The delay of Phasor Measurement Unit (PMU) measurements due to the communication network may affect the performance of real time applications accommodated in a Wide Area Monitoring and Control system. In order to miti...
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ISBN:
(纸本)9781509041695
The delay of Phasor Measurement Unit (PMU) measurements due to the communication network may affect the performance of real time applications accommodated in a Wide Area Monitoring and Control system. In order to mitigate the effect of the PMU measurement delays, a data delay compensation technique based on a linear predictor is proposed in this paper. The proposed method incorporates autocorrelation linear Predictive coding (LPC) to predict the future values of the measured signals and a data delay compensator to compensate the data delays actively. The proposed data delay compensation technique is applied to the IEEE 9-bus test system where it is indicated that the proposed method can compensate the data delays in wide area signals effectively.
This paper presents a real-time robust formant tracking system for speech signals and electroglottography (EGG) signals using a real-time phase equalization-based autoregressive exogenous model (RT-PEAR). PEAR can est...
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ISBN:
(纸本)9781467369985
This paper presents a real-time robust formant tracking system for speech signals and electroglottography (EGG) signals using a real-time phase equalization-based autoregressive exogenous model (RT-PEAR). PEAR can estimate formant frequencies robustly even for speech with high fundamental frequencies using phase equalization preprocessing and LPC with an impulse train. To reduce the computational complexity of original PEAR, a novel formulation of LPC with an impulse train is derived. EGG signals were used for stable detection of pitch marks since PEAR requires them. Formant estimation errors for the proposed method were less than 5 % regardless of fundamental frequencies with 12-ms processing delay. This technique will be useful for real-time speech conversion and speech-language therapy.
This paper is motivated by the recognition that sources of uncertainties in the electric power systems are multifold and that they may have potentially far-reaching effects. In the past, only system load forecast was ...
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ISBN:
(纸本)9781467327275
This paper is motivated by the recognition that sources of uncertainties in the electric power systems are multifold and that they may have potentially far-reaching effects. In the past, only system load forecast was considered to be the main challenge. More recently, uncertain price of electricity and hard-to-predict power produced by the renewable resources, such as wind and solar, are making the operating and planning environment much more challenging. It is, therefore, becoming very important to develop modeling methods for predicting uncertain load and wind power, in particular. In this paper we first transform historic time-stamped data into their Fourier Transform (FT) representation. The frequency domain data representation is used to decompose the wind and load power signals and to derive predictive models relevant for short-term and long-term predictions. The short-term results are interpreted next as a linear prediction coding Model (LPC) and its accuracy is analyzed. Next, the Discrete Markov Process (DMP) representation is applied to help assess probabilities of most likely short-, medium- and long-term states and the related multi-temporal risks. Throughout the paper we use publicly available data for the New York Control Area (NYCA).
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