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Abstract The increasing penetration levels of renewable distributed generation (RDG) into a power system have proven to bring both positive and negative impacts. The occurrence of under voltage at the far end of a conventional electrical distribution network (DN) may not raise concern anymore with RDGs integration into a power system. However, a penetration of RDGs into power system may cause problems such as voltage rise or over-voltage and reverse power flows at the Point of Common Coupling (PCC) between RDG and DN. This research paper presents the impact of voltage rise effect and reverse power flow constraint in power system with high concentration of RDG. The analysis is conducted on a sample DN, i.e., IEEE 13-bus test system, with RDG penetration by considering the most critical scenario such as low power demand in DN and a peak power injection by RDG. For studying the impact of voltage rise and reverse power flow, a mathematical model of a DN integrating RDG is developed. Furthermore, a controller incorporating an advance control-algorithm is proposed to be installed at PCC between DN and RDG to regulate the voltage rise effects and to mitigate the reverse power flow when operating at a worst critical scenario of minimum load and maximum generation from RDG. The proposed control strategy also mitigates the voltage–current harmonic distortions, improves the power factor, and maintain the voltage stability at PCC. The simulations are carried out using MATLAB/Simulink software. Finally, recommendations are provided for the power producers to counteract the effects of voltage rise at PCC. The study has demonstrated that, voltage at PCC can be sustained with a high concentration of RDG during a worst-case scenario without a reverse power flow and voltage rise beyond grid code limits.

The sudden demand rises in security made researchers come up with solutions that provide instantaneous safety better than the state of the art solutions. The quest for securing data began in the Spartan era. People are now looking to expand this field of research by attacking the existing paradigms and inventing new algorithms that prove to be better than their vulnerable counterparts. Unmanned aerial vehicles (UAVs) are very much prevailing due to their sleek design and flexible mobility in many sectors such as agriculture, army, healthcare, monitoring and surveillance, and many more. We discuss the growth and demand of drone technology along with its importance in this article. The paper also throws some light on the ongoing security issues in real-time scenarios and the role of quantum cryptography in securing the information over the traditional solutions. Motivated by this, we present a survey on quantum cryptography’s importance, role, and benefits in securing UAV communications underlying beyond 5G networks. A novel quantum cryptography-based layered architectural solution is also proposed to achieve high data security and efficient transmission. This paper also present a case study on the battlefield application on the Internet of military things. The performance of the proposed case study system is evaluated by considering the latency, security, and reliability.

The 5G network is an emerging field of the research community. 5G is a multi-disciplinary network that aims to support a wide range of services. 5G network has an objective to support a massive number of connected devices. Game theory has an extensive role in wireless network management. Game theory is an approach to analyzing and modeling the system where multiple actors have a role in decision-making with independent objectives and actions. The game theory is an exciting methodology to control the strategic behavior of players and generate an efficient outcome. Coalition game theory can play a crucial role in ensuring cooperation among a massive number of devices. This article provides insight into the current research trends in 5G using coalition games. The work presented in the survey is divided into three categories, namely resource management, interference management, and miscellaneous. This article also provides the foundation about 5G and coalition games highlight the scope of future research.

Cryptocurrencies are peer-to-peer-based transaction systems where the data exchanges are secured using the secure hash algorithm (SHA)-256 and message digest (MD)-5 algorithms. The prices of cryptocurrencies are highly volatile and follow stochastic moments and have reached their unpredictable limits. They are commonly used for investment and have become a substitute for other types of investment like metals, estates, and the stock market. Their importance in the market raises the strict requirement for a sturdy forecasting model. However, cryptocurrency price prediction is quite challenging due to its dependency on other cryptocurrencies. Many researchers have used machine learning and deep learning models, and other market sentiment-based models to predict the price of cryptocurrencies. As all the cryptocurrencies belong to a specific class, we can infer that the increase in the price of one cryptocurrency can lead to a price change for other cryptocurrencies. Researchers had also utilized the sentiments from tweets and other social media platforms to increase the performance of their proposed system. Motivated by these, in this paper, we propose a hybrid and robust framework, DL-Gues, for cryptocurrency price prediction, that considers its interdependency on other cryptocurrencies and also on market sentiments. We have considered price prediction of Dash carried out using price history and tweets of Dash, Litecoin, and Bitcoin for various loss functions for validation. Further, to check the usability of DL-GuesS on other cryptocurrencies, we have also inferred results for price prediction of Bitcoin-Cash with the price history and tweets of Bitcoin-Cash, Litecoin, and Bitcoin.

Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, Dash coin is an individual cryptocurrency, but it is derived from Bitcoin and Litecoin. The change in Bitcoin and Litecoin prices affects the Dash coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of Dash coin through the past days’ prices of Dash, Litecoin, and Bitcoin as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.

Regulatory changes, economic challenges, environmental concerns, and changing public perception have contributed to the profound changes observed globally in the electricity industry. Since 2008, South Africa has been experiencing electric power deficits and outages. This has been due in part to generation capacity constraints, belated investment in new electricity infrastructure, deferred maintenance of existing power assets, load growth in areas which were not adequately planned for, high population and economic growth over the last two decades. This has resulted in peak electricity demand outstripping available power generation capacity, leading to electricity shortages and load shedding, which is now impeding economic growth. In South Africa, forced under frequency load shedding, rising electricity tariffs, energy efficiency, declining cost of solar PV systems, the introduction of Carbon taxes, high cost of unserved energy has led consumers to explore embedded generation options to assist with reducing their energy bills, hence investments in solar PV has become an option to municipal customers. The simple payback period of solar PV systems is an important indicator for customers to ascertain whether to invest in these systems or not. Revenue loss remains a significant concern for municipalities who have historically designed single and two-rate bundled tariffs, which rely on municipalities selling electricity to ensure its business’s sustainability. Municipalities have now proposed new tariff structures designed to minimize the adverse impact of reducing electricity sales from solar PV by creating net billing tariffs with a built in network access charge component based on the customer’s inverter size. Case studies were carried out to better understand the impact on the feasibility of solar PV systems with and without the implementation of these new tariffs and its impact on the customer’s payback periods. A calculation of the levelised cost of electricity and customers rate of return for the different customer classes were also calculated to provide a better picture on the financial feasibility of rooftop solar PV. Results obtained from these case studies indicate lucrative payback periods for customers installing solar PV systems with improved revenue recovery for the municipality.

The COVID-19 pandemic situation has proved to be disastrous for humanity throughout the world. However, during this period, people must take precautions for safety purposes. One of the essential steps towards eliminating or reducing the effect of COVID-19 is maintaining social distancing while in public places. Some people are neglecting the social distancing norms while on the move. Still, no surveillance system exists, which monitors the people’s movement for social distancing and securely & efficiently shares the information with the concerned administration department. There also exists no penalty system which forces the people to ensure social distancing. Motivated from the aforementioned facts, in this paper, we present a blockchain and artificial intelligence (AI)-envisioned scheme for monitoring social distancing to combat COVID-19 situations. The proposed scheme uses fast region-based convolutional neural networks (RCNN) and you only look once (YOLO) models for the object (i.e., human) detection through the live video feed captured from the static CCTV cameras as well as lens-equipped drones. Further, the efficient euclidean distance calculation is embedded for calculating the distance between two humans. Blockchain technology ensures the secure and trusted exchange of information between the entities at the physical layer and the administration departments. Blockchain wallets are also used to pay the fine when people do not follow social distance norms. The performance of the proposed scheme is evaluated based on three broad parameters such as (i) human detection and violation identification, (ii) blockchain simulation and analysis, and (iii) network performance comparison. The parameters considered for (i) is confidence score, for (ii) are scalability, hash rate, and simulation interface, and for (iii) are network bandwidth, throughput, packet loss rate, and network latency. By analyzing all the parameters mentioned above, we observe the proposed scheme outperforms the traditional approaches.

