Research

23 pages, 4241 KiB

Open AccessArticle

Methodology to Implement a Microgrid in a University Campus

by Yuly V. Garcia, Oscar Garzon, Fabio Andrade, Agustin Irizarry and Omar F. Rodriguez-Martinez

Appl. Sci. 2022, 12(9), 4563; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094563 - 30 Apr 2022

Cited by 4 | Viewed by 2605

This paper presents the method we followed to design a microgrid at a university campus based on available resources. Due to introduction of renewable resources to produce energy, a methodology that allows design a microgrid in a university campus is very useful. Hence, [...] Read more.

This paper presents the method we followed to design a microgrid at a university campus based on available resources. Due to introduction of renewable resources to produce energy, a methodology that allows design a microgrid in a university campus is very useful. Hence, we present a series of steps that must be carried out to estimate the resource to be used, the installation, area needed, and the capacity of the systems needed are also described. In addition, the models of the distributed resources that constitutes the microgrid are presented and explained. To validate the proposed methodology, simulations were performed using Opal-RT-LAb. As a test scenario we selected the Mayagüez campus of the University of Puerto Rico where we conducted analysis of the available resource and capacity of the systems is needed to satisfy demand of critical loads, considering a predetermined number of days of austerity. Our study results in determination of dimensions, cost and effectiveness of the microgrid. Simulations results also show that the proposed microgrid satisfy demand with the same reliability, or better, than the traditional electrical network. Additionally, the best options for this purpose are photovoltaic, batteries, and combined heat and power, if the technological advances and availability of resources to the date for Puerto Rico are considered. Full article

(This article belongs to the Special Issue Boosting Wind Power Integration)

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20 pages, 2388 KiB

Open AccessArticle

Comparison of Optimal Control Designs for a 5 MW Wind Turbine

by Yiza-srikanth Reddy and Sung-ho Hur

Appl. Sci. 2021, 11(18), 8774; https://0-doi-org.brum.beds.ac.uk/10.3390/app11188774 - 21 Sep 2021

Cited by 8 | Viewed by 2564

Abstract

Optimal controllers, namely Model Predictive Control (MPC),

H_{\infty}

Control (

H_{\infty}

), and Linear Quadratic Gaussian control (LQG), are designed for a 5 MW horizontal-axis variable-speed wind turbine. The control design models required as part of the optimal control design are [...] Read more.

Optimal controllers, namely Model Predictive Control (MPC),

H_{\infty}

Control (

H_{\infty}

), and Linear Quadratic Gaussian control (LQG), are designed for a 5 MW horizontal-axis variable-speed wind turbine. The control design models required as part of the optimal control design are obtained by using a high fidelity aeroelastic model (i.e., DNV Bladed). The optimal controllers are eventually designed in three operating modes: below-rated, just below-rated, and above rated-wind speeds, based on linearized control design models. The linearized models are reduced by using a model reduction technique to facilitate the design of optimal controllers. The controllers are analyzed not only in the time domain but also in the frequency domain and on the torque/speed plane. Simulation results demonstrated that optimal controllers perform better than the standard proportional-integral-derivative (PID) controller, particularly for removing oscillation due to the drive-train mode without incorporating a drive-train damper. Full article

(This article belongs to the Special Issue Boosting Wind Power Integration)

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30 pages, 5042 KiB

Open AccessFeature PaperArticle

Quantification of the Information Loss Resulting from Temporal Aggregation of Wind Turbine Operating Data

by Mattia Beretta, Karoline Pelka, Jordi Cusidó and Timo Lichtenstein

Appl. Sci. 2021, 11(17), 8065; https://0-doi-org.brum.beds.ac.uk/10.3390/app11178065 - 31 Aug 2021

Cited by 3 | Viewed by 2688

Abstract

SCADA operating data are more and more used across the wind energy domain, both as a basis for power output prediction and turbine health status monitoring. Current industry practice to work with this data is by aggregating the signals at coarse resolution of [...] Read more.

