Overview

pvops is a python package for PV operators & researchers. It consists of a set of documented functions for supporting operations research of photovoltaic (PV) energy systems. The library leverages advances in machine learning, natural language processing and visualization tools to extract and visualize actionable information from common PV data including Operations & Maintenance (O&M) text data, timeseries production data, and current-voltage (IV) curves.

Module Overview
Module	Type of data	Highlights of functions
text	O&M records	fill data gaps in dates and categorical records visualize word clusters and patterns over time
timeseries	Production data	estimate expected energy with multiple models evaluate inverter clipping
text2time	O&M records and production data	analyze overlaps between O&M and production (timeseries) records visualize overlaps between O&M records and production data
iv	IV records	simulate IV curves with physical faults extract diode parameters from IV curves classify faults using IV curves

Statement of Need

Continued interest in PV deployment across the world has resulted in increased awareness of needs associated with managing reliability and performance of these systems during operation. Current open-source packages for PV analysis focus on theoretical evaluations of solar power simulations (e.g., pvlib; [HHM18]), specific use cases of empirical evaluations (e.g., RdTools; [DJN+18] and Pecos; [KS16] for degradation analysis), or analysis of electroluminescene images (e.g., PVimage; [PKL+20]). However, a general package that can support data-driven, exploratory evaluations of diverse field collected information is currently lacking. To address this gap, we present pvOps, an open-source, Python package that can be used by researchers and industry analysts alike to evaluate different types of data routinely collected during PV field operations.

PV data collected in the field varies greatly in structure (i.e., timeseries and text records) and quality (i.e., completeness and consistency). The data available for analysis is frequently semi-structured. Furthermore, the level of detail collected between different owners/operators might vary. For example, some may capture a general start and end time for an associated event whereas others might include additional time details for different resolution activities. This diversity in data types and structures often leads to data being under-utilized due to the amount of manual processing required. To address these issues, pvOps provides a suite of data processing, cleaning, and visualization methods to leverage insights across a broad range of data types, including operations and maintenance records, production timeseries, and IV curves. The functions within pvOps enable users to better parse available data to understand patterns in outages and production losses.

Available resources: