Clonal evolutionary particle swarm optimization for congestion management and compensation scheme in power system

N. Z. Mohd Ali, I. Musirin, H. Mohamad


This paper presents computational intelligence-based technique for congestion management and compensation scheme in power systems. Firstly, a new model termed as Integrated Multilayer Artificial Neural Networks (IMLANNs) is developed to predict congested line and voltage stability index separately. Consequently, a new optimization technique termed as Clonal Evolutionary Particle Swarm Optimization (CEPSO) was developed. CEPSO is initially used to optimize the location and sizing of FACTS devices for compensation scheme. In this study, Static VAR Compensator (SVC) and Thyristor Control Static Compensator (TCSC) are the two chosen Flexible AC Transmission System (FACTS) devices used in this compensation scheme. Comparative studies have been conducted between the proposed CEPSO and traditional Particle Swarm Optimization (PSO). Results obtained by the developed IMLANNs demonstrated high accuracy with respect to the targeted output. Consequently, the proposed CEPSO implemented for single objective in single unit of SVC and TCSC has resulted superior results as compared to the traditional PSO in terms of achieving loss reduction and voltage profile improvement.


Integrated multi-layer artificial neural network (IMLANNs), Clonal evolutionary particle swarm optimization (CEPSO), Static VAR compensator (SVC), Thyristor control static compensator (TCSC), Flexible AC transmission system (FACTS)

Full Text:




  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics