Phase and synchronous detector theory as applied to beam position and intensity measurements
A popular signal processing technique for beam position measurements uses the principle of amplitude-modulation-to-phase-modulation (AM/PM) conversion and phase detection. This technique processes position-sensitive beam-image-current probe-signals into output signals that are proportional to the beam's position. These same probe signals may be summed and processed in a different fashion to provide output signals that are proportional to the peak beam current. This paper derives the transfer functions for the AM/PM beam position and peak beam current processors.
An RF Beam Position Measurement Module for the Fermilab Energy Doubler
This paper describes the design, realization and performance of the RF Beam Position Measurement Module designed for the Fermilab Energy Doubler. This module measures beam off-axis position and intensity over a 60db dynamic range. For 20 or more contiguous 53MHz beam bunches, the unit will derive a position signal for 108 to 1011 protons per bucket with a position accuracy of ±0.5 mm. Also, the unit will measure the position of 1010 or more protons in a single isolated rf bunch. A beam intensity signal is also generated for triggering purposes. Since 220 of these modules are to be built, cost and performance were optimized. The entire unit is housed in a 1-wide NIM module.
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.