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This paper presents an improved mathematical model for calculating the solar test factor (STF) and solar reliability factor (SRF) of a photovoltaic (PV) automated equipment. By employing a unified metrics system and a combined testing suite encompassing various energy-efficient testing techniques, the aim of this paper is to determine a general fault coverage and improve the global SRF of a closed-loop dual-axis solar tracking system. Accelerated testing coupled with reliability analysis are essential tools for assessing the performance of modern solar tracking devices since PV system malfunctioning is directly connected to economic loss, which is an important aspect for the solar energy domain. The experimental results show that the unified metrics system is potentially suitable for assessing the reliability evaluation of many types of solar tracking systems. Additionally, the proposed combined testing platform proves efficient regarding fault coverage (overall coverage of 66.35% for all test scenarios), test time (an average of 275 min for 2864 test cycles), and power consumption (zero costs regarding electricity consumption for all considered test cases) points of view.

This paper presents a modified global energy production computation formula that replaces the traditional Performance Ratio (PR) with a novel Solar Reliability Factor (SRF) for mobile solar tracking systems. The SRF parameter describes the reliability and availability of a dual-axis solar tracker, which powers a smart home automation system entirely by using clean energy. By applying the SRF in the global energy production formula of solar tracking systems, we can predict the energy generation in real time, allowing proper energy management of the entire smart home automation system. Regarding static deployed Photovoltaic (PV) systems, the PR factor is preserved to compute the power generation of these devices accurately. Experimental results show that the energy production computation constantly fluctuates over several days due to the SRF parameter variation, showing a 26.11% reduction when the dual-axis solar tracker’s availability is affected by system errors and maximum power generation when the solar tracking device is operating in optimal conditions.

This paper presents a mathematical approach for determining the reliability of solar tracking systems based on three fault coverage-aware metrics which use system error data from hardware, software as well as in-circuit testing (ICT) techniques, to calculate a solar test factor (STF). Using Euler’s named constant, the solar reliability factor (SRF) is computed to define the robustness and availability of modern, high-performance solar tracking systems. The experimental cases which were run in the Mathcad software suite and the Python programming environment show that the fault coverage-aware metrics greatly change the test and reliability factor curve of solar tracking systems, achieving significantly reduced calculation steps and computation time.

This paper presents a modified global energy production computation formula that replaces the traditional Performance Ratio (PR) with a novel Solar Reliability Factor (SRF) for mobile solar tracking systems. The SRF parameter describes the reliability and availability of a dual-axis solar tracker, which powers a smart home automation system entirely by using clean energy. By applying the SRF in the global energy production formula of solar tracking systems, we can predict the energy generation in real time, allowing proper energy management of the entire smart home automation system. Regarding static deployed Photovoltaic (PV) systems, the PR factor is preserved to compute the power generation of these devices accurately. Experimental results show that the energy production computation constantly fluctuates over several days due to the SRF parameter variation, showing a 26.11% reduction when the dual-axis solar tracker's availability is affected by system errors and maximum power generation when the solar tracking device is operating in optimal conditions. [ABSTRACT FROM AUTHOR]

This paper presents an improved mathematical model for calculating the solar test factor (STF) and solar reliability factor (SRF) of a photovoltaic (PV) automated equipment. By employing a unified metrics system and a combined testing suite encompassing various energy-efficient testing techniques, the aim of this paper is to determine a general fault coverage and improve the global SRF of a closed-loop dual-axis solar tracking system. Accelerated testing coupled with reliability analysis are essential tools for assessing the performance of modern solar tracking devices since PV system malfunctioning is directly connected to economic loss, which is an important aspect for the solar energy domain. The experimental results show that the unified metrics system is potentially suitable for assessing the reliability evaluation of many types of solar tracking systems. Additionally, the proposed combined testing platform proves efficient regarding fault coverage (overall coverage of 66.35% for all test scenarios), test time (an average of 275 min for 2864 test cycles), and power consumption (zero costs regarding electricity consumption for all considered test cases) points of view. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical approach for determining the reliability of solar tracking systems based on three fault coverage-aware metrics which use system error data from hardware, software as well as in-circuit testing (ICT) techniques, to calculate a solar test factor (STF). Using Euler's named constant, the solar reliability factor (SRF) is computed to define the robustness and availability of modern, high-performance solar tracking systems. The experimental cases which were run in the Mathcad software suite and the Python programming environment show that the fault coverage-aware metrics greatly change the test and reliability factor curve of solar tracking systems, achieving significantly reduced calculation steps and computation time. [ABSTRACT FROM AUTHOR]