Blockchain technology is becoming increasingly popular because of its applications in various fields. It gives an edge over the traditional centralized methods as it provides decentralization, immutability, integrity, and anonymity. The most popular application of this technology is cryptocurrencies, which showed a massive rise in their popularity and market capitalization in recent years. Individual investors, big institutions, and corporate firms are investing heavily in it. However, the crypto market is less stable than traditional commodity markets. It can be affected by many technical, sentimental, and legal factors, so it is highly volatile, uncertain, and unpredictable. Plenty of research has been done on various cryptocurrencies to forecast accurate prices, but the majority of these approaches cannot be applied in real-time. Motivated from the aforementioned discussion, in this paper, we propose a deep-learning-based hybrid model (includes Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM)) to predict the price of Litecoin and Zcash with inter-dependency of the parent coin. The proposed model can be used in real-time scenarios and it is well trained and evaluated using standard data sets. Results illustrate that the proposed model forecasts the prices with high accuracy compared to existing models.

The intercloud represents a logical evolution of cloud computing that extends its computational scale and geographic footprint by collaborating with disparate Cloud Service Providers (CSPs) for resource sharing. Discovering resources belonging to heterogeneous CSPs is not only the primary but critical operation for the intercloud. However, achieving resource discovery in a deterministic manner within this global distributed environment is non-trivial. The literature has proposed several resource discovery approaches for the federated intercloud based on trusted and centralized third-party entities. Few approaches, however, exist for the non-federated intercloud, which by definition has no central entity to enable the resource discovery process. Some P2P-based resource discovery techniques have been proposed by researchers, industry players and standardization bodies like Global InterCloud Technology Forum (GICTF). However, existing P2P-based approaches in the non-federated intercloud do not adequately address authentication, non-repudiation of resource information, secure storage and management of transactional records, management of trust/reputation and optimal resource selection and provisioning. This research paper presents BIRD, a Blockchain-based Intercloud Resource Discovery framework that involves participating CSPs connected in a P2P network using blockchain to manage resource information and maintain transactional records. The BIRD framework alleviates the requirement of a trusted third party for discovering and managing resources. The main features involved in the BIRD framework are i) latency optimization, ii) fine-grained control mechanism, and iii) Quality-of-Service, Trust and Reputation (QTR) indices. Latency optimization achieves faster resource discovery, fine-grained control mechanism for intercloud resource discovery, and QTR is for quality CSP or resource selection. BIRD uses blockchain to maintain transactions between CSPs securely.

The digital content wave has proliferated the financial and industrial sectors. Moreover, with the rise of massive internet-of-things, and automation, technologies like augmented reality (AR) and virtual reality (VR) have emerged as prominent players to drive a range of applications. Currently, sixth-generation (6G) networks support enhanced holographic projection through terahertz (THz) bandwidths, ultra-low latency, and massive device connectivity. However, the data is exchanged between autonomous networks over untrusted channels. Thus, to ensure data security, privacy, and trust among stakeholders, blockchain (BC) opens new dimensions towards intelligent resource management, user access control, audibility, and chronology in stored transactions. Thus, the BC and 6G coalition in future AR/VR applications is an emerging investigative topic. To date, authors have proposed surveys that study the integration of BC and 6G in AR/VR in isolation, and hence a coherent survey is required. Thus, to address the gap, the survey is the first-of-its-kind to investigate and study the coalition of BC and 6G in AR/VR space. Based on the proposed research questions in the survey, a solution taxonomy is presented, and different verticals are studied in detail. Furthermore, an integrative architecture is proposed, and open issues and challenges are presented. Finally, a case study, BvTours, is presented that presents a unique survey on BC-based 6G-assisted AR/VR virtual home tour service. The survey intends to propose future resilient frameworks and architectures for different industry 4.0 verticals and would serve as starting directions for academia, industry stakeholders, and research organizations to study the coalition of BC and 6G in AR/VR in industrial applications, gaming, digital content manufacturing, and digital assets protection in greater detail.

In this paper, a fault protection diagnostic scheme for a power distribution system is proposed. The scheme comprises a wavelet packet decomposition (WPD) for signal processing and analysis and a support vector machine (SMV) for fault classification and location. The scheme is tested on a reduced Eskom 132 kV power line. The WPD is used to extract fault signatures of interest and the SVM is subsequently used for fault classification and locating various fault conditions. Furthermore, we investigate the effectiveness of the SVM scheme using different samples of the cycles for fault classification and location. The results show that the fault classification and location on a distribution line can be determined rapidly and efficiently irrespective of the fault impedance and incipient angle with minimum estimation error. Lastly, the proposed scheme is tested on a grid-integrated system with renewable energy sources.

