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With the rapid development of renewable energy resources, harmonics in modern distribution systems have become more serious owing to the wide adoption of power electronic devices. Today, distribution-level phasor measurement units (D-PMUs), which can provide synchronized harmonic data with high-time resolution, are being rapidly developed. To utilize the D-PMUs, a hierarchical harmonic contribution (HC) determination method is proposed in this paper for systems with multiple harmonic sources. The HCs were determined using a widely linear complex partial least squares (WL-CPLS) method, which is both accurate and efficient. In an 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 a radial distribution system with any number of D-PMU placements. The methodologies are explained and verified using the IEEE 37-bus system. The real case study results based on the demonstration project are also analyzed in this paper.

Accurate modeling of arc is significant for the researches on the high impedance arcing fault (HIAF), which performs variations of nonlinear distortions 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 distortions of currents with improved controllability and higher accuracy. Firstly, with field HIAFs experimented in a 10 kV distribution network, three major characteristics varying in the distortions of currents 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 after mathematical assumptions and transformations. The three characteristics of distortions can be independently controlled by the proposed model, specifically, by the three parameters with practical meanings. After illustrating the implementation of the model in PSCAD, an automatic parameter determination method is presented by taking advantage of the particle swarm optimization in a Python-PSCAD-MATLAB co-simulation platform. Finally, the controllability and accuracy of the proposed model are verified by comparing it with the existing typical black-box models.

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.

In the active distribution networks (ADN), the interharmonic distortion level is aggravated due to the growing penetration of distributed generations and wide application of power-electronic loads. The interharmonics, even at a low amplitude level, can cause power-quality problems, deteriorate the measurement accuracies 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 to estimate their frequencies and magnitudes. In the scheme, a totally data-dependent threshold is proposed to detect interharmonics adaptively. This adaptive threshold is determined 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 adaptive 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 electric vehicle's charging station.