Actual System State Research Articles

Enabling Highly Efficient Eigen-Analysis of Large Delayed Cyber-Physical Power Systems by Partial Spectral Discretization

The existing spectral discretization-based methods (SDMs) are capable of accurately computing critical eigenvalues of large power systems when time delays in wide-area damping control loops are considered. However, SDMs suffer from huge dimension of the discretized matrices of spectral operators, which is usually dozens of times of actual system states. To resolve the problem, an idea of partial spectral discretization (PSD) is proposed in this paper where only the retarded state variables instead of all system states are discretized. Following the PSD idea, a partial and explicit infinitesimal generator discretization (PEIGD) method is presented for highly efficient eigen-analysis of large closed-loop delayed cyber-physical power system (DCPPS). In contrast to the original EIGD method, the order of the resultant discretized matrix of the infinitesimal generator is greatly reduced and close to the number of actual state variables. The computational burden of PEIGD can be an order of magnitude less than that of EIGD and nearly the same as eigen-analysis of a system without time delay. Moreover, PEIGD is endowed with exactly the same accuracy as EIGD in capturing critical oscillation modes. Both theoretical analyses and intensive tests on the 16-generator 68-bus test system as well as a 516-bus and a 33028-bus real-life large power systems verify the accuracy and efficiency of the presented method.

Input-to-State Stability of Perturbed Nonlinear Systems With Event-Triggered Receding Horizon Control Scheme

In this paper, input-to-state stability (ISS) properties of perturbed systems with event-triggered receding horizon control (RHC) schemes are studied. Two event-triggered control schemes, which are the event-triggered quasi-infinite RHC (EQRHC) and the event-triggered dual-mode RHC (EDRHC) strategies, respectively, are considered. In the EQRHC scheme, an optimal control problem (OCP) should be considered at triggering time and the event is triggered if the error between the actual system state and the optimal system state violating a threshold. While in the EDRHC strategy, an OCP is only solved outside the terminal region and a local control law will be used inside the terminal region. The corresponding event condition is redesigned based on if the system state enters the terminal region or not. The event-triggering condition outside the terminal region is the same with that of the EQRHC scheme and the event-triggering condition inside the terminal region is proposed based on the difference between the actual system state and the predictive system state. Sufficient conditions of feasibility are proposed and ISS properties of both event-triggered control schemes are studied, respectively. At last, numerical simulations show the validity of the proposed methods.

Unscented Rauch–Tung–Streibel smoother‐based power system forecasting‐aided state estimator using hybrid measurements

A hybrid estimator to track the power system states considering fast-rate PMUs and slow-rate supervisory control and data acquisition (SCADA) system is proposed in this article. SCADA measurements arrive at the control centre with various time delays. The estimates obtained using these delayed measurements significantly differs from the actual power system states and is known as the time skew problem. The missing SCADA measurements, owing to their time skewness, are replaced by their predicted values which lead to reduced estimation accuracy. Optimal smoothing algorithms can be utilised to include dynamics in the future measurements to reduce the errors introduced by the time skew problem. Unscented Rauch–Tung–Streibel smoother is used in this study to handle the time skew problem. The performance of the proposed techniques in handling anomalous situations is also studied. The proposed estimator is validated under real-time environment using RTDS, a real-time simulation tool, to assess its applicability in the control centre.

Real Time Simulation of Observer Based State Feedback Load Frequency Regulator for Optimally Reduced Power System Model

This paper presents a study of real time simulation of observer based state feedback load frequency regulator for optimally reduced order power system model. The actual system states can be replaced by the observer based states for feedback control design without losing the system stability. State space model of two area interconnected power system model having non-reheat thermal turbine in each area is investigated to develop the proposed control scheme. Moreover, the power system model is optimally reduced with the objective of retaining the dominant characteristics of original system and reducing the complexity and computational burden for the real time simulation of the proposed scheme. Real time simulation is performed on two PC configurations, out of which continuous and discrete time observer based scheme is designed and simulated in non-real time environment on host PC and target PC is used for real time simulation of continuous and discrete time models. It was found that discrete time observer based control scheme for reduced power system model is quite appreciable in terms of dynamic performance and computational burden as compared to continuous time observer based load frequency control for reduced power system model.