The work presents the power-sharing in a standalone low voltage AC microgrid consisting of three parallel grid supporting inverters using a virtual impedance-based droop system. Typically, isolated microgrids suffer unique challenges regarding voltage, current, frequency regulation, power flow control, and power-sharing due to the absence of a stiff AC grid source. This work investigated the power flow and sharing technical challenges using three inverter-based distributed energy sources, three static loads, and one dynamic load. These distributed energy sources are interconnected with the loads using low voltage line impedance within the microgrid to implement the uneven power distribution. Furthermore, a distributed grid supporting control utilizing virtual impedance is proposed in this work to improve power-sharing. The microgrid model is developed with the MATLAB Simulink environment and validated using the Opal RT OP4510 simulator. The proposed technique ensured improved power-sharing and mitigated the effect of voltage drops introduced through a virtual impedance using combined positive and negative virtual.

In this study, a distributed secondary control is proposed alongside the conventional primary control to form a hierarchical control scheme for the Low Voltage Ride-Through (LVRT) control and applications in the inverter-based microgrid. The secondary control utilizes a fast Delayed Signal Cancelation (DSC) algorithm for the secondary control loop to control the reactive and active power reference by controlling the sequences generated. The microgrid consists of four Distributed Energy Resources (DER) sources interfaced to the grid through interfacing inverters coordinated by droop for effective power-sharing according to capacities. The droop also allows for grid supporting application for microgrid’s participation in frequency and voltage regulation in the main grid. The proposed decentralized fast DSC performance is evaluated with centralized secondary and traditional primary control using OPAL-RT Lab computation and MATLAB/SIMULINK graphical user interface for offline simulations and real-time digital simulator verification. This study presents and discusses the results.

This research presents a secondary control for a grid-supporting microgrid with photovoltaics sources to guarantee grid code compliance and ancillary services. The secondary control accomplishes the fault ride-through, which implements a delayed signal cancellation (DSC) algorithm for negative sequence detection. Without mode switching, the proposed control strategy meets grid code requirements and ensures voltage regulation at the secondary level, which is active and more salient throughout the transient period of host grid disturbances. This control also ensures a constant supply of the microgrid’s sensitive local load while adhering to grid code requirements. Similarly, active power injection into the main grid is limited by progressively altering the MPPT operating point dependent on the depth of voltage sag to optimize reactive power injection to sustain grid voltage sag. The recommended secondary control is triggered by utilizing the DSC process’s detection algorithm to identify the occurrence of a fault in a tiny fraction of a half-cycle in a grid fault. Consequently, while satisfying microgrid load needs, the devised technique guaranteed that increases in DC-link voltage and AC grid current were controlled. MATLAB Simscape ElectricalTM and OPAL-RT Lab are used to do time-domain simulations of the model using the recommended secondary control systems.

Earthing and protective devices such as line surge arrestors (LSAs) play an important role in areas with high lightning occurrence for overhead HVAC lines’ performance. A lightning stroke of high magnitude can lead to back flash-overs, and the resultant power surge on the phase conductor can cause instigate the line breaker operating to extinguish the power surge. This operation of the protective devices leads to consumer interruptions on the network, a loss of production, and negatively affects the economy. Studies have shown that reducing an earthing system’s values, which itself is costly, may not be sufficient to prevent back flashover and the associated customer production cost loss. A code was developed to determine the possibility of back flashover and the cost of various earthing schemes utilizing the MATLAB software analysis tool. This paper determines the possibility of a back flashover for various combinations of lightning strokes and earthing profiles. Tower Footing Resistances as low as 9.8 Ω can cause back flashover, provided the lightning stroke exceeds 12 kA. Furthermore, the paper presents and discusses an innovative hybrid power line protection scheme, which estimates and considers the high cost associated with establishing an earthing system; it examines the impracticality of re-engineering an earthing scheme for implementation and results obtained by the inclusion of lightning surge arrester’s (LSA). The cost-saving resulting from dips is also established over 25 years for an 88 kV line, and the breakeven point is established. The results showed that the best scenario would be to reduce the tower footing resistance to 29.1 Ω and install 11 LSA per phase.

