State Estimation Approach Research Articles

A broad learning-based state estimation method for power system

With the fast growth of developing smart grid technology in the last several years, traditional state estimation methods have proven insufficient for the real-time and precise study of emerging complex power systems. The state assessment of electric grid depending on deep learning and other related intelligent algorithms often encounter problems such as time-consuming model training and easy to fall into local optimum. In this background, this research provides a broad learning-based state estimation approach for power system, which is quite different from the existing artificial intelligence algorithms. Due to the operation theory of matrix pseudo-inverse, the broad learning system can not only swiftly compute the connection weights between various network layers, but also has the advantage of incremental learning. Finally, the proposed method is verified based on IEEE 39-node system combined with actual load data.

New Optimization Approach of State Estimation for Neural Networks with Mixed Delays

New Optimization Approach of State Estimation for Neural Networks with Mixed Delays

Authentic Microgrid State Estimation

An error-resilient state estimation is devised to calculate authentic states for microgrids equipped with hierarchical controls. New contributions include: 1) a state estimation incorporating droop control which helps mitigate estimation errors due to transient disturbance; 2) a secondary-control-empowered microgrid state estimation for leveraging power sharing and voltage regulation among DERs. Case studies demonstrate the robustness, efficiency and excellent convergence performance of the authentic state estimation approach.

An adaptive gain based approach for event-triggered state estimation with unknown parameters and sensor nonlinearities over wireless sensor networks

The distributed state and parameter estimation problem is investigated in this paper for discrete-time nonlinear systems subject to sensor nonlinearities and stochastic disturbances over a wireless sensor network. A novel architecture for distributed state estimator is introduced that incorporates adaptive coupling gains to govern the information exchange between the sensor nodes under event-triggering mechanism. The aim of this paper is to provide a scalable structure for unknown parameter identification independent of sensor networks’ complexity. The boundedness of estimation error is ensured in the framework of uniformly ultimately bounded stability by developing an algebraic connectivity based criterion. The estimator gains including proposed coupling gains are then presented as solution to matrix inequalities. Finally, two simulation examples are presented to demonstrate the effectiveness of proposed estimation architecture.

Orbit Estimation for Spacecraft Based on Intermittent Measurements: An Event-Triggered UKF Approach

This article addresses a problem of spacecraft orbit estimation with measurement transmission restrictions through an event-triggered state estimation approach. Specifically, we consider the scenario that the ground station communicates with the spacecraft through multiple channels and each channel decides whether or not to transmit the measurements to the spacecraft based on separate deterministic event-triggering conditions. Considering $J_2$ perturbations, an event-triggered unscented Kalman filter-based state estimation algorithm is designed on the basis of a model of orbit dynamics and a measurement model of the ground station. The performance of the estimator is evaluated based on real-time two-line element data. The obtained results indicate that the proposed algorithm can achieve satisfactory estimation performance at reduced measurement transmission rates.

A GMM-Based Secure State Estimation Approach against Dynamic Malicious Adversaries

We consider the secure state estimation of linear time-invariant Gaussian systems subject to dynamic malicious attacks. An error compensator is proposed to reduce the impact of local error data on state estimation. Based on that, a new estimation algorithm based on the Gaussian mixture model (GMM) aiming at dynamic attacks is proposed, which can cluster the local state estimates autonomously and improve the remote estimation accuracy effectively. The superiority of the proposed algorithm is verified by numerical simulations.

Robust strong tracking unscented Kalman filter for non‐linear systems with unknown inputs

This paper proposes a state estimation approach ‘robust strong tracking unscented Kalman filter with unknown inputs’ that can be applied to non-linear systems with unknown inputs. Specifically, the non-linear state and measurement equations are linearised by statistical linearisation. Then, the estimation equation of the unknown input is derived based on the weighted least squares method. The multiple suboptimal fading factor is introduced into a priori error covariance matrix to improve the tracking ability for the inaccuracy of the system model and the abrupt change of state variables caused by unknown inputs. Finally, based on the unbiased minimum variance estimation, the unbiased state estimation and the error covariance matrix are derived. Singular value decomposition is performed on the error covariance matrix to improve the stability of the algorithm. Simulated results validate the effectiveness of the proposed method.

