能控制增益、自适应故障预测准确性、智能化运维效率具体包括，数据准备与特征提取：Step 12. Introduce machine learning-assisted index optimization: Use machine learning algorithms (machine learning algorithms include decision trees, random forests, and deep learning) to analyze wind farm operation data, automatically identify key factors affecting performance, dynamically adjust or add evaluation indicators, and add new evaluation indicators including intelligent control gain, adaptive fault prediction accuracy, and intelligent operation and maintenance efficiency to adapt to the characteristics of smart wind farms; Use machine learning algorithms to analyze wind farm operation data, automatically identify key factors affecting performance, dynamically adjust or add evaluation indicators, and add new evaluation indicators including intelligent control gain, adaptive fault prediction accuracy, and intelligent operation and maintenance efficiency. Specifically, data preparation and feature extraction:
首先，收集智慧风电场的运行数据，这些数据涵盖新型风电机组的各种技术参数(如功率输出、转速、温度等)、控制系统的工作状态(如控制模式、调节参数等)、故障记录(如故障类型、发生时间、处理情况等)、以及与效能相关的环境因素(如风速、风向、气温等)。通过对这些原始数据进行预处理(如清洗、标准化、归一化等)，提取出对风电场效能有显著影响的特征变量，为后续的机器学习分析做好准备。First, the operation data of the smart wind farm is collected. These data cover various technical parameters of the new wind turbines (such as power output, speed, temperature, etc.), the working status of the control system (such as control mode, adjustment parameters, etc.), fault records (such as fault type, occurrence time, handling status, etc.), and environmental factors related to performance (such as wind speed, wind direction, temperature, etc.). By preprocessing these raw data (such as cleaning, standardization, normalization, etc.), characteristic variables that have a significant impact on the performance of the wind farm are extracted to prepare for subsequent machine learning analysis.
机器学习模型应用与关键因素识别，决策树/随机森林：这类算法以其直观易理解、易于解释的特点适用于风电场效能关键因素的识别。它们通过构建决策树结构或集合多个决策树(随机森林)，依据运行数据中的特征变量对效能进行分类或回归分析。在树状结构中，重要的效能影响因素位于树的较深层次或在多个树中出现频率较高，通过观察节点分裂的条件和特征重要性排序，可以识别出诸如特定风速范围下的功率曲线偏离、特定故障类型的频发、特定控制策略对发电效率的影响等关键因素。Application of machine learning models and identification of key factors, decision trees/random forests: These algorithms are intuitive, easy to understand and explain, and are suitable for identifying key factors in wind farm performance. They construct a decision tree structure or combine multiple decision trees (random forests) to classify or regress performance based on characteristic variables in the operating data. In the tree structure, important performance influencing factors are located at deeper levels of the tree or appear more frequently in multiple trees. By observing the conditions for node splitting and the order of feature importance, key factors such as power curve deviations under specific wind speed ranges, the frequency of specific fault types, and the impact of specific control strategies on power generation efficiency can be identified.
深度学习：如卷积神经网络(CNN)、循环神经网络(RNN)或长短期记忆网络(LSTM)等深度学习模型，由于其强大的非线性建模能力和对复杂数据关系的学习能力，适用于处理风电场运行数据中潜在的高维、时空相关特征。通过训练深度学习模型，可以挖掘出隐藏在大量数据背后的深层次效能影响因素，例如风电机组间相互作用导致的集群效应、风速风向预测精度对智能调度的影响、运维策略的时间序列依赖性。尽管深度学习模型的解释性相对较低，但可以通过后处理方法(如SHAP值、LIME)来解析模型内部工作机制，揭示影响效能的关键因素。Deep learning: Deep learning models such as convolutional neural networks (CNN), recurrent neural networks (RNN) or long short-term memory networks (LSTM) are suitable for processing potential high-dimensional, spatiotemporal correlation features in wind farm operation data due to their powerful nonlinear modeling capabilities and learning capabilities for complex data relationships. By training deep learning models, we can dig out the deep-level performance influencing factors hidden behind a large amount of data, such as the clustering effect caused by the interaction between wind turbines, the impact of wind speed and direction prediction accuracy on intelligent scheduling, and the time series dependency of operation and maintenance strategies. Although the interpretability of deep learning models is relatively low, post-processing methods (such as SHAP values and LIME) can be used to analyze the internal working mechanism of the model and reveal the key factors affecting performance.
动态调整与新增评估指标：Dynamic adjustment and new evaluation indicators:
基于上述机器学习分析得到的关键影响因素，评估指标体系会进行相应的动态调整或新增：Based on the key influencing factors obtained from the above machine learning analysis, the evaluation indicator system will be dynamically adjusted or added accordingly:
智能控制增益：如果机器学习识别出特定的控制策略或智能控制算法显著提升了风电机组的发电效率或减少了能量损失，那么可以引入“智能控制增益”这一指标，用于量化评估这些控制措施在不同运行条件下的效果。Intelligent control gain: If machine learning identifies that a specific control strategy or intelligent control algorithm significantly improves the power generation efficiency of the wind turbine or reduces energy losses, then the indicator "intelligent control gain" can be introduced to quantitatively evaluate the effectiveness of these control measures under different operating conditions.
