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Energies
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Methods, Algorithms and Circuits for Photovoltaic Systems Diagnosis and Control
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Methods, Algorithms and Circuits for Photovoltaic Systems Diagnosis and Control

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 (31 October 2020) | Viewed by 26929

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Mattia Ricco

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40126 Bologna, Italy
Interests: power electronics; charging infrastractures; multilevel converters; renewable energy; FPGA-based controllers; energy management systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In modern photovoltaic systems, there is an ever-increasing need to improve the system efficiency, to detect internal faults and to guarantee service continuity. The only way to meet these objectives is to utilize the create synergies between diagnostic techniques and control algorithms. Diagnostic methods can be implemented through module-dedicated electronics, by running on real-time embedded systems or by using a huge database on the cloud, profiting from artificial intelligence, machine learning, and classifiers. Model-based diagnostic approaches and data-driven methods, including broadband impedance spectroscopy techniques, are attracting the interest of the scientific community for the automatic detection of phenomena like the occurrence of hot spots, the increase of the ohmic losses, the degradation due to unexpected potentials (PID), switch failures in power electronic converters, and also the reduction of the power production due to soiling or partial shadowing. The detection of malfunctioning or even faults affecting the whole power conversion chain, from the photovoltaic modules to the power conversion stages, allows us to perform proper control actions, also in terms of MPPT. Control algorithms, running on an embedded system, such as DSP or FPGA, are optimized, e.g., through the online adaptation of their own parameters, by suitably processing data coming from the diagnostic algorithms.

This Special Issue has the objective of collecting recent original results about the diagnostic approaches to photovoltaic modules and related power electronics and control strategies with the aim to maximize the photovoltaic output power, to increase the whole system efficiency and to guarantee service continuity.

Prof. Dr. Giovanni Spagnuolo
Dr. Mattia Ricco
Guest Editors

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Keywords

  • Online and offline diagnosis techniques
  • Photovoltaic module
  • Artificial intelligence
  • Single diode model
  • Embedded systems
  • Model-based diagnosis
  • Data-driven diagnosis
  • Real-time fault detection
  • MPPT techniques

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Published Papers (5 papers)

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Research

17 pages, 1241 KiB  
Article
Automatic Faults Detection of Photovoltaic Farms: solAIr, a Deep Learning-Based System for Thermal Images
by Roberto Pierdicca, Marina Paolanti, Andrea Felicetti, Fabio Piccinini and Primo Zingaretti
Energies 2020, 13(24), 6496; https://doi.org/10.3390/en13246496 - 9 Dec 2020
Cited by 82 | Viewed by 5748
Abstract
Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, photovoltaic (PV) power plants represent one of the main systems adopted to produce clean energy. Monitoring the state of health of a system is fundamental. [...] Read more.
Renewable energy sources will represent the only alternative to limit fossil fuel usage and pollution. For this reason, photovoltaic (PV) power plants represent one of the main systems adopted to produce clean energy. Monitoring the state of health of a system is fundamental. However, these techniques are time demanding, cause stops to the energy generation, and often require laboratory instrumentation, thus being not cost-effective for frequent inspections. Moreover, PV plants are often located in inaccessible places, making any intervention dangerous. In this paper, we propose solAIr, an artificial intelligence system based on deep learning for anomaly cells detection in photovoltaic images obtained from unmanned aerial vehicles equipped with a thermal infrared sensor. The proposed anomaly cells detection system is based on the mask region-based convolutional neural network (Mask R-CNN) architecture, adopted because it simultaneously performs object detection and instance segmentation, making it useful for the automated inspection task. The proposed system is trained and evaluated on the photovoltaic thermal images dataset, a publicly available dataset collected for this work. Furthermore, the performances of three state-of-art deep neural networks, (DNNs) including UNet, FPNet and LinkNet, are compared and evaluated. Results show the effectiveness and the suitability of the proposed approach in terms of intersection over union (IoU) and the Dice coefficient. Full article
(This article belongs to the Special Issue Methods, Algorithms and Circuits for Photovoltaic Systems Diagnosis and Control)
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22 pages, 1028 KiB  
Article
An Interval-Arithmetic-Based Approach to the Parametric Identification of the Single-Diode Model of Photovoltaic Generators
by Martha Lucia Orozco-Gutierrez
Energies 2020, 13(4), 932; https://doi.org/10.3390/en13040932 - 19 Feb 2020
Cited by 2 | Viewed by 2136
Abstract
Parametric identification of the single diode model of a photovoltaic generator is a key element in simulation and diagnosis. Parameters’ values are often determined by using experimental data the modules manufacturers provide in the data sheets. In outdoor applications, the parametric identification is [...] Read more.
Parametric identification of the single diode model of a photovoltaic generator is a key element in simulation and diagnosis. Parameters’ values are often determined by using experimental data the modules manufacturers provide in the data sheets. In outdoor applications, the parametric identification is instead performed by starting from the current vs. voltage curve acquired in non-standard operating conditions. This paper refers to this latter case and introduces an approach based on the use of interval arithmetic. Photovoltaic generators based on crystalline silicon cells are considered: they are modeled by using the single diode model, and a divide-and-conquer algorithm is used to contract the initial search space up to a small hyper-rectangle including the identified set of parameters. The proposed approach is validated by using experimental data measured in outdoor conditions. The information provided by the approach, in terms of parametric sensitivity and of correlation between current variations and drifts of the parameters values, is discussed. The results are analyzed in view of the on-site application of the proposed approach for diagnostic purposes. Full article
(This article belongs to the Special Issue Methods, Algorithms and Circuits for Photovoltaic Systems Diagnosis and Control)
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Graphical abstract

