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This paper presents a non‐isolated single‐switch ultra‐high step‐up (UHSU) DC–DC converter with a three‐winding coupled inductor (CI) which is utilized to achieve ultra‐high voltage gain with a small amount of duty cycle leading to low conduction losses of the power switch and higher efficiency. The voltage gain of the suggested UHSU converter is adjusted by two methods: the duty cycle of the power switch and the three‐winding CI turn ratio, therefore enhancing the design flexibility of the suggested converter. Due to the utilization of the passive clamp circuit in the structure of the proposed converter, it is possible to select a power switch with low voltage rated and small ON‐state resistance, further enhancing the converter efficiency. The operation modes are discussed in detail and to clarify the salient features of the proposed converter, a comparison with other configurations is provided. Finally, to accredit the performance of the proposed converter, a 150‐W laboratory archetype with an input and output voltage of 20 and 300 V, respectively, at 50 kHz switching frequency is fabricated. [ABSTRACT FROM AUTHOR]

Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements. [ABSTRACT FROM AUTHOR]