This work presents a novel methodology for variable speed high power Transfer Capability of a self-excited induction generator (SEIG). The proposed methodology is based on the selection of a suitable firing angle of Fixed Capacitor-Thyristor Controlled Reactor (FC-TCR) for achieving constant rated stator current. WDSEIG would produce a variable speed high power without overheating under variable wind speed and connected load. The analytical approach for the proposed methodology has been implemented to predict the optimal operating firing angle of FC-TCR for full load stator current achievement within the allowed operating range of load and prime mover speed. Also, Soft Computing (SC) techniques have been implemented based on Harmony Search Algorithm (HSA), Flower Pollination Algorithm (FPA), and Moth-Flame Optimization (MFO) algorithm to achieve the proposed methodology. A comparison between different SC techniques, analytical approach and experimental work are given and evaluated to verify SC techniques accuracy. This evaluation study can be useful in specifying the appropriateness of the SC techniques for High Power Transfer Capability for a SEIG.
Wireless Sensor Networks (WSNs) are a type of self-organizing networks with limited energy supply and communication ability. One of the most crucial issues in WSNs is to use an energy-efficient routing protocol to prolong the network lifetime. We therefore propose the novel Energy-Efficient Load Balancing Ant-based Routing Algorithm (EBAR) for WSNs. EBAR adopts a pseudo-random route discovery algorithm and an improved pheromone trail update scheme to balance the energy consumption of the sensor nodes. It uses an efficient heuristic update algorithm based on a greedy expected energy cost metric to optimize the route establishment. Finally, in order to reduce the energy consumption caused by the control overhead, EBAR utilizes an energy-based opportunistic broadcast scheme. We simulate WSNs in different application scenarios to evaluate EBAR with respect to performance metrics such as energy consumption, energy efficiency, and predicted network lifetime. The results of this comprehensive study show that EBAR provides a significant improvement in comparison to the state-of-the-art approaches EEABR, SensorAnt, and IACO.
