An inverter design for a new permanent magnet synchronous generator

Design and simulation of a new inverter scheme are reported in the paper. The inverter is especially developed for an axial flux permanent synchronous generator (AFPMSG), which can be used for low power wind energy systems. The system includes a battery charge unit in addition to the inverter. Initially, the permanent magnet synchronous generator (PMSG) model has been created for MATLAB/Simulink environment. Since frequency and amplitude of the waveforms generated by the PMSG strictly depend on the rotation speed, several tests have been performed under different wind speed regimes. Then, an appropriate inverter model has been designed and connected to the output of the PMSG in order to convert the generated voltage from AC to DC. Thereby, stabilizing voltage and frequency has been assured and the charge of the battery unit has been realized for efficient energy storage. The simulations show that the system designed has lower THD on the current signal according to the similar ones presented in recent literature. Furthermore, the THD value has been much improved (i.e. 0.72%) thanks to the design of an additional filter unit. The proposed system can be implemented to low power wind energy systems.     

Experimental validation of CFD modelling for heat transfer coefficient predictions in axial flux permanent magnet generators

This paper describes the experimental validation of CFD modelling for heat transfer coefficients in an axial flux permanent magnet (AFPM) generator. A large scale low speed test rig was designed and constructed. The geometric parameters and the rotational speed of the test rig were determined by dimensional analysis, to ensure the flow characteristic remains unchanged as compared with commercial AFPM generators. The heat transfer coefficients in the test rig were measured at rotational Reynolds number, Re_ω from 0 to 2 × 10⁶, non-dimensional flow rate, C_w up to 11,000 and gap ratio, G = 0.016, by using the combination of heat flux sensors and thermocouples. Due to the large size of the scaled-up rig, natural convection played a significant part in the heat transfer and this had to be compensated for in the forced convection heat transfer coefficient calculations. Extra experiments were designed and conducted to identify the effect of natural convection on the machine’s cooling. The experimentally determined results were compared to heat transfer coefficients predicted by CFD models and good agreement was obtained.     

An analytical approach to non‐linear dynamical model of a permanent magnet synchronous generator

This paper proposes an analytical approach to investigate the transitory dynamic regime of a low‐power permanent magnet synchronous generator that works in an actual wind power station. The governing non‐linear differential equations are solved by means of the optimal homotopy asymptotic method, and explicit analytical solutions are obtained. Four cases are analysed for different moments of inertia and electrical resistances specific to sudden short circuit produced at the generator terminals and sudden change of load. The proposed procedure is highly efficient and controls the convergence of the approximate solutions, ensuring a very fast convergence. Such analytical approach allows modeling and simulating turbine generator systems for real‐time computations, offline applications or stability problems. Numerical investigations are also performed in order to validate the analytical results. Copyright © 2014 John Wiley & Sons, Ltd.     

A new outer‐rotor flux switching permanent magnet generator for wind farm applications

In this paper a 1.5 kW flux switching permanent magnet (FSPM) generator is presented for direct drive small scale wind turbine applications. For maximizing induced voltage and the output torque while minimizing cogging torque and unbalanced radial magnetic force (UMF), the proposed machine exhibits a new 6/19 stator pole/rotor teeth number and an outer rotor configuration. At first, in the paper an analytical design has been developed, then a finite element method (FEM) analysis is carried out for validating the analytical procedure and for design improvement. The simulation results extracted by FEM confirm the theoretical analysis procedure and help in the understanding of the performance analysis of the machine against the variations of the design variables. Furthermore, an experimental laboratory prototype of the proposed FSPM is implemented to confirm the analytical design and FEM modelling approaches. A comparison of induced voltage, torque, UMF and cogging torque produced by different FSPM configurations present in literature respect to the proposed generator has been developed. The results show the goodness of the adopted methodology and prove that, because of suitable electromagnetic performance of the proposed FSPM generator, it could be counted as a proper candidate for small scale wind turbine applications. Copyright © 2016 John Wiley & Sons, Ltd.     

Estimation of the energy of a PV generator using artificial neural network

The integration of grid-connected photovoltaic (GCPVS) systems into urban buildings is very popular in industrialized countries. Many countries enhance the international collaboration efforts which accelerate the development and deployment of photovoltaic solar energy as a significant and sustainable renewable energy option. A previous method, based on artificial neural networks (ANNs), has been developed to electrical characterisation of PV modules. This method was able to generate V–I curves of si-crystalline PV modules for any irradiance and module cell temperature. The results showed that the proposed ANN introduced a good accurate prediction for si-crystalline PV modules performance when compared with the measured values. Now, this method, based on ANNs, is going to be applied to obtain a suitable value of the power provided by a photovoltaic installation. Specifically this method is going to be applied to obtain the power provided by a particular installation, the “Univer generator”, since modules used in these works were the same as the ones used in this photovoltaic generator.     

