An interleaved converter consists of multiple phases operating out of phase with the well known benefits of reduced input output current and ripples, improved efficiency and better thermal performance. When the interleaved converter is connected to a constant power load, its negative incremental impedance characteristics leads to instability. This leads to an increased oscillation in the DC link voltage and the source current which is interfaced with the interleaved converter. In this manuscript, a robust control consisting of the sliding mode control is proposed to damp the oscillations due to CPL and maintain the DC link voltage and source current within the permissible desired limits. The proposed sliding manifold consists of the power error terms and a voltage error term. The proposed control performance is validated through simulation and experimental results on a 500W three phase interleaved boost feeding a buck converter fed resistive load setup.

As the demand for electrical distribution systems increases due to advancements in distributed generation (DG), energy storage, electric vehicles, and industrial upgrades, the necessity for efficient and manageable grid operations becomes crucial. A comprehensive grid restructuring is needed to tackle these challenges, ensuring effective power transfer, reducing losses, and maintaining existing infrastructure. One promising solution is the implementation of high-frequency AC (HFAC) technology, which operates at frequencies higher than the conventional 50-60 Hz power grid. This paper presents a standardized 33-bus High-Frequency AC testbed to fill a gap in the literature. Case studies using the 33-bus system demonstrate the benefits of integrating HFAC technology into current networks. Results from the testbed, which comply with international guidelines for both balanced and unbalanced loads, confirm the HFAC system’s compatibility with existing power infrastructure. MATLAB simulations support HFAC’s practicality by showing enhancements in power quality, voltage stability, power flow, and DG integration. Additionally, the study highlights the potential for downsizing components due to improved rating efficiencies. These results emphasize HFAC’s capability to integrate smoothly with present infrastructure and its potential as a sustainable advancement for power systems.

This paper proposes an Improved Grey Wolf Optimization (IGWO) algorithm to address the challenge of reduced Maximum Power Point Tracking (MPPT) efficiency in photovoltaic (PV) systems under partial shading conditions. The IGWO algorithm enhances the standard Grey Wolf Optimization (GWO) by updating the alpha vector with each iteration, aiming to improve convergence rates and reduce the risk of local optima. Simulation results using MATLAB-SIMULINK on a single-diode Solar-PV system demonstrate that the IGWO algorithm outperforms traditional GWO-based MPPT algorithms by overcoming reduced tracking efficiency and steady-state oscillations. The IGWO algorithm leads to improved tracking speed and overall performance under partial shading conditions.

This article represents the steady-state analysis and control of a non-ideal single-input/double-output DC/DC converter (SIDOC) named an integrated dual-output converter at continuous conduction mode (CCM). This converter offers positive voltage levels at outputs using a step-up boost converter and a step-down buck converter. This topology is obtained by combining half-bridge pair transistors, a boost converter, and a buck converter. In this way, it has a continuous input current waveform, which is desired for renewable energy systems. This topology is also suitable for residential applications and low-voltage auxiliary power supplies since it obtains multiple voltage levels. To ensure a stable operation, the decoupled-control method is suggested to regulate the output voltages. This method separates the control loops of different output voltages to remove the cross-regulation and cross-coupling of outputs. Then, among available classical compensators, the type-three lead-lag controller is chosen for the compensation. The state-space equations are used to model the converter for non-ideal conditions. Finally, simulations are done using MATLAB/Simulink, which validates the theoretical calculations.

This paper focuses on the reliability studies of the output voltages in a multi-port DC-DC converter called the integrated dual-output converter (IDOC). The Markov process and Bayesian networks (BNs) are used to perform reliability analyses. Bayesian networks (BNs), developed based on machine learning techniques, are suitable for unhiding the dependencies along with estimating the conditional probabilities for reliability studies. The BNLearn and the pgmpy are libraries developed in the Python environment that include the main parts of obtaining a BN: structure learning, parameter learning, and inference. A dataset including the reliability model of each device is created using state sampling and Monte Carlo simulation, considering the open-circuit fault. Next, the hill-climbing (HC) and Bayesian information criterion (BIC) methods are applied to the dataset as searching and scoring functions, respectively, to determine the structure of the BN. Finally, using parameter learning and inference, conditional and joint probability distributions (CPDs and JPDs) are obtained, yielding the reliability of output voltages. Theoretical calculations through the Markov process validate the accuracy of the obtained BN.

Many Electrical Vehicle (EV) batteries are expected to be retired in the next 5-10 years. These batteries are retired when no longer suitable for energy-intensive EV operations despite having 70–80% capacity left. Second-life use of these battery packs has the potential to address the increasing demand for battery energy storage systems (BESS) in the grid and to create a robust circular economy for EV batteries. However, the degradation of second-life batteries (SLBs) significantly differs from that of their fresh counterparts. This paper presents a mixed-integer linear programming optimization algorithm that considers the degradation of SLBs. A Wöhler curve is derived for an SLB using real-world degradation data of SLBs. The algorithm is tested on a real-world extreme fast charging station (XFCS) load and real-time pricing (RTP) data from a utility. The results show that using that using this algorithm the SLBESS can provide 10 years of operation with minimum degradation cost.

