Frequency regulation scheme of doubly-fed induction generators (DFIGs) are implemented to improve the frequency stability achieving by rotor speed control. However, the coupling dynamics between the maximum power point tracking operation control loop and frequency regulation control loop would impair the frequency regulation capability. To eliminate the adverse influences, firstly, analytical derivation for coupling output power dynamics is represented when adopting frequency regulation scheme of the DFIG. Then, an adaptive frequency droop feedback control based on power tracking operation strategy, which is a decoupling control scheme for frequency regulation, is proposed to respond the dynamic grid frequency without reserving power. The frequency deviation-based droop control with dynamics gains is used to calculate the coefficient of power tracking operation of the DFIG. The system stability with various power tracking operation of the DFIG is analyzed, in addition, the stable boundary is used to limit the setting of coefficient of power tracking operation. Simulations are carried based on an EMTP-RV simulator. It is found that the suggested adaptive frequency droop feedback control based on power tracking strategy can provide dual frequency regulation capability and reduce the maximum frequency excursion under various scenarios.

This paper provides a comprehensive analysis of several hybrid state estimators (HSEs) to assess their performance and identify their unique characteristics in transmission network. The results reveal that the formulation of a HSE can have a significant impact on both the estimation accuracy and the convergence properties of the estimator, which are the two most important performance factors for an HSE. Furthermore, the analysis performed has identified the formulation that maximizes these factors. The assessment can be divided into three parts. The first part conducts comprehensive simulations for the IEEE 14 bus test system for every possible PMU placement. The second part validates the conclusions made in the first part using simulations of the IEEE 118 bus test system with both random PMU placements and optimally and sub-optimally selected placements. The third part further validates these conclusions and directly explains the reasons for the differences in estimation accuracy observed for the different formulations of HSEs through mathematical analysis.

As the penetration rate of distributed generators (DG) in active distribution networks (ADNs) gradually increases, it is necessary to accurately estimate the operating state of the ADNs to ensure their safe and stable operation. However, the high randomness and volatility of distributed generator output and active loads have increased the difficulty of state estimation. To solve this problem, a method is proposed for forecasting-aided state estimation (FASE) in ADNs, which integrates the optimal extreme random forest based on the maximum average energy concentration (MAEC) and variable mode decomposition (VMD) of states. Firstly, a parameter optimization model based on MAEC is constructed to decompose the state variables of the ADNs into a set of intrinsic mode components using VMD. Then, strongly correlated weather and date features in ADNs state prediction are selected using the multivariate rapid maximum information coefficient (RapidMIC) based on Schmidt orthogonal decomposition. Finally, by combining the set of intrinsic mode functions of the ADNs state, calendar rules, and weather features, an ensemble FASE method based on the extreme random tree (ERT) ensemble for the ADNs based on cubature particle filtering (CPF) is developed. An optimization model based on mean absolute error and root mean square error is established to obtain the optimal integration strategy and final estimation results. Simulation verification is performed on the IEEE 118-bus standard distribution system. The results show that the proposed method achieves higher accuracy compared to other estimation methods, with root mean square errors of 1.4902 × 10⁻⁴ for voltage magnitude and 4.8915 × 10⁻³ for phase angle.

Robustness is an important performance index of power system state estimation, which is defined as the estimator’s capability to resist the interference. However, improving the robustness of state estimation often reduces the estimation accuracy. To solve this problem, this paper proposes a power system state estimation method for generalized M-estimation of optimized parameters based on sampling. Compared with the traditional robust state estimator, the generalized M-estimator based on projection statistics improves the robustness of state estimation, and the proposed optimized parameter determination method improves the overall accuracy of state estimation by appropriately adjusting its robustness. Considering different degrees of non-Gaussian distributed measurement noises and bad data, the estimation accuracy the proposed method is demonstrated to be up to 23% higher than the traditional generalized M-estimator through MATLAB simulations in IEEE 14, 118 bus test systems, and Polish 2736 bus system.