Critical System States Research Articles

Leveraging Behavioral Correlation in Distribution System State Estimation for the Recognition of Critical System States

State estimation for distribution systems faces the challenge of dealing with limited real-time measurements and historical data. This work describes a Bayesian state estimation approach tailored for practical implementation in different data availability scenarios, especially when both real-time and historical data are scarce. The approach leverages statistical correlations of the state variables from a twofold origin: (1) from the physical coupling through the grid and (2) from similar behavioral patterns of customers. We show how these correlations can be parameterized, especially when no historical time series data are available, and that accounting for these correlations yields substantial accuracy gains for state estimation and for the recognition of critical system states, i.e., states with voltage or current limit violations. In a case study, the approach is tested in a realistic European-type, medium-voltage grid. The method accurately recognizes critical system states with an aggregated true positive rate of 98%. Compared to widely used approaches that do not consider these correlations, the number of undetected true critical cases can be reduced by a factor of up to 9. Particularly in the case where no historical smart meter time series data is available, the recognition accuracy of critical system states is nearly as high as with full smart meter coverage.

Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems

The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To expedite data analysis, preprocessing through machine learning algorithms becomes essential. This paper introduces the advanced FedSVD algorithm, utilizing Singular Value Decomposition (SVD), which efficiently decomposes large datasets, establishes relationships, and identifies irrelevant data. The algorithm operates in federated machine learning systems, enabling local data processing on private devices while sharing only results with the global learning model. This approach enhances information processing confidentiality and facilitates the exchange of anomaly detection outcomes among network devices. The results of the study demonstrate that the modified FedSVD processing is 5 ms faster on average in comparison to the non-modified one. The proposed FedSVD algorithm calculates anomaly detection with higher accuracy by an average of 1–3% compared to the non-modified FedSVD and SVD ones. The advanced FedSVD algorithm proves to be a decentralized, confidential, and efficient solution for anomaly detection in smart grid systems.

An efficient analytical approach for operational reliability evaluation of integrated electricity‐heat energy systems with variable wind power

AbstractThe increase in wind power penetration leads to the risk of energy supply interruption in integrated electricity‐heat energy systems (IHES). This paper presents an analytical‐based approach for the efficient operational reliability evaluation of the IHES. The wind power distribution characteristic model considering correlation is constructed based on Hidden Markov Model (HMM). To reduce computational complexity, a critical system state identification (CSSI) method is proposed that characterises both random device failures and wind power uncertainty. Under each critical system state, the analytical function relationship between the reliability index and the uncertainty factors is established based on the virtual stochastic response surface (VSRS) method. The analytical function can directly calculate the reliability index when the wind power output varies, avoiding a large number of repeated calculations of the optimal load‐shedding model of the system and reducing the time required for reliability assessment. This allows the operator to evaluate the operational reliability of the integrated energy system in real‐time. The numerical simulation of the test system combining the IEEE 33‐node distribution system and the existing 28‐node heating system proves the effectiveness of the proposed method.

Physical–statistical learning in resilience assessment for power generation systems

Upswing in extreme weather conditions and natural disasters in conjunction with the relentless penetration of the intermittent renewable energy have brought resilience of the power generation systems into sharp relief. In this study, we adopt a high-order physical model to characterize the full-detail sub-transient behaviors in synchronous generator dynamics, and consequently utilize basin stability (BS) to quantify system resilience against potentially large perturbations. This high-fidelity model has not been extensive probed in estimate of BS, largely owing to the tremendous computational overhead involved. We conduct sensitivity analysis to pick out the most critical system states, whose perturbation exerts huge impact and hence are sensitive on BS or system resilience. Following this, we develop a diversity-enhanced active learning framework to sequentially identify the informative perturbed states, which will be further evaluated by the high-fidelity sub-transient model. This approach only incurs a paltry of simulation effort compared to the crude Monte Carlo simulation but with comparable accuracy on BS estimation.

Critical state of non-coherent multi-state system

Non-coherent systems represent a specific group of systems in context of reliability analysis. These systems are characteristic by existence of situations in which degradation of a system component can result in improvement of the whole system. Non-coherent systems are mostly investigated in case of Binary-State Systems (BSSs) that admit two performance levels for modeling system as well as its components. Properties of these systems have been studied and algorithms for calculation of most of reliability measures have been developed. However, in case of Multi-State Systems (MSSs), the situation is completely different. Non-coherent MSSs have been studied only marginally with focus on homogeneous MSSs. In this paper, we deal with heterogeneous non-coherent MSSs, generalize existing definitions for them and develop a new approach for computation of their critical system states, which play a key role in importance analysis. The analysis of non-coherent MSSs considered in this paper is implemented based on its representation in a form of time independent structure function.

