Evaluation of Harmonic Contributions for Multi Harmonic Sources System Based on Mixed Entropy Screening and an Improved Independent Component Analysis Method

Evaluating the harmonic contributions of each nonlinear customer is important for harmonic mitigation in a power system with diverse and complex harmonic sources. The existing evaluation methods have two shortcomings: (1) the calculation accuracy is easily affected by background harmonics fluctuation; and (2) they rely on Global Positioning System (GPS) measurements, which is not economic when widely applied. In this paper, based on the properties of asynchronous measurements, we propose a model for evaluating harmonic contributions without GPS technology. In addition, based on the Gaussianity of the measured harmonic data, a mixed entropy screening mechanism is proposed to assess the fluctuation degree of the background harmonics for each data segment. Only the segments with relatively stable background harmonics are chosen for calculation, which reduces the impacts of the background harmonics in a certain degree. Additionally, complex independent component analysis, as a potential method to this field, is improved in this paper. During the calculation process, the sparseness of the mixed matrix in this method is used to reduce the optimization dimension and enhance the evaluation accuracy. The validity and the effectiveness of the proposed methods are verified through simulations and field case studies.

Feature Extraction of EEG Signal upon BCI Systems Based on Steady-State Visual Evoked Potentials Using the Ant Colony Optimization Algorithm

This work presents the use of swarm intelligence algorithms as a reliable method for the optimization of electroencephalogram signals for the improvement of the performance of the brain interfaces based on stable states visual events. The preprocessing of brain signals for the extraction of characteristics and the detection of events is of paramount importance for the improvement of brain interfaces. The proposed ant colony optimization algorithm presents an improvement in obtaining the key features of the signals and the detection of events based on visual stimuli. As a reference model, we used the Independent Component Analysis method, which has been used in recent research for the removal of nonrelevant and detection of relevant data from the brain’s electrical signals and also allows the collection of information in response to a stimulus and separates the signals that were generated independently in certain zones of the brain.

Contribution Study on Cab Noise Source of Construction Machinery

NVH control in construction machinery is multi-disciplinary, comprehensive and complex. In order to achieve noise and vibration control, it is necessary to identify the main noise sources, vibration sources and the corresponding characteristics. To establish a relationship between the machine vibration with the noise sources of an excavator cab, the ICA (independent component analysis) method is employed to separate the multi-channel noise signals into statistically independent components, then utilize time-frequency analysis and correlation analysis to determine the distinct independent noise sources. By introducing energy calculation factor and the mixing matrix A, the contribution corresponding to each noise source can be obtained, which can be utilized to determine the main noise sources. Then by introducing simulation, the correction of the contribution can be verified. By analysis and simulation validation, the effectiveness of our proposed method is demonstrated. Finally, the main noise source is found. Our proposed method can offer an effective guidance to the practical engineering.