Energies

Research

10 pages, 2297 KiB

Open AccessCommunication

Simulating Power Generation from Photovoltaics in the Polish Power System Based on Ground Meteorological Measurements—First Tests Based on Transmission System Operator Data

by Jakub Jurasz, Marcin Wdowikowski and Mariusz Figurski

Energies 2020, 13(16), 4255; https://doi.org/10.3390/en13164255 - 17 Aug 2020

Cited by 2 | Viewed by 2526

Abstract

The Polish power system is undergoing a slow process of transformation from coal to one that is renewables dominated. Although coal will remain a fundamental fuel in the coming years, the recent upsurge in installed capacity of photovoltaic (PV) systems should draw significant [...] Read more.

The Polish power system is undergoing a slow process of transformation from coal to one that is renewables dominated. Although coal will remain a fundamental fuel in the coming years, the recent upsurge in installed capacity of photovoltaic (PV) systems should draw significant attention. Owning to the fact that the Polish Transmission System Operator recently published the PV hourly generation time series in this article, we aim to explore how well those can be modeled based on the meteorological measurements provided by the Institute of Meteorology and Water Management. The hourly time series of PV generation on a country level and irradiation, wind speed, and temperature measurements from 23 meteorological stations covering one month are used as inputs to create an artificial neural network. The analysis indicates that available measurements combined with artificial neural networks can simulate PV generation on a national level with a mean percentage error of 3.2%. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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18 pages, 2133 KiB

Open AccessArticle

Multi-Step Solar Irradiance Forecasting and Domain Adaptation of Deep Neural Networks

by Giorgio Guariso, Giuseppe Nunnari and Matteo Sangiorgio

Energies 2020, 13(15), 3987; https://doi.org/10.3390/en13153987 - 02 Aug 2020

Cited by 28 | Viewed by 6107

Abstract

The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by using three kinds of predictor structures. Two approaches are introduced: Multi-Model (MM) and Multi-Output (MO). Model parameters are identified for two kinds of neural [...] Read more.

The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by using three kinds of predictor structures. Two approaches are introduced: Multi-Model (MM) and Multi-Output (MO). Model parameters are identified for two kinds of neural networks, namely the traditional feed-forward (FF) and a class of recurrent networks, those with long short-term memory (LSTM) hidden neurons, which is relatively new for solar radiation forecasting. The performances of the considered approaches are rigorously assessed by appropriate indices and compared with standard benchmarks: the clear sky irradiance and two persistent predictors. Experimental results on a relatively long time series of global solar irradiance show that all the networks architectures perform in a similar way, guaranteeing a slower decrease of forecasting ability on horizons up to several hours, in comparison to the benchmark predictors. The domain adaptation of the neural predictors is investigated evaluating their accuracy on other irradiance time series, with different geographical conditions. The performances of FF and LSTM models are still good and similar between them, suggesting the possibility of adopting a unique predictor at the regional level. Some conceptual and computational differences between the network architectures are also discussed. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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15 pages, 601 KiB

Open AccessArticle

A Novel Module Independent Straight Line-Based Fast Maximum Power Point Tracking Algorithm for Photovoltaic Systems

by Anjan Debnath, Temitayo O. Olowu, Imtiaz Parvez, Md Golam Dastgir and Arif Sarwat

Energies 2020, 13(12), 3233; https://doi.org/10.3390/en13123233 - 22 Jun 2020

Cited by 12 | Viewed by 2561

Abstract

The maximum power point tracking (MPPT) algorithm has become an integral part of many charge controllers that are used in photovoltaic (PV) systems. Most of the existing algorithms have a compromise among simplicity, tracking speed, ability to track accurately, and cost. In this [...] Read more.