A new integrated system with thermal energy storage for five useful outputs: A case study

This paper proposes the design of a novel renewable energy based integrated system with thermal energy storage options which is capable of producing up to five useful outputs consisting of electricity, heating, cooling, freshwater, and hot water. The system is also capable of moderate thermal energy storage through the use of molten salts, NaNO3 and KNO3. Following the design of this integrated multigenerational system, an extensive energetic and exergetic thermodynamic analysis is conducted by studying heating and cooling, power outputs, storage capacities, and exergy destruction rates. The various parametric studies performed will help determine how effective the present system is able to operate under varying conditions such as changes in ambient temperature, and critical system state point values such as different temperatures, pressures, mass flow rates, etc. The proposed system is intended for a high school located within Calgary Alberta, Canada and applied as a case study. The specified system has overall energetic and exergetic efficiencies of 54.42% and 30.24%, respectively. The energetic coefficients of performance during heating and cooling are 0.8169 and 2.265, respectively while the exergetic coefficients of performance during heating and cooling are found 0.2683 and 0.3864, respectively.

A Novel Thick Ellipsoid Approach for Verified Outer and Inner State Enclosures of Discrete-Time Dynamic Systems

Simulating dynamic systems with bounded uncertainty in initial conditions and selected parameters is a common task for the reliability analysis of closed-loop control structures as well as for a simulation-based parameter identification on the basis of uncertain measurements. However, dealing with bounded uncertainty is not a trivial task. On the one hand, the naive application of interval analysis often leads to excessively large bounds which may yield state enclosures that are by far too pessimistic to be useful in practice. On the other hand, the use of grid-based or probabilistic and Monte-Carlo like simulation approaches suffers from the disadvantage that they do not provide a guarantee of the correctness of the obtained solutions. Using insufficiently many or badly chosen samples may lead to the phenomenon that critical system states are not detected so that the computed results underestimate the range of reachable states. Therefore, a novel ellipsoidal state enclosure technique is presented which does not require the online solution of linear matrix inequalities (LMIs). It uses the newly introduced representation of state domains by means of thick ellipsoids. These domains simultaneously represent inner and outer enclosures of the reachable states and directly provide a measure for the tightness of the obtained results, regardless of whether the system is linear or nonlinear or whether it is stable or not.

A Novel Framework for Synchrophasor Based Online Recognition and Efficient Post-Mortem Analysis of Disturbances in Power Systems

Synchrophasor based applications become more and more popular in today’s control centers to monitor and control transient system events. This can ensure secure system operation when dealing with bidirectional power flows, diminishing reserves and an increased number of active grid components. Today’s synchrophasor applications provide a lot of additional information about the dynamic system behavior but without significant improvement of the system operation due to the lack of interpretable and condensed results as well as missing integration into existing decision-making processes. This study presents a holistic framework for novel machine learning based applications analyzing both historical as well as online synchrophasor data streams. Different methods from dimension reduction, anomaly detection as well as time series classification are used to automatically detect disturbances combined with a web-based online visualization tool. This enables automated decision-making processes in control centers to mitigate critical system states and to ensure secure system operations (e.g., by activating curate actions). Measurement and simulation-based results are presented to evaluate the proposed synchrophasor application modules for different use cases at the transmission and distribution level.

Pulse Width Insensitive Design and Verification Methods

Many embedded controllers have some critical system states that depend on an asynchronous event. Currently handling them in design depends on the availability of always-on slow clocks. In this paper we present a generic asynchronous design scheme that doesn't require a clock and ensure a reliable functionality without associated deadlock scenarios sensitive to exact arrival times of asynchronous events. This is enabled by a novel pulse width insensitive design method, which also requires unconventional verification methodology that ensures thorough and comprehensive pre-silicon design quality. These have been applied on the latest, ultra-low cost embedded micro-controller design targeted for cost sensitive applications.

Non-equilibrium steady states in quantum critical systems with Lifshitz scaling

We study out-of-equilibrium energy transport in a quantum critical fluid with Lifshitz scaling symmetry following a local quench between two semi-infinite fluid reservoirs. The late time energy flow is universal and is accommodated via a steady state occupying an expanding central region between outgoing shock and rarefaction waves. We consider the admissibility and entropy conditions for the formation of such a non-equilibrium steady state for a general dynamical critical exponent z in arbitrary dimensions and solve the associated Riemann problem. The Lifshitz fluid with z = 2 can be obtained from a Galilean boost invariant field theory and the non-equilibrium steady state is identified as a boosted thermal state. A Lifshitz fluid with generic z is scale invariant but without boost symmetry and in this case the non-equilibrium steady state is genuinely non-thermal.

