Wind Energy Systems Research Articles

A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies.

Direct Fractional-Order Adaptive Control Design for Cascaded Doubly-Fed Induction Generator (CDFIG) in a Wind Energy System

This paper is devoted to a fractional-order model reference adaptive control (FO-MRAC) synthesis for the independent control of the active and reactive power flows in the cascaded doubly fed induction generator (CDFIG) in wind energy systems. The proposed adaptive control law combines a second-order-like fractional reference model and a direct MIT adaptation law using a fractional order integrator. This generator configuration can be an interesting alternative to standard double-output wound rotor induction generators. It is made up of two identical wound rotor induction motors such that their rotors are mechanically and electrically coupled. Using two cascaded induction machines permits the elimination of the brushes and copper rings in the traditional doubly-fed induction generator DFIG, which makes the system more resistant and reduces maintenance costs. In the first step, we propose a classical PI controller synthesis to regulate the active and reactive power produced by CDFIG. Then, the FO-MRAC design is realized and a comparative study based on numerical simulations is performed between the classical regulators PI, MRAC, and FO- MRAC, to demonstrate the superiority of the proposed fractional-order adaptive controller relative to conventional integer order PI and MRAC controllers. These results illustrate the reliability and efficiency of the proposed adaptive control scheme.

Virtual Inertia Extraction from a DC Bus Capacitor in a Three−Phase DC/AC Inverter-Based Microgrid with Seamless Synchronisation Operation Modes

It is crucial to employ power electronics converters for energy transfer in distributed energy resources such as photovoltaics, and wind energy system. Thus, parameters such as voltage, current, active and reactive powers, and frequency referred to the AC side need to be controlled. The local load could be operated either from converter itself or grid, hence the system could be called microgrid. In this paper, the active and reactive powers would be controlled separately when the inverter is connected to the grid. While in the islanded or autonomous mode, the proposed control would support the voltage and frequency. The proposed controller would be designed and function as the synchronous machine’s voltage regulator and governor. The virtual synchronous machine is adopted to obtain the inertia and the concept is that the frequency of the grid is linked to the virtual frequency or so called the virtual speed of the rotor. The virtual frequency is obtained directly from the DC bus voltage of the inverter and this is achieved by allowing the DC link capacitor voltage to swing boarder than the grid frequency by making the capacitor voltage imitating frequency of grid. Where the frequency of grid is associated to the virtual frequency which is derived directly from DC capacitor voltage, thus, large level of inertia is extracted. The basic formulation, supervisory control, extraction the inertia from DC capacitor voltage, coordination transition, and evaluation the stability is presented in this paper. The mimic operation of the inverter as Synchronous Machine SM can make the distributed energy resources to be operated in either grid connected or islanded modes without the need to adopt control structure for the required mode. In addition, it provides robust performance in comparison with the traditional current, voltage and frequency control approaches. Moreover, the controller would be implemented for inverter having any type of filters and it is resilient to any variations in the filter and grid parameters. Furthermore, there is no concerning regarding instability problems, where it achieves the synchronisation smoothly and swiftly, and it is appropriate for implemented digitally.

Photonics in offshore wind energy system development: A systematic review

Photonics in offshore wind energy system development: A systematic review

Offshore wind project installations: an advanced approach for weather risk assessments

Abstract This paper presents the development of a sequence-based simulation tool for the weather risk assessment of offshore work processes. The approach developed at the Fraunhofer Institute for Wind Energy Systems is the Advanced Weather Time Series Scheduling (Ad-WATSS) method. For statistical significance, this method combines hindcast weather time series of many years with the weather restrictions associated with offshore activities. The method is incorporated into the software tool COAST, where a project plan schedule is simulated by placing activities according to their operational limits, constraints and relationships, and minimum-required time for each operation. A scenario of an offshore wind installation project in the North Sea is used as reference test case for a weather risk assessment using the developed tool and ERA5 weather data. The assessment is then compared with the weather window statistics approach, based on the same weather data sets. The results from both approaches are analysed and compared on a project and task level detail. The results from the sequence-based simulations show that the method is able to capture the long-term variability and extreme events in weather conditions, making it a reliable tool for the simplification of the weather data-based planning, validation, and assessment of offshore projects.

Present and Future Actions of CETYS University to Mitigate Climate Action

The strategic location of CETYS University, in Baja California state in the northwest of Mexico, gives it exceptional conditions for the use of renewable energies. With an installed photovoltaic solar energy capacity of 1.37 MW, it is the academic institution with the greatest generation potential in Latin America. The importance that CETYS University gives to the subject has stimulated the institution since 2015 to offer a degree and master's degree in renewable energy to have greater and better collaboration with solar and wind energy technology companies located in the state. With a vision of the future, in 2023 the CETYS Institute for Sustainability Studies (ISS) was created to promote and coordinate, among other actions, those related to energy and climate change. With a planning horizon until 2036, the ISS, looking forward, has proposed the following actions: a) an internal audit to increase the energy efficiency of their buildings and classrooms; b) support for research projects to build on-campus operational small-scale wind energy systems; c) the deployment of the 300 x 500 program, which seeks to plant at least 300 native trees in the region with low water consumption each year, to offset the atmospheric emissions of 500 vehicles; d) a policy to achieve 80% carbon neutrality, and e) the incorporation of CETYS to the network of Mexican universities for climate action. Among many others, these actions position CETYS as a leading institution in sustainable development at the regional level.

Experimental Study of an Industrial Data Transmission Network in the Automatic Control System of a Wind Turbine

This article explores and optimizes network technologies for wind energy systems, focusing on the RS-485 interface to ensure reliable data transmission in extreme conditions. The study aims to address the impact of various distortions on data quality and wind turbine management. A system was proposed with two wind turbines, each equipped with a Raspberry Pi 4, connected to sensors measuring temperature, vibration, and wind speed. The research examined how data transmission rates affect signal shape, calculating the distortion coefficient. At 460,800 baud, the signal was almost completely distorted, with significant amplitude loss. The distortion coefficients were 1.84 for logic ‘1’ and 1.92 for logic ‘0’. The optimal speed to minimize distortions was found to be 19,200 baud, providing the most stable signal. Additionally, temperature significantly impacted transmission quality, highlighting the need to consider climatic conditions in system design. The findings and methods can help improve existing data transmission systems and enhance wind turbine performance.

Optimized hybrid osprey with PSO control for improved VSC-HVDC-wind power integration

Innovative power systems include offshore wind farms (OWFs) to generate electricity from renewable sources. So, reliable control systems are also necessary to make sure these power systems run smoothly. The purpose of this study is to combine the Osprey Optimization with Particle Swarm Optimization (OOPSO) to fine-tune the gains of Proportional-Integral (PI) controllers within the investigated wind energy conversion system (WECS) to enhance its performance. The study confirms the effectiveness of using the Hybrid OOPSO algorithm to optimize PI controllers in a WECS. The WECS includes two voltage source converter (VSC) stations at each end of a high voltage direct current (HVDC) transmission system. The transmission system links OWFs to the grid. The research demonstrates that the Hybrid OOPSO algorithm is superior to the genetic algorithm (GA) and PSO by enhancing setup stability and allowing fast voltage regaining following different fault cases. The introduced method performs more remarkably than traditional methods such as GA and PSO. The results of this study have shown that the proposed OOPSO enhances the overshoot in onshore voltage transient response under a symmetrical fault condition by 26% over PSO, 24% over GA, and 16% over OOA. The study is conducted using MATLAB/Simulink. The study underscores the importance of advanced optimization methods to guarantee offshore wind energy systems' efficient and dependable functioning.

