Abstract

     The paper studies the performance of Offshore Wind Turbines (OWTs) founded on Suction Bucket Jackets (SBJs) in clay under combined wind and seismic loading. A detailed 3D FE model of the soil–foundation–structure (SFS) system is developed and used as a benchmark to assess the efficiency of an enhanced Winkler-based “Caisson-on-Winkler-Soil” (CWS) model, where the soil is replaced by nonlinear hysteretic elements. The proposed CWS model captures residual deformations and hysteresis and offers physical coupling between vertical and moment loading. It allows excellent prediction of the  failure envelope in the most relevant first quadrant of  space, where the loads act in the same direction. Despite its successful application for the simulation of inertial loading, the CWS model fails to reproduce the dual shearing mechanism that develops at the caisson shaft during shaking, stemming from the combination of kinematic soil shearing due to the vertically propagating shear waves, and shearing due to the superstructure inertial response, thus underpredicting the co-seismic caisson settlements. For the prediction of the latter, the research utilizes spectrum compatible input motions and 3D FE models of varying geometric and material properties to derive linear regression equations that correlate the co-seismic dimensionless settlement of caissons ) with characteristic dimensional variables of the problem under investigation and the Arias Intensity  of the surface ground motion. As a final step, the paper proposes a hybrid method for performance-based assessment of SBJ OWTs. The proposed method employs the simplified CWS model to calculate the VHM loads and approximately estimate horizontal displacements and rotations at the jacket legs, followed by a preliminary assessment of caisson settlements using the correlations of  with  on the basis of spectrum-compatible input motions.

Introduction

     Following the ambitious energy targets set by countries worldwide, the offshore wind sector has seen impressive growth over the last decade, transforming from niche technology to a global industry. As of 2019, Europe alone has 22 GW of installed offshore wind capacity – enough to cover 2.3% of its electricity consumption [1], while similar or even more potent growth is observed in many countries around the world, such as China, the USA, and India. Part of this energy transition is led by technology-driven innovation, with offshore wind turbines reaching new heights and larger capacities, while wind farms are moving deeper into the sea to harness the increased energy potential. Giventhis new “deep-water” environment, traditional OWT support structures are gradually being replaced by more cost-effective and agile foundation systems in a quest for reduction of investment costs.

Conclusions

     The paper has developed a simplified performance-based assessment technique for SBJ OWTs founded in clay under the combined action of wind () and earthquake () loading. Using an example of an 8 MW jacket-supported OWT, installed at 60 m depth in the Adriatic Sea, system performance was assessed employing a detailed 3D FE model of the soil–foundation–structure (SFS) system (global 3D FE model). After deriving insights on system performance, the global 3D FE model was used as a benchmark to assess the efficiency of an enhanced Winkler-based “Caisson-on-Winkler-Soil” (CWS) model. Soil–suction caisson interaction is represented by nonlinear hysteretic elements, capturing residual deformations and hysteresis. The proposed CWS model offers physical coupling between vertical and moment loading by introducing distributed vertical hysteretic elements along the caisson shaft, simultaneously contributing to vertical and moment shaft resistance.

Abstract

Introduction

Methods

Results

Discussion

Conclusion

References

Abstract

     The industry 4.0 has created a paradigm shift in how industrial equipment could be monitored and diagnosed with the help of emerging technologies such as artificial intelligence (AI). AI-driven troubleshooting tools play an important role in high-efficacy diagnosis and monitoring processes, especially for systems consisting of several components including wind turbines (WTs). The utilization of such approaches not only reduces the troubleshooting and diagnosis time but also enables fault prevention by predicting the behavior of different components and calculating the probability of near future failure. This not only decreases the costs of repair by providing constant component’s monitoring and identifying faults’ causes but also increases the efficacy of the apparatus by lowering the downtimes due to the AI-driven early warning system. This article evaluated, compared, and contrasted eight different artificial neural network (ANN) models for diagnosis and monitoring of WTs that predict the machinery’s system failure based on internal components’ sensor signals and generation temperature. This article employed a machine learning model approach with two hidden layers using multilayer linear regression to achieve its objective. The developed system predicted the output of the WT’s generator temperature with an accuracy of 99.8% with 2 months in advance measurement prediction.

