Analysis of Power Swing Impact on Distance Relay Performance in a 500 kV Transmission System Based on Real-Time Digital Simulator Model

Wiryawan, Muhammad Adityo
Puteri, Riana Defi Mahaji

MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering
Vol 6 No 3 (2024): Motivection : Journal of Mechanical, Electrical and Industrial Engineering

Distance relays in high-voltage transmission systems detect faults by measuring impedance; however, fluctuations caused by power swings can mimic fault patterns and trigger false trips. This paper analyzes the impact of power swings on the performance of distance relays in a 500 kV transmission system using Real-Time Digital Simulator (RTDS) simulations. The results indicate that power swings cause 267 to 299 false trip signals before the actual fault occurs in the observed system. Recommendations include the development of protection algorithms such as Power Swing Blocking (PSB) and artificial intelligence (AI) technologies, including Wavelet Transform, Support Vector Machine (SVM), Chaos Theory, Prony Analysis, as well as hybrid combinations such as Wavelet-Chaos Theory and Adaptive Neuro-Fuzzy Inference System (ANFIS), to detect fault patterns in real-time. Although this study focuses on a 500 kV HVAC system, the findings can be generalized to other nominal voltage systems utilizing HVAC technology. However, further research is required for applications in HVDC-based systems. Distance relay pada sistem transmisi tegangan tinggi mendeteksi gangguan dengan mengukur impedansi, tetapi fluktuasi akibat power swing dapat menyerupai pola gangguan dan memicu kesalahan trip. Artikel ini menganalisis pengaruh power swing pada performa distance relay di sistem transmisi 500 kV menggunakan simulasi Real-Time Digital Simulator (RTDS). Hasil menunjukkan power swing menyebabkan kesalahan trip sebanyak 267 hingga 299 sinyal sebelum gangguan aktual terjadi pada sistem yang diamati. Rekomendasi mencakup pengembangan algoritma proteksi seperti Power Swing Blocking (PSB) dan teknologi kecerdasan buatan (AI), termasuk Wavelet Transform, Support Vector Machine (SVM), Chaos Theory, Prony Analysis, serta kombinasi hybrid seperti Wavelet-Chaos Theory dan Adaptive Neuro-Fuzzy Inference System (ANFIS), untuk mendeteksi pola gangguan secara real-time. Meskipun penelitian ini difokuskan pada sistem HVAC 500 kV, hasilnya dapat digeneralisasi untuk sistem tegangan nominal lainnya selama menggunakan teknologi HVAC. Namun, penelitian lebih lanjut diperlukan untuk aplikasi pada sistem berbasis HVDC.