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Inter-harmonic parameter identification method based on transform with local maximum spectrum

Sun Lin, Song Jing, Jin Yan

Archives of Electrical Engineering

2022

Vol. 71, nr 1

189--209

In order to improve the detection accuracy of harmonics/inter-harmonics in power systems, a new method of harmonic/inter-harmonic detection based on synchrosqueezed transform and the Hilbert operator based on local spectrum maximum is proposed. Firstly, the spectrum of inter-harmonic signals is obtained through short-time Fourier transform, and the local maximum value of the spectrum in the frequency direction is detected. Then, based on the maximum frequency of the spectrum, a new frequency redistribution operator and synchronous extraction operator are constructed. It combines the operators with ridge detection for the decomposition of harmonic/inter-harmonic signals, so as to obtain a series of intrinsic mode function (IMF) components. Finally, the instantaneous amplitude and frequency of the IMF components is obtained by using the Hilbert operator. Meanwhile, according to the instantaneous frequency mutation point in the spectrum, the starting and ending time of transient harmonics/inter-harmonics is located accurately. Based on a low signal-to-noise ratio (SNR), the wavelet packet method (WP), Hilbert Marginal Spectrum method (HMS), synchrosqueezing wavelet transform method (SST), the Hybrid SST method (HSST), enhanced empirical wavelet transform (EEWT) and the proposed method are used to identify the harmonic/inter-harmonic parameters, respectively. The experimental results show that the proposed LMSST method can effectively separate the steady-state and transient harmonic/inter-harmonic signals, and has higher detection accuracy and better noise robustness.

Dynamic probability fields for risk assessment and guidance solutions

Williams Edwin, Jin Yan

Annual of Navigation

2019

No. 26

33--45

Standard Guidance, Navigation, and Control (GN&C) systems take state data from a navigation system and create a trajectory that minimizes some a-priori determined cost function. These cost functions are typically time, money, weight, or any general physically realizable quantity. Previous work has been done to show the effectiveness of using risk as the sole objective function. However, this previous work used Poisson distributions and historical estimates to achieve this goal. In this paper we present the situation-risk assessment (SRA) method contained within the intelligent situation assessment and collision avoidance (iSC) platform. The SRA method uses data clustering, and pattern recognition to create a historically based estimate of guidance probabilities. These are then used in data driven, dynamic models to create the future probability fields of the situation. This probability, along with the other agent’s goals and objectives, are then used to create a minimum risk guidance solution in the nautical environment.