自适应故障预测准确性：如果故障预测系统的准确性和及时性被确定为影响风电场整体效能的关键因素，那么可以新增“自适应故障预测准确性”指标，衡量故障预警系统的预测成功率、提前报警时间等，以反映故障预防和快速响应能力对风电场稳定运行的贡献。Adaptive fault prediction accuracy: If the accuracy and timeliness of the fault prediction system are identified as key factors affecting the overall performance of a wind farm, a new indicator, “Adaptive fault prediction accuracy”, can be added to measure the prediction success rate, advance alarm time, etc. of the fault warning system to reflect the contribution of fault prevention and rapid response capabilities to the stable operation of the wind farm.
智能化运维效率：如果数据分析显示运维活动(如定期维护、故障检修、部件更换等)的时机选择、资源调配、任务执行效率等对风电场效能有显著影响，那么可以设计“智能化运维效率”指标，结合运维记录数据，评价智慧风电场在智能化运维管理方面的表现，包括故障平均修复时间(MTTR)、计划外停机时间减少比例、运维成本效益比。Intelligent O&M efficiency: If data analysis shows that the timing selection, resource allocation, and task execution efficiency of O&M activities (such as regular maintenance, troubleshooting, and component replacement) have a significant impact on the performance of wind farms, then an "intelligent O&M efficiency" indicator can be designed to evaluate the performance of smart wind farms in intelligent O&M management, combined with O&M record data, including the mean time to repair (MTTR), the reduction in unplanned downtime, and the O&M cost-effectiveness ratio.
步骤2：客观化与数据驱动的评估过程：.Step 2: Objective and data-driven evaluation process:.
步骤21.标准化功率曲线评估：采用数据驱动的方法替代专家打分制，通过收集实际运行数据，构建功率曲线的实际分布模型，与理想功率曲线进行对比分析；利用统计方法(统计方法包括Kolmogorov-Smirnov检验、均方误差)量化评估功率曲线的拟合程度，减少主观判断的影响；Step 21. Standardized power curve evaluation: Use a data-driven approach instead of the expert scoring system. By collecting actual operating data, build an actual distribution model of the power curve and compare and analyze it with the ideal power curve. Use statistical methods (including Kolmogorov-Smirnov test and mean square error) to quantitatively evaluate the degree of fit of the power curve and reduce the influence of subjective judgment.
采用数据驱动的方法替代专家打分制，通过收集实际运行数据，构建功率曲线的实际分布模型，与理想功率曲线进行对比分析；利用统计方法量化评估功率曲线的拟合程度，具体包括：数据收集与实际分布模型构建：A data-driven approach is used to replace the expert scoring system. By collecting actual operating data, an actual distribution model of the power curve is constructed, and compared with the ideal power curve for analysis. Statistical methods are used to quantitatively evaluate the degree of fit of the power curve, including: Data collection and actual distribution model construction:
首先，系统利用智慧风电场的物联网(IoT)，实时、连续地收集新型风电机组在不同风速条件下的实际功率输出数据。这些数据涵盖各种工况，包括正常运行、部分负荷、满负荷及极端气候条件下的表现。通过对海量数据的清洗、整理和归一化处理，形成一个详实、全面且结构化的实际运行数据库。First, the system uses the Internet of Things (IoT) of smart wind farms to continuously collect the actual power output data of new wind turbines under different wind speed conditions in real time. These data cover various operating conditions, including normal operation, partial load, full load and performance under extreme climate conditions. Through the cleaning, sorting and normalization of massive data, a detailed, comprehensive and structured actual operation database is formed.
接下来，运用统计学方法对收集到的实际功率数据进行分析，以构建功率曲线的实际分布模型，对数据进行概率密度函数估计，包括使用核密度估计、直方图方法来描绘不同风速下功率输出的概率分布情况。实际分布模型应能准确反映风电机组在各种风速条件下的实际功率产出特征，包括但不限于平均功率、峰值功率、功率波动范围以及不同风速区间内的功率分布模式。Next, statistical methods are used to analyze the collected actual power data to construct an actual distribution model of the power curve and estimate the probability density function of the data, including the use of kernel density estimation and histogram methods to depict the probability distribution of power output under different wind speeds. The actual distribution model should accurately reflect the actual power output characteristics of wind turbines under various wind speed conditions, including but not limited to average power, peak power, power fluctuation range, and power distribution patterns in different wind speed ranges.
理想功率曲线设定与对比分析：Ideal power curve setting and comparative analysis:
理想功率曲线是指按照风电机组设计标准和制造商提供的性能参数所确定的理论功率输出曲线，它反映了在理想条件下(无机械损耗、控制系统完美响应等)风电机组在不同风速下的预期功率输出。理想功率曲线由制造商提供，或者根据风电机组的额定功率、切入风速、切出风速、功率系数等关键参数计算得出。The ideal power curve refers to the theoretical power output curve determined according to the wind turbine design standards and the performance parameters provided by the manufacturer. It reflects the expected power output of the wind turbine at different wind speeds under ideal conditions (no mechanical loss, perfect response of the control system, etc.). The ideal power curve is provided by the manufacturer or calculated based on key parameters such as the rated power, cut-in wind speed, cut-out wind speed, and power factor of the wind turbine.
评估过程中，将构建的实际分布模型与理想功率曲线进行细致的对比分析，对比内容不仅包括整体形状的相似度，还包括在各个风速段上实际功率与理想功率的偏差情况，以及在特定风速阈值(如切入、切出风速点)的实际响应。这种对比有助于直观揭示风电机组实际运行性能与设计预期之间的差异。During the evaluation process, the actual distribution model constructed is carefully compared and analyzed with the ideal power curve. The comparison includes not only the similarity of the overall shape, but also the deviation between the actual power and the ideal power in each wind speed range, as well as the actual response at specific wind speed thresholds (such as cut-in and cut-out wind speed points). This comparison helps to intuitively reveal the difference between the actual operating performance of the wind turbine and the design expectations.
