This paper assesses the ability of four integralbased indices calculated using the post fault rotor angles, speed deviations, and accelerations to evaluate power system's transient stability. First, the impact of the integration time for the calculation of each of the indices to provide an adequate assessment of the system stability status is assessed. Then, a more detailed evaluation and calculation of the accuracies achieved by the indices is done by looking into the direct relationship between the index values and the stability status of test simulations. Results show that the proposed indices are able to represent correctly the instability degree of the system to different extents, while at the same time identifying the limitations in their use.

We developed a large area and low cost optical fingerprint recognition product based on LTPS (low temperature poly silicon) process. A high efficient a‐Si pin sensor is introduced, the quantum efficiency under white light is 70% and the dark state current density is E‐10 A / mm2. The “one‐to‐one” and “one to multiple” structures of sensor and micro lens are compared, and the “one‐to‐one” optical path structure is systematically analyzed and evaluated. The optical path focal length is about 100 μm. The fingerprint recognition effect is significantly improved by adding a BM shading layer, and this scheme can effectively avoid the screen burning problem caused by the high luminous intensity of pixels in the fingerprint unlocking area.

Data clustering has been widely applied in numerous areas in order to pave the way for adequate and efficient modelling, control and operation. In the past, most of the data clustering was carried out on static data. However, wider application of time series data has increased the need for time series clustering techniques. This paper presents a comprehensive analysis of the applicability of a standard clustering algorithm, the k-medoids algorithm, for clustering of two diverse time series datasets. The k-medoids algorithm is tested on dynamic power responses of a hybrid renewable energy source plant and neuroscience spike-train data. The main stages in clustering process, that is, data processing, the selection of the optimal distance measure and the estimation of the optimal number of clusters, are analyzed in detail.

This paper proposes a Principal Component Analysis (PCA)-based method to construct composite indices to rapidly assess power system transient stability. By assigning appropriate weights to a few selected sub-indicators, a composite index can be constructed to guarantee a better coverage of worst-case stability scenarios of the power system. This paper provides an automatic method to construct a composite index with four sub-indicators calculated by the integral of acceleration, speed deviation and rotor angle of generators over a certain integration period using PCA. The effectiveness of the sub-indicators is validated first, followed by a comprehensive example of process of composite index construction, and the assessment of the influence of the effect of integration period on the performance of the composite index. High accuracy of classification has been demonstrated using composite index with appropriate settings of the integration periods of the sub-indicators.

This paper assesses the ability of a Transient Stability Index (TSI) to evaluate power systems' transient stability. The assessment is first accomplished by looking into the TSI's classification accuracy for identifying stable or unstable cases when different transient stability thresholds are used. Although suitable values for these thresholds have been found, there are still some pitfalls in the use of TSI for stability classification. A new approach for the TSI calculation, called Truncated TSI, is proposed, reducing the index's time window calculation limits. High accuracy of stability classification has been demonstrated using the proposed approach with appropriate settings of the time window parameters.