Generic characteristic of medium voltage reference network for the Malaysian power distribution system

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Ibrahim K.A.B.
Au M.T.
Gan C.K.
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Institute of Electrical and Electronics Engineers Inc.
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The emergence of new technologies in distributed generations (DG), renewable energy (RE), plugged-in electric vehicles (PEV) connected to the distribution network has necessitated the need to analyze and assess its associated impact on the system. The impact analysis on of distribution systems often involves decisions on major issues pertaining to operation and planning for the entire network. However, it may not always be possible or convenient to analyze such complex systems as the time taken to perform studies on such large and diverse systems would be substantial and costly. One feasible solution is to analyze only a portion of the system, but this reduced analysis may not be accurate if the portion of the system is not acceptably representative of the whole system. This paper introduces a robust approach using statistical method to synthesize generic characteristics from a significantly reduced parameters in order to create a set of representative network (RN). 400 feeder datasets of the 11kV medium voltage (MV) network in Peninsular Malaysia are statistically analyzed to synthesize six RN based on: i) voltage transformation, and ii) geographic locations (urban, semi-urban and rural community). The MV feeder datasets are disaggregated using data stratification technique. The parameters analyzed includes, the number of transformers and its capacity, number of feeders and its length, and aggregated maximum demand of each substation. The results shows that this method is able to extract the most prominent characteristics of the stratified datasets. � 2015 IEEE.
Crashworthiness; Feeding; Local area networks; Sampling; Statistical methods; Distribution systems; Generic characteristics; Medium voltage networks; Power distribution system; Reference network; Stratification techniques; Stratified sampling; Voltage transformation; Complex networks