An analytical methodology for assessment of smart monitoring impact on future electric power distribution system reliability

Abstract

Smart grid is referred to a modernized power grid which can mitigate fault detection and allow self‐healing of the system without the intervention of operators. This article proposes an innovative analytical formulation using Markov method to evaluate electric power distribution system reliability in smart grids, which incorporates the impact of smart monitoring on the overall system reliability. An accurate reliability model of the main network components and the communication infrastructure have been also considered in the methodology. The proposed approach was applied to a well‐known test bed (Roy Billinton Test System) and various reliability case studies with monitoring provision and monitoring deficiency are analyzed. This article involves the developing possibilities of communication technologies and next‐generation control systems of the entire smart network based on the real‐time monitoring and modern control system to achieve a reliable, economical, safe, and high efficiency of electricity. The implementations indicate that using an appropriate set of the smart grid monitoring devices for power system components can virtually influence all the reliability indices although the amount of improvement varies between techniques. The proposed approach determined that smart monitoring for which components of the electric power distribution systems are tailored and deduce to major economical benefits. The described approach also reveals which reliability indices drastically influenced using monitoring. © 2014 Wiley Periodicals, Inc. Complexity 21: 99–113, 2015

A new metaheuristic algorithm based on shark smell optimization

“…The shape of Rastrigin benchmark function is shown in Figure 4, which illustrates the large number of its local optima. The results obtained from SSO algorithm for the test case are shown in Table 1 and compared with the results obtained from 32 other metaheuristic optimization approaches including EA [17], GA [37], improved GA (IGA) [37], DE [18], PSO [6], iteration PSO (IPSO) [38], chaotic PSO (CPSO) [32], APSO [32], PSO with time varying acceleration coefficients (PSOTVAC) [6], PSO with improved inertia weight (PSOIIW) [39], hybrid GA-PSO (HGAPSO) [40], dynamic PSO (DPSO) [41], fuzzy PSO (FPSO) [42], harmony search algorithm (HSA) [43], improved HSA (IHSA) [43], ACO [7], chaotic ACO (CACO) [8], bacterial foraging algorithm (BFA) [13], GSA [20], IGSA [21], seeker optimization algorithm (SOA) [44], imperialist competitive algorithm (ICA) [45], ABC [12], improved ABC (IABC) [11], chaotic ABC (CABC) [46], parallel ABC (PABC) [36], discrete ABC (DABC) [47], rosenberg ABC (RABC) [48], modified ABC (MABC) [48], HBMO [9], improved HBMO (IHBMO) [10], and honey bee optimization (HBO) [49]. All of these 32 benchmark methods have frequently been used in engineering applications and, for this reason, have been considered here for comparison with the proposed SSO algorithm.…”

“…Many of real-world optimization problems involve with complexities such as nonlinearity, nonconvexity, nonsmoothness, nondifferentiability, mixed integer nature, and discontinuous domain, which challenge the numerical optimization methods [3]. Accordingly, to tackle the mentioned complexities, several metaheuristic optimization techniques have been proposed in the literature in the recent decades such as genetic algorithm (GA) [4], particle swarm optimization (PSO) [5,6], ant colony optimization (ACO) [7,8], honey bee mating optimization (HBMO) [9,10], artificial bee colony (ABC) [11,12], bacterial foraging (BF) [13], clonal selection algorithm (CSA) [14], invasive weed optimization (IWO) [15], shuffled frog leaping (SFL) [16], evolutionary algorithm (EA) [17], differential evolution (DE) [18], Correspondence to: O. Abedinia, E-mail: abediniaoveis@ gmail.com simulated annealing (SA) [19], and gravitational search algorithm (GSA) [20,21]. Due to their high flexibility, simplicity and modeling efficiency, these optimization methods have been widely used in many scientific and engineering areas.…”

Improved Reduced‐Order Fault Detection Filter Design for Polytopic Uncertain Discrete‐Time Markovian Jump Systems with Time‐Varying Delays

“…The past decades have witnessed a boom of advanced studies on theories and applications of Markov jump systems in many fields, such as networks communication systems, automotive systems, energy systems, biological systems, cyber-physical systems, aerospace systems, manufacturing, automation, smart grids, vehicular networking and connected vehicles, power systems, robotics, economic systems, and social systems [1][2][3][4]. MJLSs can effectively model dynamic hybrid systems involving stochastic switching (generally autonomous) subject to the Markov chains.…”

Financial planning for the preventive maintenance of the power distribution systems critical components using the reliability-centered approach

“…E-mail: noradin.ghadimi@yahoo.com Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License the maintenance strategies to be inclined towards the reliability-centered strategies and away from the timebased strategies. The Reliability-Centered Maintenance (RCM) strategy attempts to present an organized framework for the improvement of network reliability and the reduction of maintenance expenses by relying on cost/benefit studies and the reliability analysis of networks (Schneider et al, 2006;Ghadimi, 2012;Ahadi et al, 2014a). In the RCM strategy, the corrective and preventive maintenance strategies are subjected to cost/benefit analysis, and the optimum strategy is selected (Ahadi et al, 2014b).…”