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    Öğe
    Kağıt fabrikası elektrik enerjisi kalitesi analizi
    (Hitit Üniversitesi, 2025) Gafar, Onur; Gençol, Kenan
    Energy quality is important in terms of production efficiency, equipment lifespan, and system reliability. This becomes even more critical in sectors with high energy consumption, such as paper industry. In this thesis, the quality of electrical energy in a paper factory was examined, with a particular focus on the effects of harmonic distortions on asynchronous motors. Field measurements and analyses revealed that the most prevalent harmonics in the system were the 3rd, 5th, 7th, 11th, 13th, 17th, and 19th. These harmonics caused the Total Harmonic Distortion (THD) values to exceed the limits set by IEEE standards. As a result, issues such as motor overheating, torque imbalances, and insulation damage have been observed. These effects became more visible in areas with a high number of nonlinear loads. To address these problems, a feedforward neural network (FFNN) model was developed. Optimized using the Levenberg-Marquardt algorithm, the model predicted the amplitudes of harmonic components with high correlation (R = 0.98949) and achieved a very low error rate (MSE < 6.4401e-05). The training, testing, and validation results demonstrated the model's generalization capability. One of the prominent features of the model is its ability to maintain prediction performance even in high-noise environments (with Signal-to-Noise Ratio values as low as 0 dB), and its practical structure allows it to be integrated into online monitoring systems. This enables a transition from conventional periodic monitoring to real-time and adaptive monitoring of energy quality. In conclusion, this study presents quantative data on the effects of energy quality on production processes and offers a meaningful contribution to its field by demonstrating the potential of artificial intelligence-supported analysis approaches in industrial applications.