This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sp...
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This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight *** different electrical quantities are selected as observations in the compressed sensing *** entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance ***,by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,an improved Joint Generalized Orthogonal Matching Pursuit(J-gomp)algorithm is utilized for *** reconstructed sparse vectors are divided into three *** at least two parts have consistent node identifiers,the node is identified as the disturbance *** the node identifiers in all three parts are inconsistent,further analysis is conducted considering the weights to determine the disturbance *** results based on the IEEE 39-bus system model demonstrate that the proposed method,utilizing electrical quantity information from only 8 measurement points,effectively locates disturbance positions and is applicable to various disturbance types with strong noise resistance.
In order to solve the problem of SAR imaging with azimuth missing data, a novel missing data SAR imaging algorithm is proposed in this paper. In the algorithm, the complete echo can be recovered at the two-dimensional...
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ISBN:
(数字)9781665427920
ISBN:
(纸本)9781665427920
In order to solve the problem of SAR imaging with azimuth missing data, a novel missing data SAR imaging algorithm is proposed in this paper. In the algorithm, the complete echo can be recovered at the two-dimensional frequency-domain by using the generalized orthogonal matching pursuit (gomp) algorithm. The simulation result verifies the effectiveness of the proposed algorithm. Since the proposed algorithm only needs to recover the echo corresponding to sparse target-located range gates, compared with the state-of-the-art SAR imaging algorithm with azimuth missing data, it shows the advantage in the computational complexity when the targets are sparse enough in the range direction and it has a better imaging performance than the state-of-the-art azimuth missing data algorithm in noiseless and noisy settings.
In this paper, an adaptive structured-generalized orthogonal matching pursuit (AS-gomp) algorithm is proposed for time domain channel estimation by utilizing the characteristics of multiple-input multiple-output ortho...
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ISBN:
(纸本)9781538637586
In this paper, an adaptive structured-generalized orthogonal matching pursuit (AS-gomp) algorithm is proposed for time domain channel estimation by utilizing the characteristics of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. This algorithm uses the properties of the PN sequence to obtain partial channel prior information firstly, then the remaining support sets are obtained by the improved generalized orthogonal matching pursuit (gomp) algorithm in MIMO system. A good channel estimation result is achieved by exploiting the characteristics of PN sequence and the common sparsity in spatial and time domain. The simulation results show that the proposed method can reduce the bit error rate (BER) of channel estimation and improve the performance of the MIMO-OFDM system.
In this paper, an adaptive structured-generalized orthogonal matching pursuit (AS-gomp) algorithm is proposed for time domain channel estimation by utilizing the characteristics of multiple-input multiple-output ortho...
详细信息
In this paper, an adaptive structured-generalized orthogonal matching pursuit (AS-gomp) algorithm is proposed for time domain channel estimation by utilizing the characteristics of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. This algorithm uses the properties of the PN sequence to obtain partial channel prior information firstly, then the remaining support sets are obtained by the improved generalized orthogonal matching pursuit (gomp) algorithm in MIMO system. A good channel estimation result is achieved by exploiting the characteristics of PN sequence and the common sparsity in spatial and time domain. The simulation results show that the proposed method can reduce the bit error rate (BER) of channel estimation and improve the performance of the MIMO-OFDM system.
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