Data analysis for effective monitoring of partially shaded photovoltaic systems

The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high-resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinguish it from any additional energy loss due to other malfunctions

PV System Performance Evaluation by Clustering Production Data to Normal and Non-Normal Operation

The most common method for assessment of a photovoltaic (PV) system performance is by comparing its energy production to reference data (irradiance or neighboring PV system). Ideally, at normal operation, the compared sets of data tend to show a linear relationship. Deviations from this linearity are mainly due to malfunctions occurring in the PV system or data input anomalies: a significant number of measurements (named as outliers) may not fulfill this, and complicate a proper performance evaluation. In this paper a new data analysis method is introduced which allows to automatically distinguish the measurements that fit to a near-linear relationship from those which do not (outliers). Although it can be applied to any scatter-plot, where the sets of data tend to be linear, it is specifically used here for two different purposes in PV system monitoring: (1) to detect and exclude any data input anomalies; and (2) to detect and separate measurements where the PV system is functioning properly from the measurements characteristic for malfunctioning. Finally, the data analysis method is applied in four different cases, either with precise reference data (pyranometer and neighboring PV system) or with scattered reference data (in plane irradiance obtained from application of solar models on satellite observations)

PV System Performance Evaluation by Clustering Production Data to Normal and Non-Normal Operation

The most common method for assessment of a photovoltaic (PV) system performance is by comparing its energy production to reference data (irradiance or neighboring PV system). Ideally, at normal operation, the compared sets of data tend to show a linear relationship. Deviations from this linearity are mainly due to malfunctions occurring in the PV system or data input anomalies: a significant number of measurements (named as outliers) may not fulfill this, and complicate a proper performance evaluation. In this paper a new data analysis method is introduced which allows to automatically distinguish the measurements that fit to a near-linear relationship from those which do not (outliers). Although it can be applied to any scatter-plot, where the sets of data tend to be linear, it is specifically used here for two different purposes in PV system monitoring: (1) to detect and exclude any data input anomalies; and (2) to detect and separate measurements where the PV system is functioning properly from the measurements characteristic for malfunctioning. Finally, the data analysis method is applied in four different cases, either with precise reference data (pyranometer and neighboring PV system) or with scattered reference data (in plane irradiance obtained from application of solar models on satellite observations)

Data analysis for effective monitoring of partially shaded photovoltaic systems

The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinct it from any additional energy loss due to other malfunctions

Effects of solar cell group granularity and modern system architectures on partial shading response of crystalline silicon modules and systems

Partial shading is widely considered to be a limiting factor in the performance of photovoltaic (PV) systems applied in urban environments. Modern system architectures combined with per module deployment of power electronics have been used to improve performance especially at heterogeneous irradiance conditions. In this work another approach is used to combine modern system architecture with alternate module designs. The granularity of cell groups in PV modules is investigated together with the so-called Tessera concept, in which single cells are cut in 16 parts. Typical meteorological year yield calculations show that these alternate module designs in combination with modern system architectures can retrieve up to half the shading losses compared to standard modules and string inverters under identical shading conditions

Data analysis for effective monitoring of partially shaded photovoltaic systems

The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinct it from any additional energy loss due to other malfunctions

Data analysis for effective monitoring of partially shaded photovoltaic systems

The purpose of this study is the development of an algorithmic tool to automate the process of analyzing monitoring data of partially shaded PV systems. The approach is to compare long-term and high-resolution yield data of a partially shaded Photovoltaic (PV) system (investigated PV) with the yield data of an unshaded PV system or the tilted irradiance (reference PV system) and automatically detect the energy loss due to the expected shadow, caused by any surrounding obstacles and distinguish it from any additional energy loss due to other malfunctions