Modeling power system load using intelligent methods.

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dc.contributor.author He, Shengyang
dc.date.accessioned 2011-08-17T16:15:46Z
dc.date.available 2011-08-17T16:15:46Z
dc.date.issued 2011-08-17
dc.identifier.uri http://hdl.handle.net/2097/12036
dc.description.abstract Modern power systems are integrated, complex, dynamic systems. Due to the complexity, power system operation and control need to be analyzed using numerical simulation. The load model is one of the least known models among the many components in the power system operation. The two different load models are the static and dynamic models. The ZIP load model has been extensively studied. This has widely applied to composite load models that could maintain constant impedance, constant current, and/or constant power. In this work, various Neural Networks algorithms and fuzzy logic have been used to obtain these ZIP load model coefficients for determining the percentage of constant impedance, current, or power for the various load buses. The inputs are a combination of voltage, voltage change, and power change, or voltage and power, and the outputs consist of the ZIP load model coefficients for determining the type and the percentage of load at the bus. The trained model is used to predict the type and percentage of constant load at other buses using simulated transient data from the 16-generator system. A small study was also done using a dynamic induction machine model in addition to the ZIP load model. As expected, the results show that the dynamic model is more difficult to determine than the static model. en_US
dc.description.sponsorship Kansas State University Power Affiliates project en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Power systems en_US
dc.subject Load modeling en_US
dc.subject Neural networks en_US
dc.subject Fuzzy logic en_US
dc.subject Backpropagation en_US
dc.subject ZIP load model en_US
dc.title Modeling power system load using intelligent methods. en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Electrical Engineering en_US
dc.description.advisor Shelli K. Starrett en_US
dc.subject.umi Electrical Engineering (0544) en_US
dc.subject.umi Energy (0791) en_US
dc.subject.umi Engineering (0537) en_US
dc.date.published 2011 en_US
dc.date.graduationmonth August en_US


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