Introduction

     Industry 4.0 introduced a new paradigm to the machineries’ monitoring and diagnosis procedure. Enabled by advances in artificial intelligence (AI) and notions such as Internet of Things (IoT) in recent years, Industry 4.0 has proved to be an effective and reliable trend toward digitization and automation (Haag and Anderl 2018). Industry 4.0 is the fourth paradigm shift and major breakthrough in industrial revolution made possible by the advancements in electronics and information technology; a continuation of the evolution of automation commenced from the invention of steam engines and mass production as a result of assembly lines and standardization (Xu, Xu, and Li 2018).

     The wind power industry and the whole renewable energy sector could benefit significantly from the employment of industry 4.0. Most of the machineries including wind turbines (WTs) produce a huge amount of data related to power consumption, current, voltage, vibration, and environmental factors that are not necessarily utilized. The information processed from these gathered data could be used to improve the troubleshooting, monitoring, and maintenance procedures. Sensors attached to different parts of WTs will provide important data of the health state of the apparatus, which require interpretation and processing.

Conclusion

     Eight different ML models were developed with different sensors’ data based on SCADA data collected from nine WTs over 10 years received from the Westmill Windfarm located in Swindon, United Kingdom, to predict the generator failure in WTs in advance using pattern recognition based on historical data. The results of each model’s accuracy in terms of minimum, maximum, and standard deviation offsets between the predicted and actual generator temperature values were compared and contrasted, and the effect of the input sensor data was explored. Overall, this research showed the possibility of utilizing ML-driven regression algorithms to predict WTs’ generator failure caused by heat, lowering the maintenance costs related to downtime and staff,and, at the same time, improving the operational availability of the apparatuses. For the future works, the authors of this article aim to explore the possibility of implementing transfer learning for fast adaptation and deployment of the trained models to new WTs, allowing quick training of new assets and lowering the readiness time required for the model.

Abstract

     An increasing number of offshore wind farms are being constructed in seismic regions over liquefaction susceptible soils. This paper presents a methodology for the analysis and design of monopiles in seismically liquefiable soils by extending the established “10-step methodology” with an additional 7 steps. These additional steps include assimilation of seismic data, site response analysis, stability check of the structure (ULS check through the concept of load-utilization ratio), input motion selection, prediction of permanent tilt/rotation, and ground settlement post liquefaction. A flow chart, which shows the interdependence of the different disciplines, is presented and can be extended to routine design. This proposed method is validated using the observed performance of an offshore and nearshore turbine from the Kamisu wind farm during the 2011 Great East Japan earthquake. Predicted results based on the proposed methodology compare well with the field observation and demarcate the (i) good overall performance of the offshore turbines and (ii) limit state exceedance of the nearshore turbine. It is envisaged that the proposed method will be useful towards the design of monopiles-supported wind turbines in seismic areas.

Introduction

     Towards the end of 2020, the global wind energy generation capacity amounted to 733 GW, with new installations accounting for 111 GW, almost doubling that of 2019 [1]. Due to higher efficiencies and more stable wind conditions offshore than onshore sites, an increasing proportion of wind power is produced through large offshore wind farms. This demand for cost-efficient wind energy production has facilitated the industry to develop larger turbines with higher capacities. Fig. 1 presents the evolution of turbine capacity over the last 16 years, color-coded with rotor diameter. However, due to the high capital investments involved, profitability of these wind farms requires continuous operation and immediate functionality post-natural hazards. Therefore, the seismic resilience of OWTs is an important consideration in their engineering design and is, therefore, an area of active research. Readers are referred to recent publications [[2], [3], [4], [5], [6], [7], [8], [9]] on various aspects of seismic design.

Discussion and conclusions

     An existing framework for the foundation design of monopile-supported wind turbines was extended to include seismic load considerations in liquefiable and non-liquefiable soil. The framework was validated using a case study from Wind Power Kamisu, a near-shore farm that performed well during the 2011 Tohoku earthquake and subsequent tsunami.

The discussion and conclusions are presented succinctly below:

     1. A quantitative appraisal of appropriate damping, as recommended by Lombardi and Bhattacharya [58] and Adhikari and Bhattacharya [57], could vary the structural demands and therefore needs proper consideration. Back analysis of a case study from the Kamisu wind farm further indicated that structural demands could vary if suitable damping models are not selected.

