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Energies
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Fault Detection and Diagnosis of Photovoltaic Systems
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Fault Detection and Diagnosis of Photovoltaic 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 November 2020) | Viewed by 11954

Special Issue Editor

Prof. Dr. Santiago Silvestre

E-Mail Website
Guest Editor
Department of Electronic Engineering, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Interests: photovoltaic modules; photovoltaic systems; fault detection; modeling and simulation of PV systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The cumulative global photovoltaic (PV) capacity has grown exponentially in recent years worldwide and will continue to grow. This fact is mainly due to advances in PV technologies and the significant cost reduction of PV systems. At present, the cost of the PV energy generated is moving toward a grid parity scenario in most countries. Due to these trends, PV energy production will play a key role in global electricity generation and should be one of the global strategies to reduce CO2 emissions and stop climate change. However, there are still significant efforts to be made in terms of the performance and reliability of PV systems. In this context, an important issue in the coming years will be the development of automatic supervision strategies for PV systems in order to achieve higher yields and better performance. Automatic supervision of PV systems is based on an effective and rapid detection of faults present in the PV system together with a correct diagnosis to identify the most probably cause of failures.

This Special Issue aims to collect original research or review articles on different PV systems automatic supervision strategies from an applied point of view

Dr. Santiago Silvestre
Guest Editor

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Keywords

  • Photovoltaic systems
  • Automatic supervision
  • Fault detection

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Published Papers (4 papers)

