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As an AC interconnected power system is subjected to a large load with rapid change, system frequency may be severely disturbed and becomes oscillatory. To stabilize the frequency oscillations, the dynamic power flow control of the Static Synchronous Series Compensator (SSSC) located in series with the tie line between interconnected power systems, is employed. By regarding the system interconnection as the control channel, the power flow control by an SSSC via the interconnection creates a sophisticated method of frequency stabilization. To implement this concept, the robust design method of the lead/lag controller equipped with the SSSC is proposed. In the design process, not only the attenuation performance of system disturbances, but also the robust stability against system uncertainties are taken into consideration. The optimal parameters of the lead/lag controller are obtained by using the tabu search algorithm (TSA), so that both performance and robustness are satisfied. In addition, the technique of overlapping decompositions is applied to reduce the order of the study power system, meanwhile the physical characteristic is still preserved. Simulation study exhibits the significant effect of designed controller on the study four-area interconnected power system under different load disturbances and variation of system parameters. [Copyright &y& Elsevier]

Abstract Background A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs. Method Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors. Result The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration. Conclusion The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.