      2. Definition of mechanism-specific p-y spring models for liquefiable and non-liquefiable soil is necessary as their selection could underpredict or overpredict structural response (deformation, tilt, RNA acceleration). As near-level ground conditions prevailed in this study, the authors employed hyperelastic (strain hardening) p-y curves for liquefiable soil to account for the increased resistance attributed to shear strain-induced soil dilation. During shear strain excursions, such dilative tendencies can transfer high acceleration pulses to the RNA and therefore need to be accounted for in the design stage.

1- Introduction

2- System architecture

3- Modelling of the WECS

4- Performance analysis of proposed DC nanogrid topology

5- Conclusion

References

Abstract

Wind energy conversion system (WECS) based DC nanogrid topology is proposed in this paper. The WECS topology is based on self-excited induction generator (SEIG) which is cost effective, and has rugged construction and combination of diode bridge rectifier (DBR) with DC-DC converter to implement the maximum power point tracking technique. The combination of diode DBR with DC-DC converter allows simple control structure due to one controllable switch as compared to complex control in the conventional controlled/semi-controlled rectifier with six controllable switches. Further, proposed DC nanogrid topology enhances the SEIG efficiency due to the reduction of harmonic injection on machine side from DBR implementation. The detailed harmonic spectrum analysis is carried out by developing the laboratory scale prototype of DBR based SEIG topology for WECS to justify the selection of DBR instead of the controlled rectifier. Further, to analyze the effect of different types DC-DC converter to make combination with the DBR. Afterwards, the dynamic performance of the proposed DC nanogrid topology is analyzed to understand the selection of the DC-DC converter with DBR for different DC grid voltage conditions.

Introduction

Electricity plays a vital role in energy supply, however, the electricity generation is the single largest source of CO2 emission, which is more than 40% of CO2 emission related to global energy. By the year 2030, global electricity demand is likely to double [1,2]. Hence, climate change mitigation and increasing energy demand motivates for the development of renewable energy resources. The renewable energy resources such as solar and wind energy are distributed in nature which favors the electricity generation and consumption on site. With technological advancement and regulatory changes, the concept of supplying the load locally has led to a concept of nanogrid. Thus, the development of renewable based nanogrid are gaining attention in recent past for providing environment-friendly, sustainable and economical electricity [3]. Further, the development of power electronics has transformed the load profile of end user as the modern appliances operate on DC supply [4]. Having this understanding, the concept of DC nanogrid has been proposed in the literature to elude multiple AC-DC conversion and vice-versa [5]. The DC distribution enables easy control, no issue of frequency, power angle, phase sequence, and power factor. Mashood et al.

China is the largest power producer and consumer and has the largest installed capacity of wind turbines (WTs) worldwide. In the last two decades, China’s installed capacity of WTs has exploded, significantly impacting its wind power (WP) industry and promoting the development of the industry globally. This paper examines China as one part of the world and elucidates its role, status, and impact on global WP development from a global perspective. Research indicates that China has become the global leader in WP, its WP development has greatly promoted the growth rate of global WP, and it has become an indispensable force in global WP development. However, the comprehensive capability of China’s WP generation is not only lower than that of other major WP countries but also lower than the global average. China’s WTs have low global recognition and are difficult to export. In addition, the proportion of WP in China’s power structure is lower than that of most other major WP countries. Fortunately, these gaps between China and other major WP countries are gradually narrowing. The goal of this paper is to help researchers and investors better understand China’s situation and to provide a reference for the rational investment in and development of China’s WP.

Introduction

Many countries have signed agreements to address global climate change, such as the Kyoto Protocol and the Paris Agreement, and these countries are striving to fulfill their commitments [1,2]. There is consensus on the need to reduce fossil energy and to accelerate the development and utilization of renewable energy among most countries, and they are engaging in concerted action [3,4]. As a source of clean energy with high storage, no pollution, and using mature technology, many countries are seeking to utilize wind energy [5] and consider wind power (WP) to be a promising energy [6]. China, a major energy-consuming carbon emission country, is one of many countries that have installed wind turbines (WTs) (as shown in Fig. 1). The Chinese government is actively supporting the development of clean energy through laws, regulations, finance, and taxation. With the strong support of the Chinese government, the country’s WP has experienced explosive growth [7,8]. In 2018, the cumulative capacity of WP installed in China accounted for a record high of 35.7% of the total globally, ranking first. China’s WP development has attracted worldwide attention. To date, a high volume of literature has reported on country’s WP status, development and policies.

