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Xiangang Peng
Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China

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Journal article
Published: 25 August 2021 in Applied Sciences
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Fault-cause identification plays a significant role in transmission line maintenance and fault disposal. With the increasing types of monitoring data, i.e., micrometeorology and geographic information, multiview learning can be used to realize the information fusion for better fault-cause identification. To reduce the redundant information of different types of monitoring data, in this paper, a hierarchical multiview feature selection (HMVFS) method is proposed to address the challenge of combining waveform and contextual fault features. To enhance the discriminant ability of the model, an ε-dragging technique is introduced to enlarge the boundary between different classes. To effectively select the useful feature subset, two regularization terms, namely l2,1-norm and Frobenius norm penalty, are adopted to conduct the hierarchical feature selection for multiview data. Subsequently, an iterative optimization algorithm is developed to solve our proposed method, and its convergence is theoretically proven. Waveform and contextual features are extracted from yield data and used to evaluate the proposed HMVFS. The experimental results demonstrate the effectiveness of the combined used of fault features and reveal the superior performance and application potential of HMVFS.

ACS Style

Shengchao Jian; Xiangang Peng; Haoliang Yuan; Chun Sing Lai; Loi Lei Lai. Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection. Applied Sciences 2021, 11, 7804 .

AMA Style

Shengchao Jian, Xiangang Peng, Haoliang Yuan, Chun Sing Lai, Loi Lei Lai. Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection. Applied Sciences. 2021; 11 (17):7804.

Chicago/Turabian Style

Shengchao Jian; Xiangang Peng; Haoliang Yuan; Chun Sing Lai; Loi Lei Lai. 2021. "Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection." Applied Sciences 11, no. 17: 7804.

Journal article
Published: 01 December 2015 in Energies
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Distributed generation (DG) systems are integral parts in future distribution networks. In this paper, a novel approach integrating crisscross optimization algorithm and Monte Carlo simulation (CSO-MCS) is implemented to solve the optimal DG allocation (ODGA) problem. The feature of applying CSO to address the ODGA problem lies in three interacting operators, namely horizontal crossover, vertical crossover and competitive operator. The horizontal crossover can search new solutions in a hypercube space with a larger probability while in the periphery of each hypercube with a decreasing probability. The vertical crossover can effectively facilitate those stagnant dimensions of a population to escape from premature convergence. The competitive operator allows the crisscross search to always maintain in a historical best position to quicken the converge rate. It is the combination of the double search strategies and competitive mechanism that enables CSO significant advantage in convergence speed and accuracy. Moreover, to deal with system uncertainties such as the output power of wind turbine and photovoltaic generators, an MCS-based method is adopted to solve the probabilistic power flow. The effectiveness of the CSO-MCS method is validated on the typical 33-bus and 69-bus test system, and results substantiate the suitability of CSO-MCS for multi-objective ODGA problem.

ACS Style

Xiangang Peng; Lixiang Lin; Weiqin Zheng; Yi Liu. Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem. Energies 2015, 8, 13641 -13659.

AMA Style

Xiangang Peng, Lixiang Lin, Weiqin Zheng, Yi Liu. Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem. Energies. 2015; 8 (12):13641-13659.

Chicago/Turabian Style

Xiangang Peng; Lixiang Lin; Weiqin Zheng; Yi Liu. 2015. "Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem." Energies 8, no. 12: 13641-13659.