Milad Behnamfar

This paper proposes a multiobjective distribution optimal power flow formulation to improve the resiliency of distribution feeders. The formulated base resiliency strategy prioritizes critical infrastructures (CIs) by ranking them and ensuring their continuous power supply during extreme events through the optimal sizing and placement of hybrid distributed energy resources (DERs). A DER hosting formulation with uncertainty quantification to maximize the capacity integration of the DER is proposed. To ensure effective voltage regulation during extreme events, the DER's smart inverter (SI)'s grid following and grid forming control capabilities are modeled. To capture the probability of subsystem failure during an extreme weather event, this paper proposes a fragility formulation that considers both the windspeed and the age of the components within the grid. Since the load profiles of the CIs will vary during extreme weather events, this paper proposes a weather-adjusted load profile formulation that uses a sigmoid function to model the CI's occupancy behavioral pattern. An extreme event caused by two hurricane categories is simulated on an IEEE 34 distribution feeder using the proposed probabilities of failures. A mixed integer nonlinear programming formulation that aims to maximize the penetration of the DERs, improve the network resiliency, and minimize the overall network losses while satisfying all network and power flow constraints is formulated. The effectiveness of the proposed resiliency enhancement approach is validated via numerical simulations.

Dynamic Wireless Power Transfer (DWPT) is a promising technology for alleviating range anxiety and reducing the size, cost, and volume of electric vehicle batteries. However, DWPT systems often experience voltage fluctuations due to variations in mutual coupling caused by vehicle movement. This paper proposes the use of small auxiliary coils, referred to as ’assisted coils,’ placed between adjacent transmitter coils to mitigate voltage fluctuations. These coils are significantly smaller than the main transmitter coils, resulting in minimal cross-coupling between the assisted and main coils. Despite this, the mutual coupling between the assisted coils and the receiver coil is too small to fully compensate for voltage drops during transitions between main transmitter coils. To address this, the paper introduces a design strategy to define the ratio between the LCC compensation inductors of the main and assisted coils, ensuring stable voltage during transitions. The dimensions of the assisted coils are optimized using ANSYS Maxwell, focusing on minimizing voltage fluctuations and reducing cross-coupling. Simulation and experimental results validate the proposed approach, showing a stable voltage with a 55.06% improvement in output voltage during transitions between adjacent main transmitter coils.

This article introduces an innovative control strategy that combines passivity-based control (PBC) with integral terminal sliding mode control (ITSMC) to address fluctuations in the output voltage of dynamic wireless charging (DWC) systems for electric vehicles (EVs). The proposed passivity-based integral terminal sliding mode (PBITSM) control strategy achieves stable output voltage with minimal fluctuation, even amidst variations in mutual coupling due to receiver movement. Notably, this composite control strategy offers the features of both PBC and ITSMC to enhance dynamic response, mitigate chattering issues inherent in traditional SMC, and improve system robustness, all while preserving passivity. Moreover, the proposed PBITSM controller possesses finite-time property, providing advantages such as faster convergence and superior disturbance rejection capabilities. The detailed design procedure of the proposed controller is presented, and the stability of the controller is demonstrated. Simulation results highlight the superiority of the proposed control method in suppressing voltage fluctuations compared to a proportional-integral (PI) controller and passivity-based proportional-integral control (PIPBC). Moreover, experimental results validate the effectiveness of the proposed control scheme in suppressing output voltage fluctuations during receiver motion, limiting the fluctuation rate to ±3.3%.

In commercial applications, the operation of DC/DC converters significantly impacts overall system performance and long-term reliability. This study introduces a data-driven digital twin ( DT ) approach for estimating critical degradation parameters of DC/DC BUCK converter under steady-state condition. Initially, a circuit-level MATLAB/Simulink digital model ( DM_C ) is refined against a hardware prototype’s switching model dataset using offline particle swarm optimization. The optimized digital model’s steady-state response is then verified with its average model response while varying the duty and load. Subsequently, degradation profiles are imposed on the inductor, capacitor, MOSFET in the DM_C . A large dataset is generated from this model, allowing training, validation, and testing of machine learning (ML) models for component health regression tasks. The proposed method employs random forest ML models, achieving impressive regression results with a squared R value as high as 0.99978 and a root mean square error of 4.2× 10 ⁻⁶ . The method is further validated on a medium power level DC/DC BUCK prototype with varying load conditions, and includes the analysis of MOSFET’s on-resistance under degradation conditions. This data-driven DT method shows promise for identifying parasitic degradation and ohmic loss parameters, enhancing converter reliability assessments in a non-invasive, generalized, and computationally efficient manner.

