Research On Adaptive Kalman Filter-Based Grid Synchronization Algorithms

The energy crisis and environmental pollution have accelerated the transformation of the traditional power industry and promoted the development of clean and renewable energy-based power systems.During this process,grid synchronization techniques are very significant in the fields of grid state detection,power quality control,and gridtied converter control.These techniques are mainly developed in two aspects: on the one hand,the grid synchronization techniques should have certain filtering capabilities to fit into the grid,which is contaminated with harmonic pollution.On the other hand,dynamic responses should be enhanced to achieve fast tracking under phase jump,voltage sag,and frequency deviation conditions,etc.However,the traditional grid synchronization techniques have fixed control gains,which makes their bandwidth unchangeable.Therefore,the dynamic response is usually sacrificed to maintain a relatively good filtering capability.As an adaptive algorithm,the linear Kalman filter(LKF)is able to dynamically change the Kalman gain,providing a possibility to solve the above problems.This paper focuses on LKFs,studies their steady-state and dynamic processes,comes up with a design guideline,and generalizes the study results.The main contributions of this dissertation are as follows:1)Different Kalman synchronous phase-locking models are established according to different power grid scenarios.On the basis of orthogonal vector-based LKF(OV-LKF),the space-extended OV-LKF(SEOV-LKF)and LKF-based frequency-locked-loop(LKF-FLL)are proposed to deal with the harmonic pollution and frequency deviation,respectively.Through steady-state analysis,the OV-LKF-FLL is compared with the derivative vector-based LKF-FLL(DV-LKF-FLL),which has a similar state space model with the OV one.And meanwhile,a more stable grid synchronization model named integrator vector-based LKF-FLL(IV-LKF-FLL)is also proposed.For the threephase system,the three-phase OV-LKF-FLL combined with symmetric component method and the LKF-based phase-locked-loop(LKF-PLL)are also introduced.2)Aiming at the steady-state characteristic analysis and parameter tuning problem of LKF,this dissertation proposes a steady-state Kalman gain calculation method and a parameter tuning guideline that are based on frequency domain analysis,model optimization,and the linear time-invariant(LTI)model.The continuous model of LKF is derived,which finds the interaction between Kalman gains and initial parameters.Then the LTI model of LKF is established to linearize the nonlinear part of the algorithm,and the steady-state characteristics analysis and the parameter design schemes are presented for the OV-LKF and its extension algorithms.Besides,for SEOV-LKF,a decoupling method is proposed,which reduces the coupling among parameters and among harmonic models as well as the computational cost.Thus,the researchers can freely design LKF based on different engineering demands.3)Aiming at the stability and robustness analyses of steady-state LKFs,the linear time-periodic(LTP)model combined with generalized Nyquist theory is proposed.Firstly,the LTP model of the LKF is derived.The model further considers the periodic component and coupling between the amplitude and phase estimations,which makes it reflect the characteristics of the algorithm more accurately.Then,based on the LTP model,the harmonic transfer function is derived,and the generalized Nyquist theory is introduced to realize the stability and robustness analyses.The limitations of OVLKF-FLL and DV-LKF-FLL are pointed out through LTP analyses,and a more stable model named IV-LKF-FLL is also introduced.The effectiveness of the LTP model and the results of LTP analyses are verified by numerical results under some classic grid conditions.4)Faced with the slow dynamic response when the Kalman filter reaches its steadystate,a dynamic tracking algorithm is proposed.Firstly,the adaptive process of LKF is studied,which indicates that the error covariance matrix is the key to the LKF adaptive process.Then,the interaction between the error covariance matrix and Kalman gains is studied,and based on the results,a dynamic tracking algorithm based on a relationship between the estimated error and Kalman gains is proposed.Finally,the effectiveness of the proposed method is evaluated by both numerical results and experimental results.The proposed method is also applied to the control of gridconnected three-phase inverter.Two cases including changing the desired power and the input voltage signal of the power grid are considered to verify its practicability.This dissertation starts with modeling,steady-state analysis,and dynamic analysis to design guidelines for steady-state and dynamic conditions.It complimented the studies of LKF in the field of grid synchronization through theoretical analysis and experimental analysis.The results of the analyses will lay the foundation for applying LKF under different engineering conditions,which has a profound significance.