In this paper a simplified Chaotic Pulse Width Modulation (CPWM) method is proposes for minimizing conducted electromagnetic interference (EMI) in high-gain DC-DC converters, address the shortcomings of traditional PWM techniques. In contrast to conventional methods, CPWM uses deterministic chaos to randomize switching pulses, which efficiently distributes spectrum energy and reduces high-frequency harmonic peaks without changing the output voltage and convertor performance. A randomized carrier frequency modulation with a fixed duty ratio (RCFMFD) technique is used for achieving spectral spreading with minimum hardware complexity. The proposed high-gain converter is modelled and simulated in MATLAB/Simulink under periodic and chaotic PWM settings. A hardware prototype is developed with a DC input of 12 V and it was stepped up to a 200 V DC at a 200 kHz switching frequency with a 35% duty cycle. An ARM based processor generates both conventional and CPWM switching pulses, interfaced to the MOSFET gate through a driver. A power spectral density (PSD) comparison reveals that CPWM achieves a maximum conducted EMI suppression of about 23.08 dB<inline-formula> <tex-math notation="LaTeX">$\mu \text {V}$ </tex-math></inline-formula> (equivalent to <inline-formula> <tex-math notation="LaTeX">$22.6~\mu \text {V}$ </tex-math></inline-formula>) compared to conventional PWM. The results verify that CPWM offers a compact, cost-effective, and high-performance EMI suppression solution suitable for EMI-sensitive applications such as electric vehicles, aerospace, and medical electronics.
Modular multilevel converter based multi-terminal direct current (MMC-MTDC) systems rely on energy-dissipating devices to handle surplus power caused by AC-side faults at the receiving-end, which suffers from poor economic efficiency and significant energy waste. To fully exploit the inherent surplus power absorption capability of MMC-MTDC systems and reduce dependence on energy-dissipating devices. A master-slave energy coordination strategy is proposed for interactive power absorption among multiple converter stations. Firstly, an MMC-MTDC control model is established, and the feasibility of surplus power absorption through energy-based control is analyzed. Subsequently, a three-dimensional energy model of the MMC is introduced to achieve decoupled energy control for each pole of the converter stations. Based on a simplified MMC-MTDC system model, active energy control schemes are designed for different types of converter stations. Furthermore, inspired by the master-slave control concept, a timing-based energy coordination logic is developed to address various AC-side fault scenarios at different receiving-end stations and two categories of surplus power levels, thereby enabling coordinated utilization of available energy margins across multiple converter stations. Finally, a MMC-MTDC system is implemented in PSCAD/EMTDC for simulation validation. Results demonstrate that the proposed strategy effectively coordinates multiple converter stations energy control without requiring energy-dissipating devices. The strategy can adapt to diverse surplus power conditions and successfully achieve fault ride-through.
Permanent magnet–less motors are increasingly attracting research interest due to the scarcity and high cost of rare-earth magnet resources. Among them, induction motors, particularly multi-phase induction machines, are gaining attention for their superior fault-tolerant capabilities and higher torque and power density. Pole-changing induction motors (PCIMs), a subclass of multi-phase induction machines, offer enhanced torque performance and an extended constant power speed range (CPSR), both of which are critical for vehicular applications. Despite these advantages, systematic design frameworks for PCIMs tailored to electric vehicle application remain limited in the literature. This paper presents a comprehensive design methodology for PCIMs that incorporates optimization to maximize torque output in both pole modes while simultaneously extending the CPSR, subject to current and voltage constraints. The torque behavior during pole transition is also investigated, with sensitivity analysis emphasizing the influence of inductance selection on transient torque dynamics during pole switching. This work therefore provides a structured and practical approach that incorporates parameter-dependent transient characteristics, enabling improved dynamic performance alongside steady-state optimization. The proposed framework is demonstrated through finite element analysis in ANSYS Maxwell 2D and dynamic simulations in MATLAB<inline-formula> <tex-math notation="LaTeX">$/$ </tex-math></inline-formula>Simulink. Furthermore, a 5-hp prototype PCIM is fabricated and experimentally validated, confirming the practical feasibility and effectiveness of the proposed optimization-based design methodology.
ABSTRACT A speed sensor in a motor drive system decreases the reliability and increases the cost and hardware complexity of the system. In this paper, a speed‐sensorless model‐free predictive torque control (MF‐PTC) scheme has been proposed for an induction motor (IM) drive. An extended Kalman filter (EKF) is employed to estimate the motor speed and thus requirements of a speed sensor and pulse width modulation signals are avoided. The rotor flux is also estimated using the EKF based on the current model of the IM. An ultra‐local model is employed instead of the mathematical model of the IM for predicting the current and flux in the prediction step of the proposed MF‐PTC. The unknown state of the ultra‐local model is estimated using a linear extended state observer. Simulation and experimental results confirm that the proposed controller is robust against motor parameter variations. The speed estimator can estimate the motor speed accurately from the maximum speed down to a minimum of 1 Hz. The controller also demonstrates good performance in terms of torque response, stator flux response and stator current total harmonic distortion under various operating conditions.