Event‐based model predictive control of discrete‐time non‐linear systems with external disturbances

The event-based model predictive control (MPC) problem for discrete-time non-linear control systems with external disturbances is studied. Two triggering conditions are designed based on whether the system state is within the terminal region or not. The first one is triggered outside the terminal region when the difference between the actual system state trajectory and the corresponding optimal state trajectory violates a relative threshold, while the second one is triggered inside the terminal region if the difference between the actual system state trajectory and the predicted nominal state trajectory violates a desired relative threshold. The feasibility analysis and stability analysis are given in detail. Sufficient conditions for feasibility relating to the triggering threshold and disturbance bound are obtained. Sufficient conditions for stability of the event-based control system referring to system parameters, triggering threshold and disturbance bound are given. Besides, the upper bound of the state trajectory is obtained and a robust invariant set that the system state will enter in finite time is given. Finally, simulation examples are given to validate the effectiveness of the proposed method.

Adaptive job shop scheduling strategy based on weighted Q-learning algorithm

Given the dynamic and uncertain production environment of job shops, a scheduling strategy with adaptive features must be developed to fit variational production factors. Therefore, a dynamic scheduling system model based on multi-agent technology, including machine, buffer, state, and job agents, was built. A weighted Q-learning algorithm based on clustering and dynamic search was used to determine the most suitable operation and to optimize production. To address the large state space problem caused by changes in the system state, four state features were extracted. The dimension of the system state was decreased through the clustering method. To reduce the error between the actual system states and clustering ones, the state difference degree was defined and integrated with the iteration formula of the Q function. To select the optimal state-action pair, improved search and iteration update strategies were proposed. Convergence analysis of the proposed algorithm and simulation experiments indicated that the proposed adaptive strategy is well adaptable and effective in different scheduling environments, and shows better performance in complex environments. The two contributions of this research are as follows: (1) a dynamic greedy search strategy was developed to avoid blind searching in traditional strategy. (2) Weighted iteration update of the Q function, including the weighted mean of the maximum fuzzy earning, was designed to improve the speed and accuracy of the improved learning algorithm.

An unsupervised data completion method for physically-based data-driven models

Data-driven methods are an innovative model-free approach for engineering and sciences, still in process of maturation. The idea behind is the combination of data analytics techniques, to handle the huge amount of data derived from continuous monitoring or experimental measurements, and of the constraints imposed by universal physical laws, particular to the field in hands. A well-known problem in the former corresponds to the quality and completeness of the available data that, sometimes, are so poor that make the predictions useless. In data-driven simulation-based engineering and sciences (DDSBES), the intrinsic physical constraints may help in completing the missing data in a more precise manner, by forcing them to remain in the manifold defined by the physical laws. In this work, a suitable imputation method to complete incomplete data that preserves the data context-dependent structure is presented. This is accomplished by enforcing the data to fulfill the set of physical constraints, specific to the problem. For this purpose, a generalization of the weighted mean concept is proposed, where the distance to the admissible points (in a physical sense) is used as a weighting function to get the optimal candidate. The method is evaluated in a classical regression problem, where it is compared with other standard methods, showing better results. Then, its application is illustrated in two data-driven problems, where no filling data procedure has been yet proposed, showing good predictive capability, provided that the data are close enough to the actual system state.

Stochastic thresholds in event-triggered control: A consistent policy for quadratic control

We propose an event-triggered control scheme for discrete-time linear systems subject to Gaussian white noise disturbances. The event-conditions are given in terms of the deviation between the actual system state and the state of a nominal undisturbed system whose state is identical to the real system state at the event times. In order to ensure that the conditional distribution of the deviation between the two systems, under the condition that no event occurs, remains a normal distribution, we employ thresholds that are themselves random variables. This allows us to: (i) provide expressions for the probability mass function of the times between events and, in turn, arbitrarily select this function; (ii) synthesize controllers associated with the proposed transmissions scheduler that are optimal in terms of an average quadratic cost. In particular, these two properties allow us to show that our event-triggered scheme is consistent in the sense that it outperforms (in a quadratic cost sense) traditional periodic control for the same average transmission rate and does not generate transmissions in the absence of disturbances. We demonstrate the effectiveness of our scheme in a numerical example and describe a way to solve the non-convex optimization problem arising in the approach.

Robust Component Fault Diagnostic Observer Design for Underactuated Surface Vessels Using Moving Horizon Optimization

For the consideration of improving the safety and reliability for underactuated surface vessel, the design scheme of a robust component fault detection (FD) is investigated in this paper. The main idea is to formulate the observer design as the $H_{-}/H_{\infty }$ problem of satisfying the disturbance attenuation and optimizing the fault detectability. The sufficient conditions for performance indexes are derived in the formulation of linear matrix inequality. Considering the remarkable fact that the actual system states are not involved in the iteration process for the parameters optimization, based on the moving horizon optimization strategy, the robust component FD design approach is proposed to achieve the improvements of FD performance and feasibility. The simulation results are given to demonstrate the effectiveness of the proposed approach.

