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Phasor Measurement Units (PMUs) are widely used in power system state estimation due to its high sampling rate and real-time estimation of power state in power systems. However, it is also vulnerable to cyberattacks (GPS spoofing attacks) since it uses unencrypted GPS civil signals for synchronization. The GPS spoofing attacks (GSAs) will intentionally manipulate the time reference of PMUs, which is equivalent to falsifying the phase angle of the PMU measurements. In this paper, we consider the secure power state estimation and attacks detection with the unknown GSAs in the power systems. Particularly, we derived the PMU-based GSAs model in the power state estimation problem, which is formulated as a mixed maximum likelihood problem. In the formulated problem, the involved unknown parameters are coupled and the objective function is non-convex, which are of significant challenges in finding optimal solutions. To tackle these challenges, a joint maximum a posteriori and maximum likelihood (JMAP-ML) algorithm is proposed to securely estimate the power state and detect the GSAs in the mixed measurements of PMUs. Different testing scenarios in IEEE 14 bus system are simulated to show the proposed algorithm’ performance on GSAs detection and state estimation. Numerical examples demonstrate the improved accuracy of our algorithm compared with classical algorithms when GSAs are present. And we conclude that when a sufficient number of PMUs are deployed in the system, the impact of GSAs will be largely compensated in the estimation stage.

The global transition from fossil-based automobile systems to their electric-driven counterparts has made the use of a storage device inevitable. Owing to its high energy density, lower self-discharge, and higher cycle lifetime the lithium-ion battery is of significant consideration and usage in electric vehicles. Nevertheless, the state of charge (SOC) of the battery, which cannot be measured directly, must be calculated using an estimator. This paper proposes, by means of a modified priori estimate and a compensating proportional gain, an improved extended Kalman filter (IEKF) for the estimation task due to its nonlinear application and adaptiveness to noise. The improvement was achieved by incorporating the residuals of the previous state matrices to the current state predictor and introducing an attenuating factor in the Kalman gain, which was chosen to counteract the effect of the measurement and process noise resulting in better accuracy performance than the conventional SOC curve fitting-based estimation and ampere hour methods. Simulation results show that the standard EKF estimator results in performance with an error bound of 12.9% due to an unstable start, while the modified EKF reduces the maximum error to within 2.05% demonstrating the quality of the estimator. [ABSTRACT FROM AUTHOR]

The Phasor Measurement Unit (PMU) with a GPS signal receiver is a synchronized sensor widely used for power system state estimation. While the GPS receiver ensures time accuracy, it is vulnerable to network attacks. GPS spoofing attacks can alter the phase angle of PMU measurement signals and manipulate system states. This paper derives a power system state model based on PMUs under GPS spoofing attacks, according to the characteristics of changes in bus voltages and branch currents after GSA. Based on the characteristics of this model, a detection and correction algorithm for attacked data is proposed to detect GSA and correct attacked measurements. The corrected measurements can be used for power system state estimation. Simulation results on the IEEE 14-bus system show that the proposed algorithm improves the accuracy of state estimation under one or multiple GSAs, especially when multiple GSAs are present, compared to classical Weighted Least Squares Estimation (WLSE) and Alternating Minimization (AM) algorithms. Further research indicates that this algorithm is also applicable to large-scale networks. [ABSTRACT FROM AUTHOR]

Shipboard Integrated Power System (IPS) has been considered as the trending technology for future Ships. To ensure the safe and stable operation not only in normal mode, but also when unknown faults occur or other emergency situation, this paper proposes a novel reconfiguration strategy under partial observation. Hybrid automata is used to descript the hybrid characteristics of SPS, then the fault-tolerant control and recoverability analysis can be performed. After that, state estimate under partial observation during fault operation is derived, as well as the reconfiguration strategy. To evaluate the performance of the reconfiguration strategy, case study is presented using a four-zone SPS faced with parallel faults, and considering the exist of unobservable states and uncontrollable events. Simulation results indicate that the proposed reconfiguration strategy is capable of determining the optimal reconfiguration strategy under partial observation with multiple faults occur.