Analytic modelling and optimization of slip synchronous permanent magnet wind turbine generator topologies

The modular slip‐synchronous permanent magnet generator (SSPMG) is viewed as an induction‐synchronous machine pair that is electromagnetically decoupled by a free‐rotating rotor that in turn houses two different sets of permanent magnets. This machine pair combines the advantages of both conventional induction and permanent magnet synchronous machines. It therefore has the potential to realize a new path in reliable, robust and cost‐effective wind turbine drivetrains. However, which electromagnetic SSPMG topology is best and how does it compare with conventional drivetrain designs for various capacities? To date, the most published SSPMG advances are specific to winding design, torque quality and performance optimization in the small capacity range. This paper presents optimized analytic electrical designs of modular, radially and axially separable, radial flux SSPMG topologies of capacities ranging from 100 kW to 5 MW. Designs are based on lumped analytic models and are optimized for minimum specific active mass (mass/torque). A rated efficiency of 95 % and an inductive power factor of 0.95 are applied to all designs. The analytic models are validated with transient two‐dimensional finite element analysis results. The best SSPMG topologies are determined and compared with conventional drivetrain designs. The axially separable topology seems to be the best SSPMG design. Copyright © 2014 John Wiley & Sons, Ltd.     

Low power wind energy conversion system based on variable speed permanent magnet synchronous generators

This paper presents a low power wind energy conversion system (WECS) based on a permanent magnet synchronous generator and a high power factor (PF) rectifier. To achieve a high PF at the generator side, a power processing scheme based on a diode rectifier and a boost DC–DC converter working in discontinuous conduction mode is proposed. The proposed generator control structure is based on three cascaded control loops that regulate the generator current, the turbine speed and the amount of power that is extracted from the wind, respectively, following the turbine aerodynamics and the actual wind speed. The analysis and design of both the current and the speed loops have been carried out taking into consideration the electrical and mechanical characteristics of the WECS, as well as the turbine aerodynamics. The power loop is not a linear one, but a maximum power point tracking algorithm, based on the Perturb and Observe technique, from which is obtained the reference signal for the speed loop. Finally, to avoid the need of mechanical sensors, a linear Kalman Filter has been chosen to estimate the generator speed. Simulation and experimental results on a 2‐kW prototype are shown to validate the concept. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling and control of a permanent magnet synchronous generator dedicated to standalone wind energy conversion system

The interest for the use of renewable energies has increased, because of the increasing concerns of the environmental problems. Among renewable energies, wind energy is now widely used. Wind turbines based on an asynchronous generator with a wound rotor present the inconvenience of requiring a system of rings and brooms and a multiplier, inferring significant costs of maintenance. To limit these inconveniences, certain manufacturers developed wind turbines based on synchronous machines with large number of pairs of poles coupled directly with the turbine, avoiding using the multiplier. If the generator is equipped with permanent magnets, the system of rings and brooms is eliminated. The control of the permanent magnet synchronous generator (PMSG) can be affected with the implementation of various techniques of control. This paper presented a new approach mainly based on the control strategy of power production system based on the PMSG. In fact, a mathematical model that simulates the Matlab chain was established with the introduction of control techniques, such as direct control of the torque (DTC) to control the load side converter (LSC), the control of the speed of the turbine and the DC-bus voltage ensured by PI regulators. To show the performance of the correctors used, some simulation results of the system were presented and analyzed.     

Adaptive back-stepping control for a permanent magnet synchronous generator wind energy conversion system

The maximize energy captured from the wind of a grid-connected variable speed Wind Energy Conversion System (WECS) based on a Permanent Magnet Synchronous Generator (PMSG) is investigated in this paper. An adaptive back-stepping control scheme is applied to achieve maximum power point tracking in the coefficient of maximum power. The features of the proposed control scheme are that it deals with the random nature of wind speed, the uncertainties and external perturbations the acting on WECS effectively, where the bounds of the perturbations are not known in advance. At the same time, a proof of the convergence of the closed-loop system under the proposed controller is derived using the Lyapunov stability theory. Finally, simulations are conducted to illustrate the effectiveness of the proposed approach.     

