Ultra low power consumption operation of SOA assisted extended reach EADFB laser (AXEL)
We propose a novel structure to reduce the power consumption and extend the transmission distance of a 10-Gbit/s, 1.55-µm EADFB laser. The power consumption is reduced by 1/2 and the transmission distance is extended.
Low-Power Consumption 28-Gb/s 80-km Transmission With 1.3-μm SOA-Assisted Extended-Reach EADFB Laser
We extend the transmission distance for 28-Gb/s optical links from 40 to 80 km in a single-mode fiber (SMF) in the 1.3-μm-wavelength window by using an intensity-modulation direct-detection scheme. To extend the link budget, and compensate for the optical loss of SMF, we try to increase the optical output power of an electroabsorption modulator integrated with distributed feedback (EADFB) laser. We apply a semiconductor optical amplifier (SOA)-assisted extended-reach EADFB laser (AXEL) to a 1.3-μm-wavelength light source. To increase the optical output power while using energy efficiently, we design the SOA length in terms of achieving a balance between the laser diode (LD) and the SOA length with a constant injection current of 160 mA. We fabricate an AXEL and an EADFB laser and compare their basic characteristics. By using the high-power AXEL, we achieve the first 28-Gb/s 80-km SMF transmission without the need for digital signal processing or an in-line optical amplifier. The AXEL consumes only 0.26 W and does not require any digital processing or an in-line amplifier and can, therefore, reduce the power needed for an optical network system by tens of watts.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.