Energy security (ES) has great impact on power grids. Therefore it is important to have power security service (PSS). The PSS should be designed to handle interference and interruption attack in the grid. The interference and interruption attack in the grid is handled by incursion-detection system (IDS). The IDS is the most obtainable strategy to sense and classify different security issues and abnormal issues that occur in power grids. Therefore the IDS have to be up to date with the latest issues that happened in the entire grid. The proposed concept illustrate the collection of data with reference to intrude-attacks in power-grid and to test and evaluate different machine-learning strategy to illustrate the different attacks and issues that happen in power system grid. The research work is illustrated with a modified J48 decision tree algorithm.

Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset.

Internet of Things (IoT) has now become an embryonic technology to elevate the whole sphere into canny cities. Hasty enlargement of smart cities and industries leads to the proliferation of waste generation. Waste can be pigeon‐holed as materials‐based waste, hazard potential based waste, and origin‐based waste. These waste categories must be coped thoroughly to make certain of the ecological finest run‐throughs irrespective of the origin or hazard potential or content. Waste management should be incorporated into ecological preparation since it is a grave piece of natural cleanliness. The most important goalmouth of waste management is to maintain the pecuniary growth and snootier excellence of life by plummeting and exterminating adversative repercussions of waste materials on environment and human health. Disposing of unused things is a significant issue, and this ought to be done in the best manner by deflecting waste development and keeping hold of cost, and it involves countless human resources to deal with the waste. These current techniques predominantly focus on cost‐effective monitoring of waste management, and results are not imprecise, so that it could not be developed in real time or practically applications such as in educational organizations, hospitals, and smart cities. Internet of things‐based waste management system provides a real‐time monitoring system for collecting the garbage waste, and it does not control the dispersion of overspill and blowout gases with poor odor. Consequently, it leads to the emission of radiation and toxic gases and affects the environment and social well‐being and induces global warming. Motivated by these points, in this research work, we proposed an automatic method to achieve an effective and intelligent waste management system using Internet of things by predicting the possibility of waste things. The wastage capacity, gas level, and metal level can be monitored continuously using IoT based dustbins, which can be placed everywhere in city. Then, our proposed method can be tested by machine learning classification techniques such as linear regression, logistic regression, support vector machine, decision tree, and random forest algorithm. The proposed method is investigated with machine learning classification techniques in terms of accuracy and time analysis. Random forest algorithm gives the accuracy of 92.15% and time consumption of 0.2 milli seconds. From this analysis, our proposed method with random forest algorithm is significantly better compared to other classification techniques.

This article presents a new protection scheme for the µgrid structure is implemented on low voltage side as DC system’s protection methods are quite new when compared to the AC systems. The protection system designed in AC system faces many difficulties like frequency matching, reactive power violation, and instability. The main objective of the proposed system is to eliminate the faulty segment promptly while keeping the healthy segments functioning unaltered. In the proposed method, a unitwise controller is designed and is connected in feedback so as to maintain stability under all conditions. The unitwise controller comprises primary and secondary controllers which continually monitor all the units of DC grid comprising semiconductor bidirectional switches and snubber circuits. A primary controller is used for monitoring the complete variation in the parameters in all the units. If the observed parameter value is greater than the acceptable range, the secondary controllers receive the command so as to take the required action to eliminate the faulty segment. Both lines and ground faults are tested on all the sections separately and also simultaneously. The tested results are analyzed in all aspects and the results of the states that the designed protection scheme competent in isolating the defective segment within a short period of time. Once the defective segment is isolated, the other segments retain the stabilized voltage and current limits. The protection scheme is designed and verified using MATLAB/Simulink.

This study presents the implementation of Artificial Bee Colony (ABC) optimization in an island hybrid power system model using fuzzy logic-based load frequency control. The Island Hybrid Power System considered in this study consisted of various generation units and an energy storage system. The optimized control parameters of PID using ABC were used in an intelligent fuzzy logic controller. The profiles (power & Frequency) of isolated hybrid power system were improved using a Super Conducting Magnetic Energy Storage (SMES) System. Individual controllers were used for wind turbine and diesel generators to control the power output for balancing the demand (frequency change control). Comparative analysis of power and frequency with the help of various classical and intelligent control configurations is presented. The outcome of the study shows that a minimum deviation in frequency and power is obtained through the proposed Intelligent Fuzzy Control approach for the considered isolated power system model.