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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.