System Performance Loss Research Articles

Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems

The increasing penetration of grid-enabled electric vehicles (EVs) renders road networks (RNs) and power networks (PNs) increasingly interdependent for normal operation. For this reason, recently few studies have started to investigate the vulnerability of a highly coupled traffic-power system in the presence of disruptive events. Actually, however, only very few of these studies have considered the impact of EVs on the interdependent traffic-power system during restoration from a disruptive event. In an attempt to fill this gap, in this study, we investigate the restoration planning of both independent RNs and PNs, and interdependent traffic-power systems. A mixed integer program model is formulated to provide optimal reconfiguration and operational solutions for post-disruption traffic-power systems recovery. The objective of the model is to minimize the total cost incurred by system performance loss, which is quantified by the cumulative unmet traffic demand for RNs and load shedding cost for PNs. Several reconfiguration strategies are considered, including links reversing in RNs and line switching in PNs, to optimize system resilience. In the proposed model, the integrated problem of system optimal dynamic traffic assignment and optimal power flow is solved to derive the optimal traffic-power flow. RNs and PNs are coupled through the coordinately allocated spatio-temporal charging demand of EVs. A partial highway network in North Carolina (NC), USA, and a modified IEEE-14 bus system are used to illustrate the application of the model. The numerical results obtained show the added value of coordinately planning restoration for traffic-power systems and the effects of different levels of EV penetration.

A novel optimal approach for control law of multi-rate systems with different rate operations

Abstract The main purpose of this study is to propose a method to achieve optimal response and performance of the multi rate systems in existence of different sampling rates and to avoid data losses in such systems. In this paper, a new approach based on the multi-rate digital system is proposed. Based on this method, we will be able to maintain optimal performance of these systems without losing data, in addition to establishing connections between subsystems with different sampling rates. By the proposed method, not only the effect of different sampling rates in multi-rate systems is ignored, but also, we can connect subsystems with different rate operations without compromising system performance and data loss which simplified system design and sensor selection by the digital system designers. This means the designer can choose any sensor without concerning about the sampling rate and the operation rate of the system and matching them together. Finally, a guided missile with different sampling rates between the seeker, autopilot, guidance law and the output feedback are simulated and its effective performance in pursuit, hitting and smooth motion toward the target in a motion scenario is shown.

Hypersonic Re-entry Vehicle Fault-Tolerant Control Against Actuator Failure via Integral Sliding Mode

A fault-tolerant control (FTC) scheme is proposed based on integral sliding mode(ISM) for attitude control of hypersonic re-entry vehicle (HRV) under partial loss of actuator effectiveness. First, the inner/outer loop mathematical model of HRV in the present of actuator failure is given with specific analysis on torque. Then prescribed performance approach is introduced to the ISM controller design by error transformation. Specifically, the transformation error rather than tracking error is chosen as the sliding variable to establish the sliding mode surface and reaching law. As a result, the attitude tracking error of HRV is limited into the expected range. Meanwhile, the transient process and the steady state behavior are both considered. Considering that the unknown fault information of actuator can cause adverse effect on HRV, an extended state observer(ESO) is adopted to estimate the unknown actuator failure. Thus the observer result is served as compensation for the loss of the controlled system performance. Simulation results show that the proposed scheme can fulfil the tracking task with prescribed performance.

New PV Performance Loss Methodology Applying a Self-Regulated Multistep Algorithm

Analyses of performance loss rates in photovoltaic (PV) systems are not yet standardized, and are typically carried out by a linear regression of the evolution of a certain performance metric (e.g., performance ratio, yield, etc.) over time. In this article, we propose a novel methodology of advanced PV system performance loss rate modeling applying a self-regulated multistep algorithm. The developed algorithm automatically detects the number and positions of breakpoints in nonlinear performance time series and divides the performance trend into an adequate number of linear segments. Instead of calculating one linear performance loss rate, given in percentage per year, as is common practice, multiple linear performance loss values are determined, depending on the trend of the time series and subsequently the number of breakpoints. The algorithm is fully automated. We have applied our methodology on data of an experimental PV installation in Bolzano/Italy, which consists of 26 different PV systems. The overall linear performance loss rate of the facility's crystalline silicon systems is between -0.5 and -1.3%/year, whereas the thin-film PV systems experience values between -0.6 and -2.4%/year. Based on our results, the algorithm appears to be stable and accurate. The methodology is to be used as a fast and automated check of PV systems in operation to detect anomalies affecting performance (in an early stage). By building up a large database of detected issues in the field this algorithm will enable us to better understand the performance evolution of different PV system types in varying climates.

