ORCID Profile
0000-0001-8060-3386
Current Organisation
University of the Witwatersrand
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Electrical and Electronic Engineering | Electrical Engineering
Wind |
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2008
Publisher: IEEE
Date: 03-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2007
Publisher: IEEE
Date: 06-2018
Publisher: IEEE
Date: 04-2017
Publisher: AIP Publishing
Date: 15-04-2006
DOI: 10.1063/1.2162822
Abstract: In many cases, the normal operation of switched reluctance machines requires excitation of two or more phases simultaneously. When multiple phases are conducting simultaneously, the flux paths from each phase will overlap, which may lead to localized saturation. In such cases, the flux linkage must be considered a function not just of the current in the test winding but of all excited windings. The degree of mutual coupling between phases influences the per-phase magnetization curves and torque characteristics. In machines with even phase numbers, the degree of mutual coupling between phases varies due to discontinuities in the phase polarity arrangement. From nonlinear finite element simulations, it is possible to compare the i-ψ loop diagrams under single-phase and multiphase excitations, and hence the torque produced. The mutual flux linkage from each phase can be calculated separately for each rotor position using the frozen permeability method, to further analyze the mutual coupling effects. For a given excitation current profile, the torque can be maximized by careful arrangement of the phase polarities.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
Publisher: IEEE
Date: 10-2017
Publisher: Elsevier BV
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2009
Publisher: IEEE
Date: 05-2015
Publisher: Institution of Engineering and Technology (IET)
Date: 1996
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2008
Publisher: Wiley
Date: 17-06-2019
DOI: 10.1002/ETT.3653
Publisher: IEEE
Date: 09-2011
Publisher: IEEE
Date: 06-2017
Publisher: IEEE
Date: 09-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2005
Publisher: IEEE
Date: 04-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2010
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 06-2204
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Publisher: MDPI AG
Date: 24-11-2019
DOI: 10.20944/PREPRINTS201911.0272.V1
Abstract: A cognitive radio sensor network (CRSN) based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and also different from the conventional SG that uses a static resource allocation technique to allocate resources to sensor nodes and communication devices in the SG network. Due to the challenges associated with competitive sensor nodes and communication devices in accessing and utilizing radio resources, the need for dynamic radio resource allocation (RRA) has been proposed as a solution for allocating radio resources to sensor nodes in a CRSN based smart grid ecosystem (network). These challenges include energy ower constraints, poor quality of service (QoS), interference, delay, spectrum efficiency issues, and excessive spectrum hand-offs. Hence, the optimization of resource allocation criteria, such as energy efficiency, throughput maximization, QoS guarantee, fairness, priority, interference mitigation/avoidance, etc., will go a long way in addressing the problems of RRA in a CRSN based SG. Consequently, this work explores RRA in CRSNs for SGs. Various resource allocation schemes, as well as its architecture in a CRSN for SG environment, are presented. The work reported in this paper introduces a model called the & guaranteed network connectivity channel allocation& for throughput maximization (GNC-TM) and optimal spectrum band determination in RRA for improved throughput criteria in CRSNs for SGs. The results show that the model outperforms the existing protocol in terms of throughput and error probability. Finally, the contribution to knowledge and future research direction, such as energy efficiency and hybrid energy harvesting schemes are highlighted.
Publisher: IEEE
Date: 10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Publisher: IEEE
Date: 04-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-1994
Publisher: IEEE
Date: 10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 11-2009
Publisher: IEEE
Date: 09-2009
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 04-2018
Publisher: IEEE
Date: 11-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2011
Publisher: IEEE
Date: 10-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-1994
DOI: 10.1109/60.300144
Publisher: Bentham Science Publishers Ltd.
