scholarly journals Energy Production Benefits by Wind and Wave Energies for the Autonomous System of Crete

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2741 ◽  
Author(s):  
George Lavidas ◽  
Vengatesan Venugopal
Keyword(s):  
Wind Turbine ◽  
Wave Energy ◽  
Energy Production ◽  
Large Scale ◽  
Capital Expenditure ◽  
Temporal Correlation ◽  
Clean Energy ◽  
Limiting Factor ◽  
Spatio Temporal ◽  
Visual Impacts

At autonomous electricity grids Renewable Energy (RE) contributes significantly to energy production. Offshore resources benefit from higher energy density, smaller visual impacts, and higher availability levels. Offshore locations at the West of Crete obtain wind availability ≈80%, combining this with the installation potential for large scale modern wind turbines (rated power) then expected annual benefits are immense. Temporal variability of production is a limiting factor for wider adaptation of large offshore farms. To this end multi-generation with wave energy can alleviate issues of non-generation for wind. Spatio-temporal correlation of wind and wave energy production exhibit that wind and wave hybrid stations can contribute significant amounts of clean energy, while at the same time reducing spatial constrains and public acceptance issues. Offshore technologies can be combined as co-located or not, altering contribution profiles of wave energy to non-operating wind turbine production. In this study a co-located option contributes up to 626 h per annum, while a non co-located solution is found to complement over 4000 h of a non-operative wind turbine. Findings indicate the opportunities associated not only in terms of capital expenditure reduction, but also in the ever important issue of renewable variability and grid stability.

A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

Sensor Review ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Jinghan Du ◽  
Haiyan Chen ◽  
Weining Zhang
Keyword(s):  
Deep Learning ◽  
Sensor Networks ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Correlation ◽  
Data Recovery ◽  
Series Data ◽  
Learning Method ◽  
Content Type ◽  
Spatio Temporal

Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network. Findings This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness. Originality/value A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

scholarly journals Test campaign of a ducted wind turbine in real operating conditions

2019 ◽  
Vol 113 ◽  
pp. 03005
Author(s):  
Enrico Valditerra ◽  
Massimo Rivarolo ◽  
Aristide F. Massardo ◽  
Marco Gualco
Keyword(s):  
Wind Turbine ◽  
Urban Areas ◽  
Energy Production ◽  
Electrical Energy ◽  
Clean Energy ◽  
Operating Conditions ◽  
Pollutant Emissions ◽  
Ambient Conditions ◽  
Wind Speeds ◽  
Electrical Energy Production

Wind turbine installation worldwide has increased at unrested pace, as it represents a 100% clean energy with zero CO2 and pollutant emissions. However, visual and acoustic impact of wind turbines is still a drawback, in particular in urban areas. This paper focuses on the performance evaluation of an innovative horizontal axis ducted wind turbine, installed in the harbour of Genova (Italy) in 2018: the turbine was designed in order to minimize visual and acoustic impacts and maximize electrical energy production, also during low wind speed periods. The preliminary study and experimental analyses, performed by the authors in a previous study, showed promising results in terms of energy production, compared to a traditional generator ( factor >2.5 on power output). In the present paper, the test campaign on a scaled-up prototype, installed in the urban area of Genova, is performed, with a twofold objective: (i) comparison of the ducted innovative turbine with a standard one, in order to verify the increase in energy production; (ii) analysis of the innovative turbine for different wind speeds and directions, evaluating the influence of ambient conditions on performance. Finally, based on the obtained results, an improved setup is proposed for the ducted wind turbine, in order to further increase energy production mitigating its visual impact.

scholarly journals Impact of Economic Indicators on the Integrated Design of Wind Turbine Systems

2018 ◽  
Vol 8 (9) ◽  
pp. 1668 ◽  
Author(s):  
Jianghai Wu ◽  
Tongguang Wang ◽  
Long Wang ◽  
Ning Zhao
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Large Scale ◽  
Wind Farm ◽  
Net Present Value ◽  
Integrated Design ◽  
Aerodynamic Performance ◽  
System Level ◽  
Blade Length

This article presents a framework to integrate and optimize the design of large-scale wind turbines. Annual energy production, load analysis, the structural design of components and the wind farm operation model are coupled to perform a system-level nonlinear optimization. As well as the commonly used design objective levelized cost of energy (LCoE), key metrics of engineering economics such as net present value (NPV), internal rate of return (IRR) and the discounted payback time (DPT) are calculated and used as design objectives, respectively. The results show that IRR and DPT have the same effect as LCoE since they all lead to minimization of the ratio of the capital expenditure to the energy production. Meanwhile, the optimization for NPV tends to maximize the margin between incomes and costs. These two types of economic metrics provide the minimal blade length and maximal blade length of an optimal blade for a target wind turbine at a given wind farm. The turbine properties with respect to the blade length and tower height are also examined. The blade obtained with economic optimization objectives has a much larger relative thickness and smaller chord distributions than that obtained for high aerodynamic performance design. Furthermore, the use of cost control objectives in optimization is crucial in improving the economic efficiency of wind turbines and sacrificing some aerodynamic performance can bring significant reductions in design loads and turbine costs.

