Matching Items (4)
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- All Subjects: Electric transformers
- All Subjects: Renewable energy sources
- Creators: Tylavsky, Daniel J
Description
Recent trends in the electric power industry have led to more attention to optimal operation of power transformers. In a deregulated environment, optimal operation means minimizing the maintenance and extending the life of this critical and costly equipment for the purpose of maximizing profits. Optimal utilization of a transformer can be achieved through the use of dynamic loading. A benefit of dynamic loading is that it allows better utilization of the transformer capacity, thus increasing the flexibility and reliability of the power system. This document presents the progress on a software application which can estimate the maximum time-varying loading capability of transformers. This information can be used to load devices closer to their limits without exceeding the manufacturer specified operating limits. The maximally efficient dynamic loading of transformers requires a model that can accurately predict both top-oil temperatures (TOTs) and hottest-spot temperatures (HSTs). In the previous work, two kinds of thermal TOT and HST models have been studied and used in the application: the IEEE TOT/HST models and the ASU TOT/HST models. And, several metrics have been applied to evaluate the model acceptability and determine the most appropriate models for using in the dynamic loading calculations. In this work, an investigation to improve the existing transformer thermal models performance is presented. Some factors that may affect the model performance such as improper fan status and the error caused by the poor performance of IEEE models are discussed. Additional methods to determine the reliability of transformer thermal models using metrics such as time constant and the model parameters are also provided. A new production grade application for real-time dynamic loading operating purpose is introduced. This application is developed by using an existing planning application, TTeMP, as a start point, which is designed for the dispatchers and load specialists. To overcome the limitations of TTeMP, the new application can perform dynamic loading under emergency conditions, such as loss-of transformer loading. It also has the capability to determine the emergency rating of the transformers for a real-time estimation.
ContributorsZhang, Ming (Author) / Tylavsky, Daniel J (Thesis advisor) / Ayyanar, Raja (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2013
Description
t temperature (HST) and top-oil temperature (TOT) are reliable indicators of the insulation temperature. The objective of this project is to use thermal models to estimate the transformer's maximum dynamic loading capacity without violating the HST and TOT thermal limits set by the operator. In order to ensure the optimal loading, the temperature predictions of the thermal models need to be accurate. A number of transformer thermal models are available in the literature. In present practice, the IEEE Clause 7 model is used by the industry to make these predictions. However, a linear regression based thermal model has been observed to be more accurate than the IEEE model. These two models have been studied in this work.
This document presents the research conducted to discriminate between reliable and unreliable models with the help of certain metrics. This was done by first eyeballing the prediction performance and then evaluating a number of mathematical metrics. Efforts were made to recognize the cause behind an unreliable model. Also research was conducted to improve the accuracy of the performance of the existing models.
A new application, described in this document, has been developed to automate the process of building thermal models for multiple transformers. These thermal models can then be used for transformer dynamic loading.
This document presents the research conducted to discriminate between reliable and unreliable models with the help of certain metrics. This was done by first eyeballing the prediction performance and then evaluating a number of mathematical metrics. Efforts were made to recognize the cause behind an unreliable model. Also research was conducted to improve the accuracy of the performance of the existing models.
A new application, described in this document, has been developed to automate the process of building thermal models for multiple transformers. These thermal models can then be used for transformer dynamic loading.
ContributorsRao, Shruti Dwarkanath (Author) / Tylavsky, Daniel J (Thesis advisor) / Holbert, Keith E. (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Power generation in remote isolated places is a tough problem. Presently, a common source for remote generation is diesel. However, diesel generation is costly and environmental unfriendly. It is promising to replace the diesel generation with some clean and economical generation sources. The concept of renewable generation offers a solution to remote generation. This thesis focuses on evaluation of renewable generation penetration in the remote isolated grid. A small town named Coober Pedy in South Australia is set as an example. The first task is to build the stochastic models of solar irradiation and wind speed based on the local historical data. With the stochastic models, generation fluctuations and generation planning are further discussed. Fluctuation analysis gives an evaluation of storage unit size and costs. Generation planning aims at finding the relationships between penetration level and costs under constraint of energy sufficiency. The results of this study provide the best penetration level that makes the minimum energy costs. In the case of Coober Pedy, cases of wind and photovoltaic penetrations are studied. The additional renewable sources and suspended diesel generation change the electricity costs. Results show that in remote isolated grid, compared to diesel generation, renewable generation can lower the energy costs.
ContributorsZhu, Yujia (Author) / Holbert, Keith E. (Thesis advisor) / Karady, George G. (Committee member) / Tylavsky, Daniel J (Committee member) / Arizona State University (Publisher)
Created2012
Description
The development of new policies favoring integration of renewable energy into the grid has created a need to relook at our existing infrastructure resources and at the way the power system is currently operated. Also, the needs of electric energy markets and transmission/generation expansion planning has created a niche for development of new computationally efficient and yet reliable, simple and robust power flow tools for such studies. The so called dc power flow algorithm is an important power flow tool currently in use. However, the accuracy and performance of dc power flow results is highly variable due to the various formulations which are in use. This has thus intensified the interest of researchers in coming up with better equivalent dc models that can closely match the performance of ac power flow solution.
This thesis involves the development of novel hot start dc model using a power transfer distribution factors (PTDFs) approach. This document also discusses the problems of ill-conditioning / rank deficiency encountered while deriving this model. This model is then compared to several dc power flow models using the IEEE 118-bus system and ERCOT interconnection both as the base case ac solution and during single-line outage contingency analysis. The proposed model matches the base case ac solution better than contemporary dc power flow models used in the industry.
This thesis involves the development of novel hot start dc model using a power transfer distribution factors (PTDFs) approach. This document also discusses the problems of ill-conditioning / rank deficiency encountered while deriving this model. This model is then compared to several dc power flow models using the IEEE 118-bus system and ERCOT interconnection both as the base case ac solution and during single-line outage contingency analysis. The proposed model matches the base case ac solution better than contemporary dc power flow models used in the industry.
ContributorsSood, Puneet (Author) / Tylavsky, Daniel J (Thesis advisor) / Vittal, Vijay (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2014