Dynamic wireless charging (DWC) is an emerging technology designed to alleviate range anxiety by enabling continuous charging for electric vehicles in motion. As an electric vehicle travels over transmitter coils, fluctuations in mutual coupling can lead to variations in output voltage, potentially reducing battery lifespan and causing efficiency fluctuations. In response to these challenges, this paper introduces an innovative composite control method that combines the backstepping control approach with passivity-based control to stabilize output voltage during vehicle travel and changing load conditions. The paper details the design procedure of the proposed composite control method, and simulation results validate its effectiveness in suppressing output voltage fluctuations while the vehicle is in motion, limiting the fluctuation rate to almost 1%. The performance of the proposed controller is analyzed under different operating conditions, such as load changing and voltage reference changing, while the receiver is in motion. The results demonstrate the superior disturbance rejection and robustness of the proposed controller.

This research paper discusses the reliability analysis of Direct Current (DC) Electric Vehicle (EV) charging stations, focusing on their crucial role in advancing sustainable transportation. Utilizing Continuous Markov Processes, the methodology assesses the dependability of key components in DC charging systems, such as inverters, resonant circuits, transformers, and rectifiers. Real-world operational data and failure rates derived from MIL-HDBK-217 are employed for analysis, and the study utilizes the Markov process to model the system’s dynamic behavior, presenting results that affirm the high reliability (0.9993) of the DC charging infrastructure over a ten-year period. The impact of different Mean Time to Repair (MTTR) on system reliability is considered. The reliability analysis is conducted using dedicated software designed for Markov modeling and system reliability assessment.

In this study, we utilize a Nonlinear Auto-Regressive Exogenous (NARX) observer to estimate voltage and current states within an islanded DC microgrid across normal, load change, and attack scenarios. We implement a distributed cooperative control mechanism to synchronize distributed generation (DG) units for proportional load distribution. Specifically, we introduce a false data injection (FDI) attack to compromise control functions and system stability by intercepting sensor data or communication flows. Detecting such attacks is imperative for system resilience. Additionally, we compare Support Vector Machine (SVM), Neural Network (NN), and NARX methods to assess their estimation capabilities, finding that NARX outperforms in both estimation and attack detection. This study involves the initial operation of the DC microgrid under normal conditions, generating a dataset for training neural networks. This dataset incorporates load variations, enabling the distinction between load changes and potential cyber-attacks. Trained networks are subsequently applied in real-time to estimate the voltages and currents of Distributed Energy Resources (DER) units, allowing for the detection of cyber-attacks based on estimation errors. The efficacy of our approach is verified through MATLAB simulations and real-time validation using the OPAL-RT platform.

This study presents an active defense mechanism aimed at identifying cyber-attacks on an Islanded DC microgrid utilizing distributed consensus control. The proposed mechanism involves injecting a unique random signal, termed as a "watermark," into the control inputs of the system. This watermark signal is demonstrated to propagate through the actuator, converter, and microgrid components, thus becoming evident in all measurement data used for system control. The proposed method uses the gathered data to create a system identification model, which includes a constant system matrix for representing the system in state-space form, enabling accurate estimation of future states.Under normal operating conditions, the properties of the watermark signal remain unchanged, while they are altered during attack scenarios. Subsequently, two statistical sequence tests are employed to accurately detect cyber-attacks. This approach effectively detects any malicious manipulation of measurement data, such as false data injection or record and replay attacks, perpetrated by malicious actors seeking to gain control of the microgrid or disrupt its operations, potentially leading to significant grid damage.The robustness of the proposed active defense mechanism is verified through validation with various attack scenarios in the MATLAB environment.

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. The approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

In order to cope with the phenomenal growth of mobile data traffic, unlicensed spectrum can be utilized by the Long Term Evolution (LTE) cellular systems. However, ensuring fair coexistence with WiFi is a mandatory requirement. In one approach, periodically configurable transmission gaps can be used to facilitate a coexistence between WiFi and LTE. In this paper, a Multi-Armed Bandit (MAB) based dynamic duty cycle selection method is proposed for configuration of transmission gaps ensuring a better coexistence for both technologies. Then the concept is further strengthened with downlink power control mechanism using the same algorithm leading to a high energy efficiency and interference reduction. Performance results are given for different user equipment and WiFi station densities in which it is shown that significant improvements in overall throughput and energy efficiency can be achieved.

One of the most important features of the Smart Grid (SG) is real-time self-assessment which may threat that target power system stability. In order to improve robustness of power systems against such attacks, accurate estimation of the power system operation is required and conventional power flow methods should be upgraded. In this paper, we derive minimum variance unbiased estimators (MVUEs) for active power based on the voltage phase at each node of the power system. The state variables are the voltage phases and the received measurement signals are active power measurements. The proposed method is implemented on a four-bus test system. Three scenarios are defined to investigate the effect of covariance matrix topology on the estimation accuracy. The results shows that lower correlation between the noise vector elements leads to a more accurate estimation of power system operation.