This paper presents a novel approach for the state estimation of poorly-observable low voltage distribution networks, characterized by intermittent and erroneous measurements. The developed state ...estimation algorithm is based on the Extended Kalman filter, where we have modified the execution of the filtering process. Namely, we have fixed the Kalman gain and Jacobian matrices to constant matrices; their values change only after a larger disturbance in the network. This allows for a fast and robust estimation of the network state. The performance of the proposed state-estimation algorithm is validated by means of simulations of an actual low-voltage network with actual field measurement data. Two different cases are presented. The results of the developed state estimator are compared to a classical estimator based on the weighted least squares method. The comparison shows that the developed state estimator outperforms the classical one in terms of calculation speed and, in case of spurious measurements errors, also in terms of accuracy.
Estimating the boost-phase trajectory of a ballistic missile using line of sight measurements from space-borne passive sensors is an important issue in missile defense. A well-known difficulty of ...this issue is the poor-observability of the target motion. A profile-based maximum penalised likelihood estimator is presented, which is expected to work in poor-observability scenarios. Firstly, a more adaptable boost-phase profile is proposed by introducing unknown parameters. Then, the estimator is given based on the Bayesian paradigm. After that, a special penalty for box constraint is constructed based on a mixed distribution. Numerical results for some typical scenarios and sensitivity with respect to a priori information are reported to show that the proposed estimator is promising.
In the tracking problem for the maritime radiation source by a passive sensor, there are three main difficulties, i.e., the poor observability of the radiation source, the detection uncertainty ...(false and missed detections) and the uncertainty of the target appearing/disappearing in the field of view. These difficulties can make the establishment or maintenance of the radiation source target track invalid. By incorporating the elevation information of the passive sensor into the automatic bearings-only tracking (BOT) and consolidating these uncertainties under the framework of random finite set (RFS), a novel approach for tracking maritime radiation source target with intermittent measurement was proposed. Under the RFS framework, the target state was represented as a set that can take on either an empty set or a singleton; meanwhile, the measurement uncertainty was modeled as a Bernoulli random finite set. Moreover, the elevation information of the sensor platform was introduced to ensure observability of passive measurements and obtain the unique target localization. Simulation experiments verify the validity of the proposed approach for tracking maritime radiation source and demonstrate the superiority of the proposed approach in comparison with the traditional integrated probabilistic data association (IPDA) method. The tracking performance under different conditions, particularly involving different existence probabilities and different appearance durations of the target, indicates that the method to solve our problem is robust and effective.
Induction motor sensorless control drive is a hot topic in Industry and academia due to its low cost and reliability. However, the performance near or at zero current frequency is unsatisfying ...without extra excitation signals due to poor observability at zero current frequency. Here, for Q-MRAS method, authors propose new state equations. Based on these equations, authors analyze the observability and stability of IM drive with and without virtual voltage injection. Finally, based on virtual voltage injection, authors propose a new control scheme to solve the instability problem at zero current frequency.
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