Traveling wave based fault location for power transmission lines using morphological filters and clarke modal components

This article presents a fast and accurate fault location approach for power transmission lines based on the theory of traveling waves. In fact, when faults occur, they give rise to transient voltages and currents that propagate at a speed close to that of light along the transmission line as traveling waves. Moreover, according to the superposition theorem, each of these transients is a combination of a steady-state quantity and an incremental quantity. These transient signals measured at both ends of the line are first transformed to the Clarke (0-α-β components) components in order to categorize the type of faults, and then multi-scale morphological gradient filters are used to extract equivalent quantities to the incremental quantities to form what are called characteristic signals. These latter will be used to identify the fault location according to the proposed algorithm

Multi-scale morphological gradient algorithm based ultra-high-speed directional transmission line protection for internal and external fault discrimination

This paper introduces an ultra-high-speed directional transmission line protection scheme based on multi-scale morphological gradient algorithm (MSMGA). The directional protection scheme sets down the rules for determining the fault position in relation to the relaying point. The MSMGA is used to extract the fault-induced transient characteristics contained in the voltage and current signals. The associated signals are formed from these transient characteristics and the polarity of their local modulus maxima allow the discrimination between internal and external faults

A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS

Meta-heuristic optimization methods applied to renewable distributed generation planning: A review

Due to its proven efficiency and computational speed, the most recent developed meta-heuristic optimization methods are widely used to better integrate renewable distributed generation (RDG) into the electricity grid. The main objective of this paper is to obtain a better knowledge of current trends in meta-heuristics applied to optimally integrate RDGs to the distribution network. This is a review of well known meta-heuristic approaches, used to solve the problem of optimal renewable distributed generation allocation planning (ORDGAP). In this context, some research gaps were mentioned, and recommendations were proposed to expand the scope of research in this field