This paper presents a novel salient pole synchronous generator i.e. permanentmagnet- assisted salient-pole synchronous generator (PMa-SGs). Due to saturation of conventional synchronous generators (SGs), permanent-magnet-assisted salientpole synchronous generators (PMa-SGs) are presented. PMa-SGs are a new type of salient-pole synchronous machines with extra permanent magnets (PMs) between the adjacent pole shoes. Placing PMs between adjacent pole shoes leads to a reduction in flux saturation plus an increase in armature flux linkage. In other words, the generator can operate at higher capacity. In this paper, a comparative study is carried out between conventional SGs and PMa-SGs based on finite element analysis (FEA). This is done via simulation of a PMa-SG compared to a conventional SG. Simulation Results show superiority of PMa-SGs over SGs. In fact, in PMa-SG maximum flux density in stator core is increased and pole bodies are not saturated. Besides, PMa-SG has higher flux linkage compared to conventional SG. Therefore, higher voltage could be produced in the generator. In other words, the output performance of the PMa-SG is considerably better than that of a conventional SG. پرونده مقاله

Protection systems have vital role in network reliability in short circuit mode and proper operating for relays. Current transformer often in transient and saturation under short circuit mode causes mal-operation of relays which will have undesirable effects. Therefore, proper and quick identification of Current transformer saturation is so important. In this paper, an Artificial Neural Network (ANN) which is trained by two different swarm based algorithms; Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) have been used to discriminate between Current transformer saturation and fault currents in power transformers. In fact, GSA operates based on gravity law and in opposite of other swarm based algorithms, particles have identity and PSO is based on behaviors of bird flocking. Proposed approach has two general stages. In first step, obtained data from simulation have been processed and applied to an ANN, and then in second step, using training data considered ANN has been trained by GSA & PSO. Finally, a proposed technique has been compared with one of the common training approach which is called Genetic algorithm (GA). پرونده مقاله

Permanent magnet synchronous generators (PMSGs) are novel generators which can be used in high-performance wind farms. High efficiency and flexibility in producing electricity from variable rotation make them good candidate for wind power applications. Furthermore, because these kinds of generators have no excitation winding, there is no copper loss on rotor; hence, they can operate at high power factor. Besides, performance characteristics of such generators could be further improved by design optimization. This paper presents design optimization of PMSGs used in small wind turbines using novel and efficient optimization algorithm i.e. Artificial Bee Colony (ABC) Algorithm. Then, a well-known optimization algorithm i.e. Genetic Algorithm (GA) is used to show the validity and efficiency of the before-mentioned algorithm. For this purpose, the necessary equations are provided. Objective function of this study is to reduce the total volume of motor. Case study of this study is a 5 kW, 220 V, 50 Hz, 100 rpm generator. Finally, results obtained by optimization are verified with Maxwell software which is based on finite element method (FEM). Comparison shows that the results of optimization approach are in good agreement with that of FEM ones. پرونده مقاله