统计方法量化拟合程度：Statistical methods quantify the goodness of fit:
为了定量评估实际功率曲线与理想功率曲线的拟合程度，可以运用多种统计方法，其中提及的两种具体方法包括：In order to quantitatively evaluate the degree of fit between the actual power curve and the ideal power curve, a variety of statistical methods can be used. Two specific methods mentioned include:
Kolmogorov-Smirnov检验(KS检验)：这是一种非参数检验方法，用于比较一个样本数据分布与某一理论分布(如正态分布、均匀分布或已知的理想功率曲线对应的分布)之间的差异。在功率曲线评估中，KS检验可以用来衡量实际功率数据分布与理想功率曲线所代表的理论分布之间的最大偏离度(KS统计量D)。如果该统计量小于预设的显著性水平对应的临界值，可以认为实际功率曲线与理想功率曲线没有显著差异，即二者拟合较好。Kolmogorov-Smirnov test (KS test): This is a nonparametric test method used to compare the difference between a sample data distribution and a theoretical distribution (such as normal distribution, uniform distribution, or the distribution corresponding to a known ideal power curve). In power curve evaluation, the KS test can be used to measure the maximum deviation between the actual power data distribution and the theoretical distribution represented by the ideal power curve (KS statistic D). If the statistic is less than the critical value corresponding to the preset significance level, it can be considered that there is no significant difference between the actual power curve and the ideal power curve, that is, the two fit well.
均方误差(Mean Squared Error,MSE)：MSE是一种常用的评价回归模型预测精度的指标，同样适用于评估实际功率曲线与理想功率曲线的拟合优度。计算方法是将实际功率数据点与对应风速下的理想功率值作差，然后对所有差值的平方求平均。MSE值越小，说明实际功率曲线与理想曲线的偏差越小，拟合程度越好。Mean Squared Error (MSE): MSE is a commonly used indicator for evaluating the prediction accuracy of regression models. It is also applicable to evaluating the goodness of fit between the actual power curve and the ideal power curve. The calculation method is to make a difference between the actual power data point and the ideal power value at the corresponding wind speed, and then average the squares of all the differences. The smaller the MSE value, the smaller the deviation between the actual power curve and the ideal curve, and the better the fit.
步骤22.智能故障诊断与修复时间预测：利用智慧风电场的故障诊断系统记录并分析故障事件，结合历史数据和机器学习算法预测平均故障修复时间(MTTR)，提高评估的准确性与客观性；Step 22. Intelligent fault diagnosis and repair time prediction: Use the fault diagnosis system of the smart wind farm to record and analyze fault events, combine historical data and machine learning algorithms to predict the mean time to repair (MTTR), and improve the accuracy and objectivity of the assessment;
利用智慧风电场的故障诊断系统记录并分析故障事件，结合历史数据和机器学习算法预测平均故障修复时间(MTTR)具体包括，故障事件记录与诊断：The fault diagnosis system of the smart wind farm is used to record and analyze fault events, and the mean time to repair (MTTR) is predicted by combining historical data and machine learning algorithms. Specifically, fault event recording and diagnosis:
故障事件记录：智慧风电场配备先进的监控系统与物联网设备，能够实时监测各类设备的状态参数，包括温度、振动、电流。一旦检测到异常数据或触发预设的故障阈值，系统会自动记录下故障事件的发生时间、位置(具体风机编号或系统模块)、初始故障症状及初步诊断信息。这些详细的故障记录不仅包括即时报警，还涵盖故障发生前后的相关数据，以便于后续的故障原因分析。Fault event records: Smart wind farms are equipped with advanced monitoring systems and IoT devices that can monitor the status parameters of various equipment in real time, including temperature, vibration, and current. Once abnormal data is detected or the preset fault threshold is triggered, the system will automatically record the time and location of the fault event (specific wind turbine number or system module), initial fault symptoms, and preliminary diagnostic information. These detailed fault records include not only immediate alarms, but also relevant data before and after the fault occurs, to facilitate subsequent fault cause analysis.
故障诊断：故障诊断系统利用内置的专业知识库和智能算法对记录的故障事件进行深入分析。它结合故障树分析、规则推理、模式匹配等方法，快速定位故障源，确定故障类型和严重程度。诊断结果将详细描述故障的具体原因(如机械磨损、电气元件故障、控制系统错误等)以及的连锁影响，为后续的故障处理和维修计划提供依据。Fault diagnosis: The fault diagnosis system uses built-in professional knowledge base and intelligent algorithms to conduct in-depth analysis of recorded fault events. It combines fault tree analysis, rule reasoning, pattern matching and other methods to quickly locate the source of the fault and determine the type and severity of the fault. The diagnosis results will describe in detail the specific cause of the fault (such as mechanical wear, electrical component failure, control system error, etc.) and the chain effect, providing a basis for subsequent fault handling and maintenance planning.
历史数据整合与分析：Historical data integration and analysis:
历史数据整合：收集并整理智慧风电场过去一段时间内的所有故障事件记录，形成故障历史数据库；这些数据应包含故障类型、故障发生时间、故障持续时间、最终修复时间、维修措施、故障部件信息等详细内容。此外，也包括与故障相关的环境条件(如风速、气温、湿度等)、设备使用年限、维护历史等背景信息。Historical data integration: Collect and organize all fault event records of the smart wind farm in the past period of time to form a fault history database; these data should include details such as fault type, fault occurrence time, fault duration, final repair time, maintenance measures, fault component information, etc. In addition, it also includes background information such as environmental conditions related to the fault (such as wind speed, temperature, humidity, etc.), equipment age, maintenance history, etc.
