Conventional multilevel inverters, such as neutral-point-clamped and T-type inverters, have gained popularity in electric vehicle applications due to their advantages, including high voltage range, high power capability, low switching losses, low total harmonic distortion, and low electromagnetic interference. However, these traditional multilevel inverters require a neutral point connection to generate a zero-voltage vector. The neutral point current oscillates at three times the fundamental frequency, leading to voltage imbalance and overvoltage stress on the power modules. Additionally, the use of two stacked DC-link capacitors increases the volume required for the same overall capacitance and complicates packaging due to ripple current and heat dissipation from separate components. This is a significant concern for traction drive units, where space is limited. In this paper, a neutral-point-less multilevel dual three-phase inverter topology is investigated for traction inverter applications. Simulation results demonstrate that the proposed topology effectively retains the benefits of multilevel operation while utilizing a single DC-link capacitor. The inverter model was simulated in conjunction with an industry-standard battery model to evaluate the potential for capacitor size reduction compared to a conventional three-level inverter.

Industrial, commercial, and residential facilities are progressively adopting automation and generation capabilities.By having flexible demand and renewable energy generation, traditional passive customers are becoming active participants in electric power system operations.Through profound coordination among grid operators and active customers, the facilities' capability for demand response (DR) and distributed energy resource (DER) management will be valuable asset for ancillary services (ASs).To comply with the increasing demand and flexible energy, utilities urgently require standards, regulations, and programs to efficiently handle load-side resources without trading off stability and reliability.This study reviews different types of customers' flexibilities for DR, highlighting their capabilities and limitations in performing local ancillary services (LASs), which should benefit the power grid by profiting from it through incentive mechanisms.Different financial incentives and techniques employed around the world are presented and discussed.The potential barriers in technical and regulatory aspects are successfully identified and potential solutions along with future guidance are discussed.

Military bases, communities, and university-scale microgrids are being implemented to serve critical loads of high priority. Remote switch scheduling and distributed energy resources (DERs) operation strategies for post-disaster service restoration have been explored in previous literature. Flexible buildings offer the central microgrid management system an opportunity to ensure available energy is directed to critical loads. This work presents a novel bi-level optimal sequence of operations for managing the controllable devices in small-scale microgrids to serve loads based on a priority scheme in campus-scale microgrids. This study introduces a technique for step-by-step restoration of customers' granular loads. After and during an outage scenario, the facilities' internal loads are energized in sequence according to their customer and local criticality levels, as well as the amount of energy available from the bulk system and DERs. The proposed methodology is formulated as a mixed-integer linear programming (MILP) model and adapts to various operating conditions. The proposed method is validated by performing controller hardware-in-loop (CHIL) case studies on the Banshee microgrid benchmark model on a real-time simulator.

The COVID-19 pandemic has brought chaos on education systems worldwide, affecting several billion students all over the world. This had far-reaching consequences in every part of our life. Traditional educational techniques have been considerably disrupted because of social alienation and restricting mobility policies. Innovation, implementation of alternative educational systems and evaluation methodologies are in demand. The COVID-19 outbreak has given us a chance to lay the groundwork for digital learning. The Army Educational Research Program (AEOP) is a summer internship program that allows students to do research in a real laboratory environment with the help of a professional STEM mentor and researcher. For the past 12 years, this program has been delivered as an in-person apprenticeship at Texas Tech University (TTU). Face-to-face apprenticeships were canceled and shifted online because of the pandemic, resulting in the emergence of online learning, which has allowed learners to complete their study. Students, instructors, administrators, and education leaders faced several issues because of the abrupt shift from face-to-face to online learning. This article aims to provide a comprehensive assessment on the impact of the COVID-19 pandemic on the transition of the AEOP program from face-to-face to an online teaching mode at TTU.

Community-scale microgrids play an essential role in serving critical loads during emergency conditions, involving the operation of breakers, tie-switches, distributed energy resources (DERs), and loads. Electric loads are primarily considered as lumped loads without many granular levels of controls. Flexible buildings offer the central microgrid management system an opportunity to shed multiple non-critical loads at granular levels by adopting Internet-of-Things (IoT) based controls. This work presents a novel bi-level optimal sequence of operations for managing the controllable devices in microgrids to serve loads, based on a priority scheme in community scale-scale microgrids. The proposed methodology is formulated as a mixed-integer linear programming (MILP) model and adapts to various operating conditions. The proposed method is validated through case studies that are performed on the Banshee microgrid benchmark model.