This paper introduces an innovative rotor field-oriented control (RFOC) approach tailored for a low-voltage three-phase induction motor (IM) driven by a reduced-structure delta inverter (B3-VSI). The delta inverter, featuring only three power switches, three freewheeling diodes, and three DC voltage sources, represents a promising solution for cost-effective, low-power applications, such as electric vehicles (EVs) and photovoltaic (PV)-fed systems. However, due to the limited voltage vectors provided by this inverter topology, advanced control strategies are essential for achieving optimal motor performance. The proposed RFOC method utilizes a look-up table to generate the control signals for the inverter switches. This table is developed through precise selection of the Boolean variables produced by hysteresis controllers applied to the stator currents. Simulation and experimental results validate the proposed approach, demonstrating its potential to improve system performance in low-power applications while maintaining simplicity and efficiency.
Abstract To address performance degradation and demagnetization risks in hybrid electric vehicle oil pump motors under high-temperature conditions, this article proposes a novel rare-earth-free interior tangential brushless DC motor. Leveraging the negative temperature coefficient characteristics of ferrite materials, the design enhances the motor’s high-temperature performance and stability. Simultaneously, by improving the flux concentration capability, it effectively reduces magnetic leakage and lowers motor costs.Firstly, the impact of leakage flux coefficient on the mechanical characteristics of brushless DC motors was investigated, leading to the proposal of a novel air-isolated injection-molded rotor structure for interior tangential motors. Through finite element modeling and magnetic circuit integration method analysis, the quantitative correlation between magnetic bridge width and leakage flux coefficient was determined. While satisfying mechanical strength requirements, the new air-isolated injection-molded structure demonstrated 25.3% reduction in leakage flux coefficient compared to conventional magnetic bridge structure configurations accompanied by 21.7% enhancement in electromagnetic torque, 43% increase in viscous damping coefficient, and 10% improvement in power density.The mechanical characteristic curve exhibited greater stiffness, with the structural reliability of the rotor’s air-isolated flux barrier design being further validated through simulation studies.Secondly, a new ‘mirror image’ segmented rotor structure is proposed, which can reduce the motor cogging torque by 75.4% and significantly suppress the oil pump pressure fluctuation and noise; Finally, the effectiveness of the new rotor structure and the accuracy of the optimisation method are verified through prototype tests, and the test prototype has an increase in output power by 11%, while the cogging torque is effectively reduced by 73.5%.This article pioneers the integrated design combining non-rare-earth material applications, optimized air-isolated injection-molded rotor structures, and a novel “mirror-image” segmented configuration, meeting hybrid electric vehicles’ requirements for cost-effectiveness, performance, and operational reliability.
As inverter-based resources (IBRs) penetrate power systems, the dynamics become more complex, exhibiting multiple timescales, including electromagnetic transient (EMT) dynamics of power electronic controllers and electromechanical dynamics of synchronous generators. Consequently, the power system model becomes highly stiff, posing a challenge for efficient simulation using existing methods that focus on dynamics within a single timescale. This paper proposes a Heterogeneous Multiscale Method for highly efficient multi-timescale simulation of a power system represented by its EMT model. The new method alternates between the microscopic EMT model of the system and an automatically reduced macroscopic model, varying the step size accordingly to achieve significant acceleration while maintaining accuracy in both fast and slow dynamics of interests. It also incorporates a semi-analytical solution method to enable a more adaptive variable-step mechanism. The new simulation method is illustrated using a two-area system and is then tested on a detailed EMT model of the IEEE 39-bus system.
Muhammad Bilal Shahid, Weidong Jin, Muhammad Abbas Abbasi
et al.
