Details

Autor(en) / Beteiligte

• Nguyen, Thang Trung

Titel

A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization

Ist Teil von

• Energy (Oxford), 2019-03, Vol.171, p.218-240

Ort / Verlag

Oxford: Elsevier Ltd

Erscheinungsjahr

2019

Link zum Volltext

Quelle

ScienceDirect Journals (5 years ago - present)

Beschreibungen/Notizen

• The paper proposes a novel improved social spider optimization algorithm (NISSO) for solving optimal power flow (OPF) problem to independently optimize electricity generation fuel cost, power loss, polluted emission, voltage deviation and L index. The proposed NISSO method is first developed in the paper by performing three modifications with intent to improve optimal solution quality and speed up convergence of conventional social spider optimization (SSO). The first and the second modifications are to focus on new solution generation by changing the movement strategy of female spiders and male spiders while the third modification is to fix the female spider rate to an appropriate ratio. The performance of the proposed method is evaluated by testing on three IEEE systems with 30, 57 and 118 buses. As a result, the proposed method has advantages over SSO such as simpler application, fewer number of control parameters, spend less time tuning control parameter values, faster convergence to optimal solutions and more stable search ability. In addition, the proposed method's results are also compared to other existing methods and the indications are that the proposed method can find better optimal solutions, use lower number of generated solutions and faster convergence. •Novel Improved Social Spider Optimization (NISSO) is first proposed in the paper.•Optimal power flow (OPF) problem is one of the most complicated problems.•NISSO is executed for OPF problem with three IEEE systems with 30, 57 and 118 buses.•NISSO can find high quality solutions of OPF problem with fast search ability.•NISSO is superior to approximately all compared methods for all study cases.