The three-level submodule (3L SM) has emerged as a viable alternative to the conventional half-bridge submodule in modular multilevel converters (MMC), primarily due to its reduced footprint. Despite this, there has been no in-depth exploration of MMC topologies utilizing 3L SMs, particularly in the context of motor drive applications. The 3L SM features two capacitors, and the MMC relies on a cascade connection of multiple 3L SMs per phase, necessitating voltage-balancing control for these capacitors. This paper introduces a voltage-balancing control strategy for multiple capacitors, incorporating high-frequency voltage and circulating current injections to ensure optimal performance in low-speed motor drive operations. The voltage commands are normalized and compared with phase-shifted carrier (PSC) PWM triangular waveforms to generate gating signals. The proposed voltage-balancing control method has been validated through a 4160-V/1-MW simulation model using a hardware-in-the-loop (HIL) system.

As the use of combustion engine vehicles plays a deciding role in global warming, we can observe a trend to replace them with electric vehicles (EV) driven by new environmentally conscious policies and increasing technological capabilities. With improvements in driving range and reduction in prices come new challenges that may hamper the progress towards complete battery driven transportation. A major challenge for the increasing EV adoption is the planning of extensions to existing infrastructure or the inclusion of new infrastructure components in the planning process. This demands increasingly complex planning tools that can simulate the interplay between different stakeholders in modern transportation scenarios such as EVs, charging stations, energy providers, and general transportation participants. Simulation platforms for agent-based modeling in transportation have been developed as effective interactive tools that allow planners to explore different trade-offs across different scenarios with the ability to simulate the impact of policy or infrastructure decisions on the different stakeholders in the simulation. This article surveys several of the major simulation platforms that include modern EV-based forms of transportation and allow the simulation of relevant infrastructure components alongside the well established transportation simulations. These tools allow researchers to analyze expected traffic flow, identify possible charging station locations based on area demand, predict electrical grid demand, and more. This survey intends to make it easier for researchers to identify and apply a simulation platform in the context of supporting the increasing electrification of the transportation sector, enabling more efficient simulation and planning capabilities in this domain.

In this article, an accurate secured framework to detect and stop data integrity attacks in wireless sensor networks in microgrids is proposed. An intelligent anomaly detection method based on prediction intervals (PIs) is introduced to distinguish malicious attacks with different severities during a secured operation. The proposed anomaly detection method is constructed based on the lower and upper bound estimation method to provide optimal feasible PIs over the smart meter readings at electric consumers. It also makes use of the combinatorial concept of PIs to solve the instability issues arising from the neural networks. Due to the high complexity and oscillatory nature of the electric consumers' data, a new modified optimization algorithm based on symbiotic organisms search is developed to adjust the NN parameters. The high accuracy and satisfying performance of the proposed model are assessed on the practical data of a residential microgrid.

This article proposes a deep learning based approach for cyber attack detection in the vehicles. The proposed method is constructed based on generative adversarial network (GAN) classification to assess the message frames transferring between the electric control unit (ECU) and other hardware in the vehicle. To this end, two networks called generator (G) and discriminator (D) will run an adversarial game to fool each other. In such a process, the most optimal structure is found which distinguish between the model normal behavior and abnormalities. Due to the instabilities existing in the GAN model, a new optimization method based on firefly algorithm is proposed to create a class of generators in a feasible region, i.e. the discriminator D. A three-stage modification method is also devised to increase the algorithm population diversity and reduce the possibility of falling in local optima. The performance of the model is assessed on the experimental dataset recorded from the OBD-II port of an undefined vehicle.

This paper proposes a univariate prognostic approach based on wavelet transform and support vector regression (SVR) to predict the tidal current speed and direction with high accuracy. The proposed model decomposes the tidal current data into some subharmonic components. The details and approximation components are later fed to several SVR models to attend the prediction process. In order to increase the robustness of the model, the idea of combined prediction is used to model each subharmonic signal by several SVRs. The median operator is further used to determine the aggregated forecast tidal current data. Due to the high reliance of SVR model on the kernel function and hyperplane parameters, a new optimization method based on the bat algorithm is used to train the SVR model. The final forecast tidal current data are constructed using an aggregation operator in the output of the SVRs. The accuracy and satisfying performance of the proposed model are examined on the practical tidal data collected from the Bay of Fundy, NS, Canada. The experimental results reveal the high capability and robustness of the proposed hybrid model for the tidal current prediction.

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have received increasing attention because of their low pollution emissions, petroleum independence, and high fuel economy. The large market penetration of these vehicles is dramatically changing the view of the power distribution system. Unlike other power loads, these vehicles can be connected to power grids anywhere and anytime, which brings more spatial and temporal diversity and uncertainty. There is an urgent need to investigate the impact of PHEV/PEV charging on the power distribution system considering multidisciplinary complexities (e.g., driving behavior, route and departure time choice, charging station location, engineering, policy, economic, environment, technology, and social impact). This paper consolidates the modeling and simulation of power distribution system and transportation network in order to assess the emerging electric vehicle technologies. Moreover, this paper proposes a comprehensive co-modeling/simulation framework for investigating the impact of the electrification of transportation in the real world.

Economics and environmental incentives, as well as advances in technology, are reshaping the traditional view of industrial systems. The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) into the market brings up many technical problems that are highly related to industrial information technologies within the next ten years. There is a need for an in-depth understanding of the electrification of transportation in the industrial environment. It is important to consolidate the practical and the conceptual knowledge of industrial informatics in order to support the emerging electric vehicle (EV) technologies. This paper presents a comprehensive overview of the electrification of transportation in an industrial environment. In addition, it provides a comprehensive survey of the EVs in the field of industrial informatics systems, namely: 1) charging infrastructure and PHEV/PEV batteries; 2) intelligent energy management; 3) vehicle-to-grid; and 4) communication requirements. Moreover, this paper presents a future perspective of industrial information technologies to accelerate the market introduction and penetration of advanced electric drive vehicles.