The transition to smart grids introduces significant cybersecurity vulnerabilities, particularly with the rise of False Data Injection Attacks (FDIAs). These attacks allow malicious actors to manipulate sensor data, alter the internal state of the grid, and bypass traditional Bad Data Detection (BDD) systems. FDIAs pose a serious threat to grid security, potentially leading to incorrect state estimation and destabilization of the power system, which could result in system outages and economic losses. To address this challenge, this paper proposes a novel detection and localization method. First, false data and measurement errors are modeled as non-Gaussian noise. Recognizing the limitations of the traditional Extended Kalman Filter (EKF) under non-Gaussian conditions, the Maximum Correntropy Criterion (MCC) is integrated into the EKF to improve the robustness of state estimation. Additionally, the Maximum Correntropy Criterion Extended Kalman Filter (MCCEKF) is combined with Weighted Least Squares (WLS), and cosine similarity is introduced to quantify the differences between these two estimators for FDIA detection. A partition approach is then used to construct a logical localization matrix, with cosine similarity detection applied in each section to generate a detection matrix. By performing a logical AND operation on these matrices, the attacked bus is identified. Simulations on IEEE-14-bus and IEEE-30-bus systems validate the proposed approach, demonstrating its effectiveness in reliably detecting and localizing FDIAs in smart grids. • In this study, we enhance EKF with Maximum Correntropy Criterion to improve state estimation. • This paper proposes a detection method for FDIA by using the cosine similarity between MCCEKF and WLS. • This paper proposes a localization method for FDIA by using a partition strategy and detection matrix. [ABSTRACT FROM AUTHOR]

The Phasor Measurement Unit (PMU) with a GPS signal receiver is a synchronized sensor widely used for power system state estimation. While the GPS receiver ensures time accuracy, it is vulnerable to network attacks. GPS spoofing attacks can alter the phase angle of PMU measurement signals and manipulate system states. This paper derives a power system state model based on PMUs under GPS spoofing attacks, according to the characteristics of changes in bus voltages and branch currents after GSA. Based on the characteristics of this model, a detection and correction algorithm for attacked data is proposed to detect GSA and correct attacked measurements. The corrected measurements can be used for power system state estimation. Simulation results on the IEEE 14-bus system show that the proposed algorithm improves the accuracy of state estimation under one or multiple GSAs, especially when multiple GSAs are present, compared to classical Weighted Least Squares Estimation (WLSE) and Alternating Minimization (AM) algorithms. Further research indicates that this algorithm is also applicable to large-scale networks.

Phasor Measurement Units (PMUs) are widely used in power system state estimation due to its high sampling rate and real-time estimation of power state in power systems. However, it is also vulnerable to cyberattacks (GPS spoofing attacks) since it uses unencrypted GPS civil signals for synchronization. The GPS spoofing attacks (GSAs) will intentionally manipulate the time reference of PMUs, which is equivalent to falsifying the phase angle of the PMU measurements. In this paper, we consider the secure power state estimation and attacks detection with the unknown GSAs in the power systems. Particularly, we derived the PMU-based GSAs model in the power state estimation problem, which is formulated as a mixed maximum likelihood problem. In the formulated problem, the involved unknown parameters are coupled and the objective function is non-convex, which are of significant challenges in finding optimal solutions. To tackle these challenges, a joint maximum a posteriori and maximum likelihood (JMAP-ML) algorithm is proposed to securely estimate the power state and detect the GSAs in the mixed measurements of PMUs. Different testing scenarios in IEEE 14 bus system are simulated to show the proposed algorithm' performance on GSAs detection and state estimation. Numerical examples demonstrate the improved accuracy of our algorithm compared with classical algorithms when GSAs are present. And we conclude that when a sufficient number of PMUs are deployed in the system, the impact of GSAs will be largely compensated in the estimation stage. • Explores the utility of PMUs for real-time power system state estimation, leveraging their high sampling rate. • Proposing a novel algorithm for secure power state estimation and GSA detection. • Improved the detection and localization performance of GSA. [ABSTRACT FROM AUTHOR]

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The accurate diagnostic of internal short circuit (ISC) is critical to the safety of lithium-ion battery (LIB), considering its consequence to disastrous thermal runaway. Motivated by this, this article proposes a novel ISC diagnostic method with a high robustness to measurement disturbances and the capacity fading. Particularly, a multistate-fusion ISC diagnostic method leveraging polarization dynamics instead of the conventional charge depletion is proposed within a model-switching framework. This is well-proven to eliminate the vulnerability of diagnostic to battery aging. Within this framework, the recursive total least squares method with variant forgetting is exploited, for the first time, to mitigate the adverse effect of measurement disturbances, which contributes to an unbiased estimation of the ISC resistance. The proposed method is validated both theoretically and experimentally for high diagnostic accuracy as well as the strong robustness to battery degradation and disturbance. [ABSTRACT FROM AUTHOR]

This study is devoted to solution of the problem of construction of the state estimate for a finite Markov chain with discrete time. The Markov chain is considered as a linear dynamic system with incomplete state information. The state estimate is constructed based on the Luenberger observer. The conditions of invariance of the state estimate to perturbations of transition probabilities in the Markov chain are obtained. A numerical example is considered. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of state estimation and detection in the context of timed discrete-event systems. Specifically, we study the verification of detectability, a fundamental state estimation property for dynamic systems. Existing works on this topic mainly focus on untimed DESs. In some applications, however, real-time information is critical for the purpose of system analysis. To this end, in this paper, we investigate the verification of detectability for timed DESs modeled by partially-observed timed automata. Three notions of detectability, strong detectability, weak detectability and delayed detectability, are studied in a dense-time setting, which characterizes detectability by time elapsing rather than event updating steps. We show that verifying strong detectability and delayed detectability for partially-observed timed automata is decidable by providing verifiable necessary and sufficient conditions. Furthermore, we show that weak detectability is undecidable in the timed setting by reducing the language universality problem for timed automata to the verification problem of weak detectability. Our results extend the detectability analysis of DESs from the untimed setting to a timed setting. [ABSTRACT FROM AUTHOR]

Abstract A description and assessment of the first release of the Arctic Subpolar gyre sTate Estimate (ASTE_R1), a data‐constrained ocean‐sea ice model‐data synthesis, is presented. ASTE_R1 has a nominal resolution of 1/3° and spans the period 2002–2017. The fit of the model to an extensive (O(109)) set of satellite and in situ observations was achieved through adjoint‐based nonlinear least squares optimization. The improvement of the solution compared to an unconstrained simulation is reflected in misfit reductions of 77% for Argo, 50% for satellite sea surface height, 58% for the Fram Strait mooring, 65% for Ice Tethered Profilers, and 83% for sea ice extent. Exact dynamical and kinematic consistency is a key advantage of ASTE_R1, distinguishing the state estimate from existing ocean reanalyses. Through strict adherence to conservation laws, all sources and sinks within ASTE_R1 can be accounted for, permitting meaningful analysis of closed budgets at the grid‐scale, such as contributions of horizontal and vertical convergence to the tendencies of heat and salt. ASTE_R1 thus serves as the biggest effort undertaken to date of producing a specialized Arctic ocean‐ice estimate over the 21st century. Transports of volume, heat, and freshwater are consistent with published observation‐based estimates across important Arctic Mediterranean gateways. Interannual variability and low frequency trends of freshwater and heat content are well represented in the Barents Sea, western Arctic halocline, and east subpolar North Atlantic. Systematic biases remain in ASTE_R1, including a warm bias in the Atlantic Water layer in the Arctic and deficient freshwater inputs from rivers and Greenland discharge.

