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Muktevi Chakravarthy
Vasavi College of Engineering
Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Dynamic state power system fault monitoring and protection with phasor measurements and fuzzy based expert system Ravi Ponnala; Muktevi Chakravarthy; Suraparaju Venkata Naga Lakshmi Lalitha
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3585

Abstract

The main aim of this paper is building of a small micro grid test bed system with a few sources, and few loads with various types of lumped resistive (R), capacitive (C) and inductive (L) elements. Before the advent of electric smart grid every load is facilitated with its own electromagnetic. Slowly with an advancement of protection system numerical relays came into existence. Because in the numerical relays the communication between the relays and communication with central computer is possible, with this a coordinative protection system was evolved. For the monitoring and protection of system in dynamic state many of the numerical relays were now replaced by intelligent electronic devices. The system is to be centrally controlled with an improvement in data communications and the artificial intelligence into power system made it is possible to control the power system centrally. In this paper, it is presented that the fault is monitoring and protection against different types of faults, such as frequency variations, voltage variations and different fault conditions using fuzzy based expert system (FBES). The proposed protection scheme is adaptive for frequency variations, voltage variations and fault levels. The FBES scheme is implemented in the LabVIEW software with MAMDANI structure.
Performance and comparison of different phasor calculation techniques for the power system monitoring Ravi Ponnala; Muktevi Chakravarthy; Suraparaju Venkata Naga Lakshmi Lalitha
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3833

Abstract

Day to day to electrical power demand increases very rapidly with linear and non-linear load demands. Especially the nonlinear loads are creating the harmonics in the current and voltage signals. The current and voltage signal values are measured with the phasor measurement unit (PMU) for the proper magnitude and phase angle calculation even in the presence of harmonic components in the signals. The performance of the PMU is depending upon the phasor calculation technique. Different technique/methods are available for the phasor calculation, from the method to method there is difference in the accuracy, phasor computation time and complexity. In this paper various techniques for phasor calculation are presented. For better performance of PMU, more accurate and less computation time for phasor calculation technique is required. But in real time, accuracy and speed both may not satisfied with single technique. Need to find a satisfactory technique, which satisfies the speed of phasor computation and accuracy. In this paper it is proposed that direct phasor estimation technique, which gives the better results in terms of accuracy and time and this method, satisfies the requirements for the dynamic monitoring of power system according to the IEEE std. C37.118.1-2011.
Power system contingency classification using machine learning technique Sandhya Rani Gongada; Muktevi Chakravarthy; Bhukya Mangu
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4031

Abstract

One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighbor machine learning strategy is presented to categorize these patterns. The proposed machine learning classifiers are trained on the IEEE 30 bus system before being evaluated on the IEEE 14, IEEE 57, and IEEE 118 bus systems. The suggested k-nearest neighbor (KNN) classifier increases the accuracy of power system security assessments categorization. A fuzzy logic approach was also investigated and implemented for the IEEE 14 bus test system to forecast the aforementioned five classifications.
Effective monitoring of power system with phasor measurement unit and effective data storage system Ravi Ponnala; Muktevi Chakravarthy; Suraparaju Venkata Naga Lakshmi Lalitha
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4085

Abstract

In the recent years the monitoring and operation of the power system became complex, due to the more demand from the different linear and non-linear loads and generation from the different sources. For the effective monitoring and operation of the power system, existing power system monitoring methods need to improve or new technologies are required. For the effective monitoring and operation of the power system phasor measurement unit (PMU) based monitoring is suitable, because it provide the dynamic state monitoring system. In this paper PMU based monitoring is proposed with effective data storage system and protection. With this method phasor values of voltage and current signals are calculated at the location of PMU and with the help of software based program effective data storage also possible. With this proposed model the phasor values in the power system at different locations monitoring also possible and required phasor data only stored and total data is only monitored. The phasor values of signals are calculated with direct phasor measurement technique in LabVIEW and by adding time stamping to the each phasor value accurate measurement of power flow is possible.
Co-Authors Bhukya Mangu Ravi Ponnala Sandhya Rani Gongada Suraparaju Venkata Naga Lakshmi Lalitha