This paper outlines a novel design of low-cost, portable, fast, and precise Current-Voltage Curve Tracer (IVCT) with automated parameter extraction for high power rated Solar Photovoltaic (SPV) modules to effectively and efficiently determine the outdoor operating status of SPV power generators. The developed IVCT is based on a Raspberry Pi microprocessor, a super-capacitive load, heat sinkable discharge resistances, and sensors with high sensitivity and resolution for measuring light irradiance, module temperature, current, and voltage. The proposed Outdoor Test Facility (OTF) consists of an Current-Voltage (I − V ) and a Power-Voltage (P − V ) curve tracer that uses a dynamic loading supercapacitor to safely and quickly scan the SPV module performance characteristics under real-world operating conditions. It also helps to achieve uniform sampling with better data accuracy. It uses Raspberry Pi as a central processing unit for low-cost data acquisition, data logging, and data computation. Furthermore, results from on-field testing of various small-scale SPV modules show that the I − V tracer can acquire higher-resolution characteristics curves and perform accurate model parameter recognition in real-time. Proposed IVCT can measure individual SPV modules without altering the electrical interconnection circuit, and the operating point can be shifted to 20 A and 45 V in few seconds. The proposed system recomposes the SPV module I − V characteristics based on this variance, with accuracies of 1 to 3% for the region near maximum power. INDEX TERMSI-V curve tracer, Capacitive load, Internet of Things (IoT), On-site I − V curve measurement, Solar Photovoltaic VOLUME x, 20xx

Solar energy is the most promising renewable resource with an unbounded energy source, capable of meeting all human energy requirements. Solar Photovoltaic (SPV) is an effective approach to convert sunlight into electricity, and it has a promising future with consistently rising energy demand. In this work, we propose a smart solution of outdoor performance characterization of the SPV module utilizing a robust, lightweight, portable, and economical Outdoor Test Facility (OTF) with the Internet of Things (IoT) capability. This approach is focused on the capacitive load-based method, which offers improved accuracy and cost-effective data logging using Raspberry Pi and enables the OTF to sweep during the characterization of the SPV module automatically. A demonstration using an experimental setup is also provided in the paper to validate the proposed OTF. This paper further discusses the advantages of using the capacitive load approach over the resistive load approach. IoT’s inherent benefits empower the proposed OTF method on the backgrounds of real-time tracking, data acquisition, and analysis for outdoor output performance characterization by capturing Current–Voltage (I–V) and Power–Voltage (P–V) curves of the SPV module.

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Single or double diode electrical modeling of SPV module gives valuable results which will help to identify the exact behavior of SPV module under the normal operating condition. Accurate modelling of SPV module will also help to calculate internal resistances (Rs, Rsh) and parasitic of the SPV module. The main contribution of this work is the stepwise simplification of the current equation of single and double diode electrical model of SPV module. Then the single diode model of SPV module having 36 SPV cells in series is simulated in LTspice simulator. Simulated results are compared with labelled electrical parameters which shows close proximity to the labelled parameter at particular values of series and shunt resistance. This paper also presents the effect of variation in series resistance (Rs) and shunt resistance (Rsh) on the performance of the SPV module under normal operating condition. The dependency of SPV electrical parameters (Imax, Vmax, Pmax, FF, η) with the variation of series resistance (Rs) and shunt resistance (Rsh) is simulated, and the effects are discussed in details.

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