基于EDPF-CP系统进行风机主控国产化改造的设计及应用

对1.5 MW风机进行国产化主控系统改造分析,将原来的进口倍福主控系统替换为双引擎结构的EDPF-CP主控系统。对系统的软件、硬件进行了分析,进行了国产化EDPF-CP主控系统的整体替代,保证原有的IO通道及变流器、变桨等重要设备的通讯均正常;在保证与原有SCADA通讯的同时,也建立了一套新的SCADA系统,SCADA系统可通过上位机自由组态,风机数据均可正常地统计、分析、显示,并可正确接收调度的指令。     

基于DOTA的风电场远程监控“概念界面”设计

在大型风电场中,SCADA系统是整个风场风机安全、可靠、经济运行的保证。在满足控制性能要求的前提下,良好的人机界面在提高运行人员的工作效率及积极性方面具有重要的作用。DOTA作为受众最广的竞技游戏,其游戏界面极具吸引力。本文介绍了SCADA系统主要功能、DOTA游戏界面特点及人机界面设计原则,并依据该原则将DOTA界面特点合理地应用到SCADA系统,提出了一种全新风格的风电场监控界面设计理念。     

基于IEC61400-25标准的风电场SCADA系统建模及应用

IEC 61400-25系列标准为风电场的监控系统提供了关键技术要求和建模指南.文中根据IEC61400 25系列标准的定义,给出了SCADA监控系统的结构和软件设计模型,并对软件模型中的风机IED服务器和数据库设计方案进行详细说明.该设计方案成本低、扩展性好、结构清晰,数据检索快速.方便.     

风电场分布式SCADA系统设计与研究

面向湘电风能XE72风力发电机组,提出了一套风电场分布式SCADA系统构建方案.实现了多个用户对风电场风机状态在线实时监测以及对风机设备的远程故障诊断和维护.通过构建多个RTU服务软件,实现对风机的分组监测与管理:通过系统时间同步,实现了风电场各个设备单元之间的时间统一.保证了监测数据的时间一致性.经过实际应用.该方案完全满足风力发电场的现场监测与维护需求,适合于大型风电场的构建和运营.     

贝加莱WTC风电主控系统

本文概述了风力发电设备对控制系统的要求,介绍了以B&R系统为背景的风电解决方案。重点描述了基于APROL(DCS)的风电场监控系统和基于X20PCC及其分布式I/O模块、POWERLINK工业实时以太网和WTC软件包的风机主控系统。其中,对风机单机SCADA系统、风机仿真模型和风电安全系统等也进行了全面的介绍。     

贝加莱开放式结构的WTC风电主控系统

该文概述了风力发电设备对控制系统的要求,介绍了以B＆R系统为背景的风电解决方案。重点描述了基于APROL（DCS）的风电场监控系统和基于X20 PCC及其分布式I／O模块、POWERLINK工业实时以太网和WTC软件包的风机主控系统。其中,对风机单机SCADA系统、风机仿真模型和风电安全系统等也进行了全面的介绍。     

风电场风功率预测系统研究

对风电场发电功率的准确预测,可以有效优化电网调度,使得电网经济运行,同时提高风电场在电力市场中的竞争能力.文中提出了基于先进统计方法的风电功率预测系统,以高精度数值气象预报为基础,搭建完备的数据库系统,利用各种通讯接口采集风电场监控数据.提出并实现了“自适应逻辑网络”(ALN),为风电场建立功率预测算法引擎,可以准确预报风电场未来72小时短期功率负荷,并能针对单台风机、风电场或风机集群进行预测和上报,为风电场管理提供重要辅助手段.现场运行测试验证了系统在风速预测以及针对不同型号风机风功率预测的可行性和有效性.     

风电场风功率预测系统研究

对风电场发电功率的准确预测,可以有效优化电网调度,使得电网经济运行,同时提高风电场在电力市场中的竞争能力。文中提出了基于先进统计方法的风电功率预测系统,以高精度数值气象预报为基础,搭建完备的数据库系统,利用各种通讯接口采集风电场监控数据。提出并实现了"自适应逻辑网络"(ALN),为风电场建立功率预测算法引擎,可以准确预报风电场未来72小时短期功率负荷,并能针对单台风机、风电场或风机集群进行预测和上报,为风电场管理提供重要辅助手段。现场运行测试验证了系统在风速预测以及针对不同型号风机风功率预测的可行性和有效性。     

研华WebAccess组态软件在风力发电风场管理系统中的应用

本文介绍研华WebAccess组态软件在风力发电风场管理系统中的应用。在风电场现场控制级监控站点上,SCADA Node节点数据采集是通过标准的以太网接口直接采集现场的实时数据和参数,即采集各风轮机在线监测点控制器PLC的全部现场数据,如：有功功率、无功功率、效率、风机总数、运行数、停机数、待机、故障、运行等实时数据。     

城市燃气管网SCADA系统设计与应用

SCADA系统在城市燃气管网中应用非常广泛.本文描述了SCADA系统的三个重要部分：调度中心,监控终端站,通信系统.该系统可以完成对城市燃气管网的监控.本文提出了先进完整的现场解决方案,在城市燃气管网中可以得到广泛应用.     

