Estimation Of Current State Research Articles

Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density.

Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state‐space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin‐producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near‐term (1‐ to 4‐week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4‐week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1‐week‐ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long‐term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

A multivariate adaptive gradient algorithm with reduced tuning efforts

Large neural networks usually perform well for executing machine learning tasks. However, models that achieve state-of-the-art performance involve arbitrarily large number of parameters and therefore their training is very expensive. It is thus desired to implement methods with small per-iteration costs, fast convergence rates, and reduced tuning. This paper proposes a multivariate adaptive gradient descent method that meets the above attributes. The proposed method updates every element of the model parameters separately in a computationally efficient manner using an adaptive vector-form learning rate, resulting in low per-iteration cost. The adaptive learning rate computes the absolute difference of current and previous model parameters over the difference in subgradients of current and previous state estimates. In the deterministic setting, we show that the cost function value converges at a linear rate for smooth and strongly convex cost functions. Whereas in both the deterministic and stochastic setting, we show that the gradient converges in expectation at the order of O(1/k) for a non-convex cost function with Lipschitz continuous gradient. In addition, we show that after T iterates, the cost function of the last iterate scales as O(log(T)/T) for non-smooth strongly convex cost functions. Effectiveness of the proposed method is validated on convex functions, smooth non-convex function, non-smooth convex function, and four image classification data sets, whilst showing that its execution requires hardly any tuning unlike existing popular optimizers that entail relatively large tuning efforts. Our empirical results show that our proposed algorithm provides the best overall performance when comparing it to tuned state-of-the-art optimizers.

Postfault Direct Field-Oriented Control of Induction Motor Drive Using Adaptive Virtual Current Sensor

Electric drives immune to failures of selected elements of the power system or measuring sensors have been the subject of research in recent years, due to the growing interest in systems with an increased level of safety. This article concerns the analysis of the direct field-oriented control induction motor drive after the failure of all stator current sensors. An adaptive virtual current sensor (AVCS) was used to estimate the stator current, based on the measurement of the motor angular velocity and the voltage in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{DC}\ (u_{\text{DC}})$</tex-math></inline-formula> of the voltage-source inverter. This article presents the possibility of improving the accuracy of stator current estimation using the original approximation of changes in rotor resistance depending on the drive operating point when the measurement information about the stator current has been lost. This approximation was determined experimentally during the normal operation of the drive using a rotor resistance estimator. The determined approximate lookup functions were used to adapt the rotor resistance in the AVCS during the drive system operation in a postfault mode. This approach has made it possible to significantly improve the accuracy of the stator current reconstruction, especially under low speeds and load torques, which was demonstrated in experimental studies <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</b>

A Current Sharing State Estimation Method of Redundant Switched-Mode Power Supply Based on LSTM Neural Network

Redundant Switched-mode Power supplies (SMPSs) are commonly used to improve electronic systems’ reliability, and accurate estimation of the current sharing state is significant for evaluating the system’s health. Currently, the current sharing state estimation is mainly realized by using current sensors to detect each branch’s current, and the deployment and maintenance costs are high. In this paper, a method for power supply current sharing state estimation based on LSTM recurrent neural network is proposed. By taking advantage of subtle differences in the inherent spectral characteristics of SMPSs, this method only needs to detect the voltage ripple at the switching frequency of the load terminal to estimate the output current of each power supply branch. The verification experiment on the three-power redundant experimental platform shows that the estimation error is less than 10%. The method has the characteristics of simple structure, non-invasion, convenient deployment and maintenance, so it has high application and promotion value.

A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool

• DT model based meta-action, collected data from multi-sensors on transmission unit. • Monitoring and simulation of performance degradation on the wear of transmission. • Combining the data-driven and the model-based method by particle filter algorithm . • Integrating mechanism and real-time data, mutual modified to a High fidelity model . Precision performance prediction of transmission system is considered as a key technology to modern equipment health management. Given the importance of maintaining a transmission system's precision, this paper presents a hybrid approach framework driven by digital twin technology (DT), to predict performance degradation. Firstly, a DT model based on meta-action theory is established, and real-time monitoring and digital simulation, driven by DT data, is realized in order to analyze the precision of the transmission units in machine tools. Secondly, the wear of gear in transmission unit is studied through Achard wear theory, which considered the comprehensive influence of gear load and speed on surface wear of the gear pair tooth, based on the model driving method. The performance degradation of the transmission unit is obtained by using the RBF neural network algorithm based on the data-driven method to extrapolate the wear data to the field-measurable precision index value. In addition, the hybrid predictive approach of the performance degradation model through the particle filter algorithm is built, and the real-time data is used to update the current state estimation to improve the prediction accuracy. By combining the mechanism of the physical degradation processes with the real-time and historical data and turning them into a cooperative architecture, this prediction method uses the complementary advantages offered by the fusion of these methods to bridge the link between data-driven prediction and model-based prediction. Finally, the method has been successfully applied to the precision prediction of the transmission unit in CNCMT turntable, and it is compared with the single prediction method to verify the effectiveness and feasibility.