历史数据分析：对故障历史数据进行深入统计分析，识别故障发生的规律、趋势以及各种故障之间的关联性；分析涉及故障频次、故障周期、故障季节性、故障与特定环境条件的相关性。这些分析结果有助于理解风电场设备的整体健康状况，以及哪些故障类型更易发生或需要更长时间来修复。Historical data analysis: Conduct in-depth statistical analysis of historical fault data to identify the patterns and trends of fault occurrence and the correlation between various faults; analyze fault frequency, fault cycle, fault seasonality, and the correlation between faults and specific environmental conditions. These analysis results help understand the overall health of wind farm equipment and which types of faults are more likely to occur or take longer to repair.
平均故障修复时间(MTTR)预测：Mean time to repair (MTTR) prediction:
机器学习模型构建：选择合适的机器学习算法(如决策树、随机森林、支持向量机、神经网络等)，根据历史故障数据及其对应的修复时间，训练一个预测MTTR的模型；模型的输入特征包括故障类型、故障严重程度、故障发生时的环境条件、设备使用年限、维修资源可用性，输出则为目标变量MTTR；Machine learning model construction: Select appropriate machine learning algorithms (such as decision trees, random forests, support vector machines, neural networks, etc.) and train a model to predict MTTR based on historical fault data and their corresponding repair times. The input features of the model include fault type, fault severity, environmental conditions when the fault occurs, equipment age, and maintenance resource availability. The output is the target variable MTTR.
模型训练与验证：使用历史数据集对模型进行训练，通过交叉验证等方法调整模型参数，优化模型性能。确保模型在训练集和独立验证集上都能展现出良好的预测能力，即能准确地估计各类故障的平均修复时间。Model training and validation: Use historical data sets to train the model, adjust model parameters through cross-validation and other methods, and optimize model performance. Ensure that the model can demonstrate good predictive capabilities on both the training set and the independent validation set, that is, it can accurately estimate the mean repair time for various types of faults.
MTTR预测应用：对于新发生的故障事件，利用已训练好的预测模型，根据其特定的故障特征输入模型，得出预期的平均故障修复时间。这个预测结果可以帮助运维团队提前规划资源调度、备件准备以及维修工作流程，从而缩短实际修复时间，减少停机损失，提高风电场的运营效率。MTTR prediction application: For new fault events, the trained prediction model is used to input the model according to its specific fault characteristics to obtain the expected average fault repair time. This prediction result can help the operation and maintenance team plan resource scheduling, spare parts preparation and maintenance workflow in advance, thereby shortening the actual repair time, reducing downtime losses, and improving the operational efficiency of the wind farm.
步骤3：融合多源数据与智能算法的综合评估：Step 3: Comprehensive evaluation of multi-source data and intelligent algorithms:
步骤31.多源数据整合：集成智慧风电场的气象数据、机组状态数据、电网交互数据、运维记录多元信息，形成全面的效能评估数据集；Step 31. Multi-source data integration: Integrate the meteorological data, unit status data, grid interaction data, and operation and maintenance record information of the smart wind farm to form a comprehensive performance evaluation data set;
步骤32.深度学习效能评估模型：训练深度神经网络模型(深度神经网络模型包括卷积神经网络CNN、循环神经网络RNN或长短期记忆网络LSTM)，模型输入为多源数据集，输出为风电场综合效能评分；模型训练过程中，利用已有的风电场效能标签数据(风电场效能标签数据包括基于现有评估体系的历史评分)进行监督学习，确保评估结果与行业标准保持一致，同时能够捕捉复杂非线性关系；Step 32. Deep learning performance evaluation model: train a deep neural network model (deep neural network models include convolutional neural network CNN, recurrent neural network RNN or long short-term memory network LSTM), the model input is a multi-source data set, and the output is a comprehensive wind farm performance score; during the model training process, supervised learning is performed using existing wind farm performance label data (wind farm performance label data includes historical scores based on the existing evaluation system) to ensure that the evaluation results are consistent with industry standards and can capture complex nonlinear relationships;
步骤33.模型解释与透明化评估：利用模型解释(模型解释包括SHAP值、LIME)揭示深度学习模型在做出评估决策时各因素的影响力，增加评估过程的透明度，便于理解和验证评估结果；Step 33. Model explanation and transparent evaluation: Use model explanation (model explanation includes SHAP value and LIME) to reveal the influence of various factors when the deep learning model makes evaluation decisions, increase the transparency of the evaluation process, and facilitate the understanding and verification of evaluation results;
利用模型解释(模型解释包括SHAP值、LIME)揭示深度学习模型在做出评估决策时各因素的影响力，具体包括：模型解释的重要性：Model explanations (including SHAP values and LIME) are used to reveal the influence of various factors when deep learning models make evaluation decisions, including: The importance of model explanations:
深度学习模型(如CNN、RNN、LSTM等)具有强大的非线性建模能力，能够从复杂的多源数据中学习到风电场综合效能的潜在规律。然而，这些模型被视为“黑箱”，其内部决策逻辑和权重分配对于人类用户来说并不直观。这导致评估结果难以理解和接受，尤其是在涉及关键决策时，缺乏透明度导致信任度降低。因此，运用模型解释技术，包括SHAP(SHapleyAdditive exPlanations)和LIME(Local Interpretable Model-AgnosticExplanations)，能够帮助我们理解模型是如何利用输入特征来做出评估决策的，以及每个特征对最终评估分数的具体贡献，从而提高评估过程的可解释性和结果的可信度。Deep learning models (such as CNN, RNN, LSTM, etc.) have powerful nonlinear modeling capabilities and can learn the underlying laws of the comprehensive effectiveness of wind farms from complex multi-source data. However, these models are regarded as "black boxes" and their internal decision logic and weight distribution are not intuitive for human users. This makes the evaluation results difficult to understand and accept, especially when it comes to key decisions, where the lack of transparency leads to reduced trust. Therefore, the use of model interpretation techniques, including SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-AgnosticExplanations), can help us understand how the model uses input features to make evaluation decisions, and the specific contribution of each feature to the final evaluation score, thereby improving the interpretability of the evaluation process and the credibility of the results.
