Neural Network Observers for Tracking Synchronous Machine Parameters and Incipient Failure Detection

俄亥俄州立大学、亚利桑那州立大学和亚利桑那公共服务公司提议进行一项合作研究，以开发同步发电机在线参数识别和早期故障检测技术。该程序将基于基于神经网络的观测器的发展来跟踪机器阻尼器电流，参数，在动态扰动中磁场绕组与定子匝数比，并识别机器参数，机器中性电流和转矩角的特征。发电机关键指标的在线参数跟踪和特征识别的影响将对现场绕组退化的在线早期故障检测和识别产生重要影响。例如，亚利桑那州公共服务公司四角单元被迫停电的经济成本约为每天20万美元。早期故障的在线检测将有利于有序停机检修。预计该项目将影响大型同步发电机原有的维护计划。此外，在未来几十年，美国的电力系统将面临大容量电力传输能力的瓶颈，因为几乎没有新的输电系统。现有设施的利用率不断提高，以应对日益增长的大规模电力传输和第三方接入，这将需要更密切地关注系统稳定性要求。机器参数的跟踪将有助于更准确的稳定性研究。因此，拟议研究的经济影响是相当显著的，通过增加输电能力和推迟新建设的需要，允许更接近稳定极限的电力系统运行

ECS-9722844 Keyhani The Ohio - State University, Arizona State University and Arizona Public Service Company propose to undertake a collaborative research effort in development of technology for on-line parameter identification and incipient failure detection of synchronous generators. The procedure will be based on the development of neural network based observers to track the machine damper currents, parameters, field winding to-stator turns ratio in dynamic disturbances and identify the signatures of the machine parameters, the machine neutral current and torque angle. The impact of on-line parameter tracking and signature identification of the generator key indicators will have significant impact on-line incipient failure detection and identification of field winding degradation. For example, the economic cost of a forced outage of The Arizona Public Service company Four-Corner unit is approximately two hundred thousand dollars a day. The on-line detection of incipient failure will facilitate the orderly shutdown of generators for repair. It is expected that the project will impact the original maintenance scheduling, of large synchronous generators. Furthermore, in the next decades, the power systems of the United States will face bottlenecks of bulk power transfer capabilities because of the virtual absence of new transmission. The increased utilization of existing facilities to cop with growing magnitude of bulk power transfers and third-party access will require a closer look at system stability requirements. The tracking of the machine parameters will facilitate more accurate stability studies. Therefore, the economic impact of the proposed research is quite significant in permitting operation of power systems much closer to the stability limit by increasing the transfer capabilities and postponing the need for new construction