Design and Operation of a High Power L-Band Multiple Beam Klystron
A 10 MW, 1.3 GHz multiple beam klystron (MBK) has been developed for the DESY X-FEL and International Linear Collider projects. The device uses six electron beams set off-axis on a large bolt circle which interact with a combination of higher-order-mode and conventional klystron cavities to efficiently produce high power RF. Extensive two and three dimensional simulations were used to design the device. Recent test results have validated the basic design concepts and procedure. Ten megawatts of peak and 150 kW of average power have been stably generated with 59% efficiency and 48 dB of gain.
Design of a multiple beam klystron for TESLA
In this paper, we reported on the design of a 10 MW, 1300 MHz multiple beam klystron for TESLA superconducting linear accelerator. The multiple beam klystron is used in the accelerator because of its ability to produce high RF output power at moderate electron beam energies. The beam optics design has been verified by fully 3D simulations using the codes Michelle and Mafia. Electron trajectories in the gun region as calculated from Mafia codes.
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.