Transfer Strategy for Power Output Estimation of Wind Farm at Planning Stage Based on a SVR Model

The conventional wind farm(WF)power generation modelling method highly relies on wind hindcast produced by record time-series data or numerical weather modelling.However,estimating production at future sites is challenging in the absence of local wind monitoring.To address this,a data-driven WF modelling and model transfer strategy is proposed in this work.It considers the challenge of how to transpose metered data from existing operational WFs to sites that might feature as a prospective site for a new WF.By modelling 14 WFs distributed across Scotland using a machine learning(ML)approach,this study proved it was possible to effectively model metered production at a site using modelled wind speed and direction.In addition,this study also found when the latitude difference between two WFs is less than 0.2 degrees and the distance is less than 5o km,two WFs in non-mountainous areas can share an ML model.The results of the shared ML model remain superior to the results of the given power curve from manufacturers,after adjusting the results by the ratio of the power curve in these two WFs.The WF model transfer strategy investigated in this work offered a novel approach to transposing WF production estimates to new sites and appeared to offer better value than simple power curves,which is of importance at the early planning stage for site selection,although it would likely not fully replace detailed micro-siting modelling which are well established in the industry.Index Terms-Machine learning,model transfer strategy,power curve,power output estimation,wind farm.

Unified Analysis and Output Estimation for a Strong Coupling Contactless Slipring System

A contactless slipring (CS) system utilizing inductive-power-transfer (IPT) technology is a good candidate for traditional mechanical slipring assemblies. However, suffering from the high harmonic currents in strong coupling CS systems, the output power will deviate from the theoretical values estimated by the fundamental harmonic approximation (FHA) and its extension method, i.e., E-FHA, in which the power is transferred by both the fundamental current and the high order harmonic currents. In order to achieve high precise output estimation, a unified analysis is proposed in this paper. First, “Fundamental-harmonic Double Resonance Phenomenon” is revealed via impedance analysis, to address the nature of the high harmonic currents in strong coupling systems. Then, a unified output current expression owning high precision is derived, and followed by a unified fundamental load impedance. Discussions show that both the output and the fundamental load impedance of FHA, and E-FHA are the special cases of the unified expressions proposed. FHA and E-FHA are precise enough for the loose coupling system, whereas the proposed method is indispensable for the strong coupling system with k>0.4 . Finally, simulations and experimental measurements of a 1.6kW CS system, as well as the comparative studies related to FHA, E-FHA, and the proposed method, are presented, indicating that the proposed method is effective for high precise output estimation.

Estimation of Boost Converter State-Space Variables Using Fast Output Sampling Method

In this paper a fast output sampling （FOS） estimator is designed for estimation of state-space variables of DC-DC boost converter. Estimated state-space variables are output voltage of the converter and its first derivative, which are suitable for model reference adaptive controllers and sliding mode controllers design. Estimator is designed for operation in continuous and discontinuous conduction modes. The simulation results show that proposed FOS estimator provides good estimation of state-space variables despite the voltage ripple caused by high frequency switching in converter and disturbances （change of load and input voltage）.