Brain storm optimization algorithms with k-medians clustering algorithms

Author(s):  
Haoyu Zhu ◽  
Yuhui Shi
Author(s):  
Babatunde Olusegun Adewolu ◽  
Akshay Kumar Saha

Applications of Flexible AC Transmission Systems (FACTS) devices for enhancement of Available Transfer Capability (ATC) is gaining attention due to economic and technical limits of the conventional methods involving physical network expansions. FACTS allocation which is sine-qua-non to its performance is a major problem and it is being addressed in recent time with heuristic algorithms. Brain Storm Optimization Algorithms (BSOA) is a new heuristic and predicting optimization algorithms which revolutionizes human brainstorming process. BSOA is engaged for the optimum setting of FACTS devices for enhancement of ATC of a deregulated electrical power system network in this study. ATC enhancement, bus voltage deviation minimization and real power loss regulation are formulated into multi-objective problems for FACTS allocation purposes. Thyristor Controlled Series Capacitor (TCSC) is considered for simulation and analyses because of its fitness for active power control among other usefulness. ATC values are obtained for both normal and N-1-line outage contingency cases and these values are enhanced for different bilateral and multilateral power transactions. IEEE 30 Bus system is used for demonstration of the effectiveness of this approach in a Matlab software environment. Obtained enhanced ATC values for different transactions during normal evaluation cases are then compared with enhanced ATC values obtained with Particle Swarm Optimization (PSO) set TCSC technique under same trading. BSO behaved much like PSO throughout the achievements of other set objectives but performed better in ATC enhancement with 27.12 MW and 5.24 MW increase above enhanced ATC values achieved by the latter. The comparative of set objectives values relative to that obtained with PSO methods depict suitability and advantages of BSOA technique.


Author(s):  
Shi Cheng ◽  
Yifei Sun ◽  
Junfeng Chen ◽  
Quande Qin ◽  
Xianghua Chu ◽  
...  

Author(s):  
Sajad Ahmad Rather ◽  
P. Shanthi Bala

In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were introduced to solve innumerable optimization problems. These optimization algorithms show better performance than conventional algorithms. Recently, the gravitational search algorithm (GSA) is proposed for optimization which is based on Newton's law of universal gravitation and laws of motion. Within a few years, GSA became popular among the research community and has been applied to various fields such as electrical science, power systems, computer science, civil and mechanical engineering, etc. This chapter shows the importance of GSA, its hybridization, and applications in solving clustering and classification problems. In clustering, GSA is hybridized with other optimization algorithms to overcome the drawbacks such as curse of dimensionality, trapping in local optima, and limited search space of conventional data clustering algorithms. GSA is also applied to classification problems for pattern recognition, feature extraction, and increasing classification accuracy.


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