Date: 25-08-2021
DOI: 10.2174/1872212115999201125123708
Abstract: A cognitive radio sensor network (CRSN)-based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and conventional SG. Currently, an SG uses a static resource allocation technique to allocate resources to sensor nodes in the SG network. Static resource allocation is not efficient due to the heterogeneous nature of CRSN-based SGs. Hence, an appropriate mechanism such as dynamic radio resource allocation (RRA) is required for efficient resource allocation in CRSNs for SGs. The objective of this paper is to investigate and propose suitable dynamic RRA for efficient resource allocation in CRSNs-based SGs. This involves a proposal for appropriate strategy that will address poor throughput and excessive errors in resource allocation. In this paper, the dynamic RRA approach is used to allocate resources such as frequency, energy, channels and spectrum to the sensor nodes. This is because of the heterogeneity in a CRSN which differs for SG applications. The dynamic RRA approach is based on optimization of resource allocation criteria such as energy efficiency, throughput maximization, QoS guarantee, etc. The methods include an introduced model called “guaranteed network connectivity channel allocation for throughput maximization” (GNC-TM). Also used, is an optimal spectrum-band determination in RRA for improved throughput. The results show that the model outperforms the existing protocol of channel allocation in terms of throughput and error probability. This study explores RRA schemes for CRSNs for SGs. The paper proposed a GNC-TM model, including demonstration of suitable spectrum band operation in CRSNs for SGs.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: IEEE
Date: 11-2009
Publisher: Institution of Engineering and Technology (IET)
Date: 04-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2005
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 10-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2006
Publisher: Elsevier BV
Date: 03-2020
Publisher: Inderscience Publishers
Date: 2009
Publisher: IEEE
Date: 02-2014
Publisher: Inderscience Publishers
Date: 2009
Publisher: MDPI AG
Date: 18-10-2018
DOI: 10.3390/S18103526
Abstract: This work presents a study of the traction, normal and stall forces in a two-sided planar actuator with orthogonal planar windings and a mover that comprises two cars magnetically coupled to each other through two pairs of permanent magnets (PMs). There is no ferromagnetic armature core because of the permanent magnets array in the mover and orthogonal traction forces can be generated in order to move both cars jointly in any direction on a plane. The stall force is the minimal force necessary to break up the magnetic coupling between the two cars. When one of the cars is subjected to an external force through the x- or y-axis, the cars can become out of alignment with respect to each other and the planar actuator cannot work properly. The behavior of the forces was modelled by numerical and analytical methods and experimental results were obtained from tests carried out on a prototype. The average sensitivity of the measured static propulsion planar force along either axis is 4.48 N/A. With a 20-mm displacement between the cars along the direction of the x-axis and no armature current, a magnetic stall force of 17.26 N is produced through the same axis in order to restore the alignment of the two cars
Publisher: IEEE
Date: 11-2009
Publisher: MDPI AG
Date: 06-07-2023
DOI: 10.3390/EN16135206
Abstract: With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing need for robust cybersecurity mechanisms to protect these systems from cyberattacks. A cyber-physical system is a combination of physical and cyber components, and a security breach in either component can lead to catastrophic consequences. Cyberattack detection and mitigation methods in CPSs involve the use of various techniques such as intrusion detection systems (IDSs), firewalls, access control mechanisms, and encryption. Overall, effective cyberattack detection and mitigation methods in CPSs require a comprehensive security strategy that considers the unique characteristics of a CPS, such as the interconnectedness of physical and cyber components, the need for real-time response, and the potential consequences of a security breach. By implementing these methods, CPSs can be better protected against cyberattacks, thus ensuring the safety and reliability of critical infrastructure and other vital systems. This paper reviews the various kinds of cyber-attacks that have been launched or implemented in CPSs. It reports on the state-of-the-art detection and mitigation methods that have been used or proposed to secure the safe operation of various CPSs. A summary of the requirements that CPSs need to satisfy their operation is highlighted, and an analysis of the benefits and drawbacks of model-based and data-driven techniques is carried out. The roles of machine learning in cyber assault are reviewed. In order to direct future study and motivate additional investigation of this increasingly important subject, some challenges that have been unaddressed, such as the prerequisites for CPSs, an in-depth analysis of CPS characteristics and requirements, and the creation of a holistic review of the different kinds of attacks on different CPSs, together with detection and mitigation algorithms, are discussed in this review.