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Research

15 pages, 6505 KiB  
Article
Diagnostics of PID Early Stage in PV Systems
by Tomáš Finsterle, Ladislava Černá, Pavel Hrzina, David Rokusek and Vítězslav Benda
Energies 2021, 14(8), 2155; https://doi.org/10.3390/en14082155 - 13 Apr 2021
Cited by 9 | Viewed by 2341
Abstract
Potential induced degradation (PID) is a serious threat for the photovoltaic (PV) industry. The risk of PID may increase with increasing operating voltage of PV systems. Although PID tests are currently standard tests, the expansion of floating PV power plants and installation in [...] Read more.
Potential induced degradation (PID) is a serious threat for the photovoltaic (PV) industry. The risk of PID may increase with increasing operating voltage of PV systems. Although PID tests are currently standard tests, the expansion of floating PV power plants and installation in humid climates show that PID-free modules are still sensitive to this type of degradation. Therefore, a method that can detect PID in the initial phase before standard tests reveal it, is necessary to increase the reliability of PV systems and maintain their lifetime. One possible tool for revealing early-stage PID manifestations is impedance spectroscopy and I-V dark curves measurements. Both IS and dark current measurement methods are sensitive to cell shunt resistance (RSH), which is strongly influenced by PID before significant power loss and can act as an early stage PID detection mechanism. The paper describes the differences of the common P-type PV module parameters both during the degradation process and also during the regeneration process when diagnosed by conventional and IS and dark current measurement methods. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Photovoltaic Systems)
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19 pages, 4384 KiB  
Article
A Fault Diagnosis Mechanism with Power Generation Improvement for a Photovoltaic Module Array
by Kuei-Hsiang Chao and Pei-Lun Lai
Energies 2021, 14(3), 598; https://doi.org/10.3390/en14030598 - 25 Jan 2021
Cited by 1 | Viewed by 1690
Abstract
This paper aims to develop an online diagnostic mechanism, doubling as a maximum power point tracking scheme, for a photovoltaic (PV) module array. In case of malfunction or shadow event occurring to a PV module, the presented diagnostic mechanism is enabled, automatically and [...] Read more.
This paper aims to develop an online diagnostic mechanism, doubling as a maximum power point tracking scheme, for a photovoltaic (PV) module array. In case of malfunction or shadow event occurring to a PV module, the presented diagnostic mechanism is enabled, automatically and immediately, to reconfigure a PV module array for maximum output power operation under arbitrary working conditions. Meanwhile, the malfunctioning or shaded PV module can be located instantly by this diagnostic mechanism according to the array configuration, and a PV module replacement process is made more efficient than ever before for the maintenance crew. In this manner, the intended maximum output power operation can be resumed as soon as possible in consideration of a minimum business loss. Using a particle swarm optimization (PSO)-based algorithm, the PV module array is reconfigured by means of switch manipulations between modules, such that a load is supplied with the maximum amount of output power. For compactness, the PSO-based online diagnostic algorithm is implemented herein using a TMS320F2808 digital signal processor (DSP) and is experimentally validated as successful to identify a malfunctioning PV module at the end of this work. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Photovoltaic Systems)
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14 pages, 4860 KiB  
Article
Bollinger Bands Based on Exponential Moving Average for Statistical Monitoring of Multi-Array Photovoltaic Systems
by Silvano Vergura
Energies 2020, 13(15), 3992; https://doi.org/10.3390/en13153992 - 3 Aug 2020
Cited by 14 | Viewed by 3687
Abstract
Monitoring the performance of a photovoltaic (PV) system when environmental parameters are not available is very difficult. Comparing the energy datasets of the arrays belonging to the same PV plant is one strategy. If the extension of a PV plant is limited, all [...] Read more.
Monitoring the performance of a photovoltaic (PV) system when environmental parameters are not available is very difficult. Comparing the energy datasets of the arrays belonging to the same PV plant is one strategy. If the extension of a PV plant is limited, all the arrays are subjected to the same environmental conditions. Therefore, identical arrays produce the same energy amount, whatever the solar radiation and cell temperature. This is valid for small- to medium-rated power PV plants (3–50 kWp) and, moreover, this typology of PV plants sometimes is not equipped with a meteorological sensor system. This paper presents a supervision methodology based on comparing the average energy of each array and the average energy of the whole PV plant. To detect low-intensity anomalies before they become failures, the variability of the energy produced by each array is monitored by using the Bollinger Bands (BB) method. This is a statistical tool developed in the financial field to evaluate the stock price volatility. This paper introduces two modifications in the standard BB method: the exponential moving average (EMA) instead of the simple moving average (SMA), and the size of the width of BB, set to three times the standard deviation instead of four times. Until the produced energy of each array is contained in the BB, a serious anomaly is not present. A case study based on a real operating 19.8 kWp PV plant is discussed. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Photovoltaic Systems)
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21 pages, 2517 KiB  
Article
Tailored Algorithms for Anomaly Detection in Photovoltaic Systems
by Pedro Branco, Francisco Gonçalves and Ana Cristina Costa
Energies 2020, 13(1), 225; https://doi.org/10.3390/en13010225 - 2 Jan 2020
Cited by 14 | Viewed by 3469
Abstract
The fastest-growing renewable source of energy is solar photovoltaic (PV) energy, which is likely to become the largest electricity source in the world by 2050. In order to be a viable alternative energy source, PV systems should maximise their efficiency and operate flawlessly. [...] Read more.
The fastest-growing renewable source of energy is solar photovoltaic (PV) energy, which is likely to become the largest electricity source in the world by 2050. In order to be a viable alternative energy source, PV systems should maximise their efficiency and operate flawlessly. However, in practice, many PV systems do not operate at their full capacity due to several types of anomalies. We propose tailored algorithms for the detection of different PV system anomalies, including suboptimal orientation, daytime and sunrise/sunset shading, brief and sustained daytime zero-production, and low maximum production. Furthermore, we establish simple metrics to assess the severity of suboptimal orientation and daytime shading. The proposed detection algorithms were applied to a set of time-series of electricity production in Portugal, which are based on two periods with distinct weather conditions. Under favourable weather conditions, the algorithms successfully detected most of the time-series labelled with either daytime or sunrise/sunset shading, and with either sustained or brief daytime zero-production. There was a relatively low percentage of false positives, such that most of the anomaly detections were correct. As expected, the algorithms tend to be more robust under favourable rather than under adverse weather conditions. The proposed algorithms may prove to be useful not only to research specialists, but also to energy utilities and owners of small- and medium-sized PV systems, who may thereby effortlessly monitor their operation and performance. Full article
(This article belongs to the Special Issue Fault Detection and Diagnosis of Photovoltaic Systems)
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