Study of a longitudinal flux permanent magnet linear generator for wave energy converters

A directly coupled linear permanent magnet generator of longitudinal flux‐type is investigated. The generator will be used for power take‐off in a wave energy converter. A combined field‐ and circuit model, solved by a time stepping finite element technique, is used to model and analyse the electromagnetic behaviour of the machine. A large number of simulations form the basis of a design study where the influence of armature current level, number of cables per slot, and pole width is investigated with respect to efficiency, generator size, and the load angle. A case study is performed for a chosen generator design. The electromagnetic behaviour is examined both for nominal load and for overloads. The generator has a nominal output power of 10 kW for a constant piston speed of 0.7 ms^?1. The electromagnetic efficiency at nominal load is 86.0%, the load angle 6.6°, and the power fluctuation 1.3%. At 300% overload the load angle barely exceeds 12° and the cable temperature is below 25°C provided that the stator back is thermally connected to the sea water. The numerical calculations have been verified for small speeds by experiments. Copyright © 2006 John Wiley & Sons, Ltd.     

Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant

The objective of this study has been to create an online system for condition monitoring and diagnosis of a combined heat and power plant in Sweden. The system in question consisted of artificial neural network models, representing each main component of the combined heat and power plant, connected to a graphical user interface. The artificial neural network models were integrated on a power generation information manager server in the computer system of the combined heat and power plant, and the graphical user interface was made available on workstations connected to this server.     

Modelling and control of variable‐speed multi‐pole permanent magnet synchronous generator wind turbine

Emphasis of this article is on variable‐speed pitch‐controlled wind turbines with multi‐pole permanent magnet synchronous generator (PMSG) and on their extremely soft drive‐train shafts. A model and a control strategy for a full back‐to‐back converter wind turbine with multi‐pole PMSG are described. The model comprises submodels of the aerodynamic rotor, the drive‐train by a two‐mass model, the permanent magnet generator and the full‐scale converter system. The control strategy, which embraces both the wind turbine control itself and the control of the full‐scale converter, has tasks to control independently the active and reactive powers, to assist the power system and to ensure a stable normal operation of the wind turbine itself. A multi‐pole PMSG connected to the grid through a full‐scale converter has no inherent damping, and therefore, such configuration can become practically unstable, if no damping by means of external measures is applied. In this work, the frequency converter is designed to damp actively the drive‐train oscillations, thus ensuring stable operation. The dynamic performance of the presented model and control strategy is assessed and emphasized in normal operation conditions by means of simulations in the power system simulation tool DIgSILENT. Copyright © 2008 John Wiley & Sons, Ltd.     

Wind turbine power curve modelling using artificial neural network

Technical improvements over the past decade have increased the size and power output capacity of wind power plants. Small increases in power performance are now financially attractive to owners. For this reason, the need for more accurate evaluations of wind turbine power curves is increasing. New investigations are underway with the main objective of improving the precision of power curve modeling. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, Artificial Neural Network (ANN) has proven to be well suited for power curve modelling. It has been shown that a multi-stage modelling techniques using multilayer perceptron with two layers of neurons was able to reduce the level of both the absolute and random error in comparison with IEC methods and other newly developed modelling techniques. This newly developed ANN modeling technique also demonstrated its ability to simultaneously handle more than two parameters. Wind turbine power curves with six parameters have been modelled successfully. The choice of the six parameters is crucial and has been selected amongst more than fifty parameters tested in term of variability in differences between observed and predicted power output. Further input parameters could be added as needed.     

Multi-objective design optimization of a large-scale directdrive permanent magnet generator for wind energy conversion systems

This paper presents a simultaneous multiobjective optimization of a direct-drive permanent magnet synchronous generator and a three-blade horizontal-axis wind turbine for a large scale wind energy conversion system. Analytical models of the generator and the turbine are used along with the cost model for optimization. Three important characteristics of the system i.e., the total cost of the generator and blades, the annual energy output and the total mass of generator and blades are chosen as objective functions for a multi-objective optimization. Genetic algorithm (GA) is then employed to optimize the value of eight design parameters including seven generator parameters and a turbine parameter resulting in a set of Pareto optimal solutions. Four optimal solutions are then selected by applying some practical restrictions on the Pareto front. One of these optimal designs is chosen for finite element verification. A circuit-fed coupled time stepping finite element method is then performed to evaluate the no-load and the full load performance analysis of the system including the generator, a rectifier and a resistive load. The results obtained by the finite element analysis (FEA) verify the accuracy of the analytical model and the proposed method.     

Investigating the overload capacity of a direct‐driven synchronous permanent magnet wind turbine generator designed using high‐voltage cable technology

Utilization of wind energy to maximum permissible limits for generating electrical power has become necessary to meet the global energy demands. Under such circumstances the present day conventional wind turbine generators pose a limitation regarding the overload capability, specifically significant when they need to operate in high‐energy wind conditions. It is proposed that by employing a completely different generator winding concept, based on high‐voltage cable technology, to a specific generator, it is possible to increase the generator overload capabilities and thereby making it operationally efficient in high wind speed situations. Therefore, the possibility of extracting more energy is predicted to increase. Simulations, based on finite‐element methods combined with external circuit models for the generator, have been performed. The results demonstrate that under given thermal and electrical restrictions, a direct‐driven permanent magnet synchronous wind turbine generator, based on high‐voltage cable windings, is capable of being overloaded more than twice the rated power, thus making it very suitable for strong wind situations. Copyright © 2007 John Wiley & Sons, Ltd.     

