This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm.

Smart computing environments have evolved with the dawn of the Internet of Things, incorporating pervasive or ubiquitous computing. Besides using sensors and smart devices, the main objective has been to make these environments intelligent by utilizing different kinds of artificial intelligent methods and algorithms. Making a system intelligent requires inclusion and implementation of various tools and technologies to facilitate artificial intelligence. This chapter focuses on identifying the most prominent enabling technologies in making smart computing environments intelligent. The ten foremost intelligence-enabling technologies – predictive analysis, deep learning, artificial neural network, big data analytics, intelligent edge, human-computer interaction, computer vision, explainable artificial intelligence, natural language processing and robotics have been discussed in this chapter.

Aiming ultra-reliable and mountable connectivity of tremendously high data rates in the Tb/s era with zero-perceived latency of 6G systems, next generation smart grids will need to captivate the advantages of breakthrough novel technology perceptions, incorporating THz wireless links, broadband and machine learning-based models designs, protocols and management practices. Motivated by the transformation potential of 6G systems, this paper deals with an island detection approach in 6G paradigm for an active distribution network. The proposed island detection method is tailored to adopt the breakthrough technologies of 6G, particularly, ultra-low latency, high volume data transmission and machine learned intelligent system. This methodology calculates the angular sum of the positive and the zero-sequence phase angle of the voltage at point of common coupling combined with random forest (RF) machine learning to identify islanding conditions. The technique has a robust resistance to noise due to the inherent nature of RF, has no consequences on power quality and exhibits a zero non-detection zone. It has the ability to differentiate between islanding and non-islanding events precisely even during active and reactive power discrepancy and transients similar to islanding. The method depicts a high testing accuracy of 98.46% with an island detection time of 52 ms.

Abstract Protection of critical components in the power sector especially the transmission line system is very important to minimize loss of revenue, resources, and productivity. Improvement of power transfer capacity and voltage profile in smart the power grid is achieved by Series Compensation (SC). However, it poses many serious issues in the protection of compensated transmission lines. The majority of faults occurring in transmission lines are transient in nature for which service continuity can be regained by an auto-reclosing scheme. An efficient auto-reclosing scheme that can provide adaptive protection with reliable operation is required to prevent transient and permanent faults in the series compensated transmission line (SCTL). This work highlights the emulation of an Internet of Things (IoT) aided adaptive auto-reclosing scheme in association with an ESP32 controller that provides adaptive dead time control. The reclosing instance has been identified by means of a synchro-check relay. A real-time IoT-aided auto-reclosing scheme has been realized using the ESP32 controller that can generate a trip/reclosing signal instantaneously using the concepts of edge computing. The projected system has been also corroborated by simulating the modeled smart power grid network with wide variations in fault context in PSCAD. The validation of the set of rules is done in MATLAB software for the proposed auto-reclosing scheme. The extracted test outcomes of the simulation and its laboratory hardware implementation indicate an appreciable reduction in dead time which reflects the efficacy of the developed protection scheme.

Heat transfer and entropy generation are crucial considerations in the nuclear industry, where the safe and efficient transfer of heat is essential for the operation of nuclear reactors and other nuclear systems. Casson fluid is a useful tool in the nuclear industry for simulating the flow behavior of nuclear fuels and coolants, and for optimizing the design and operation of nuclear reactors. In view of this, the current investigation deals with the heat and fluid flow of unsteady Casson fluid in a circular pipe under the influence of magnetic field, internal heat generation, entropy generation and porous media. The governing equations have been simplified under suitable assumptions and nondimensional quantities. The simplified dimensionless governing equations have been solved using the method of separation of variables along with Bessel functions. It is concluded from the investigation that the temperature increases with time. The Casson fluid parameter raises the temperature and entropy generation. The temperature, entropy generation and Bejan number are the decreasing functions of the Prandtl number.