The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, sel...The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self-tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will also be taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.展开更多
随着我国风电产业高速发展,风电机组服役时间延长,故障率和运维成本随之增加。利用人工智能算法对风电大数据进行数据挖掘,实现风电机组的状态监测与故障诊断,对风电产业提质增效具有重要的现实意义,近年来逐渐成为研究热点。文中介绍...随着我国风电产业高速发展,风电机组服役时间延长,故障率和运维成本随之增加。利用人工智能算法对风电大数据进行数据挖掘,实现风电机组的状态监测与故障诊断,对风电产业提质增效具有重要的现实意义,近年来逐渐成为研究热点。文中介绍了风电机组数据采集与监控(Supervisory Control and Data Acquisition, SCADA)系统和振动信号数据的特性,阐述了风电机组状态监测和故障诊断智能算法的框架,归纳总结了相关研究成果,并对风电机组状态监测和故障诊断技术所面临的挑战和发展趋势进行了展望。展开更多
文摘The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self-tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will also be taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.
文摘随着我国风电产业高速发展,风电机组服役时间延长,故障率和运维成本随之增加。利用人工智能算法对风电大数据进行数据挖掘,实现风电机组的状态监测与故障诊断,对风电产业提质增效具有重要的现实意义,近年来逐渐成为研究热点。文中介绍了风电机组数据采集与监控(Supervisory Control and Data Acquisition, SCADA)系统和振动信号数据的特性,阐述了风电机组状态监测和故障诊断智能算法的框架,归纳总结了相关研究成果,并对风电机组状态监测和故障诊断技术所面临的挑战和发展趋势进行了展望。
基金国家自然科学基金(52076013)北京市自然科学基金(3212024)2023年度中韩青年科学家交流计划,the Government of the Republicof Korea(Ministry of Science and ICT),and the National Research Foundation of Korea in 2023。