New Approaches for Supervision of Systems with Sliding Wear: Fundamental Problems and Experimental Results Using Different Approaches

Reliability and availability of technically complex and safety-critical systems are of increasing importance. Besides the degree of wear, the quality of mechanical systems is significant for the system reliability. The focus of this contribution is the development and application of readily applicable and easily interpretable algorithms for industrial data obtained from technical systems during operation. The methods are within the focus of the production-oriented automation programs (Industrial Internet, Automation 4.0, China 2025). In this contribution as example a hydraulically driven machine in which parts slide over each other is chosen as sliding wear example. Monitoring is applied to distinguish normal and abnormal operation as well as to define end of useful lifetime. In this contribution four different methods will be introduced and experimentally compared without the availability of objective information about the wear state. The approaches differ with respect to the used measurements and data preparation. As measurements Acoustic Emission and the hydraulic pressure of the driving machine are used. For processing the accumulation of damage related values, a machine learning algorithm, and a sensitivity matrix are used. For comparison the experimental validation is based on identical data sets. Different operational states of the system denoted as actual system state are defined and classified. The comparison shows that the four introduced methods provide similar classification results although the underlying measurements are based on different physical principles. The newly introduced approaches allow online evaluation of the actual system state and can serve within improved maintenance strategies.

The Design of Output Feedback Distributed Model Predictive Controller for a Class of Nonlinear Systems

For a class of nonlinear systems whose states are immeasurable, when the outputs of the system are sampled asynchronously, by introducing a state observer, an output feedback distributed model predictive control algorithm is proposed. It is proved that the errors of estimated states and the actual system's states are bounded. And it is guaranteed that the estimated states of the closed-loop system are ultimately bounded in a region containing the origin. As a result, the states of the actual system are ultimately bounded. A simulation example verifies the effectiveness of the proposed distributed control method.

An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system

In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm.

Integration of SPC and performance maintenance for supply chain system

In this paper, a supply chain system is viewed as a maintainable system, and the economic-statistical design of a likelihood ratio control chart with a maintenance application is considered for this system. The supply chain system is described by a three-state: normal state, warning state and failure state. A likelihood ratio control chart is used to monitor the system given that only categorical observations can be obtained. When the chart signals, a full inspection is performed to determine the actual system state (normal or warning), and preventive maintenance is immediately performed in the warning state. In addition, the supply chain system must be corrected upon failure (i.e. corrective maintenance), and should be maintained in a scheduled time (i.e. planned maintenance). A mathematical model is developed for the joint optimisation of the control chart parameters and planned maintenance time based on renewal theory. An example is presented to illustrate how to determine the optimal design parameters. We also investigate the effect of coefficients and statistical constraints on the decision variables and the expected cost.

Economic analysis of condition monitoring systems for offshore wind turbine sub‐systems

The use of condition monitoring systems on offshore wind turbines has increased dramatically in recent times. However, their use is mostly restricted to vibration based monitoring systems for the gearbox, generator and drive train. A survey of commercially available condition monitoring systems and their associated costs has been completed for the blades, drive train, tower and foundation. This paper considers what value can be obtained from integrating these additional systems into the maintenance plan. This is achieved by running simulations on an operations and maintenance model for a wind farm over a 20 year life cycle. The model uses Hidden Markov Models to represent both the actual system state and the observed condition monitoring state. The CM systems are modelled to include reduced failure types, false alarms, detection rates and 6 month failure warnings. The costs for system failures are derived, as are possible reductions in costs due to early detection. The detection capabilities of the CM systems are investigated and the effects on operational costs are examined. Likewise, the number of failures detected 6 months in advance by the CM systems is modified and the costs reported.