scholarly journals Piezoeletricity as an Alternative Source of Electric Power Generation in an Education Institution in the Amazon

2019 ◽  
Vol 7 (11) ◽  
pp. 467-474
Author(s):  
Yasmin Souza de Carvalho ◽  
Elizeu Moraes da Silva ◽  
Fabiana Rocha Pinto ◽  
David Barbosa de Alencar ◽  
Igor Felipe Oliveira Bezerra
Keyword(s):  
Energy Production ◽  
Large Scale ◽  
New Technology ◽  
Education Institution ◽  
Clean Energy ◽  
Piezoelectric Ceramics ◽  
Production Model ◽  
Small Scale ◽  
Alternative Source ◽  
Short Time

The development of technologies for the generation of clean and sustainable energy has brought significant changes to the energy sector in Brazil and worldwide. The newest technology is piezoelectricity, which although it has been studied for years, has not yet gained its proper space in the national and international electrical matrices. With this in mind, the present work aims to describe the process of installing a prototype carpet using piezoelectric ceramics that, through a force applied by any individual, is capable of generating enough energy for the operation of a turnstile in a HEI from Manaus-AM. The application was tested by modeling applying mathematical equations in the working of the prototype developed by APC International. Different answers were obtained considering the different dimensions for the piezoelectric parts. However, it is understood that this energy production model, treated as a new technology, presents economic viability in its implementation. One of the results demonstrates that the smaller the ceramic piece, the greater the energy production and can be adapted over time to respond to large productions. Thus, it is concluded from the calculations made that piezoelectric ceramics is an excellent alternative for the production of clean energy on a small scale, in a short time, and in the long term can reach large scale.

scholarly journals Super-large-scale flow visualization using natural snowfall for the study of utility-scale wind turbine flows

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Aliza Abraham ◽  
Jiarong Hong
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Large Scale ◽  
Complex Dynamic ◽  
Wide Range ◽  
Spatio Temporal ◽  
Large Scale Flow ◽  
Utility Scale ◽  
Fundamental Physical ◽  
Turbine Installation

With the rapid growth of wind turbine installation in recent decades, fundamental physical understanding of the flow around wind turbines and farms is becoming increasingly critical for further efficiency increases. However, the effort to develop this understanding is hindered by the significant challenges involved in modelling such a complex dynamic system with a wide range of relevant scales (blade boundary layer thickness at ∼ 1 mm to atmospheric scales at ∼ 1 km). Additionally, conventional methods used to measure air flow around wind turbines in the field (e.g., lidar) are limited by low spatio-temporal resolutions.

scholarly journals A Multiscale Spatio-Temporal Convolutional Deep Belief Network for Sensor Fault Detection of Wind Turbine

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3580 ◽  
Author(s):  
Hong Wang ◽  
Hongbin Wang ◽  
Guoqian Jiang ◽  
Yueling Wang ◽  
Shuang Ren
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Temporal Correlation ◽  
Deep Belief Network ◽  
Stable Operation ◽  
Sensor Fault ◽  
Temporal Characteristics ◽  
Belief Network ◽  
Sensor Fault Detection ◽  
Spatio Temporal

Sensor fault detection of wind turbines plays an important role in improving the reliability and stable operation of turbines. The supervisory control and data acquisition (SCADA) system of a wind turbine provides promising insights into sensor fault detection due to the accessibility of the data and the abundance of sensor information. However, SCADA data are essentially multivariate time series with inherent spatio-temporal correlation characteristics, which has not been well considered in the existing wind turbine fault detection research. This paper proposes a novel classification-based fault detection method for wind turbine sensors. To better capture the spatio-temporal characteristics hidden in SCADA data, a multiscale spatio-temporal convolutional deep belief network (MSTCDBN) was developed to perform feature learning and classification to fulfill the sensor fault detection. A major superiority of the proposed method is that it can not only learn the spatial correlation information between several different variables but also capture the temporal characteristics of each variable. Furthermore, this method with multiscale learning capability can excavate interactive characteristics between variables at different scales of filters. A generic wind turbine benchmark model was used to evaluate the proposed approach. The comparative results demonstrate that the proposed method can significantly enhance the fault detection performance.

scholarly journals Risk Assessment of a Wind Turbine Using an AHP-MABAC Approach with Grey System Theory: A Case Study of Morocco

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Rim Bakhat ◽  
Mohammed Rajaa
Keyword(s):  
Risk Factors ◽  
Wind Turbine ◽  
Energy Production ◽  
Failure Modes ◽  
Systems Design ◽  
Multicriteria Decision Making ◽  
Clean Energy ◽  
Grey System Theory ◽  
Grey System ◽  
Criticality Analysis

