scholarly journals Multi-Harmonic Source Localization Based on Sparse Component Analysis and Minimum Conditional Entropy

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 65
Author(s):  
Du ◽  
Yang ◽  
Ma
Keyword(s):  
Independent Component Analysis ◽  
Distribution System ◽  
Conditional Entropy ◽  
Component Analysis ◽  
Independent Component ◽  
Minimum Condition ◽  
Harmonic Current ◽  
Sparse Component Analysis ◽  
System Node ◽  
Harmonic Source

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.

Multi-harmonic sources location based on sparse component analysis and complex independent component analysis

2020 ◽  
Vol 14 (19) ◽  
pp. 4195-4206
Author(s):  
Guangui Wang ◽  
Xiaoyang Ma ◽  
Weikang Wang ◽  
Honggeng Yang ◽  
Chang Chen ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Sparse Component Analysis

INDEPENDENT COMPONENT ANALYSIS (ICA) DAN SPARSE COMPONENT ANALYSIS (SCA) DALAM PEMISAHAN VOKAL DAN INSTRUMEN PADA SENI GEGUNTANGAN

2019 ◽  
Vol 8 (1) ◽  
pp. 105
Author(s):  
Angga Pramana Putra ◽  
Ni Wayan Wiantari ◽  
Putu Mira Novita Dewi ◽  
I Dewa Made Bayu Atmaja Darmawan
Keyword(s):  
Independent Component Analysis ◽  
Mean Square Error ◽  
Blind Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Mean Square ◽  
Blind Signal ◽  
Sparse Component Analysis

Geguntangan adalah pesantian dalam upacara keagamaan yang diiringi dengan gamelan. Indra  pendengaran manusia cenderung memiliki keterbatasan, yang menyebabkan tidak semua vokal yang  tercampur dengan gamelan bisa didengar jelas. Oleh karena itu diperlukan suatu sistem yang dapat digunakan untuk memisahkan vokal dengan gamelan pada geguntangan. Pemisahan sumber suara ini dikategorikan sebagai Blind Source Separation (BSS) atau disebut juga Blind Signal Separation yang  artinya sumber tidak dikenal. Algoritma yang digunakan untuk menangani BSS adalah algoritma Independent Component Analysis (ICA) dan Sparse Component Analysis (SCA) dengan berfokus  pada pemisahan sinyal suara pada file suara berformat *.wav. Algoritma SCA dan ICA digunakan  untuk proses pemisahan suara dengan parameter nilai yang digunakan adalah Mean Square Error (MSE) dan Signalto Interference Ratio(SIR). Dari hasil simulasi menunjukkan Hasil perhitungan MSE dan SIR dengan dengan menggunakan mixing matriks [0.3816, 0.8678], [0.8534, -0.5853] didapatkan untuk metode ICA nilai MSE sebesar 4.169380402433175 x 10-6 untuk instrumennya dan 2.884749383815846 x 10-5 untuk vokalnya dan didapatkan nilai SIR sebesar 53.79928479270223 untuk instrumennya dan 45.39891910741724 untuk vokalnya. Selanjutnya untuk metode SCA, nilai MSE sebesar 3.382207103335018 x 10-5 untuk instrumennya dan 3.099942460987607 x 10-5 untuk vokalnya dan didapatkan nilai SIR sebesar 44.707998026869014 untuk instrumennya dan 45.08646367168143 untuk vokalnya.

Leakage discharge separation from the total water supply flow in water distribution system by constrained independent component analysis (CICA)

2015 ◽  
Vol 16 (3) ◽  
pp. 611-619
Author(s):  
Chengzhi Zheng ◽  
Jinliang Gao ◽  
Wenjie He
Keyword(s):  
Independent Component Analysis ◽  
Water Supply ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Component Analysis ◽  
Independent Component ◽  
Total Water ◽  
Simulation Accuracy ◽  
Constrained Independent Component Analysis

The blind source separation theory was introduced and the trend and amplitude (TAA) model was established in order to overcome the shortcomings of some traditional global leakage discharge analysis models in water distribution systems (WDS). The TAA model considers the leakage discharge as one part of the total water supply flow, consisting of constrained independent component analysis (CICA) model and amplitude solving model. In the CICA model, the CICA algorithm was chosen and two reference vectors were constructed, and then the trend of leakage discharge was obtained. In the amplitude solving model, the two-element coupled linear overdetermined equations were derived and the amplitude was calculated. The TAA model was optimized and verified based on the data from three kinds of WDS (the laboratory WDS, the emulational WDS and the actual WDS). The simulation accuracy of the TAA model was high enough when the total water supply flow was a non-Gaussian signal in the WDS with one entrance only; the TAA model can effectively avoid the complexity (and reflect the uncertainty) of the relationship between leakage discharge and pressure head. More importantly, the model has good transplant performance.

An Identification Method of Harmonic Source in the Power System Based on Independent Component Analysis

2013 ◽  
Vol 380-384 ◽  
pp. 4088-4093
Author(s):  
Shan Jiang ◽  
Min You Chen ◽  
Hao Lin ◽  
Zhi Sheng Lv ◽  
Ang Fu
Keyword(s):  
Independent Component Analysis ◽  
Power System ◽  
Harmonic Distortion ◽  
Component Analysis ◽  
Independent Component ◽  
Identification Method ◽  
Harmonic Voltage ◽  
Harmonic Source ◽  
Signal Sources ◽  
Mutually Independent

In this research, an identification method of harmonic source, based on independent component analysis, was proposed. An unknown harmonic source in the equivalent circuit of power system was regarded as the signal source in the independent component analysis. The known voltage and current of PCC were taken as observed quantities. The independent component analysis and the optimization algorithm were utilized to construct a matrix that could linearly transform voltage and current of PCC into mutually independent signal sources. The harmonic impedance and harmonic source of both the user side and system side were obtained. Then the contribution of users to the harmonic distortion at PCC could be identified. With a good anti-jamming property, this algorithm only needs a condition that variations in the harmonic voltage sources at system side and user side are independent to each other. Simulation results indicated the effectiveness of the method to identify the harmonic source of PCC.

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