Environmental and Socio-Economic Impact Assessment of Renewable Energy Using Machine Learning Models

Renewable energy sources, such as solar, hydro, wind, and geothermal energy, have emerged as key alternatives to fossil fuels in combating climate change and addressing energy security concerns in the USA and ad worldwide. Strategic use of this renewable resource is important not only for carbon emission reduction and improvement of environmental sustainability but also for maintaining future energy supplies. At the same time, such transition raises thorough assessments of environmental and socio-economic impacts. Machine learning (ML) models offer a powerful tool for predicting and analyzing such impacts, allowing for more efficient decision-making and long-term planning. These models are supposed to analyze patterns in energy production, land use, and emissions to make a more dynamic and predictive understanding of how renewable energy adoption influences CO2 levels. The principal aim of this research project was to develop and curate machine learning algorithms for predicting CO2 emissions based on renewable energy data, using the knowledge to better understand how solar, wind, hydro, and geothermal energy systems affect environmental outcomes. The predictive models developed in this research would serve as useful tools for the policymakers and major stakeholders in decision-making on investments in energy infrastructure and characterization of regulatory frameworks. These datasets for this research project were retrieved from several prominent institutions, including governmental agencies, international organizations such as the International Energy Agency-IEA and the World Bank, satellite data repositories, and USA environmental monitoring agencies. For this research project, 3 machine learning algorithms in the experiment were used, namely Logistic Regression, XG-Boost, and Random Forest. Amongst these three, the linear regression model gave the best performance, as it had the least MSE; indicating that its predictive capability was impressive. The comparative analysis of renewable energy projects in Germany, China, and California underlines that effective policy-making plays a very decisive role in the transition toward sustainable energy.

Nonlinear control of the permanent magnet synchronous motor PMSM using input-output linearization control and sliding mode control

Today, electric machines are becoming increasingly important in all sectors (industrial drives, the automotive industry, aviation, traction systems, and agriculture, etc.). Among these machines, permanent magnet synchronous machines (PMSMs) hold a significant place in the control of industrial mechanisms, automated systems, and in the fields of renewable energy like (wind energy, solar energy, and hybrid systems, etc ). Currently, in variable speed drives, the use of PMSM, especially for low power and certain special industrial applications, is replacing DC motors and asynchronous motors because they offer higher efficiency, power factor, and torque density. Moreover, PMSM do not have an excitation circuit in the rotor, which leads to reduced maintenance demands. The control of permanent magnet synchronous machines (PMSM) presents significant challenges due to their nonlinear behavior. Classical linear controllers, such as PI, perform well with linear systems that have constant parameters. However, for nonlinear systems with varying parameters, the conventional control methods may prove insufficient and exhibit a lack of robustness. To address the issues and achieve decoupled machine control, various methods have been proposed. Nonlinear robust control techniques have been extensively explored within the domain of power electronics and drive systems. Included in these, sliding mode control and nonlinear input-output feedback linearization (IOFL). Sliding mode control (SMC) is a control technique known for its excellent dynamic performance in PMSM drives. It offers high robustness and straightforward implementation in both software and hardware. However, a significant drawback of this method is the chattering phenomenon. By the way the input/output linearization control can provide good behavior in steady and dynamic regimes and also provides good decoupling between system variables. This article presents a design of tow control laws based on sliding mode and feedback linearization controller based on input-output feedback linearization to adjust the speed of a permanent magnet synchronous motor (PMSM). To highlight the effectiveness of the designed controllers, a comparison was carried out Matlab Simulink, revealing that the feedback linearization controller outperforms the SMC.

Control of Large Wind Energy Systems Throughout the Shutdown Process

This contribution examines the control problem for very large wind energy converters during shutdown operation and analyses the most important control approaches. The control methods make use of the built-in conventional control infrastructure, but control system reconfigurations are undertaken in order to meet the demands of the shutdown control operation. Hence, the torque controller as well as the collective pitch controller (CPC) are redesigned from their regulator functions to reference tracking control systems with constraints. In addition, the CPC is combined with a feedforward controller in order to gain responsiveness. Constraints in magnitude and rate are managed by a modified anti-windup mechanism. Simulations of a 20 MW reference wind turbine verify the performance of the approaches.

