Applied Sciences

Research

16 pages, 2544 KiB

Open AccessArticle

Linear Programming Coordination for Overcurrent Relay in Electrical Distribution Systems with Distributed Generation

by Daniel Alcala-Gonzalez, Eva M. García del Toro, M. Isabel Más-López, Sara García-Salgado and Santiago Pindado

Appl. Sci. 2022, 12(9), 4279; https://doi.org/10.3390/app12094279 - 23 Apr 2022

Cited by 4 | Viewed by 1448

Abstract

Electric power distribution networks are generally radial in nature, with unidirectional power flows transmitted from the highest voltage levels to the consumption levels. The protection system in these distribution networks is relatively simple and consists mainly of fuses, reclosers (RC) and overcurrent relays [...] Read more.

Electric power distribution networks are generally radial in nature, with unidirectional power flows transmitted from the highest voltage levels to the consumption levels. The protection system in these distribution networks is relatively simple and consists mainly of fuses, reclosers (RC) and overcurrent relays (OCRs). The installation of distributed generation (DG) in a network causes coordination problems between these devices, because the power flows are no longer unidirectional and can flow upstream to the substation. For this reason, the work proposed here analyzes the most significant impacts that DG has on the protection devices and proposes an adjustment method for the OCRs based on linear programming (LP) techniques with the aim of improving their response time to the different faults that may occur in the main feeder of the network. The distribution system selected for the study is the IEEE 34 bus system using DIgSILENT 14.1 software for its modeling and Matlab for the adjustment of the overcurrent devices. Results indicate that better coordination between protection devices are achieved if LP is used. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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19 pages, 4982 KiB

Open AccessArticle

Detection Criterion for Progressive Faults in Photovoltaic Modules Based on Differential Voltage Measurements

by Luis Diego Murillo-Soto and Carlos Meza

Appl. Sci. 2022, 12(5), 2565; https://doi.org/10.3390/app12052565 - 01 Mar 2022

Cited by 1 | Viewed by 1344

Abstract

PV modules may experience degradation conditions that affect their power efficiency and affect the rest of the PV array. Based on the literature review, this paper links the parameter variation on a PV module with the six most common degradation faults, namely, series [...] Read more.

PV modules may experience degradation conditions that affect their power efficiency and affect the rest of the PV array. Based on the literature review, this paper links the parameter variation on a PV module with the six most common degradation faults, namely, series resistance degradation, optical homogeneous degradation, optical heterogeneous degradation, potential induced degradation, micro-cracks, and light-induced degradation. A Monte Carlo-based numerical simulation was used to study the effect of the faults mentioned above in the voltage of the modules in a PV array with one faulty module. A simple expression to identify faults was derived based on the obtained results. The simplicity of this expression allows integrating the fault detection technique in low-cost electronic circuits embedded in a PV module, optimizer, or microinverter. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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19 pages, 1863 KiB

Open AccessArticle

Generation of Data-Driven Expected Energy Models for Photovoltaic Systems

by Michael W. Hopwood and Thushara Gunda

Appl. Sci. 2022, 12(4), 1872; https://doi.org/10.3390/app12041872 - 11 Feb 2022

Cited by 3 | Viewed by 1453

Abstract

Although unique expected energy models can be generated for a given photovoltaic (PV) site, a standardized model is also needed to facilitate performance comparisons across fleets. Current standardized expected energy models for PV work well with sparse data, but they have demonstrated significant [...] Read more.

Although unique expected energy models can be generated for a given photovoltaic (PV) site, a standardized model is also needed to facilitate performance comparisons across fleets. Current standardized expected energy models for PV work well with sparse data, but they have demonstrated significant over-estimations, which impacts accurate diagnoses of field operations and maintenance issues. This research addresses this issue by using machine learning to develop a data-driven expected energy model that can more accurately generate inferences for energy production of PV systems. Irradiance and system capacity information was used from 172 sites across the United States to train a series of models using Lasso linear regression. The trained models generally perform better than the commonly used expected energy model from international standard (IEC 61724-1), with the two highest performing models ranging in model complexity from a third-order polynomial with 10 parameters (