This paper proposes a computationally efficient building energy management algorithm for demand response that can serve as a grid-ancillary system. The controller aims to regulate flexible loads and intelligent switches, complying with the utility’s request. The control algorithm dynamically optimizes the load’s configuration of the building. This optimization is based on the required power consumption level and the resident’s actual comfort constraints. Since the load-matrix considered by the proposed algorithm is computationally expensive, a novel region-selection approach is incorporated in the algorithm to make the strategy computationally efficient. The proposed algorithm is validated through OPAL-RT Real-Time Digital Simulation with Raspberry Pi. The test results show that the algorithm is capable of curtailing controllable loads during emergencies and outage scenarios to maintain an uninterrupted supply to the critical loads and respect the power limit request of the building.

– This paper proposes an efficient and optimal reduced control set model predictive flux control (RCS-MPFC) for a three-level neutral-point-clamped voltage source inverter (3L-NPC VSI) fed induction motor. The proposed algorithm reduces the computational time in the prediction stage without causing any suboptimality. The optimal voltage vector selected by the proposed method produces the same cost function value as that of the conventional FCS-MPFC which requires enumerating all 27 voltage vectors. Therefore, the proposed algorithm achieves the same performance as the conventional method in the entire range of operation of IM drives while the computational effort is significantly reduced. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the existing RCS-MPFC scheme.

This article presents an optimal model predictive flux control (MPFC) for a two-level inverter fed induction motor. Integrating discrete SVM into FCS-MPFC enhances the performance of the IM drive. However, conventional DSVM-MPFC requires to enumerate and evaluate a higher number of virtual vectors in the prediction loop. In this paper, a high-efficient and low complexity voltage selection method is proposed to reduce the number of candidate voltage vectors from 38 to 15 without any suboptimality. Both steady-state and transient performances of the proposed method remain the same as the 38-vector based conventional DSVM-MPFC, producing the same cost-function values in all operating conditions. Furthermore, an online switching frequency reduction technique is proposed to achieve a minimum commutation per inverter vector change within each sampling cycle and between adjacent cycles. By appropriately arranging the sequence of real voltage vectors in each sampling cycle, a lower average switching frequency is achieved. The proposed switching frequency reduction method decreases the switching losses without compromising the performance of DSVM-MPFC as only the applied sequences of the real voltage vectors are optimized. Experimental studies are conducted to verify the effectiveness of the proposed algorithm.

This work proposes the large-scale adoption of self-synchronized universal droop controller (SUDC)-based inverters to enable ancillary services for different modes of distribution system operations. The IEEE 123 bus system was modeled on a real-time simulator to study the performance of large-scale adoption of SUDC inverters in a distribution system. The resulting data collected shows that the voltage and the frequency were regulated within ranges, such as less than 5% for voltage and less than 0.5% for frequency, under different load variations and grid operations. Also, the black start was achieved within 0.4 s without any voltage overshoot. Through the simulation and validation on a small microgrid and the IEEE 123 bus distribution system, it can be concluded that the SUDC was successfully adopted to regulate the voltage and the frequency within the given ranges, and black start achieved within 1 s without voltage overshoot for different modes of distribution system operations.

Tuning of weighting factors is a very complex task in the implementation of conventional finite set predictive torque control (FS-PTC) for three-level neutral-point-clamped (3L-NPC) inverter fed IM drive. The choice of these weighting factors strongly influences the control objectives such as flux, torque, and neutral point voltage (NPV). Moreover, the computational burden is high as all 27 switching states are enumerated for the evaluation of a cost function involving multiple objectives. To reduce the complexity of the conventional FS-PTC, this article proposes a high-efficient single-vector-based predictive flux control for an IM drive fed by a 3L-NPC inverter. The proposed cascaded predictive flux and NPV control evaluates 13 vectors for outer predictive flux control and two switching states for the inner predictive NPV control. It significantly reduces the computational load as the number of enumerations is reduced by more than 50%. The nontrivial process of weighting factor calculation is no longer required. The complexity reduction and the elimination of weighting factors are achieved without sacrificing the transient and steady-state performances in terms of torque and stator flux responses, total harmonic distortion of stator currents, NPV variation, and average switching frequency, compared to the conventional method as verified by experimental results.