Wind Rose Analysis of Temperature Variation with Sensor Implantation Technique for Wind Turbine

This study focuses on analysing the impact of seasonal variations in heat on different parts of a "wind energy system," serving as a representative mechanical system. The proposed methodology utilizes wind rose analysis, offering a straightforward means to assess heat transfer effects, applicable to any mechanical system comprising numerous small heat-dissipating components. Furthermore, this work elucidates the correlation between mechanical component performance and heat dissipation impact, providing breakdown alerts for various wind turbine components. Additionally, it analyses the influence of mechanical part temperatures on energy generation, highlighting how high temperatures can indicate component deterioration, such as bearing damage. Notably, the study focuses on wind speeds ranging from 10 to 15 m/s, a typical operational range for wind turbines. By employing wind rose charts and real-time readings, the graded heat dissipating potential of mechanical parts under various wind velocities and weather conditions is determined. Moreover, the study emphasizes the significance of heat transfer analysis in optimizing wind turbine performance, demonstrating how temperature differentials between mechanical components and the environment, along with increased surface area, affect heat transfer. The proposed analysis underscores the importance of considering heat's impact on each mechanical component in tandem with seasonal environmental temperature fluctuations. The wind rose methodology, integrated with sensor implantation, emerges as a cost-effective tool for studying -level heat variances in mechanical systems, enabling the development of heat profiles for future turbine designs and computational fluid dynamics (CFD) analyses. As heat transfer coefficient increases the wind turbine performance decrease. The heat transfer coefficient decreases 14.28% from rainy to winter season and 42.85% from winter to summer season. The wind rose analysis provides valuable insights into wind patterns and characteristics at a specific location, which can inform decision-making in various fields such as energy, construction, environmental management, and safety planning. As the turbine temperature increases the wind velocity decreases. Here it decreases 28 % from rainy to winter season.

Adaptive event-triggered stochastic estimator-based sampled-data fuzzy control for fractional-order permanent magnet synchronous generator-based wind energy systems

This study aims to develop an adaptive event-triggered estimation sampled-data control method for a fractional-order permanent magnet synchronous generator (PMSG)-based wind energy system (WES) using the Takagi–Sugeno (T–S) fuzzy approach. Unlike existing PMSG-based WES control schemes, we propose an aperiodic event-triggered communication scheme with an adaptive mechanism to reduce data transmission. To address the challenge of limited communication resources, we introduce an adaptive event-triggered (AET) estimation method with aperiodic sampling, significantly reducing communication overhead while maintaining stable WES performance. This method employs transmission techniques based on absolute errors, resulting in high data transmission efficiency. First, the fractional-order model for the PMSG-based WES is represented using linear sub-models through the T–S fuzzy approach. Next, a fuzzy aperiodic AET mechanism is proposed for the PMSG model with augmented estimation error systems. Then, the fractional Lyapunov function theory is employed to derive linear matrix inequalities (LMIs), ensuring bounded mean-square stability for the PMSG-based WES with the augmented estimation error systems. Additionally, the desired estimator control gains are determined through solvable LMIs. Finally, simulation studies are presented to demonstrate the superiority and feasibility of the proposed control scheme.

Optimization by Genetic Algorithm of a Wind Energy System applied to a Dual-feed Generator

Optimization by Genetic Algorithm of a Wind Energy System applied to a Dual-feed Generator

Power Quality Disturbance Monitoring in PV Integrated Power System with Mode Decomposition and Ensemble Extreme Learning Machine

Power quality disturbance (PQD) monitoring has become an important issue in modern power system due to integration of several renewable energy sources such as photovoltaic (PV), wind energy system (WES), Fuel cells etc. This research presents a mode decomposed based ensemble extreme learning machine (EELM) to recognise and classify the PQD events with higher accuracy in terms of rapid learning speed and smaller computational burden in a complete PV based power system, The PQD signals are decomposed using a variational mode decomposition (VMD) to obtain effective band limited intrinsic mode functions (BIIMF) which leads to compute robust features and improves classification accuracy. For Power Quality Disturbances detection and classification, an ensemble extreme learning machine is suggested since an ensemble outperforms any single contributing model in terms of performance and prediction. The proposed VMD-EELM approach is validated in a modified IEEE 13 Bus system integrating PV with eleven types of PQDs. The proposed research having 100% classification accuracy for no noise and 99.88%, 99.94% 99.94% for 20dB, 30dB and 40dB noises respectively. It is being demonstrated that the suggested method can reliably identify and track PQD occurrences both with and without noise.