R_{a d j}^{2}

= 0.994) to a simpler, second-order polynomial with 4 parameters (

R_{a d j}^{2} = 0.993

), the latter of which is subject to further evaluation. Subsequently, the trained models provide a more robust basis for identifying potential energy anomalies for operations and maintenance activities as well as informing planning-related financial assessments. We conclude with directions for future research, such as using splines to improve model continuity and better capture systems with low (≤1000 kW DC) capacity. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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11 pages, 4063 KiB

Open AccessArticle

SCADA-Compatible and Scaleable Visualization Tool for Corrosion Monitoring of Offshore Wind Turbine Structures

by Joachim Verhelst, Inge Coudron and Agusmian Partogi Ompusunggu

Appl. Sci. 2022, 12(3), 1762; https://doi.org/10.3390/app12031762 - 08 Feb 2022

Cited by 6 | Viewed by 2412

Abstract

The exploitation of offshore windfarms (WFs) goes hand in hand with large capital expenditures (CAPEX) and operational expenditures (OPEX), as these mechanical installations operate continuously for multiple decades in harsh, saline conditions. OPEX can account for up to 30% of the levelised cost [...] Read more.

The exploitation of offshore windfarms (WFs) goes hand in hand with large capital expenditures (CAPEX) and operational expenditures (OPEX), as these mechanical installations operate continuously for multiple decades in harsh, saline conditions. OPEX can account for up to 30% of the levelised cost of energy (LCoE) for a deployed offshore wind farm. To maintain the cost-competitiveness of deployed offshore WFs versus other renewable energy sources, their LCoE has to be kept in check, both by minimising the OPEX and optimising the offshore wind energy production. As corrosion, in particular uniform corrosion, is a major cause of failure of offshore wind turbine structures, there is an urgent need for corrosion management systems for deployed offshore wind turbine structures (WTs). Despite the fact that initial corrosion protection solutions are already integrated on some critical structural components such as WT towers, WT transition pieces or WT sub-structure (fixed or floating platforms), these components can still be harshly damaged by the corrosive environmental offshore conditions. The traditional preventive maintenance strategy, in which regular manual inspections by experts are necessary, is widely implemented nowadays in wind farm applications. Unfortunately, for such challenging operating environments, regular human inspections have a significant cost, which eventually increase the OPEX. To minimise the OPEX, remote corrosion monitoring solutions combined with supporting software (SW) tools are thus necessary. This paper focuses on the development of a software (SW) tool for the visualisation of corrosion measurement data. To this end, criteria for efficient structural corrosion analysis were identified, namely a scaleable, SCADA-compatible, secure, web accessible tool that can visualise 3D relationships. In order to be effective, the SW tool requires a tight integration with decision support tools. This paper provides three insights: Firstly, through a literature study and non-exhaustive market study, it is shown that a combined visualisation and decision SW tool is currently non-existing in the market. This gap motivates a need for the development of a custom SW tool. Secondly, the capabilities of the developed custom software tool, consisting of a backend layer and visualisation browser designed for this task are demonstrated and discussed in this paper. This indicates that a SCADA-compatible visualisation software tool is possible, and can be a major stepping stone towards a semi-automated decision support toolchain for offshore wind turbine corrosion monitoring. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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20 pages, 27868 KiB

Open AccessArticle

Ultrasound-Based Smart Corrosion Monitoring System for Offshore Wind Turbines

by Upeksha Chathurani Thibbotuwa, Ainhoa Cortés and Andoni Irizar

Appl. Sci. 2022, 12(2), 808; https://doi.org/10.3390/app12020808 - 13 Jan 2022

Cited by 9 | Viewed by 3359

Abstract

The ultrasound technique is a well-known non-destructive and efficient testing method for on-line corrosion monitoring. Wall thickness loss rate is the major parameter that defines the corrosion process in this approach. This paper presents a smart corrosion monitoring system for offshore wind turbines [...] Read more.

