Solar Engineering Of Thermal Processes

pvlib python is a community-supported open source tool that provides a set of functions and classes for simulating the performance of photovoltaic energy systems. pvlib python aims to provide reference implementations of models relevant to solar energy, including for example algorithms for solar position, clear sky irradiance, irradiance transposition, DC power, and DC-to-AC power conversion. pvlib python is an important component of a growing ecosystem of open source tools for solar energy (William F. Holmgren, Hansen, Stein, & Mikofski, 2018).

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pvlib python: a python package for modeling solar energy systems

A cell-to-module-to-array detailed model for photovoltaic panels

A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations

Fault detection in solar photovoltaic (PV) arrays is an essential task for increasing reliability and safety in PV systems. Because of PV's nonlinear characteristics, a variety of faults may be difficult to detect by conventional protection devices, leading to safety issues and fire hazards in PV fields. To fill this protection gap, machine learning techniques have been proposed for fault detection based on measurements, such as PV array voltage, current, irradiance, and temperature. However, existing solutions usually use supervised learning models, which are trained by numerous labeled data (known as fault types) and therefore, have drawbacks: 1) the labeled PV data are difficult or expensive to obtain, 2) the trained model is not easy to update, and 3) the model is difficult to visualize. To solve these issues, this paper proposes a graph-based semi-supervised learning model only using a few labeled training data that are normalized for better visualization. The proposed model not only detects the fault, but also further identifies the possible fault type in order to expedite system recovery. Once the model is built, it can learn PV systems autonomously over time as weather changes. Both simulation and experimental results show the effective fault detection and classification of the proposed method.

Graph-Based Semi-supervised Learning for Fault Detection and Classification in Solar Photovoltaic Arrays

The U.S. Department of Energy has supported development of the Solar Advisor Model (SAM) to provide a common platform for evaluation of the solar energy technologies being developed with the support of the Department. This report describes a detailed comparison of performance-model calculations within SAM to actual measured PV system performance in order to evaluate the ability of the models to accurately predict PV system energy production. This was accomplished by using measured meteorological and irradiance data as an input to the models, and then comparing model predictions of solar and PV system parameters to measured values from co-located PV arrays. The submodels within SAM which were examined include four radiation models, three module performance models, and an inverter model. The PVWATTS and PVMod models were also evaluated.

Comparison of PV system performance-model predictions with measured PV system performance

The proper modeling of Photovoltaic(PV) systems is critical for their financing, design, and operation. PV LIB provides a flexible toolbox to perform advanced data analysis and research into the performance modeling and operations of PV assets, and this paper presents the extension of the PV LIB toolbox into the python programming language. PV LIB provides a common repository for the release of published modeling algorithms, and thus can also help to improve the quality and frequency of model validation and inter comparison studies. Overall, the goal of PV LIB is to accelerate the pace of innovation in the PV sector.

https://energy.sandia.gov/wp-content/gallery/uploads/PV_LIB_Python_final_SAND2014-18444C.pdf

Introduction to the open source PV LIB for python Photovoltaic system modelling package

A model, suitable for a row or multiple rows of photovoltaic (PV) modules, is presented for estimating the backside irradiance for bifacial PV modules. The model, which includes the effects of shading by the PV rows, is based on the use of configuration factors to determine the fraction of a source of irradiance that is received by the backside of the PV module. Backside irradiances are modeled along the sloped height of the PV module, but assumed not to vary along the length of the PV row. The backside irradiances are corrected for angle-of-incidence losses and may be added to the front side irradiance to determine the total irradiance resource for the PV cell. Model results are compared with the measured backside irradiances for NREL and Sandia PV systems, and with results when using ray tracing software.

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A Practical Irradiance Model for Bifacial PV Modules

A novel model-based prognostics and health management (PHM) system has been designed to monitor the health of a photovoltaic (PV) system, measure degradation, and indicate maintenance schedules. Current state-of-the-art PV monitoring systems require module and array topology details or extensive modeling of the PV system. We present a method using an artificial neural network (ANN) which eliminates the need for a priori information by teaching the algorithm “good” performance behavior based on the initial performance of the array. The PHM algorithm was tested on two PV systems under test at the Outdoor Test Facility (OTF) at the National Renewable Energy Laboratory (NREL). The PHM algorithm was trained using two months of AC power production. The model then predicted the output power of the system using irradiance, wind, and temperature data. Based on the deviation in measured AC power from the AC power predicted by the trained ANN model, system outages and other faults causing a reduction in power were detected. Had these been commercial installations, rather than research installations, an alert for maintenance could have been initiated. Further use of the PHM system may be able to indicate degradation, detect module or inverter failures, or detect excessive soiling.

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Photovoltaic prognostics and heath management using learning algorithms

We identify the key inputs and measurement data needed for accurate energy rating of concentrator photovoltaic (CPV) modules based on field observations of multiple CPV modules. Acceptance angle is shown to correlate with the observed module-level performance ratio (PR) for the modules studied. Using power ratings based on concentrator standard test conditions, PRs between 90% and 95% were observed during the summers with up to ~10% lower PRs during the winters. A module fabricated by Semprius showed 94% ±0.7% PR over almost 2 years with seasonal variation in PR of less than 1% showing how a module with relatively large acceptance angle may show very consistent average efficiency (calculated from the energy generated relative to the energy available), potentially simplifying energy ratings. The application of the results for translation of energy rating from one location to another is discussed, concluding that most of the translation differences may be correlated with temperature differences between sites with the largest variation happening when optical efficiency depends on temperature. Copyright © 2014 John Wiley & Sons, Ltd.

Daniel Riley

Papers

Comparison of PV system performance-model predictions with measured PV system performance

Introduction to the open source PV LIB for python Photovoltaic system modelling package

A Practical Irradiance Model for Bifacial PV Modules

Photovoltaic prognostics and heath management using learning algorithms

Key Parameters in Determining Energy Generated by CPV Modules