The expected fifth industrial revolution or Industry 5.0 (I-5.0) is human-centered and concerns societal values, and sustainability. I-5.0 focuses on human and machine coworking by augmenting human-collaborative intelligent robots. The current developments in information communications and the increasing market need for high agility and innovative ways to tailor products urge the world for an I-5.0 transformation. Artificial intelligence (AI) plays a key role in I-5.0 in offering intelligent components, smart digital twins, smart cyber-physical systems, and cobotics for efficient, precise and human-centered decision-making. In this context, AI boosts human-robot collaboration in production for timely and precise responses to the market and thereof realizing the concept of mass personalization. In addition, since I-5.0 is based on the idea of the widespread connection of devices, and identities in and out of the production process, there is a high level of vulnerability in front of cyberattacks where AI assists in smart cyber defense techniques. This article is a review of I-5.0 human-center concerns, involvement of social values, and I-5.0 technological elements in connection with human-collaborative artificial intelligence. The role of AI in mass personalization and defense in front of cybersecurity threats is analyzed, and the horizon of I-5.0 in AI context is depicted. Finally, the realization challenges are reviewed as I-5.0 aims to make the future instead of waiting to face the future, and there is a variety of challenges on this path.

In practical operating conditions, the Solar-Photo Voltaic (SPV) system experiences multifarious irradiation and temperature levels, which generate power with multiple peaks. This is considered as the nonuniform operating condition (NUOC). This requires accurate tracking of global power peaks to achieve maximum power from SPV, which is a challenging task. Hence, this paper presents an incremental Conductance based Particle Swarm Optimization (ICPSO) algorithm for accurate tracking of maximum global power from active power multiple peaks generated by the SPV. The proposed algorithm continuously adjusts the individual particle’s weight component, which depends on its distance from the global best position during the tracking process. The proposed algorithm has the merit of continuous adjustment of weight components which reduces active power oscillations at the optimal global position area. Proposed ICPSO algorithm has been successfully designed and implemented for Solar-photo voltaic (PV) under nonuniform operating condition. It is established that the proposed algorithm enhances the output power of the Solar-PV up to 7% with the maximum power tracking of 0.1 s compared to other maximum power point tracking algorithms.

Enhancement in solar energy (SE) injection into the power system network creates power quality (PQ) issues in the supply. This article presents an approach supported by Stockwell transform ( $S$ -transform) for assessment of PQ issues related with the grid interfaced solar photovoltaic (SPV) system under various operating conditions. This will help to enhance the SE integration level into the utility grid. The set up, to perform assessment of the PQ issues includes an emulated SPV system interfaced with the utility at the point of common coupling (PCC). Measurements of voltage and current signals are performed by utilizing power network analyzer. The captured voltage signals are analyzed using $S$ -transform for the detection of a variety of PQ problems associated with the grid interfacing and outage of the SPV system. Effects on PQ due to presence of the various types of loads at PCC have also been investigated under the same operating conditions. Effect of partial shading of SPV plates on the PQ is also investigated. Harmonic analysis is performed for all the investigated events. The proposed algorithm proved to be successful for detecting different PQ disturbances under all the investigated operating conditions.

This paper presents a binary coded evolutionary computational method inspired from the evolution in plant genetics. It introduces the concept of artificial DNA which is an abstract idea inspired from inheritance of characteristics in plant genetics through transmitting dominant and recessive genes and Epimutaiton. It involves a rehabilitation process which similar to plant biology provides further evolving mechanism against environmental mutation for being better and better. Test of the effectiveness, consistency, and efficiency of the proposed optimizer have been demonstrated through a variety of complex benchmark test functions. Simulation results and associated analysis of the proposed optimizer in comparison to Self-learning particle swarm optimization (SLPSO), Shuffled Frog Leap Algorithm (SFLA), Multi-species hybrid Genetic Algorithm (MSGA), Gravitational search algorithm (GSA), Group Search Optimization (GSO), Cuckoo Search (CS), Probabilistic Bee Algorithm (PBA), and Hybrid Differential PSO (HDSO) approve its applicability in solving complex problems. In this paper, we have shown effective results on thirty variables benchmark test problems of different classes.

Electrical energy is critical to a country’s socioeconomic progress. Distribution system expansion planning addresses the services that must be installed for the distribution networks to meet the expected load need, while also meeting different operational and technical limitations. The incorporation of distributed generation sources (DGs) alters the operating characteristics of modern power systems, resulting in major economic and technical benefits, such as simplified distribution network expansion planning, lower power losses, and improved voltage profile. Thus, in this study, an analytical method is used to design the expansion planning of the Addis North distribution network considering the integration of optimal sizes of distributed generations for the projected demand growths. To evaluate the capability of the existing Addis North distribution network and its capability to supply reliable power considering future expansion, the load demand forecast for the years 2020–2030 is done using the least square method. The performance evaluation of the existing and the upgraded network considering the existing and forecasted load demand for the years 2030 is done using ETAP software. Accordingly, the results revealed that the existing networks cannot meet the existing load demand of the town, with major problems of increased power loss and a reduced voltage profile. To mitigate this problem, the Addis North feeder-1 distribution network is upgraded and for each study case, the balanced and positive sequence load flow analysis was executed and the maximum total real and reactive power losses were found at bus 29. The result shows that the upgraded network of bus 29 was the optimal location of DG and its size was 9.93 MW. After the optimal size of DG was placed at this bus, the real and reactive power losses of the upgraded networks were 0.2939 MW and 0.219 MVAr, respectively. At bus 29 the maximum power losses reduction and voltage profile improvements were found. The active and reactive power losses were minimized by 21.285% and 19.633% respectively and the voltage profiles were improved by 8.78%. Thus, in the predicted year 2030, DG power sources could cover 61.12% of the feeder-1 power requirements.