The ultrasound technique is a well-known non-destructive and efficient testing method for on-line corrosion monitoring. Wall thickness loss rate is the major parameter that defines the corrosion process in this approach. This paper presents a smart corrosion monitoring system for offshore wind turbines based on the ultrasound pulse-echo technique. The solution is first developed as an ultrasound testbed with the aim of upgrading it into a low-cost and low-power miniaturized system to be deployed inside offshore wind turbines. This paper discusses different important stages of the presented monitoring system as design methodology, the precision of the measurements, and system performance verification. The obtained results during the testing of a variety of samples show meaningful information about the thickness loss due to corrosion. Furthermore, the developed system allows us to measure the Time-of-Flight (ToF) with high precision on steel samples of different thicknesses and on coated steel samples using the offshore standard coating NORSOK 7A. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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27 pages, 21168 KiB

Open AccessArticle

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

by Andrés Pérez-González, Nelson Benítez-Montoya, Álvaro Jaramillo-Duque and Juan Bernardo Cano-Quintero

Appl. Sci. 2021, 11(24), 12093; https://doi.org/10.3390/app112412093 - 19 Dec 2021

Cited by 16 | Viewed by 3664

Abstract

Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous [...] Read more.

Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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19 pages, 5081 KiB

Open AccessArticle

Modeling and Control of a Microgrid Connected to the INTEC University Campus

by Miguel Aybar-Mejía, Lesyani León-Viltre, Félix Santos, Francisco Neves, Víctor Alonso Gómez and Deyslen Mariano-Hernández

Appl. Sci. 2021, 11(23), 11355; https://doi.org/10.3390/app112311355 - 30 Nov 2021

Cited by 1 | Viewed by 1847

Abstract

A smart microgrid is a bidirectional electricity generation system—a type of system that is becoming more prevalent in energy production at the distribution level. Usually, these systems have intermittent renewable energy sources, e.g., solar and wind energy. These low voltage networks contribute to [...] Read more.

A smart microgrid is a bidirectional electricity generation system—a type of system that is becoming more prevalent in energy production at the distribution level. Usually, these systems have intermittent renewable energy sources, e.g., solar and wind energy. These low voltage networks contribute to decongestion through the efficient use of resources within the microgrid. In this investigation, an energy management strategy and a control scheme for DG units are proposed for DC/AC microgrids. The objective is to implement these strategies in an experimental microgrid that will be developed on the INTEC university campus. After presenting the microgrid topology, the modeling and control of each subsystem and their respective converters are described. All possible operation scenarios, such as islanded or interconnected microgrids, different generation-load possibilities, and state-of-charge conditions of the battery, are verified, and a seamless transition between different operation modes is ensured. The simulation results in Matlab Simulink show how the proposed control system allows transitions between the different scenarios without severe transients in the power transfer between the microgrid and the low voltage network elements. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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19 pages, 1669 KiB

Open AccessArticle

Solar Power System Assessments Using ANN and Hybrid Boost Converter Based MPPT Algorithm

by Imran Haseeb, Ammar Armghan, Wakeel Khan, Fayadh Alenezi, Norah Alnaim, Farman Ali, Fazal Muhammad, Fahad R. Albogamy and Nasim Ullah

Appl. Sci. 2021, 11(23), 11332; https://doi.org/10.3390/app112311332 - 30 Nov 2021

Cited by 14 | Viewed by 2297

Abstract

The load pressure on electrical power system is increased during last decade. The installation of new power generators (PGs) take huge time and cost. Therefore, to manage current power demands, the solar plants are considered a fruitful solution. However, critical caring and balance [...] Read more.

The load pressure on electrical power system is increased during last decade. The installation of new power generators (PGs) take huge time and cost. Therefore, to manage current power demands, the solar plants are considered a fruitful solution. However, critical caring and balance output power in solar plants are the highlighted issues. Which needs a proper procedure in order to minimize balance output power and caring issues in solar plants. This paper investigates artificial neural network (ANN) and hybrid boost converter (HBC) based MPPT for improving the output power of solar plants. The proposed model is analyzed in two steps, the offline step and the online step. Where the offline status is used for training various terms of ANNs in terms of structure and algorithm while in the online step, the online procedure is applied with optimum ANN for maximum power point tracking (MPPT) using traditional converter and hybrid converter in solar plants. Moreover, a detail analytical framework is studied for both proposed steps. The mathematical and simulation approaches show that the presented model efficiently regulate the output of solar plants. This technique is applicable for current installed solar plants which reduces the cost per generation. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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18 pages, 3988 KiB

