Framework for effective PV system instrumentation focused on fault diagnosis

A comprehensive framework for instrumentation in photovoltaic (PV) systems is proposed to enhance fault detection accuracy and diagnostic capability across varied PV applications. The framework is structured around four key components: (i) system design considerations, which include PV topology, scale, and sensor placement strategies to maximize detection sensitivity; (ii) data acquisition, detailing sensor selection, sampling rate optimization, and communication protocols adaptable to different configurations; (iii) data management and preprocessing, encompassing storage strategies, data quality control, and normalization pipelines; and (iv) a review of existing monitoring platforms, identifying their limitations for fault-specific diagnostics. Unlike existing standards such as International Electrotechnical Commission (IEC) 61724, which focus on performance monitoring, this framework is explicitly tailored to address diagnostic challenges, offering a fault-oriented perspective on instrumentation design. It provides structured guidelines for aligning spatial resolution, sensor types, and data granularity with the specific needs of fault localization and characterization. The framework also promotes scalable and cost-effective solutions by balancing the trade-offs between instrumentation complexity and diagnostic accuracy. By emphasizing dynamic sampling strategies and preprocessing workflows, the framework supports the development of more responsive and reliable monitoring architectures. This contribution aims to guide practitioners and researchers in improving the resilience, maintainability, and performance of PV systems through more intelligent and diagnosis-ready instrumentation infrastructures. • Framework for PV instrumentation focused on improving fault diagnosis. • Analysis of limitations in current PV monitoring platforms. • Guidelines for sensor type, location, and system communication design. • Adaptive sampling and preprocessing to boost diagnostic performance. • Scalable recommendations beyond current monitoring standards.