Modeling and simulation of an induction motor
Due to their affordability, dependability, and longevity, induction motors are among the most widely used motors. The induction motor is modeled and simulated within a stationary reference frame using the qd0 transformation theory. The motor's dynamic activity is captured by the differential equations of the system. MATLAB/SIMULINK is used to carry out simulations, which concentrate on important motor output parameters as phase current, motor speed, and electromagnetic torque. The benefits of applying the qd0 transformation theory to motor modeling are amply illustrated by the simulation results.
Përdorimi i inteligjencës artificiale në hartat mjedisore në Tiranë. Një rrugë drejt planifikimit urban të qëndrueshëm.
Tirana, the capital of Albania, has experienced rapid urban development, resulting in significant environmental challenges. Historical data reveal a drastic reduction in green space per capita, from approximately 10 m² during the communist period to a concerning 0.5 m² in recent years. This de-cline highlights the urgent need for sustainable urban planning strategies that prioritize ecological resilience. This study explores the innovative application of Artificial Intelligence (AI) platforms to compile comprehensive environmental maps of Tirana, aiming to address the city’s sustainability challenges. By integrating AI techniques with urban planning, the research seeks to offer a novel approach to enhancing urban environments and ecological sustainability. Utilizing a diverse array of AI techniques, including machine learning algorithms, remote sensing data analysis, and predictive modeling, the study aggregates and interprets vast datasets. These meth-ods are employed to produce detailed environmental maps that highlight key features and challenges within the city, such as the distribution of green spaces, pollution hotspots, urban heat islands, and water management areas. The generated maps provide unprecedented insights into Tirana’s environmental dynamics, illumi-nating the areas most in need of intervention. This mapping enables the classification of the city into climatic zones, aiding in the identification of urgent regulatory plan interventions. The findings underscore the potential of AI in revolutionizing environmental monitoring and management, facili-tating informed decision-making for urban development strategies. By presenting Tirana as a focal point, this research showcases the practical applications and benefits of AI in environmental mapping and sets a precedent for other cities aiming to integrate advanced technologies into their environmental planning processes. The study emphasizes the importance of creating public and green spaces within urban settings, highlighting Tirana as a case study for avoid-ing environmental degradation in similar contexts. With population projections indicating significant growth, the necessity for adopting AI-driven environmental planning becomes increasingly evident as a means to ensure sustainable urban development and ecological resilience.