Forming coordination group for coordinated traffic congestion management schemes

It has been well known that properly coordinating travelers’ route choices will help mitigate urban traffic congestion. Accordingly, coordinated routing mechanisms (CRMs), functioned as coordinated network congestion management schemes, have been developed in recent years. They often employ game theory models to strategically coordinate the route decisions of a group of travelers en route with the aim to reduce network traffic congestion. However, existing studies do not provide the solution about how to form the traveler groups, such as who and how many travelers the CRMs should involve. This issue may jeopardize the efficiency and scalability of the CRMs in practical applications. To make up this gap, this study seeks to develop the clustering aided network modeling approaches to form the coordination groups (CGs), which have the proper members and sizes for supporting the merits of the congestion management schemes. Specifically, we define coordination potential as an index to indicate the potential benefit for coordinating travelers’ route decisions on network congestion reduction. Then we develop rigorous approaches to understand the mathematical features and further quantify the coordination potential between two travelers, and among multiple travelers. Built upon that, we form the CGs through a well-designed adaptive centroid-based clustering algorithm (ACCA). It secures a local optimal clustering solution, balancing the intra-cluster and inter-cluster CP, so that we can ensure a small system performance loss as we implement a CRM on each CG. The efficiency of the CG formation approach is evaluated by the CG-CRM scheme, which partitions the travelers into multiple CGs and independently coordinates the travelers’ route choices in each CG by a CRM. The numerical experiments built upon both Sioux Falls and Hardee city networks confirm the efficiency and effectiveness of the CG formation approach. Mainly, the CG-CRM outperforms the independent best response routing mechanism in system performance. As compared to the CRM working on a single group involving all travelers, the CG-CRM requires significantly less computation load with a minor compromise on the system performance. This merit becomes more apparent under high penetration and congested traffic condition. Therefore, we claim that the CG formation approach will significantly help the implementation of the coordinated congestion mitigation schemes in practice.

Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System.

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

Performance Study on IEEE Bus System for Contingency Problem and Mitigation

The increasing dependence of most businesses on electricity requires regular improvements in distribution systems. The main objective of this study is to provide load balancing in the distribution network by reducing the loss of system performance without violating the voltage and current limits of the power supplies. And the selection of the power system contingency by calculating the transmission line failure performance indices using the load flow analysis on the IEEE 14 bus system. To determine and tabulate the voltage on each bus, the actual current flow on each line and power system leak for specified bus or terminal conditions. Use of DC studies where stress conditions also vary. In this paper, the selection and classification of potential liabilities, important for the analysis of potential liabilities, was carried out by evaluating two important performance indices, namely the active and reactive power index (PIp & PIv). The global performance index (OPI) is calculated. It is the sum of two severity indices, namely the real power index and the voltage power index using the direct current flow method. The work was carried out on the bus system 14 and the line between the buses (9-10) was extremely sensitive to the emergency problem of the described system.

Differential Permutation Index DCSK Modulation for Chaotic Communication System

This letter proposes a differential permutation index differential chaos shift keying (DPI-DCSK) modulation for wireless communications. The conventional PI-DCSK has the disadvantage of wasting transmission energy and the loss of system performance. In $M$ -ary DPI-DCSK, one chaotic sequence to be sent represents current modulated ${M}$ -ary symbol as well as the reference of next modulation symbol in a data frame, which can fully utilize the transmission energy with the same hardware implementation as PI-DCSK. The bit error performance of the proposed DPI-DCSK over the multipath Rayleigh fading channel is theoretically analyzed. The simulation results show that the proposed DPI-DCSK achieves a performance gain of more than 3 dB over PI-DCSK, especially in high-delay multipath fading channels. The performance advantage of the proposed scheme is also illustrated in practical ultra-wideband (UWB) channels. Thus, DPI-DCSK is an efficient alternative transmission scheme for chaos-based wireless communication, such as indoor applications of transmitted-reference UWB communications.