1. Introduction

2. Methods

3. Results

4. Limitations

5. Conclusion

Acknowledgments

Appendix A. Supplementary data

References

Abstract

This paper discusses the impact of bulk electric storage on the production from dispatchable power plants for rising variable renewable electricity shares. Two complementary optimization frameworks are used to represent power systems with a varying degree of complexity. The corresponding models approximate the wholesale electricity market, combined with the rational retirement of dispatchable capacity. Two different generic storage technologies are introduced exogenously to assess their impact on the system. The analysis covers two countries: France, where the power supply’s large nuclear share allows for the discussion of storage impact on a single generator type; and Germany, whose diverse power supply structure enables storage interactions with multiple electricity generators. In the most general case, additional storage capacity increases dispatchable power production (e.g. nuclear, coal) for small wind and solar shares, i.e. it compensates the replacement induced by renewable energies. For larger variable renewable electricity volumes, it actively contributes to dispatchable power replacement. In a diverse power system, this results in storage-induced sequential mutual replacements of power generation from different plant types, as wind and solar capacities are increased.

This mechanism is strongly dependent on the technical parameters of the storage assets. As a result, the impact of different storage types can have opposite signs under certain circumstances. The influence of CO2 emission prices, wind and solar profile shapes, and power plant ramping costs is discussed.

Introduction

Todecarbonize the electricity sector, windandphotovoltaic (PV) power are likely to cover increasing shares of future electricity production (Creutzig et al., 2017; Luderer et al., 2017). To cope with the inherently variable nature of these renewable resources, their integration requires additional system flexibility (International Energy Agency, 2018). This flexibility is necessary in order to follow steeper loadramps (Huber et al., 2014),tomanage short-termpowerfluctuations (International Energy Agency, 2018), and to counteract the market value erosion of resources with inflexible profiles (Hirth, 2013). Ultimately, very high shares of variable renewable electricity (VRE) require the system to absorb otherwise curtailed peak generation (Denholm and Hand, 2011; Després et al., 2017).

The future needs for additional flexibility of supply and demand can be satisfied in a multitude of ways, relying both on improvements of the legacy system (such as power plant upgrades (International Energy Agency, 2018)) and on dedicated measures, including storage installation and cross-sector coupling (Lund et al., 2015). Among these options, electricity-to-electricity storage stands out as especially versatile due its ability to provide a multitude of (e.g. ancillary) services and due to its lack of scalability constraints (with the notable exception of pumped hydro storage—PHS) (Fitzgerald et al., 2015; Palizban and Kauhaniemi, 2016). This is in addition to several more technology-specific advantages like estimated low capital costs per energy capacity (e.g. compressed air energy storage (Lazard, 2016)), pronounced anticipated cost decreases (e.g. battery technologies (Schmidt et al., 2017)), very fast deployment time, modularity, and increasing maturity due to rapidly accelerating worldwide use (batteries (REN21, 2018; Tortora, 2014)).

This paper demonstrates the optimum design and techno-economic assessment of hybrid renewable energy system (HRES) for rural electrification in the remote district of Korkadu, India. The renewable energy resources namely solar photovoltaic, wind turbine and bio generators are considered as the main sources because of its high potential in Korkadu district. This paper estimates the load forecasting for the selected district, which is a mixture of different load patterns namely residential, commercial, institutional and agricultural demands. The desired HRES has to meet the forecasted load demand for reliable electrification. The optimum design and techno-economic analysis of proposed HRES system is carried using powerful tool HOMER. This study also compares HRES’s operational behavior with different operational strategies such as load following strategy, cycle charging strategy and combined strategy of the system. The outcome of the proposed dispatched strategy expressed the contribution of solar power as 86.8%, wind as 12.7% and bio gen power as 0.5% to meet out developed rural load pattern with 6.8% of battery bank losses and 1.78% of converter losses. This research work also illustrate the HRES based power generation can be a cost effective sustainable power alternative to the conventional grid extension system.

