Predicting cardiac disease is considered one of the most challenging tasks in the medical field. It takes a lot of time and effort to figure out what’s causing this, especially for doctors and other medical experts. In this paper, various Machine Learning algorithms such as LR, KNN, SVM, and GBC, together with the GridSearchCV, predict cardiac disease. The system uses a 5-fold cross-validation technique for verification. A comparative study is given for these four methodologies. The Datasets for both Cleveland, Hungary, Switzerland, and Long Beach V and UCI Kaggle are used to analyze the models’ performance. It is found in the analysis that the Extreme Gradient Boosting Classifier with GridSearchCV gives the highest and nearly comparable testing and training accuracies as 100% and 99.03% for both the datasets (Hungary, Switzerland &; Long Beach V and UCI Kaggle). Moreover, it is found in the analysis that XGBoost Classifier without GridSearchCV gives the highest and nearly comparable testing and training accuracies as 98.05% and 100% for both the datasets (Hungary, Switzerland & Long Beach V and UCI Kaggle). Furthermore, the analytical results of the proposed technique are compared with previous heart disease prediction studies. It is evident that amongst the proposed approach, the Extreme Gradient Boosting Classifier with GridSearchCV is producing the best hyperparameter for testing accuracy. The primary aim of this paper is to develop a unique model-creation technique for solving real-world problems.

Energy security is one of the most crucial factor in the development of any nation. Interconnections among different power system networks are made to lower the overall price of power generation as well as enhance the reliability and the security of electric power supply. Different types of interconnection technologies are employed, such as AC interconnections, DC interconnections, synchronous interconnections, and asynchronous interconnections. It is necessary to control the power flow between the interconnected electric power networks. The power flow controllers are used to (i) enhance the operational flexibility and controllability of the electric power system networks, (ii) improve the system stability and (iii) accomplish better utilization of existing power transmission systems. These controllers can be built using power electronic devices, electromechanical devices or the hybrid of these devices. In this paper, control techniques for power system networks are discussed. It includes both centralized and decentralized control techniques for power system networks. This paper also presents a comprehensive review of HVDC interconnections, asynchronous AC interconnections, synchronous AC interconnections and different types of power flow controllers used in these interconnections. Moreover, some important and multivariable flexible AC transmission system (FACTS) devices such as UPFC and IPFC are also discussed with their merits and limitations. Finally, a new asynchronous AC link called flexible asynchronous AC link (FASAL) system is also described in detail. At last, a summary of the comparative analysis of power system link and power flow controllers is given based on recent publications. More than 400 research articles and papers on the topic of power transfer control are covered in this review and appended for a quick reference.

An interconnection of electric power networks enables decarbonization of the electricity system by harnessing and sharing large amounts of renewable energy. The highest potential renewable energy areas are often far from load centers, integrated through long-distance transmission interconnections. The transmission interconnection mitigates the variability of renewable energy sources by importing and exporting electricity between neighbouring regions. This paper presents an overview of regional and global energy consumption trends by use of fuel. A large power grid interconnection, including renewable energy and its integration into the utility grid, and globally existing large power grid interconnections are also presented. The technologies used for power grid interconnections include HVAC, HVDC (including LCC, VSC comprising of MMC-VSC, HVDC light), VFT, and newly proposed FASAL are discussed with their potential projects. Future trends of grid interconnection, including clean energy initiatives and developments, UHV AC and DC transmission systems, and smart grid developments, are presented in detail. A review of regional and global initiatives in the context of a sustainable future by implementing electric energy interconnections is presented. It presents the associated challenges and benefits of globally interconnected power grids and intercontinental interconnectors. Finally, in this paper, research directions in clean and sustainable energy, smart grid, UHV transmission systems that facilitate the global future grid interconnection goal are addressed.

This research investigates implementing and optimizing microgrid energy management systems (EMS) utilizing artificial intelligence (AI). Inspired by the need for efficient resource utilization and the limitations of traditional control methods, it addresses essential aspects of microgrid design, such as cost-effectiveness, system capacity, power generation mix, and customer satisfaction. The primary goals are to optimize energy management, control techniques, and AI applications in microgrids. The study critically examines the classification of energy management systems, various EMS applications, and their associated challenges. Additionally, it discusses different optimization techniques relevant to EMS, highlighting their applications, benefits, and challenges. The research emphasizes the importance of hybrid systems, demand-side management, and energy storage in addressing the intermittency of renewable energy sources. AI techniques, such as unsupervised learning (USL), supervised learning (SL), and semi-supervised learning (SSL), are extensively analyzed in relation to their specific applications. The study explores AI-based hierarchical controls at primary, secondary, and tertiary levels. Furthermore, AI methods like deep learning for load forecasting and reinforcement learning for optimal control are emphasized for their substantial contributions to enhancing microgrid reliability and efficiency. The research concludes that integrating distributed energy resources (DER) and using advanced optimization algorithms can lead to significant financial benefits and improved sustainability in microgrid operations. Over 200 research papers were referenced in this study.

Heart disease is a danger to people’s health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart disease for accurate forecasts and concrete steps for future diagnosis. The manual analysis and prediction of a massive volume of data are challenging and time-consuming. In this paper, a unique heart disease prediction model is proposed to predict heart disease correctly and rapidly using a variety of bodily signs. A heart disease prediction algorithm based on the analysis of the predictive models’ classification performance on combined datasets and the train-test split technique is presented. Finally, the proposed technique’s training results are compared with the previous works. For the Cleveland, Switzerland, Hungarian, and Long Beach VA heart disease datasets, accuracy, precision, recall, F1-score, and ROC-AUC curves are used as the performance indicators. The analytical outcomes for Random Forest Classifiers (RFC) of the combined heart disease datasets are F1-score 100%, accuracy 100%, precision 100%, recall 100%, and the ROC-AUC 100%. The Decision Tree Classifiers for pooled heart disease datasets are F1-score 100%, accuracy 98.80%, precision 98%, recall 99%, ROC-AUC 99%, and for RFC and Gradient Boosting Classifiers (GBC), the ROC-AUC gives 100% performance. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. In this paper, several reduction methods are incorporated. It is found that the accuracy of the RFC classification algorithm is high. Moreover, the developed method is efficient and reliable for predicting heart disease.

Energy is vital input for economy and social development in every society. Presently, the global primary energy demands are met largely from oil, natural gas, coal, nuclear and hydroelectric energy among which coal is dominant thermal power stations add to environmental degradation problems through gaseous emissions, particulate matter, fly ash, bottom ash, which are very harmful to human life. The proper siting, installation and operation of renewable and non-conventional energy power sources will help to control, replace and avoid adverse environmental impacts. The carbon dioxide (CO₂) and other gases released by burning fossil fuels contribute to the "green house" effect which cause global climate changes. Renewable energy sources mainly wind, small hydro, biomass and solar photovoltaic (SPY) etc. are the most suitable means to achieve the mitigation of CO₂ emission. This study gives estimation of CO₂ mitigation potential of renewable energy sources in comparison with pollution caused by pulverized coal boilers based thermal power plants. In this paper emissions (kg/KWH) from Indian coal-fired thermal power plants have been calculated by taking emission data from 81 thermal power plants around the country. The total amounts of emission (kg/year) from existing thermal power plants have been estimated and the mitigation of emissions through renewable energy generation have been shown.