A description and assessment of the first release of the Arctic Subpolar gyre sTate Estimate (ASTE_R1), a data‐constrained ocean‐sea ice model‐data synthesis, is presented. ASTE_R1 has a nominal resolution of 1/3° and spans the period 2002–2017. The fit of the model to an extensive (O(109)) set of satellite and in situ observations was achieved through adjoint‐based nonlinear least squares optimization. The improvement of the solution compared to an unconstrained simulation is reflected in misfit reductions of 77% for Argo, 50% for satellite sea surface height, 58% for the Fram Strait mooring, 65% for Ice Tethered Profilers, and 83% for sea ice extent. Exact dynamical and kinematic consistency is a key advantage of ASTE_R1, distinguishing the state estimate from existing ocean reanalyses. Through strict adherence to conservation laws, all sources and sinks within ASTE_R1 can be accounted for, permitting meaningful analysis of closed budgets at the grid‐scale, such as contributions of horizontal and vertical convergence to the tendencies of heat and salt. ASTE_R1 thus serves as the biggest effort undertaken to date of producing a specialized Arctic ocean‐ice estimate over the 21st century. Transports of volume, heat, and freshwater are consistent with published observation‐based estimates across important Arctic Mediterranean gateways. Interannual variability and low frequency trends of freshwater and heat content are well represented in the Barents Sea, western Arctic halocline, and east subpolar North Atlantic. Systematic biases remain in ASTE_R1, including a warm bias in the Atlantic Water layer in the Arctic and deficient freshwater inputs from rivers and Greenland discharge. Plain Language Summary: A 2002–2017 ocean‐sea ice reconstruction, ASTE_R1, is distributed for use in climate studies over the early 21st century in the northern high latitudes. The product is a model‐data synthesis, using a numerical model to interpolate approximately a billion satellite and in situ observations. The primary strength of ASTE_R1 compared to most existing ocean reanalyses is that strict adherence to the equations describing the fluid flow and conservation laws is built into the product, thus making ASTE_R1 free from artificial un‐physical sources or sinks and associated "jumps" in the time‐evolving state. Furthermore, the product is consistent with most available observations, both used in the synthesis and retained for independent verification. This indicates good large‐scale representation of evolving sea‐ice, ocean currents and water properties, including year‐to‐year variability and decadal trends in heat and freshwater storage in the Arctic and subpolar North Atlantic. Some systematic data‐model differences remain in the product and highlight where extra data and/or model development will improve the next release. The product and underlying model configuration are freely available to the research community. Key Points: The 2002–2017 medium‐resolution Arctic Subpolar gyre sTate Estimate (ASTE) is constrained to 109 satellite and in situ observationsStrict adherence to conservation laws ensures all sources/sinks can be accounted for, enabling application for meaningful budget analysesASTE captures the large‐scale dynamics of the Arctic ocean‐sea ice system including variability and trends in heat and freshwater storage [ABSTRACT FROM AUTHOR]

Talcing account of the problems that the standard probability hypothesis density filter is difficult to estimate the target state correctly and has high computational complexity when there are multiple targets move close to each other in clutter environments, this paper put forward a fast close-spaced target tracking algorithm based on the probability hypothesis density. The proposed algorithm firstly used an adaptive gate technology to divide the measurement set originated from the real targets from the sensor measurement set. And then used the real target measurement set to update the predicted intensity. Finally, a detection-guided close-spaced target reweight approach selectively redistributed the inaccurate component weights within the posterior intensity at each discrete time. Experimental results illustrate that the proposed algorithm not only has target tracking with high efficiency, but also has good stability. [ABSTRACT FROM AUTHOR]

A regional data‐constrained coupled ocean‐sea ice general circulation model and its adjoint are used to investigate mechanisms controlling the volume transport variability through Bering Strait during 2002 to 2013. Comprehensive time‐resolved sensitivity maps of Bering Strait transport to atmospheric forcing can be accurately computed with the adjoint along the forward model trajectory to identify spatial and temporal scales most relevant to the strait's transport variability. The simulated Bering Strait transport anomaly is found to be controlled primarily by the wind stress on short time scales of order 1 month. Spatial decomposition indicates that on monthly time scales winds over the Bering and the combined Chukchi and East Siberian Seas are the most significant drivers. Continental shelf waves and coastally trapped waves are suggested as the dominant mechanisms for propagating information from the far field to the strait. In years with transport extrema, eastward wind stress anomalies in the Arctic sector are found to be the dominant control, with correlation coefficient of 0.94. This implies that atmospheric variability over the Arctic plays a substantial role in determining Bering Strait flow variability. The near‐linear response of the transport anomaly to wind stress allows for predictive skill at interannual time scales, thus potentially enabling skillful prediction of changes at this important Pacific‐Arctic gateway, provided that accurate measurements of surface winds in the Arctic can be obtained. The novelty of this work is the use of space and time‐resolved adjoint‐based sensitivity maps, which enable detailed dynamical, that is, causal attribution of the impacts of different forcings. Plain Language Summary: An ocean circulation model, that was adjusted to match observations, is used to investigate what are the important factors controlling the oceanic flow of water through the Bering Strait. Results show that the flow through the strait is related to surface atmospheric winds over the Bering Sea Shelf (south of the strait) and the near coastal regions of the Arctic Ocean (north of the strait). In the model, knowledge of these winds over the preceding 1 month allows us to reconstruct most of the changes in the flow through the strait. A somewhat surprising result is that winds in the Arctic have a greater influence on the amount of water flowing through the Bering Strait than winds over any region of the Pacific Ocean or the Bering Sea. The connection between the winds and the flow through the strait is strong enough that interannual changes in the winds may be used to predict interannual change in the flow. This predictive skill opens up the prospect for an improved understanding of the causes and mechanisms of flow changes at this important Pacific‐Arctic gateway, provided that accurate measurements of surface winds over the Arctic can be obtained. Key Points: An adjoint sensitivity analysis is performed to quantify the role of atmospheric forcing on the variability of Bering Strait throughflowPrimary driver of the variability is the wind stress over the Bering Sea and Arctic shelves, on time scales matching shelf wave propagationImpact of precipitation, although consistent with steric flow control, yields insignificant variability on monthly to interannual time scales [ABSTRACT FROM AUTHOR]