SHAP值解释，SHAP value explanation,
SHAP值是一种基于博弈论的解释方法，它基于Shapley值的概念，计算每个特征对于模型输出变化的边际贡献。SHAP值考虑了特征间的交互效应，为每个样本的每个特征赋予一个数值，表示该特征对该样本评估得分的正向或负向影响程度。主要特点包括：全局解释：通过对所有样本的SHAP值进行汇总分析，可以得到全局层面的特征重要性排序，即哪些特征对风电场综合效能评估的影响最为显著。SHAP value is an explanation method based on game theory. It is based on the concept of Shapley value and calculates the marginal contribution of each feature to the change of model output. SHAP value takes into account the interaction effect between features and assigns a numerical value to each feature of each sample, indicating the degree of positive or negative impact of the feature on the evaluation score of the sample. The main features include: Global explanation: By summarizing and analyzing the SHAP values of all samples, the global level feature importance ranking can be obtained, that is, which features have the most significant impact on the comprehensive performance evaluation of wind farms.
局部解释：针对单个风电场实例，SHAP值能够展示每个特征对其评估得分的具体影响值，以及这些值是如何累加起来得出最终评估分数的。以SHAP值散点图、beeswarmplot或force plot等形式呈现，直观展示各特征对评估结果的推动或抑制作用。Local explanation: For a single wind farm example, the SHAP value can show the specific impact of each feature on its evaluation score, and how these values are accumulated to get the final evaluation score. It is presented in the form of SHAP value scatter plot, beeswarmplot or force plot, which can intuitively show the driving or inhibiting effect of each feature on the evaluation result.
特征交互：SHAP值能够揭示特征间的交互效应，即某些特征组合在一起时对评估结果产生不同于各自单独作用的效果。这对于理解复杂非线性模型中特征如何协同工作至关重要。Feature Interaction: SHAP values can reveal the interaction effects between features, that is, when certain features are combined, they have different effects on the evaluation results than when they are combined individually. This is crucial for understanding how features work together in complex nonlinear models.
LIME解释，LIME(Local Interpretable Model-Agnostic Explanations)是一种针对个体预测结果提供可解释模型的本地解释方法。它通过在样本周围生成一系列扰动样本，并用简单模型(如线性回归或决策树)拟合这些扰动样本的预测结果与原始特征之间的关系，以此来近似复杂模型在该样本上的行为。LIME解释的主要特点包括：LIME explanation, LIME (Local Interpretable Model-Agnostic Explanations) is a local explanation method that provides an interpretable model for individual prediction results. It approximates the behavior of complex models on the sample by generating a series of perturbed samples around the sample and fitting the relationship between the prediction results of these perturbed samples and the original features with a simple model (such as linear regression or decision tree). The main features of LIME explanation include:
局部解释：LIME专注于解释深度学习模型在某个特定风电场实例上的决策过程，生成易于理解的特征重要性排名和可视化解释，包括特征权重条形图。Local Explanations: LIME focuses on explaining the decision-making process of a deep learning model on a specific wind farm instance, generating easy-to-understand feature importance rankings and visual explanations, including feature weight bar charts.
模型无关性：LIME方法适用于任何类型的模型，包括深度学习模型，只需模型能提供对新样本的预测分数。Model independence: The LIME method is applicable to any type of model, including deep learning models, as long as the model can provide prediction scores for new samples.
可解释模型：LIME通过构建一个简单的、易于理解的本地模型(如线性模型)来近似复杂模型在特定样本上的行为。这个本地模型的权重可以直接解读为特征对该样本评估得分的影响程度。Interpretable model: LIME approximates the behavior of a complex model on a specific sample by building a simple, easy-to-understand local model (such as a linear model). The weight of this local model can be directly interpreted as the degree of influence of the feature on the evaluation score of the sample.
应用示例：在智慧风电场综合效能评估中，模型解释(如SHAP值、LIME)的应用包括：Application example: In the comprehensive performance evaluation of smart wind farms, the application of model interpretation (such as SHAP value, LIME) includes:
关键特征识别：通过分析SHAP值或LIME特征重要性排名，可以识别出对风电场综合效能评估最具影响力的几个关键特征，包括故障率、智能控制增益、风速稳定性等，为运维决策提供明确的方向。Identification of key features: By analyzing the SHAP value or LIME feature importance ranking, we can identify several key features that have the greatest impact on the comprehensive performance evaluation of wind farms, including failure rate, intelligent control gain, wind speed stability, etc., providing a clear direction for operation and maintenance decisions.