Evaluating the effects of inventory stockouts in the performance of production control systems

Studies on the performance of production control systems typically assume that materials are continuously available at stations, thereby neglecting the potential impact of inventory stockouts. Contrarily, this study assesses the performance behavior of two of the most known production control systems, namely POLCA (Paired-cell Overlapping Loops of Cards with Authorization) and WLC (Workload Control) under inventory stockouts, in an assembly-to-order system. For a better understanding of such behavior, Load-Based POLCA (LB-POLCA), i.e. a variant of the POLCA system that takes into account the workload of jobs, is also considered in the study. The study is carried out using discrete event simulation. Results show that stockouts induce loss of system performance, as expected, but the relative performance of the production control systems tested does not change in relation to the situation of continuous availability of materials. Moreover, delivery performance is best using WLC followed first by LB-POLCA and after by POLCA.

Resiliency-based restoration optimization for dependent network systems against cascading failures

Due to the increasing importance of large-scale and complex network systems and the potential for massive cascading failures in these real-world systems, modeling of system resiliency and optimization of restoration strategies to mitigate system performance loss caused by diverse disruptions is of significant interest among researchers and practitioners. Although society has experienced many incidents that demonstrate the influence of cascading failures aggravated by dependencies inside network systems, existing resiliency-based restoration optimization research rarely if ever jointly considers the impact of system dependencies on cascading failures. In this paper, different restoration prioritization strategies are applied to network systems subject to cascading failures that take into account system dependencies. By conducting case studies on synthetic networks and the U.S. airport network system, the effects of restoration strategies are evaluated using a system resiliency metric and two system performance measurements. The influence of system dependency characteristics and the interplay between them and restoration strategies on system resiliency regarding cascading failures are also investigated. This work demonstrates the distinct effects of restoration prioritization actions against cascading failures to mitigate the performance loss in network systems with different properties. It also provides insights about restoration improvement by considering dependency impacts to effectively reduce the intensity and extent of cascading failures.

Performance analysis on a novel photovoltaic-hydrophilic modified tubular seawater desalination (PV-HMTSD) system

To make more effective use of photovoltaic waste heat, a novel photovoltaic-hydrophilic modified tubular seawater desalination system was proposed in this paper. Based on the first and second laws of thermodynamics, energy analysis and exergy analysis models of the system were established. The influences of operation and structure parameters on system performance, heat and exergy losses of each component and performance comparisons among different solar tubular seawater desalination systems were analyzed and discussed. The results manifest that freshwater yield rate, thermal efficiency, energy efficiency and exergy efficiency are enhanced by raising the mass flow rate of working medium and heating pipe length; particularly when the length is small, the increase of heating pipe length can significantly elevate system performance. Moreover, 69.2% of the whole system's heat losses and 90.6% of the whole system's total exergy losses are attributed to the PV module. Compared with other solar tubular seawater desalination systems, the photovoltaic-hydrophilic modified tubular seawater desalination system proposed in this paper has the highest average energy efficiency and exergy efficiency, which are 32.6%, 10.4% respectively. Meanwhile, the photovoltaic thermal collector can provide up to 49.1 W of electricity to maintain the operation of system without consuming extra energy.

Thermodynamic evaluation of CCHP system based on biomass gasification by exploring the feasibility of using CO2 as gasification agent

The initial purpose of adopting combined cooling heating and power (CCHP) system is to achieve cascade utilization of fuel energy as well as stepping into low-carbon society. In this study, CO2 is used as gasification agent instead of conventional option of using steam in the biomass gasification unit for reducing CO2 emissions and increasing energy efficiency. To determine the thermodynamic feasibility during the process, exergy analysis has been performed within the whole process as well as its individual units. From the analytical results, it discloses that gasification and combustor irreversibilities yield the highest exergy destruction in the CCHP system, which account for 44.19% and 20.79% of the overall exergy destruction, respectively. Exergy analysis emphasises that both losses and irreversibilities can contribute to loss of efficiency system performance. Additionally, focusing on the influences of key parameters on system performance has been highlighted as well. The outcomes suggest that the optimum gasification temperature and CO2/C are 825 °C and 0.25 respectively. Under the optimal design parameters, the energy efficiency and PESR can reach 53.25% and 10.17% in summer respectively. In addition, a preliminary economic analysis is involved and the financial feasibility of this CCHP system is further demonstrated.