The Konect value – a quantitative method for estimating perception time and accuracy for HMI designs

ABSTRACTA human operator monitoring a safety-critical system has to perceive information quickly and accurately to detect critical system states and execute countermeasures in time. So far, testing such human machine interfaces (HMIs) is a complex task as HMI design prototypes have to be implemented for simulation environments to perform tests with professional operators. We propose Konect Value, a quantitative method to estimate the relative perception accuracy and operator reaction time at an early design stage. The model-based method solely requires a task model and HMI design sketches as input. To validate the Konect Value, we applied the quantitative measure to seven different HMIs in a truck platooning use case. A comparison of the calculated value to the measured accuracy and reaction times in a lab study (n=33) revealed high correlations for the relative reaction time (r=0.83, p<0.05) and relative perception accuracy (r=−0.90, p<0.01). This indicates that Konect Value is a promising method for early HMI design evaluation in the safety-critical system domain.

Violation of the Bell inequality in quantum critical random spin-1/2 chains

We investigate the entanglement and nonlocality properties of two random XX spin-1/2 critical chains, in order to better understand the role of breaking translational invariance to achieve nonlocal states in critical systems. We show that breaking translational invariance is a necessary but not sufficient condition for nonlocality, as the random chains remain in a local ground state up to a small degree of randomness. Furthermore, we demonstrate that the random dimer model does not have the same nonlocality properties of the translationally invariant chain, even though they share the same universality class for a certain range of randomness.

Tools 4.0 – Intelligence starts on the cutting edge

Predominantly milling tools with indexable inserts, so-called inserted-tooth cutters, are used to produce plane metal surfaces. The remarkable features of efficient milling tools are that great production qualities can be attained at large material removal rates and that they are very reliable in use. Hence, it is important for the user that the cutting process is performed at the greatest possible material removal rate with the best possible surface quality. An important cost factor here is the tool life, i.e. the service life of the tool until the cutting edge has to be replaced.Today under Industry 4.0, production is combined with cutting-edge information and communication technology. The goal of a joint project initiated by industry is to increase reliability and save costs by a continuous predictive analysis of the milling process. In this project, the relevant information of the cutting process were permanently established directly in the milling tool during cutting, then processed with intelligent algorithms and displayed to the operator. The physical parameters here are the cutting force directly on the cutting edge, the rotational speed of the tool, the hardness of the machining as well as the temperature close to the cutting edge. An integrated accelerometer is used to determine the rotational speed and also serves as an on/off switching function of the electronics.The measuring data already prefiltered are received by an intelligent tool changer for indexable inserts, which is located right in the middle of several machines. This tool changer functions as charging station and tool for indicating worn indexable inserts. Moreover, it serves as a base for up to five milling tools and as a transmitting terminal for transferring process data to various terminals. Critical system states and necessary changes of indexable inserts can be displayed on terminals, such as computers, smartphones, tablets or smartwatches.This paper presents a functional information chain with the main focus on the design of an intelligent and sensitive tool.

Indicators as modular-hierarchical models for valuation

Indicators are necessary and widely used means to understand system behavior. This paper will provide a pragmatic discussion where indicators come from, how they are used especially in the field of modelling and simulation and how a complex indicator should be designed. Starting with the basic definition, indicators primarily are system quantities that can easily be observed and measured but standing for a more difficult to measure or hidden or inaccessible value the user is interested in intrinsically. This basic usage however loses importance when indicators are applied to system modelling and simulation. In the modelling context, there are no “inaccessible” model quantities because model description does not distinguish between readable and not readable variables. In this context, indicators mainly are used to manage complex, modular hierarchically built systems (or the models of them). Indicators in such a context help to understand how to optimize those complex systems and how to avoid critical system states by indicating them as crossing a critical threshold for the indicator value.For those cases, the paper analyses the situation by a discussion of the following five main topics: Some deliberations about a pragmatic definition of indicators especially for modelling and simulation purposes. Some differentiation concerning the functionality of indicators. Some aspects how to build indicators, a process that will have many commons with the system modelling process. Some basic structural settings for building complex, hierarchical indicators mathematically. And finally, a few words about the sensitivity of indicator values concerning the composition functions and the weightings of the sub indicators used for indicator construction. In general, this paper will show that indicators are proper valuation models for system or model behavior and thus have to be handled like any other model by executing the typical steps of the modelling process as modelling by finding adequate mathematical interrelations between the components of the indicator, model construction by finding adequate parameter values and weightings, model validation by checking whether the indicator fulfills the user defined demands, and finally a proper sensitivity analysis for the model- respective the indicator-design.

