Grounding arc fault (GAF) happening in the medium-voltage (MV) distribution system may result in great damages to devices and human security. However, difficulties still exist in identifying those faults with higher grounding impedances due to the weaker feature and the varieties when grounded with different surfaces. This paper presents an integrated algorithm to detect the high impedance arc faults (HIAFs) with high-resolution waveform data provided by distribu-tion-level PMUs (D-PMUs) deployed in the system. An im-proved arc model is proposed which can continuously imitate the randomness and intermittence during the ‘unstable arcing period’ of arc faults. The integrated algorithm consists of two branches. Firstly, the variations of HIAFs during unstable arcing period is identified with the unified harmonic energy and global randomness index, which can unify the scale of harmonic content in different fault situations and enlarge the disparities from non-fault conditions. Then, the waveform distortions of HIAFs during the stable arcing period are identified with discrete wavelet transform (DWT) to extract the detailed distribution characteristics. The reliability and security of the proposed algorithm is verified with numerical simulations and field tests in a 10kV distribution system.

The accurate modelling of the arc is significant for the researches on the high impedance arcing fault (HIAF), which performs sig-nificant variations of nonlinearity under different fault conditions. The diversity of the waveform distortions during HIAFs is rarely investigated previously. This paper proposes a black-box HIAF model to simulate the nonlinear current distortions with im-proved controllability and higher accuracy. Firstly, with field HIAF faults experimented in a 10kV distribution network, three major characteristics varying in the distortions of the fault cur-rent are summarized and illustrated, including the offset, extent, and duration. Secondly, a distortion-controllable (DIST-C) HIAF model is proposed based on the heat balance equation of the arc after reasonable assumptions and transformations. The three characteristics of distortions can be independently and directly controlled. Then, the implementation of the DIST-C model in PSCAD is detailedly illustrated. An automatic parameter deter-mination method using particle swarm optimization in a Py-thon-PSCAD-MATLAB co-simulation platform is presented. Finally, the controllability and accuracy of the proposed model are verified by comparing it with the other existing typical black-box models.

With the fast development of renewable energy resources, harmonics in the modern distribution system become more serious due to the wide adoption of power electronic devices. Nowadays, the distribution-level phasor measurement units (D-PMUs), which can provide synchronized harmonic data with high time resolution, are under fast development. To take advantage of the D-PMUs, a hierarchical harmonic contribution (HC) determination method is proposed in this paper for systems with multiple harmonic sources. The HCs are determined by the widely linear complex partial least squares (WL-CPLS) method which is both accurate and efficient. In the actual power system, the D-PMUs are not fully installed at all buses and the harmonics monitored by each D-PMU are actually the aggregate effects of multiple harmonic sources in the subarea. Based on the above consideration, a hierarchical HC strategy is proposed to locate the main harmonic contributors layer by layer. The proposed method can be applied to the radial distribution system with any number of D-PMU placement. The methodologies are illustrated and verified by the IEEE 37-bus system.

Phasor measurement is widely used in fault detection and localization of power systems. To improve the phasor measurement accuracy when the fault signal contains the decaying dc component (DDC), this article proposes a half-cycle DFT (HCDFT)-based phasor estimation algorithm. The error caused by DDC is estimated and removed from the phasor estimate by combining multiple HCDFT results that are computed using both the conventional basis vector and its corresponding complex conjugate. Both the numerical tests and the IEEE 39-bus system simulation tests are implemented to evaluate the performance of the proposed algorithm in comparison with four related phasor estimation algorithms. Test results show that the proposed algorithm can achieve accurate phasor estimation in the presence of DDC of a wide range of time constants under the interference of harmonics and noise. Moreover, the proposed algorithm is computationally efficient and its performance is independent of sampling rate. These merits make the proposed algorithm suitable to be applied in measurement and protection devices in real power systems.

In the active distribution network (ADN), the interharmonic dis-tortion level is aggravated due to the growing penetration of dis-tributed generations and wide application of power electronic loads. The interharmonics, even at a low amplitude level, can cause power quality problems, deteriorate the measurement ac-curacies of the advanced metering infrastructures, and increase the possibilities of power system oscillations. In this paper, an interharmonic detection scheme is proposed to effectively extract the interharmonic components of the power system even with the extremely noisy signals, and then estimate their frequencies and magnitudes. A totally data dependent threshold is proposed to detect interharmonics adaptively. This adaptive threshold is de-termined iteratively based on the statistical character of the background noise bins. And then the time-varying frequencies and magnitudes of the detected interharmonics are estimated using the time-domain method. Case studies show that the adap-tive threshold is robust under the conditions with high noisy signal, and the proposed scheme can estimate precisely the frequencies and magnitudes of the interharmonics detected for both the steady-state and dynamic interharmonic conditions. Furthermore, the proposed scheme is also verified using the field 10-kV current signal of one fuel company and 0.4-kV voltage signal of one elec-tric vehicles charging station.

Distributed energy resources and new types of electrical loads are morphologically changing the signals of power grids. Grasping the wideband information of field data will play an important role in helping smart grids to operate in a safe manner. A wideband signal model composed of deterministic and stochastic components is proposed to enhance the wideband measurement. Based on the model, an adaptive decomposition scheme is designed for the offline analysis of wideband signals. In the scheme, a modified robust local regression smoothing method is presented to denoise the wideband signal. A totally data-dependent threshold is established to extract adaptively interharmonic components. Further, the Taylor-Fourier transform algorithm is used to estimate the parameters of deterministic components based on the subsignals extracted by the Chebyshev-II infinite impulse response filter bank. Finally, the stochastic components are extracted using the adaptive threshold based on the signal that the deterministic components are removed. The performances of the decomposition scheme under different conditions have been validated. In the end, the proposed decomposition scheme is carried out and verified with the field data of the 24-h current signals at one residence community.