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Transformers are considered as significant equipments in electrical power systems, once failure ,the economic operation will be lost. To overcome this difficulty and to maintain economic operation of facilities, diverse diagnosis methods are developed to implement fault forecasting. According to intelligent complementary ideas, a fault diagnosis is proposed when there is a missing failure symptom of transformer. The core of the proposed approach is a soft hybrid induction system called the Generalized Distribution Table and Rough Set System (GDT-RS) to discover classification rules. The system is based on a combination of Generalized Distribution Table (GDT) and the Rough Set methodologies. The proposed approach is applied into transformer fault diagnosis and the results indicate that it is very effective and accurate.

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A novel approach for optimized feedback gains of a stable sensorless induction motor (IM) drives at low speeds in the regenerating mode is presented. The proposed approach depends on the rough set (RS) and genetic algorithm (GA) in a cascading construction. The RS is used to obtain the most dominant machine parameters that affect the stability of the sensorless IM drive at very low speeds in the regenerating mode. The parameter's values are randomly selected to investigate their influence on the stability. Then, a reduction is obtained for the most dominant machine parameters affecting the stability. GA is applied to search for the optimal design of the observer feedback gains under the dominant parameter deviation. The proposed RS theory and GA guarantees a stable speed estimate and efficient sensorless IM drive at very low speeds in the regenerating mode. Theoretical analysis, design procedure, and simulation work of the proposed approach are presented. A sensorless IM drive is executed in the laboratory using the digital signal processor (DSP)-DS1104 control board. Extensive results in the different operating conditions to verify the efficacy of the proposed approach are presented and compared with previous works.

Autonomy is considered an important criterion that characterizes the performance of electric vehicles. It is represented by the distance that could be traveled by a fully electric vehicle which mainly depends on several parameters such as the vehicle model, type of battery, type of motor, etc. In this context, to improve the autonomy of electric vehicles, this paper represents an optimization study for the electric motor based on two contributions. The first devise an energy optimization algorithm to reduce the motor losses by calculation of the stator flux reference according to the electromagnetic torque and the rotation speed. The second is concerned with controller parameters adjustment using the Particle Swarm Optimization (PSO) technique to improve the efficacy and robustness of the drive. The performance of this strategy is evaluated in terms of torque, flux ripples, and transient response to step variations of the torque control. A comparative study of the designed PI controllers based on PSO with four other control algorithms and tuning methods is established in order to prove the efficiency of PI_PSO. The analysis, modeling, and simulation results are presented to verify the validity of the proposed overall optimization study.

The subject of mutually orthogonal Latin squares (MOLSs) has fascinated researchers for many years. Although there is a number of intriguing results in this area, many open problems remain to which the answers seem as elusive as ever. Mutually orthogonal graph squares (MOGSs) are considered a generalization to MOLS. MOLS are considered an area of combinatorial design theory which has many applications in optical communications, cryptography, storage system design, wireless communications, communication protocols, and algorithm design and analysis, to mention just a few areas. In this paper, we introduce a technique for constructing the mutually orthogonal disjoint union of graphs squares and the generalization of the Kronecker product of MOGS as a generalization to the MacNeish's Kronecker product of MOLS. These are useful for constructing many new results concerned with the MOGS.

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