Open AccessArticle

Diagnosis of Broken Bars in Wind Turbine Squirrel Cage Induction Generator: Approach Based on Current Signal and Generative Adversarial Networks

by Yuri Merizalde Zamora, Luis Hernández-Callejo, Oscar Duque-Pérez and Víctor Alonso-Gómez

Appl. Sci. 2021, 11(15), 6942; https://doi.org/10.3390/app11156942 - 28 Jul 2021

Cited by 4 | Viewed by 1591

Abstract

To ensure the profitability of the wind industry, one of the most important objectives is to minimize maintenance costs. For this reason, the components of wind turbines are continuously monitored to detect any type of failure by analyzing the signals measured by the [...] Read more.

To ensure the profitability of the wind industry, one of the most important objectives is to minimize maintenance costs. For this reason, the components of wind turbines are continuously monitored to detect any type of failure by analyzing the signals measured by the sensors included in the condition monitoring system. Most of the proposals for the detection and diagnosis of faults based on signal processing and artificial intelligence models use a fault-free signal and a signal acquired on a system in which a fault has been provoked; however, when the failures are incipient, the frequency components associated with the failures are very close to the fundamental component and there are incomplete data, the detection and diagnosis of failures is difficult. Therefore, the purpose of this research is to detect and diagnose failures of the electric generator of wind turbines in operation, using the current signal and applying generative adversarial networks to obtain synthetic data that allow for counteracting the problem of an unbalanced dataset. The proposal is useful for the detection of broken bars in squirrel cage induction generators, which, according to the control system, were in a healthy state. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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16 pages, 1236 KiB

Open AccessArticle

Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

by Andrés Pérez-González, Álvaro Jaramillo-Duque and Juan Bernardo Cano-Quintero

Appl. Sci. 2021, 11(14), 6524; https://doi.org/10.3390/app11146524 - 15 Jul 2021

Cited by 7 | Viewed by 2122

Abstract

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output [...] Read more.

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (

I o U

) and the Dice Coefficient (

D C

) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to

90.42

% and

91.42

% as calculated by

I o U

and

D C

metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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20 pages, 911 KiB

Open AccessArticle

A Learning-Based Methodology to Optimally Fit Short-Term Wind-Energy Bands

by Claudio Risso and Gustavo Guerberoff

Appl. Sci. 2021, 11(11), 5137; https://doi.org/10.3390/app11115137 - 31 May 2021

Cited by 1 | Viewed by 1600

Abstract

The increasing rate of penetration of non-conventional renewable energies is affecting the traditional assumption of controllability over energy sources. Power dispatch scheduling methods need to integrate the intrinsic randomness of some new sources, among which, wind energy is particularly difficult to treat. This [...] Read more.

The increasing rate of penetration of non-conventional renewable energies is affecting the traditional assumption of controllability over energy sources. Power dispatch scheduling methods need to integrate the intrinsic randomness of some new sources, among which, wind energy is particularly difficult to treat. This work aims at the optimal construction of energy bands around wind energy forecasts. Complementarily, a remarkable fact of the proposed technique is that it can be extended to integrate multiple forecasts into a single one, whose band width is narrower at the same level of confidence. The work is based upon a real-world application case, developed for the Uruguayan Electricity Market, a world leader in the penetration of renewable energies. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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23 pages, 12973 KiB

Open AccessArticle

Analytical Modeling of Current-Voltage Photovoltaic Performance: An Easy Approach to Solar Panel Behavior

by José Miguel Álvarez, Daniel Alfonso-Corcuera, Elena Roibás-Millán, Javier Cubas, Juan Cubero-Estalrrich, Alejandro Gonzalez-Estrada, Rocío Jado-Puente, Marlon Sanabria-Pinzón and Santiago Pindado

Appl. Sci. 2021, 11(9), 4250; https://doi.org/10.3390/app11094250 - 07 May 2021

Cited by 13 | Viewed by 2862

Abstract

In this paper, we propose very simple analytical methodologies for modeling the behavior of photovoltaic (solar cells/panels) using a one-diode/two-resistor (1-D/2-R) equivalent circuit. A value of a = 1 for the ideality factor is shown to be very reasonable for the different photovoltaic [...] Read more.

