Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter

International audienc

Adaptive Vectorial Filter for Grid Synchronization of Power Converters Under Unbalanced and/or Distorted Grid Conditions

This paper presents a new synchronization scheme for detecting multiple positive-/negative-sequence frequency harmonics in three-phase systems for grid-connected power converters. The proposed technique is called MAVF-FLL because it is based on the use of multiple adaptive vectorial filters (AVFs) working together inside a harmonic decoupling network, resting on a frequency-locked loop (FLL) which makes the system frequency adaptive. The method uses the vectorial properties of the three-phase input signal in the αβ reference frame in order to obtain the different harmonic components. The MAVF-FLL is fully designed and analyzed, addressing the tuning procedure in order to obtain the desired and predefined performance. The proposed algorithm is evaluated by both simulation and experimental results, demonstrating its ability to perform as required for detecting different harmonic components under a highly unbalanced and distorted input grid voltage

Smart control architecture for microgrid application

This research proposes non-linear control architectures dedicated towards improving transient response, reliability and computational burden for grid connected inverters applicable for ac micro-grids. Also this work proposes an optimization procedure applied to a small microgrid to reduce the billing cost for power incorporating battery degradation mechanism. Three works are discussed in this research that discusses methodologies to improve the operation of a three phase grid connected inverters. The first work discusses a globally stable estimation architecture for estimating the plant parameters for a grid connected inverter during its operation. Then a Lyapunov based control architecture is utilized and online parameter update scheme is used to optimize the controller performance. The second work discusses a Lyapunov based control architecture during a contingency that the grid voltage sensor fails. In this work an internal model based grid voltage estimation architecture has been proposed which successfully estimates the grid voltage and controls the grid current. The last work shows a methodology to optimally utilize a battery in a microgrid based on Markov Decision Process. Dynamic algorithm is used to solve the problem so that the cost is minimized at the end of the day. Furthermore, in this research detailed stability analysis of the first two works along with the controller design has been presented. Also in this work, battery degradation is modelled empirically and the overall cost function is obtained for the optimization of billing cost for a small microgrid. Detailed plant modeling, controller design, simulation and experimental results are presented for all of the proposed schemes --Abstract, page iv

Achieving quantum precision limit in adaptive qubit state tomography

The precision limit in quantum state tomography is of great interest not only to practical applications but also to foundational studies. However, little is known about this subject in the multiparameter setting even theoretically due to the subtle information tradeoff among incompatible observables. In the case of a qubit, the theoretic precision limit was determined by Hayashi as well as Gill and Massar, but attaining the precision limit in experiments has remained a challenging task. Here we report the first experiment which achieves this precision limit in adaptive quantum state tomography on optical polarization qubits. The two-step adaptive strategy employed in our experiment is very easy to implement in practice. Yet it is surprisingly powerful in optimizing most figures of merit of practical interest. Our study may have significant implications for multiparameter quantum estimation problems, such as quantum metrology. Meanwhile, it may promote our understanding about the complementarity principle and uncertainty relations from the information theoretic perspective.Comment: 9 pages, 4 figures; titles changed and structure reorganise

Adaptive Quadrant Filter Based Phase Locked Loop System

Phase-Locked-Loop (PLL) is one of the key technologies extensively used in grid connected power electronics system. A good PLL system can detect the grid phase angle and frequency fast and accurately, and additionally it can extract the positive sequence (or fundamental component for single phase system) exactly. In real applications, source signal (voltage or current) sensed for PLL usually includes harmonic distortion, unbalanced components, noises and frequency variations. Conventional PLL strategy cannot solve all the problems, especially the unbalanced and harmonic distortion. There is a trade-off between the dynamic response and phase angle tracking accuracy. Different PLL solutions are proposed in literature in recent years. The general considerations for these different approaches are to design positive sequence estimator to eliminate the negative sequence components and use filters to filter out the higher order harmonic distortions from the PLLs. In this paper, an adaptive quadrature filter based synchronous reference frame PLL (SRF-PLL) with positive sequence estimation feature is presented. The proposed PLL has good performances in filtering harmonic, eliminating unbalanced components and auto-adjusting frequency change. The simulation model is built in Matlab/simulink and the simulation results are given to verify the mathematical analysis

Maximum likelihood frequency estimation in smart grid applications

International audienceThis paper focuses on the estimation of the fundamental frequency in balanced three-phase power systems. Specifically, we propose a Maximum Likelihood Estimator (MLE) that exploits the multidimensional nature of electrical signals. For perfectly sinusoidal signals, we show that the MLE can be expressed according to the periodogram of the instantaneous positive component. For harmonic signals, we demonstrate that the MLE can be approximated by a cumulated periodogram of the zero, positive and negative sequence components. As compared to single-phase estimators, statistical analysis and simulation results prove that the proposed estimator decreases the Mean Square Error by a factor of three, whatever the Signal to Noise Ratio (SNR) or data length. Furthermore, simulations with experimental data show that the proposed technique outperforms classical spectral estimators such as MUSIC