Identification of wavefronts in Partial Discharge acoustic signals using discrete wavelet transform

Arrival time estimation methodology for partial discharge acoustic signals in power transformers based on a double-threshold technique

“…The first category is simple iteration methods [8], such as the Newton-Raphson, the Chan, and steepest descent methods. The second category includes intelligent optimization algorithms, such as the genetic algorithm (GA) [9], the particle-swarm-optimization (PSO) algorithm [10], and the imperial competition algorithm (ICA) [11]. The latter two categories are based on the TDOA method, and the coordinates of PD sources are derived by solving TDOA equations where propagation routes are regarded as direct wave paths.…”

Partial discharge localisation methodology for power transformers based on improved acoustic propagation route search algorithm

“…However, because of improper initial values or the use of a single search path, these methods suffer local convergence or nonconvergence. Later studies focused some intelligent optimisation algorithms, such as the genetic algorithm (GA) [1], the particleswarm-optimisation algorithm [6,7], the ant system algorithm [8], and the simulated annealing algorithm [9]. Moreover, some researchers located PD sources by comparing the measured signals and reference signals [10,11], and others used feature extraction and support vector machines to discriminate PD sources at different locations [12].…”

Simultaneous location of two partial discharge sources in power transformers based on acoustic emission using the modified binary partial swarm optimisation algorithm

“…In order to localise PD sources, acoustic signals should be analysed [7–9]. Currently, the wavelet transform is extensively used for denoising and analysing acoustic signals [7–9]. This transform is capable of extracting signal spectrum components [7–9].…”

“…Currently, the wavelet transform is extensively used for denoising and analysing acoustic signals [7–9]. This transform is capable of extracting signal spectrum components [7–9]. So, it can be used for de‐noising the signals [9–11].…”

“…This transform is capable of extracting signal spectrum components [7–9]. So, it can be used for de‐noising the signals [9–11].…”