An assessment of the value of principal component analysis for photovoltaic IV trace classification of physically-induced failures

Hopwood, Michael; Gunda, Thushara; Seigneur, Hubert; Walters, Joseph

doi:10.1109/pvsc45281.2020.9300601

Cited by 5 publications

(8 citation statements)

References 12 publications

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“…The covariance matrix, which is defined as the expected value of the product of xi and xj , represents the relationships among the variables and is not a zero vector (Hong & Wu, 2012; Li et al, 2018; Naderi et al, 2023; Rastogi et al, 2021). By finding the eigenvectors and eigenvalues of the covariance matrix, we can determine which PCs (Hopwood et al, 2020; Nikkhah et al, 2019) account for the largest variances and subsequently explain most of the total variability of the vector.…”

Section: Preliminaries and Methodologymentioning

confidence: 99%

“…Additionally, PCA is generally beneficial for studies requiring the discovery of latent common structures of factors and the interpretation of the structural meaning of each PC. Numerous studies have applied PCA to research questions in electricity markets and relevant areas (Hopwood et al, 2020; Ji et al, 2017; Li et al, 2018; Liu et al, 2020; K. Wang et al, 2019). For example, PCA has been employed to investigate the dynamics of implied price volatility and capture long‐ and short‐term fluctuations of the volatility term structure for the stock index.…”

Section: Preliminaries and Methodologymentioning

confidence: 99%

“…Relatedly, Hopwood et al (2020) showcased that PCA, when applied to current–voltage (IV) traces, could enhance failure classification accuracy by producing uncorrelated variables, ultimately reducing dimensionality and refining interpretability. Nikkhah et al (2019) combined PCA with artificial neural networks (ANN) for forecasting energy flows in agricultural contexts.…”

Section: Literature Review and Contributionsmentioning

confidence: 99%

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Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets

Nyangon,

Akintunde

2023

WIREs Energy & Environment

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Electricity price forecasting is crucial for grid management, renewable energy integration, power system planning, and price volatility management. However, poor accuracy due to complex generation mix data and heteroskedasticity poses a challenge for utilities and grid operators. This paper evaluates advanced analytics methods that utilize principal component analysis (PCA) to improve forecasting accuracy amidst heteroskedastic noise. Drawing on the experience of the California Independent System Operator (CAISO), a leading producer of renewable electricity, the study analyzes hourly electricity prices and demand data from 2016 to 2021 to assess the impact of day‐ahead forecasting on California's evolving generation mix. To enhance data quality, traditional outlier analysis using the interquartile range (IQR) method is first applied, followed by a novel supervised PCA technique called robust PCA (RPCA) for more effective outlier detection and elimination. The combined approach significantly improves data symmetry and reduces skewness. Multiple linear regression models are then constructed to forecast electricity prices using both raw and transformed features obtained through PCA. Results demonstrate that the model utilizing transformed features, after outlier removal using the traditional method and SAS Sparse Matrix method, achieves the highest forecasting performance. Notably, the SAS Sparse Matrix outlier removal method, implemented via proc RPCA, greatly contributes to improved model accuracy. This study highlights that PCA methods enhance electricity price forecasting accuracy, facilitating the integration of renewables like solar and wind, thereby aiding grid management and promoting renewable growth in day‐ahead markets.This article is categorized under: Energy and Power Systems > Energy Management Energy and Power Systems > Distributed Generation Emerging Technologies > Digitalization

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Section: Preliminaries and Methodologymentioning

confidence: 99%

Section: Preliminaries and Methodologymentioning

confidence: 99%

Section: Literature Review and Contributionsmentioning

confidence: 99%

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Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets

Nyangon,

Akintunde

2023

WIREs Energy & Environment

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“…IV curves are typically described by a set of cardinal points or parameters that include short-circuit current (I sc ) or current when V = 0, open-circuit voltage (V oc ) or voltage when I = 0, maximum power point (V mp , I mp ), and fill factor (I mp × V mp )/(I sc × V oc ). Many module failures and degradation modes result in distinct changes to the IV curve shape, and while they can affect the summary parameters, much more information is available if the whole IV curve is available [31][32][33][34]. Standard practice, however, is to monitor either DC or AC power from each inverter and not collect IV curves automatically.…”

Section: Introductionmentioning

confidence: 99%

“…In this publication, we focus on string-level IV curves as the input feature for our machine learning models. String-level IV curves offer a rich data source for classifying failures, as they characterize the electrical behavior of modules (often) in series at all possible operating points [33]. In order to extract the valuable device health information contained within IV curves using machine learning techniques, a diverse IV curve dataset labeled by failure(s) is required.…”

Section: Introductionmentioning

confidence: 99%

Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

et al. 2022

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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.

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Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach

Sepúlveda-Oviedo,

Travé-Massuyès,

Subias

et al. 2023

Heliyon

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An assessment of the value of principal component analysis for photovoltaic IV trace classification of physically-induced failures

Cited by 5 publications

References 12 publications

Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets

Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets

Physics-Based Method for Generating Fully Synthetic IV Curve Training Datasets for Machine Learning Classification of PV Failures

Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach

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