In this paper, we propose very simple analytical methodologies for modeling the behavior of photovoltaic (solar cells/panels) using a one-diode/two-resistor (1-D/2-R) equivalent circuit. A value of a = 1 for the ideality factor is shown to be very reasonable for the different photovoltaic technologies studied here. The solutions to the analytical equations of this model are simplified using easy mathematical expressions defined for the Lambert W-function. The definition of these mathematical expressions was based on a large dataset related to solar cells and panels obtained from the available academic literature. These simplified approaches were successfully used to extract the parameters from explicit methods for analyzing the behavior of solar cells/panels, where the exact solutions depend on the Lambert W-function. Finally, a case study was carried out that consisted of fitting the aforementioned models to the behavior (that is, the I-V curve) of two solar panels from the UPMSat-1 satellite. The results show a fairly high level of accuracy for the proposed methodologies. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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16 pages, 4409 KiB

Open AccessArticle

Use of Ecofriendly Glass Powder Concrete in Construction of Wind Farms

by Eva M. García del Toro, Daniel Alcala-Gonzalez, María Isabel Más-López, Sara García-Salgado and Santiago Pindado

Appl. Sci. 2021, 11(7), 3050; https://doi.org/10.3390/app11073050 - 29 Mar 2021

Cited by 6 | Viewed by 2167

Abstract

Silicon is the main element in the composition of glass and it has been seen that it can be used as a partial replacement for cement in the manufacture of concrete. Different dosages of glass powder and cement were applied to manufacture the [...] Read more.

Silicon is the main element in the composition of glass and it has been seen that it can be used as a partial replacement for cement in the manufacture of concrete. Different dosages of glass powder and cement were applied to manufacture the concrete mixes. Initially, the characteristics of fresh concrete were studied, such as consistency, air content, apparent density and workability. Secondly, compressive strength tests were performed on the different concrete mixtures produced. The consistency tests allowed us to classify these concretes within the group of fluids. The air content of these concretes increased with the rate of substitution of cement by glass powder, resulting in lighter concretes. Density tests showed that this parameter decreased as the rate of substitution of cement increased. A coefficient k has been calculated for the substitution of glass powder by cement in the binder, using the Bolomey formula. Also, a mathematical model has been proposed to further analyze the experimental data. Major contributions of this work were to study the possible application of this concrete in different dispersions as a surface protection layer against the action of corrosion, in wind turbine foundations as well as the stabilization of the wind farm roads. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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15 pages, 2430 KiB

Open AccessFeature PaperArticle

Online Distributed Measurement of Dark I-V Curves in Photovoltaic Plants

by José Ignacio Morales-Aragonés, María del Carmen Alonso-García, Sara Gallardo-Saavedra, Víctor Alonso-Gómez, José Lorenzo Balenzategui, Alberto Redondo-Plaza and Luis Hernández-Callejo

Appl. Sci. 2021, 11(4), 1924; https://doi.org/10.3390/app11041924 - 22 Feb 2021

Cited by 7 | Viewed by 3608

Abstract

The inspection techniques for defects in photovoltaic modules are diverse. Among them, the inspection with measurements using current–voltage (I-V) curves is one of the most outstanding. I-V curves, which can be carried under illumination or in dark conditions, are widely used to detect [...] Read more.

