ABSTRACT
Oscillations, commonly known as a universal, propagative, and intricate event in the new power system, often give rise to generator tripping and load shedding, not only adversely affecting the power flow limit and the power angle stability but also posing threats to the lines of defense for stability and protection. Traditionally, emphasis has been laid on post-fault oscillation management, an emergency measure to deal with the impact and damage that have already affected the power grid. As such, this paper focuses on an oscillation prediction technique to detect oscillation energy early and intervene proactively to prevent further faults. This technique effectively lessens the damage caused by impacts and disconnects to the power grid. Firstly, this paper proposes the concept of disturbance power density and establishes the correlation between disturbance energy and the time domain, thereby exploring a method for evaluating the pattern of electrical quantities before power system oscillation. Secondly, it speeds up the time it takes to detect faults by catching nuances of voltage-current phase angle and impedance. Lastly, it puts forward a technique to cope with the intricacy and variety of power grid equipment using the convolutional neural network (CNN). This technique incorporates an integrated attention mechanism within a one-dimensional CNN model to capture the implicit mapping between voltage, active power, and reactive power at any time in the power system. This enables the model to self-learn multi-device characteristics and enhances the possibility of using theory in practical ways. Moreover, practical case studies also show that the prediction technique proposed in this paper can effectively issue warnings eight minutes before the occurrence of oscillation.
ABSTRACT
Objective@#To exmain reliability and validity of the Chinese version of Family Hardiness Index(FHI) among patients with cancer.@*Methods@#A convinent sample of 344 patients with cancer was investigated with a self-designed questionnaire about personal information, the Herth Hope Index (HHI) and the Chinese version of FHI. Item analysis was conducted on each item of the Chinese version of FHI; The construct validity of the scale were evaluated by explorotory factor analysis, the reliability of the scale was also evaluated by calculating the Cronbach α coefficient, the split-half reliability and the retest reliability.@*Results@#The Chinese version of FHI consisted of 20 items. Five factors were extracted by factor analysis which explained 57.900% of the total variance. The score of the Chinese version of FHI was significantly correlated to the total score of HHI (r=0.716, p<0.01) .The Cronbach α coefficient of the Chinese version of FHI was 0.806, and the test-retest reliability coefficient was 0.790 and The split-half reliability was 0.793.@*Conclusion@#The Chinese version of FHI demonstrates good reliability and validity during initial testing among patients with cancer.
