Automated early detection and identification of switch faults are essential in high-voltage applications. Modular multilevel converter (MMC) is a new and promising topology for such applications. MMC is composed of many identical controlled voltage sources called modules or cells. Each cell may have one or more switches and a switch failure may occur in anyone of these cells. The steady-state normal and fault behavior of a cell voltage will also significantly vary according to the changes in the load current and the fault timing. This makes it a challenging problem to detect and identify such faults as soon as they occur. In this study, we propose a real-time and highly accurate MMC circuit monitoring system for early fault detection and identification using adaptive 1D Convolutional Neural Networks (CNNs). The proposed approach is directly applicable to the raw voltage and current data and thus eliminates the need for any feature extraction algorithm, resulting in a highly efficient and reliable system. Simulation results obtained using a 4-cell, 8-switch MMC topology demonstrate that the proposed system has a high reliability to avoid any false alarm and achieves a detection probability of 0.989, and average identification probability of 0.997 in less than 100 ms.

To mitigate global warming and energy shortage, integration of renewable energy generation sources, energy storage systems, and plug-in electric vehicles (PEVs) have been introduced in recent years. The application of electric vehicles (EV) in the smart grid has shown a significant option to reduce carbon emission. However, due to the limited battery capacity, managing the charging and discharging process of EV as a distributed power supply is a challenging task. Moreover, the unpredictable nature of renewable energy generation, uncertainties of plug-in electric vehicles associated parameters, energy prices, and the time-varying load create new challenges for the researchers and industries to maintain a stable operation of the power system. The EV battery charging management system plays a main role in coordinating the charging and discharging mechanism to efficiently realize a secure, efficient, and reliable power system. More recently, there has been an increasing interest in data-driven approaches in EV charging modeling. Consequently, researchers are looking to deploy model-free approaches for solving the EV charging management with uncertainties. Among many existing model-free approaches, Reinforcement Learning (RL) has been widely used for EV charging management. Unlike other machine learning approaches, the RL technique is based on maximizing the cumulative reward. This article reviews the existing literature related to the RL-based framework, objectives, and architecture for the charging coordination strategies of electric vehicles in the power systems. In addition, the review paper presents a detailed comparative analysis of the techniques used for achieving different charging coordination objectives while satisfying multiple constraints. This article also focuses on the application of RL in EV coordination for research and development of the cutting-edge optimized energy management system (EMS), which are applicable for EV charging.

One of the key solutions to address the issue of energy efficiency and sustainable mobility is to integrate plug-in electric vehicle (EV) infrastructure and photovoltaic (PV) systems. The research proposes a comprehensive EV infrastructure planning and analysis tool (EVI-PAT) with solar power generation for micro-scale projects for the deployment of EV Charging Stations (EVCS). For the evaluation of the proposed infrastructure, a case study of Qatar University (QU) campus is chosen for the integration of the EV charging infrastructure and PV power generation to evaluate the performance of the presented framework. The model estimates the EV adoption and the number of vehicles based on the inputs related to the country’s EV adoption, campus vehicle count, and driving behavior. Economic and environmental indicators are used for evaluating policy choices. The findings in the paper show that the proposed planning framework can find the optimum staging plan for EV and PV infrastructure based on the policy choices. The staging plan optimizes the sizes and times of installing EVCSs combined with solar PV keeping the EV-PV project at maximum economic and environmental targets. The optimum policy can affect the optimum power infrastructure limit to maximize the economic benefit by the solar tariff.

Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures.

This paper presents the design and implementation of solar powered V/f controlled single phase capacitor start induction motor. Multi Level Quasi Impedance Source Inverter (MLqZSI) controls the power flowing from PV array to single - phase induction motor. In solar powered drive systems, main concern is stable intended operation of drive when subjected to variations in power generation of PV array. For same environmental conditions, PV power extraction is different at different torques for constant speed application. Due to this, extraction of maximum power with MPPT algorithm is not achieved with only motor load. To address this concern, concept of battery storage system is introduced in the system which helps in achieving maximum power when PV power generation capability exceeds rated motor input power. In addition to this, battery storage system can supply power to the load when PV power generation is less than the rated motor input power. Conclusively, design of control algorithm must address issues of MPPT algorithm, control of battery storage system and stable operation of V/f - controlled induction motor drive operation. MATLAB®/ Simulink model of the proposed system with 4kW PV array rating is developed. Proposed Control Algorithm achieves satisfactory operation of single - phase motor drive in all the three operation modes (depending upon PV power generation). Variation of solar irradiation and temperature are simultaneously considered for introducing perturbation in the PV power generation. Hardware results for this system are also presented which validates the effectiveness of the control algorithm for the proposed system.

An ultra-high step-up, non-isolated DC–DC converter with a continuous input current was developed as a result of this research. This converter’s architecture consists of a voltage multiplier cell (VMC), a positive output super lift Luo converter (POSLLC), and a quadratic boost converter (QBS) (also referred to as a cascaded boost topology (CBT)). Thus, the bold points of the topologies mentioned earlier enhance the voltage gain of the proposed topology. It is important to note that when the duty cycle is at 50%, the converter attains a voltage gain of ten. Additionally, the constant input current of the topology reduces the current stress on the input filter capacitor. This converter’s topology was investigated and studied under various operating conditions: ideal and non-ideal modes, as well as continuous and discontinuous current modes (CCM/DCM). The converter’s efficiency and voltage gain were also compared to those of newly proposed converters. PLECS and MATLAB software tools were used in the investigation of the proposed topology. A 200 V/200 W prototype was constructed. The experimental results validated the theoretical study and the simulation results. The extracted efficiency was 91%.