Predictive control of electrical drives

In this work, the application of the Predictive Control Technique to the electrical drives has been considered and discussed, especially in comparison with the employment of the traditional control techniques. First of all, a predictive control algorithm for the Brushless DC drive is developed with the aim of improving the traditional current commutation as best as possible. Then, a novel predictive control algorithm is proposed by imposing both the reference torque value and the minimum Joule losses condition. Then, several predictive control algorithms are proposed for the Synchronous Reluctance Machine, taking into account the magnetic saturation effects too. They are based either on the traditional control strategy or on optimization criteria, such as the minimum steady state Joule losses condition and the fastest achievement of the reference torque value. Finally, a novel predictive Direct Torque Control algorithm is synthesized for the Asynchronous Machine, by taking into account both voltage saturation and current limitation constraints. The synthesizing procedure adopted is also shown by an interesting graphical representation. The effectiveness of all the proposed algorithms has been properly tested by appropriate simulation studies, performed in the Matlab Simulink environment. The corresponding results have highlighted how the employment of the Predictive Control Technique allows better performances compared to those achievable by the traditional control ones

A Novel Highly Integrated Hybrid Energy Storage System for Electric Propulsion and Smart Grid Applications

This chapter addresses potentialities and advantages of a highly integrated hybrid energy storage system (HESS) for electric propulsion and smart grids. This configuration consists of a highly integrated battery-ultracapacitor system (HIBUC) and aims to benefit from the advantages of both passive and active HESS configurations. Particularly, the integration of the ultracapacitor module (UM) within the DC-link of the DC/AC multilevel converter enables the decoupling between DC-link voltage and energy content without the need for any additional DC/DC converter. As a result, HIBUC benefits from simplicity and energy flow management capabilities very similar to those achieved by passive and active HESS configurations, respectively. This is highlighted properly by a theoretical analysis, which also accounts for a comparison between HIBUC and both passive and active HESS configurations. Some HIBUC application examples are also reported, which highlight the flexibility and potentialities of HIBUC for both electric propulsion systems and smart grids

Design of flux-weakening space vector control algorithms for permanent magnet brushless DC machines on suitable synchronous reference frames

The design of Space Vector Control (SVC) systems suitable for flux-weakening operation of Permanent Magnet Brushless DC Machines (PMBDCMs) is presented in this paper. The proposed design approach enables overcoming the critical issues arising from the non-linearities of PMBDCM voltage and torque equations; these issues derive from the trapezoidal shapes of back-emfs and affect PMBDCM constraint management significantly. The SVCs presented in this paper have been developed within two different synchronous reference frames, both of which enable distinguishing torque and demagnetizing current components clearly. Therefore, reference torque current component is determined in accordance with PMBDCM torque demand, while reference demagnetizing current component is computed through a voltage follower PI regulator, which processes the voltage deficit detected on the DC-link. In this regard, a novel synchronous reference frame is proposed in this paper, which improves PMBDCM constraint management and results into a wider constant-power speed range, but at the cost of some torque ripple. The enhanced performances achievable by SVC approaches are highlighted by numerical simulations, which regard the comparison among the SVCs and an SVC with no flux-weakening capability, at different operating conditions

A Genetic Algorithm for the Definition of Nodal Load Time Evolutions in Micro Grids Assessment

One of the on-going research topic in smart grid planning and assessment is the definition of suitable time evolution of load profiles in micro grids by using the information about the network topology and the available electrical measurements. This paper presents an approach for a heuristic definition of nodal load profiles in micro grids when the available measurements are not exhaustive for its state evaluation. In particular, in order to develop the preliminary micro grids assessment, a Genetic Algorithm (GA) has been employed to determine possible evolution of nodal load profiles that satisfy the power system constraints and input measurements. In order to verify the effectiveness of proposed methodology a real micro grid has been considered as case of study. The micro grid has been simulated in Digsilent and the used GA has been implemented in Matlab environment. Finally, Digsilent Programming Language (DPL) has been employed for interfacing the GA with Digsilent

suppression of dc link voltage unbalance in three level neutral point clamped converters

Abstract Two different control approaches for suppressing DC-link voltage unbalance in Three-Level Neutral-Point Clamped Converters (NPCs) are presented in this paper. They both guarantee DC-link voltage equalization over any NPC operating conditions, i.e. when the NPC feeds or is supplied by the main AC grid at different active and/or reactive power rates. The proposed control approaches consist of either a hysteresis or a proportional regulator, each of which synthesizes the most suitable control action based on the actual DC-link voltage unbalance. Particularly, two different PWM techniques have been developed in order to achieve DC-link voltage equalization successfully, preserving NPC voltage and current waveforms at the same time. The performances achievable by means of both the proposed control approaches have been compared to each other through an extensive simulation study in order to highlight their most important advantages and drawbacks, as well as their effectiveness over any operating conditions. Particularly, both control approaches are validated in the Matlab-Simulink environment referring to DC-link voltage equalization of an NPC that represents the point of common coupling between a DC microgrid and the main AC grid