This article presents a simplified discrete space vector modulation (DSVM)-based predictive torque control (PTC) scheme in order to improve the performance of a two-level inverter-fed induction motor drive. DSVM technique creates a number of virtual vectors which are evaluated in the conventional all vector-based discrete space vector modulation-based model predictive torque control (DSVM-MPTC) method. The high number of admissible vectors increases the computational burden of DSVM-MPTC, significantly. In this article, an efficient optimal voltage vector selection method is proposed to reduce the computational load of DSVM-MPTC from 37 to 13 enumerations. The vector selected from the reduced set of admissible voltage vectors produces the same cost function value as that of all vector-based DSVM-MPTC in the entire range of operation of induction motor (IM) drives. The proposed method reduces the computational burden effectively without causing any suboptimization issues in both transients and steady states. Experimental results verify the effectiveness of the proposed algorithm and its superior performance compared to the switching-table-based DSVM-MPTC and the classic finite-control-set model-predictive-control which only utilizes the real voltage vectors.

This paper proposes an improved modulated model predictive torque control as an alternative of linear controller based direct torque and flux control (DTFC) for high-speed IPMSM drive. The complicated controller design and tuning in PI-regulators based DTFC are eliminated. Moreover, the transient performance is greatly improved while maintaining similar steady-state performances. It also proposes analytical solutions for machine trajectories, which combine with modulated model predictive control (M2PC) to achieve high electrical efficiency in wide speed operation. M2PC is modified to select an optimal vector combination among one-active, two-active, one-active-one-zero, or two-active-one-zero voltage vectors based on their corresponding cost function. The control precision is improved further by introducing virtual vectors (VVs) associated with real VVs in the control set, especially in high-speed operation. A two-stage optimization is then employed to reduce the computational burden added due to the extended control set. Extensive experimental results are shown to validate the effectiveness of the proposed method.

This paper presents an analytic approach to direct torque and flux controller (DTFC) for Interior permanent magnet synchronous machine (IPMSM) which includes deep flux weakening. Unlike the conventional lookup tables (LUTs) based DTFC, this paper proposes a direct mathematical calculation approach for the stator flux reference as well as the torque limit in field weakening (FW) and maximum-torque-per-voltage (MTPV) regions. The proposed method will thus allow flexibility of using online estimated machine parameters directly for an optimized IPMSM drive. This study also explores the voltage and current limit trajectories on a torque-flux linkage plane to operate the machine in deep field weakening. It incorporates maximum-torque-per-ampere (MTPA), FW, and MTPV trajectories control under voltage and current constraints. Experimental results are shown to verify the effectiveness of the proposed algorithm.

The computational burden in Finite State Predictive torque control (FS-PTC) increases with the number of voltage vectors and number of variables in the cost function. The conventional FS-PTC for three level neutral-point clamped voltage source inverter (3L-NPC VSI) fed motor drive has a disadvantage of long execution time since all available vectors are evaluated in the prediction stage. This paper proposes a new method for reducing the number of voltage vectors from 27 to 17 in the prediction stage, which reduces the computational burden of conventional FS-PTC. The performance of the proposed voltage selection method is investigated for an induction motor drive in terms of electromagnetic torque and stator flux responses (transient and ripple), total harmonic distortion of stator currents, neutral point voltage variations, average switching frequency reduction and robustness of the drive. Experimental results confirm that the execution time is reduced by 30% compared to conventional FS-PTC while similar dynamic and steady-state performances are preserved.

This paper proposes a two-stage optimization of voltage vector selection method integrated with reference stator flux vector calculation (RSFVC) for predictive torque control of a three-level neutral point clamped inverter (3L NPC VSI) fed induction motor. The RSFVC technique simplifies the cost function by avoiding the weighting factor tuning between stator flux and electromagnetic torque and the proposed two-stage method reduces the number of voltage vectors in the prediction stage. Hence, with the proposed control algorithm, two major contributions are achieved. Firstly, the computational burden is reduced due to less number of voltage vector candidates in the prediction stage. Secondly, the design for the cost function becomes simplified as the weighting factor of stator flux error, which is essential in the cost function, is eliminated. Moreover, experimental results confirms that, selection of the two remaining weighting factors required for neutral point voltage (λ cv ) and number of switching transitions (λ n ) becomes less complex. The dynamic and steady-state performances of the proposed control method is investigated in terms of electromagnetic torque and stator flux, total harmonic distortion of stator currents, neutral point voltage variations and robustness of the drive.