Introduction

Total power generation capacity of India was 340 GW as on March 2018, a major portion of which 65% was from fossil fuels such as coal, oil etc. whole the renewable energy, natural gases based energy generation contributed to 19.2% [1]. The research says that still 240 millions of India are living in dark without electricity and most of the electrified villages have poor quality of electricity especially during the peak demand period i.e., during summer [1,2]. Government of India has initiated several schemes, namely, Remote Village Electrification Program (RVEP), Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY), Jawaharlal Nehru National Solar mission (JNNSM) etc., for addressing this issue and for ensuring electrification of the rural areas in India [1,3]. The main objective of these schemes is to ensure clean source of electricity generation for the electrification of un-electrified villages in India. Various researchers have proposed an attractive solution called the Renewable Energy based Hybrid Rural Electrification System (REHRES). But the main challenges in this proposed solution relate to system reliability, power management and economical cost of energy. In addition, wide variations are seen in the renewable energy availability and its potential depending on their geographical location. Hence the optimum sizing is considered as an important parameter for the development of an efficient and economical REHRES. In recent years, various researchers have proposed the solution for REHRES worldwide such as Hydrogen fuel system based hybrid energy system has been deployed in Newfoundland [4], Solar/wind/hydrogen based HRE [5], solar/wind powered reversed osmosis system [6], PV/ wind/battery based isolated island system [7] these study clearly expressed the issues related with HRE system sizing and necessity of performance optimization of the system.

1. Introduction

2. Characterization of wind energy

3. Problem formulation

4. Exchange market algorithm (EMA)

5. Application of the EMA method for solving the optimization problem

6. Numerical results

7. Conclusion

References

Abstract

This paper presents an Exchange Market Algorithm (stocktickerEMA) method for solving the Economic Emission Dispatch (EED) problem including wind farms in the power systems. The stocktickerEMA algorithm is a powerful and useful method for finding the optimal value of an optimization problem with high accuracy. In recent years, because of the emission of harmful gases from fossil fuels and global warming issues, the penetration level of cleaner energies such as the wind and solar energy has been increased in order to produce the desired electrical energy. Therefore, it is vital to consider the wind turbines and wind farms in the EED optimization problem. Due to the probabilistic nature of wind speed in wind turbines, the generated power by wind turbines and wind farms has uncertain nature. Hence, the Weibull probability distribution function is used to model the wind power in the EED problem. The proposed method is tested on the IEEE 40-units test system. The analysis shows that, compared to other algorithms the EMA method has faster convergence and better ability in finding the optimal solution for the EED problem.

Introduction

Economic dispatch (ED) problem is very important for the economical operation of power systems. The purpose of the ED problem is to determine the optimal output values for the generation units to fulfill the load demand and other constraints of a power system, as well as minimizing the cost of electrical energy produced [1]. In the recent years the diffusion of harmful gases such as sulfur oxides (SOX ) and nitrogen oxides (NOX ), which pollutes the atmosphere and exacerbates the global warming situation, has become a critical issue. The main source of these greenhouse gases are the thermal units. One way to limit the emission of these gasses is to impose stricter policies on the thermal units. To apply the new regulations and tax issues for excessive generated greenhouse gases, a combination of economic dispatch and constraints on emission has been introduced which is known as Economic Emission Dispatch (EED) problem. In the EED optimization problem, in addition to minimizing the cost of energy, minimization of the amount of emission has been considered as well [2]. EED is an optimization problem with the following two main goals [3]:

(1) Minimizing the fuel cost of thermal units.

(2) Minimizing the emission of harmful gases into the air.

In this paper, a distributed optimal reactive power control (DORPC) scheme is proposed for minimizing the total losses of doubly fed induction generator (DFIG)-based wind farms (WFs), including the losses of generators, converters, filters, and networks. The DORPC minimizes total WF losses by optimally coordinating reactive power outputs of the DFIG stator and the grid-side converter. The optimal control problem is solved in a distributed manner by using the consensus alternating direction method of multipliers (ADMM). With the consensus ADMM, the total WF loss optimization problem is transformed into a distributed optimal power flow problem considered with DFIGs’ optimal operation. The optimization problem with local constraints considers the reactive power limit of DFIG-based wind turbines (WTs) and the voltage limits at all WT terminal buses inside the WF. In the DORPC, the optimal control problem is solved by the collector bus station controller and WT controllers in parallel, only with the information exchange between immediate neighbors. It eliminates the need of a central controller and centralized communication, implying better robustness and plug-and-play capability. A WF with 20 DFIG-based WTs was used to validate the proposed DORPC scheme.

