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In this paper, a starting current limitation method of a cereals milling system powered by a photovoltaic micro-grid is proposed. Today, the milling system consisting of a locally designed hammer mill driven by an induction motor is recognized as an innovative and efficient cereal milling solution. For this purpose, they are increasingly being developed in urban and rural areas. Although they offer many advantages, they have some limitations, such as the electromagnetic torque oscillations and the high inrush current at startup. A starting current limitation method based on soft starting technique is proposed to mitigate these negative effects in the transient state. To develop the proposed method, the scalar control principle is used. The scalar control is a simple technique, and it is operated to control the voltage magnitude and the supply frequency. The proposed method has been simulated and compared to conventional direct-on-line starting to evaluate its performance. Simulation results show that the method achieves a predominant performance over the conventional, providing a significant reduction in inrush current while having a lower electromagnetic torque ripple. To validate the method in real-time, an experimental test environment was developed using a dSPACE R&D 1104 controller board, an induction motor, and a SEMIKRON converter. Real-time experimental results show positive feedback on the proposed current limitation method's efficiency and effectiveness by corroborating the simulation results.

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First order Takagi-Sugeno model is mainly used in the consequent part of ANFIS models enabling ease of implementation and fast training due to the linearity in the consequent parameters. The input space is then partitioned starting from a well known number of partitions. For complex systems, fuzzy clustering of the input feature space is often used in order to gather data which may have some "similarities" or may represent some system's specificities, forming then the rules for an ANFIS model. We present in this work a new approach for designing ANFIS models where rules are extracted by means of a fuzzy clustering technique of the input feature space and polynomial functions with various orders are used in the consequent part. An algorithm for adapting the consequent part structure to the extracted rules is presented. Tested on a non linear dynamic system, the proposed approach yields very good results in terms of approximation accuracy, outperforming ANFIS with first order Takagi-Sugeno models.

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