Adaptive, Reconfigurable, Self-Healing Solar Arrays

自适应、可重构、自愈太阳能电池阵列

• 批准号: 0901439
• 负责人: Bradley Lehman
• 金额: $ 29.71万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0901439&HistoricalAwards=false
• 关键词: Adaptive; Reconfigurable; Self; Healing; Solar

GOALI: Adaptive, Reconfigurable, Self-Healing Solar ArraysThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This GOALI project proposes a fundamentally new approach to solar photovoltaic (PV) array operation. We propose the ability for a solar array to adaptively reconfigure its internal connections to reduce the detrimental effects of shadows or damaged solar cells on the solar panels. A switching matrix connects a solar adaptive bank to a fixed part of a solar PV array, according to a model based control algorithm that increases the power output of the solar PV array. Control algorithms are implemented in real-time. Neural networks are used to learn shadow patterns, such as trees on rooftop installations, to help adjust the solar cell reconfigurations. An experimental reconfiguration PV system with resistive load is proposed to verify the new approach to reconfigurations. Finally, a fundamentally new solar array structure is proposed that is able to merge power electronic converters within solar arrays, creating new classifications of renewable energy power systems.Intellectual MeritThis project includes several transformative concepts in scientific multi-disciplinary research advancements, all of which represent fundamentally new research approaches in renewable energy:1) The notion of real-time reconfigurable, self-healing adaptive solar arrays will be introduced and developed and shown to improve solar PV array power output for several important applications. Current state-of-the-art utilizes fixed connection solar arrays only.2) New neural network learning models will be proposed to model shading factors and to learn/classify types of shadows and solar cell degradations. These can be used either for reconfiguration or for alarms to be sent to the user of the solar PV array.3) New synergisms are proposed that are able to merge features of switching power supplies with solar arrays to create fundamentally new classifications of renewable energy systems.Broader ImpactThis project will benefit society by developing methods to extract more power production out of an inexhaustible, environmentally safe renewable energy. The solar array reconfiguration schemes will aid to mitigate hot-spot effects within the solar array, which are known to reduce life-expectancy of the arrays. Further, since reconfiguration of solar arrays allows for improved power extraction of solar arrays when they are shaded, it is possible to consider installations of solar PV systems in locations that are usually ignored.To ensure high impact of the research results, formal technology transfer partnerships have been formed with two industrial companies. Specifically, joint university/industry lab experiments (at industry), solar array customer data from industry, and joint graduate student advising help ensure that the problems being studied are relevant and important. These relationships also permit the simulation, modeling and design recommendations from this proposal to be broadly disseminated to industrial solar energy applications. The results of the project will further be disseminated as journal publications, conference presentations, and possible patents.The project plans to involve students from under-represented groups and will build solar energy demonstrations for existing K-12 existing programs at Northeastern University. Specifically, the PI will perform solar energy demonstrations in Northeastern University Engineering Open Houses and at a local Middle School Science Night in the Boston area. A new multi-disciplinary renewable energy course will also be designed, to be taken as an elective by engineering undergraduates.

GOALI：自适应、可重构、自愈太阳能阵列该奖项根据 2009 年美国复苏和再投资法案（公法 111-5）提供资助。该 GOALI 项目提出了一种全新的太阳能光伏 (PV) 阵列运行方法。我们提出太阳能电池阵列能够自适应地重新配置其内部连接，以减少阴影或损坏的太阳能电池对太阳能电池板的有害影响。根据基于模型的控制算法，开关矩阵将太阳能自适应组连接到太阳能光伏阵列的固定部分，从而增加太阳能光伏阵列的功率输出。控制算法是实时实现的。神经网络用于学习阴影模式，例如屋顶装置上的树木，以帮助调整太阳能电池的重新配置。提出了一种带有电阻负载的实验性重构光伏系统来验证新的重构方法。最后，提出了一种全新的太阳能电池阵列结构，能够将电力电子转换器合并到太阳能电池阵列中，创建可再生能源电力系统的新分类。智力优点该项目包括科学多学科研究进展中的几个变革性概念，所有这些都代表了可再生能源领域的全新研究方法：1）将引入和开发实时可重构、自愈自适应太阳能电池阵列的概念，并展示其可提高太阳能光伏阵列的功率输出，以用于几个重要的应用。目前最先进的技术仅使用固定连接太阳能电池阵列。2）将提出新的神经网络学习模型来对阴影因素进行建模并学习/分类阴影和太阳能电池退化的类型。这些可用于重新配置或用于发送给太阳能光伏阵列用户的警报。3) 提出了新的协同作用，能够将开关电源与太阳能阵列的功能合并起来，从根本上创建可再生能源系统的新分类.更广泛的影响该项目将通过开发从取之不尽、用之不竭、对环境安全的可再生能源中提取更多电力的方法来造福社会。太阳能电池阵列重新配置方案将有助于减轻太阳能电池阵列内的热点效应，众所周知，这会缩短阵列的预期寿命。此外，由于太阳能电池阵列的重新配置可以提高太阳能电池阵列在被遮挡时的电力提取，因此可以考虑在通常被忽视的位置安装太阳能光伏系统。为了确保研究成果的高影响力，正式的技术转让合作伙伴关系与两家工业公司组建。具体来说，大学/行业联合实验室实验（在行业中）、来自行业的太阳能电池阵列客户数据以及联合研究生建议有助于确保所研究的问题具有相关性和重要性。这些关系还允许该提案中的模拟、建模和设计建议广泛传播到工业太阳能应用。该项目的结果将进一步以期刊出版物、会议演示和可能的专利的形式传播。该项目计划让来自代表性不足群体的学生参与进来，并将为东北大学现有的 K-12 项目建立太阳能示范。具体来说，PI 将在东北大学工程开放日和波士顿地区当地的中学科学之夜进行太阳能演示。还将设计一门新的多学科可再生能源课程，作为工程本科生的选修课。