Suppressing white‐noise in partial discharge measurements—part 1: construction of complex Daubechies wavelet and complex threshold

Abstract

SUMMARYOnline partial discharge (PD) detection still remains a very challenging task because of the strong electromagnetic interferences. In this paper, a new method of de-noising, using complex Daubechies wavelet (CDW) transform, has been proposed. It is a relatively recent enhancement to the real-valued wavelet transform because of two important properties, which are nearly shift invariant and availability of phase information. Those properties give CDW transform superiority over other real-valued wavelet transform. On the basis of CDW transform, complex threshold algorithm and combined information are devised. In order to take into account the difference of the real part and imaginary part of complex wavelet coefficients, complex threshold is devised to modify the real part and imaginary part respectively. Because the restored signal by inverse CDW transform is complex one, combined information is devised to combine the real part and imaginary part of complex signal to construct a real one, furthermore making full use of the simple information from complex signal.

Quantitative analysis of the influence of regularity of SF6 decomposition characteristics with trace O2 under partial discharge

“…However, when the O 2 concentration (%) is considerably small (within specific range of trace O 2 in the operation level), the rate of formula (3) is too small so that the influence of H 2 O on SO 2 F 2 generation is relatively significant. H 2 O released by the electrodes and internal wall of the decomposition equipment is diluted by the increasing of trace O 2 concentration (%), thus decreases the rate of reactions in formulae (6) to (10) and reduces the generated SOF 4 amount. In addition to the reduction of H 2 O volume fraction, the reaction in formula (11) will be double restrained.…”

“…Since UHF method can monitor PD with ocular and timely merits, it is treated as the most popular approach to assess PD status of SF 6 electrical equipment and has been used widely [5][6][7][8]. Nevertheless, since the fully-enclosed structure of SF 6 electrical equipment and unavoidable strong on-site electromagnetism and random narrow-band interference in detection band, many difficulties exist when using UHF to monitor PD and diagnose insulation fault quantitively [10,11]. As a result, to explore new methods to monitor and diagnose the inner insulation faults of SF 6 electrical equipment is a major research concern.…”

Creeping discharges features propagating in air at atmospheric pressure on various materials under positive lightning impulse voltage – part 1: noise suppression using the discrete wavelet transform approach

“…The pulse frequency features are quite similar to those of PD 1D signals and need advanced filtering techniques to extract noise [59,60]. Thus, the PDs signal analysis using the DWT is an interesting possibility when comparing its performance to those provided by existing methods [61][62][63][64][65][66]. Indeed, the DWTbased PDs de-noising technique has shown its effectiveness for de-noising many sources of noise for those cited above except for the DSI interference to which additional efforts should be provided [62,67].…”

Partial discharge recognition based on SF6 decomposition products and support vector machine

“…In the UHF method, the ingress of external interference directly affects the sensitivity and reliability of acquired PD data, especially with on-line detection. There are also problems on discharge calibration and pattern recognition in this method [7].…”