Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Model-driven full system dynamics estimation of PMSM-driven chain shell magazine

Based on the system dynamic model, a full system dynamics estimation method is proposed for a chain shell magazine driven by a permanent magnet synchronous motor(PMSM). An adaptive extended state observer(AESO) is proposed to estimate the unmeasured states and disturbance, in which the model parameters are adjusted in real time. Theoretical analysis shows that the estimation errors of the disturbances and unmeasured states converge exponentially to zero, and the parameter estimation error can be obtained from the extended state. Then, based on the extended state of the AESO, a novel parameter estimation law is designed. Due to the convergence of AESO, the novel parameter estimation law is insensitive to controllers and excitation signal. Under persistent excitation(PE) condition, the estimated parameters will converge to a compact set around the actual parameter value. Without PE signal, the estimated parameters will converge to zero for the extended state. Simulation and experimental results show that the proposed method can accurately estimate the unmeasured states and disturbance of the chain shell magazine, and the estimated parameters will converge to the actual value without strictly continuous PE signals.

Coupled dynamic model of state estimation for hypersonic glide vehicle

Aiming at handling complicated maneuvers or other unpredicted emergencies for hypersonic glide vehicle tracking,three coupled dynamic models of state estimation based on the priori information between guidance variables and aerodynamics are presented. Firstly, the aerodynamic acceleration acting on the target is analyzed to reveal the essence of the target’s motion.Then three coupled structures for modeling aerodynamic parameters are developed by different ideas: the spiral model with a harmonic oscillator, the bank model with trigonometric functions of the bank angle and the guide model with the changing rule of guidance variables. Meanwhile, the comparison discussion is concluded to show the novelty and advantage of these models.Finally, a performance assessment in different simulation cases is presented and detailed analysis is revealed. The results show that the proposed models perform excellent properties. Moreover, the guide model produces the best tracking performance and the bank model shows the second; however, the spiral model does not outperform the maneuvering reentry vehicle(MaRV) model markedly.

Vehicle Dynamic State Estimation： State of the Art Schemes and Perspectives

Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.

Robust H_(∞)state estimation for a class of complex networks with dynamic event-triggered scheme against hybrid attacks

We investigate the dynamic event-triggered state estimation for uncertain complex networks with hybrid delays suffering from both deception attacks and denial-of-service attacks.Firstly,the effects of time-varying delays and finitedistributed delays are considered during data transmission between nodes.Secondly,a dynamic event-triggered scheme(ETS)is introduced to reduce the frequency of data transmission between sensors and estimators.Thirdly,by considering the discussed plant,dynamic ETS,state estimator,and hybrid attacks into a unified framework,this framework is transferred into a novel dynamical model.Furthermore,with the help of Lyapunov stability theory and linear matrix inequality techniques,sufficient condition to ensure that the system is exponentially stable and satisfies H∞performance constraints is obtained,and the design algorithm for estimator gains is given.Finally,two numerical examples verify the effectiveness of the proposed method.

Vehicle State and Parameter Estimation Based on Dual Unscented Particle Filter Algorithm

Acquisition of real-time and accurate vehicle state and parameter information is critical to the research of vehicle dynamic control system.By studying the defects of the former Kalman filter based estimation method,a new estimating method is proposed.First the nonlinear vehicle dynamics system,containing inaccurate model parameters and constant noise,is established.Then a dual unscented particle filter(DUPF)algorithm is proposed.In the algorithm two unscented particle filters run in parallel,states estimation and parameters estimation update each other.The results of simulation and vehicle ground testing indicate that the DUPF algorithm has higher state estimation accuracy than unscented Kalman filter(UKF)and dual extended Kalman filter(DEKF),and it also has good capability to revise model parameters.

Dynamic State Forecasting in Electric Power Networks

The real time monitoring and control have become very important in electric power system in order to achieve a high reliability in the system. So, improvement in Energy Management System (EMS) leads to improvement in the monitoring and control functions in the control center. In this paper, DSE is proposed based on Weighted Least Squares (WLS) estimator and Holt’s exponential smoothing to state predicting and Extended Kalman Filter to state filtering. The results viewing the dynamic state the estimator performance under normal and abnormal operating conditions.

ARCHITECTURE OF DYNAMIC DATA DRIVEN SIMULATION FOR WILDFIRE AND ITS REALIZATION

Dynamic data driven simulation （DDDS） is proposed to improve the model by incorporaing real data from the practical systems into the model. Instead of giving a static input, multiple possible sets of inputs are fed into the model. And the computational errors are corrected using statistical approaches. It involves a variety of aspects, including the uncertainty modeling, the measurement evaluation, the system model and the measurement model coupling ,the computation complexity, and the performance issue. Authors intend to set up the architecture of DDDS for wildfire spread model, DEVS-FIRE, based on the discrete event speeification （DEVS） formalism. The experimental results show that the framework can track the dynamically changing fire front based on fire sen- sor data, thus, it provides more aecurate predictions.

Vector tracking loops in GNSS receivers for dynamic weak signals

Vehicle positioning with the global navigation satellite system （GNSS） in urban environments faces two problems which are attenuation and dynamic. For traditional GNSS receivers hardly able to track dynamic weak signals, the coupling between all visible satellite signals is ignored in the absence of navigation state feedback, and thermal noise error and dynamic stress threshold are contradictory due to non-coherent discriminators. The vector delay/frequency locked loop （VDFLL） with navigation state feedback and the joint vector tracking loop （JVTL） with coherent discriminator which is a synchronization parameter tracking loop based on maximum likelihood estimation （MLE） are proposed to improve the tracking sensitivity of GNSS receiver in dynamic weak signal environments. A joint vector position tracking loop （JVPTL） directly tracking user position and velocity is proposed to further improve tracking sensitivity. The coherent navigation parameter discriminator of JVPTL, being able to ease the contradiction between thermal noise error and dynamic stress threshold, is based on MLE according to the navigation parameter based linear model of received baseband signals. Simulation results show that JVPTL, which combines the advantages of both VDFLL and JVTL, performs better than both VDFLL and JVTL in dynamic weak signal environments.