Publisher: IEEE
Date: 09-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2005
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 21
Publisher: IEEE
Date: 05-2015
Publisher: IEEE
Date: 09-0009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2007
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 05-2015
Publisher: IEEE
Date: 11-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1997
DOI: 10.1109/28.567073
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: IEEE
Date: 05-2006
Publisher: IEEE
Date: 08-2017
Publisher: IEEE
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: IEEE
Date: 09-2017
Publisher: IEEE
Date: 09-2009
Publisher: IEEE
Date: 07-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 09-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-0009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2007
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 11-2009
Publisher: Institution of Engineering and Technology (IET)
Date: 07-2014
Publisher: IEEE
Date: 11-2009
Publisher: IEEE
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2012
Publisher: IEEE
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 06-2015
Publisher: Elsevier BV
Date: 2019
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 04-2017
Publisher: Institution of Engineering and Technology (IET)
Date: 03-05-2018
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 11-2006
Publisher: IEEE
Date: 06-2018
Publisher: IEEE
Date: 04-2018
Publisher: Elsevier BV
Date: 10-2017
Publisher: Elsevier BV
Date: 09-2017
Publisher: IET
Date: 2011
DOI: 10.1049/CP.2011.0220
Publisher: Institution of Engineering and Technology (IET)
Date: 08-2014
Publisher: Elsevier BV
Date: 07-2019
Publisher: Institution of Engineering and Technology
Date: 2016
DOI: 10.1049/CP.2016.0374
Publisher: IEEE
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
Publisher: IEEE
Date: 10-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2011
Publisher: MDPI AG
Date: 27-07-2022
DOI: 10.3390/EN15155411
Abstract: Stand-alone DC microgrids have multiple distributed generation (DG) sources that meet the required demand (load) by using droop control to achieve load (current) sharing between the DGs. The use of droop control leads to a voltage drop at the DC bus. This paper presents a distributed secondary control scheme to simultaneously ensure current sharing between the DGs and regulate the DC bus voltage. The proposed control scheme eliminates the voltage deviation and ensures balanced current sharing by combining the voltage and current errors in the designed secondary control loop. A new flight-based artificial bee colony optimization algorithm is proposed. This selects the parameters of the distributed secondary control scheme to achieve the objectives of the proposed controller. A state–space model of the DC microgrid is developed by using eigenvalue observation to test the impacts of the proposed optimized distributed secondary controller on the stability of the DC microgrid system. A real-time test system is developed in MATLAB/Simulink and used in a Speedgoat real-time simulator to verify the performance of the proposed control scheme for real-world applications. The results show the robustness of the proposed distributed secondary control scheme in achieving balance current sharing and voltage regulation in the DC microgrid with minimal oscillations and fast response time.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2010
Publisher: AEDERMACP (European Association for the Development of Renewable Energies and Power Quality)
Date: 03-2005
DOI: 10.24084/REPQJ03.271
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2011
Publisher: IEEE
Date: 11-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: IEEE
Date: 09-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2009
Publisher: IEEE
Date: 03-2019
Publisher: IEEE
Date: 11-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2010
Publisher: Elsevier BV
Date: 05-2016
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 09-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2012
Publisher: IEEE
Date: 05-2012
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2009
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 05-2006
Publisher: IEEE
Date: 2019
Publisher: IEEE
Date: 09-2013
Publisher: IEEE
Date: 05-2006
Publisher: IEEE
Date: 09-2019
Publisher: IEEE
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: IEEE
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
Publisher: IEEE
Date: 09-2014
Publisher: IEEE
Date: 09-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
Publisher: IEEE
Date: 09-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: IEEE
Date: 09-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 10-2008
Publisher: IEEE
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2012
Publisher: IEEE
Date: 09-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: IEEE
Date: 04-2018
Publisher: IEEE
Date: 2019
Publisher: IEEE
Date: 08-2017
Publisher: IEEE
Date: 2019
Publisher: Elsevier BV
Date: 07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1996
DOI: 10.1109/60.536997
Publisher: IEEE
Date: 2019
Publisher: Emerald
Date: 17-09-2018
DOI: 10.1108/COMPEL-01-2018-0022
