Kowsalya M

Department of Energy and Power Electronics

School of Electrical Engineering

Vellore Campus

AR Nageswa Rao

Priya Vijaya

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Voltage stability indices for stability assessment: a review

International Journal of Ambient Energy

Continuous assessment of voltage stability (VS) is an important aspect to safeguard electrical power system (EPS) operation. The conservative methods for online assessment in terms of stability check for voltage are extremely time consuming and also absurd for supervising any application online. In agreement with this, a model which is an amalgamation of the Salp swarm algorithm (SSA) and support vector machine (SVM) is aimed at supervising the VS in the paper. The method anticipated for the aforesaid problem utilises the magnitude of voltage and its corresponding phase angle which are attained from the PMU as the inputs to the ML model and the respective output are considered to be the voltage stability margin index (VSMI). The proficiency of the anticipated model (SSA–SVM) is verified by means of various test cases under the real-time scenario and compared with the same data set to attest its pre-eminence. © 2019, © 2019 Informa UK Limited, trading as Taylor &amp; Francis Group.

A robust machine learning model for monitoring online voltage stability

Incessant assessment of voltage stability is a lively aspect to safeguard the electrical power system (EPS) operation. The conservative methods for online assessment in terms of stability check for voltage are extremely time engrossing and also absurd for supervising any application online. In line to this, a Salp Swarm Algorithm-based artificial neural network (SSA–ANN) model is opted for online monitoring of voltage stability in this manuscript. Artificial neural network is an influential and promising predictive tool. To upsurge the efficacy and accuracy and minimalize the training time. SSA is used for tuning the metaparameters such as the activation functions and number of nodes along with the learning rate. The method anticipated for the aforesaid problem utilizes the magnitude of voltage and its corresponding phase angle which are attained from the PMU as the inputs to the neural network model and the output is the voltage stability margin index (VSMI). The efficiency of the proposed model is verified with ICA–ANN and GA–ANN underrobust test cases and compared with the same data set to attest its preeminence. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

International Journal of Computers and Applications

A robust neural network model for monitoring online voltage stability

Continuous assessment of voltage stability is vivacious in safeguarding the electrical power system (EPS) operation. The final outcome of voltage instability is proximity to voltage collapse. The critical operating conditions of the transmission lines may possibly lead to voltage collapse with certain disturbances in electrical power supply. Quite a lot of Voltage Stability Indices (VSIs) have been technologically advanced in to estimate the proximity to voltage collapse. A few VSIs are computationally efficient, while the rest are said not to accomplish as expected under all circumstances. The study recommends a voltage stability index such as Composite Severity Index (CSI) and New Voltage Stability Index (NVSI) which are vigorous based on its theoretical nitty-gritties. The recommended index has been meritoriously tested on IEEE 30 bus and IEEE 118 bus test system under various operating conditions in order to weigh its efficacy. Also, it is shown that CSI and NVSI index could be used for estimating the distance to Voltage Collapse Point (VCP). The validation is done with Lmn, LQP and FVSI indices. The results designate its possibilities for the assessment of voltage stability. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Propinquity of voltage collapse prediction for power system using indices

International Journal of Electrical and Computer Engineering (IJECE)

Power system voltage collapse prediction using a new line stability index (NLSI-1): A case study of the 330-kV nigerian national grid

IEEE Transactions on Power Systems

Development and Application of a New Voltage Stability Index for On-Line Monitoring and Shedding

Power System Dynamics and Stability: With Synchrophasor Measurement and Power System Toolbox 2e

Synchronized Phasor Measurement

Voltage Stability Monitoring From a Transmission Bus PMU

International Journal of Electrical Power & Energy Systems

Wide-area measurement-based voltage stability sensitivity and its application in voltage control

Journal	International Journal of Ambient Energy
Publisher	Informa UK Limited
ISSN	0143-0750
Open Access	0