Dynamic Positioning System Design for A Marine Vessel with Unknown Dynamics Subject to External Disturbances Including Wave Effect

In this paper, a new control system is proposed for dynamic positioning(DP) of marine vessels with unknown dynamics and subject to external disturbances. The control system is composed of a substructure for wave filtering and state estimation together with a nonlinear PD-type controller. For wave filtering and state estimation, a cascade combination of a modified notch filter and an estimation stage is considered. In estimation stage, a modified extended-state observer(ESO) is proposed to estimate vessel velocities and unknown dynamics. The main advantage of the proposed method is its robustness to model uncertainties and external disturbances and it does not require prior knowledge of vessel model parameters. Besides, the stability of the cascade structure is analyzed and input to state stability(ISS) is guaranteed. Later on, a nonlinear PD-type controller with feedforward of filtered estimated dynamics is utilized. Detailed stability analyses are presented for the closed-loop DP control system and global uniform ultimate boundedness is proved using large scale systems method. Simulations are conducted to evaluate the performance of the proposed method for wave filtering and state estimation and comparisons are made with two conventional methods in terms of estimation accuracy and the presence of uncertainties. Besides, comparisons are made in closed-loop control system to demonstrate the performance of the proposed method compared with conventional methods. The proposed control system results in better performance in the presence of uncertainties,external disturbance and even in transients when the vessel is subjected to sudden changes in environmental disturbances.

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.

Cyber-Attack Detection for Autonomous Driving Using Vehicle Dynamic State Estimation

As intelligent vehicles become increasingly computerized and networked,they gain more autonomous capabilities.However,they are also becoming more exposed to cyber-threats which are likely to be a more prominent concern.This paper proposes a cyber-attack detection method for autonomous vehicles based on secure estimation of vehicle states,with an example application under attacks in the vehicle localization system.To investigate the effects of vehicle model and estimator on the attack detection performance,different nonlinear vehicle dynamic models and estimation approaches are employed.The deviation between the measurement from the onboard sensors and the state estimation is monitored in real time.With the designed vehicle state estimator and preset threshold,the cyber-attack detection algorithm is further developed for autonomous vehicles,whose performance is tested in simulations where the vehicle localization system is assumed to be compromised during a double lane change maneuver.The test results demonstrate the feasibility and effectiveness of the proposed cyber-attack algorithm.In addition,the results illustrate the impacts of vehicle nonlinear characteristics on the cyber-attack detection performance.Beyond this,the effects of different vehicle models on the attack detection performance,as well as the selection of suitable filtering approaches for the attack detection,are also discussed.

State Estimation of Vehicle’s Dynamic Stability Based on the Nonlinear Kalman Filter

An accurate estimation of a vehicle’s state of motion is the basis of dynamic stability control.Two different nonlinear Kalman filters are adopted for the estimation of the vehicle’s lateral/rollover stability state.First,the overall structure of the state estimation with four inputs and four outputs is introduced.After determining tire-cornering stiffness using a recursive leastsquares(RLS)method,the equations of state and of observation for the nonlinear Kalman filter are established based on a vehicle model with four degrees of freedom including planar and rollover dynamics.Then,the specific steps of real-time state estimation using the extended Kalman filter(EKF)and unscented Kalman filter(UKF)are both given.In a co-simulation,we find that the RLS algorithm estimates tire-cornering stiffness accurately and quickly,and the UKF improves the effect of state estimation compared with EKF.In addition,the UKF is verified against data from vehicle tests.The results show the proposed method is reliable and practical in estimating vehicle states.

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.

Distributed state estimation for linear timeinvariant dynamical systems:A review of theories and algorithms

Distributed state estimation is of paramount importance in many applications involving the large-scale complex systems over spatially deployed networked sensors.This paper provides an overview for analysis of distributed state estimation algorithms for linear time invariant systems.A number of previous works are reviewed and a clear classification of the main approaches in this field are presented,i.e.,Kalman-filter-type methods and Luenberger-observer-type methods.The design and the stability analysis of these methods are discussed.Moreover,a comprehensive comparison of the existing results is provided in terms of some standard metrics including the graph connectivity,system observability,optimality,time scale and so on.Finally,several important and challenging future research directions are discussed.

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 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.

Local Hybrid Linear State Estimation for Electric Power Systems Using Stream Processing

The increasing penetration of renewable energy resources with highly fluctuating outputs has placed increasing concern on the accuracy and timeliness of electric power system state estimation(SE).Meanwhile,we note that only a fraction of system states fluctuate at the millisecond level and require to be updated.As such,refreshing only those states with significant variation would enhance the computational efficiency of SE and make fast-continuous update of states possible.However,this is difficult to achieve with conventional SE methods,which generally refresh states of the entire system every 4–5 s.In this context,we propose a local hybrid linear SE framework using stream processing,in which synchronized measurements received from phasor measurement units(PMUs),and trigger/timingmode measurements received from remote terminal units(RTUs)are used to update the associated local states.Moreover,the measurement update process efficiency and timeliness are enhanced by proposing a trigger measurement-based fast dynamic partitioning algorithm for determining the areas of the system with states requiring recalculation.In particular,non-iterative hybrid linear formulations with both RTUs and PMUs are employed to solve the local SE problem.The timeliness,accuracy,and computational efficiency of the proposed method are demonstrated by extensive simulations based on IEEE 118-,300-,and 2383-bus systems.