Introduction

Wind power has become a widely used renewable energy source (RES) with substantial potential and mature technology. With wind power generation expanding, the intermittency of wind power and the interaction between wind farms (WFs) and power systems introduce challenges [1]. The doubly fed induction generator (DFIG)-based wind turbine (WT) has been widely used in modern WFs due to high controllability and small converter rating [2]. With power electronic converters, DFIG-based WFs can regulate reactive power independently and provide reactive power support for power systems [3]. Voltage and reactive power control of WFs has motivated numerous studies. The WF is required to maintain the power factor within the limit at the point of connection (POC) [4,5] or provide reactive power support for power systems while tracking the dispatch command from the transmission system operator (TSO) [6]. Dynamic power electronic devices, such as static var compensators (SVC) and static synchronous compensators (STATCOM), are used in WFs for providing rapid reactive compensation and voltage control [7,8]. In DFIG-based WFs, each DFIG-based WT is equipped with power electronic converters. The DFIG-based WF can utilize the capabilities of the DFIG-based WTs for providing reactive power support to meet grid code requirements. The most widely used reactive power control scheme in WFs is the proportional dispatch (PD) scheme, which is simple, easy to implement, and considers the reactive power margin of each DFIG-based WT [9,10]. However, without the optimizing reactive power references for individual WTs, the WF controller cannot achieve WF optimal operation. In [11], the particle swarm optimization (PSO) was adopted to dispatch reactive power of WTs by minimizing total active power losses along the cables and the transformers of WTs.

1- Introduction

2- Proposed methodology

3- Architecture of hybrid wind power forecasting model

4- Cases study and analysis

5- Conclusion

References

Abstract

Given the intermittency and randomness of wind energy, the mass grid connection of wind power poses great challenges in power system and increases the threat in power system balance. Wind power forecasting can predict the fluctuation of output wind power in wind farms, which can effectively reduce wind power uncertainty. Improving the accuracy of wind power is indispensable for enhancing the efficiency of wind power utilization. To improve the forecasting accuracy, this research proposed a novel wind power forecasting method based on singular spectrum analysis and a new hybrid Laguerre neural network. First, singular spectrum analysis was used to analyze the wind power series, which decomposes the series into two subsequences, namely, trend and harmonic series and noise series. Then, Laguerre neural network and new Laguerre neural network were proposed to build the hybrid forecasting model optimized by the opposition transition state transition algorithm. The two decomposed signals were used for forecasting the future wind power value by using a forecasting model. Finally, the proposed hybrid forecasting method was investigated with respect to the wind farm in Xinjiang, China. Prediction performance results demonstrated that the proposed model has higher accuracy than the Laguerre neural network, hybrid Laguerre neural network, hybrid Laguerre neural network with singular spectrum analysis, hybrid Laguerre neural network with opposition transition state transition algorithm and singular spectrum analysis, and other popular methods.

Introduction

Efficient, clean, and low carbon has become the mainstream of the world’s energy development direction. Wind energy, as one of the most popular renewable clean energies, has developed rapidly in recent years. The total installed capacity of 14 years (from 2005 year to 2018 year) is reported in Fig. 1. It can be seen from Fig. 1 that the wind power installed capacity maintains sustained growth state in recent years, indicating that wind energy utilization remains rapidly increasing. The overall capacity of all wind turbines installed worldwide by the end of 2018 reached 597 GW [1]. Given the intermittency and randomness of wind energy, the mass grid connection of wind power poses great challenges in power system and increases the threat in power system balance. Wind power forecasting is a technology for predicting the fluctuation of output wind power in wind farms, which can effectively reduce wind power uncertainty. In other words, the accuracy of wind power forecasting is the basis of wind system optimization scheduling and efficient accommodation [2]. Improving the forecasting accuracy of wind power is indispensable for enhancing the efficiency of wind power utilization [3].