Abstract State‐of‐the‐art model‐based predictive control techniques for AC motor drives are reviewed in this paper. A plethora of MPC algorithms with vast number of complex ideas has emerged in the last decade and this work makes an attempt to present those concepts in an intuitive, comprehensive and hierarchical manner. More emphasis is laid on finite control set model predictive control (FCS‐MPC) methods, especially predictive torque control (PTC) and predictive current control (PCC) because of their emergence as the prime focus of ongoing research in energy efficient drive control. The main focus of this review is to analyse the most recent work, signpost the future research directions, identify the core challenges and consolidate the ideas into a coherent and concise reference. A comprehensive classification based on actuation signals is presented and reviewed in detail. Then, the important challenges in MPC implementation, such as computational complexity reduction and delay compensation, weighting factor selection for multi‐objective cost functions, steady state performance and ripple reduction, parameter variations/model mismatching and achieving extended prediction horizons, are surveyed and most relevant solutions are reviewed. A detailed analysis of the last five years related work is given at the end and it is concluded that the future course seems to be diverting towards voltage vector selection with optimised phase, magnitude and duty ratios. Computational burden is still one of the main hurdle towards MPC proliferation and adaptation in AC drive control at the industrial level. However, with advent of high speed and cheaper signal processors and development of efficient algorithms, MPC is rapidly becoming the control method of choice for energy‐efficient drive control.
Raik Orbay, Aditya Pratap Singh, Johannes Emilsson
et al.
An effortless charging experience will boost electric vehicle (xEV) adoption and assure driver satisfaction. Tailoring the charging experience incorporating smart algorithms introduces an exciting set of development opportunities. The goal of a smart charging algorithm is to lay down an accurate estimation of charging power needs for each user. As recommender systems (RS) are frequently used for tailored services and products, a novel RS based approach is developed in this study. Based on a collaborative-filtering principle, an RS agent will customize charging power transient prioritizing the physical principles governing the battery system, correlated to customer preferences. However, parallel to other RS applications, a collaborative-filtering for charging power transient design may suffer from the cold-start problem. This paper thus aims to prescribe a remedy for the cold-start problem encountered in RS specifically for charging power transient design. The RS is cold-started based on multiphysical modelling, combined with customer driving styles. It is shown that using 7 fundamental charging power transients would capture about 70% of a set of representative charging power transient population. Matching a unsupervised learning based clustering pipeline for 7 possible customer driving styles, an RS agent can prescribe 7 charging power transients automatically and cold-start the RS until more data is available.
Transportation engineering, Transportation and communications
Scenario reduction (SR) aims to identify a small yet representative scenario set to depict the underlying uncertainty, which is critical to scenario-based stochastic optimization (SBSO) of power systems. Existing SR techniques commonly aim to achieve statistical approximation to the original scenario set. However, SR and SBSO are commonly considered as two distinct and decoupled processes, which cannot guarantee a superior approximation of the original optimality. Instead, this paper incorporates the SBSO problem structure into the SR process and introduces a novel problem-driven scenario reduction (PDSR) framework. Specifically, we project the original scenario set in distribution space onto the mutual decision applicability between scenarios in problem space. Subsequently, the SR process, embedded by a distinctive problem-driven distance metric, is rendered as a mixed-integer linear programming formulation to obtain the representative scenario set while minimizing the optimality gap. Furthermore, <i>ex-ante<i> and <i>ex-post<i> problem-driven evaluation indices are proposed to evaluate the SR performance. Numerical experiments on two two-stage stochastic economic dispatch problems validate the effectiveness of PDSR, and demonstrate that PDSR significantly outperforms existing SR methods by identifying salient (e.g., worst-case) scenarios, and achieving an optimality gap of less than 0.1% within acceptable computation time.
In the transition to achieving net zero emissions, it has been suggested that a substantial expansion of electric power grids will be necessary to support emerging renewable energy zones. In this paper, we propose employing battery-based feedback control and nonlinear negative imaginary (NI) systems theory to reduce the need for such expansion. By formulating a novel Luré-Postnikov-like Lyapunov function, stability results are presented for the feedback interconnection of two single nonlinear NI systems, while output feedback consensus results are established for the feedback interconnection of two networked nonlinear NI systems based on a network topology. This theoretical framework underpins our design of battery-based control in power transmission systems. We demonstrate that the power grid can be gradually transitioned into the proposed NI systems, one transmission line at a time.
Leen Al Homoud, Katherine Davis, Shamina Hossain-McKenzie
et al.
Modern-day power systems have become increasingly cyber-physical due to the ongoing developments to the grid that include the rise of distributed energy generation and the increase of the deployment of many cyber devices for monitoring and control, such as the Supervisory Control and Data Acquisition (SCADA) system. Such capabilities have made the power system more vulnerable to cyber-attacks that can harm the physical components of the system. As such, it is of utmost importance to study both the physical and cyber components together, focusing on characterizing and quantifying the interdependency between these components. This paper focuses on developing an algorithm, named CyberDep, for Bayesian network generation through conditional probability calculations of cyber traffic flows between system nodes. Additionally, CyberDep is implemented on the temporal data of the cyber-physical emulation of the WSCC 9-bus power system. The results of this work provide a visual representation of the probabilistic relationships within the cyber and physical components of the system, aiding in cyber-physical interdependency quantification.