A combined strategy of torque error feed-forward control and blade-pitch angle servo control is proposed to improve the dynamic power capture for wind turbine maximum power point tracking (MPPT). Aerodynamic torque is estimated using the unscented Kalman filter (UKF). Wind speed and tip speed ratio (TSR) are estimated using the Newton–Raphson method. The error between the estimated aerodynamic torque and the steady optimal torque is used as the feed-forward signal to control the generator torque. The gain parameters in the feed-forward path are nonlinearly regulated by the estimated generator speed. The estimated TSR is used as the reference signal for the optimal blade-pitch angle regulation at non-optimal TSR working points, which can improve the wind power capture for a wider non-optimal TSR range. The Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code is used to simulate the aerodynamics and mechanical aspects of wind turbines while MATLAB/SIMULINK is used to simulate the doubly-fed induction generator (DFIG) system. The example of a 5 MW wind turbine model reveals that the new method is able to improve the dynamic response of wind turbine MPPT and wind power capture. [ABSTRACT FROM AUTHOR]

Using a 26-year average of a dynamically consistent ECCO state-estimate, an effort is made to find a few simple descriptive, but quantitative, patterns of properties of the ocean circulation that are near-globally applicable outside the Arctic regions. The conceptual assumption is made that such an average is physically meaningful. Even with a 26-year average, complex spatial variations in the flow field remain, particularly below about 2000 m where the intricate structure of the underlying topography becomes manifest. Nonetheless, certain constructs do describe the great bulk of the ocean. These constructs consist of thermal wind balance (quasi-geostrophy), spiral-like flow behavior in the near-surface boundary layers with orientation analogous to that of an Ekman layer—abruptly changing sign across the equator. In contrast, evidence for beta-spirals is very thin, consistent with the spatially complex meridional and vertical velocities. As expected, integration so as to remove spatial dependence in one coordinate (e.g. zonal) does produce much simplified structures, albeit in the process suppressing diverse dynamical regimes. Predominantly zonal structures persist in the zonal velocity at depth, and are presumed sensitive to the (parameterized) mean eddy fluxes. An unanswered question, and one perhaps unanswerable at the present time, is whether a much longer averaging interval would significantly further simplify the upper-ocean circulation. The abyssal circulation almost everywhere appears dominated by the topography and slopes and whose structure likely would persist in averages of arbitrary duration. • It is now possible to compute the time-average global ocean circulation during the data dense period since about 1990. • Large-scale patterns of flow appear, including near-universal Ekman-like surface flows, and quasi-geostrophy over much of the ocean. • Some discussion of the utility of turbulence closures in ocean models. [ABSTRACT FROM AUTHOR]

Constrained filters, through utilising the prior state constraint information, are designed to obtain more accurate state estimates in applications, and most of them deal with the estimation problem of systems with deterministic constraints. In practice, complex environmental disturbance, incomplete information or uncooperative behaviour often brings out uncertainties of the constraints. This paper tackles the filtering problem of dynamic systems subject to the stochastic linear equality constraints expressed by random weighted basis matrices. The corresponding constrained dynamic model is constructed first and the linear-minimum-mean-square-error filter is derived based on the orthogonality principle. Due to the effect of constraint randomness, the resultant filter encounters the problem of nonlinear stochastic calculation of random parameters, which is solved by the Taylor-based and the UT-based schemes, respectively, and the computational complexity as well as the tractability of both schemes are analysed. Finally, a simulation study on a road-constrained vehicle tracking demonstrates that the proposed filter has better performance than the classical estimation projection method in terms of estimation accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