The inspection techniques for defects in photovoltaic modules are diverse. Among them, the inspection with measurements using current–voltage (I-V) curves is one of the most outstanding. I-V curves, which can be carried under illumination or in dark conditions, are widely used to detect certain defects in photovoltaic modules. In a traditional way, these measurements are carried out by disconnecting the photovoltaic module from the string inside the photovoltaic plant. In this work, the researchers propose a methodology to perform online dark I-V curves of modules in photovoltaic plants without the need of disconnecting them from the string. For this, a combination of electronic boards in the photovoltaic modules and a bidirectional inverter are employed. The results are highly promising, and this methodology could be widely used in upcoming photovoltaic plants. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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16 pages, 5548 KiB

Open AccessArticle

A Study on the Improvement of Efficiency by Detection Solar Module Faults in Deteriorated Photovoltaic Power Plants

by Myeong-Hwan Hwang, Young-Gon Kim, Hae-Sol Lee, Young-Dae Kim and Hyun-Rok Cha

Appl. Sci. 2021, 11(2), 727; https://doi.org/10.3390/app11020727 - 13 Jan 2021

Cited by 15 | Viewed by 2782

Abstract

In recent years, photovoltaic (PV) power generation has attracted considerable attention as a new eco-friendly and renewable energy generation technology. With the recent development of semiconductor manufacturing technologies, PV power generation is gradually increasing. In this paper, we analyze the types of defects [...] Read more.

In recent years, photovoltaic (PV) power generation has attracted considerable attention as a new eco-friendly and renewable energy generation technology. With the recent development of semiconductor manufacturing technologies, PV power generation is gradually increasing. In this paper, we analyze the types of defects that form in PV power generation panels and propose a method for enhancing the productivity and efficiency of PV power stations by determining the defects of aging PV modules based on their temperature, power output, and panel images. The method proposed in the paper allows the replacement of individual panels that are experiencing a malfunction, thereby reducing the output loss of solar power generation plants. The aim is to develop a method that enables users to immediately check the type of failures among the six failure types that frequently occur in aging PV panels—namely, hotspot, panel breakage, connector breakage, busbar breakage, panel cell overheating, and diode failure—based on thermal images by using the failure detection system. By comparing the data acquired in the study with the thermal images of a PV power station, efficiency is increased by detecting solar module faults in deteriorated photovoltaic power plants. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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16 pages, 6555 KiB

Open AccessArticle

Effect of Distributed Photovoltaic Generation on Short-Circuit Currents and Fault Detection in Distribution Networks: A Practical Case Study

by Daniel Alcala-Gonzalez, Eva Maria García del Toro, María Isabel Más-López and Santiago Pindado

Appl. Sci. 2021, 11(1), 405; https://doi.org/10.3390/app11010405 - 04 Jan 2021

Cited by 13 | Viewed by 3272

Abstract

The increase in the installation of renewable energy sources in electrical systems has changed the power distribution networks, and a new scenario regarding protection devices has arisen. Distributed generation (DG) might produce artificial delays regarding the performance of protection devices when acting as [...] Read more.

The increase in the installation of renewable energy sources in electrical systems has changed the power distribution networks, and a new scenario regarding protection devices has arisen. Distributed generation (DG) might produce artificial delays regarding the performance of protection devices when acting as a result of short-circuits. In this study, the preliminary research results carried out to analyze the effect of renewable energy sources (photovoltaic, wind generation, etc.) on the protection devices of a power grid are described. In order to study this problem in a well-defined scenario, a quite simple distribution network (similar to the ones present in rural areas) was selected. The distribution network was divided into three protection zones so that each of them had DG. In the Institute of Electrical and Electronic Engineers (IEEE) system 13 bus test feeder, the short-circuits with different levels of penetration were performed from 1 MVA to 3 MVA (that represent 25%, 50%, and 75% of the total load in the network). In the simulations carried out, it was observed that the installation of DG in this distribution network produced significant changes in the short-circuit currents, and the inadequate performance of the protection devices and the delay in their operating times (with differences of up to 180% in relation to the case without DG). The latter, that is, the impacts of photovoltaic DG on the reactions of protection devices in a radial distribution network, is the most relevant outcome of this work. These are the first results obtained from a research collaboration framework established by staff from ETSI Civil and the IDR/UPM Institute, to analyze the effect of renewable energy sources (as DG) on the protection devices of a radial distribution network. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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7 pages, 1186 KiB

Open AccessArticle

Distribution Grid Stability—Influence of Inertia Moment of Synchronous Machines

by Tomáš Petrík, Milan Daneček, Ivan Uhlíř, Vladislav Poulek and Martin Libra

Appl. Sci. 2020, 10(24), 9075; https://doi.org/10.3390/app10249075 - 18 Dec 2020

Cited by 10 | Viewed by 1610

Abstract

This paper shows the influence of grid frequency oscillations on synchronous machines coupled to masses with large moments of inertia and solves the maximum permissible value of a moment of inertia on the shaft of a synchronous machine in respect to the oscillation [...] Read more.