Radix-Partition-Based Over-the-Air Aggregation and Low-Complexity State Estimation for IoT Systems Over Wireless Fading Channels

We consider remote state estimation for an internet-of-things (IoT) system, where a number of distributed IoT sensors monitor a dynamic plant and deliver the state measurements to a remote state estimator over an unreliable wireless network. We propose novel radix-partition-based over-the-air aggregation to coordinate the transmissions of the IoT sensors, which strongly enhances the spectral efficiency of radio resources and enables a smaller state estimation mean square error (MSE) at the remote state estimator, taking into consideration the quantization effect and radio resource allocation for the IoT sensors. We consider a fixed-filtering design at the remote state estimator, which significantly reduces the signal processing overhead at the remote state estimator compared to using the conventional Kalman-filtering-based state estimation approach. We show that the proposed low-complexity state estimation scheme enables state estimation stability via the linear matrix inequality (LMI) technique for on-off fading channels and via Lyapunov stability analysis for random fading channels. Based on this, we further propose efficient algorithms for the offline design of the filtering gain, connection topology and radix representation for IoT systems. Numerical results show that the proposed scheme has superior scalability performance and can achieve a smaller state estimation MSE compared to various state-of-the-art baselines.

State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks.

Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach for topology identification (TI) and unbalanced three-phase distribution system state estimation (DSSE). Two deep neural networks (DNNs) are trained for time-synchronized DNN-based TI and DSSE, respectively, for systems that are incompletely observed by synchrophasor measurement devices (SMDs) in real-time. A data-driven approach for judicious SMD placement to facilitate reliable TI and DSSE is also provided. Robustness of the proposed methodology is demonstrated by considering non-Gaussian noise in the SMD measurements. A comparison of the DNN-based DSSE with more conventional approaches indicates that the DL-based approach gives better accuracy with smaller number of SMDs.

Intelligent State Estimation for Online Optimizing Control of a Reactor System exhibiting Input Multiplicity

Combinations of real-time optimization (RTO) and model predictive control (MPC) have been widely employed in the process industry for tracking the economic optimum in the face of drifting disturbances and parameters. Online update of model parameters is a critical step in the implementation of RTO. In this work, an intelligent state and parameter estimation approach is developed by combining a fault diagnosis approach with a moving window-based online state and parameter estimator. The estimation of unmeasured disturbance(s)/ parameter(s)/ sensor bias(es) is carried out only when required and triggered by the fault identification scheme. Thus, the subset of parameters/faults that are being estimated online can change with time. This can avoid difficulties that arise due to the observability condition. The intelligent state and parameter estimator is further combined with an online optimizing control scheme consisting of integrated frequent RTO and adaptive MPC. The integrated scheme has embedded intelligence to auto-correct models used for estimation, control, and optimization and to decide whether the detected changes require the invocation of RTO. The efficacy of the proposed scheme is investigated using a benchmark CSTR system that exhibits input multiplicity behavior. The optimum operating point of this system is sensitive to mean shifts in unmeasured disturbances or system parameters. The proposed approach successfully isolates the parameter/ unmeasured disturbance/ sensor bias that has undergone abrupt change and tracks the shifting economic optimum without significant delays. Thus, the proposed integrated approach has the ability to handle normal, o¤-normal, and abnormal operating envelopes of the system.

Extent-based reconstruction of the inlet composition matrix in process systems with feed variability and unknown reaction dynamics

Extent-based representation is a powerful tool for reaction systems since it decouples the reaction related dynamics and inlet flow dynamics. However it assumes that the inlet flow composition is exactly known. In this paper, we present the design of an estimation procedure to partially reconstruct the approximate inlet composition matrix based on the extent representation of the process. This is especially necessary when process systems have recycles streams or the inlet composition matrix contains traces of other species. The estimation is done by transforming the measured moles to the extent basis based on a nominal but incorrect inlet composition. The use of this incorrect composition will reveal the difference between the true process and the nominal model. The error between these two can be exploited to be cast a constrained linear optimization problem to estimate the uncertainty in the inlet composition. The correction of the inlet composition matrix will be crucial in obtaining an appropriate extent-based representation that can be used for control, and state and parameter estimation approaches. The use of extents is motivated because it allows to represent the system with a set of decoupled dynamics, avoiding the necessity of the reaction dynamics in the estimation procedure. The techniques developed in this paper are ultimately tested in a simple case study of a CSTR with a recycle stream from a flash evaporator.