Wind energy as one of the renewable energies is serving as an indispensable role in generating new electric power. The worldwide installation of wind farms has considerably increased recently. To extract more wind resources, multi-megawatt wind turbines are usually designed and constructed with large rotors and slender tower. These flexible structures are susceptible to external dynamic excitations such as wind, wave and seismic loads. The excessive vibrations can compromise the wind energy conversion, lead to the structural fatigue damage and even result in the catastrophic failure of wind turbines in harsh environmental conditions. Various control devices have been proposed and used to mitigate the unwanted vibrations of wind turbines to enhance their safety and serviceability. This paper aims to provide a state-of-the-art review of the current vibration control techniques and their applications to wind turbines. Firstly, the widely used control strategies in engineering structures are briefly introduced. Their applications to suppress the adverse vibrations of the structural components of wind turbines, mainly the tower and blades, are then reviewed and discussed in detail. It can be concluded that the vibration mitigation of wind turbines is very challenging due to the fact that the dynamic behaviours of wind turbines are very complicated, which are associated with the aerodynamics, rotation of the blades, interaction between the tower and rotating blades, and soil-structure interaction, etc. Moreover, it is a challenge to straightforwardly use many of the conventional control devices because of the limited spaces in the tower and blades.

Introduction

Wind energy as one of the renewable energies plays an attractive means to generate new electric power. Wind farms have experienced rapid growth and expansion recently especially in the last decade. As reported by the Global Wind Energy Council, the worldwide installations of wind turbines reached about 539 GW at the end of 2017, with an increase of 2155% compared to that in 2011 [1]. Moreover, numbers of wind turbines are erected far away from coastlines to more efficiently extract the huge wind resources. The offshore wind power reached a historical record in 2017 with 4334 MW new installations, and the cumulative capacity was 18,814 MW [1]. Fig. 1 shows the global annual and cumulative installations of wind turbines from 2001 to 2017, and the total installations of offshore wind farms in 2011–2017 are shown in Fig. 2. It can be seen that though the offshore wind capacity in 2017 was only about 3.5% of the total wind capacity, it is growing very quickly and with great prospects. Wind turbines can be broadly grouped into the horizontal and vertical axis categories depending on the orientation of the rotation axis of the blades. Fig. 3 shows the typical geometrical configurations of the horizontal and vertical axis wind turbines (HAWTs and VAWTs) [2]. As implied by the name, the blades of HAWTs rotate about the horizontal axis, and they are perpendicular to the direction of wind flow, while the blades of VAWTs rotate vertically and they are not required to face the wind. As the HAWTs dominate the utility-scale wind turbine market currently, only the research works on the vibration control of HAWTs are reviewed and discussed herein.

The energy interaction between smart homes can be a solution for developing renewable energy systems in residential sections and optimal energy consumption in homes. The main objectives of such energy interactions are to increase consumer participation in energy management‚ boost economic efficiency‚ increase the user’s satisfaction by choosing between electricity sellers and buyers‚ and reduce the electricity purchased from the grid especially at peak hours. Thus, the innovations of this study includes defining an energy exchange method between smart buildings in an off-grid mode considering renewable energy systems, considering both thermal and electrical equilibrium and studying the lightning loads. it is assumed, here, that smart homes are off-grid‚ and the critical loads are supplied by the energy transfer between the homes using mixed integer linear programming. A compromise between the cost and time interval for using home appliances is considered to provide consumer’s comfort. An objective function is introduced considering programmable and non-programmable loads‚ thermal and electrical storages and lighting loads aiming to optimize the cost of energy between different smart buildings. Based on the method, which is tested in two different cases not only does the total cost of the smart buildings decrease but also the cost is reduced significantly when lightning loads are managed.

Introduction

The applications of hybrid energy systems have been studied with the focus on smart homes (SHs) and their optimal management in various studies. In (Yin, et al., 2017)‚ a hybrid DC Microgrid (MG) (based on power supply from photovoltaic (PV) cells) was investigated. This research shows that if the smart grid (SG) is in the off-grid state‚ the diesel generator with the electrochemical storage can be a better choice as a backup source. On the other hand‚ diesel generator cannot supply a stable power at the start-up. In (Arabul, et al., 2017)‚ the smart home loads are supplied by fuel cells (FCs), wind and solar energy resources. The method used in this research which is balancing and optimization of energy production and consumption using the Energy Management Algorithm (EMA), has been investigated. This method enhances the efficiency of the whole system improves the penetration of renewable energy sources‚ and increases the reliability of the system compared to conventional load shifting methods. In (Pilloni, et al., 2018)‚ the energy management method based on renewable energies in MG is presented., The energy sharing between SHs considering renewable energy participation is analysed in (Celik, et al., 2018). (Wu, et al., 2018)‚ adopt a method for energy management in SHs which is discussed considering renewable energy systems.