For the problem that the standard probability hypothesis density is unable to correctly estimate the states of targets and their number when tracking multiple targets in possible missed detection environments, an improved probability hypothesis density filter based multi-target tracking algorithm is proposed under the linear Gaussian conditions. Two assisted parameters, namely label and probability of existence, are introduced to expand the standard target state in the proposed algorithm which includes three robust schemes compared with the Gaussian mixture probability hypothesis density filter. Firstly, the extended component set of target states representing the target intensity can be correctly updated in the proposed target intensity update scheme. Secondly, by optimizing the component set that approximates the target posterior intensity, the excess and invalid components are effectively reduced in the improved component fusion scheme. Lastly, a new target state extraction scheme is able to accurately estimate the states of targets by comprehensively utilizing both the weight and existent probability of the target. Simulation results show that the proposed algorithm not only provides relatively accurate multi-target estimates, but also has a relatively low computational burden. [ABSTRACT FROM AUTHOR]

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

Ocean heat content (OHC) is a key climate variable that needs to be monitored to know how Earth's energy imbalance is changing, yet observing OHC remains a challenge. The present study examines whether a depth integral of the ocean's electrical conductivity ("conductance"), which may be inferred from both in situ methods and satellite magnetometers over the global ocean, could help monitor OHC. The ocean's electrical conductivity locally depends on temperature, salinity, and pressure, but it is not as well known how the conductance depends on OHC and ocean salt content. By examining the output of an ocean state estimate shown to agree well with observations that have not been assimilated, this study evaluates the fundamental limitations of using perfectly known ocean conductance to predict OHC, rather than the challenges associated with accounting for observational error. It is found that the ocean's conductance and OHC fields are nonlinearly related but nevertheless highly correlated. A statistical framework tends to predict OHC more accurately than ocean salt content from ocean conductance in regions where conductivity is more sensitive to salinity than temperature. The annually (bidecadally) averaged OHC can be predicted from a combination of conductance and depth‐averaged conductivity ocean fields to within nearly 0.1% (1%) error globally and even more accurately in many poorly observed (e.g., ice‐covered) regions. Practical application of this statistical framework to monitor OHC requires examination of the effect of uncertainties in the observed bathymetry and ocean conductance, which vary with application. Plain Language Summary: Seawater's electrical conductivity describes the ocean's ability to conduct electricity, which has an associated magnetic field. It may be possible to get a conductivity averaged over all depths from a combination of satellite observations and our knowledge of the seafloor. Because conductivity depends upon how warm/cold the ocean is, how salty the ocean is, and how deep you are in the ocean, we investigate whether this depth‐averaged conductivity can provide us with information about how much heat is in the ocean over different time period lengths. We develop some frameworks for predicting how much heat is in the ocean from the averaged conductivity and find that there is a trade‐off between the accuracy of the predictions and the length of the time period over which changes in the amount of heat can be detected in the ocean. We also note that there will be additional uncertainties added when our frameworks are applied to observations. Key Points: Predictions of ocean heat content (OHC) from conductance are more accurate on yearly time scales than bidecadal onesOHC can be more accurately predicted than salt content in regions where conductivity is more sensitive to salinityElectrical conductance could be used to monitor OHC in ice‐covered regions in particular [ABSTRACT FROM AUTHOR]

A combined strategy of torque error feed-forward control and blade-pitch angle servo control is proposed to improve the dynamic power capture for wind turbine maximum power point tracking (MPPT). Aerodynamic torque is estimated using the unscented Kalman filter (UKF). Wind speed and tip speed ratio (TSR) are estimated using the Newton−Raphson method. The error between the estimated aerodynamic torque and the steady optimal torque is used as the feed-forward signal to control the generator torque. The gain parameters in the feed-forward path are nonlinearly regulated by the estimated generator speed. The estimated TSR is used as the reference signal for the optimal blade-pitch angle regulation at non-optimal TSR working points, which can improve the wind power capture for a wider non-optimal TSR range. The Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code is used to simulate the aerodynamics and mechanical aspects of wind turbines while MATLAB/SIMULINK is used to simulate the doubly-fed induction generator (DFIG) system. The example of a 5 MW wind turbine model reveals that the new method is able to improve the dynamic response of wind turbine MPPT and wind power capture.

