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.

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.

Conventional map-matching (MM) algorithms take blind eyes to the complexity in realistic traffic conditions and hence present significant limitations in distinguishing the detailed driving paths of vehicles within complex urban road networks. The popularity of vehicle-borne cameras and advances in image recognition technologies provide an opportunity to remedy the gap through integrating vehicle-borne image semantic information with MM algorithms. Following this logic, this article proposes a novel MM algorithm with semantic fusion from vehicle-borne images (VIS-MM) suited to the parallel road scenes. First, a multipath output algorithm is developed using the hidden Markov model to obtain candidate paths. Second, image recognition techniques are employed to extract vehicle-borne image semantics. Finally, the entropy weight method is performed to determine the most promising driving path among the candidate paths. The experimental results show that semantic fusion from vehicle-borne images contributes to a significant improvement of accuracy from 66.18% to 99.88% against the parallel road scenes. The proposed map-matching algorithm can be applied into the fields of unmanned autonomous navigation and crowdsourcing updating of high-definition maps.

Urban landmarks play an important role as spatial references in spatial cognition, navigation, map design and urban planning. However, the current landmark extraction methods do not consider the imbalance between the landmark and non-landmarknon-landmark samples in a dataset, so the extraction results are biased toward the class with the majority of sample data, resulting in poor classification performance for the class with the fewest sample data. This study introduces a random forest (RF) classifier combined with cost-sensitive learning to extract urban landmarks automatically from a basic spatial database. First, the optimal feature set is determined according to the importance of features. Next, a cost-sensitive RF algorithm is applied to extract landmarks, which determines the misclassification cost according to the class distribution, and each decision tree is weighted by the classification results. The method has good performance, with a recall and area under the ROC curve (AUC) greater than 90%, and the model is also applicable to small sample sets, which can reduce the cost of manual labor.

In this paper, we address the optimal autonomous vehicle (AV) coordination problem at road intersections, which is of great importance in modern intelligent transportation systems (ITS). We first formulate it as a general collision-free traffic scheduling framework. Aiming at the dynamic characteristics of vehicles' arrival, a corresponding dynamic coordination strategy is thus proposed to achieve better quality of service (QoS), i.e., the traffic throughput and delay. The road stability is also guaranteed using the Rate Stability Theorem and Lyapunov Theorem. Provided numerical results validate our analysis and show the performance improvements achieved by the proposed framework.