Purpose
This paper aims to address not only technical and economic challenges in electrical distribution system but also environmental impact and the depletion of conventional energy resources due to rapidly growing economic development, results rising energy consumption.
Design/methodology/approach
Generally, the network reconfiguration (NR) problem is designed for minimizing power loss. Particularly, it is devised for maximizing power loss reduction by simultaneous NR and distributed generation (DG) placement. A loss sensitivity factor procedure is incorporated in the problem formulation that has identified sensitivity nodes for DG optimally. An adaptive weighted improved discrete particle swarm optimization (AWIDPSO) is proposed for ascertaining a feasible solution.
Findings
In AWIDPSO, the adaptively varying inertia weight increases the possible solution in the global search space and it has obtained the optimum solution within lesser iteration. Moreover, it has provided a solution for integrating more amount of DG optimally in the existing distribution network (DN).
Practical implications
The AWIDPSO seems to be a promising optimization tool for optimal DG placement in the existing DN, DG placement after NR and simultaneous NR and DG sizing and placement. Thus, a strategic balance is derived among economic development, energy consumption, environmental impact and depletion of conventional energy resources.
Originality/value
In this study, a standard 33-bus distribution system has been analyzed for optimal NR in the presence of DG using the developed framework. The power loss in the DN has reduced considerably by indulging a new and innovative approaches and technologies.
Abstract:In this paper a metaheuristic based newfangled adaptive weighted improved discrete particle swarm optimization (AWIDPSO) algorithm is applied to minimize the load balancing index in radial distribution network reconfiguration (RDNR) problem. It is devised as extremely nonlinear and multimodal optimization problem under practical constraints. In order to improve the solution quality the constraint violations are augmented with objective function. Further, adaptively varying inertia weight increases the possible solution in the global search space and the proposed algorithm has obtained the optimal solution within lesser executing time. In this study, 33-bus system is analyzed for optimal network reconfiguration using the developed framework. Comparison of the simulated results with the results of well known prudent optimization technique confirms the applicability of the AWIDPSO algorithm for RDNR problem.
In the scenario of Distributed Denial of Service (DDoS) attacks are increasing in a significant manner, the attacks should be mitigated in the beginning itself to avoid its devastating consequences for any kind of business. DDoS attack can slow down or completely block online services of business like websites, email or anything that faces internet. The attacks are frequently originating from cloud virtual machines for anonymity and wide network bandwidth. Hyper-Calls Analysis(HCA) enables the tracing of command flow to detect any clues for the occurrence of malicious activity in the system. A DDoS attack detection approach proposed in this paper works in the hypervisor side to perform hyper calls based introspection with machine learning algorithms. The system evaluates system calls in hypervisor for the classification of malicious activities through Support Vector Machine and Stochastic Gradient Descent (SVM & SGD) Algorithms. The attack environment created using XOIC attacker tool and CPU death ping libraries. The system’s performance also evaluated on CICDDOS 2019 dataset. The experimental results reveal that more than 99.6% of accuracy in DDoS detection without degrading performance.
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