SCADA operating data are more and more used across the wind energy domain, both as a basis for power output prediction and turbine health status monitoring. Current industry practice to work with this data is by aggregating the signals at coarse resolution of typically 10-min averages, in order to reduce data transmission and storage costs. However, aggregation, i.e., downsampling, induces an inevitable loss of information and is one of the main causes of skepticism towards the use of SCADA operating data to model complex systems such as wind turbines. This research aims to quantify the amount of information that is lost due to this downsampling of SCADA operating data and characterize it with respect to the external factors that might influence it. The issue of information loss is framed by three key questions addressing effects on the local and global scale as well as the influence of external conditions. Moreover, recommendations both for wind farm operators and researchers are provided with the aim to improve the information content. We present a methodology to determine the ideal signal resolution that minimized storage footprint, while guaranteeing high quality of the signal. Data related to the wind, electrical signals, and temperatures of the gearbox resulted as the critical signals that are largely affected by an information loss upon aggregation and turned out to be best recorded and stored at high resolutions. All analyses were carried out using more than one year of 1 Hz SCADA data of onshore wind farm counting 12 turbines located in the UK. Full article

(This article belongs to the Special Issue Boosting Wind Power Integration)

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17 pages, 2166 KiB

Open AccessArticle

Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis

by Mattia Beretta, Yolanda Vidal, Jose Sepulveda, Olga Porro and Jordi Cusidó

Appl. Sci. 2021, 11(16), 7523; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167523 - 17 Aug 2021

Cited by 13 | Viewed by 2598

Abstract

The goal of this paper is to develop, implement, and validate a methodology for wind turbines’ main bearing fault prediction based on an ensemble of an artificial neural network (normality model designed at turbine level) and an isolation forest (anomaly detection model designed [...] Read more.

The goal of this paper is to develop, implement, and validate a methodology for wind turbines’ main bearing fault prediction based on an ensemble of an artificial neural network (normality model designed at turbine level) and an isolation forest (anomaly detection model designed at wind park level) algorithms trained only on SCADA data. The normal behavior and the anomalous samples of the wind turbines are identified and several interpretable indicators are proposed based on the predictions of these algorithms, to provide the wind park operators with understandable information with enough time to plan operations ahead and avoid unexpected costs. The stated methodology is validated in a real underproduction wind park composed by 18 wind turbines. Full article

(This article belongs to the Special Issue Boosting Wind Power Integration)

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19 pages, 3841 KiB

Open AccessFeature PaperArticle

Exploring the Effect of Temporal Aggregation on SCADA Data for Wind Turbine Prognosis Using a Normality Model

by Pere Marti-Puig, Alejandro Bennásar-Sevillá, Alejandro Blanco-M. and Jordi Solé-Casals

Appl. Sci. 2021, 11(14), 6405; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146405 - 11 Jul 2021

Cited by 6 | Viewed by 1478

Abstract

Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data provides four statistical [...] Read more.

Today, the use of SCADA data for predictive maintenance and forecasting of wind turbines in wind farms is gaining popularity due to the low cost of this solution compared to others that require the installation of additional equipment. SCADA data provides four statistical measures (mean, standard deviation, maximum value, and minimum value) of hundreds of wind turbine magnitudes, usually in a 5-min or 10-min interval. Several studies have analysed the loss of information associated with the reduction of information when using five minutes instead of four seconds as a sampling frequency, or when compressing a time series recorded at 5 min to 10 min, concluding that some, but not all, of these magnitudes are seriously affected. However, to our knowledge, there are no studies on increasing the time interval beyond 10 min to take these four statistical values, and how this aggregation affects prognosis models. Our work shows that, despite the irreversible loss of information that occurs in the first 5 min, increasing the time considered to take the four representative statistical values improves the performance of the predicted targets in normality models. Full article

(This article belongs to the Special Issue Boosting Wind Power Integration)

