14782

13304

كارشناسي ارشد

قدرت

اصفهان : دانشگاه صنعتي اصفهان

1398

شانزده، 150ص.: مصور، جدول، نمودار

محمد اسماعيل همداني گلشن

حميدرضا كارشناس، محمد امين لطيفي

1398/04/31

كتابنامه

مهندسي برق

مهندسي برق و كامپيوتر

1398/04/31

2549529

Real time monitoring of short term voltage stability Milad Aslanian m aslanian@ec iut ac ir Date of Submission 2019 05 29 Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language FarsiSupervisor Mohammad Esmaeel Hamedani Golshan hgolshan@cc iut ac irAbstract In recent years due to the ever increasing electrical demand and the limitations associated with energyresources power systems are operated near the limits of their transmission capabilities Therefore real timemonitoring and control of the power system are necessary for secure operation of the system Due to the increaseof dynamic loads especially asynchronous motors the monitoring of short term voltage stability SVS isnecessary to be taken into consideration With the introduction of wide area measurement systems WAMS andphasor measurement units PMUs real time voltage and current phasors are provided for online monitoring andcontrol of power systems Therefore data based or model free methods can be developed for online stabilitymonitoring In this thesis two data based methods are proposed for real time monitoring of SVS The first methodis based on SVS indices and the other uses data mining techniques In SVS indices based methods elevenappropriate indices are defined for online short term voltage stability The indices are calculated using the datameasured by the PMUs and compared with pre defined thresholds to assess the short term voltage stability of thesystem The performance of these indices is studied in terms of the time and accuracy required for determiningthe short term voltage stability instability On the other hand in methods which use data mining techniques asuitable database is created by the PMUs measured data from the various disturbances over many years Thisdatabase is then clustered and classified and an efficient classifier is designed for online SVS assessment Inthe data mining technique the concept of time series shapelet classification TSSC is used for online SVSassessment which uses decision tree DT to distinguish the short term voltage stability instability situations Furthermore a new method is proposed for creating the DT which uses the eleven indices as the decisionfeatures To evaluate the performance of the proposed online SVS assessment methods the IEEE 39 bus test systemis simulated and the performance of the SVS indices are assessed Furthermore a database consist of variousscenarios related to short term voltage phenomena is created and then the DT is designed in an offline manner using the SVS indices as the decision features Finally the developed DT is used for online SVS assessmentof the system Furthermore the performance of the proposed methods are compared The constituted DT candetect the stable or unstable states at almost one second which indicates the superiority of the proposed onlineSVS assessment over the previous methods Keywords Short term voltage stability real time monitoring WAMS PMU voltage stability indices datamining decision tree

محمد اسماعيل همداني گلشن

حميدرضا كارشناس، محمد امين لطيفي