This paper addresses the challenge of improving the efficiency of solar photovoltaic systems, which often suffer from intermittent energy harvesting and suboptimal power extraction methods. Solar energy, a promising renewable source, can replace fossil fuels, but its efficiency is hindered by fluctuating power output. To tackle this issue, the study introduces a novel approach utilizing wavelet-based ripple extraction. By employing a ten-level wavelet decomposition process, this method aims to obtain smooth, ripple-free power from solar canopies. The research examines the tracking efficiency of maximum power point tracking (MPPT) and the power oscillation issue. Notably, the study compares the Perturb and Observe method with the Artificial Neural Network MPPT approach, assessing ripple power during wavelet decomposition stages. Results reveal that the proposed wavelet and signal analyzer-based technique achieves a remarkable tracking efficiency of nearly 99.9% while minimizing power fluctuations, surpassing prior studies. This innovative approach enhances the performance and efficiency of solar PV systems, ultimately contributing to the broader adoption of solar energy. Real-time validation through OPAL-RT further validates its effectiveness.

This study investigates the vulnerability of DC microgrid systems to cyber threats, focusing on false data injection attacks (FDIAs) affecting sensor measurements. These attacks pose significant risks to equipment, generation units, controllers, and human safety. To address this vulnerability, we propose a novel solution utilizing a Non-Linear Autoregressive Network with Exogenous Input (NARX) observer. Trained to differentiate between normal conditions, load changes, and cyber-attacks, the NARX network estimates DC currents and voltages. The system initially operates without FDIAs to collect data for training NARX networks, followed by online deployment to estimate output DC voltages and currents of Distributed Energy Resources (DER). An attack mitigation strategy using a PI controller aligns NARX output with actual converter output, generating a counter-attack signal to nullify the attack impact. Comparative analysis with other AI-based methods is conducted, demonstrating the effectiveness of our approach. MATLAB simulations validate the method's performance, with real-time validation using OPAL-RT further confirming its applicability.

This paper presents a novel composite control scheme aimed at ensuring stable and consistent output power in dynamic wireless charging (DWC) systems subject to mutual coupling variations caused by electric vehicle (EV) movement. The proposed control strategy combines backstepping control and second-order sliding mode control, offering both finite-time stability and large-signal stability. The finite-time stability property enhances control robustness and improves disturbance rejection capability, while the large-signal stability feature enables the controller to effectively handle significant disturbances. Furthermore, this paper presents a detailed design procedure for the proposed composite control method, along with stability analysis to demonstrate its finite-time stability property. Simulation and experimental results confirm the effectiveness of the proposed composite controller in stabilizing power at the reference value despite mutual coupling variation and load changes, minimizing fluctuations to just 0.1%. Additionally, the proposed controller consistently maintains an efficiency of 84% across varied positions as the vehicle moves over the transmitter coils.

Dynamic wireless charging systems (DWCS) for electric vehicles is an effective solution to overcome driving range anxiety, high volume, and cost of battery by offering continuous charging for electric vehicles in motion. However, this technology suffers from some challenges associated with the high cost of infrastructure and power pulsation at the battery side, hindering the widescale commercial adoption of the technology. As the electric vehicle moves along the transmitter coils, power pulsation arises due to fluctuation in the mutual coupling, which deteriorates the battery’s lifetime and the overall system’s efficiency. In recent years, a significant volume of research methodologies have been proposed to address power fluctuation in DWCS. These methods can be categorized into three main areas, which include developing magnetic coil structures to suppress the coupling variation, optimizing the compensation network for coping with the coupling fluctuation, and applying controllers for output power stabilization. This paper presents a comprehensive review of the current literature on magnetic coil structures, compensation network topologies, and control schemes aiming to address the impacts of power variation in DWCS. This paper gives a new and holistic insight into the challenges and the corresponding solutions to address the power pulsation issues in DWCS.