Efficient state estimation strategies for stochastic optimal control of financial risk problems

<abstract><p>In this paper, a financial risk model, which is formulated from the risk management process of financial markets, is studied. By considering the presence of Gaussian white noise, the financial risk model is reformulated as a stochastic optimal control problem. On this basis, two efficient computational approaches for state estimation, which are the extended Kalman filter (EKF) and unscented Kalman filter (UKF) approaches, are applied. Later, based on the state estimate given by the EKF and UKF approaches, a linear feedback control policy is designed from the stationary condition. For illustration, some parameter values and the initial conditions of the financial risk model are used for the simulation of the stochastic optimal control problem. From the results, it is noticed that the UKF algorithm provides a better state estimate with a smaller value of the sum of squared errors (SSE) as compared to the SSE given by the EKF algorithm. Thus, the estimated output trajectory has a high accuracy that is close to the real output. Moreover, the control effort assists in estimating the state dynamics at the minimum cost. In conclusion, the efficiency of the computational approaches for optimal control of the financial risk model has been well presented.</p></abstract>

Carleman Linearization Approach for State Estimation of Stochastic Boost Converter with Constant Power Load

Carleman Linearization Approach for State Estimation of Stochastic Boost Converter with Constant Power Load

State estimation for censored system with colored noises: system reconstruction approach

A state estimation approach based on system reconstruction for a class of discrete-time linear censored systems with colored noises is proposed. Firstly, the censored system is re-modeled by compensating the censored measurement and adding the weight factor. Then, avoiding the traditional state expansion approach, and according to the random property of adjacent correlation colored noises and projective theory, the innovation variance, prediction gain and prediction error variance with recursive structure are obtained, and the state one-step predictor is given. Finally, a numerical example is applied to verify the feasibility of the proposed method.

Distributed nonsynchronous event-triggered state estimation of genetic regulatory networks with hidden Markovian jumping parameters.

In this paper, the distributed state estimation problem of genetic regulatory networks (GRNs) with hidden Markovian jumping parameters (HMJPs) is explored. Furthermore, in order to improve the communication efficiency among state estimation sensors, the event-triggered strategy is employed in the distributed framework for sensor networks. Particularly, by considering the fact that the true modes are always unaccessible, a novel nonsynchronous state estimation (NSE) strategy is utilized based on observed hidden mode information. By means of Lyapunov-Krasovski method, sufficient stochastic state estimation analysis and synthesis results are established, such that the concentrations of mRNA and protein in GRNs can be both well estimated by convex optimization. Finally, an illustrative example with relevant simulations results is provided to validate the applicability and effectiveness of the developed state estimation approach.

Combination of Stochastic State Estimation with Online Identification of the Open-Circuit Voltage of Lithium-Ion Batteries

Typical equivalent circuit models for Lithium-ion batteries are represented in the form of the so-called Thévenin form. These models comprise a state of charge (SOC) controlled voltage source as well as lumped elements such as series resistances as well as at least one RC subnetwork to describe dynamic effects by the terminal current and voltage as input and output of the battery. In classical state estimation approaches, these characteristics are assumed to be identified beforehand, while aging of battery cells is related to a loss of the total capacity, changes in the charging/discharging efficiency, an increase of the Ohmic cell resistance, and/or changes of the time constants of the above-mentioned RC networks. Such variations can be estimated by means of additional parameters that are included in the system's state vector. However, the typically applied approaches do not allow for a direct identification of the nonlinear dependencies of the circuit elements on the SOC and other influence factors such as currents or the cell temperature. Therefore, this paper proposes a real-time capable, two-stage identification of these dependencies on the example of the open-circuit voltage by means of an Unscented Kalman Filter (UKF) approach. In the first stage, the state variables of the dynamic system model are estimated together with a lumped disturbance term (an additive correction of the open-circuit voltage). In the second stage, this disturbance term is used to correct the a-priori knowledge of the open-circuit voltage and to simultaneously express the identification quality in terms of covariances for the coefficients of its piecewise polynomial approximation in each temporal discretization step. The efficiency of the proposed methodology is demonstrated in simulations for an experimentally validated system model.

