In this paper, we propose chaotic compressive sensing (CS) encryption algorithms for orthogonal frequency division multiplexing passive optical network (OFDM-PON), aiming at compressing the transmitted data and enhancing the security of data transmission. Bitstream transmission using CS directly is restricted due to its inability to satisfy the sparsity in neither time nor frequency domain. While the sparsity of the transmitted data can be constructed when transmitting the multimedia. A sensor can be then used to identify whether the data is multimedia. If it is, the CS technique is used, and the sensor’s result is set as side information inserted into the pilot and transmitted to the terminal simultaneously. For encryption processing, a 2-dimensional logistic-sine-coupling map (2D-LSCM) is used to generate pseudo-random numbers to construct the first row of a measurement matrix to encrypt the system. Four transform formats are then applied to generate the sparsity of the transmitted data. Due to the restriction of data transmission in the physical layer, the discrete cosine transform (DCT) is chosen to conduct the CS technique. Four approximation algorithms are also proposed to optimize the performance of compressing the length of bits. We find that ‘Round + Set negative to 0’ shows the best performance. The combination of this chaotic CS encryption technique with the OFDM-PON systems saves the bandwidth and improves the security.
The identification of coherent clusters plays an important role in dynamic equivalence and active split control of power systems. The existing coherent clustering methods often adopt a single indicator, e.g. only based on the power angle curve to identify coherent clusters. In addition, in the coherency identification process, the feature extraction is not sufficient, which may cause the problem of inaccurate grouping. In this paper, a coherent clustering method based on weighted clustering of multi-indicator panel data (WCMPD) is proposed. First, the measurements including power angle increment, terminal voltage, and rotor kinetic energy increment from phasor measurement units (PMU) are selected from panel data to reflect the coherence of the generators. Second, the indicator weights and time weights are calculated based on the cross-sectional and time dimension of the panel data. In order to suppress the shortcomings of the coherent clustering method based on Euclidean distance, three distance functions (''horizontal absolute value,'' ''rate of change at adjacent time points,'' and ''fluctuation variation degree'') are defined, and then aggregated. At last, the distance matrices among generators are calculated and the coherent generators can be obtained based on the system cluster method. The simulation results on the EPRI-36 bus system and the North China power grid demonstrate that the proposed method has better clustering results than traditional methods.INDEX TERMS Coherency clustering, weighted clustering algorithm of panel data, multiple indicator, index weight, system clustering.
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