Considerations on the preliminary sizing of electrical machines with hairpin windings

Although the standard preliminary sizing of electrical machines equipping random windings is well consolidated and is worldwide acknowledged to be a good starting point for the design, there is no proof of accuracy and confidence when it comes to hairpin windings. This winding technology is gaining extensive attention due to its inherently high slot fill factor, good heat dissipation, strong rigidity, and short end-windings. These features make hairpin windings a potential candidate for some traction application to enhance power and/or torque densities. In this paper, a comparative design is done using the classical sizing tools available in literature between two surface-mounted permanent magnet synchronous machines, one featuring a random winding and one with a hairpin layout. The study aims at highlighting the hairpin winding challenges at high frequency operations and at showing limits of applicability of these standard approaches when applied to this technology. For verification purposes, finite element evaluations are also performed

Intelligent control of battery energy storage for microgrid energy management using ANN

In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique

Multifunctional control design for modular plug-and-play battery storage in DC microgrids

Plug-and-Play (P&P) performance facilitates the modularity of DC microgrids. The realization of P&P operation relies on the control design of DC microgrids. Conventional control methods are normally designed for steady operation of a DC microgrid, neglecting or partially sacrifices the availability of P&P operations. Some bottom layer’s control designs such as droop control, from a hierarchical control scheme perspective for example, are inherently able to realize P&P operations. However, such methods have limitations in terms of power sharing accuracy. This paper proposes a control scheme that reconfigures hierarchical control and makes it more compatible for different P&P operation situations in DC microgrids. In this control scheme, Automatic Mater-Slave (AMS) control is implemented in the secondary control layer to automatically respond to those cases in the absence of communication or the failure of the master module. The proposed control scheme is validated by MATLAB/Simulink simulation

Supercapacitor assisted surge absorber (SCASA) technique: selection of magnetic components based on permeance

Supercapacitors help building long time constant resistor-capacitor circuits. This property helps them withstand high voltage transient surges and dissipate transient energy in the resistive element of the circuit without exceeding the supercapacitor’s DC voltage rating, which is usually between 2.5 to 4 V. SCASA is a patented technique, which was commercialized within the last five years. Successful implementation of this circuit topology, despite its simplicity, is quite dependent on the selection of the core of the coupled inductor utilized. This paper provides the essential details of the process of selecting the core for the magnetic component required, with a brief comparison of SCASA technique with a traditional surge protector, without any supercapacitors

Machine learning-based agoraphilic navigation algorithm for use in dynamic environments with a moving goal

This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal

Development of a Thermal Analysis Tool for Linear Machines

The thermal design is one of the most important stages in the design process of electrical machines. Thanks to software packages, like Motor-CAD, rotating machine designers can predict the thermal operation of the machines with high precision. However, a Motor-CAD equivalent for linear machines does not exist. Thus, linear machine designers must develop specific thermal analysis tools when designing the machines. In this article, a generic thermal analysis tool for different kinds of linear machines is presented. The model has been designed in MATLAB Simulink. Hence, it should be easy to implement for most engineers. The article describes the configuration of the different elements of the tool. The calibration parameters and procedure, and typical values of the calibration variables, are also given in the document. Finally, in order to demonstrate the generic nature of the tool, the model is experimentally validated via DC thermal tests to a linear induction machine and a linear switched-flux permanent magnet machine. The results show that, despite being simple and easy to implement, the model can predict the thermal operation of different machines with high precision

Model-free neural network-based predictive control for robust operation of power converters

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC

Voltage H8 control of a vanadium redox flow battery

Redox flow batteries are one of the most relevant emerging large-scale energy storage technologies. Developing control methods for them is an open research topic; optimizing their operation is the main objective to be achieved. In this paper, a strategy that is based on regulating the output voltage is proposed. The proposed architecture reduces the number of required sensors. A rigorous design methodology that is based on linear H8 synthesis is introduced. Finally, some simulations are presented in order to analyse the performance of the proposed control system. The results show that the obtained controller guaranties robust stability and performance, thus allowing the battery to operate over a wide range of operating conditions. Attending to the design specifications, the controlled voltage follows the reference with great accuracy and it quickly rejects the effect of sudden current changes.Peer ReviewedPostprint (published version