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Data-Driven Analyses for Field Failures and Faults in Water and...
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Data-Driven Analyses for Field Failures and Faults in Water and Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 6335

Special Issue Editor

Dr. Thushara Gunda

E-Mail Website
Guest Editor
Sandia National Laboratories, Albuquerque, NM 87101, USA
Interests: data science; operations and maintenance; water–energy; interdisciplinary

Special Issue Information

Dear Colleagues,

The advent of data-driven assessments has led to new approaches in how we develop, operate, and manage our water and energy-critical infrastructure systems. For this Special Issue, we welcome researchers to submit details of advancements made in the characterization, analysis, and responses to faults and failures, observed in the field, for water and energy systems. In recognition of the diverse mechanisms through which operational diagnoses can be made, we welcome the evaluation of multimedia datasets, including sensor-based information, imagery, sound, human assessments, and others. The methods utilized for analyzing the data can range from physics-driven simulations to theory-informed machine learning. Although experimental work is also underway to support field assessments, we would like to prioritize the studies that are done in uncontrolled environments to capture the complexity of myriad factors influencing accurate field diagnostics. The analysis of infrastructure can range from that of specific components (e.g., pipelines, transformers, substations, PV modules, and wind turbines) to system-wide analysis.

Dr. Thushara Gunda
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data-driven
  • algorithms
  • operations and maintenance
  • field failures
  • water
  • energy

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Published Papers (2 papers)

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Research

12 pages, 732 KiB  
Article
Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach
by Michael W. Hopwood, Lekha Patel and Thushara Gunda
Energies 2022, 15(14), 5104; https://doi.org/10.3390/en15145104 - 13 Jul 2022
Cited by 5 | Viewed by 2755
Abstract
Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this [...] Read more.
Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV. Full article
(This article belongs to the Special Issue Data-Driven Analyses for Field Failures and Faults in Water and Energy Systems)
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16 pages, 1220 KiB  
Article
Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures
by Michael W. Hopwood, Joshua S. Stein, Jennifer L. Braid and Hubert P. Seigneur
Energies 2022, 15(14), 5085; https://doi.org/10.3390/en15145085 - 12 Jul 2022
Cited by 11 | Viewed by 2974
Abstract
Classification machine learning models require high-quality labeled datasets for training. Among the most useful datasets for photovoltaic array fault detection and diagnosis are module or string current-voltage (IV) curves. Unfortunately, such datasets are rarely collected due to the cost of high fidelity monitoring, [...] Read more.
Classification machine learning models require high-quality labeled datasets for training. Among the most useful datasets for photovoltaic array fault detection and diagnosis are module or string current-voltage (IV) curves. Unfortunately, such datasets are rarely collected due to the cost of high fidelity monitoring, and the data that is available is generally not ideal, often consisting of unbalanced classes, noisy data due to environmental conditions, and few samples. In this paper, we propose an alternate approach that utilizes physics-based simulations of string-level IV curves as a fully synthetic training corpus that is independent of the test dataset. In our example, the training corpus consists of baseline (no fault), partial soiling, and cell crack system modes. The training corpus is used to train a 1D convolutional neural network (CNN) for failure classification. The approach is validated by comparing the model’s ability to classify failures detected on a real, measured IV curve testing corpus obtained from laboratory and field experiments. Results obtained using a fully synthetic training dataset achieve identical accuracy to those obtained with use of a measured training dataset. When evaluating the measured data’s test split, a 100% accuracy was found both when using simulations or measured data as the training corpus. When evaluating all of the measured data, a 96% accuracy was found when using a fully synthetic training dataset. The use of physics-based modeling results as a training corpus for failure detection and classification has many advantages for implementation as each PV system is configured differently, and it would be nearly impossible to train using labeled measured data. Full article
(This article belongs to the Special Issue Data-Driven Analyses for Field Failures and Faults in Water and Energy Systems)
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