永磁同步风电机组神经网络滑模多目标优化控制

将永磁同步风力发电机组中的变流器和电网用等效负载代替并对控制回路进行简化,得到非线性仿射形式的机组模型,利用反馈线性化方法对系统进行精确线性化。固定参数离散指数趋近律滑模控制算法主要缺陷是如两个参数匹配不当,可能会使求得的控制量过大,同时系统在滑模面附近剧烈的高频抖振会导致机组所要承受的机械应力增加,动态性能变差,利用神经网络的自适应学习能力对这两个控制参数进行实时优化,根据机组控制目标定义一个综合性能指标,通过优化该指标得到网络权值修正算法。仿真结果表明,该方法可以使系统快速到达滑模面,实现了机组对最优转速的快速跟踪;同时有效抑制了系统的抖振,减小了额外的疲劳载荷,实现了多目标优化控制。     

基于永磁同步电机逆系统解耦模糊滑模控制

基于逆系统理论和模糊滑模控制方法对永磁同步电动机调速系统进行了解耦控制研究,提出了一种新型的模糊推理滑模控制策略。根据永磁同步电动机调速系统的动态数学模型,证明了其逆系统的存在性,给出了永磁同步电动机逆系统解耦的模糊滑模控制器设计方法,系统仿真结果表明,该控制策略在不加剧滑动模态抖动的前提下,有效增强了系统的抗负载扰动能力,改善了永磁同步电动机系统的控制性能。     

Robust nonlinear control via feedback linearization and Lyapunov theory for permanent magnet synchronous generator-based wind energy conversion system

In this paper, the method for the nonlinear control design of a permanent magnet synchronous generator based-wind energy conversion system (WECS) is proposed in order to obtain robustness against disturbances and harvest a maximum power from a typical stochastic wind environment. The technique overcomes both the problem of nonlinearity and the uncertainty of the parameter compared to such classical control designs based on traditional control techniques. The method is based on the differential geometric feedback linearization technique (DGT) and the Lyapunov theory. The results obtained show the effectiveness and performance of the proposed approach.     

Wind speed prediction in the mountainous region of India using an artificial neural network model

Measured wind speed data are not available for most sites in the mountainous regions of India. The objective of present study is to predict wind speeds for 11 locations in the Western Himalayan Indian state of Himachal Pradesh to identify possible wind energy applications. An artificial neural network (ANN) model is used to predict wind speeds using measured wind data of Hamirpur location for training and testing. Temperature, air pressure, solar radiation and altitude are taken as inputs for the ANN model to predict daily mean wind speeds. Mean absolute percentage error (MAPE) and correlation coefficient between the predicted and measured wind speeds are found to be 4.55% and 0.98 respectively. Predicted wind speeds are found to range from 1.27 to 3.78 m/s for Bilaspur, Chamba, Kangra, Kinnaur, Kullu, Keylong, Mandi, Shimla, Sirmaur, Solan and Una locations. A micro-wind turbine is used to assess the wind power generated at these locations which is found to vary from 773.61 W to 5329.76 W which is suitable for small lighting applications. Model is validated by predicting wind speeds for Gurgaon city for which measured data are available with MAPE 6.489% and correlation coefficient 0.99 showing high prediction accuracy of the developed ANN Model.     

Improvement in artificial neural network-based estimation of grid connected photovoltaic power output

This paper presents a method to improve the accuracy of artificial neural network (ANN)–based estimation of photovoltaic (PV) power output by introducing two more inputs, solar zenith angle and solar azimuth angle, in addition to the most widely used environmental information, plane-of-array irradiance and module temperature. Solar zenith angle and solar azimuth angle define the solar position in the sky; hence, the loss of modeling accuracy due to impacts of solar angle-of-incidence and solar spectrum is reduced or eliminated. The observed data from two sites where local climates are significantly different is used to train and test the proposed network. The good performance of the proposed network is verified by comparing with existing ANN model, algebraic model, and polynomial regression model which use environmental information only of plane-of-array irradiance and module temperature. Our results show that the proposed ANN model greatly improves the accuracy of estimation in the long term under various weather conditions. It is also demonstrated that the improvement in estimating outdoor PV power output by adding solar zenith angle and azimuth angle as inputs is useful for other data-driven methods like support vector machine regression and Gaussian process regression.