The maximum power point tracking (MPPT) algorithm has become an integral part of many charge controllers that are used in photovoltaic (PV) systems. Most of the existing algorithms have a compromise among simplicity, tracking speed, ability to track accurately, and cost. In this work, a novel “straight-line approximation based Maximum Power Point (MPP) finding algorithm” is proposed where the intersections of two linear lines have been utilized to find the MPP, and investigated for its effectiveness in tracking maximum power points in case of rapidly changing weather conditions along with tracking speed using standard irradiance and temperature curves for validation. In comparison with a conventional Perturb and Observe (P&O) method, the Proposed method takes fewer iterations and also, it can precisely track the MPP s even in a rapidly varying weather condition with minimal deviation. The Proposed algorithm is also compared with P&O algorithm in terms of accuracy in duty cycle and efficiency. The results show that the errors in duty cycle and power extraction are much smaller than the conventional P&O algorithm. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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14 pages, 11682 KiB

Open AccessArticle

Deterioration Diagnosis of Solar Module Using Thermal and Visible Image Processing

by Heon Jeong, Goo-Rak Kwon and Sang-Woong Lee

Energies 2020, 13(11), 2856; https://doi.org/10.3390/en13112856 - 03 Jun 2020

Cited by 21 | Viewed by 2639

Abstract

Several factors cause the output degradation of the photovoltaic (PV) module. The main affecting elements are the higher PV module temperature, the shaded cell, the shortened or conducting bypass diodes, and the soiled and degraded PV array. In this paper, we introduce an [...] Read more.

Several factors cause the output degradation of the photovoltaic (PV) module. The main affecting elements are the higher PV module temperature, the shaded cell, the shortened or conducting bypass diodes, and the soiled and degraded PV array. In this paper, we introduce an image processing technique that automatically identifies the module generating the hot spots in the solar module. In order to extract feature points, we used the maximally stable extremal regions (MSER) method, which derives the area of interest by using the inrange function, using the blue color of the PV module. We propose an effective matching method for feature points and a homography translation technique. The temperature data derivation method and the normal/ abnormal decision method are described in order to enhance the performance. The effectiveness of the proposed system was evaluated through experiments. Finally, a thermal image analysis of approximately 240 modules was confirmed to be 97% consistent with the visual evaluation in the experimental results. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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13 pages, 1777 KiB

Open AccessArticle

Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells

by Premkumar Vincent, Gwenaelle Cunha Sergio, Jaewon Jang, In Man Kang, Jaehoon Park, Hyeok Kim, Minho Lee and Jin-Hyuk Bae

Energies 2020, 13(7), 1726; https://doi.org/10.3390/en13071726 - 04 Apr 2020

Cited by 13 | Viewed by 3702

Abstract

Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical [...] Read more.

Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers’ thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100% accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use 60.84% fewer simulations than the brute-force method. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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22 pages, 12535 KiB

Open AccessArticle

A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and Metaheuristic Global Optimization Algorithms

by Luis Miguel Pérez Archila, Juan David Bastidas-Rodríguez, Rodrigo Correa, Luz Adriana Trejos Grisales and Daniel Gonzalez-Montoya

Energies 2020, 13(4), 801; https://doi.org/10.3390/en13040801 - 12 Feb 2020

Cited by 4 | Viewed by 2110

Abstract

The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior [...] Read more.

The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior of a PV generator. However, the implicit model does not analyze different solution methods to reduce computation time. This paper formulates the solution of the implicit model of SP arrays as an optimization problem with restrictions for all the variables, i.e., submodules voltages, blocking diode voltage, and strings currents. Such an optimization problem is solved by using two deterministic (Trust-Region Dogleg and Levenberg Marquard) and two metaheuristics (Weighted Differential Evolution and Symbiotic Organism Search) optimization algorithms to reproduce the current–voltage (I–V) curves of small, medium, and large generators operating under homogeneous and non-homogeneous conditions. The performance of all optimization algorithms is evaluated with simulations and experiments. Simulation results indicate that both deterministic optimization algorithms correctly reproduce I–V curves in all the cases; nevertheless, the two metaheuristic optimization methods only reproduce the I–V curves for small generators, but not for medium and large generators. Finally, experimental results confirm the simulation results for small arrays and validate the reference model used in the simulations. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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16 pages, 920 KiB

Open AccessArticle

Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems

by Sunme Park, Soyeong Park, Myungsun Kim and Euiseok Hwang

Energies 2020, 13(3), 737; https://doi.org/10.3390/en13030737 - 07 Feb 2020

Cited by 10 | Viewed by 2402

Abstract

This work proposes a fault detection and imputation scheme for a fleet of small-scale photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site meteorological information, such as solar irradiance, is helpful for monitoring PV systems. However, collecting this type of weather [...] Read more.