High Voltage Direct Current (HVDC) systems has been an alternative method of transmitting electric power from one location to another with some inherent advantages over AC transmission systems. The efficiency and rated power carrying capacity of direct current transmission lines highly depends on the converter used in transforming the current from one form to another (AC to DC and vice versa). A well configured converter reduces harmonics, increases power transfer capabilities, and reliability in that it offers high tolerance to fault along the line. Different HVDC converter topologies have been proposed, built and utilised all over the world. The two dominant types are the line commutated converter LCC and the voltage source converter VSC. This review paper evaluates these two types of converters, their operational characteristics, power rating capability, control capability and losses. The balance of the paper addresses their applications, advantages, limitations and latest developments with these technologies.

Power System stability is an essential study in the planning and operation of an efficient, economic, reliable and secure electric power system because it encompasses all the facet of power systems operations, from planning, to conceptual design stages of the project as well as during the systems operating life span. This paper presents different scenario of power system stability studies on a modified IEEE 30-bus system which is subjected to different faults conditions. A scenario whereby the longest high voltage alternating current (HVAC) line is replaced with a high voltage direct current (HVDC) line was implemented. The results obtained show that the HVDC line enhances system stability more compared to the contemporary HVAC line. Dynamic analysis using RMS simulation tool was used on DigSILENT PowerFactory.

Due to the intermittent nature of wind, the wind power output tends to be inconsistent, and hence maximum power point tracking (MPPT) is usually employed to optimize the power extracted from the wind resource at a wide range of wind speeds. This paper deals with the rotor speed control of a 2 MW direct-driven permanent magnet synchronous generator (PMSG) to achieve MPPT. The proportional-integral (PI), proportional-derivative (PD), and proportional-integral-derivative (PID) controllers have widely been employed in MPPT studies owing to their simple structure and simple design procedure. However, there are a number of shortcomings associated with these controllers; the trial-and-error design procedure used to determine the P, I, and D gains presents a possibility for poorly tuned controller gains, which reduces the accuracy and the dynamic performance of the entire control system. Moreover, these controllers’ linear nature, constricted operating range, and their sensitivity to changes in machine parameters make them ineffective when applied to nonlinear and uncertain systems. On the other hand, phase-lag compensators are associated with a design procedure that is well defined from fundamental principles as opposed to the aforementioned trial-and-error design procedure. This makes the latter controller type more accurate, although it is not well developed yet, and hence it is the focus of this paper. The simulation results demonstrated the effectiveness of the proposed MPPT controller.

The growing level of grid-connected renewable energy sources in the form of microgrids has made it highly imperative for grid-connected microgrids to contribute to the overall system stability. Consequently, secondary services which include the fault ride-through (FRT) capability are expected to be possessed characteristics by inverter-based microgrids. This enhances the stable operation of the main grid and sustained microgrid grid interconnection during grid faults in conformity with the emerging national grid codes. This paper proposes an effective FRT secondary control strategy to coordinate power injection during balanced and unbalanced fault conditions. This complements the primary control to form a two-layer hierarchical control structure in the microgrids. The primary level is comprised of voltage/power and current inner loops fed by a droop control. The droop control coordinates grid power-sharing amongst the voltage source inverters. When a fault occurs, the participating inverters operate to support the grid voltage, by injecting supplementary reactive power based on their droop gains. Similarly, under unbalanced voltage condition due to asymmetrical faults in the grid, the proposed secondary control ensures the positive sequence component compensation and negative and zero sequence components clearance using a delayed signal cancellation (DSC) algorithm and power electronic switched series impedance placed in-between the point of common coupling (PCC) and the main grid. While ensuring that FRT ancillary service is rendered to the main utility, the strategy proposed ensures relatively interrupted quality power is supplied to the microgrid load. Consequently, this strategy ensures the microgrid ride-through the voltage sag and supports the grid utility voltage during the period of the main utility grid fault. Results of the study are presented and discussed.