The upper ocean dissolved inorganic carbon (DIC) concentration is regulated by advective and diffusive transport divergence, biological processes, freshwater, and air-sea CO2 fluxes. The relative importance of these mechanisms in the Southern Ocean is uncertain, as year-round observations in this area have been limited. We use a novel physical-biogeochemical state estimate of the Southern Ocean to construct a closed DIC budget of the top 650 m and investigate the spatial and temporal variability of the different components of the carbon system. The dominant mechanisms of variability in upper ocean DIC depend on location and time and space scales considered. Advective transport is the most influential mechanism and governs the local DIC budget across the 10 day-5 year timescales analyzed. Diffusive effects are nearly negligible. The large-scale transport structure is primarily set by upwelling and downwelling, though both the lateral ageostrophic and geostrophic transports are significant. In the Antarctic Circumpolar Current, the carbon budget components are also influenced by the presence of topography and biological hot spots. In the subtropics, evaporation and air-sea CO2 flux primarily balances the sink due to biological production and advective transport. Finally, in the subpolar region sea ice processes, which change the seawater volume and thus the DIC concentration, compensate the large impact of the advective transport and modulate the timing of biological activity and air-sea CO2 flux. [ABSTRACT FROM AUTHOR]

The discrete‐time state estimation problem for a class of stochastic non‐linear polynomial systems confused with Poisson noises over linear observations is presented in this study. The filtering problem is solved computing the time‐update and measurement‐update equations for the state estimate and error covariance matrix. A finite number of filtering equations can be obtained by expressing the conditional expectations of polynomial terms as functions of the estimate and error covariance. The finite‐dimensional filtering equations are explicitly derived in a closed form for third degree polynomial systems. Numerical simulations are performed for a third degree polynomial system and the performance of the designed filter is compared to the performances of the filter designed for polynomial systems confused with additive white Gaussian noises and the extended Kalman filter. [ABSTRACT FROM AUTHOR]

In this paper, we investigate recoverability of discrete-event systems with faults. For a discrete-event system, recoverability is defined as the ability to fire recovery events after the occurrence of a finite number of events for any event sequence which leads the system to faulty modes. By constructing the observer of the system, we discuss how to check whether the system is recoverable or not. Furthermore, we propose one more efficient approach to check recoverability. Finally, all the results are illustrated by a practical example. [ABSTRACT FROM AUTHOR]

SUMMARY This paper considers the problems of the state estimation and the simultaneous unknown input and measurement noise reconstruction when the observer matching condition is not satisfied. A new augmented system is constructed by introducing a new augmented state vector. An auxiliary output vector is proposed to deal with the observer matching condition issues, and a high-order sliding mode observer is considered to get the exact estimates of both the auxiliary outputs and their derivatives in a finite time. A reduced-order observer is developed to asymptotically estimate the system states, and a kind of reconstruction method of the new system's unknown inputs is proposed to reach the goal of reconstructing the original system's unknown input and measurement noise. Finally, a numerical simulation example is given to illustrate the effectiveness of the proposed methods. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This article presents a model-based controller design approach for vehicle to improve handling and stability while the vehicle parameters are uncertain and the state of body sideslip angle is immeasurable. The variations in tire cornering stiffness, which are due to model simplification, are considered and treated as parameter uncertainties. By translating parameter uncertainties into perturbed matrices and combining with state observer, a sufficient condition for the theorem of an observer-based, optimal, and robust H∞ controller subject to parameter uncertainties is derived. In the theorem, the controller and the state observer are integrated into a linear matrix inequality optimization problem and designed together. The performances of the control system are evaluated via numerical simulation under various critical maneuvers and road conditions. The simulation results demonstrated that the vehicle dynamic control system designed by the approach proposed in this article can significantly improve vehicle handling and stability regardless of the variations on the tire cornering stiffness and the absence of the measured body sideslip angle. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters and mixed time-delays. The parameters of the neural networks under consideration switch over time subject to a Markov chain. The networks involve both the discrete-time-varying delay and the mode-dependent distributed time-delay characterized by the upper and lower boundaries dependent on the Markov chain. By constructing novel Lyapunov-Krasovskii functionals, sufficient conditions are firstly established to guarantee the exponential stability in mean square for the addressed discrete-time neural networks with Markovian jumping parameters and mixed time-delays. Then, the state estimation problem is coped with for the same neural network where the goal is to design a desired state estimator such that the estimation error approaches zero exponentially in mean square. The derived conditions for both the stability and the existence of desired estimators are expressed in the form of matrix inequalities that can be solved by the semi-definite programme method. A numerical simulation example is exploited to demonstrate the usefulness of the main results obtained. [ABSTRACT FROM AUTHOR]