This work proposes a fault detection and imputation scheme for a fleet of small-scale photovoltaic (PV) systems, where the captured data includes unlabeled faults. On-site meteorological information, such as solar irradiance, is helpful for monitoring PV systems. However, collecting this type of weather data at every station is not feasible for a fleet owing to the limitation of installation costs. In this study, to monitor a PV fleet efficiently, neighboring PV generation profiles were utilized for fault detection and imputation, as well as solar irradiance. For fault detection from unlabeled raw PV data, K-means clustering was employed to detect abnormal patterns based on customized input features, which were extracted from the fleet PVs and weather data. When a profile was determined to have an abnormal pattern, imputation for the corresponding data was implemented using the subset of neighboring PV data clustered as normal. For evaluation, the effectiveness of neighboring PV information was investigated using the actual rooftop PV power generation data measured at several locations in the Gwangju Institute of Science and Technology (GIST) campus. The results indicate that neighboring PV profiles improve the fault detection capability and the imputation accuracy. For fault detection, clustering-based schemes provided error rates of 0.0126 and 0.0223, respectively, with and without neighboring PV data, whereas the conventional prediction-based approach showed an error rate of 0.0753. For imputation, estimation accuracy was significantly improved by leveraging the labels of fault detection in the proposed scheme, as much as 18.32% reduction in normalized root mean square error (NRMSE) compared with the conventional scheme without fault consideration. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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23 pages, 6091 KiB

Open AccessArticle

A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

by Nailya Maitanova, Jan-Simon Telle, Benedikt Hanke, Matthias Grottke, Thomas Schmidt, Karsten von Maydell and Carsten Agert

Energies 2020, 13(3), 735; https://doi.org/10.3390/en13030735 - 07 Feb 2020

Cited by 29 | Viewed by 5160

Abstract

A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algorithm. The main challenge [...] Read more.

A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algorithm. The main challenge of the approach was using (1) publicly available weather reports without solar irradiance values and (2) measured PV power without any technical information about the PV system. Using this input data, the developed model can predict the power output of the investigated PV systems with adequate accuracy. The lowest seasonal mean absolute scaled error of the prediction was reached by maximum size of the training set. Transferability of the developed approach was proven by making predictions of the PV power for warm and cold periods and for two different PV systems located in Oldenburg and Munich, Germany. The PV power prediction made with publicly available weather data was compared to the predictions made with fee-based solar irradiance data. The usage of the solar irradiance data led to more accurate predictions even with a much smaller training set. Although the model with publicly available weather data needed greater training sets, it could still make adequate predictions. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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18 pages, 7357 KiB

Open AccessArticle

Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

by Promphak Dawan, Kobsak Sriprapha, Songkiate Kittisontirak, Terapong Boonraksa, Nitikorn Junhuathon, Wisut Titiroongruang and Surasak Niemcharoen

Energies 2020, 13(2), 351; https://doi.org/10.3390/en13020351 - 10 Jan 2020

Cited by 22 | Viewed by 2937

Abstract

The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using [...] Read more.

The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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14 pages, 3624 KiB

Open AccessArticle

A Side-Absorption Concentrated Module with a Diffractive Optical Element as a Spectral-Beam-Splitter for a Hybrid-Collecting Solar System

by An-Chi Wei, Wei-Jie Chang and Jyh-Rou Sze

Energies 2020, 13(1), 192; https://doi.org/10.3390/en13010192 - 01 Jan 2020

Cited by 3 | Viewed by 2491

Abstract

In this paper, we propose a side-absorption concentrated module with diffractive grating as a spectral-beam-splitter to divide sunlight into visible and infrared parts. The separate solar energy can be applied to different energy conversion devices or diverse applications, such as hybrid PV/T solar [...] Read more.

