On eigen-based signal combining using the autocorrelation coefficient

Abstract

In typical signal combining scenarios, the combining weights are estimated using the criterion of maximum average signal-to-noise ratio (SNR) or maximum combined output power (COP). Eigen-based algorithms are very important and popular in signal combining. The conventional SNR EIGEN or COP EIGEN may not necessarily be effective in terms of performance or system complexity. The main contribution of this study is the introduction of the combined signal autocorrelation coefficient as a newer objective function to signal combining. The corresponding eigen-based combining algorithm AC EIGEN and its modified algorithm MAC EIGEN are also derived. Proposed algorithms have the same simple system structure as the COP EIGEN, which can successfully avoid estimating the noise correlation matrix. Simulation results indicate that the AC EIGEN and the MAC EIGEN have good combining performance for signals with white Gaussian noise when the SNR of the signals is low.Considering the system complexity of the SNR EIGEN, and the COP EIGEN being biased for non-uniform noise variance signals, the proposed algorithms are attractive.

Sensitivity Analysis of Time Length of Photovoltaic Output Power to Capacity Configuration of Energy Storage Systems

“…From the view of Figure 2b, with the time length increasing, the oscillation amplitude of the autocorrelation coefficient is decreasing and the oscillation center is approaching the 0 point. After more than 2400 h (100 days), the autocorrelation coefficient maximum value of PV sequence drops below 0.3 [26][27][28], Thus, we can think that correlation between the PV output at any time and PV output historical data changes before 100 days is weak, Energies 2017, 10, 1616 6 of 15 and its influence is very small. Therefore, in the three PV power plant capacity scenarios, it is concluded that we should take 100 days as a time length sample to analyze the PV output data.…”