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Online Voltage Stability Assessment Based on Wide Area Measurements

Beiraghi, Mojtaba | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 40923 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Ranjbar, Ali Mohammad; Mozafari, Babak
  7. Abstract:
  8. Online voltage stability assessment is essential for preventing the failure in power systems. Voltage instability in power systems usually occurs in consequence of contingency situations or continuous load increasing or both of them. A new method for online voltage stability assessment using the data acquired from phasor measurement units (PMUs) and offline trained decision trees is presented in this thesis. The index of voltage stability margin (VSM) is used for security assessment and considering WSCC criterion, different operation points are divided in two classes: safe and unsafe. In order to obtain the voltage stability margin and generate the database, a new method which has been named modified continuation power flow (MCPF), is presented. In MCPF method, the generators reactive power limit hitting points as well as the type of instability are first identified using a predictor/corrector scheme and then are used as given data in continuation power flow. MCPF algorithm is used to generate the database by considering load and generation change scenarios defined in power system planning and then different decision trees are trained using various predictors in the database. In order to increase the precision of decision trees, methods of generating ensemble models such as bagging and adaptive boosting are used and a scheme for overcoming unpredictable load change scenarios in planning and also choosing the appropriate evaluating model is presented. Finally the proposed model for online voltage stability assessment is applied to IEEE 39 and 118 bus systems
  9. Keywords:
  10. Voltage Stability ; Online Voltage Stability Analysis ; Phasor Measurement Unit ; Decision Making Tree ; Adaboost Algorithm ; Bagging

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