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In this thesis, model predictive control (MPC) techniques are investigated with their applications to modular multilevel converters (MMCs). Since normally a large number of submodule (SM) capacitor voltages and gate signals need to be handled in an MMC, the MPC schemes studied in this thesis are employed for determining only the voltage levels of converter arms, while gate signals are subsequently generated by the conventional sorting method. Emphasis is given to inner-loop current control in terms of phase current and circulating current, aiming at performance enhancement and computation reduction. A variable rounding level control (VRLC) approach is developed in this thesis, which is based on a modification of the conventional nearest level control (NLC) scheme: instead of the conventional nearest integer function, a proper rounding function is selected for each arm of the MMC employing the MPC method. As a result, the simplicity of the NLC is maintained while the current regulating ability is improved. The VRLC technique can also be generalized from an MPC perspective. Different current controllers can be considered to generate the arm voltage references as input of the VRLC block, thus refining the control sets of the MPC. Based on the decoupled current models, the accumulated effect of SM capacitor voltage ripples is investigated, revealing that the VRLC strategy may not achieve a proper performance if the accumulated ripple is nontrivial compared to the voltage per level. Two indexes are also proposed for quantifying the current controllability of the VRLC. Benefiting from this analysis, A SM-grouping solution is put forward to apply such MPC techniques to an MMC with a large number of SMs, leading to an equivalent operation of an MMC with much reduced number of SMs, which significantly increases the current regulating capability with reduced complexity. As an example, the SM-grouping VRLC proposal is analyzed and its system design principles are described. Th

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Atmospheric Remote Sensing, Geoscience and Remote Sensing

The use of spectral polarimetric filters in the range-Doppler domain shows great promise for clutter mitigation in weather radar applications. One limitation of these filters is that they cannot deal with situations in which ground clutter and precipitation overlap. In this letter, we propose a new signal recovery technique based on kriging in the spectral domain to recover the precipitation in clutter-contaminated areas. Using synthetic radar data, we test our new method and compare its performance to that of Gaussian model adaptive processing and bilinear interpolation. Our results indicate that kriging is the most accurate and robust technique out of the three., Atmospheric Remote Sensing

The finite control set-model predictive control (FCS MPC) has been adopted as an excellent choice for the applications of multilevel converters during the last two decades for its salient performance. However, in the case of modular multilevel converters (MMCs), a high amount of calculation is always involved in the implementation, making the FCS-MPC less suitable especially for an MMC with a high number of submodules. To cope with the issue, this article proposes an MPC technique for the MMC with a very low calculation cost. In each sampling period, the arm voltage references of each phase are determined analytically by solving a constrained quadratic programming problem formulated from the cost function. Both a rigorous and simplified procedure is provided to solve the optimization problem. Then, the four nearest candi dates around the arm voltage references are evaluated, leading to a proper selection of arm voltage levels. Several experimental tests on an MMC prototype are carried out to validate the effectiveness of the proposed method. Results show that compared with the con ventional FCS-MPC method which evaluates all voltage-level com binations, the proposed scheme presents an apparent advantage in terms of calculation cost while achieving similar performance.

Weather radar is well recognized as an effective sensor for obtaining the microphysical and dynamical properties of precipitation at high spatial and temporal resolution. Radar calibration is one of the most important prerequisites for achieving accurate observations. In this article, a portable, cost-effective and repeatable radar calibration technique, namely, unmanned aerial vehicle (UAV)-aided radar calibration, is proposed. A UAV serves as the stable aerial platform carrying a metal sphere, flying over the radar illumination areas to complete the calibration process. The flying routine of the UAV can be pre-programmed, and thus, the antenna pattern regarding different elevation and azimuth angles can be retrieved. To obtain the position of the sphere, the real-time single-frequency precise point positioning-type global navigation satellite system solution is developed. In addition, the radar constant is calculated in the range-Doppler domain, and only the data where the metal sphere separates from clutter and other objects are selected. The S-band polarimetric Doppler transportable atmospheric radar (TARA) is used in the calibration campaign. The experiments demonstrate the following results: 1) antenna pointing calibration can be completed and 2) antenna pattern can be retrieved and weather radar constant can be accurately calculated., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Atmospheric Remote Sensing, Geoscience and Remote Sensing, Microwave Sensing, Signals & Systems

Nowadays, there is an intensive use of wind turbines worldwide to generate green and renewable energy. In addition, the sizes of wind turbines keep increasing for higher efficiency. However, echoes from wind turbines are severe clutter for weather radar, deteriorating its performance significantly. In this work, the spectral polarimetric filters (SPFs) are designed to mitigate wind turbine clutter (WTC). The SPFs consist of the moving double spectral linear depolarization ratio (MDsLDR) filter [1] and the object-orientated spectral polarimetric (OBSpol) filter [2], which have been previously proposed to mitigate the narrow-band clutter (both stationary and moving) for polarimetric radar with and without cross-polar measurements, respectively. Data collected by a polarimetric Doppler weather radar known as the IRCTR Drizzle Radar (IDRA) are used to validate the performance of the proposed algorithm. Mounted on the top of a 213m tower in Cabauw Experimental Site for Atmospheric Research, the Netherlands, the X-band operational research radar is still affected by wind turbines which are 120m in height and 3400m far away. Different case studies demonstrate the good performance of the SPFs in sidelobe WTC suppression. Moreover, these filters can be applied in real time due to its low computation complexity.