This paper presents a simple method for the identification of controller for Permanent Magnet Synchronous Generator based Power Electronic Quadratic DC-DC Converter Inverter System. The PMSG source is used with cascaded three phase bridge rectifier. Quadratic Boost Converter Inverter System and three phase balanced load.

This paper proposes a new PWM generation scheme for the three phase five level cascaded multilevel inverter. The main objective of this paper to improve the power quality by using various multi-carrier PWM methods. Generally multi-carrier-based methods have been used widely for switching of multilevel inverters due to their simplicity, flexibility and reduced computational requirements compared to Space Vector Modulation (SVM) techniques. Several multi-carrier modulation methods with miscellaneous properties have been already known. While evaluating and comparing these methods, such as PD, POD and APOD through measures like total harmonic distortion (THD) the paper shows their advantages for applications of Cascade multi-level topologies by simulation results. Finally simulation results and experimental results are discussed.

This paper displays an simple method for identification of controller for wind based Quadratic Boost Converter Inverter system. India, being the fourth largest wind power generator has attracted numerous researchers towards the improvement of wind energy conversion system. This paper also presents improved controller techniques for a Permanent Magnet Synchronous Generator (PMSG) coupled with Cascaded Quadratic Boost Converter (QBC) and Space Vector Modulation Inverter (SVMI). The digital simulation and execution of PMSG based wind turbine along with QBC and SVM Inverter is presented in a closed loop system. The performance of closed loop system is realized using Proportional Integral (PI) and Fractional Order Proportional Integral (FOPI) controllers. Initially, the AC power from PMSG wind turbine is rectified to DC using rectifier circuit. The DC power from the bridge rectifier is then boosted to the required level using quadratic boost converter. The output from QBC is then given to the SVM inverter. The closed loop investigations are carried with PI and FOPI controllers. The simulation results of both PI and FOPI controlled QBC are compared. The outcome of FOPI controller represents that the steady state error and settling time are reduced when compared to PI controlled closed loop Quadratic Boost Converter. The overall Matlab/simulink model is applied to undergraduate/postgraduate course as a educational tool and assessed thoroughly.

The distribution of electricity system will be impacted by the growing number of electric vehicles (EVs). As a result, an infrastructure must be developed to help charging station owners efficiently handle EV customers' charging needs and guarantee that their demands are met without going over the electricity grid's limits. EV users will have issues within a few decades because charging sites will not be available, and the grid will get crowded as a lot of EVs try to charge at the same time. Thus, EV charging trends could be coordinated with the help of artificial intelligence (AI) and blockchain, and charging stations can be connected to the grid. AI is capable of identifying the best times and routes for charging electric vehicles based on the current price of energy. While distributing demand throughout the grid to prevent power spikes, blockchain technology can validate transactions at charging stations. They allow optimised management of energy, decentralised electrical networks, sustainable integration, and future charging infrastructure to expedite the resilient energy systems.

Forecasting energy demand is essential for efficient energy management and planning, allowing utilities and policymakers to make data-driven decisions regarding energy generation, distribution, and consumption. This abstract examines the latest developments in forecasting methods, such as statistical techniques, machine learning, and hybrid models, which have significantly enhanced prediction accuracy. Traditional techniques, including time series analysis and regression models, are now complemented by innovative approaches that utilize big data and artificial intelligence. These advancements enable real-time data processing and the consideration of diverse factors, including weather conditions, economic trends, and consumer behaviors. Moreover, the integration of smart grid technologies and IoT devices has revolutionized data collection methods, offering more detailed insights into energy usage patterns. Case studies demonstrate the successful application of these techniques across different regions, showcasing their effectiveness in optimizing energy supply and reducing costs. The abstract also addresses challenges in forecasting, such as data quality concerns and the necessity for continuous model updates. By tackling these challenges and adopting emerging technologies, stakeholders can improve their forecasting abilities, fostering more sustainable and resilient energy systems. This ongoing progression in energy demand forecasting is crucial for meeting the increasing challenges associated with the energy transition and climate change..

ITS, an acronym for “Intelligent Transport Systems,” has the potential to significantly transform the administration and commute of cities. More and more people are opting for advanced ITS systems to make transportation safer, more efficient, and more environmentally friendly. The ability of Graph Neural Networks (GNNs) to process complex connections and unexpected input is driving their rising popularity. Intelligent transportation systems utilize graph neural networks.Using common baseline models and statistics in each transportation domain, urban planners and decision-makers design and assess GNN-based frameworks. “Graph Neural Networks for Intelligent Transportation Systems: A Survey” examines the advantages and disadvantages of GNNs and their application in ITS. Future studies find that geographically dispersed networks (GNNs) boost the sustainability, robustness, and efficiency of transportation networks. Enhanced efficiency is another perk.