Voltage stability monitoring of power systems using reduced network and artificial neural network

• Published in: International journal of electrical power & energy systems Vol. 87; pp. 43 - 51
• Main Authors: Ashraf, Syed Mohammad, Gupta, Ankur, Choudhary, Dinesh Kumar, Chakrabarti, Saikat
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-05-2017
• Subjects: Artificial neural network; Continuation power flow; Network reduction; Voltage stability; Voltage stability; Network reduction; Artificial neural network; Continuation power flow
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2016.11.008

•Used measurements of internal area only to monitor power system voltage stability.•Included information of external area in measurements of internal area using ANN.•Proposed adaptive training of ANN for dynamic system operating conditions.•A Z-score based algorithm to detect bad data in the measured data is discussed.
This paper presents network reduction based methodologies to monitor voltage stability of power systems using limited number of measurements. In a multi-area power system, artificial neural networks (ANNs) are used to estimate the loading margin of the overall system, based on measurements from the internal area only. Information regarding the important measurements from the external areas is considered in measurement transformation through the network reduction process, to enhance the estimation accuracy of the ANNs. A Z-score based bad or missing data processing algorithm is implemented to make the methodologies robust. To account for changing operating conditions, adaptive training of the ANNs is also suggested. The proposed methods are successfully implemented on IEEE 14-bus and 118-bus test systems.