In recent times, Blockchain has emerged as a transformational technology with the ability to disrupt and evolve multiple domains. As a decentralized, immutable distributed ledger, Blockchain technology is one of the most recent entrants to the comprehensive ideology of Smart Cities. The rise of urbanization and increased citizen participation have led to various technology integrations in our present-day cities. For cities to become smart, we need standard frameworks and procedures for integrating technology, citizens and governments. In this paper, we explore the potential of Blockchain technology as an enabler for e-governance in smart cities. We examine the daily challenges of citizens and compare them with the benefits being offered by Blockchain integration. On the basis of a comprehensive literature review, we identified four key areas of e-governance wherein Blockchain can provide monumental advantages. In the context of Blockchain integration for e-governance, the paper presents a survey of prominent published works discussing various urban applications.

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier’s performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.

This paper presents the analysis of power supply restoration time after failures occurring in power lines. It found that the power supply restoration time depends on several constituents, such as the time for obtaining information on failures, the time for information recognition, the time to repair failures, and the time for connection harmonization. All these constituents have been considered more specifically. The main constituents’ results values of the power supply restoration time were analyzed for the electrical networks of regional power supply company “Oreolenergo”, a branch of Interregional Distribution Grid Company (IDGC) of Center. The Delphi method was used for determining the time for obtaining information on failures as well as the time for information recognition. The method of mathematical statistics was used to determine the repair time. The determined power supply restoration time (5.28 h) is similar to statistical values of the examined power supply company (the deviation was equal to 9.9%). The technical means of electrical network automation capable of the reduction of the power supply restoration time have also been found. These means were classified according to the time intervals they shorten.

Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a $R^2$ value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet.

Electricity supply as well as the provision of other forms of resources is one of the foundations of efficient agriculture. However, due to the reduction in the number of people living in rural settlements, there have been a large number of power lines with considerable lengths supplying small loads, hence resulting in an increase in power supply efficiency. A single-wire power transmission is an option for reducing the capital cost of power line construction by utilizing fewer conductors and fittings and lighter power transmission towers while lowering operational expenses. This paper considers the possible methods for single-wire energy transmission via the analysis of information sources such as Yandex and Google search engines; Scopus and Google Scholar scientific databases; and Cyber Leninka, eLIBRARY.ru, Elsevier, Springer, IEEE Xplore, and IGI Global electronic libraries. The conducted review revealed four alternatives: a single-wire earth return (SWER) system, a single-wire balanced line (B-Line), resonant wireless power transmission (SWPT) system, and a resonant single-wire power transmission system. The latter is of particular interest due to the lack of comprehensive and detailed information describing this technology, although it has distinct characteristics because of the peculiarities of the resonant mode of operation. The paper provides a comprehensive review of all existing published materials on the topic of “resonant systems for the transmission of electrical energy along a single wire”. The study covers the history of development and the structure of this system; describes its features, advantages, and the problems of using it; and the experience and fields of its application.

PURPOSE. Currently, modeling is one of the most reliable ways to study normal and emergency modes of operation of electrical networks. In this regard, a mathematical model of the 0.4 kV rural electric network was developed in the MATLAB Simulink program.PURPOSE. Evaluation of the accuracy of the parameters of the operating modes of the 0.4 kV rural electric network obtained by modeling in MATLAB Simulink by estimating the values of the steady-state single-phase short circuit current at different distances from the transformer substation.METHODS. The currents obtained using the 0.4 kV electrical network model developed in MATLAB Simulink were compared with the currents obtained using other calculation methods (according to GOST 28249-93, the simplified phase-zero loop method and the mathematical model «Project RZA»).RESULTS. It was revealed that the discrepancy in the results at individual points was 9-11%. When simulating a steady-state single-phase short circuit in MATLAB Simulink, it was found that the voltage of the damaged phase A decreases to 0 V, while the voltages on the two remaining intact phases increase by 1.4-1.5 times, i.e.E. by a factor of 2 to the values of 335-354 V when short-circuiting at the transformer terminals and to the values of 329-338 V when short-circuiting behind a section of a 0.4 kV power line.CONCLUSIONS. The resulting mathematical model of a 0.4 kV rural electric network in the MATLAB Simulink program can be used to study normal and emergency operating modes, study the modes of joint operation of renewable energy sources with a centralized network, and the processes of switching on backup generation sources.