Yogeswara Rao Golive

Soiling of PV panels results in energy production and revenue losses. Accurate measurements of soiling losses are essential for the optimised cleaning of solar panels. The Stochastic Rate and Recovery (SRR) model, used to measure soiling loss, uses King's model to estimate the PV cell/module temperature. King's model is a steady-state model and is likely to overestimate the changes in temperature under changing irradiance conditions. In this work, we evaluate a Distributed Model, which includes the thermal properties of a PV module to estimate the module temperature. The soiling rates obtained using the Distributed Model and King's model are compared. It is shown that the latter overestimates the soiling rate. King's model underestimates the temperature-corrected performance ratio compared to the Distributed Model. Further, the use of Distributed Model resulted in improved linear regression of the temperature-corrected performance ratio compared to the King's model.

The Standard Test Condition (STC) correction procedures are algorithms used for transforming the Photovoltaic (PV) module current‐voltage (I‐V) data measured at arbitrary conditions back to STC. The PV module Temperature Coefficients are used as inputs by various STC correction procedures and can significantly influence their accuracy. This paper presents the sensitivity analysis of accuracy of six STC correction procedures (IEC 60891‐Procedure 1, Procedure 2, Modified IEC 60891‐Procedure 1, IEC 60891‐Procedure 4 (Voltage Dependent Temperature Coefficient (VDTC) Procedure), Anderson Procedure, and Standard Irradiance and Desired Temperature Procedure) to the errors in the temperature coefficients of P_max, V_oc, and I_sc. Experimentally measured as well as simulated I‐V curves of six multi c‐Si PV modules were used in this study. IEC 60891‐Procedure 4 (Voltage Dependent Temperature Coefficient) that uses a Voltage Dependent Temperature Coefficient which can be calculated using a single measured I‐V curve was found to be most robust against errors in the temperature coefficients.

The field‐measured current–voltage (I–V) curves of photovoltaic (PV) modules need to be corrected to Standard Test Conditions (STC) in order to estimate the degradation rates. STC correction procedures have various attributes such as accuracy, requirement of minimum number and types of I–V curves, required irradiance range, and the type of correction (specific points or entire I–V curve) that determine their optimality for specific applications. This paper presents the investigation of accuracy and constraints of six different STC correction procedures for high‐throughput field I–V measurements through experimental and simulation studies. Following STC correction procedures are considered in this paper: IEC 60891‐Procedure 1, IEC 60891‐Procedure 2, Modified IEC 60891‐Procedure 1, Standard Irradiance and Desired Temperature (SIDT) procedure, Anderson procedure, and Voltage‐Dependent Temperature Coefficient (VDTC) Procedure. Eight different simulation models for predicting the performance of PV modules at arbitrary irradiance and temperature are compared, and the simulation model that yields lowest root mean square error and the most accurate estimation of power temperature coefficient is identified. The simulated I–V curves using this model and the experimentally measured I–V curves on a flash tester at different temperatures and irradiances are provided as an input to all of the STC correction procedures. The average percentage errors in correction of maximum power (P_max), open‐circuit voltage (V_oc), short‐circuit current (I_sc), and fill factor (FF) were determined as a function of irradiance and temperature during measurement. Systematic biases introduced during correction by certain procedures were also identified. Based on the error estimation, constraints of various procedures, and requirements of high‐throughput field I–V measurements, the most optimal STC correction procedure was identified. Moreover, the analysis of the root cause of superior performance of this procedure is also presented.

This paper presents analysis of Infrared (IR) imaging performed on a total of 793 modules inspected during the All India Survey of PV Module Reliability 2018. It 18% of the inspected modules had Module ΔT (difference between maximum and modal temperature) of more than 4°C under MPPT condition which correlated to a significant increase in power degradation. Also, modules with hotspots show higher degradation than modules without hotspots in both Hot and Non-Hot climates. However, effect of hot cell (Module with Module AT > 4 °C) in hot zone was more significant. It was also seen that Module AT is significantly higher for modules above the age of 5 years. Comparison of the drone based IR imaging to ground based handheld IR imaging is also provided.