Optimization of electric service quality in distribution substations in urban-rural areas of central Peru: an approach based on the Technical Standard for Electric Service Quality

• Autores: Espinoza Taquire, Eduardo Luis, Huaman Rojas, Jezzy James
• Formato: tesis de grado
• Fecha de Publicación: 2025
• Institución: Universidad Continental
• Repositorio: CONTINENTAL-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.continental.edu.pe:20.500.12394/17457
• Enlace del recurso: https://hdl.handle.net/20.500.12394/17457; https://doi.org/10.14445/23488379/IJEEE-V12I2P103
• Nivel de acceso: acceso abierto
• Materia: Electricidad; Electricity; Energía eléctrica; Electrical energy; Simulación; Simulation; https://purl.org/pe-repo/ocde/ford#2.02.01

Electric power supply in urban and rural Peru is loaded with issues, especially in the quality of service offered at distribution substations. Voltage fluctuations compromise power quality , leading to equipment breakdowns and inadequate service delivery. This research tackles the problem of power quality optimization based on the analysis of accurate data and corrective measures using simulation techniques. The effect of setting the transformer tap changer and voltage control device on supply stability was examined using the PandaPower library in Python. Through the strict implementation of the Technical Standard for the Quality of Electric Services (NTCSE) and the Technical Standard for the Quality of Rural Electric Services (NTCSER), the performance of the secondary circuits powered by a transformer of 100 kVA in urban areas and 25kVA in rural areas was evaluated. A threshold of 2.6 V was set to detect borderline cases where corrective actions could be intelligently modified. The findings proved that adjustments of tap changer positions are a viable low - cost option to enhance power quality in networks with small capacity while achieving compliance with the regulations and improving the overall services. This helps improve the power supply quality in rural areas while offering an adaptable approach for power infrastructure optimization in the region. Further studies should aim to fusion real - time monitoring systems and machine learning to improve predictive maintenance and adaptive control strategies, particularly concerning energy policy and sustainable development .