This paper shows the influence of grid frequency oscillations on synchronous machines coupled to masses with large moments of inertia and solves the maximum permissible value of a moment of inertia on the shaft of a synchronous machine in respect to the oscillation of grid frequency. Grid frequency variation causes a load angle to swing on the synchronous machines connected to the grid. This effect is particularly significant in microgrids. This article does not consider the effects of other components of the system, such as the effects of frequency, voltage, and power regulators. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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20 pages, 1971 KiB

Open AccessArticle

Short-Term Forecasting of Photovoltaic Solar Power Production Using Variational Auto-Encoder Driven Deep Learning Approach

by Abdelkader Dairi, Fouzi Harrou, Ying Sun and Sofiane Khadraoui

Appl. Sci. 2020, 10(23), 8400; https://doi.org/10.3390/app10238400 - 25 Nov 2020

Cited by 70 | Viewed by 5121

Abstract

The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. [...] Read more.

The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, and storage. This paper intends to provide efficient short-term forecasting of solar power production using Variational AutoEncoder (VAE) model. Adopting the VAE-driven deep learning model is expected to improve forecasting accuracy because of its suitable performance in time-series modeling and flexible nonlinear approximation. Both single- and multi-step-ahead forecasts are investigated in this work. Data from two grid-connected plants (a 243 kW parking lot canopy array in the US and a 9 MW PV system in Algeria) are employed to show the investigated deep learning models’ performance. Specifically, the forecasting outputs of the proposed VAE-based forecasting method have been compared with seven deep learning methods, namely recurrent neural network, Long short-term memory (LSTM), Bidirectional LSTM, Convolutional LSTM network, Gated recurrent units, stacked autoencoder, and restricted Boltzmann machine, and two commonly used machine learning methods, namely logistic regression and support vector regression. The results of this investigation demonstrate the satisfying performance of deep learning techniques to forecast solar power and point out that the VAE consistently performed better than the other methods. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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28 pages, 6342 KiB

Open AccessArticle

Fault Detection of Wind Turbine Induction Generators through Current Signals and Various Signal Processing Techniques

by Yuri Merizalde, Luis Hernández-Callejo, Oscar Duque-Perez and Raúl Alberto López-Meraz

Appl. Sci. 2020, 10(21), 7389; https://doi.org/10.3390/app10217389 - 22 Oct 2020

Cited by 8 | Viewed by 2822

Abstract

In the wind industry (WI), a robust and effective maintenance system is essential. To minimize the maintenance cost, a large number of methodologies and mathematical models for predictive maintenance have been developed. Fault detection and diagnosis are carried out by processing and analyzing [...] Read more.

In the wind industry (WI), a robust and effective maintenance system is essential. To minimize the maintenance cost, a large number of methodologies and mathematical models for predictive maintenance have been developed. Fault detection and diagnosis are carried out by processing and analyzing various types of signals, with the vibration signal predominating. In addition, most of the published proposals for wind turbine (WT) fault detection and diagnosis have used simulations and test benches. Based on previous work, this research report focuses on fault diagnosis, in this case using the electrical signal from an operating WT electric generator and applying various signal analysis and processing techniques to compare the effectiveness of each. The WT used for this research is 20 years old and works with a squirrel-cage induction generator (SCIG) which, according to the wind farm control systems, was fault-free. As a result, it has been possible to verify the feasibility of using the current signal to detect and diagnose faults through spectral analysis (SA) using a fast Fourier transform (FFT), periodogram, spectrogram, and scalogram. Full article

(This article belongs to the Special Issue Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids)

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