DEVELOPMENT OF AN OPTIMAL TECHNIQUE FOR RELIABILITY ANALYSIS OF POWER LINE COMMUNICATION SYSTEM FOR SMART GRID REALIZATION

Power Line Communication (PLC) is a communication technology that enables transmission of data on a conductor which is also used for electric power transmission. The development of a PLC system presents a significant challenge for the communication engineer due to the unusual channel characteristics that affect high-speed signal transmission since the electric power grid is designed to operate at 50/60 Hz. In addition, the topology of electric power grid network is often very irregular resulting in significant dispersion of the transmitted message signals as it is susceptible to noise and attenuation due to power lines being primarily used for transmission of electricity as a result of its viability for smart grid realization. This study therefore investigates the reliability analysis of PLC for Smart Grid (SG) realization using clipping and Turbo equalizer techniques to reduce the effects of noise and attenuation in narrowband (9-490 kHz) PLC system respectively. This optimal technique is based on the minimum Bit Error Rate (BER) search. The results show that the technique used improves the BER performance of the narrowband PLC system for smart grid realization. The result of this study provides a reliable communication and control for cost efficiency of existing power line infrastructure for Smart Grid. Keywords: PLC, Smart Grid, Attenuation, Clipping, Equalizer, Electric Power Transmission, Bit Error Rate. DOI: 10.7176/MTM/9-8-01 Publication date: August 31st 2019

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field