In this paper, we propose a side-absorption concentrated module with diffractive grating as a spectral-beam-splitter to divide sunlight into visible and infrared parts. The separate solar energy can be applied to different energy conversion devices or diverse applications, such as hybrid PV/T solar systems and other hybrid-collecting solar systems. Via the optimization of the geometric parameters of the diffractive grating, such as the grating period and height, the visible and the infrared bands can dominate the first and the zeroth diffraction orders, respectively. The designed grating integrated with the lens and the light-guide forms the proposed module, which is able to export visible and infrared light individually. This module is demonstrated in the form of an array consisting of seven units, successfully out-coupling the spectral-split beams by separate planar ports. Considering the whole solar spectrum, the simulated and measured module efficiencies of this module were 45.2% and 34.8%, respectively. Analyses of the efficiency loss indicated that the improvement of the module efficiency lies in the high fill-factor lens array, the high-reflectance coating, and less scattering. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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13 pages, 2737 KiB

Open AccessArticle

A Novel MPPT Technique for Single Stage Grid-Connected PV Systems: T4S

by Luigi Costanzo and Massimo Vitelli

Energies 2019, 12(23), 4501; https://doi.org/10.3390/en12234501 - 26 Nov 2019

Cited by 12 | Viewed by 2703

Abstract

In this paper, a novel maximum power point tracking (MPPT) technique, which has been named T4S (a technique based on the proper setting of the sign of the slope of the photovoltaic voltage reference signal), is presented and discussed. It is specifically designed [...] Read more.

In this paper, a novel maximum power point tracking (MPPT) technique, which has been named T4S (a technique based on the proper setting of the sign of the slope of the photovoltaic voltage reference signal), is presented and discussed. It is specifically designed with reference to a single-stage grid-connected PV system. Its performance is numerically compared with that of the well-known and widely used perturb and observe (P&O) MPPT technique. The results of the numerical simulations confirm the validity of the proposed MPPT technique which exhibited a slightly better performance, under stationary and also time-varying irradiance conditions. In addition, the T4S technique is characterized by the following features: it does not require explicit power detection or calculation and, moreover, it allows the tracking of the maximum average power injected into the grid rather than the tracking of the maximum instantaneous power extracted by the PV source. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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14 pages, 4359 KiB

Open AccessArticle

Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach

by Martin Ćalasan, Dražen Jovanović, Vesna Rubežić, Saša Mujović and Slobodan Đukanović

Energies 2019, 12(21), 4209; https://doi.org/10.3390/en12214209 - 04 Nov 2019

Cited by 31 | Viewed by 5194

Abstract

Estimation of single-diode and two-diode solar cell parameters by using chaotic optimization approach (COA) is addressed. The proposed approach is based on the use of experimentally determined current-voltage (I-V) characteristics. It outperforms a large number of other techniques in terms of [...] Read more.

Estimation of single-diode and two-diode solar cell parameters by using chaotic optimization approach (COA) is addressed. The proposed approach is based on the use of experimentally determined current-voltage (I-V) characteristics. It outperforms a large number of other techniques in terms of average error between the measured and the estimated I-V values, as well as of time complexity. Implementation of the proposed approach on the I-V curves measured in laboratory environment for different values of solar irradiation and temperature prove its applicability in terms of accuracy, effectiveness and the ease of implementation for a wide range of practical environment conditions. The COA-based parameter estimation is, therefore, useful for PV power converter designers who require fast and accurate model for PV cell/module. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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23 pages, 5160 KiB

Open AccessArticle

Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization

by Huawen Sheng, Chunquan Li, Hanming Wang, Zeyuan Yan, Yin Xiong, Zhenting Cao and Qianying Kuang

Energies 2019, 12(18), 3527; https://doi.org/10.3390/en12183527 - 13 Sep 2019

Cited by 55 | Viewed by 2988

Abstract

Photovoltaic (PV) models’ parameter extraction with the tested current-voltage values is vital for the optimization, control, and evaluation of the PV systems. To reliably and accurately extract their parameters, this paper presents one improved moths-flames optimization (IMFO) method. In the IMFO, a double [...] Read more.

