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.

Suppressing current harmonics is an essential issue for photovoltaics (PVs). The equivalent impedance or admittance expresses the interaction between the PV converter (PVC) and the background harmonics from the grid, where the control strategies and parameters play a crucial role in the process. Currently, multifrequency coupling characteristics have become more vital in harmonic analysis, and traditional selective harmonic suppression methods show imperfect performance. The key to solving this issue is constructing an adaptive controller for coupled harmonic mitigation according to grid conditions. Therefore, this article first constructs a harmonic coupling matrix model (HCMM) of the PVC to describe the harmonic coupling characteristics, which can represent the influence of topology and control parameters simultaneously. Second, the effect of the critical controller parameters and virtual impedance method on the coupled harmonic admittance of the HCMM is analyzed. Finally, coordinated control of adaptive parameter tuning and virtual impedance injection method is proposed to reduce coupled harmonics efficiently and to avoid the instability caused by the modulation voltage saturation. Comparative experiments are provided by a three-phase PVC with a digital signal processing (DSP) controller, and the results verify the validity of the coupled harmonic analysis and the effectiveness of the proposed method.

The widespread application of nonlinear power electronic devices in residential loads has resulted in significant harmonic distortions. Harmonic source modeling is of great importance since it is a prerequisite to estimate the harmonic pollution. In this paper, a dynamic harmonic coupled matrix model (HCMM) is proposed for the nonlinear home appliances. This model is adaptive to the load supply voltage conditions; therefore, the dynamic harmonic generating characteristics of nonlinear home appliances can be demonstrated by the proposed HCMM. Moreover, the HCMM derivation process is based on measurement, independent of the load equivalent circuit, which makes the proposed method most suitable for nonlinear home appliances with unknown circuit topologies. The widely linear complex partial least squares (WL-CPLS) method, which has a fast speed and high robustness, is used to estimate the HCMM. A procedure to establish the HCMM for multistate home appliances is also proposed in this paper. Lab experiments were performed to verify the accuracy and validity of the proposed methodology. The aggregate harmonics caused by multiple nonlinear residential loads have been analyzed based on the proposed HCMM.

As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton–Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.

Harmonics produced by residential loads have become an increasing concern in recent years due to the extensive adoption of power-electronic-based home appliances. Harmonic modeling of aggregate residential loads (ARLs) is of great significance since it is the prerequisite for harmonic evaluation and necessary mitigation measures in the residential areas. In this paper, a piecewise probabilistic harmonic model is proposed to analyze the aggregate harmonics of residential loads. Based on the field measurement data, a unified dominant harmonic coupled matrix model (UD-HCMM) is established. This model directly exhibits the coupled relationship between the harmonic voltages and currents of residential loads. In view of the random and fluctuating harmonic characteristics of home appliances, the multi-Gaussian curve fitting method is used to fit the UD-HCMM parameters. Thereafter, the probabilistic UD-HCMMs are reconstructed using the Newton-Raphson method. Finally, a piecewise UD-HCMM is derived to characterize the time-varying harmonics of ARLs. The accuracy and validity of the proposed method are verified through simulations and field measurements. In addition, the proposed method is applied to evaluate the harmonics of a medium voltage distribution system with massive residential loads.

The power system on the offshore platform is of great importance since it is the power source for oil and gas exploitation, procession and transportation. Transformers constitute key equipment in the power system, and partial discharge (PD) is its most common fault that should be monitored and identified ın a timely and accurate manner. However, the existing PD classifiers cannot meet the demand for real-time online monitoring due to their disadvantages of high memory consumption and poor timeliness. Therefore, a new MobileNets convolutional neural network (MCNN) model is proposed to identify the PD pattern of transformers based on the phase resolved partial discharge (PRPD) spectrum. The model has the advantages of low computational complexity, fast reasoning speed and excellent classification performance. Firstly, we make four typical defect models of PD and conduct a test in a laboratory to collect the PRPD spectra as the data sample. In order to further improve the feature expression ability and recognition accuracy of the model, the lightweight attention mechanism Squeeze-and-Excitation (SE) module and the nonlinear function hard-swish (h-swish) are added after constructing the MCNN model to eliminate the potential accuracy loss in PD pattern recognition. The MCNN model is trained and tested with the pre-processed PRPD spectrum, and a variety of methods are used to visualize the model to verify the effectiveness of the model. Finally, the performance of MCNN is compared with many existing PD pattern recognition models based on convolutional neural network (CNN), the results show that the proposed MCNN can further reduce the number of parameters of the model and improve the calculation speed to achieve the best performance on the premise of good recognition accuracy.