In the framework of collective measurements, efforts have been made to reconstruct one-qubit states. Such schemes find an obstacle in the no-cloning theorem, which prevents full reconstruction of a quantum state. Quantum Mechanics thus restricts us to obtaining estimates of the reconstruction of a pure qubit. We discuss the optimal estimate on the basis of the Uhlmann-Josza fidelity, respecting the limitations imposed by the no-cloning theorem. We derive a realistic optimal expression for the average fidelity. Our formalism also introduces an optimization parameter L. Values close to zero imply full reconstruction of the qubit (i. e., the classical limit), while larger L's represent good quantum optimization of the qubit estimate. The parameter L is interpreted as the degree of quantumness of the average fidelity associated with the reconstruction. [ABSTRACT FROM AUTHOR]

In this paper we are interested in robust adaptive fuzzy control of nonlinear SISO systems in the presence of parametric uncertainties. The plant model structure is represented by the Takagi-Sugeno (T-S) type fuzzy system. An indirect adaptive fuzzy controller based on model reference control scheme is proposed to provide asymptotic tracking of reference signal. The controller parameters are computed at each time. The plant state tracks asymptotically the state of the reference model for any bounded reference input signal. Inverted pendulum and mass spring damper are used to check the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

For the problem of deterministic parameter estimate, the theoretical lower bound of estimate error is the Cramér-Rao bound; while for random parameter, the lower bound of estimate error is generally termed by Posterior Cramér-Rao Bound (PCRB). Under the background of passive tracking where the target’s state can be seen as a time-varying random parameter, PCRB of the state estimate error is analyzed in this paper, and the relation between PCRB and varied condition is also fully investigated using different simulation examples. The presented analytical method provides a theoretical base for performance assessment of all kinds of suboptimal estimate algorithms used in practice. [ABSTRACT FROM AUTHOR]

Abstract: The problem of oceanographic state estimation, by means of an ocean general circulation model (GCM) and a multitude of observations, is described and contrasted with the meteorological process of data assimilation. In practice, all such methods reduce, on the computer, to forms of least-squares. The global oceanographic problem is at the present time focussed primarily on smoothing, rather than forecasting, and the data types are unlike meteorological ones. As formulated in the consortium Estimating the Circulation and Climate of the Ocean (ECCO), an automatic differentiation tool is used to calculate the so-called adjoint code of the GCM, and the method of Lagrange multipliers used to render the problem one of unconstrained least-squares minimization. Major problems today lie less with the numerical algorithms (least-squares problems can be solved by many means) than with the issues of data and model error. Results of ongoing calculations covering the period of the World Ocean Circulation Experiment, and including among other data, satellite altimetry from TOPEX/POSEIDON, Jason-1, ERS- 1/2, ENVISAT, and GFO, a global array of profiling floats from the Argo program, and satellite gravity data from the GRACE mission, suggest that the solutions are now useful for scientific purposes. Both methodology and applications are developing in a number of different directions. [Copyright &y& Elsevier]

An ensemble Kalman filter for state estimation and a bias estimation algorithm were applied to estimate individual soil moisture profiles in a small corn field with the CLM2.0 model through the assimilation of measurements from capacitance probes. Both without and with inclusion of bias correction, the effect of the assimilation frequency, the assimilation depth, and the number of observations assimilated per profile were studied. Assimilation of complete profiles had the highest impact on deeper soil layers, and the optimal assimilation frequency was about 1-2 weeks, if bias correction was applied. The optimal assimilation depth depended on the calibration results. Assimilation in the surface layer had typically less impact than assimilation in other layers. Through bias correction the soil moisture estimate greatly improved. In general, the correct propagation of the innovations for both the bias-blind state and bias filtering from any layer to other layers was insufficient. The approximate estimation of the a priori (bias) error covariance and the choice of a zero-initialized persistent bias model made it impossible to estimate the bias in layers for which no observations were available. [ABSTRACT FROM AUTHOR]

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Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy. [ABSTRACT FROM AUTHOR]

The random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) filter is a promising and efficient suboptimal approximation for the multi-target Bayes filter. However, the GM-PHD filter is unable to track nearby targets caused by the improper position distribution of target-originated measurements. Aiming at the problem, a multi-target GM-PHD filter with an improved component merging method is proposed. Based on a proposed adaptive threshold-based component similarity measure scheme, the improved component merging method is able to avoid incorrect fusion of the components of targets in close proximity and optimize the target components within the target posterior intensity. Experimental results illustrate that the proposed algorithm not only can achieve better estimation accuracy in terms of the target states and its number but also has high computation efficiency when compared against the related GM-PHD-based filters. [ABSTRACT FROM AUTHOR]