Computational Intelligence: An Introduction

Fulcher, John

doi:10.1007/978-3-540-78293-3_1

Cited by 51 publications

(36 citation statements)

References 154 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The mutation probability is related to the inverse value of the affinities. In order to enhance the strength of the algorithm, crossover operator [21] of GA is proposed in the present work to overcome the lack of memory in immune algorithm. To do this, in the cloning phase, the algorithm selects two solutions (instead of one) and performs the crossover operation.…”

Section: Proposed Immune-ga Methodsmentioning

confidence: 99%

Probabilistic determination of pilot points for zonal voltage control

Amraee

Soroudi

Ranjbar

2012

IET Gener. Transm. Distrib.

View full text Add to dashboard Cite

Abstract-Due to local nature of the voltage and reactive power control, the voltage control is managed in a zonal or regional basis. In this paper a new comprehensive scheme for optimal selection of pilot points is proposed. The uncertainties of operational and topological disturbances of the power system are included to provide the robustness of the pilot node set. To reduce the huge number of probable states(i.e. combined states of load and topological changes) a scenario reduction technique is used. The resulted optimal control problem is solved using a new Immune-based Genetic Algorithm. The performance of the proposed method is verified over IEEE 118-Bus and realistic Iranian 1274-Bus national transmission grids.

show abstract

Section: Proposed Immune-ga Methodsmentioning

confidence: 99%

Probabilistic determination of pilot points for zonal voltage control

Amraee

Soroudi

Ranjbar

2012

IET Gener. Transm. Distrib.

View full text Add to dashboard Cite

show abstract

“…In order to increase the performance capability of the algorithm, crossover operator [23] of GA is proposed in the present work to overcome this shortcoming. By doing this, when the algorithm is in the cloning phase, it will select two solutions (instead of one) and performs the crossover operation.…”

Section: The Proposed Immune-ga-based Technique For Finding Pareto Frmentioning

confidence: 99%

A practical eco-environmental distribution network planning model including fuel cells and non-renewable distributed energy resources

2011

View full text Add to dashboard Cite

Publication informationRenewable energy, 36 (1): 179-188Publisher Elsevier Item record/more information http://hdl.handle.net/10197/6209 Publisher's statementThis is the author's version of a work that was accepted for publication in Renewable Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Renewable Energy (VOL 36, ISSUE 1, (2011) AbstractThis paper presents a long-term dynamic multi-objective planning model for distribution network expansion along with distributed energy options. The proposed model optimizes two objectives, namely costs and emissions and determines the optimal schemes of sizing, placement and specially the dynamics (i.e., timing) of investments on distributed generation units and network reinforcements over the planningperiod. An efficient two-stage heuristic method is proposed to solve the formulated planning problem.The effectiveness of the proposed model is demonstrated by applying it to a distribution network and comparing the simulation results with other methods and models.

show abstract

“…The mutation operator of AI is the same as mutation operator of GA with a difference. In GA, the mutation probability is constant [26] but in IA, it is proportional to the inverse value of affinity factor for each antibody. The more affinity factor of an antibody is, the less it will be mutated.…”

Section: The Proposed Hybrid Immune-gamentioning

confidence: 99%

“…This may prevent each antibody from communicating with other antibodies and using their desired characteristics in identifying the antigens (optimization the objective function and its constraints). To overcome this shortcoming of IA, the crossover operator of GA [26] is applied and a hybrid IA and GA named IGA is proposed. The flowchart of proposed IGA is depicted in Fig.1.…”

Section: The Proposed Hybrid Immune-gamentioning

confidence: 99%

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

Soroudi

Ehsan

2010

Int Trans Elec Energy Syst

View full text Add to dashboard Cite

This paper proposes a hybrid heuristic optimization method based on genetic algorithm and immune systems to maximize the benefits of distribution network operators (DNOs) accrued due to sizing and placement of distributed generation (DG) units in distribution networks. The effects of DG units in reducing the reinforcement costs and active power losses of distribution network have been investigated. In the presented method, the integration of DG units in distribution network is done considering both technical and economical aspects. The strength of the proposed method is evaluated by applying it on a small and a realistic large scale distribution network and the results are compared with analytical classic and other heuristic methods and discussed.

show abstract

Computational Intelligence: An Introduction

Cited by 51 publications

References 154 publications

Probabilistic determination of pilot points for zonal voltage control

Probabilistic determination of pilot points for zonal voltage control

A practical eco-environmental distribution network planning model including fuel cells and non-renewable distributed energy resources

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

Contact Info

Product

Resources

About