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Honggeng Yang
College of Electrical Engineering, Sichuan University, Chengdu 610065, China

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Journal article
Published: 03 January 2020 in Entropy
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Aiming at the fact that the independent component analysis algorithm requires more measurement points and cannot solve the problem of harmonic source location under underdetermined conditions, a new method based on sparse component analysis and minimum conditional entropy for identifying multiple harmonic source locations in a distribution system is proposed. Under the condition that the network impedance is unknown and the number of harmonic sources is undetermined, the measurement node configuration algorithm selects the node position to make the separated harmonic current more accurate. Then, using the harmonic voltage data of the selected node as the input, the sparse component analysis is used to solve the harmonic current waveform under underdetermination. Finally, the conditional entropy between the harmonic current and the system node is calculated, and the node corresponding to the minimum condition entropy is the location of the harmonic source. In order to verify the effectiveness and accuracy of the proposed method, the simulation was performed in an IEEE 14-node system. Moreover, compared with the results of independent component analysis algorithms. Simulation results verify the correctness and effectiveness of the proposed algorithm.

ACS Style

Yongzhen Du; Honggeng Yang; Xiaoyang Ma. Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy. Entropy 2020, 22, 65 .

AMA Style

Yongzhen Du, Honggeng Yang, Xiaoyang Ma. Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy. Entropy. 2020; 22 (1):65.

Chicago/Turabian Style

Yongzhen Du; Honggeng Yang; Xiaoyang Ma. 2020. "Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy." Entropy 22, no. 1: 65.

Journal article
Published: 16 January 2018 in Sustainability
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With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH) technology. The proposed algorithm consists of three main stages: (1) training the basic classifier; (2) selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3) training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection) and GMDH-U (GMDH-based semi-supervised feature selection for customer classification) models.

ACS Style

Lintao Yang; Honggeng Yang; Haitao Liu. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting. Sustainability 2018, 10, 217 .

AMA Style

Lintao Yang, Honggeng Yang, Haitao Liu. GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting. Sustainability. 2018; 10 (1):217.

Chicago/Turabian Style

Lintao Yang; Honggeng Yang; Haitao Liu. 2018. "GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting." Sustainability 10, no. 1: 217.