Accurate forecasting-aided state estimation plays a vital role in reliable and secure operation of power systems. However, most of existing methods are unable to deal with the uncertainties that might be caused by uncertain model parameters or uncertain noise statistics. Therefore, the performance of these methods may be inevitably degraded significantly. To address these issues, based on the robust control theory, in this paper, by incorporating the modified innovation based Sage-Husa estimator of noise statistics and the proposed estimation error covariance matrix adaptive technique, a novel adaptive $H_{\infty}$ extended Kalman filter(AHEKF) is developed to realize robust forecasting-aided state estimation for power system with model uncertainties. Extensive simulations carried out on several different test systems demonstrate the efficiency and robustness of the proposed method.

The robust stability and stabilization of adaptive event-triggered load frequency control (LFC) with sliding mode control (SMC) for multi-area power systems under a networked environment are investigated in this paper. The adaptive event-triggered scheme is proposed to maximize network bandwidth utilization, and it can be adaptively modified according to circumstances. Furthermore, to provide stronger robustness performance which is against the frequency deviation induced by power unbalance or transmission time delays, the SMC is developed. Then, the LFC scheme for multi-area power systems under the networked environment is modeled as a Markov jump linear system model, to describe the uncertainty parameters and external disturbances better in this system. Additionally, by employing Wirtinger-based inequality and Lyapunov theory, the robust stability and stabilization criteria with less conservatism are derived. Finally, simulations are performed to demonstrate the efficacy and superiority of the developed approach.

Accurate estimation of dynamic states is the key to monitoring power system operating conditions and controlling transient stability. However, the inevitable non-Gaussian noise and randomly occurring denial-of-service (DoS) attacks may deteriorate the performance of standard filters seriously. To deal with these issues, a novel resilient cubature Kalman filter based on the Cauchy kernel maximum correntropy optimal criterion approach (termed CKMC-CKF) is developed, in which the Cauchy kernel function is utilized to describe the distance between vectors. Specifically, the errors of state and measurement in the cost function are unified by a statistical linearization technique, and the optimal estimated state is acquired by the fixed-point iteration method. Due to the salient thick-tailed feature and the insensitivity to the kernel bandwidth of Cauchy kernel function, the proposed CKMC-CKF can effectively mitigate the adverse effect of non-Gaussian noise and DoS attacks with a better numerical stability. Finally, the efficacy of the proposed method is demonstrated on the standard IEEE 39-bus system under various abnormal conditions. Compared with standard cubature Kalman filter (CKF) and maximum correntropy criterion CKF (MCC-CKF), the proposed algorithm reveals better estimation accuracy and stronger resilience.

Due to the unfavorable interference of non-Gaussian noise, abnormal system states, and rough measurement errors, dynamic state estimation (DSE) plays an important role in the safe operation of power system. A novel DSE method based on an adaptive cubature Kalman filter (CKF) with generalized correntropy loss (GCL) criterion, termed AGCLCKF, is developed to deal with the complex non-Gaussian distribution noises of power system in this article. First, a nonlinear regression model is derived to simultaneously incorporate the state and noise errors into the GCL cost function, and a fixed-point iteration is exploited to recursively update the posterior state estimate. Then, considering that the filtering performance of the estimator is largely determined by the kernel bandwidth (KB) in correntropy, an adaptive factor is established to adjust the KB of kernel function in real time, which can improve the flexibility and accuracy of DSE in the existence of bad measurement information. Finally, extensive simulation results carried out on the IEEE 39-bus test system demonstrate that the proposed method can achieve much accuracy and robustness under various situations.

It is urgent to exploit additional automatic generation control (AGC) services provided by new frequency regulation units (FRUs) for future power systems with the increasing penetration of the power-converter-interfaced distributed generators. When heterogeneous FRUs with different response speeds operate together, the conventional proportional–integral-based centralized AGC method will face new challenges in parameter design and performance improvement. To explore the potential of heterogeneous FRUs and improve the performance of AGC controllers, this article develops a novel distributed event-triggered fixed-time sliding-mode control (FTSMC)-based AGC approach. First, to make all the FRUs estimate the real area control error (ACE) value, a distributed event-triggered fixed-time ACE discovery algorithm is designed. Then, an FTSMC-based AGC approach is designed to regulate the ACE to allowable value in a fixed time. Finally, to restore the power reservation of rapid-response FRUs, a fixed-time distributed proportional power dispatch approach is designed to improve frequency regulation performance.

This paper develops an adaptive robust cubature Kalman filter (ARCKF) that is able to mitigate the adverse effects of the innovation and observation outliers while filtering out the system and measurement noises. To develop the ARCKF dynamic state estimator, a batch-mode regression form in the framework of cubature Kalman filter is first established by processing the predicted state and measurement data information simultaneously. Subsequently, based on the regression form, the outliers can be detected and downweighted by the robust projection statistics approach. Then, the adverse effects of innovation and observation outliers can be effectively suppressed by the generalized maximum likelihood (GM)-type estimator utilizing the iteratively reweighted least squares approach. Finally, an adaptive strategy is developed to adjust the state estimation error covariance matrix under different conditions. Extensive simulation results obtained from the IEEE New England 10-machine 39-bus test system under various operating conditions demonstrate the effectiveness and robustness of the proposed method, which is able to track the transients of power system in a more reliable way than the conventional cubature Kalman filter (CKF) and the unscented Kalman filter (UKF).