海上风力发电机组远程状态监测系统设计

设计了一种海上风力发电机组远程状态监测系统,系统数据包括SCADA数据、在线状态监测数据、机组内外部视频数据.采用无线通信技术传输上述3种数据,实现了系统的远程访问和显示功能,并给出了远程状态监测系统的软件界面.     

Iterative modeling of wind turbine power curve based on least‐square B‐spline approximation

Power curve plays a vital role in design, operation, control, and condition monitoring of wind turbines. Especially, it provides necessary information for wind farm maintenance decisions, allowing for not only comparison among the same type of wind turbines installed in different places, but also the same one at different time stamps. A standardized power curve iterative modeling procedure of wind turbines is proposed based on actual supervisory control and data acquisition (SCADA) data for performance evaluation in an online manner with guaranteed smoothness. The data are firstly preprocessed for anomaly detection by following normalization correction and then sliced into different wind speed bins. The least‐square B‐spline approximation method is iteratively implemented for power curve construction based on dominant points selected from the bin centroids, and turbulence intensity correction and outlier detection are conducted iteratively for refining identification results. Finally, the test results based on actual operating SCADA data demonstrate better performance in comparison with two counterparts.     

双馈风力发电机状态监测系统软件设计

采用双馈风力发电机状态监测系统可及时发现并跟踪风电机组主要部件的机械和电气故障,有效降低机组的运行维护成本,保证风电机组的安全稳定运行.软件采用模块化设计思想,主要功能包括管理、诊断、监测、查询、数据存储、数据备份等.通过对振动、电流、电压、温度等参量进行分析,该软件实现了诊断发电机定子和转子匝间短路、相间短路、相对地短路、轴承故障的功能.将该系统试用于某风电场1.5 MW双馈发电机组上,结果表明其可用于监测和诊断双馈风力发电机.     

基于聚类的超闭球算法短期风速预测研究

具有较高精度的超短期风速预测有着重要的作用,它对建立和保障并网运行风电场风电功率预测预报系统有着举足轻重的作用.但是,由于风速的影响因素较多,且存在着巨大的波动性、随机性,以及较高的自相关性.这些因素,极大地影响了传统的风速预测方法.因此,探究一种短期风速预测方法是十分必要的,此方法以聚类的小脑超闭球算法为基础,此超闭球方法,对减少数据输入的地址碰撞有着很好的作用,提高了学习速度,另通过模糊聚类对输入数据确定节点数和节点值,提高了学习精度.仿真结果证明基于聚类的小脑超闭球网络相比应用较为成熟的BP神经网络等能很好地预测未来1h风速.     

输入时滞的海上风电机群分布式控制

随着海上风电的大规模发展, 以及电网互联范围的不断扩大, 时滞主要产生在风电场广域测量系统的信号 测量和传输中, 从而导致风电机组系统性能下降甚至不稳定, 严重影响了风电场的正常运行. 因此, 该文基于Hamilton 能量理论, 研究了双馈风电机群的分布式时滞控制问题. 首先, 该文对双馈风电系统进行Hamilton实现, 得到风 电机组PCH–D模型; 然后, 针对单机系统PCH–D模型, 引入Casimir函数设计控制律, 使得风电系统在输入时滞下能 够稳定运行; 进而, 将风电机组网络化, 设计分布式时滞控制策略, 使得整个风电机群在30300 ms的时滞范围内整 体保持稳定. 最后, 通过仿真验证了所提控制策略能有效地解决系统的输入时滞问题, 同时减小了时滞引起的系统 误差, 提高了风电机群的稳定性和控制精度.     

Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines

 It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions. The models are identified using supervisory control and data acquisition (SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.     

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description

This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 normal behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months.The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components.In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.     

分布式机载风电振动信号采集系统设计与应用

针对风电设备分布广泛、振动信号采集困难、监测数据处理要求高等特点,基于分布式风机在线监测的通用架构研发了一种能够安装于风机各关键部位的小型机载分布式风电状态监测装置,就系统设计中遇到的如硬件选型与架构设计、信号采样逻辑控制与处理、风机信号特征选择、上位机通信报文设计等关键问题进行了深入研究,并将该系统应用于实际风场的设备监测与故障诊断环境,验证了该装置的可靠性、有效性与可拓展性.     

基于ZigBee和GPRS的风电场远程监控系统研究

文章设计了一种基于ZigBee无线传感器网络和GPRS技术的风电场远程监控系统,可以对风力发电机组的发电机参数、状态参数、机械参数、环境参数等信息进行现场采集和监控,编制了适合应用于各类风电场的基于ZigBee无线传感器网络和GPRS技术的监控软件,能够很好地完成对风电场监控与管理的功能。