Special Issue "Computational Intelligence in Photovoltaic Systems - Volume II"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 July 2020).

Special Issue Editors

Dr. Emanuele Ogliari
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

Among the renewable energy sources, photovoltaics has become increasingly popular. In recent years, many research topics have arisen, mainly due to the problems that are constantly faced in smart-grid and microgrid operations with regard to output power plant production forecasting, storage sizing, modelling, and control optimization of photovoltaic systems.

Computational intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques in solving problems such as modelling, identification, optimization, availability prediction, forecasting, sizing, and control of stand-alone, grid-connected, and hybrid photovoltaic systems.

Applied Sciences is an international journal that is developing a Special Issue focused on the latest scientific results and methods on both computational intelligence and optimization techniques for all possible photovoltaic applications. Our goal is to bring together scientists representing several approaches and various research communities working on these topics, with the aim of sharing top-level research and promoting research on these advanced topics.

This Special Issue "Computational Intelligence in Photovoltaic Systems" is open to both original research articles and review articles covering all relevant progress in fields including, but not restricted to:

  • forecasting techniques (deterministic, stochastic, etc.)
  • DC/AC converter control and maximum power point tracking techniques
  • sizing of photovoltaic system components and their optimization
  • photovoltaic modelling and parameter estimation
  • maintenance and reliability modelling
  • decision process for grid operators

Prof. Dr. Sonia Leva
Dr. Emanuele Giovanni Carlo Ogliari
Guest Editors

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 papers will be 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. Applied Sciences 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 2000 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.

Published Papers (3 papers)

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Research

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Article
A Novel Plant Propagation-Based Cascaded Fractional Order PI Controller for Optimal Operation of Grid-Connected Single-Stage Three-Phase Solar Photovoltaic System
Appl. Sci. 2019, 9(20), 4269; https://0-doi-org.brum.beds.ac.uk/10.3390/app9204269 - 11 Oct 2019
Viewed by 1261
Abstract
Grid-connected photovoltaic (PV) inverters are gaining attention all over the world. The optimal controller setting is key to the successful operation of a grid-connected PV system. In this paper, a novel plant propagation algorithm-based fractional order proportional-integrator (FOPI) controller for cascaded DC link [...] Read more.
Grid-connected photovoltaic (PV) inverters are gaining attention all over the world. The optimal controller setting is key to the successful operation of a grid-connected PV system. In this paper, a novel plant propagation algorithm-based fractional order proportional-integrator (FOPI) controller for cascaded DC link voltage and inner current control of a grid-connected PV controller has been proposed, which outperforms particle swarm optimization-based PI and elephant herding optimization-based FOPI in terms of multicriteria-based analysis. The performance of the proposed controller also has been measured in terms of total harmonic distortion to maintain the appropriate power quality. Also, the proposed controllers were tested under various solar irradiance and voltage sag conditions to show the effectiveness and robustness of the controllers. The whole system is developed in OPAL-RT using MATLAB/Simulink and RT-LAB as a machine-in-loop (MIL) system to validate the performance in real time. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems - Volume II)
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Article
Modeling and Analysis Framework for Investigating the Impact of Dust and Temperature on PV Systems’ Performance and Optimum Cleaning Frequency
Appl. Sci. 2019, 9(7), 1397; https://0-doi-org.brum.beds.ac.uk/10.3390/app9071397 - 03 Apr 2019
Cited by 17 | Viewed by 1647
Abstract
This paper proposes computational models to investigate the effects of dust and ambient temperature on the performance of a photovoltaic system built at the Hashemite University, Jordan. The system is connected on-grid with an azimuth angle of 0° and a tilt angle of [...] Read more.
This paper proposes computational models to investigate the effects of dust and ambient temperature on the performance of a photovoltaic system built at the Hashemite University, Jordan. The system is connected on-grid with an azimuth angle of 0° and a tilt angle of 26°. The models have been developed employing optimized architectures of artificial neural network (ANN) and extreme learning machine (ELM) models to estimate conversion efficiency based on experimental data. The methodology of building the models is demonstrated and validated for its accuracy using different metrics. The effect of each parameter was found to be in agreement with the well-known relationship between each parameter and the predicted efficiency. It is found that the optimized ELM model predicts conversion efficiency with the best accuracy, yielding an R2 of 91.4%. Moreover, a recommendation for cleaning frequency of every two weeks is proposed. Finally, different scenarios of electricity tariffs with their sensitivity analyses are illustrated. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems - Volume II)
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Review

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Review
Advanced Methods for Photovoltaic Output Power Forecasting: A Review
Appl. Sci. 2020, 10(2), 487; https://0-doi-org.brum.beds.ac.uk/10.3390/app10020487 - 09 Jan 2020
Cited by 45 | Viewed by 2751
Abstract
Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such [...] Read more.
Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems - Volume II)
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