案例分析：针对某个风电场实例，通过查看其SHAP值或LIME解释图，可以详细解析该风电场为何获得当前的综合效能评分，哪些因素起到了正向推动作用，哪些因素造成了负面影响，以及各因素的具体贡献程度。Case analysis: For a certain wind farm instance, by viewing its SHAP value or LIME explanation diagram, you can analyze in detail why the wind farm has obtained the current comprehensive performance score, which factors have played a positive role, which factors have caused a negative impact, and the specific contribution of each factor.
结果验证与沟通：将模型解释结果纳入效能评估报告中，以可视化形式呈现给管理层，有助于他们理解评估结果的合理性，增强对评估系统的信任，并基于解释结果进行有效的决策讨论和策略制定。Result verification and communication: Incorporating model interpretation results into the performance evaluation report and presenting them to management in a visual form will help them understand the rationality of the evaluation results, enhance their trust in the evaluation system, and conduct effective decision-making discussions and strategy formulation based on the interpretation results.
步骤4：智慧决策支持与持续优化：步骤41.效能评估报告自动化生成：系统自动生成包含详细评估结果、关键影响因素分析、改进建议内容的智慧风电场效能评估报告，通过可视化展示，便于管理层理解和决策。Step 4: Smart decision support and continuous optimization: Step 41. Automatic generation of performance evaluation report: The system automatically generates a smart wind farm performance evaluation report containing detailed evaluation results, key influencing factor analysis, and improvement suggestions. Through visual display, it is easy for management to understand and make decisions.
系统自动生成包含详细评估结果、关键影响因素分析、改进建议内容的智慧风电场效能评估报告，通过可视化展示包括，详细评估结果：The system automatically generates a smart wind farm performance evaluation report that includes detailed evaluation results, analysis of key influencing factors, and improvement suggestions. The detailed evaluation results are displayed visually:
评估得分与等级：报告首先会明确给出本次智慧风电场综合效能的总体评分，采用数值或等级(如优秀、良好、合格、不合格等)表示。评分基于深度学习效能评估模型计算得出，反映了风电场在当前时段内各项关键性能指标的表现。Evaluation score and grade: The report will first clearly give the overall score of the comprehensive performance of the smart wind farm, expressed in numerical values or grades (such as excellent, good, qualified, unqualified, etc.). The score is calculated based on the deep learning performance evaluation model and reflects the performance of various key performance indicators of the wind farm in the current period.
指标详情：报告详细列出各项评估指标的具体数值或得分，包括但不限于：功率曲线吻合度、智能控制增益、自适应故障预测准确性、智能化运维效率、平均故障修复时间(MTTR)。对于每项指标，报告还会提供与历史数据、行业标准或目标值的对比，以凸显变化趋势与相对优劣。Indicator details: The report lists the specific values or scores of each evaluation indicator in detail, including but not limited to: power curve fit, intelligent control gain, adaptive fault prediction accuracy, intelligent operation and maintenance efficiency, and mean time to repair (MTTR). For each indicator, the report also provides a comparison with historical data, industry standards or target values to highlight the trend of change and relative advantages and disadvantages.
时间序列分析：报告包含不同时间段(如日、周、月、季度、年)的效能评估得分变化图，通过折线图、柱状图等形式展示风电场效能随时间的波动情况，帮助管理者了解效能波动的周期性规律、异常变化点及其的原因。Time series analysis: The report includes performance evaluation score change charts for different time periods (such as day, week, month, quarter, and year). It displays the fluctuation of wind farm performance over time in the form of line charts and bar charts, helping managers understand the periodic laws of performance fluctuations, abnormal change points and their causes.
步骤42.关键影响因素分析：Step 42. Analysis of key influencing factors:
因素重要性排序：借助模型解释技术(如SHAP值、LIME)，报告将呈现对风电场综合效能影响最大的若干关键因素及其对总评分的贡献度。这些因素是运行参数(如风速、发电量)、设备状态(如故障率、维护状态)、运维策略(如检修计划执行情况、智能控制策略应用)。Factor importance ranking: With the help of model interpretation techniques (such as SHAP value, LIME), the report will present several key factors that have the greatest impact on the overall performance of the wind farm and their contribution to the total score. These factors are operating parameters (such as wind speed, power generation), equipment status (such as failure rate, maintenance status), and operation and maintenance strategies (such as maintenance plan execution, intelligent control strategy application).
因素影响可视化：通过散点图、热力图、条形图等可视化手段，直观展示关键因素与效能评分之间的关系，包括某因素值的变化如何导致评分上升或下降，或者不同因素组合如何共同影响效能。这有助于管理者理解各因素间的交互效应以及对整体效能的综合影响。Visualization of factor impact: Through visualization methods such as scatter plots, heat maps, and bar charts, the relationship between key factors and performance scores can be intuitively displayed, including how changes in a factor value lead to an increase or decrease in the score, or how different combinations of factors affect performance together. This helps managers understand the interaction effects between factors and the combined impact on overall performance.
步骤43.改进建议内容：Step 43. Improve the content of the suggestion:
针对性改进建议：基于评估结果与影响因素分析，报告将提出针对风电场效能提升的定制化改进建议。这些建议包括：Targeted improvement suggestions: Based on the evaluation results and analysis of influencing factors, the report will put forward customized improvement suggestions for improving the efficiency of wind farms. These suggestions include:
设备维护与升级：如建议对特定风电机组进行预防性维护、更换老化部件，或考虑引入更高效的新型机组。Equipment maintenance and upgrades: For example, recommending preventive maintenance on specific wind turbines, replacing aging components, or considering the introduction of new, more efficient units.