Optimal Coding Schemes for Detecting False Data Injection Attacks in Power System State Estimation

As a side-effect-free method, meter coding can successfully detect stealthy false data injection (FDI) attacks without significantly affecting physical plants by encoding the sensor outputs with an invertible matrix. However, since the relationship between the detection of stealthy FDI attacks and the cost of meter coding was not clearly analyzed in existing works, the optimal design of coding schemes has not been well studied. This paper investigates the optimal design of coding schemes based on the analysis of detection conditions for stealthy FDI attacks in power system state estimation. Specifically, detection conditions for stealthy FDI attacks are derived in both general and special coding schemes, which reveal the requirements on the encoded measurements, the coding scheme and the meters in power systems. Utilizing the constraints determined by the detection conditions, the optimization of the special coding scheme is formulated and equivalently simplified, which decouples the design of coding matrix with the other decision variables. Finally, simulation results are carried out on the alternating current (AC) state estimation in the IEEE 14-bus and 57-bus test systems to validate theoretical results on the conditions and cost of detecting stealthy FDI attacks in meter coding.

A field-oriented control (FOC) method using the virtual currents for the induction motor drive

&lt;span lang="EN-US"&gt;An improving field-oriented control technique without current sensors is proposed to control rotor speed for an induction motor drive. The estimated stator currents based on the slip frequency are used instead of feedback current signals in the FOC loop. The reference signals and the estimated currents through computation steps are used to generate the control voltage for the switching inverter. Simulations were performed in Matlab/Simulink environment at rated speed and low-speed range to demonstrate the method's feasibility. Through simulation results, the FOC method using virtual sensors has proved its effectiveness in ensuring the stable operation of the IMD over a wide speed range.&lt;/span&gt;

Invariant Extended Kalman Filtering for Underwater Navigation

Recent advances in the utilization of Lie Groups for robotic localization have led to dramatic increases in the accuracy of estimation and uncertainty characterization. One of the novel methods, the Invariant Extended Kalman Filter (InEKF) extends the Extended Kalman Filter (EKF) by leveraging the fact that some error dynamics defined on matrix Lie Groups satisfy a log-linear differential equation. Utilization of these observations result in linearization with minimal approximation error, no dependence on current state estimates, and excellent convergence and accuracy properties. In this letter we show that the primary sensors used for underwater localization, inertial measurement units (IMUs) and doppler velocity logs (DVLs) meet the requirements of the InEKF. Furthermore, we show that singleton measurements, such as depth, can also be used in the InEKF update with minor modifications, thus expanding the set of measurements usable in an InEKF. We compare convergence, accuracy and timing results of the InEKF to a quaternion-based EKF using a Monte Carlo simulation and show notable improvements in long-term localization and much faster convergence with negligible difference in computation time.

Multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter

If the system model or the statistical characteristics of noise are inaccurate, the past measurements will directly affect the accuracy of current state estimation or even lead to filtering divergence. To overcome above difficulties, a multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter is proposed. Firstly, the inverse Wishart distribution is adopted to model the measurement noise covariance matrix. Secondly, the remodified measurement noise covariance matrix and the innovation covariance matrix estimated by exponential weighting method are employed to construct the scalar fading factor. Next, the multiple fading factors are calculated to correct the predicted error covariance matrix. Finally, the local optimal estimations of measurement noise covariance matrix and state are obtained by variational Bayesian approach. The target tracking simulations verify that the proposed algorithm has better tracking ability for the predicted error covariance matrix and the measurement noise covariance matrix compared with the existing filters.

An Improved Current-Sensorless Method for Induction Motor Drives Applying Hysteresis Current Controller

A novel strategy based on the feed-forward field-oriented control (FOC) method is proposed for the Hysteresis Current technique to control the induction motor (IM) drive without current sensors (CSs). A control scheme is proposed to estimate stator currents from reference rotor flux, rotor flux angle, and state variables as a replacement for the feedback-signal of CSs used in the hysteresis current controller (HCC). Here the rotor flux angle component is extracted from the feed-forward FOC loop. MATLAB/Simulink is applied to implement the simulations under many different operating conditions. The simulation results demonstrated the feasibility of the proposed method to obtain high performance in controlling the IM drives without the current sensors.

An Improved Current-Sensorless Method for Induction Motor Drives Applying Hysteresis Current Controller

A novel strategy based on the feed-forward field-oriented control (FOC) method is proposed for the Hysteresis Current technique to control the induction motor (IM) drive without current sensors (CSs). A control scheme is proposed to estimate stator currents from reference rotor flux, rotor flux angle, and state variables as a replacement for the feedback-signal of CSs used in the hysteresis current controller (HCC). Here the rotor flux angle component is extracted from the feed-forward FOC loop. MATLAB/Simulink is applied to implement the simulations under many different operating conditions. The simulation results demonstrated the feasibility of the proposed method to obtain high performance in controlling the IM drives without the current sensors.

Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy

Lithium-ion battery State of Charge (SoC) estimation for Electric Vehicle (EV) applications must be robust and as accurate as possible to maximize battery utilization and ensure safe operation over a wide range of operating conditions. SoC estimation commonly utilizes filters such as the Extended Kalman Filter (EKF) which rely on battery models, usually in the form of Equivalent Circuit Models (ECM). At low temperatures the battery response to current draw becomes increasingly non-linear, resulting in amplified SoC estimation errors. In this study, current dependent SoC estimation at low temperature is proposed using an Interacting Multiple Model (IMM) filter with three ECMs covering a range of C-rates. The IMM is combined with the Smooth Variable Structure Filter (SVSF) to obtain robust SoC estimates within a SoC estimation error of 2%.

New Sensorless Vector Control System With High Load Capacity Based on Improved SMO and Improved FOO

In the existing sensorless vector control system of permanent magnet synchronous motor (PMSM), direct differential calculation or phase-locked loop (PLL) control is often used to estimate the rotor speed, but this method has some problems of weak load capacity and slow dynamic response. A new sensorless vector control method based on an improved sliding mode observer (SMO) and improved full-order observer (FOO) is proposed in this paper. The cut-off frequency of the low-pass filter is designed as a function of speed, the back EMF is fed back into the stator current estimation mathematical model, and the adaptive rate of feedback gains coefficient is proposed for improving the traditional SMO. The disturbance feedback matrix is introduced for improving the traditional FOO, and the estimated load torque is feedforward compensated to the given end of the quadrature current. An integrated system of corresponding algorithms is established, a test platform is built. The experimental results show that the speed and load torque estimation performance of the improved observer is better than that of the traditional observer in the process of sudden load change. The sensorless vector control system based on the proposed observer has a high dynamic response, load capacity, and reliability.

A Novel Speed and Current Sensor Fault-Tolerant Control Based on Estimated Stator Currents in Induction Motor Drives

The paper introduces a novel Fault Tolerant Control (FTC) algorithm based on estimated stator currents to diagnose some faulty states of stator current and speed sensors of Induction Motor (IM) drives. The FTC unit is integrated in the field-oriented control of induction motors to ensure that the IM drive can operate stably when current and speed sensor failures occur. The presented scheme combines two stator current estimators and a rotor speed estimator where a correction coefficient determined from the healthy current sensor is used to enhance the accuracy of the stator current estimation. To verify the capability of the proposed approach, the simulation in MATLAB-Simulink has been accomplished for various conditions of the IM drive, i.e. normal speed, low speed, and very low speed operating ranges. The results have confirmed very good features of the proposed FTC against the current and speed sensor fault in the different speed ranges.

GNSS Vector Tracking Method Using Graph Optimization

Commonly, there are two different types of signal tracking methods in Global Navigation Satellite System (GNSS) receivers: Scalar Tracking (ST) and Vector Tracking (VT). In ST, each tracking channel works independently and generates the measurements for Position, Velocity and Time (PVT) estimation. On the contrary, VT accomplishes the signal tracking and PVT solutions estimation together using a Kalman filter (KF). In this manner, mutual aiding between these channels are obtained, which contributes to the superior performance of VT than ST. KF is the method in the manner of weighted average of predicted state and the measurements derived state. Two drawbacks restrict the KF performance, (1) in KF, correlation between current state and all the past states is ignored, only adjacent state is included in the current state estimation through state transformation matrix; (2) linear KF might degrade the estimation due to the nonlinearity of the VT measurement model. In this brief, a graph optimization (GO) method is employed to substitute the KF to estimate the navigation solutions in VT. In the GO-VT, the measurements and the state transformation from past epochs are all regarded as constraints to optimize the states estimation. With the iterations during the optimization, the GO-VT is expected to alleviate the influence of the measurement model nonlinearity on the state estimation. A field test was carried out for assessing the performance of the GO-VT, its superior position accuracy compared with that from the KF supported that the GO could enhance the VT.

Stator Current-Sensorless-Modulated Model Predictive Direct Power Control of a DFIM With Magnetizing Characteristic Identification

This article presents a direct power control method based on modulated model predictive control for a doubly fed induction machine. The modulated predictive control algorithm constructs an optimal voltage vector from two inverter states that give a minimum absolute error in the active and reactive power. This article focuses on the effects of magnetic saturation and its impact on the accuracy of computed reactive power when the stator current sensors are not installed. To reduce the impact of magnetic saturation on reactive power computation, the machine's magnetizing characteristic is identified through a self-commissioning scheme introduced in this article. The identified magnetizing curve is utilized to construct a full-state stator flux observer, which is used to accurately estimate stator currents that appear in the reactive power equation. Experimental results are presented to demonstrate the accuracy of the reactive power computation in the absence of stator current sensors while conserving the rapid transient response offered by a modulated predictive control strategy for active and reactive power regulation.

