Smart grids are considered to be the ‘next generation’ of electricity supply systems, capable of increasing supply reliability, availability and energy efficiency through the use of information and communication technologies. Despite these advantages, however, the development of smart grids in rail has lagged behind the domestic sector and other industries, only recently becoming a focus of the future railway. Generally speaking, the technologies suitable for railway smart grids are already being used in other sectors, but the unique socio-political and technological environment of rail makes their implementation challenging. This review explores smart grids in the rail context, focusing on the specific drivers, benefits and challenges for the development of railway smart grids. The necessity of rail as a future transport mode is highlighted, before the following drivers and their related benefits are explored: fossil-fuel reliance, supply reliability, customer participation and the nature of rail traction demand. Finally, the railway power supply system is described and a simple railway smart grid architecture introduced before seven technical challenges are presented against the rail background. These are: interfacing new equipment, electromagnetic compatibility, developing communications, distributed generation, cybersecurity, data and standardisation and regulation. It is hoped that this review will stimulate discussion in the field of railway smart grids and direct research into addressing the railway specific challenges hindering smart grid implementation.
Accurate, rapid estimation of epicentral distance, Δ, is essential for earthquake early warning systems. To improve Δ estimation, new methods to calculate the empirical relationship between the amplitude growth rate parameter, C, and Δ are investigated. Using orthogonal regression is most appropriate for application to EEW systems for Japanese high-speed trains. Evaluation using a K-NET dataset, of earthquake epicenters up to 200 km from the recording station, showed that the proposed method reduces maximum error from 546.2 km to 209.8 km. The percentage of correct estimations, defined as estimates within ±30% of the measured epicentral distance, is increased from 38.0% to 55.3%.
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