Safe Active Learning and Safe Bayesian Optimization for Tuning a PI-Controller

In the combustion engine calibration domain, many controllers are still tuned manually or using simple adjustment laws. In order to increase workforce efficiency, automated methods for controller tuning are desirable. Often, the structure of the controller is fixed and only its parameters have to be optimized. Model based controller tuning methods require a good dynamic model of the system. Such models are often hard to obtain, as deep system knowledge or extensive measurements at the system, potentially in open loop, may be required. In some cases only closed loop measurements are possible, for example, due to system instability. If controller tuning methods interact with the real system for which the controller shall be tuned, they have to comply with safety constraints. For example, parameter sets resulting in unstable control loops or ones causing critical system states as with very high overshoot should not be tested at the real system.In this contribution, two optimization-based methods for tuning controller parameters are compared. The first method, Safe Active Learning for control, learns a loss function based on controller parameters. Subsequently, an offline optimization is pursued. The second method, a newly proposed Safe Bayesian Optimization algorithm, combines learning of a loss function model with online optimization. Both methods perform closed loop measurements and take safety constraints into account. The methods are evaluated and compared at a PI controller of a real high pressure fuel supply system in a test vehicle.

Reliability analysis of multi-state system with application of multiple-valued logic

PurposeThe purpose of this paper is to develop a new mathematical method for the reliability analysis and evaluation of multi-state system (MSS) reliability that agrees with specifics of such system. It is possible based on the application of multiple-valued logic (MVL) that is a natural extension of Boolean algebra used in reliability analysis.Design/methodology/approachSimilar to Boolean algebra, MVL is used for the constriction of the structure function of the investigated system. The interpretation of the structure function of the MSS in terms of MVL allows using mathematical methods and approaches of this logic for the analysis of the structure function.FindingsThe logical differential calculus is one of mathematical approaches in MVL. The authors develop new method for MSS reliability analysis based on logical differential calculus, in particular direct partial logical derivatives, for the investigation of critical system states (CSSs). The proposed method allows providing the qualitative and quantitative analyses of MSS: the CSS can be defined for all possible changes of any system component or group of components, and probabilities of this state can also be calculated.Originality/valueThe proposed method permits representing the MSS in the form of a structure function that is interpreted as MVL function and provides the system analyses without special transformation into Boolean interpretation and with acceptable computational complexity.

Renyi entanglement entropies of descendant states in critical systems with boundaries: conformal field theory and spin chains

We discuss the Renyi entanglement entropies of descendant states in critical one-dimensional systems with boundaries, that map to boundary conformal field theories in the scaling limit. We unify the previous conformal-field-theory approaches to describe primary and descendant states in systems with both open and closed boundaries. We provide universal expressions for the first two descendants in the identity family. We apply our technique to critical systems belonging to different universality classes with non-trivial boundary conditions that preserve conformal invariance, and find excellent agreement with numerical results obtained for finite spin chains. We also demonstrate that entanglement entropies are a powerful tool to resolve degeneracy of higher excited states in critical lattice models.

Shock waves, rarefaction waves, and nonequilibrium steady states in quantum critical systems

We re-examine the emergence of a universal non-equilibrium steady state following a local quench between quantum critical heat baths in spatial dimensions greater than one. We show that energy transport proceeds by the formation of an instantaneous shock wave and a broadening rarefaction wave on either side of the interface, and not by two shock waves as previously proposed. For small temperature differences the universal steady state energy currents of the two-shock and rarefaction-shock solutions coincide. Over a broad range of parameters, the difference in the energy flow across the interface between these two solutions is at the level of two percent. The properties of the energy flow remain fully universal and independent of the microscopic theory. We briefly discuss the width of the shock wave in a viscous fluid, the effects of momentum relaxation, and the generalization to charged fluids.

Evaluating measures of adverse financial conditions

Timely identification and anticipation of adverse conditions in the financial system are critical for macroprudential policy. However, there is no consensus on how to evaluate the quality of systemic measures. This paper provides a framework to compare measures of systemic conditions. We illustrate the proposed tests with a case study of US measures from 1976 to 2013. We find that measures which include information from multiple markets improve identification of critical system states. However, tested measures show limited capacity to anticipate critical episodes.

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Groundwater extracted from alluvial aquifers close to rivers is vulnerable to contamination by infiltrating river water. Infiltration is often increased during high discharge events, when the levels of waterborne pathogens are also increased. Water suppliers with low-level treatment thus rely on alternative measures derived from information on system state to manage the resource and maintain drinking-water quality. In this study, a combination of Self-Organizing Maps and Sammon’s Mapping (SOM-SM) was used as a proxy analysis of a multivariate time-series to detect critical system states whereby contamination of the drinking water extraction wells is imminent. Groundwater head, temperature and electrical conductivity time-series from groundwater observation wells were analysed using the SOM-SM method. Independent measurements (spectral absorption coefficient, turbidity, particle density and river stage) were used. This approach can identify critical system states and can be integrated into an adaptive, online, automated groundwater-management process.