控制策略优化：如调整智能控制策略参数以提高发电效率，或改进自适应故障预测算法以降低误报率。Control strategy optimization: such as adjusting intelligent control strategy parameters to improve power generation efficiency, or improving adaptive fault prediction algorithms to reduce false alarm rates.
运维管理改进：如优化检修计划安排以缩短MTTR，或加强人员培训以提升智能化运维效率。Improvements in operation and maintenance management: such as optimizing maintenance schedules to shorten MTTR, or strengthening personnel training to improve intelligent operation and maintenance efficiency.
外部条件应对：如针对特定气象条件优化风电机组布局，或与电网协调改善电力调度策略。Response to external conditions: such as optimizing the layout of wind turbines for specific meteorological conditions, or coordinating with the power grid to improve power dispatch strategies.
潜在效益预估：对于提出的改进建议，报告还会估算其实施后预期带来的效能提升幅度以及经济效益(如额外发电收益、运维成本节约等)，为决策者权衡投入产出比提供参考。Potential benefit estimation: For the proposed improvement suggestions, the report will also estimate the expected efficiency improvement and economic benefits (such as additional power generation income, operation and maintenance cost savings, etc.) after their implementation, providing a reference for decision makers to weigh the input-output ratio.
步骤44.可视化展示：Step 44. Visualization:
图表丰富：报告将大量使用图表、图形等可视化元素，使复杂的评估数据和分析结果一目了然。不同类型的图表用于展示不同类型的信息，包括时间序列数据、相关性分析、因素贡献度。Rich charts: The report will use a lot of charts, graphs and other visual elements to make complex evaluation data and analysis results clear at a glance. Different types of charts are used to display different types of information, including time series data, correlation analysis, and factor contribution.
交互式界面：高级的评估报告系统具备交互式功能，允许用户通过点击、滑动等方式探索数据细节，包括放大查看特定时间段的效能变化，或筛选查看特定设备或区域的评估结果。Interactive interface: The advanced assessment reporting system has interactive features that allow users to explore data details by clicking and sliding, including zooming in to view performance changes over a specific time period or filtering to view assessment results for a specific device or area.
颜色编码与标签：报告运用色彩鲜明的编码系统和清晰的标签标注，帮助用户快速识别评估结果的好坏、因素的重要程度以及建议的优先级，提升信息解读效率。Color coding and labeling: The report uses a color coding system and clear labeling to help users quickly identify the quality of evaluation results, the importance of factors, and the priority of recommendations, thereby improving the efficiency of information interpretation.
本申请新的智慧风电场综合效能评估方法通过构建动态适应性评估指标体系、实施客观化与数据驱动的评估过程、融合多源数据与智能算法进行综合评估，以及提供智慧决策支持与持续优化机制，有效解决了现有技术存在的缺点，充分体现了智慧风电场的智能化特点。The new smart wind farm comprehensive performance evaluation method of this application effectively solves the shortcomings of the existing technology by constructing a dynamic adaptive evaluation index system, implementing an objective and data-driven evaluation process, integrating multi-source data and intelligent algorithms for comprehensive evaluation, and providing intelligent decision-making support and continuous optimization mechanisms, and fully reflects the intelligent characteristics of smart wind farms.
在需要保护的实施例中，本发明提供一种智慧风电场综合效能评估方法，如图1所示，包括步骤有：In an embodiment to be protected, the present invention provides a method for evaluating the comprehensive performance of a smart wind farm, as shown in FIG1 , comprising the following steps:
步骤1：构建动态适应性评估指标体系，实时监测与更新技术参数，引入机器学习辅助指标优化；Step 1: Build a dynamic adaptive evaluation indicator system, monitor and update technical parameters in real time, and introduce machine learning to assist indicator optimization;
步骤2：客观化与数据驱动的评估过程，标准化功率曲线评估，智能故障诊断与修复时间预测；Step 2: Objective and data-driven evaluation process, standardized power curve evaluation, intelligent fault diagnosis and repair time prediction;
步骤3：融合多源数据与智能算法的综合评估，多源数据整合，深度学习效能评估模型，模型解释与透明化评估；Step 3: Comprehensive evaluation integrating multi-source data and intelligent algorithms, multi-source data integration, deep learning performance evaluation model, model interpretation and transparent evaluation;
步骤4：智慧决策支持与持续优化，效能评估报告自动化生成，关键影响因素分析，改进建议内容，可视化展示。Step 4: Intelligent decision support and continuous optimization, automatic generation of performance evaluation reports, analysis of key influencing factors, improvement suggestions, and visual display.
优选的实施例中，实时监测与更新技术参数包括利用智慧风电场的物联网，实时采集新型风电机组、控制系统的运行参数：通信网络包括LoRa、NB-IoT、Zigbee；设置数据采集终端接收传感器数据，实时监测风电机组的各项运行参数，包括叶片角度、发电机转速、输出功率、电流电压、变流器状态、偏航角度、塔筒振动，监控风电场控制系统的关键性能指标，包括控制策略执行情况、系统响应时间、控制指令的准确度、自适应调节效果，将采集到的各类运行参数按照统一的标准进行格式化处理，将风电机组、控制系统、环境参数多源数据进行整合。In a preferred embodiment, real-time monitoring and updating of technical parameters include utilizing the Internet of Things of a smart wind farm to collect real-time operating parameters of new wind turbines and control systems: communication networks include LoRa, NB-IoT, and Zigbee; setting a data acquisition terminal to receive sensor data, and real-time monitoring of various operating parameters of wind turbines, including blade angle, generator speed, output power, current and voltage, converter status, yaw angle, and tower vibration, monitoring key performance indicators of the wind farm control system, including control strategy execution, system response time, accuracy of control instructions, and adaptive adjustment effect, formatting the various types of collected operating parameters according to unified standards, and integrating multi-source data of wind turbines, control systems, and environmental parameters.