Low-Complexity Beamformer Design for Joint Radar and Communications Systems

Joint radar and communication (JRC) system is expected to significantly improve the utilization of spectrum resources, system performance, and reduce the equipment cost and size. In this letter, a JRC system containing a collocated multi-input multi-output (MIMO) radar and a multi-user communication base station is considered, which can serve several downlink users and detect multiple targets simultaneously. The radar signal covariance matrix and the communication physical precoder are jointly designed by matching the desired beampattern for radar detection while maximizing the downlink communication signal to interference and noise ratio (SINR). By relaxing the original problems, the iterative process with closed-form expressions is proposed to obtain the sub-optimal solutions of the system. Compared with solving the optimization problems directly, the proposed algorithm can significantly reduce the computational complexity with a little loss of system performance. Finally, simulation results show the effectiveness of the proposed algorithm.

Entropy Loading Design for the MIMO-OFDM Visible Light Communication System Using the OCT Precoding Technique

In this paper, an orthogonal circulant matrix transform (OCT) precoding technique is proposed to combine with the entropy loading in the multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) visible light communication (VLC) system where the space-time coding (STBC) is chosen for its robustness to the channel correlation. Benefitting from the OCT precoding technique, the uniform signal-to-noise ratio (SNR) among all the subchannels can be achieved. As a result, only one SNR value is required to be fed back, and the same distribution matcher is employed during probabilistic shaping (PS), which means much lower feedback overhead and system complexity than the conventional entropy loading scheme. Experimental results show that the OCT precoding does not cause the system performance loss where the achievable information rate (AIR) of the proposed system is comparable with the conventional system without precoding. With an available bandwidth of ∼25 MHz, the proposed scheme can realize the AIR of 50.75 Mb/s at the expense of 0.45% average forward error correction (FEC) overhead (OH).

합성곱 신경망을 사용한 회전체 오일-휩 및 오일-휩 초기 상태 상태 진단 연구

Failure of a rotating machine can lead to not only loss of system performance but massive losses. Therefore, Condition monitoring technologies of rotating machine for detecting a failure has been actively applied in many industries. Recently, as many techniques were developed to collect and analysis of data, condition monitoring which machine learning technology is applied has been studied. In this paper, the machine learning method is used to detect oil whip phenomenon and classify the normal state, oil whip initial state and oil whip. Oil whip which cand lead to large amplitude of vibration is the most common fault cause of sub-synchronous instability in hydrodynamic journal bearings. Convolution neural network which is widely used in image dimensionality reduction is used to detect oil whip phenomenon. As the input data of the neural network, an orbital image which can represent the feature of the normal state, the oil-whip initial state, and the oil-whip is used.

Nested Shallow Geothermal Systems

The long-term sustainability of shallow geothermal systems in dense urbanized areas can be potentially compromised by the existence of thermal interfaces. Thermal interferences between systems have to be avoided to prevent the loss of system performance. Nevertheless, in this work we provide evidence of a positive feedback from thermal interferences in certain controlled situations. Two real groundwater heat pump systems were investigated using real exploitation data sets to estimate the thermal energy demand bias and, by extrapolation, to assess the nature of thermal interferences between the systems. To do that, thermal interferences were modelled by means of a calibrated and validated 3D city-scale numerical model reproducing groundwater flow and heat transport. Results obtained showed a 39% (522 MWh·yr−1) energy imbalance towards cooling for one of the systems, which generated a hot thermal plume towards the downgradient and second system investigated. The nested system in the hot thermal plume only used groundwater for heating, thus establishing a positive symbiotic relationship between them. Considering the energy balance of both systems together, a reduced 9% imbalance was found, hence ensuring the long-term sustainability and renewability of the shallow geothermal resource exploited. The nested geothermal systems described illustrate the possibilities of a new management strategy in shallow geothermal energy governance.

