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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.

The increasing penetrations of converter-based renewable energy resources and nonlinear loads make it urgent to develop a detection method to adaptively monitor the sub/super-synchronous components to further detect the potential risk of the co-existence and cooperation of hundreds or thousands of converters. This paper proposes an enhanced adaptive sub/super-synchronous components detection method based on robust regression smoothing filtering (RRSF). According to simulation tests, the proposed method can detect the time-varying sub/super-synchronous components in the noisy signals with SNR of 0 dB and is robust to the colored property of background noise.

To improve the operation security and stability of power systems, the distribution-level synchrophasor measurement techniques have been developed rapidly. However, the signals in distribution systems are heavily corrupted by noise which will deteriorate synchrophasor measurement accuracy. This paper preliminarily investigates the noise in distribution systems according to the field measurement results. Then the two-stage noise filtering method is proposed based on the median filter and mean filter. According to the additive problem caused by median filter, the corresponding compensation coefficients are derived. Finally, numerical tests verify the effectiveness of the proposed filtering and compensation method under different conditions.

With expansion of the urban population, the power consumed by Chinese residential load is increasing rapidly. At the same time, power-electronic devices are widely adopted in home appliances, injecting a great amount of harmonics into the power system, which makes the harmonic problem in residential areas more serious than ever before. In this paper, a bottom-up approach is proposed to evaluate the harmonics of residential energy use. Firstly, a harmonic coupled model is proposed to calculate the harmonics generated by individual nonlinear home appliance. The model directly exhibits the couplings between different order harmonic voltages and currents. Secondly, based on the Markov chain Monte Carlo method, a random usage model is established to determine the usage pattern of home appliances. Various behavioral factors that affect the domestic use of energy in China are considered, and corresponding correction factors are proposed to improve the model accuracy. Thirdly, based on the proposed electrical and random usage models, a bottom-up approach is used to evaluate the harmonics and power of large-scale residential loads. Simulation results are compared with field measurements to verify the accuracy and validity of the proposed method. Furthermore, the proposed methodologies are applied to predict the harmonic generation and power consumption of some rapidly developing harmonic sources such as electric vehicles.

In the active distribution network (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 method is proposed to effectively detect the interharmonic components of the power system even with the extremely noisy signals using a totally data dependent threshold. This adaptive threshold is determined iteratively based on the statistical character of the background noise bins. Case studies show that the adaptive threshold is robust with severely noisy signal for both the steady-state and dynamic interharmonic conditions.

It is determined by the nature of AC power grid that the disturbance in high voltage power grid can be reflected in low voltage power grid to some extent. The idea of the light synchronized phasor measurement unit (PMU Light) and that of the light wide-area measurement system (WAMS Light) was proposed and implemented in low voltage grid. Comparing the data recorded by WAMS and that recorded by WAMS Light, it is clear that the dynamic responses in high voltage power grid and low voltage power grid are strongly correlated. WAMS Light has already recorded several typical large disturbances. So WAMS Light has a broad application prospect in many fields. It also enlightens scholars to dig the value of the dynamic behavior of low voltage grid, the grid measurement theory, and even the way of the decentralized control of the power system.