IMPROVING THE REAL TIME SUPERVISORY NETWORK PERFORMANCE IN 330KV TRANSMISSION STATION USING INTELLIGENT PHASOR MONITORING AND CONTROL SCHEME

Subject Area: Engineering

This paper focuses on improving the performance of the SCADA network in a 330/132kV transmission station by employing a phasor-based intelligent monitoring scheme. The study assesses the characteristics of the Alaoji SCADA system, which is designed with a Remote Telemetry Unit (RTU), and identifies challenges related to data synchronization and collection delays. To address these issues, the proposed methodology involves data collection from the Alaoji 330/33kV station, data processing using a three-phase shunt active filter, Phasor Measurement Unit (PMU), artificial neural network training, and classification. The system design incorporates a structural approach to model the 330/132kV substation, phasor measurement unit, feed-forward neural network, and the Intelligent Load Flow Sampling Algorithm (LFSA). The implementation of the system is carried out using the Simulink platform, and its performance is evaluated using the tenfold cross-validation technique. The results demonstrate that the new LFSA achieves data collection and transmission within 14.3ms, a significant improvement compared to the characterized result of 540ms. The mean square error is reduced to 0.053288Mu, indicating high accuracy as it approaches zero, with an overall accuracy of 98.3%. Integration of the system at the Alaoji 330/132kV transmission station enables high-quality and integrity data collection, thereby enhancing decision-making processes within the station.