A Dynamic Bayesian Network-based approach to Resilience Assessment of Engineered Systems

Traditional risk assessment approaches mainly focus on the pre-failure scenarios with certain information. For complex systems, the scope of risk assessment needs to be extended to include the post-failure phase; because the emerging hazards of these systems cannot be wholly identified and are usually highly uncertain. Thus, resilience assessment needs to be investigated. Most of the existing literature quantify resilience based on a system's performance loss caused by disruptions. These studies fail to assess the probability of a system to sustain or restore to a normal operational state after disruptions occur, how this probability changes with time, and how fast the system can be restored. The dynamic and probabilistic characteristics of resilience must be considered in systemic resilience assessment, in which the engineered system, human and organizational factors, and external disruptions are considered. This paper aims to develop a dynamic Bayesian network (DBN)-based approach to the probabilistic assessment of the system resilience by incorporating temporal processes of adaption and recovery into the analysis of system functionality. The proposed method also provides a new way to define resilience in terms of the probability of system functionality change during and after a disruption. A case study on the Chevron refinery accident is used to demonstrate the applicability of the proposed methodology.

МОДЕЛЬ БЕЗОПАСНОСТИ С «ЧАСОВЫМ» МЕХАНИЗМОМ СИСТЕМЫ ЗАЩИТЫ ИНФОРМАЦИИ

The main point of the new approach to security should be the development of effective security models that are adequate to the current level of development of software and hardware, as well as the possibility of flexible security management depending on the requirements, acceptable risk and resource consumption. The task of the kernel integrity control subsystem is to ensure the security of the system during its operation and detect incorrect interference of system subjects in its operation. The system integrity control subsystems are based on two main components: static and dynamic. The frequency of the kernel integrity control subsystem actuation determines the time that the system will be in the "information leak" state in the event of a violation of the system integrity. There are two approaches to determining the time interval T: the first approach is characterized by minimizing the loss of system performance due to the presence of the kernel integrity control subsystem in the system, the second approach is characterized by the fact that it provides a higher level of security in case of possible losses of system performance.

Numerical Evaluation of Job Finish Time Under MTD Environment

Moving target defense (MTD) has recently emerged as a game-changer in the confrontation between cyberattack and defense. MTD mechanism constantly and randomly changes the system configurations to create uncertainty of the attack surface against cyber-adversaries. To date, researches on the evaluation of MTD techniques either focused on analyzing the effectiveness of MTD or studying the system performance loss due to the use of MTD. The impact on job/service running on the protected system is always ignored. In this paper, we propose an SRN (Stochastic reward net) based analytical modeling approach to investigate how MTD techniques influence the job running on protected system from the perspective of job finish time. The SRN model developed in this paper captures the behaviors of both the adversary and the job execution process. Furthermore, we carry out numerical analysis to study the impact of different system parameters on job finish time and other evaluation metrics. The results in this paper can help defenders choose a better MTD configuration to complete the job execution as soon as possible.

Fusion-Based FDI Attack Detection in Cyber-Physical Systems

This brief studies the alarm response problem of attack detection for cyber-physical systems under false data injection attacks. Notice that the faster the alarm response of the attack detection is, the less the system performance loss is. In this case, the distributed fusion strategy, which can potentially provide higher accuracy and reliability, is introduced, for the first time, to improve the speed of alarm response. Meanwhile, to ensure real-time anomaly detection online, a group of sensors deployed by different positions send their messages to a monitoring center through communication networks. Due to the limited bandwidth, multiple finite-level logarithmic quantizers are used to reduce the size of data packages containing residual messages (generated by local state estimators). Then, an optimal weighting fusion criterion is designed by establishing convex optimization problems such that the residual evaluation threshold is more accurate in the presence of quantization errors. Finally, an F-404 aircraft example is used to illustrate that the response speed of attack detection becomes faster as compared with the case of single sensor.