14 pages, 747 KiB  
Article
Condition Monitoring in Photovoltaic Systems by Semi-Supervised Machine Learning
by Lars Maaløe, Ole Winther, Sergiu Spataru and Dezso Sera
Energies 2020, 13(3), 584; https://doi.org/10.3390/en13030584 - 27 Jan 2020
Cited by 10 | Viewed by 3724
Abstract
With the rapid increase in photovoltaic energy production, there is a need for smart condition monitoring systems ensuring maximum throughput. Complex methods such as drone inspections are costly and labor intensive; hence, condition monitoring by utilizing sensor data is attractive. In order to [...] Read more.
With the rapid increase in photovoltaic energy production, there is a need for smart condition monitoring systems ensuring maximum throughput. Complex methods such as drone inspections are costly and labor intensive; hence, condition monitoring by utilizing sensor data is attractive. In order to recognize meaningful patterns from the sensor data, there is a need for expressive machine learning models. However, supervised machine learning, e.g., regression models, suffer from the cumbersome process of annotating data. By utilizing a recent state-of-the-art semi-supervised machine learning based on probabilistic modeling, we were able to perform condition monitoring in a photovoltaic system with high accuracy and only a small fraction of annotated data. The modeling approach utilizes all the unsupervised data by jointly learning a low-dimensional feature representation and a classification model in an end-to-end fashion. By analysis of the feature representation, new internal condition monitoring states can be detected, proving a practical way of updating the model for better monitoring. We present (i) an analysis that compares the proposed model to corresponding purely supervised approaches, (ii) a study on the semi-supervised capabilities of the model, and (iii) an experiment in which we simulated a real-life condition monitoring system. Full article
(This article belongs to the Special Issue Methods, Algorithms and Circuits for Photovoltaic Systems Diagnosis and Control)
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17 pages, 6058 KiB  
Article
Advanced MPPT Algorithm for Distributed Photovoltaic Systems
by Hyeon-Seok Lee and Jae-Jung Yun
Energies 2019, 12(18), 3576; https://doi.org/10.3390/en12183576 - 19 Sep 2019
Cited by 35 | Viewed by 9421
Abstract
The basic and adaptive maximum power point tracking algorithms have been studied for distributed photovoltaic systems to maximize the energy production of a photovoltaic (PV) module. However, the basic maximum power point tracking algorithms using a fixed step size, such as perturb and [...] Read more.
The basic and adaptive maximum power point tracking algorithms have been studied for distributed photovoltaic systems to maximize the energy production of a photovoltaic (PV) module. However, the basic maximum power point tracking algorithms using a fixed step size, such as perturb and observe and incremental conductance, suffer from a trade-off between tracking accuracy and tracking speed. Although the adaptive maximum power point tracking algorithms using a variable step size improve the maximum power point tracking efficiency and dynamic response of the basic algorithms, these algorithms still have the oscillations at the maximum power point, because the variable step size is sensitive to external factors. Therefore, this paper proposes an enhanced maximum power point tracking algorithm that can have fast dynamic response, low oscillations, and high maximum power point tracking efficiency. To achieve these advantages, the proposed maximum power point tracking algorithm uses two methods that can apply the optimal step size to each operating range. In the operating range near the maximum power point, a small fixed step size is used to minimize the oscillations at the maximum power point. In contrast, in the operating range far from the maximum power point, a variable step size proportional to the slope of the power-voltage curve of PV module is used to achieve fast tracking speed under dynamic weather conditions. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations than the basic and adaptive algorithms. The theoretical analysis and performance of the proposed algorithm were verified by experimental results. In addition, the comparative experimental results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm. Full article
(This article belongs to the Special Issue Methods, Algorithms and Circuits for Photovoltaic Systems Diagnosis and Control)
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22 pages, 9839 KiB  
Article
Practical Implementation of the Backstepping Sliding Mode Controller MPPT for a PV-Storage Application
by Marwen Bjaoui, Brahim Khiari, Ridha Benadli, Mouad Memni and Anis Sellami
Energies 2019, 12(18), 3539; https://doi.org/10.3390/en12183539 - 16 Sep 2019
Cited by 25 | Viewed by 5126
Abstract
This study presents a design and an implementation of a robust Maximum Power Point Tracking (MPPT) for a stand-alone photovoltaic (PV) system with battery storage. A new control scheme is applied for the boost converter based on the combination of the adaptive perturb [...] Read more.
This study presents a design and an implementation of a robust Maximum Power Point Tracking (MPPT) for a stand-alone photovoltaic (PV) system with battery storage. A new control scheme is applied for the boost converter based on the combination of the adaptive perturb and observe fuzzy logic controller (P&O-FLC) MPPT technique and the backstepping sliding mode control (BS-SMC) approach. The MPPT controller design was used to accurately track the PV operating point to its maximum power point (MPP) under changing climatic conditions. The presented MPPT based on the P&O-FLC technique generates the reference PV voltage and then a cascade control loop type, based on the BS-SMC approach is used. The aims of this approach are applied to regulate the inductor current and then the PV voltage to its reference values. In order to reduce system costs and complexity, a high gain observer (HGO) was designed, based on the model of the PV system, to estimate online the real value of the boost converter’s inductor current. The performance and the robustness of the BS-SMC approach are evaluated using a comparative simulation with a conventional proportional integral (PI) controller implemented in the MATLAB/Simulink environment. The obtained results demonstrate that the proposed approach not only provides a near-perfect tracking performance (dynamic response, overshoot, steady-state error), but also offers greater robustness and stability than the conventional PI controller. Experimental results fitted with dSPACE software reveal that the PV module could reach the MPP and achieve the performance and robustness of the designed BS-SMC MPPT controller. Full article
(This article belongs to the Special Issue Methods, Algorithms and Circuits for Photovoltaic Systems Diagnosis and Control)
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