Application of data mining based methods in online voltage stability assessment has attracted vast attentions in recent years. To account for significant system changes, most of the data mining based methods reconstruct an entire model based on the updated training database. Instead of entirely rebuilding a model in offline mode, this paper presents a novel online learning framework for monitoring the voltage stability of a transmission grid using wide area measurements. A new enhanced online random forest (EORF) model based on the drift detection and online bagging techniques is proposed. It enables to online update the trees involving tree growth and/or tree replacement. The trees in the forest are then combined via a weighted majority voting, which makes the decision model better adapted to system changes. The framework was first demonstrated on the IEEE 57-bus system, and then applied to a practical power system, the Taiwan power (Taipower) system composed of 1821 buses. In addition to accuracy-based measures, robustness and speed of the proposed framework were also validated. Extensive studies demonstrate that the proposed framework is able to provide reliable and accurate online voltage stability assessment.

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

This letter presents a robust approach to accurate estimation of Thevenin equivalent (TE) parameters using phasor measurement unit (PMU) data for online voltage stability assessment. The TE parameter estimation is formulated as a worst-case robust least squares problem to take PMU uncertainties into account. A second-order cone programming technique is then utilized to solve the problem. Case studies on several IEEE standard networks and a practical power system, along with comparative results, illustrate the effectiveness and robustness of the presented approach.

This work presents growing amorphous, anatase, rutile, and mixed phases TiO2 thin films by mist chemical vapor deposition and post deposition annealing techniques. The annealing temperature affects crystal structures of the TiO2 films significantly. X-ray diffraction and Raman spectra are used to characterize the structures of the TiO2 films. X-ray photoelectron spectroscopy is used to analyze the chemical composition of the TiO2 film. The optical characteristics including refractive index, extinction coefficient, optical absorption coefficient, and photoluminescence spectrum are measured. Finally, the metal-semiconductor-metal photodetectors are fabricated and their current-voltage, Schottky barrier height, photoresponse characteristics, response time, and detectivity characteristics are investigated. The research results show that these crystal structures of the TiO2 demonstrate their specific advantages in the performance of the photodetectors.

This letter presents a self-adaptive data-driven learning method for enhanced probabilistic prediction of voltage stability margin (VSM). An online probabilistic extreme learning machine (ELM) algorithm based on the power transformation technique is developed. The prediction interval (PI) estimation for VSM is formulated as a Box-Cox transformation (BT) model to take into account uncertainties associated with predictions. The parameters in the transformed model are determined by the maximum likelihood estimator. The proposed PI-based VSM estimation method is applied to power grids with high proliferation of renewable energy generation. It enables to update the prediction model online and adapt to changing operating conditions. Numerical studies along with comparative results demonstrate the efficacy and robustness of the proposed method.

Transmission grid voltage collapse can be initiated by unexpected load disturbances. Secondary voltage control, which is to improve overall load bus voltage profiles by appropriate management of reactive power sources, is regarded as one of the effective countermeasures against voltage collapse. A new adaptive method based on synchronized phasor measurement unit data for secondary voltage control is developed. By formulating the voltage control problem as a nonlinear constrained optimization problem, the proposed method determines an optimal control action using a successive approximate vector ∞-norm minimization algorithm to improve voltage profiles. The proposed method is adaptive in the sense that load disturbances are estimated on-line for computing the feasible control action for the minimization of the worst case load voltage deviation. Extensive simulation studies on various IEEE benchmark systems are carried out to demonstrate the feasibility and effectiveness of the proposed method.