Abstract: Semi-active suspension systems with electromagnetic d ers allow energy regeneration and the required control strategies are easier to implement than the active suspensions are. This paper aims to address the application of a tubular linear permanent magnet synchronous machine for a semi-active suspension system. Classical rules of mechanics and electromagnetics were applied to describe a dynamic model combining vibration and electrical machines theories. A multifaceted MATLAB®/Simulink model was implemented to incorporate equations and simulate global performance. Experimental tests on an actual prototype were carried out to investigate displacement transmissibility of the passive case. In addition, simulation results were shown for the dissipative semi-active case. The application of the developed model suggests convergent results. For the passive case, numerical and experimental outcomes validate the parameters and confirm system function and proposed methodology. MATLAB®/Simulink results for the semi-active case are consistent, showing an improvement on the displacement transmissibility. These agree with the initial conceptual thoughts. The use of linear electromagnetic devices in suspension systems is not a novel idea. However, most published papers on this subject outline active solutions, neglect semi-active ones and focus on experimental studies. However, here a dynamic mechanical-electromagnetic coupled model for a semi-active suspension system is reported. This is in conjunction with a linear electromagnetic d er.
Publisher: IEEE
Date: 09-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2011
Publisher: Elsevier BV
Date: 08-2015
Publisher: IEEE
Date: 05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/60.790874
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: IEEE
Date: 2001
Publisher: IEEE
Date: 04-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: IEEE
Date: 08-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2013
Publisher: IEEE
Date: 11-2010
Publisher: IEEE
Date: 04-2017
Publisher: MDPI AG
Date: 19-03-2016
DOI: 10.3390/S16030407
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
Publisher: IEEE
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: IEEE
Date: 02-2014
Publisher: MDPI AG
Date: 15-05-2014
DOI: 10.3390/EN7053204
Publisher: IEEE
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 09-2007
DOI: 10.1109/IAS.2007.184
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 08-2017
Publisher: IEEE
Date: 09-2017
Publisher: IEEE
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2003
Publisher: IEEE
Date: 11-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2012
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 11-2008
Publisher: IEEE
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2013
Publisher: Elsevier BV
Date: 10-2019
Publisher: IEEE
Date: 11-2007
Publisher: Elsevier BV
Date: 2018
Publisher: IEEE
Date: 11-2015
Publisher: IEEE
Date: 11-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: IEEE
Date: 06-2010
Publisher: IEEE
Date: 02-2019
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 10-2017
Publisher: Elsevier BV
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2013
Publisher: IEEE
Date: 08-2017
Publisher: IEEE
Date: 09-2009
Publisher: IEEE
Date: 12-2015
DOI: 10.1109/AIMS.2015.66
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/60.790931
Publisher: IEEE
Date: 05-2006
Publisher: IEEE
Date: 2020
Publisher: IEEE
Date: 05-2006
Publisher: IEEE
Date: 11-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2008
Publisher: IEEE
Date: 11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 05-2012
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: IEEE
Date: 10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2012
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 05-2011
Publisher: MDPI AG
Date: 10-01-2019
DOI: 10.3390/EN12020214
Abstract: The matrix converter (MC) is a promising converter that performs the direct AC-to-AC conversion. Model predictive control (MPC) is a simple and powerful tool for power electronic converters, including the MC. However, weighting factor design and heavy computational burden impose significant challenges for this control strategy. This paper investigates the generalized sequential MPC (SMPC) for a three-phase direct MC. In this control strategy, each control objective has an in idual cost function and these cost functions are evaluated sequentially based on priority. The complex weighting factor design process is not required. Compared with the standard MPC, the computation burden is reduced because only the pre-selected switch states are evaluated in the second and subsequent sequential cost functions. In addition, the prediction model computation for the following cost functions is also reduced. Specifying the priority for control objectives can be achieved. A comparative study with traditional MPC is carried out both in simulation and an experiment. Comparable control performance to the traditional MPC is achieved. This controller is suitable for the MC because of the reduced computational burden. Simulation and experimental results verify the effectiveness of the proposed strategy.
