Abstract
Smart grids have been constructed so as to guarantee the security and stability of the power grid in recent years. Power transformers are a most vital component in the complicated smart grid network. Any transformer failure can cause damage of the whole power system, within which the failures caused by overloading cannot be ignored. This research gives a new insight into overload capability assessment of transformers. The hot-spot temperature of the winding is the most critical factor in measuring the overload capacity of power transformers. Thus, the hot-spot temperature is calculated to obtain the duration running time of the power transformers under overloading conditions. Then the overloading probability is fitted with the mature and widely accepted Weibull probability density function. To guarantee the accuracy of this fitting, a new objective function is proposed to obtain the desired parameters in the Weibull distributions. In addition, ten different mutation scenarios are adopted in the differential evolutionary algorithm to optimize the parameter in the Weibull distribution. The final comprehensive overload capability of the power transformer is assessed by the duration running time as well as the overloading probability. Compared with the previous studies that take no account of the overloading probability, the assessment results obtained in this research are much more reliable.
Highlights
The power grid is an important infrastructure for a nation’s economic and social development, in recent years, the objective environment to guarantee the security and stability of the power grid is undergoing tremendous changes
The overload capability of oil-immersed power transformers is assessed by the data sampled from three residential areas named Lake Neighborhood, North Neighborhood and Sunshine
The comprehensive overload capability of power transformers is obtained based on the running time duration of the power transformers under overload conditions and the overloading probability calculation results: the running time duration of the power transformer is obtained according to the given ambient temperature and the rated load first, the overloading probability is obtained from the probability of the current, which is derived from the probability of the corresponding active power
Summary
The power grid is an important infrastructure for a nation’s economic and social development, in recent years, the objective environment to guarantee the security and stability of the power grid is undergoing tremendous changes. There are many studies devoted to hot-spot temperature forecasting such as the radial basis function network [11], a genetic algorithm based technique [12], and a local memory-based algorithm [13] provided by Galdi et al, the Takagi-Sugeno-Kang fuzzy model presented by Siano [14], the optimal linear combination of artificial neural network approach used by Pradhan and Ramu [15], the grey-box model introduced by Domenico et al [16], etc Though these researches make tremendous contributions, efforts on overload capability assessments should not be stopped, and new overload capability measurement techniques with respect to power transformers still need to be developed and exploited to improve the accuracy of overload capability assessment and provide more techniques to prevent failure of transformers caused by emergency overloads.
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