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    Optimal solar power injection in distribution network based on anfis controller
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2024) Assaf, Abdullah Sami Assaf; Kurnaz, Sefer
    Both individuals and the environment may benefit greatly from renewable energy sources. The sources of it are unavoidable, and its diversity across a range of domains is seen as a source for a secure place to live and a vehicle to charge throughout life. Guidelines for renewable energy are also accessible in the majority of nations. The success from network's installation or electrical network itself determine whether networks have connections to the major electricity network or to islands within the main electricity network. Here is where techniques for managing control of power, equipment equilibrium, voltage effectiveness, efficiency reactive consumption promotional code, island identification administration, and network synchronization must be created. This work describes how an Adaptive Neuro-Fuzzy Inference -System (ANFIS)- controller can be implemented to improve the effectiveness of solar power injection within distribution networks. The goal of the research is to maximize the performance of solar power by investigating its integration into distribution networks. The distribution network's overall stability and efficiency are increased by the efficient management and control of solar power injection provided by the ANFIS controller. The influence of ANFIS-based management on the operational features of solar energy integration is examined in this work, taking system dependability, grid stability, and power quality into account. The work shows how ANFIS controllers can be used to achieve outstanding performance renewable energy integration in distribution networks through analysis and simulation. There are three primary sections to this work. Using a solar energy programming set for PV - solar energy module, the MPPT system, and the traditional control device (PID), a mathematical model including four main elements was developed in the first section. These components are simulation, correlation, and evaluation. as well as a 600 volt electrical network. This system was simulated, and the outcomes were examined. In the second section, the Adaptive Neuro-Fuzzy Inference System (ANFIS) smart scimitar was used to re-create the prior system. In the last phase, the consequences on the structure of the network were examined using the energy output along with the effects of temperature and radiation synchronization. Additional possibilities, like modifications to the load and disturbances. A discussion of the proposed system's simulation is conducted, and an analysis is done to determine how these fundamental restrictions might affect real-world applications in the network due to unexpected circumstances. The final section presents the created system's full scheme. This technology converts the light that enters it into electrical energy. With this energy, the primary 600 volt electrical network can run. MATLAB simulation models for each component of the system and simulation tapes were used to assess the suggested system. The results of the analysis and comparison between the performance of the traditional controller (PID) and the intelligent controller (ANFIS) demonstrated the efficiency of the intelligent controller and its ability to adapt to all problems and obtain highly efficient performance with acceptable stability under All working conditions.