The high proportion of nonlinear and unbalanced loads results in power quality issues in islanded microgrids. This paper presents a novel control strategy for harmonic and unbalanced power allocation among distributed generators (DGs) in microgrids. Different from the existing sharing strategies that allocate the harmonic and unbalanced power according to the rated capacities of DGs, the proposed control strategy intends to shape the lowest output impedances of DGs to optimize the power quality of the microgrid. To achieve this goal, the feasible range of virtual impedance is analyzed in detail by eigenvalue analysis, and the findings suggest a simultaneous adjustment of real and imaginary parts of virtual impedance. Because virtual impedance is an open-loop control that imposes DG to the risk of overload, a new closed-loop structure is designed that uses residual capacity and absorbed power as feedback. Accordingly, virtual impedance can be safely adjusted in the feasible range until the power limit is reached. In addition, a fuzzy integral controller is adopted to improve the dynamics and convergence of the power distribution, and its performance is found to be superior to linear integral controllers. Finally, simulations and control hardware-in-the-loop experiments are conducted to verify the effectiveness and usefulness of the proposed control strategy.

The traditional active power direction (APD) method has been proven to be unreliable to determine the dominant harmonic source at a point of common coupling (PCC), when both sides have the harmonic sources. In view of this shortcoming, this paper proposes an improved APD by imposing certain constraints to the traditional one. The constraints are developed based on the voltage and current measurements at the PCC and the impedance properties of the two sides. Through rigorous mathematical analysis, it has been proven that when those constraints are satisfied, the improved APD method is guaranteed to provide accurate results. Performance and effectiveness of the proposed method are demonstrated through simulation and field case verifications.

Because of the volatility and intermittence of the renewable energy units in the novel power system, the electrical variables usually exhibit dynamic characteristics. Under fault conditions, the electrical variables usually contain a decaying DC (DDC) component. Currently, when considering the DDC component and the dynamic characteristics of electrical variables, it is difficult to obtain highly precise estimation results in most references. In this article, a fast dynamic phasor estimation algorithm considering the DC offset is proposed to obviate errors caused by dynamic characteristics and the DDC component. The algorithm consists of two parts. The first part establishes the mathematical model of DDC using exponential functions and obtains the exact DDC component by iteration. The second part establishes the compressive sensing Taylor Fourier Multi-Frequency (CSTFM) model of the dynamic phasor. The improved sparsity adaptive matching pursuit (ISAMP) algorithm is used to quickly and accurately estimate the dynamic phasor, frequency and rate of change of frequency (ROCOF). Simulation and field data test results show that the proposed algorithm can accurately estimate the fault current phasor, frequency, and ROCOF compared with existing phasor estimation algorithms.

Line commutated converter based high voltage direct current (LCC-HVDC) has played a more and more vital role in the power systems in recent years. However, the energization inrush current generated by unloaded switched-on of converter transformer se-riously threatens the safety and stable operation of the power sys-tem. To limit the energization inrush current, a suppression method utilized resistive type superconducting fault current limit-er (R-SFCL) is investigated in this paper. Firstly, the mechanism of energization inrush current is described, and the theoretical feasibility of SFCL for such an application is discussed. Then, the structure and working principle of the R-SFCL are studied. Sim-ulation results obtained in PSCAD/EMTDC validated that the R-SFCL can effectively limit the energization inrush current.

So far, there is no literature to evaluate the fault level of sensitive equipment (FLSE) caused by voltage sag from the perspective of the power grid. In practice, although the FLSE are dominated by the voltage sag of node, the voltage sag of the node is associated with whole power grid, including the voltage grade and location of the node, the distance between the concerned node and location of fault, and the weather and date at the time of fault, which are all named as voltage sag properties (VSP). In view of this gap, this paper evaluates the FLSE using the long-time monitoring data of VSP of some regional power grid by data mining method based on multidimensional matrix simplification and the improved gray target theory. The data mining process can be divided into three steps: 1) construct a database consisted of VSP and FLSE using long-time monitoring data, 2) mine the association rules between VSP and FLSE by multidimensional matrix simplification, and 3) match some actual scenario with the mined association rules by the improved gray target theory. Performance and effectiveness of the proposed method are verified through the simulation and field case.

In large-scale applications, parts of harmonic data are inevitably lost during transmission. This study presents an approach for the recovery of missing harmonic data based on the spectral graph theory. The proposed methodology involves graph theory for constructing a Laplacian matrix and a graph signal reconstruction function, the merging K -means algorithm for building a priori information model, and the accelerated segmentation Bregman iterative algorithm for solving the reconstruction function. Compared with existing methods on data recovery in power systems, the method maintains a good recovery accuracy when the data correlation of measurement units is low and the prior information is little. The proposed method has good anti-noise performances and low computational complexity. The feasibility and accuracy of the proposed method are verified through simulation and field recorded data.