This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN) approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency. Two motors of (2.2 Kw) have been used to achieve the actual fault and desirable data at no-load, half-load and full-load conditions. Motor Current Signature analysis (MCSA) based on stator current has been used to detect eccentricity fault. Feed forward neural network and error back propagation training algorithms are used to perform the motor fault detection. The inputs of artificial neural network are the amplitudes of the positive and negative harmonics and the speed, and the output is the type of fault. The training of neural network is achieved by data through the experiments test on healthy and faulty motor and the diagnostic system can discriminate between "healthy" and "faulty" machine.

Outages and faults cause problems in interconnected power system with huge economic consequences in modern societies. In the power system blackouts, black start resources such as micro combined heat and power (CHP) systems and renewable energies, due to their selfstart ability, are one of the solutions to restore power system as quickly as possible. In this paper, we propose a model for power system restoration considering CHP systems and renewable energy sources as being available in blackout states. We define a control variable representing a level of balance between the distance and importance of loads according to the importance and urgency of the affected customer. Dynamic power flow is considered in order to find feasible sequence and combination of loads for load restoration.