Impact of Transformer Asset Management on Distribution Network Performance
Karim Ibrahim Mohamadeen Ahmed;
Abstract
ABSTRACT
In this thesis, a new methodology is adopted to develop an enhanced transformer predictive Asset Management (AM) model. The increase in the distribution networks load demand is the major motivation for this work. The distribution networks performance is highly affected by the condition and the lifetime of the working transformers.
The proposed strategy relies upon assessing the present condition of transformers by calculating the Health Indices (HI). Calculating the transformer (HI) score is undertaken utilizing (14) distinct oil transformer measurements. The measurements were acquired in the field for a total of 804 transformers. One major research step was the calculation of HI by optimizing and identifying the most significant diagnostic measurements and developing a model using Binary Cat Swarm Optimization (BCSO) with Support Vector Machines (SVM) for this purpose. A Self-Adaptive Neuro-Fuzzy Interference System (ANFIS) predictor model is adopted using the Particle Swarm Optimization (PSO) technique and utilized for the prediction of the optimized HI. The study is undertaken by processing two actual field measurements for working distribution transformers (≤69kV) within the network of industrial facilities.
In this thesis, a new methodology is adopted to develop an enhanced transformer predictive Asset Management (AM) model. The increase in the distribution networks load demand is the major motivation for this work. The distribution networks performance is highly affected by the condition and the lifetime of the working transformers.
The proposed strategy relies upon assessing the present condition of transformers by calculating the Health Indices (HI). Calculating the transformer (HI) score is undertaken utilizing (14) distinct oil transformer measurements. The measurements were acquired in the field for a total of 804 transformers. One major research step was the calculation of HI by optimizing and identifying the most significant diagnostic measurements and developing a model using Binary Cat Swarm Optimization (BCSO) with Support Vector Machines (SVM) for this purpose. A Self-Adaptive Neuro-Fuzzy Interference System (ANFIS) predictor model is adopted using the Particle Swarm Optimization (PSO) technique and utilized for the prediction of the optimized HI. The study is undertaken by processing two actual field measurements for working distribution transformers (≤69kV) within the network of industrial facilities.
Other data
| Title | Impact of Transformer Asset Management on Distribution Network Performance | Authors | Karim Ibrahim Mohamadeen Ahmed | Issue Date | 2017 |
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