The paper discusses an improved modelling of transformer windings based on bacterial swarming algorithm (BSA) and frequency response analysis (FRA). With the purpose to accurately identify transformer windings paramet...
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The paper discusses an improved modelling of transformer windings based on bacterial swarming algorithm (BSA) and frequency response analysis (FRA). With the purpose to accurately identify transformer windings parameters a model-based identification approach is introduced using a well-known lumped parameter model. It includes search space estimation using analytical calculations, which is used for the subsequent model parameters identification with a novel BSA. The newly introduced BSA, being developed upon a bacterial foraging behavior, is described in detail. Simulations and discussions are presented to explore the potential of the proposed approach using simulated and experimentally measured FRA responses taken from two transformers. The BSA identification results are compared with those using genetic algorithm. It is shown that the proposed BSA delivers satisfactory parameter identification and improved modelling can be used for FRA results interpretation. (C) 2010 Published by Elsevier B.V.
With the increasing use of distributed intelligent devices and the demand of separated power network managing, distributed control of a complex power system becomes more and more important in application. In a distrib...
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With the increasing use of distributed intelligent devices and the demand of separated power network managing, distributed control of a complex power system becomes more and more important in application. In a distributed power flow optimisation, the cost of the network can be optimised by coordinating the control of generators and taps in a subarea partition. In this paper, a bacterial swarming algorithm (BSA) is presented to solve an optimisation problem of distributed power flow. BSA is designed from a searching framework that combines the underlying mechanisms of bacterial chemotaxis and quorum sensing. The algorithm has been evaluated by simulation studies, which were undertaken on an IEEE 118-bus test system, in comparison with a genetic algorithm (GA) and a particle swarm optimiser (PSO).
In this paper, Flexible AC Transmission System (FACTS) devices are optimally allocated in a power network to achieve Optimal Power Flow (OPF) solution. The location of FACTS devices and the setting of their control pa...
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ISBN:
(纸本)9781424419050
In this paper, Flexible AC Transmission System (FACTS) devices are optimally allocated in a power network to achieve Optimal Power Flow (OPF) solution. The location of FACTS devices and the setting of their control parameters are optimized by a bacterial swarming algorithm (BSA) to improve the performance of the power network. Two objective functions are simultaneously considered as the indexes of the system performance: maximization of system loadability in system security margin and minimization of total generation fuel cost. Four types of FACTS devices are modeled and incorporated in the OPF problem. Simulation studies are undertaken on a standard IEEE 30-bus test system. Results demonstrate that the steady state performance of the power system can be effectively enhanced due to the optimal allocation of multi-type FACTS devices.
Noise removal is an underlying issue of image processing. This paper proposes a generic approach to design an optimal filter which combines linear and morphological filtering techniques, so that both Gaussian and non-...
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ISBN:
(纸本)9781424418220
Noise removal is an underlying issue of image processing. This paper proposes a generic approach to design an optimal filter which combines linear and morphological filtering techniques, so that both Gaussian and non-Gaussian noise can be rejected. The optimisation process is performed by a bacterial swarming algorithm (BSA), which is derived from the bacterial foraging algorithm (BFA) and involves underlying mechanisms of bacterial chemotaxis and quorum sensing. The performance of the combined filter optimised by BSA is analysed in comparison with the filter optimised by the genetic algorithm (GA), as well as with other commonly used filters. The simulation results demonstrated in this paper have shown the merits of the proposed filtering technique and the optimisation algorithm.
With the increasing use of distributed intelligent devices and the demand of separated power network management, distributed control of a complex power system becomes more and more important in practice. In distribute...
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With the increasing use of distributed intelligent devices and the demand of separated power network management, distributed control of a complex power system becomes more and more important in practice. In distributed Optimal Power Flow (OPF) problems, the cost of the network can be optimised by coordinating the control variables of generators and taps in a subsystem. In this paper, distributed OPF problems are handled by the proposed bacterial swarming algorithm (BSA). BSA is designed from a searching framework that combines the underlying mechanisms of bacterial chemotaxis and quorum sensing. The algorithm has been evaluated by simulation studies, which were undertaken on an IEEE 118-bus test system, in comparison with a Genetic algorithm (GA) and Particle Swarm Optimiser (PSO).
This paper presents a novel optimization algorithm for solving reactive power dispatch problem. The problem is formulated as a nonlinear constrained multi-objective optimization problem with real power losses and volt...
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ISBN:
(纸本)9787811240559
This paper presents a novel optimization algorithm for solving reactive power dispatch problem. The problem is formulated as a nonlinear constrained multi-objective optimization problem with real power losses and voltage stabilities to be optimized simultaneously. This problem is handled by a Bacteria swarmingalgorithm (BSA) proposed in this paper. The BSA has been evaluated on an IEEE 30-bus test system and the results demonstrate its capabilities of generating superior solutions to the conventional weighted sum-based methods.
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