Photovoltaic (PV) models’ parameter extraction with the tested current-voltage values is vital for the optimization, control, and evaluation of the PV systems. To reliably and accurately extract their parameters, this paper presents one improved moths-flames optimization (IMFO) method. In the IMFO, a double flames generation (DFG) strategy is proposed to generate two different types of target flames for guiding the flying of moths. Furthermore, two different update strategies are developed for updating the positions of moths. To greatly balance the exploitation and exploration, we adopt a probability to rationally select one of the two update strategies for each moth at each iteration. The proposed IMFO is used to distinguish the parameter of three test PV models including single diode model (SDM), double diode model (DDM), and PV module model (PMM). The results indicate that, compared with other well-established methods, the proposed IMFO can obtain an extremely promising performance. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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19 pages, 3684 KiB

Open AccessArticle

Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

by Amith Khandakar, Muhammad E. H. Chowdhury, Monzure- Khoda Kazi, Kamel Benhmed, Farid Touati, Mohammed Al-Hitmi and Antonio Jr S. P. Gonzales

Energies 2019, 12(14), 2782; https://doi.org/10.3390/en12142782 - 19 Jul 2019

Cited by 106 | Viewed by 7523

Abstract

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m² in the Arabian Peninsula is an exploitable [...] Read more.

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m² in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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16 pages, 2194 KiB

Open AccessArticle

An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico

by Nun Pitalúa-Díaz, Fernando Arellano-Valmaña, Jose A. Ruz-Hernandez, Yasuhiro Matsumoto, Hussain Alazki, Enrique J. Herrera-López, Jesús Fernando Hinojosa-Palafox, A. García-Juárez, Ricardo Arturo Pérez-Enciso and Enrique Fernando Velázquez-Contreras

Energies 2019, 12(14), 2662; https://doi.org/10.3390/en12142662 - 11 Jul 2019

Cited by 17 | Viewed by 2855

Abstract

In this manuscript, distinct approaches were used in order to obtain the best electrical power estimation from photovoltaic systems located at different selected places in Mexico. Multiple Linear Regression (MLR) and Gradient Descent Optimization (GDO) were applied as statistical methods and they were [...] Read more.

In this manuscript, distinct approaches were used in order to obtain the best electrical power estimation from photovoltaic systems located at different selected places in Mexico. Multiple Linear Regression (MLR) and Gradient Descent Optimization (GDO) were applied as statistical methods and they were compared against an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an intelligent technique. The data gathered involved solar radiation, outside temperature, wind speed, daylight hour and photovoltaic power; collected from on-site real-time measurements at Mexico City and Hermosillo City, Sonora State. According to our results, all three methods achieved satisfactory performances, since low values were obtained for the convergence error. The GDO improved the MLR results, minimizing the overall error percentage value from 7.2% to 6.9% for Sonora and from 2.0% to 1.9% for Mexico City; nonetheless, ANFIS overcomes both statistical methods, achieving a 5.8% error percentage value for Sonora and 1.6% for Mexico City. The results demonstrated an improvement by applying intelligent systems against statistical techniques achieving a lesser mean average error. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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14 pages, 5179 KiB

Open AccessArticle

Complex Network Analysis of Photovoltaic Plant Operations and Failure Modes

by Fabrizio Bonacina, Alessandro Corsini, Lucio Cardillo and Francesca Lucchetta

Energies 2019, 12(10), 1995; https://doi.org/10.3390/en12101995 - 24 May 2019

Cited by 6 | Viewed by 3059

Abstract

This paper presents a novel data-driven approach, based on sensor network analysis in Photovoltaic (PV) power plants, to unveil hidden precursors in failure modes. The method is based on the analysis of signals from PV plant monitoring, and advocates the use of graph [...] Read more.

This paper presents a novel data-driven approach, based on sensor network analysis in Photovoltaic (PV) power plants, to unveil hidden precursors in failure modes. The method is based on the analysis of signals from PV plant monitoring, and advocates the use of graph modeling techniques to reconstruct and investigate the connectivity among PV field sensors, as is customary for Complex Network Analysis (CNA) approaches. Five month operation data are used in the present study. The results showed that the proposed methodology is able to discover specific hidden dynamics, also referred to as emerging properties in a Complexity Science perspective, which are not visible in the observation of individual sensor signal but are closely linked to the relationships occurring at the system level. The application of exploratory data analysis techniques on those properties demonstrated, for the specific plant under scrutiny, potential for early fault detection. Full article

(This article belongs to the Special Issue Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications)

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