Techno-economic analysis of solar, wind and biomass hybrid renewable energy systems in Bhorha village, India

The present study investigates the potential use of Hybrid Renewable Energy Systems (Solar photovoltaic, wind, biomass, and diesel), both with and without the inclusion of battery/supercapacitor storage in the Bhorha village, Bihar, India. A comprehensive assessment of different possible system configurations is conducted using hybrid optimization model for electric renewable (HOMER) software to determine the most economically viable and efficient system for the designated place. The current analysis is focused on six distinct cases in the village community, in view of sufficing the daily energy requirement of 615.625 kWh and a peak demand of 86.47 kW, pertaining to major factors viz. system efficiency, financial viability, and ecological consequences. The primary aim of the research is to elucidate the comparative analysis of energy generation for different proposed designs of hybrid renewable energy systems. Detailed techno-commercial assessments are also carried out to examine the energy production, consumption, and financial impacts of each HRES configuration. The research outcome of this study obtained from HOMER software reveals that the optimized hybrid system comprises 86.7 kW solar photovoltaic, 30 kW wind turbine, 5 kW biogas generator, a 50 kW diesel generator, 280 kWh battery bank with nominal capacity, and 38.8 kW converter to sustain the for energy needs of the nominated place. This system has a minimum cost of energy of 0.309 $/kWh with a net present cost of $854894 along with operating cost 51847 $/year and net carbon dioxide emission of 56728 kg/yr. The research offers useful insights for designers, scholars, and policymakers on the existing design constraints and policies of biomass-based hybrid systems for a safe, sustainable and independent green future for the generations to come.

Fractional impulsive controller design of fractional-order fuzzy systems with average dwell-time strategy and its application to wind energy systems

In this study, the issue of adaptive impulsive control of a permanent magnet synchronous generator (PMSG)-based wind energy system (WES) with an average dwell-time strategy is investigated using a Takagi–Sugeno (T-S) fuzzy model described by fractional-order differential equations. A T-S fuzzy model is employed to describe the nonlinear fractional-order PMSG (FOPMSG) model, an average dwell time, an average impulsive condition, a Lyapunov function, and a less conservative algebraic inequality criterion that guarantees stabilization for the considered nonlinear system. First, the mathematical model of the FOPMSG in the n−m reference frame is transformed into a dimensionless chaotic system through affine transformation and time scale transformation. Then, the stabilization problem of the nonlinear chaotic FOPMSG model is considered to validate the proposed sufficient conditions, leading to the derivation of a new stability criterion for the FOPMSG model, instead of addressing the general problem to validate the proposed result. Subsequently, the desired control gains can be obtained to ensure the stabilization of the addressed closed-loop system. By combining adaptive and impulsive control, the model under consideration can be stabilized for any target dynamics. Finally, we demonstrate the efficiency and feasibility of the suggested approach through numerical simulations and comparative results.

Quasi Z source direct matrix converter based K- to three-phase wind energy conversion system using maximum constant boost current control modulation technique

The conversion system for wind energy has no restriction on the number of output phases. K numbers of output phases can be converted into standard grid-connected three-phase supply using a quasi-Z source direct matrix converter (QZSDMC). The obtained input supply may be five phases from the wind energy conversion system with continuously variable frequency as well as variable terminal voltages. Using the proposed QZSDMC converter with maximum constant boost current control modulation technique constant three-phase voltages as well as frequency can be maintained at the grid side. Additionally, the energy conversion system has high output voltage gain. The proposed converter as well as the modulation technique produces the maximum voltage transfer ratio of 1.05. The output voltage gain reached nearly 1.99 compared with the traditional power converters. The voltage THD and current THD are maintained within the acceptable limit of 5.3 and 3.6 respectively. The power factor have been maintained nearly unity throughout the all mode of operations. To stabilize the grid frequencies and grid voltages additional control blocks are used. This research article presents simulation results for the proposed QZSDMC converter with a maximum constant boost current control modulation technique. Also, the proposed scheme has been validated with suitable experimental results.

Control of Floating Offshore Wind Energy Systems: An Introduction to the Special Issue

Control of Floating Offshore Wind Energy Systems: An Introduction to the Special Issue

Control of Floating Wind Energy Systems [About this Issue

Control of Floating Wind Energy Systems [About this Issue