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

用于跟踪同步机器参数和初期故障检测的神经网络观察器

• 批准号: 9722844
• 负责人: Ali Keyhani
• 金额: $ 23.95万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-10-01 至 2001-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9722844&HistoricalAwards=false
• 关键词: Neural; Network; Observers; Tracking; Synchronous

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

ECS-9722844 在俄亥俄州-州立大学，亚利桑那州州立大学和亚利桑那州公共服务公司建议进行合作研究的努力，在同步发电机的在线参数识别和早期故障检测技术的发展。该程序将基于神经网络的发展，基于观察器跟踪电机阻尼器电流，参数，磁场绕组定子匝数比在动态干扰和识别的机器参数，机器中性电流和转矩角的签名。 发电机关键指标的在线参数跟踪和特征识别的影响将对在线早期故障检测和励磁绕组退化识别产生重大影响。 例如，亚利桑那州公共服务公司四角单元的强制停机的经济成本约为每天二十万美元。 对初期故障的在线检测将有助于有秩序地关闭发电机进行修理。 预计该项目将影响大型同步发电机的原始维护计划。 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，因为几乎没有新的传输。 增加现有设施的利用率，以应对日益增长的大规模电力传输和第三方接入，这将需要更仔细地研究系统稳定性要求。 机器参数的跟踪将有助于更准确的稳定性研究。 因此，所提出的研究的经济影响是相当重要的，在允许运行的电力系统更接近稳定极限，通过增加传输能力和推迟需要新的建设