SBIR Phase I: Intelligent Monitoring and Control of Revenue-Critical PV Power Plants
SBIR Phase I: Intelligent Monitoring and Control of Revenue-Critical PV Power Plants
批准号:
1014404
负责人:
Shahar Ben-Menahem
金额:
$14.98万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-07-01 至 2010-12-31
中文摘要
这个小型企业创新研究第一阶段项目旨在开发新的方法和算法,用于分析和控制光伏(PV)系统中的退化。目前,光伏阵列普遍存在性能不佳的情况,而且往往没有被发现。虽然在太阳能电池装置的设计阶段使用了许多现场评估和分析工具,但目前缺乏在线监测和诊断工具来检测和评估性能不佳以及可以提高性能的相关控制算法。表现不佳的光伏电池可能是由一系列相互关联的异常造成的,包括阴影、灰尘堆积、热点和组件故障。为了提高性能,可以使用智能控制来通过在模块或模块串级操作点的激励来改善功率输出劣化。本建议的目的是开发并在模拟中测试混合MAP-MPPT估计和控制算法,该算法能够智能地调节运行点,以最大限度地提高该项目的更广泛的影响/商业潜力,包括增强大规模光伏阵列的性能,以及更好地估计商业和住宅光伏系统的能源回收时间,从而在全球范围内进一步部署光伏。领先的收入关键型光伏发电厂所有者-运营商经常利用专用运营中心来监控计算机屏幕的墙壁,有时还会通过逆变器触发警报机制。通常情况下,使用特别方法,决策主要基于操作员的经验和直觉。尽管一些网站使用基于网络的商业监控服务,使用工业级传感器,但这一过程远远不是严格的,而且只取得了参差不齐的成功。此外,智能控制操作点的不足还没有得到充分发展。建议的解决方案与监测光伏电站状态的能力相结合,将通过智能控制方法纠正故障,从而在大规模光伏阵列降级的情况下实现最佳电力生产。潜在客户是收入关键的太阳能发电站,包括:购电协议(PPA)承包商和承租人主机。
英文摘要
This Small Business Innovation Research Phase I project seeks to develop new methods and algorithms for the analysis and control of degradations in Photovoltaic (PV) systems. There currently exists widespread, and often undetected, underperformance in PV arrays. Although there are numerous site evaluation and analysis tools used in the design phase of solar cell installations, there is currently a lack of on-line monitoring and diagnostic tools to detect and estimate underperformance and associated control algorithms that can improve performance. Underperforming PV cells can result from a host of inter-related abnormalities including shading, dust build up, hot spots and component failures. To improve performance intelligent control can be used to ameliorate power output degradation via actuation at the module- or module-string-level operating points. The purpose of this proposal is to develop, and test in simulation, a hybrid MAP-MPPT estimation and control algorithm, capable of intelligently modulating the operating point to maximize The broader impact/commercial potential of this project include the enhancement of performance of large scale PV arrays and the ability to better estimate the energy payback time for commercial and residential PV systems enabling further deployment of PV worldwide. Leading revenue-critical, PV power plant owner-operators often utilize dedicated operation centers to monitor walls of computer screens, sometimes with alarm mechanisms triggered by inverters. It is often the case that ad-hoc methods are utilized and decisions are based primarily on operator experience and intuition. Although some sites utilize commercial web-based monitoring services, with industrial grade sensors, this process is far from rigorous, and has resulted in only spotty success. Furthermore, the lack of intelligently controlling the operating point has not yet been fully developed. The proposed solution, used in conjunction with the ability to monitor the state of the PV plant, will correct faults via intelligent control methods enabling optimal power production under degradations for large scale PV arrays. Potential customers are revenue critical solar power sites including: Power Purchase Agreement (PPA) contractors, and lease-holder hosts.
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