Divergence Between Flight Crew Mental Model and Aircraft System State in Auto-Throttle Mode Confusion Accident and Incident Cases

In many aircraft accidents relating to pilot error, flight crews held an inconsistent assumption of the actual state of the system when reacting to a failure or abnormal situation. This paper formally defines divergence between flight crew mental model and actual system state, describes a developed framework to understand the mechanisms behind divergence, and discusses results of analysis of divergence in eight auto-throttle mode confusion accidents and incidents. The framework revealed that divergence occurred in all auto-throttle cases. As divergence was analyzed, it became apparent that reconvergence was also prominent in the accidents and incidents, where seven of eight cases indicated that the crew’s mental model and actual system state regained consistency at some point during the accident timeline. Success of recovery actions was dependent on the timing of reconvergence in relation to the criticality of the situation. Consistent with existing literature, the framework also revealed that breakdowns in situation awareness were apparent during the onset of divergence and as an inhibition of reconvergence. The analysis using the framework of divergence showed that for auto-throttle mode confusion accidents, mitigation efforts would be most effective if targeted toward promoting situation awareness of system state prior to the situation degrading beyond recoverability.

Optimal control strategies for single-machine family scheduling with sequence-dependent batch setup and controllable processing times

The problem of scheduling jobs on an unreliable single machine is considered in this paper. The scheduling problem is characterized by the following features: jobs are grouped into classes of equivalent jobs; the generalized due date model is adopted for each class of jobs; it is possible to reduce the processing time of a job, at the price of the payment of an extra cost; a costly setup is required when switching between jobs of different classes. The scheduling problem is solved from a perspective which is different from the traditional determination of an optimal sequence of jobs; in fact, the objective of the paper is to determine optimal control strategies (functions of the system state) which allow generating the optimal decisions during the evolution of the system, taking into account the actual system state. In this way, optimal decisions can be promptly taken also in the presence of perturbations which affect the single machine (such as breakdowns and slowdowns). To this aim, a specific optimal control problem is stated and solved in the paper.

Adaptive vector sliding mode fault-tolerant control of the uncertain Stewart platform based on position measurements only

SUMMARYThis paper investigates the trajectory tracking of a Stewart platform, which is a typical multi-input multi-output nonlinear system, with unmodeled dynamics, parameter uncertainties, friction, and unpredictable actuator faults. An adaptive vector sliding mode fault-tolerant control law is derived to ensure the system is insensitive to uncertainties and drive the state variable errors of the closed-loop system to converge to the origin. Moreover, novel adaptive laws are proposed to update the upper boundary of uncertainty according to the actual system state, which greatly reduces the chattering of sliding mode control. Furthermore, velocity signals are estimated by introducing a simple nonlinear observer, resulting in the proposed controller requiring position measurements only. Finally, numerical simulations illustrate the effectiveness of the proposed control scheme.

A Unified Approach for Power System Predictive Operations Using Viterbi Algorithm

A paradigm shift in the renewable energy proliferation in the U.S. necessitates a paradigm shift in power system operations to accommodate large-scale intermittent power while keeping the grid reliable and secure. Energy management systems (EMS) will benefit from an auxiliary function, which integrates the wind and load forecasting to state estimation and forecasting. This auxiliary function will create a predictive database for the power system states using the historical states as well as wind and load forecasts. The predictive database can be utilized to provide pseudo-measurements to a static state estimator in the case of loss of observability and bad data processing, or it can be used for short-term congestion and ramping predictions. This paper proposes an auxiliary tool for look-ahead power system state forecasting in electrical power systems with high intermittent renewable energy penetration. The method utilizes Markov models (MMs) and the Viterbi algorithm (VA) with a grid of feasible power system states obtained and updated by using the past states. The proposed algorithm is evaluated on the IEEE 14-bus and 118-bus systems using wind and load data available from the Bonneville Power Administration (BPA). The results show good correlation between the predictions and the actual power system states.

An online outlier identification and removal scheme for improving fault detection performance.

Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions.

Robust moving horizon estimation based output feedback economic model predictive control

In this work, we develop an economic model predictive control scheme for a class of nonlinear systems with bounded process and measurement noise. In order to achieve fast convergence of the state estimates to the actual system state as well as the robustness of the observer to measurement and process noise, a deterministic (high-gain) observer is first applied for a small time period with continuous output measurements to drive the estimation error to a small value; after this initial small time period, a robust moving horizon estimation scheme is used on-line to provide more accurate and smoother state estimates. In the design of the robust moving horizon estimation scheme, the deterministic observer is used to calculate reference estimates and confidence regions that contain the actual system state. Within the confidence regions, the moving horizon estimation scheme is allowed to optimize its estimates. The output feedback economic model predictive controller is designed via Lyapunov techniques based on state estimates provided by the deterministic observer and the moving horizon estimation scheme. The stability of the closed-loop system is analyzed rigorously and conditions that ensure the closed-loop stability are derived. Extensive simulations based on a chemical process example illustrate the effectiveness of the proposed approach.