Adaptive Two-stage Unscented Kalman Filter for Dynamic State Estimation of Synchronous Generator Under Cyber Attacks Against Measurements

This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber attacks.To achieve high fidelity,considering the excitation system of SGs,a detailed 9~(th)-order SG model for dynamic state estimation is established.Then,for several common cyber attacks against measurements,a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in parallel.Subsequently,to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions,a multi-dimensional adaptive factor matrix is derived to modify the noise covariance matrix.Finally,a large number of simulation experiments are carried out on the IEEE 39-bus system,which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.

Time-domain Dynamic State Estimation for Unbalanced Three-phase Power Systems

In this paper,we present a time-domain dynamic state estimation for unbalanced three-phase power systems.The dynamic nature of the estimator stems from an explicit consideration of the electromagnetic dynamics of the network,i.e.,the dynamics of the electrical lines.This enables our approach to release the assumption of the network being in quasi-steady state.Initially,based on the line dynamics,we derive a graphbased dynamic system model.To handle the large number of interacting variables,we propose a port-Hamiltonian modeling approach.Based on the port-Hamiltonian model,we then follow an observer-based approach to develop a dynamic estimator.The estimator uses synchronized sampled value measurements to calculate asymptotic convergent estimates for the unknown bus voltages and currents.The design and implementation of the estimator are illustrated through the IEEE 33-bus system.Numerical simulations verify the estimator to produce asymptotic exact estimates,which are able to detect harmonic distortion and sub-second transients as arising from converterbased resources.

Dynamic State Estimation of Power Systems with Uncertainties Based on Robust Adaptive Unscented Kalman Filter

In this paper,a robust adaptive unscented Kalman filter(RAUKF)is developed to mitigate the unfavorable effects derived from uncertainties in noise and in the model.To address these issues,a robust M-estimator is first utilized to update the measurement noise covariance.Next,to deal with the effects of model parameter errors while considering the computational complexity and real-time requirements of dynamic state estimation,an adaptive update method is produced.The proposed method is integrated with spherical simplex unscented transformation technology,and then a novel derivative-free filter is proposed to dynamically track the states of the power system against uncertainties.Finally,the effectiveness and robustness of the proposed method are demonstrated through extensive simulation experiments on an IEEE 39-bus test system.Compared with other methods,the proposed method can capture the dynamic characteristics of a synchronous generator more reliably.

Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation

Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG)with unknown inputs based on adaptive interpolation and cubature Kalman filter(AICKF-UI).DFIGs adopt different control strategies in normal and fault conditions;thus,the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases.Consequently,the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs,which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter.Furthermore,as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs,a large estimation error may occur or the DSE approach may diverge.To this end,in this paper,a local-truncation-error-guided adaptive interpolation approach is developed.Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can(1)effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient;(2)accurately track the dynamic states and unknown inputs of the DFIG;and(3)deal with various types of system operating conditions such as time-varying wind and different system faults.

Dynamic State Estimation for Integrated Electricity-gas Systems Based on Kalman Filter

In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to establish anappropriate state estimation (SE) model for IEGS to filter theraw measured data. Considering that power systems and naturalgas systems have different time scales and sampling periods, thispaper proposes a dynamic state estimation (DSE) method basedon a Kalman filter that can consider the dynamic characteristicsof natural gas pipelines. First, the standardized state transitionequations for the gas system are developed by applying the finitedifference method to the partial differential equations (PDEs) ofthe gas system;then the DSE model for IEGS is formulatedbased on a Kalman filter;also, the measurements from theelectricity system and the gas system with different samplingperiods are fused to ensure the observability of DSE by using theinterpolation method. The IEEE 39-bus electricity system and the18-nodes Belgium gas system are integrated as the test systems.Simulation results verify the proposed method’s accuracy andcalculation efficiency.

Protection and control of microgrids using dynamic state estimation

High penetration of Converter Interfaced Generations(CIGs)presents challenges in both microgrid(μGrid)circuit and other system with CIG resources,such as wind farms and PV plants.Specifically,protection challenges are mainly brought by the insufficient separation between fault and load currents,especially forμGrids in islanded operation,and the short connection length inμGrids.In addition,CIG resources exhibit limited inertia and weak coupling to any rotating machinery,which can result in large transients during disturbances.To address the above challenges,this paper proposes a Dynamic State Estimation(DSE)based algorithm for protection and control of systems with substantial CIG resources such as aμGrid.It requires a high-fidelity dynamic model and time domain(sampled value)measurements.ForμGrid circuit protection,the algorithm dependably and securely detects internal faults by checking the consistency between the circuit model and available measurements.For CIG control,the algorithm estimates the frequency at other parts of aμGrid using CIG local information only and then utilizes it to provide supplementary feedback control.Simulation results prove that DSE based protection algorithm detects internal faults faster,ignores external faults and has improved sensitivity towards high impedance faults when compared to conventional protection methods.DSE based CIG control scheme also minimizes output oscillation and transient during system disturbances.

Dynamic State Estimation of Medium-voltage DC Integrated Power System with Pulse Load

The dynamic characteristic evaluation is an important prerequisite for safe and reliable operation of the mediumvoltage DC integrated power system(MIPS),and the dynamic state estimation is an essential technical approach to the evaluation.Unlike the electromechanical transient process in a traditional power system,periodic change in pulse load of the MIPS is an electromagnetic transient process.As the system state suddenly changes in the range of a smaller time constant,it is difficult to estimate the dynamic state due to periodic disturbance.This paper presents a dynamic mathematical model of the MIPS according to the network structure and control strategy,thereby overcoming the restrictions of algebraic variables on the estimation and developing a dynamic state estimation method based on the extended Kalman filter.Using the method of adding fictitious process noise,it is possible to solve the problem that the linearized algorithm of the MIPS model is less reliable when an abrupt change occurs in the pulse load.Therefore,the accuracy of the dynamic state estimation and the stability of the filter can be improved under the periodic disturbance of pulse load.The simulation and experimental results confirm that the proposed model and method are feasible and effective.

Resilient Smart Power Grid Synchronization Estimation Method for System Resilience with Partial Missing Measurements

With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid synchronization.To solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency dynamically.First,a threephase unbalanced network’s positive sequence fast estimation model is established.Then,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss information.Finally,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).

A multi-functional dynamic state estimator for error validation:measurement and parameter errors and sudden load changes

We propose a new and efficient algorithm to detect, identify, and correct measurement errors and branch parameter errors of power systems. A dynamic state estimation algorithm is used based on the Kalman filter theory. The proposed algorithm also successfully detects and identifies sudden load changes in power systems. The method uses three normalized vectors to process errors at each sampling time: normalized measurement residual, normalized Lagrange multiplier, and normalized innovation vector. An IEEE 14-bus test system was used to verify and demonstrate the effectiveness of the proposed method. Numerical results are presented and discussed to show the accuracy of the method.