Abstract:Wind power prediction research shows that it is difficult to accurately and effectively estimate the probability distribution (PD) of wind power. When only partial information of the wind power probability distribution function is available, an optimal available transfer capability (ATC) assessment strategy considering the uncertainty on the wind power probability distribution is proposed in this paper. As wind power probability distribution is not accurately given, the proposed strategy can efficiently maximize ATC with the security operation constraints satisfied under any wind power PD function case in the uncertainty set. A distributional robust chance constrained (DRCC) model is developed to describe an optimal ATC assessment problem. To achieve tractability of the DRCC model, the dual optimization, S-lemma and Schur complement are adopted to eliminate the uncertain wind power vector in the DRCC model. According to the characteristics of the problem, the linear matrix inequality (LMI)-based particle swarm optimization (PSO) algorithm is used to solve the DRCC model which contains first and second-order moment information of the wind power. The modified IEEE 30-bus system simulation results show the feasibility and effectiveness of the proposed ATC assessment strategy.
Infrared thermography has been a very important nondestructive evaluation (NDE) in the detection of concrete due to its non-contactness, rapidity, capability of imaging large area. More generally, there are some frames in the infrared image sequence. It costs more time to read the information behind the infrared image directly, and the result is influenced by subjective factors in the most degree. Principal component analysis (PCA) is used to convert an infrared image sequence into a set of principal components. Then, the detection result can be got quickly by keeping the lower principal components and ignoring the higher ones.
Infrared thermography has been a very important nondestructive evaluation (NDE) in the detection of concrete due to its non-contactness, rapidity, capability of imaging large area. More generally, there are some frames in the infrared image sequence. It costs more time to read the information behind the infrared image directly, and the result is influenced by subjective factors in the most degree. Correlation analysis is used to convert an infrared image sequence into a single clear image. Then, the detection result can be got quickly.
Propeller cavitation noise continuous spectrum results from noise radiated from random collapses of a large number of cavitation bubbles. In consideration of randomicity of initial collapse of cavitation bubbles and similarity of frequency spectrums of various cavitation bubble radiated noises, theoretical analysis is conducted on four types of cavitation radiated noise continuous spectrum characteristics, which shows that simulation modeling could be carried out for cavitation noise continuous spectrum waveform by use of Monte Carlo method based on statistics of cavitation bubble radius and initial collapse time. The model parameters are expected values, difference degree and randomicity of cavitation bubble radius, which will be respectively used to determine base waveform of radiated noise, similarity of various cavitation bubble noise frequency spectrums and random collapse process of a larger number of cavitation bubbles. The input parameters have specific physical meaning and are controlled simply and could simulate the characteristics of cavitation noise continuous spectrum in a better way and therefore the simulation results could be better matching with theoretical analysis.
A passive long wave cochlea model is applied to extract audial feature of naval vessel radiation noise. Based on the model, a two dimensional time space distribution Spectrum of noise signal is calculated. Four one dimensional features with simple form are presented. The experiment results shows that the features based on cochlea model is consistent with auditory perception of noise signal. The approach is a new method to extract feature for passive sonar target recognition.
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