Clean energy has become a growing concern, and many organizations pay attention to environmental protection and energy production as well. In the last few decades, the wind turbine has become the core of clean energy production and has advanced in generating electricity from 40 kW to 5 mW. However, the new design of the wind turbine causes several potential failures which frequently lead to the inability to accomplish the operational requirements intended to meet the customers’ expectations. As a solution to this problem, the present paper proposes a novel systematic approach that combines Multicriteria Decision-Making (MCDM) techniques and Failure Mode Effects and Criticality Analysis (FMECA) tool to reveal the fatal failures and optimize the maintenance actions. To further develop the preceding framework, this work will not only rely on the three risk factors that are involved in the traditional Risk Priority Numbers (RPN) approach but also will consider the economic aspect of the system. In the proposed approach, the grey Analytic Hierarchy Process (AHP) method is applied in the first place to calculate the weights of the four risk factors criteria. Second, the grey Multiattribute Border Approximation area Comparison (MABAC) technique is applied to rank the failure modes and their criticality on the whole system. The proposed model is verified within an organization of renewable energy production in Morocco. Furthermore, the results of the comparative and the sensitivity analysis affirm that the proposed research framework is adequate for enhancing other complex systems design, especially in a developing world where funds and resources are scarce.

scholarly journals Agrivoltaics: A Climate-Smart Agriculture Approach for Indian Farmers

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1277
Author(s):  
Rakeshkumar Mahto ◽  
Deepak Sharma ◽  
Reshma John ◽  
Chandrasekhar Putcha
Keyword(s):  
Water Conservation ◽  
Energy Production ◽  
Large Scale ◽  
Swot Analysis ◽  
Production Capacity ◽  
Clean Energy ◽  
Economic Stability ◽  
Smart Agriculture ◽  
Sustainable Income ◽  
Commit Suicide

India is a leader when it comes to agriculture. A significant part of the country’s population depends on agriculture for livelihood. However, many of them face challenges due to using unreliable farming techniques. Sometimes the challenges increase to the extent that they commit suicide. Besides, India is highly populated, and its population is steadily increasing, requiring its government to grow its GDP and increase its energy supply proportionately. This paper reviews integrating solar farming with agriculture, known as Agrivoltaics, as a Climate-Smart Agriculture (CSA) option for Indian farmers. This study is further supported by the Strength, Weaknesses, Opportunities, and Threats (SWOT) analysis of agrivoltaics. Using the SWOT analysis, this article presents how agrivoltaics can make agriculture sustainable and reliable. This paper identifies rural electrification, water conservation, yield improvement, sustainable income generation, and reduction in the usage of pesticides as the strengths of agrivoltaics. Similarly, the paper presents weaknesses, opportunities, and threats to agrivoltaics in India. The research concludes with the findings that agrivoltaics have the potential of meeting multiple objectives such as meeting global commitments, offering employment, providing economic stability, increasing clean energy production capacity, conserving natural resources, and succeeding in several others. The paper also includes a discussion about the findings, suggestions, and implications of adopting agrivoltaics on a large scale in India.

scholarly journals Research on optimization strategy of grid frequency modulation based on doubly-fed wind turbines

2020 ◽  
Author(s):  
Chao Wang ◽  
Jianyuan Xu ◽  
Liang Wang ◽  
Dan Song
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Wind Turbines ◽  
Frequency Modulation ◽  
Control Strategy ◽  
Large Scale ◽  
Synchronous Generator ◽  
Clean Energy ◽  
Frequency Regulation ◽  
Doubly Fed

Abstract The increasing global energy and environmental problems are encouraging to the development and utilization of renewable and clean energy in various countries. Wind power is one of the major source in large-scale renewable energy applications. However, the frequency regulation becomes a critical issue while the technology is spreading. Research on the frequency modulation (FM) technology of wind turbines and its control strategy for future power grids become significant. The paper proposes a novel coordinated frequency control strategy with the synchronous generator to solve the unmatched state between the output power of the doubly-fed wind turbines (doubly-fed induction generators) and the grid frequency, combined with the frequency response characteristics of the synchronous generator. The FM coordination strategy is formulated by the modulation coefficient from current wind speed and operation mode of each wind turbine. By coordinating the FM output of the doubly-fed wind turbine and the synchronous generator within the allowable range of frequency deviation, it will achieve the dual goal of reducing the frequency regulation pressure of the synchronous generator and indirectly reducing the abandoned wind volume of the wind turbine. The simulation is carried out on the MATLAB/SIMULINK platform. The results show that the presenting variable coefficient frequency modulation strategy could significant smooth the wind power fluctuation, and allow the reserve power of the doubly-fed wind turbine can fully engaged in frequency modulation which will reduces the frequency modulation pressure of the synchronous generator in the system.

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