Real‐time open‐switch fault diagnosis in automotive permanent magnet synchronous motor drives based on Kalman filter

This study proposes an effective diagnosis method for single and multiple open-switch faults (OSFs) in three-phase permanent magnet synchronous motor (PMSM) drives. The proposed method is based on the real-time estimation of the three-phase motor currents using three Kalman filters (KFs). Three residual signals are defined as the differences between the three-phase measured and estimated stator currents. The averaged normalised residual signals are used as diagnostic criteria for the detection of OSFs. The proposed method accurately detects the OSFs, localises the faulty switches and effectively discriminates between the OSFs and current sensor faults. The main superiorities of the proposed KF-based method are its fast detection time and high robustness to measurement noises/errors and load variations. Moreover, the proposed method is useful for both closed-loop and open-loop PMSM drives. The proposed method can be embedded as a subroutine in the drive control unit without any hardware extension. The effectiveness of the proposed method is confirmed through extensive simulations and several hardware-in-the-loop experiments on a testbed with a 1.5 kW PMSM and dSPACE1104 control board. In addition, the real-time feasibility of the proposed method using low-cost microprocessor technology is guaranteed by testing it on TMS320F28335 from Texas Instruments © .

An integrated state-estimation framework for interdependent water and energy systems

Recent studies have shown indisputable evidence indicating water and energy networks are two interdependent cyber-physical networks and should be operated cooperatively to maximize their efficiency and resiliency. However, current state-estimation models of water and energy infrastructures are implemented independently in their respective control centers. Such independent state-estimation in energy or water network might not be able to detect false data injections in the other network. In this paper, an integrated state-estimation framework is proposed to process the consistency of the data and estimate the parameters of a combined water and energy network. Compared to standalone state-estimation, an integrated framework proposed in this work makes it more difficult for the attackers to launch stealthy false data injections, as more parameters are involved in the state-estimation that need to be modified for a successful attack. The findings of this research confirm that by accounting for interlinks between hydrological and energy systems, the security of both systems will be improved. The interconnection between the state variables and measurements of the water-energy network is reflected in the energy consuming components of the water system, and is achieved through the measurements of pump’s active power consumption in the proposed integrated estimation framework. The framework includes Newton’s iterative method for an alternating current (AC) state-estimation procedure for the energy network. The state-estimation of the energy network includes the estimation of voltages using measured parameters of the energy network such as active/reactive power flow/injection or voltages. Moreover, a three-tier state-estimation process based on Newton’s iterative method for the water network is contemplated to attain nodal heads, nodal demands, and the flow rate of the pipes without pumps. An integrated bad data detection algorithm is also supplemented to the proposed framework to detect potential bad data in water distribution system or energy network and enhance the resilience of the integrated water energy systems.

Distributed State Estimation of a Non-linear process system with interconnected subsystems

In this paper authors propose a distributed state estimation scheme for the hybrid system with interconnected subsystems to estimate the states. The system considered in this work has different subsystems which can interact with each other via their states over a communication network. The objective is to implement the distributed state estimation scheme for interconnected subsystems in which each subsystem sensors are connected to the communication network, the estimator has been used for each subsystem to estimate the current states by utilizing the states of the neighboring subsystems over the communication network. The proposed state estimation framework utilizes unscented Kalman filter algorithm. Unscented Kalman filter has been designed for each subsystem to estimate the current state estimates which corrupted by state and measurement noise. The benchmark system taken to implement distributed state estimation is continuous stirred tank reactor units which are strongly interconnected via their neighboring states. The reactors temperature is maintained at unstable operating point by a decentralized proportional integral controller for each subsystem by utilizing the measurements of the sensors from the network. The estimation framework has been verified in the absence of the network failure to stabilize the plant at unstable operating point. The estimate of the corresponding subsystems is used to compute the controller output in the absence of the network failure with minimal sharing over the network.

Verification of Detectability for Unambiguous Weighted Automata

In this article, we deal with the detectability problem for unambiguous weighted automata (UWAs). The problem is to determine if, after a finite number of observations, the set of possible states is reduced to a singleton. Four types of detectabilities, namely, strong detectability, detectability, strong periodic detectability, and periodic detectability are defined in terms of different requirements for current state estimation. We first construct a deterministic finite state automaton (called observer) over a weighted alphabet and prove that it can be used as the current-state estimator of the studied UWA. Finally, necessary and sufficient conditions based on the observer are proposed to verify detectabilities of a UWA.