A Novel Binary Coding Particle Swarm Optimization for Feeder Reconfiguration

Tsai, Men‐Shen; Wu, Wu-Chang

doi:10.5772/6764

Cited by 5 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One more advantage of the proposed method is that it guarantees fast convergence because answer space will reduce significantly. For example, in Figure 2 feasible states are equal to 60 (as already mentioned), but all states are equal to 2 16 (=65536); then answer space is about 0.09% of the search space.…”

Section: Sifting Algorithmmentioning

confidence: 86%

See 1 more Smart Citation

A Heuristic Multiobjective Method for Radial Distribution Networks Reconfiguration

Shojaeian

Ghandehari

2013

Chinese Journal of Engineering

View full text Add to dashboard Cite

This paper introduces a novel algorithm for radial distribution networks reconfiguration, called “Sifting algorithm.” It not only has a simple structure but also has high speed and accuracy. It works by eliminating infeasible states and reduces the search space and then it uses a simple method to find optimum answer in remaining space. To ensure the effectiveness of this algorithm, a 16-switch test network was tested and the results were compared with three other well-known algorithms. The results of simulations showed high speed and accuracy of this algorithm.

show abstract

Section: Sifting Algorithmmentioning

confidence: 86%

“…It avoids the algorithm to trap in a local optimum solution. At this step just feasible codes will be checked, but in other algorithms like BPSO codes will be updated and then changed to feasible states [15,16]. This change will reduce the speed of algorithm and make it difficult too.…”

Section: Sifting Algorithmmentioning

confidence: 99%

A Heuristic Multiobjective Method for Radial Distribution Networks Reconfiguration

Shojaeian

Ghandehari

2013

Chinese Journal of Engineering

View full text Add to dashboard Cite

show abstract

“…But the proposed GA is useable when it is implemented for huge network systems only and efficiently for the restoration system problem solving and load balancing study. A part from that, an improved of optimization method such as Particle Swarm Optimization (PSO) for power losses minimization has been done in [5][6][7][8]. The simulation results for this optimization method have shown a good healthiness of the approach in term of power loss reduction but they are less optimal solution for computational time.…”

Section: Introductionmentioning

confidence: 99%

Power Distribution Network Reconfiguration by Using EPSO for Loss Minimizing

Sulaima

Jali

Bukhari

et al. 2014

AMM

View full text Add to dashboard Cite

Due to the complexity of modern power distribution network, a hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for network reconfiguration. The objectives of this work are to reduce the power losses and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster in order to find the optimal solution. The proposed method is applied and it impacts to the network reconfiguration for real power loss and voltage profiles is investigated respectively. The proposed method is tested on a IEEE 33-bus system and it is compared to the traditional PSO and EP method accordingly. The results of this study is hoped to help the power engineer to configure the smart and less lossed network in the future.

show abstract