A new class of bias‐compensated affine‐projection‐like (APL) algorithms is proposed, in which a bias‐compensation vector is derived to eliminate the bias caused by the noisy input. In addition, a new estimation method for the input noise variance is proposed which does not require the input–output noise variance ratio in advance. Simulations in a system identification context show that the proposed algorithms achieve significant improvements in steady‐state misalignment as compared with the conventional APL algorithms.

This paper proposes an unscented Kalman filter (UKF)-based unbiased minimum-variance estimation (UMV) method for the nonlinear system with unknown inputs. By utilizing the statistical linearization, the nonlinear system and measurement functions are transformed into a “linear-like” regression form. The latter preserves the nonlinearity of the system and the measurement models. To this end, the unknown inputs can be estimated by the weighted least-squares. This “linear-like“ regression form also allows us to resort to the UMV state estimation framework for the development of new nonlinear filter to handle unknown inputs. Specifically, two approaches have been developed: 1) given the estimated inputs, we derive a filter by minimizing the trace of the state error covariance matrix; 2) without input estimation, we derive the filter by minimizing the trace of the state error covariance matrix subject to a constraint imposed on the gain matrix. We prove that these two approaches provide the same results. Numerical results validate the effectiveness of the proposed method.

This paper presents a family of robust set-membership normalized subband adaptive filtering (RSM-NSAF) algorithms for acoustic echo cancellation (AEC). By using a new robust set-membership error bound, the RSM-NSAF algorithm obtains improved robustness against impulsive noises and decreased steady-state misalignment relative to the conventional set-membership NSAF (SM-NSAF) algorithm. To exploit the sparsity of the impulse response, the L ₀ norm constraint robust set-membership NSAF (L ₀ -RSM-NSAF), robust set-membership improved proportionate NSAF (RSM-IPNSAF), and L ₀ norm constraint robust set-membership improved proportionate NSAF (L ₀ -RSM-IPNSAF) algorithms are derived by minimizing a differentiable cost function that utilizes the Riemannian distance between the updated and previous weight vectors as well as the L ₀ norm of the weighted updated weight vector. Simulations in AEC application confirm the improvements of the proposed algorithms in performance.

This paper proposes a correlation-aided robust adaptive unscented Kalman filter (UKF) for power system decentralized dynamic state estimation with unknown inputs, termed as robust AUKF-UI. The temporal and spatial correlations among the unknown inputs are used to derive a vector auto-regressive (VAR) model in an adaptive manner. This VAR model is further integrated together with state transition and measurement models for joint state and unknown inputs estimation. This allows taking into account the implicit cross-correlations between the states and the unknown inputs. As a result, the rank requirement for unknown input vector estimation is relaxed and the local generator frequency measurement is not required. The temporal correlations of time series innovation vectors, predicted state and input vectors are also leveraged by the robust AUKF-UI to detect, identify and process bad data. Without these correlations, it is very challenging to address bad data with unknown inputs. Simulation results carried out on the IEEE 39-bus system demonstrate that, thanks to the use of hidden system temporal and spatial correlations, the proposed robust AUKF-UI has lower requirement of the number of measurements for dynamic state estimation while achieving better robustness against bad data as compared to the existing UKF methods with unknown inputs.

A phasor measurement unit (PMU) is the core measurement component of power system monitoring and analysis. The placement of PMUs directly impacts the state estimation confidence, and hence the optimal PMU placement (OPP), that, minimizing the number of PMUs and ensuring system observability, is appealing to power engineers. However, the current observability analysis mostly relies on topological methods, which cannot reflect the influence of the operating environment. Moreover, intricate OPP models are driving higher demand for efficient solvers. Confronting these challenges, we propose a reinforcement learning graph convolutional network-deep deterministic policy gradient algorithm-based OPP strategy, which effectively captures the system graph structure and PMU state, thereby independently identifying the PMUs value. Furthermore, the degree of system observability is considered, and three observability quantitative indicators are proposed in the OPP strategy, which will enhance the confidence of the perceived state under complex operating environment. The effectiveness of the proposed strategy have been validated in multiple test systems.

With the increase of the train speed, the pantograph-catenary (PC) detachment becomes more frequent, which results in more serious PC detachment arc. In order to analyze the influence of PC detachment arc on the train system, several arc models are established. However, these models do not consider the actual PC detachment process. In this paper, an extended black-box model of PC arc is proposed, which considers the dynamic detachment process of PC based on the PC dynamics. First, the Habedank arc model is presented and the relationship of the PC detachment distance with the arc voltage and arc dissipated power is established. Next, the dynamic detachment trajectory is obtained from the dynamic simulation of the PC structure model. Then, by introducing the dynamic PC detachment trajectory model into the arc model, the dynamic arc model is built. Finally, the performance of the arc current and arc voltage is analyzed and the effectiveness of the dynamic arc model is verified.