In recent years a trend of using CubeSat-class nanosatellites for commercial and scientific missions has taken the lead; this is due to the advantage of the available and low-cost products on the market without need for a large development and infrastructure investments. On our behalf, the advanced approach is to build a CubeSat platform dedicated to academic and engineering training on the different satellite subsystems, including development of a transponder based on products and software/hardware solutions accessible to the research and academic community in order to promote its use to engage in cross-team development on the basis of this platform and allowing the aggregation of a know-how.
In this context, the work presented in this paper, handles partly the problematic by a proposal of the communication system design, intended to be integrated on our platform; with a particular focus on the implementation of an FSK modulator based on the Bell 202 standard. The reported development project will include simulations, implantation and the validation tests performed to achieve our first prototype.
Applications of electric power, Electric apparatus and materials. Electric circuits. Electric networks
This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay.
Convex relaxations of the optimal power flow (OPF) problem provide an efficient alternative to solving the intractable alternating current (AC) optimal power flow. The conic subset of OPF convex relaxations, in particular, greatly accelerate resolution while leading to high-quality approximations that are exact in several scenarios. However, the sufficient conditions guaranteeing exactness are stringent, e.g., requiring radial topologies. In this short communication, we present two equivalent ex post conditions for the exactness of any conic relaxation of the OPF. These rely on obtaining either a rank-1 voltage matrix or self-coherent cycles. Instead of relying on sufficient conditions a priori, satisfying one of the presented ex post conditions acts as an exactness certificate for the computed solution. The operator can therefore obtain an optimality guarantee when solving a conic relaxation even when a priori exactness requirements are not met. Finally, we present numerical examples from the MATPOWER library where the ex post conditions hold even though the exactness sufficient conditions do not, thereby illustrating the use of the conditions.
The long-term resilient property of ecosystems has been quantified as ecological robustness (RECO) in terms of the energy transfer over food webs. The RECO of resilient ecosystems favors a balance of food webs' network efficiency and redundancy. By integrating RECO with power system constraints, the authors are able to optimize power systems' inherent resilience as ecosystems through network design and system operation. A previous model used on real power flows and aggregated redundant components for a rigorous mapping between ecosystems and power systems. However, the reactive power flows also determine power systems resilience; and the power components' redundancy is part of the global network redundancy. These characteristics should be considered for RECO-oriented evaluation and optimization for power systems. Thus, this paper extends the model for quantifying RECO in power systems using real, reactive, and apparent power flows with the consideration of redundant placement of generators. Recalling the performance of RECO-oriented optimal power flows under N-x contingencies, the analyses suggest reactive power flows and redundant components should be included for RECO to capture power systems' inherent resilience.
Predictive control offers many advantages such as simple design and a systematic way to handle constraints. Model predictive control (MPC) belongs to predictive control, which uses a model of the system for predictions used in predictive control. A major drawback of MPC is the dependence of its performance on the model of the system. Any discrepancy between the system model and actual plant behavior will greatly affect the performance of the MPC. Recently, model-free approaches have been gaining attention because they are not dependent on the system model parameters. To obtain the advantages of both a model-free approach and predictive control, model-free predictive control (MFPC) is being explored and reported in the literature for different applications such as power electronics and electric drives. This paper presents an overview of model-free predictive control. A comprehensive review of the application of MFPC in power converters, electric drives, power systems, and microgrids is presented in this paper. Moreover, challenges, opportunities, and emerging trends in MFPC are also discussed in this paper.
In recent years, multiport DC-DC converters are seen in a variety of power converter applications in electric vehicles. The design of multiport converter architectures plays a major role in DC microgrids and electric vehicle applications. This research examines a modified multiport converter structure interface with dual inputs and dual outputs used in electric vehicles. The versatility of accommodating energy sources with varying voltage and current nature characteristics is the most notable feature of this converter. During operation, the proposed architecture can offer a boost as well as buck operations at the same time. The suggested dual input-dual output (DIDO) converter is built with fewer components and a simpler control technique which makes it more dependable and the converter is cost-effective. Furthermore, this structure allows the power to flow in both directions making it to be utilized in electric vehicle battery charging during regenerative braking. The converter’s steady-state and dynamic behavior are investigated, and a control strategy for regulating the power flow among the varied input energies is proposed. To develop the suggested converter, a small-signal model is modeled. MATLAB simulation and experimental findings are used for the verification of converter design and validated the performance behavior experimentally using a hardware setup.