EAGER: Arc Flash Detection in Photovoltaic DC Arrays Using Wavelets

研究目标和方法本研究的目的是通过实验确定小波理论用于检测光伏系统中的电弧闪光作为早期电弧故障预警的可行性。该方法是识别、开发和验证小波变换算法，在数字信号处理硬件中实施该技术，建立一个弧光闪光测试室，并使用德克萨斯农工大学的光伏阵列基础设施进行测试。从小型住宅系统到大型公用事业系统，弧光对人类安全构成严重威胁，并阻碍光伏能源的广泛采用。智能价值该项目将通过概念验证设计和测试小波变换的有效性进行实验验证，潜在地改变进行电弧故障检测的方式。这项研究具有变革性，因为目前最先进的方法只能通过检测直流系统中的电流来检测串联电弧故障，并且依赖于频率或时间域模式识别算法，而不是基波信号分解。更广泛的影响建议的方法检测母线电压，并使用比电流所需传感器更简单、更便宜的传感器，从而降低安全、可靠的光伏能源成本。这些概念的使用在任何直流系统中都有广泛的应用，包括微电网、建筑系统、服务器系统和电信系统。PI有技术转让的历史，积极参与相关标准的制定(UL 1699B)，并表现出对培养多样化的清洁技术/绿色技术劳动力的承诺。该项目将为学生提供动手操作仪器的培训，以及研究、分析和出版的机会。

Research Objectives and ApproachesThe objective of this research is to experimentally determine the feasibility of wavelet theory to detect arc flash in photovoltaic energy systems as a warning of incipient arc fault. The approach is to identify, develop, and verify wavelet transform algorithms, implement the technique in digital signal processing hardware, build an arc flash test chamber, and test using the photovoltaic array infrastructure at Texas A&M University. From small-scale residential systems to large-scale utility systems, arcing poses serious threats to human safety and hinders widespread adoption of photovoltaic energy. Intellectual MeritThis project will conduct experimental verification through proof-of-concept design and testing of the efficacy of the wavelet transform, potentially transforming how arc fault detection is conducted. The proposed research is transformative because present state-of-the-art methods can only detect series arc faults by sensing the current in a direct current system and relying upon frequency or time-domain pattern recognition algorithms rather than fundamental signal decomposition. Broader impactsThe proposed method senses the bus voltage and uses simpler, less expensive sensors than those needed to current, thus lowering the cost of safe, reliable photovoltaic energy. Use of these concepts has broad applications to any direct-current system, including microgrid, building systems, server systems and telecom systems. The PI has a history of technology transfer, is active in relevant standards development (UL 1699B), and has a demonstrated commitment to educating a diverse cleantech/greentech workforce. This project will offer students hands-on instrumentation training as well as opportunities for research analysis and publications.