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14 pages, 2681 KiB

Open AccessArticle

Low-Voltage Ride-Through of the Novel Voltage Source-Controlled PMSG-Based Wind Turbine Based on Switching the Virtual Resistor

by Shun Sang, Binhui Pei, Jiejie Huang, Lei Zhang and Xiaocen Xue

Appl. Sci. 2021, 11(13), 6204; https://0-doi-org.brum.beds.ac.uk/10.3390/app11136204 - 04 Jul 2021

Cited by 4 | Viewed by 2347

Abstract

Voltage source (VS) control based on inertia synchronization is a novel phase lock loop (PLL)-less autonomous grid-synchronization control strategy suitable for the permanent magnet synchronous generator (PMSG)-based wind turbine. It can autonomously sense grid frequency fluctuations by adopting the dynamics of DC-link capacitor, [...] Read more.

Voltage source (VS) control based on inertia synchronization is a novel phase lock loop (PLL)-less autonomous grid-synchronization control strategy suitable for the permanent magnet synchronous generator (PMSG)-based wind turbine. It can autonomously sense grid frequency fluctuations by adopting the dynamics of DC-link capacitor, and it has the advantage of stable operation in an extremely weak grid. This paper further studies the low-voltage ride-through (LVRT) of the PMSG-based wind turbine under the VS control, and presents a wind turbine structure with the additional energy storage battery on the DC side, which not only improves its LVRT capability but also enables the wind turbine to participate in the grid primary frequency regulation. The transient characteristics of VS-controlled wind turbines after the occurrence of the short-circuit fault are analyzed, and a current suppression strategy via switching the virtual resistor in the control loop of the grid-side converter (GCS) is presented. Through coordination with the energy storage battery, the LVRT of the PMSG-based wind turbine is realized, which has the advantage of withstanding a long-time short-circuit fault. Finally, based on the PSCAD/EMTDC simulation platform, the feasibility of the control strategy and the correctness of the theoretical analysis are verified. Full article

(This article belongs to the Special Issue Boosting Wind Power Integration)

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19 pages, 3738 KiB

Open AccessArticle

Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control

by Sung-ho Hur and Yiza-srikanth Reddy

Appl. Sci. 2021, 11(12), 5661; https://0-doi-org.brum.beds.ac.uk/10.3390/app11125661 - 18 Jun 2021

Cited by 3 | Viewed by 2040

Abstract

The estimation of variables that are normally not measured or are unmeasurable could improve control and condition monitoring of wind turbines. A cost-effective estimation method that exploits machine learning is introduced in this paper. The proposed method allows a potentially expensive sensor, for [...] Read more.

The estimation of variables that are normally not measured or are unmeasurable could improve control and condition monitoring of wind turbines. A cost-effective estimation method that exploits machine learning is introduced in this paper. The proposed method allows a potentially expensive sensor, for example, a LiDAR sensor, to be shared between multiple turbines in a cluster. One turbine in a cluster is equipped with a sensor and the remaining turbines are equipped with a nonlinear estimator that acts as a sensor, which significantly reduces the cost of sensors. The turbine with a sensor is used to train the estimator, which is based on an artificial neural network. The proposed method could be used to train the estimator to estimate various different variables; however, this study focuses on wind speed and aerodynamic torque. A new controller is also introduced that uses aerodynamic torque estimated by the neural network-based estimator and is compared with the original controller, which uses aerodynamic torque estimated by a conventional aerodynamic torque estimator, demonstrating improved results. Full article

(This article belongs to the Special Issue Boosting Wind Power Integration)

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16 pages, 1267 KiB

Open AccessArticle

Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

by Satyendra Singh, Manoj Fozdar, Hasmat Malik, Maria del Valle Fernández Moreno and Fausto Pedro García Márquez

Appl. Sci. 2021, 11(10), 4438; https://0-doi-org.brum.beds.ac.uk/10.3390/app11104438 - 13 May 2021

Cited by 13 | Viewed by 1825

Abstract

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, [...] Read more.

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach. Full article

(This article belongs to the Special Issue Boosting Wind Power Integration)

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