Energy Management and Control System Design of an Integrated Flywheel Energy Storage System for Residential Users

This paper presents the energy management and control system design of an integrated flywheel energy storage system (FESS) for residential users. The proposed FESS is able to draw/deliver 8 kWh at 8 kW, and relies on a large-airgap surface-mounted permanent magnet synchronous machine, the inner rotor of which integrates a carbon-fiber flywheel, leading to a compact and efficient FESS. The proposed energy management system is based on four different operating modes, which are defined and can be selected in accordance with FESS speed and/or user’s preference, while FESS control system is devoted to power/current tracking at both machine- and grid-side converters. The effectiveness of the proposed solutions, as well as the overall energy performance of the proposed FESS, are verified by real-time simulations, which regard different operating conditions and/or realistic scenarios

Inductor losses estimation in DC-DC converters by means of averaging technique

A suitable inductor modeling for power electronic DC-DC converters is presented in this paper. It is developed with the aim of improving inductor losses estimation achievable by averaged models, which inherently neglect inductor current ripple. In order to account for its contribution to the overall inductor losses, an appropriate parallel resistance is thus enclosed into the inductor model, whose value should be chosen in accordance with the DC-DC converter operating conditions. This allows the development of improved averaged models of DC-DC converters, especially in terms of power losses estimation. The effectiveness of the proposed modeling approach has been validated through a simulation study, which refers to the case of a boost DC-DC converter and is performed by means of a suitable circuit simulator designed for rapid modelling of switching power systems (SIMetrix/SIMPLIS)

Vehicle-to-Grid Technology: State-of-the-Art and Future Scenarios

An overview of V2G (vehicle-to-grid) technology is presented in this paper. It aims to highlight the main features, opportunities and requirements of V2G. Thus, after briefly resuming the most popular charging strategies for PEVs (plug-in electric vehicles), the V2G concept is introduced, especially highlighting its potentiality as a revenue opportunity for PEV owners; this is mainly due to the V2G ability to provide ancillary services, such as load leveling, regulation and reserve. Such solutions have been thoroughly investigated in the literature from both the economic and technical points of view and are here reported. In addition, V2G requirements such as mobility needs, charging stations availability and appropriate PEV aggregative architectures are properly taken into account. Finally, future developments and scenarios have also been reported

Design of a High-Speed Ferrite-based Brushless DC Machine for Electric Vehicles

In the present paper an analytic procedure for the preliminary design of a High-Speed ferrite-based Brushless DC Machine (HS-BLDC) has been proposed. In particular, mechanical and electromagnetic modeling have been developed in order to take into account their mutual influence in the definition of the geometry of the electrical machine. In addition, suitable design targets have been imposed in accordance with electric vehicle application requirements. Hence, several mechanical and electromagnetic constraints have been introduced in order to comply with high-speed operation, preventing demagnetization issues of ferrite magnets as well. Subsequently, an HS-BLDC characterized by an inner rotor configuration has been designed in accordance with the proposed methodology. The analytical procedure and the corresponding results have been reported and validated by means of Finite Element Analyses (FEAs), highlighting the effectiveness of the proposed configuration and design solutions

3D vs. 2D MRI radiomics in skeletal Ewing sarcoma: Feature reproducibility and preliminary machine learning analysis on neoadjuvant chemotherapy response prediction

ObjectiveThe extent of response to neoadjuvant chemotherapy predicts survival in Ewing sarcoma. This study focuses on MRI radiomics of skeletal Ewing sarcoma and aims to investigate feature reproducibility and machine learning prediction of response to neoadjuvant chemotherapy. Materials and methodsThis retrospective study included thirty patients with biopsy-proven skeletal Ewing sarcoma, who were treated with neoadjuvant chemotherapy before surgery at two tertiary sarcoma centres. 7 patients were poor responders and 23 were good responders based on pathological assessment of the surgical specimen. On pre-treatment T1-weighted and T2-weighted MRI, 2D and 3D tumour segmentations were manually performed. Features were extracted from original and wavelet-transformed images. Feature reproducibility was assessed through small geometrical transformations of the regions of interest mimicking multiple manual delineations, and intraclass correlation coefficient >0.75 defined feature reproducibility. Feature selection also consisted of collinearity and significance analysis. After class balancing in the training cohort, three machine learning classifiers were trained and tested on unseen data using hold-out cross-validation. Results1303 (77%) 3D and 620 (65%) 2D radiomic features were reproducible. 4 3D and 4 2D features passed feature selection. Logistic regression built upon 3D features achieved the best performance with 85% accuracy (AUC=0.9) in predicting response to neoadjuvant chemotherapy. ConclusionCompared to 2D approach, 3D MRI radiomics of Ewing sarcoma had superior reproducibility and higher accuracy in predicting response to neoadjuvant chemotherapy, particularly when using logistic regression classifier