This study proposes a simplified flux-error based voltage vector selection method for predictive flux control of a three-level neutral point clamped inverter (3L-NPC) fed induction motor. The controller of a 3L-NPC fed IM drive has 27 voltage vectors for cost function evaluation. The proposed optimization method reduces the number of voltage vectors from 27 to 14 for prediction and minimization of cost function by using a two-stage optimization technique. The reference stator flux vector calculation (RSFVC) technique is employed in order to reduce the computational complexity of the prediction further, by regulating the torque outside of the cost function. Compared with conventional finite state predictive torque control (FS-PTC) for 3L-NPC VSI fed induction motor drive, the proposed method reduces the computational time by 40%. The dynamic and steady-state performances of the proposed method are investigated and compared with the conventional FS-PTC in terms of electromagnetic torque and stator flux responses, total harmonic distortion of stator currents, neutral point voltage variations and robustness of the drive.

Computational burden is a major hurdle for practical implementation of finite-state predictive torque control (FS-PTC) of motor drive fed by a multilevel inverter. One of the reasons of computational complexity is that all voltage vectors are evaluated for prediction and actuation. This paper proposes a reduced number of voltage vectors for the prediction and actuation, which are called prediction vectors in FS-PTC. The performance is investigated for a three-level neutral-point clamped inverter fed motor drive in terms of torque and flux response, stator current total harmonic distortion, robustness, average switching frequency, and neutral-point voltage variation. The number of prediction vectors is reduced based on the position of stator flux and the deviation in stator flux from its reference. Experimental results confirm that the computational burden could be reduced by 38%, while the dynamic performance is comparable with the conventional all voltage vectors based FS-PTC.

Conventional Finite State Predictive Torque control (FS-PTC) for three-level Neutral Point Clamped voltage source inverter (3L-NPC VSI) uses 27 voltage vectors for prediction and actuation. Using all voltage vectors for the prediction loop is not an ideal method as it increases computational burden. This paper proposes a less complex prediction loop method with selected number of voltage vectors for FS-PTC of a three level NPC driven induction motor. The number of voltage vectors is reduced based on a two-stage optimal vector selection algorithm. In the first stage, the algorithm considers the VSI as two-level and selects the most favourable long vector. In the second stage, among the short and the medium voltage vectors closest to the long vector which is selected in the first stage, the optimum vector is selected for prediction. Compared to 27 voltage vectors based prediction, this algorithm evaluates 15 selected vectors in total for prediction and actuation. The effectiveness of the proposed algorithm in terms of speed, torque and flux responses and capacitor voltage balancing is presented through simulation results from MATLAB/Simulink. Computational time is measured from a real-time simulation implemented on dSPACE DS1104 platform. The results show that the proposed method reduces the computation time significantly (by about 45%), while the dynamic and steady-state performances of the motor drive are retained similar to the conventional FS-PTC.

This paper reviews the existing structures for converter stages in renewable energy and motor drive systems that offer high voltage gain at the input of an inverter. The inverter may also connect the DC supply to the utility grid. For traction motor drives in vehicle and rail transport and in high-frequency transformer connected grids, bi-directional power flow capability is also required. Cascaded high-boost non-isolated DC stage which offers multi-stage or multi-level DC inputs and output connections is the focus of attention in this paper. The high voltage DC supply to the inverter allows connections to inverters for motor drive and utility at the required voltage level. This arrangement may lead to reduced battery management issues and reduced size of storage elements, flexibility of connections with several input DC supplies at different levels, and improved reliability through series/parallel connection of converters with some redundancy. Several options for the high-boost stage which are currently available will be reviewed. This will be followed by considerations for energy storage elements and converters suitable for bi-directional power transfer capabilities. Converter topologies and control issues using a high-frequency link for bi-directional power flow between two AC grids will also be discussed.