A Two-Filter Approach for State Estimation Utilizing Quantized Output Data.

Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitalization process for storing or transmitting the measurements through a communication channel using a minimum amount of resources. Thus, obtaining suitable state estimates in this context is essential. In this paper, Gaussian sum filtering and smoothing algorithms are developed in order to deal with noisy measurements that are also subject to quantization. In this approach, the probability mass function of the quantized output given the state is characterized by an integral equation. This integral was approximated by using a Gauss–Legendre quadrature; hence, a model with a Gaussian mixture structure was obtained. This model was used to develop filtering and smoothing algorithms. The benefits of this proposal, in terms of accuracy of the estimation and computational cost, are illustrated via numerical simulations.

A survey on state estimation of complex dynamical networks

The analysis and synthesis issues have gained widespread attention for complex dynamical networks (CDNs) over the past few years. Accordingly, some challenges including protocol-based scheduling, security vulnerability, limited communication resources as well as network-induced phenomena have to be handled by developing effective analysis and design approaches. In this paper, we make an attempt to review the latest state estimation schemes for CDNs especially those over the networked environment. Firstly, the engineering background and the current attractive topics in regard to CDNs are presented. Subsequently, we provide a review of several types of state estimation methods under different performance indices, for example, state estimation, set-membership state estimation, optimal state estimation and fault estimation. Next, particular effort is devoted to show the up-to-date progresses on the protocol-based state estimation and compensation-based state estimation approaches for CDNs. Finally, some challenging problems are outlined regarding the future research to promote the theoretical developments in related fields.

An extended Kalman filter approach for real-time state estimation in multi-region MFD urban networks

The problem of traffic state estimation for large-scale urban networks modeled with MFD dynamics is studied here. Given a network partitioned in a number of regions, aggregated traffic dynamics describe the vehicle accumulation in each region, as well as transfer flows to and from neighboring regions. Considering that MFD accumulation-based models have been integrated in perimeter control approaches, this work tackles the real-time estimation problem when limited data is available. An estimation engine is developed according to the Extended Kalman Filter (EKF) theory; it seeks to estimate the real state of the multi-region dynamic system based on traffic sensors’ measurements. First, a stochastic model is presented for the dynamics of the process (plant). Then, the EKF estimation scheme is described based on a simpler aggregated model of dynamics and some real-time measurements. Estimation accuracy is investigated through detailed micro-simulation of downtown Barcelona by studying a realistic configuration of real-time measurement availability through loop detector data; however, the developed methodology is generic. The state vector we seek to estimate, as well as the available measurements configuration, can be altered according to the application. The proposed methodology is tested both in macro- and micro-simulation; resulting estimated traffic states (i.e., regional accumulations, demands, and distribution of outflows) are compared to actual ones obtained from the stochastic plant. The developed algorithm can be utilized by closed-loop online urban traffic management strategies to feed the estimated traffic state back to the controller.

Probabilistic Appearance-Invariant Topometric Localization With New Place Awareness

Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data. Recently, probabilistic localization systems utilizing appearance-invariant visual place recognition (VPR) methods as the primary exteroceptive sensor have demonstrated state-of-the-art performance in the presence of substantial appearance change. However, existing systems 1) do not fully utilize odometry data within the motion models, and 2) are unable to handle route deviations, due to the assumption that query traverses exactly repeat the mapping traverse. To address these shortcomings, we present a new probabilistic topometric localization system which incorporates full 3-dof odometry into the motion model and furthermore, adds an "off-map" state within the state-estimation framework, allowing query traverses which feature significant route detours from the reference map to be successfully localized. We perform extensive evaluation on multiple query traverses from the Oxford RobotCar dataset exhibiting both significant appearance change and deviations from routes previously traversed. In particular, we evaluate performance on two practically relevant localization tasks: loop closure detection and global localization. Our approach achieves major performance improvements over both existing and improved state-of-the-art systems.