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 11-2015
Publisher: Hindawi Limited
Date: 15-10-2019
DOI: 10.1002/ER.4222
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 09-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2019
Publisher: IEEE
Date: 08-2016
Publisher: Elsevier BV
Date: 06-2011
Publisher: IEEE
Date: 09-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Publisher: MDPI AG
Date: 23-05-2022
DOI: 10.3390/EN15103848
Abstract: Large induction motors can have a high inrush and run-up current during starting, often up to ten times the rated current. In weak supplies, this could be a problem, causing system stability issues and the voltage to dip below acceptable levels. In islanded systems, the capacity could be pulled below its maximum. There are several different starting methods possible, but they are often only suitable for smaller machines. One method not investigated is the use of parallel capacitor compensation during the starting because large induction motors are very inductive during the starting sequence, so that supplying reactive power may be more effective than supplying energy. This paper first investigates several different induction motors with increasing size and assesses their compensation requirements in terms of the reactive power requirement during starting. It is shown that they do generally become more inductive as they increase in size. It is illustrated, using simulations, that using parallel capacitors during starting can significantly reduce the starting current. The concept is tested using a small experimental motor. Series inductors are added to the motor to make it more inductive and look more like a larger machine. The experimental results confirm that the method works. It is also found that the parallel capacitors require series filters to prevent harmonic current, and these can be realized using inductors in series with the compensation capacitors. Point-on switching using solid-state relays was tested, and these can reduce the transient switch-on current over the first voltage cycle.
Publisher: IEEE
Date: 10-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/60.815075
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2009
Publisher: IEEE
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
Publisher: IEEE
Date: 11-2006
Publisher: AEDERMACP (European Association for the Development of Renewable Energies and Power Quality)
Date: 03-2005
DOI: 10.24084/REPQJ03.314
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: IEEE
Date: 2019
Publisher: IEEE
Date: 02-2019
Publisher: Elsevier BV
Date: 2015
Publisher: IEEE
Date: 11-2008
Publisher: IEEE
Date: 10-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
Publisher: MDPI AG
Date: 24-01-2020
DOI: 10.3390/EN13030564
Abstract: In the work reported in this paper, a novel application of the artificial bee colony algorithm is used to implement a virtual inertia control strategy for grid-connected wind energy conversion systems. The proposed control strategy introduces a new heuristic optimization technique that uses the artificial bee colony (ABC) algorithm to calculate the optimal gain value of an additional derivative control loop added to the control scheme of the machine side converter in a wind energy system to enable wind farms to participate in frequency control as specified by recent grid codes. This helps to minimize the frequency deviations, reduce active power deviation in the system, and increase the penetration level of wind energy in power systems. The study was performed in a restructured power system environment. The proposed control scheme and its robustness were evaluated using load–frequency analysis for three real-life transaction scenarios that can occur in an interconnected open-energy market and the validation was carried out using eigenvalue analysis. The results in this study show that the optimal gain of the proposed controller reduces the frequency deviations and improves stability and overall performance of the system.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2014
Publisher: IEEE
Date: 05-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: IEEE
Date: 11-2008
Publisher: Informa UK Limited
Date: 02-01-2018
Publisher: IEEE
Date: 05-2013
Publisher: IEEE
Date: 11-2008
Publisher: IEEE
Date: 05-2013
Publisher: IEEE
Date: 06-2010
Publisher: IEEE
Date: 11-2016
Publisher: Elsevier
Date: 2005
Publisher: IEEE
Date: 10-2014
Publisher: MDPI AG
Date: 12-01-2022
DOI: 10.3390/EN15020511
Abstract: The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 06-2010
End Date: 05-2014
Amount: $310,000.00
Funder: Australian Research Council
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