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Record W4255456588 · doi:10.1049/el.2020.0226

interview

2020· article· en· W4255456588 on OpenAlexaboutno aff

Bibliographic record

VenueElectronics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemSnowRenewable energySolar energyEnvironmental scienceMeteorologyCold climateEngineeringElectrical engineeringGeography

Abstract

fetched live from OpenAlex

Shamsodin Taheri Shamsodin Taheri, from the University of Quebec Outaouais, Canada, talks to us about his group's submission “Analysis of electrical behaviour of PV arrays covered with nonuniform snow” page 192. I am an Associate Professor of Electrical Engineering in the Department of Computer Science and Engineering at the Université du Québec en Outaouais (UQO). The expertise of my research group in the field of power energy has advanced our understanding of the performance of photovoltaic (PV) sources in cold-climate conditions. This research was inspired by the issues that PV plants in Canada experience during cold months. In recent years, we have collaborated with several industry partners, who own utility-scale PV plants, to evaluate the performance of their facilities. This ongoing research helps mitigate the technical challenges facing the integration of renewable energy sources into power grids in cold regions. PV systems have found different applications in cold areas for a long time, such as remote research facilities in the South and North Poles and space travelling. The majority of PV energy plants are presently installed in geographical cold locations with a considerable amount of snowfall every year. Furthermore, the solar energy industry is expanding competitively in cold climate regions. For example, in Canada, the installed capacity of solar PV grew from 95 MW to more than 2500 MW during 2009–2016. PV panel efficiency is not only influenced by PV technology, but environmental conditions can also influence their energy production. Accumulation of snow/ice decreases solar radiation reaching the PV panels, which results in a significant loss of power generation. Non-uniform snow accretion on PV panels often occurs due to ambient conditions such as wind, temperature variation, partial snow shedding and ground interference. This leads to power loss that is dependent on the configuration of the PV system. Owing to the increasing deployment of PV systems, there is a significant interest in optimal utilisation of PV potential in cold climate regions. A fundamental need for knowledge of the impact of nonuniform snow accretion on PV systems is therefore essential. This research has shown that the snow-covered PV panels can still produce a significant amount of energy depending on the pattern and depth of the snow accumulation. The portion of this energy that could be collected is decided by configuration of the PV array as well as the maximum power point tracking (MPPT) technique. Investigations concerning the effect of bypass diodes have shown that the PV string without bypass diodes generally experiences more power loss. In addition, the vertical and horizontal PV array layouts were tested to determine power loss due to nonuniform snow accretion. The horizontal PV array layout would be more effective in snowy climates. This work is helpful for PV system performance assessment, MPPT development and testing, power generation prediction, and proper arrangement of panels in cold regions. The accumulation of snow on PV modules weakens the intensity of incoming solar radiation and hence limits their ability to generate electricity during cold months. However, the true extent of such impact on the energy production of PV modules cannot be specified in a straightforward way, since it is not proportional to the snow-covered area. In some instances, snow covering only one cell can reduce the efficiency of a whole module by a noticeable amount. Snow is a complex phenomenon, and a quantitative characterisation of radiation transmittance through snow coverage requires knowledge of the physical properties of snow. In fact, the interaction of sunlight with snow to determine the influence of snow coverage on the PV modules is a challenge. This leads to changing the electrical characteristics of the PV array and producing multiple, local peaks on PV characteristics. Accurate modelling of the electric behaviour of PV modules and systems is a key element in improving system operation and efficiency. Hence, proper modelling approaches of the operation of PV generators under varying environmental conditions that improve their performance control and assist the design of PV converters and their MPPT controller have been achieved. Moreover, an appropriate tool has been proposed to determine the efficiency of PV farms under cold conditions. Furthermore, a high performance MPPT technique has been proposed to harvest the maximum energy of a PV array with multipeak characteristic under partial shading condition. Major progress must be made on the proper design and arrangement of PV arrays and power electronic interfaces, minimising the effects of snow on large-scale PV farms and the increasing the use of bifacial PV modules as a candidate for cold regions over the next ten years.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.184
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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