This study presents an algorithmic approach for optimal placement of phasor measurements units (PMUs) to ensure complete observability in the presence of conventional measurements and zero injection buses. The financi...
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This study presents an algorithmic approach for optimal placement of phasor measurements units (PMUs) to ensure complete observability in the presence of conventional measurements and zero injection buses. The financial or technical restrictions prohibit the deployment of PMUs at every bus, which in turn motivates their strategic placement around the power system. Topology-based transformations are implemented for observability analysis. Tau he PMU problem allocation is optimized based on measurement observability criteria for achieving solvability of the power state estimation. The Branch-and-Bound algorithm (BB) and binary-coded genetic algorithm (BCGA) are applied to solve the optimization problem. The BCG algorithm incorporates a special truncation procedure to handle integer restrictions on decision variables along with a penalty parameter approach for handling constraints. The proposed algorithms detect the minimum PMU number and their locations required to make the power system numerically observable. The proposed algorithms are applied to IEEE systems as well as a large-scale system with 1011 buses to exhibit the applicability of them to practical power systems. The solution points located using the BCGA are interpreted as nonstrict global minima since they are in complete agreement with those obtained by the BB algorithm in solving the (zero-one) constraint integer linear program.
Train arrivals and departures should be scheduled over a certain period when talking about train timetabling problems. For the midnight train operations, passengers significantly concern about the network transfer iss...
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Train arrivals and departures should be scheduled over a certain period when talking about train timetabling problems. For the midnight train operations, passengers significantly concern about the network transfer issue. Currently, some existing studies address the last train timetabling problem by only optimizing the timetable for the last train on a single subway line whereas this study takes into consideration the complete last-shift period. We first put forward a last-shift train scheduling model aiming to minimize the transfer waiting time and maximize the network connectivity. Two genetic-based algorithms, an integer-codedgeneticalgorithm (ICGA) and a binary-coded genetic algorithm (BCGA) are developed. The relevance and applicability of the algorithms have been demonstrated by several testing networks and real-world implementation. The ICGA and the branch-and-bound approaches show high efficiency in obtaining the optimal solutions for a small network, while the BCGA approach that bases on an integer-programming model shows low efficiency in addressing problems of sparse solution spaces. However, the branch-and-bound approach has limited ability in solving medium-sized networks. On the contrary, the ICGA generates satisfactory results in solution quality and computational efficiency when applied to large-sized networks. (C) 2021 Elsevier B.V. All rights reserved.
Generation maintenance scheduling plays a crucial role in power system operation and planning;as it makes a significant effect on the outage time of generating units, and subsequently, to have a more secure and reliab...
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Generation maintenance scheduling plays a crucial role in power system operation and planning;as it makes a significant effect on the outage time of generating units, and subsequently, to have a more secure and reliable power system with an optimal maintenance plan. Conventional maintenance scheduling methods are no longer applicable when the restructured power systems introduced. It is thus essential to develop novel approaches for the maintenance coordination. In this paper, an ISO-based security-constrained generation maintenance coordination approach is proposed. The approach comprises two main phases, the security-based maintenance scheduling, and the bidding based maintenance coordination. Moreover, the N - 1 contingency analysis is considered in this study. The network security constraints are also incorporated in the investigation, using the security-constrained optimal power flow model. The model is solved by a binary-coded genetic algorithm which is combined with a primal-dual interior-point method. For systematic analysis, the IEEE 30-Bus and the IEEE-118 test systems are employed. The presented results emphasize the effectiveness and applicability of the proposed approach.
Embedded systems have become integral parts of today's technology-based life, starting from various home appliances to satellites. Such a wide range of applications encourages for their economic design using optim...
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Embedded systems have become integral parts of today's technology-based life, starting from various home appliances to satellites. Such a wide range of applications encourages for their economic design using optimization-based tools. The JPEG encoder is an embedded system, which is applied for obtaining high quality output from continuous-tone images. It has emerged in recent years as a problem of optimum partitioning of its various processes into hardware and software components. Realizing pairing and conflicting nature among its various cost terms, for the first time the JPEG encoder is formulated and partitioned here as a multi-objective optimization problem. A multi-objective binary-coded genetic algorithm is proposed for this purpose, whose effectiveness is demonstrated through the application to a real case study and a number of large-size hypothetical instances. (C) 2013 Elsevier B.V. All rights reserved.
Integrated production-distribution plan considering three major objectives, viz., total cost minimization, change in labor level reduction, and underutilization minimization, is developed for a renowned bearing manufa...
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Integrated production-distribution plan considering three major objectives, viz., total cost minimization, change in labor level reduction, and underutilization minimization, is developed for a renowned bearing manufacturing industry in India. The total cost minimization objective minimizes the regular, overtime, and outsourced production costs along with inventory holding, backorder, hiring/laying-off, and trip-wise distribution costs. The multi-criteria model is solved using a novel simulation-based analytic hierarchy process (AHP)-discrete particle swarm optimization (DPSO) algorithm. The solutions of the AHP-DPSO algorithm are verified using the AHP-binary-coded genetic algorithm solutions. The proposed simulation-based AHP-DPSO solutions are found to be superior. Demand is assumed to vary uniformly, and the simulation-based AHP-DPSO algorithm is used for obtaining the best production-distribution plan that serves as a trade-off between holding inventory and backordering products. In addition to bearing manufacturing industry dataset, two other test datasets are also solved.
In this paper, we propose a visualization method to grasp the search process and results in the binary-coded genetic algorithm. The representation, the choices of operations, and the associated parameters can each mak...
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ISBN:
(纸本)9783540691594
In this paper, we propose a visualization method to grasp the search process and results in the binary-coded genetic algorithm. The representation, the choices of operations, and the associated parameters can each make a major difference to the speed and the quality of the final result. These parameters are decided interactively and very difficult to disentangle their effects. Therefore, we focus on the chromosome structure, the fitness function, the objective function, the termination conditions, and the association among these parameters. We can indicate the most important or optimum parameters in visually. The proposed method is indicated all individuals of the current generation using the pseudo-color. The pixels related a gene of the chromosome are painted the red color when the gene of the chromosome represents '1', and the pixels related to one are painted the blue color when one represents '0'. Then the brightness of the chromosome changes by the fitness value, and the hue of the chromosome changes by the objective value. In order to show the effectiveness of the proposed method, we apply the proposed method to the zero-one knapsack problems.
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