In this paper the established performance indices for screening and ranking of power system contingencies are applied. The indices capture the change in the system state variables and deliver the degree to which it is provoked under different operating states. The indices refer to the generator coherency, transient energy conversion, dot products derived from the classical concept of the transient energy function, and generator angular deviation. The indices are calculated using the time domain simulation and a power system model.

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The development in subsea technology relies increasingly on sophisticated and power-intensive equipment, such as pumps, compressors, processing, and flow assurance systems, for which there is a growing need to provide ultra-reliable electrical power transmission, distribution and conversion systems in order to supply subsea processing equipments through long step-outs. In May 2015, the 163km long Martin Linge submarine cable was successfully energised and tested, setting the world record for the longest High Voltage AC (HV AC) subsea cable. This key achievement has demonstrated the feasibility and maturity of AC subsea power transmission for long stepouts. The next technical challenge would be to develop and mature a concept where the full field development is made subsea, whilst using power from shore through a long AC step out. This paper provides an overview of potential architectures for long step-out subsea electrical power transmission and distribution systems. The paper describes the design and main technical challenges of the Martin Linge AC transmission step-out. Finally, the paper provides an insight in the point of view of the manufacturer and operator, with lessons learned in a successful development of a power from shore solution.

This paper presents the first complete approach to achieving environmental intelligence support in the management of vegetation within electrical power transmission corridors. Contrary to the related studies that focused on the mapping of power lines, together with encroaching vegetation risk assessment, we realised predictive analytics with vegetation growth simulation. This was achieved by following the JDL/DFIG data fusion model for complementary feature extraction from Light Detection and Ranging (LiDAR) derived data products and auxiliary thematic maps that feed an ensemble regression model. The results indicate that improved vegetation growth prediction accuracy is obtained by segmenting training samples according to their contextual similarities that relate to their ecological niches. Furthermore, efficient situation assessment was then performed using a rasterised parametrically defined funnel-shaped volumetric filter. In this way, RMSE≈ 1 m was measured when considering tree growth simulation, while a 0.37 m error was estimated in encroaching vegetation detection, demonstrating significant improvements over the field observations.

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