优选的实施例中，引入机器学习辅助指标优化包括利用机器学习算法对风电场运行数据进行分析，自动识别影响效能的关键因素，动态调整或新增评估指标，新增评估指标包括智能控制增益、自适应故障预测准确性、智能化运维效率，具体包括，数据准备与特征提取，机器学习模型应用与关键因素识别，动态调整与新增评估指标。In a preferred embodiment, the introduction of machine learning-assisted indicator optimization includes using machine learning algorithms to analyze wind farm operating data, automatically identifying key factors affecting performance, dynamically adjusting or adding evaluation indicators, and adding new evaluation indicators including intelligent control gain, adaptive fault prediction accuracy, and intelligent operation and maintenance efficiency, specifically including data preparation and feature extraction, application of machine learning models and identification of key factors, and dynamic adjustment and addition of evaluation indicators.
优选的实施例中，客观化与数据驱动的评估过程包括标准化功率曲线评估：采用数据驱动的方法替代专家打分制，通过收集实际运行数据，构建功率曲线的实际分布模型，与理想功率曲线进行对比分析；利用统计方法量化评估功率曲线的拟合程度，具体包括：数据收集与实际分布模型构建，运用统计学方法对收集到的实际功率数据进行分析，以构建功率曲线的实际分布模型，对数据进行概率密度函数估计，包括使用核密度估计、直方图方法来描绘不同风速下功率输出的概率分布情况，理想功率曲线设定与对比分析，评估过程中，将构建的实际分布模型与理想功率曲线进行细致的对比分析，对比内容不仅包括整体形状的相似度，还包括在各个风速段上实际功率与理想功率的偏差情况，以及在特定风速阈值的实际响应。In a preferred embodiment, the objective and data-driven evaluation process includes standardized power curve evaluation: using a data-driven method to replace the expert scoring system, by collecting actual operating data, constructing an actual distribution model of the power curve, and comparing and analyzing it with the ideal power curve; using statistical methods to quantitatively evaluate the degree of fit of the power curve, specifically including: data collection and actual distribution model construction, using statistical methods to analyze the collected actual power data to construct an actual distribution model of the power curve, and estimating the probability density function of the data, including using kernel density estimation and histogram methods to depict the probability distribution of power output at different wind speeds, ideal power curve setting and comparative analysis, during the evaluation process, the constructed actual distribution model is carefully compared and analyzed with the ideal power curve, and the comparison content includes not only the similarity of the overall shape, but also the deviation between the actual power and the ideal power in each wind speed segment, and the actual response at a specific wind speed threshold.
优选的实施例中，智能故障诊断与修复时间预测包括利用智慧风电场的故障诊断系统记录并分析故障事件，结合历史数据和机器学习算法预测平均故障修复时间，具体包括，故障事件记录与诊断，历史数据整合与分析，收集并整理智慧风电场过去一段时间内的所有故障事件记录，形成故障历史数据库；对故障历史数据进行深入统计分析，识别故障发生的规律、趋势以及各种故障之间的关联性；平均故障修复时间预测：选择机器学习算法，根据历史故障数据及其对应的修复时间，训练一个预测MTTR的模型；模型的输入特征包括故障类型、故障严重程度、故障发生时的环境条件、设备使用年限、维修资源可用性，输出则为目标变量MTTR。In a preferred embodiment, intelligent fault diagnosis and repair time prediction includes using the fault diagnosis system of the smart wind farm to record and analyze fault events, combining historical data and machine learning algorithms to predict the average fault repair time, specifically including fault event recording and diagnosis, historical data integration and analysis, collecting and organizing all fault event records of the smart wind farm in the past period of time to form a fault history database; conducting in-depth statistical analysis of fault history data to identify the patterns and trends of fault occurrence and the correlation between various faults; average fault repair time prediction: selecting a machine learning algorithm to train a model for predicting MTTR based on historical fault data and its corresponding repair time; the input features of the model include fault type, fault severity, environmental conditions when the fault occurs, equipment service life, and maintenance resource availability, and the output is the target variable MTTR.
优选的实施例中，融合多源数据与智能算法的综合评估包括多源数据整合：集成智慧风电场的气象数据、机组状态数据、电网交互数据、运维记录多元信息，形成全面的效能评估数据集；深度学习效能评估模型：训练深度神经网络模型，模型输入为多源数据集，输出为风电场综合效能评分；模型训练过程中，利用已有的风电场效能标签数据进行监督学习，模型解释与透明化评估：利用模型解释展示深度学习模型在做出评估决策时各因素的影响力。In a preferred embodiment, the comprehensive evaluation integrating multi-source data and intelligent algorithms includes multi-source data integration: integrating meteorological data, unit status data, power grid interaction data, and operation and maintenance record information of smart wind farms to form a comprehensive performance evaluation data set; deep learning performance evaluation model: training a deep neural network model, the model input is a multi-source data set, and the output is a comprehensive performance score of the wind farm; during the model training process, supervised learning is performed using existing wind farm performance label data, model interpretation and transparent evaluation: using model interpretation to demonstrate the influence of various factors of the deep learning model when making evaluation decisions.
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