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The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Purpose of this study is to investigate the problem of how typical (or, conversely, unique) is the phenomenon of explosive synchronization in networks of nonlinear oscillators with topologies of links such as “ring” and “small world”, and, in turn, how the partial frequencies of the interacting oscillators must correlate with each other for the phenomenon of explosive synchronization in these networks can be possible. Methods. In this paper, we use an analytical description of the synchronous behavior of networks of nonlinear elements with “ring” and “small world” link topologies. To confirm the obtained results the numerical simulation is used. Results. It is shown that in networks of nonlinear oscillators with topologies of links such as “ring” and “small world”, the phenomenon of explosive synchronization can be observed for the different distributions of partial frequencies of network oscillators. Conclusion. The paper considers an analytical description of the behavior of network oscillators with “ring” and “small world” topologies of links and shows that the phenomenon of explosive synchronization in such networks is atypical, but not unique.

Measuring the population diversity in metaheuristics has become a common practice for adaptive approaches, aiming mainly to address the issue of premature convergence. Understanding the processes leading to a diversity loss in a metaheuristic algorithm is crucial for designing successful adaptive approaches. In this study, we focus on the relation of the neighborhood size and the rate of diversity loss in the Particle Swarm Optimization algorithm with local topology (also known as LPSO). We argue that the neighborhood size is an important input to consider when designing any adaptive approach based on the change of population diversity. We used the extensive benchmark suite of the IEEE CEC 2014 competition for experiments.

Measuring the population diversity in metaheuristics has become a common practice for adaptive approaches, aiming mainly to address the issue of premature convergence. Understanding the processes leading to a diversity loss in a metaheuristic algorithm is crucial for designing successful adaptive approaches. In this study, we focus on the relation of the neighborhood size and the rate of diversity loss in the Particle Swarm Optimization algorithm with local topology (also known as LPSO). We argue that the neighborhood size is an important input to consider when designing any adaptive approach based on the change of population diversity. We used the extensive benchmark suite of the IEEE CEC 2014 competition for experiments.

The stability and convergence time become an essential factor in network availability performance. Multiprotocol Label Switching (MPLS) is one of the Virtual Private Network (VPN) technologies that can support the quality of communication media on the high-speed backbone network. Therefore, it is necessary to determine the proper protocol routing in espousing VPN technology based on MPLS supported by direct-link backup to improve network availability in the Data Center. The purpose of this study is comparing the convergence time and Quality of Service (QoS) among the three IGP protocols routing, namely Routing Information Protocol version 2 (RIP), Open Shortest Path First (OSPF), and Enhanced Interior Gateway Routing Protocol (EIGRP) based on two autonomous system number using Ring topology design between Data Center and DRC. Network scenario is created using the Graphic Network Simulator (GNS3) application to measure convergence time and QoS parameters of the three protocols routing and the use of MPLS-TE and RR in enhancing MPLS backbone performance. The results revealed that QoS in the three protocols routing has a good quality level according to TIPHON’s standard with the number of indexes up to 3.25 (Good). On the other hand, the fastest convergence time when interruption on the main link (VPN) is EIGRP with convergence time for about 15 seconds.

This study exploits the modeling and behavioral analysis of electrical coupling medium between neurons. A new electrical coupling model that represents two resistor, inductor and capacitor (RLC) circuits for a single link between two neurons is introduced by employing the biological evidences and electrical circuit theory knowledge. Herein, a model consisting of a network of multiple FitzHugh–Nagumo (FHN) neurons in a ring topology is also proposed. The presented model follows direction-dependent transmission based on resistive, inductive, and capacitive inter-neuronal electrical coupling medium for generalization purposes. The coupling medium, attributable to the direction-dependent information transmittance, admitting resistive, inductive and capacitive nature, reveals the astonished results such as the existence of two PID controllers for a single link among two neurons. Furthermore, the effects of difference in PID parameters by fixing the parametric difference and by increasing the parametric difference on synchronization are studied in order to explore the behavior of electrical coupling medium. Astoundingly, such behavioral analysis reveals that the coupling medium truly tracks the well-known proportional integral derivative (PID) characteristics curve.

Particle swarm optimization (PSO) is an evolutionary algorithm that is well known for its simplicity and effectiveness. It usually has strong global search capability but has the drawback of being easily trapped by local optima. A scaling mutation strategy and an elitist learning strategy are presented in this paper. Based on these strategies, an improved PSO variant (LSERPSO) is developed through a local search and ring topology strategy. The new scaling mutation strategy involved an exploration and exploitation balance focusing on mutation operation. A collection of elite individuals is maintained such that an array of current particles can learn from them. A ring topology-based neighborhood structure is adopted to maintain the population diversity and to reduce the possibility of particles being trapped in local optima. Finally, a quasi-Newton-based local search is incorporated to enhance the fine-grained capability. The effects of these proposed strategies and their cooperation are verified step by step. The performance of LSERPSO is comprehensively studied using IEEE CEC2015 benchmark functions.

Distance adaptive spectrum allocation exploits the tradeoff between spectrum width and reach to improve resource utilisation by tailoring the modulation format to the level of impairments along the path. The authors first show that the distance‐adaptive routing and spectrum assignment (DA‐RSA) problem is a special case of a multiprocessor scheduling problem. The authors then develop a suite of efficient and effective DA‐RSA algorithms for ring networks, that build upon list scheduling concepts. This work explores the tradeoffs involved in DA‐RSA algorithm design, and opens up new research directions that may leverage the vast literature in scheduling theory.

Today, passive optical networks (PONs) are mostly used as modern high-speed access networks for various applications. However, there are also several specific applications, such as in business, office, army or science sector, which require a complex protection and backup system against failures and malfunctions. Typically, tree or star topologies are used for passive optical networks PONs. These topologies are vulnerable mainly against the failures of central optical line termination (OLT) unit. This paper presents an innovative method for protecting PONs by using ring topologies, especially the OLT unit. The method is described in the article, and an elementary mathematical model for calculations of asymmetric passive optical splitters together with an example is included as well.