The existing division of system stability region does not fully consider the influence of nonlinear characteristics of the system. To address the challenge that it is difficult to inscribe the stability region of wind power grid-connected systems accurately and quantitatively, this paper proposes a nonlinear system multi-parameter practical stability region analysis method based on limit cycle amplitude tracking. Firstly, the nonlinear dynamic model of multi-machine system with doubly fed induction generator (DFIG) is established, and the bifurcation diagram of the system is obtained by nonlinear analysis of the model. Then, the limit cycle-parameter diagram is obtained by tracking the limit cycle of the Hopf bifurcation point of the system, and the problem of how the system oscillation evolves with the change of operating parameters is analyzed. Afterwards, the single parameter practical stability region of the nonlinear system is obtained by combining the system equilibrium manifold. Finally, the multi-parameter practical stability region of the nonlinear system is obtained by the three-dimensional bifurcation map with the system time delay, DFIG output active power and reactive load as the bifurcation parameters. The division of the stability region has important guiding significance for the parameter adjustment in the actual operation of the system.

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.

To reduce the dimensions of a multiterminal DC (MTDC) distribution system connected with multiple voltage source converters (VSCs), a dynamic equivalent model is normally established, assuming that the VSCs are either identical or have a specified topology. These assumptions are inconsistent with practical scenarios, and thus, may lead to errors in the analyzed results. In this study, differences of the VSCs are considered, and a dynamic equivalent model of a DC-voltage-droop-controlled MTDC distribution system is established by reconstructing the dynamics of the VSCs and the DC network. Based on that, a dynamic-reconstruction-based reduced-order modal calculation method is proposed to quickly calculate the DC voltage oscillation mode and determine the stability. For a MTDC distribution system with N different VSCs, the highest dimension is NH + T, where the orders of the VSC and DC network are H and T, respectively. The highest dimension using the proposed method can be reduced to H + 1. Finally, the advances of the proposed method are demonstrated by comparing with other reduced-order methods. The conclusions are verified by both a simple example and a large-scale DC distribution system with 100 different VSCs.

Dense frequency signal refers to the signal containing harmonic and interharmonic components that are very close to each other in frequency domain. Due to the limited frequency resolution and the existence of spectrum leakage and fence effects, it is difficult to accurately detect the harmonic and interharmonic components in dense frequency signals. The spectral interference under asynchronous sampling conditions can lead to the failure of measuring the multiple adjacent harmonics and interharmonics. Therefore, this article proposes a mixed dense signal harmonic and interharmonic analysis method. The dense frequency signal model is first established. Based on the model, the discrete Fourier transform results of the dense frequency signal are processed by local mean decomposition extension to construct that single multichannel signal as a virtual multichannel. Then, the parameters of each frequency component are obtained by means of principal component analysis and fast independent component analysis. Finally, the measurement of dense harmonic and interharmonic components is implemented. The simulation and experimental results show the high accuracy and the noise resistance ability of the proposed method in identifying and measuring the dense harmonic and interharmonic components.

Accurate harmonic modeling is key for the assessment and mitigation against harmonic issues. Existing modeling methods face difficulties in reflecting the influence of uncertain factors such as operating states of the harmonic source, resulting in poor accuracy. To address this problem, we propose in this study a general harmonic probability model (GHPM) based on operating states identification. Firstly, a load operating state identification algorithm is described, to support modelling according to the different operating states. Secondly, the proposed GHPM is presented. It includes the possible probability density function of the emission by the typical harmonic sources, which overcomes the poor generality of the traditional methods. Furthermore, this study proposes the parameter estimation method of the proposed GHPM based on the monitored data. Finally, the proposed method is verified by a 110 kV steelmaking plant, PV power station and wind farm in Central China, and the IEEE 13-bus test system. The effects of practical issues are also discussed to show the applicability of the proposed method.

Voltage sag can result in extensive losses of sensitive users. However, such losses have yet to be quantified based on power quality monitoring data. The load loss rate (LLR) is a practical index for evaluating the consequences of voltage sag, providing vital information for mitigation decisions. This study proposes a data-driven LLR evaluation method of sensitive users under voltage sag, which can be used as a basis for configuring mitigation devices. First, the LLR calculation index is proposed, based on the sensitive load response characteristics and the active power trajectory under voltage sags. Second, a feature indices system is established for the active power trajectory, based on the basic attributes and load response characteristics of sensitive load, to characterize the change of active power under voltage sag. Furthermore, a voltage sag data augmentation model is constructed to generate sample data, considering the characteristic calculation model of voltage sag and the probability density distribution of characteristics. Finally, LLR evaluation model is proposed, based on random forest regression for sensitive users under voltage sags. The effectiveness and accuracy of the proposed method are verified through a case study.