Single Fault Scenarios Research Articles

Joint optimal PMU and DULR placement in distribution network considering fault reconstruction and state estimation accuracy

Due to the rapid development of the distribution network, there is an urgent need for high-precision state estimation and safe and stable operation, this paper proposes a joint optimal placement method for Phasor Measurement Unit (PMU) and Dual Use Line Relay (DULR) in distribution networks considering fault reconstruction and state estimation accuracy. The proposed method aims to minimize the cost of PMU and DULR configuration. Based on the known pseudo-measurements and Supervisory Control and Data Acquisition (SCADA) measurements, we use the E-optimal standard design of the state estimation error covariance matrix to form the state estimation accuracy index and take the relay protection function of Feeder Terminal Unit (FTU) device and DULR device into consideration of the fault reconstruction. By solving the mixed integer semidefinite programming (MISDP) model, the optimal placement result is obtained, so that the network under normal condition and the network after multiple fault reconstruction can meet the constraints of state estimation accuracy and load loss rate respectively. The proposed method is tested on IEEE 33-node, 123-node systems as an example. The experimental results show that the proposed method is effective and practical.Note to Practitioners—This study is stimulated by the need for optimizing the placement of Micro-PMU (µPMU) in distribution network. Since the new µPMU device DULR has the same relay protection function as FTU, and distributed generation can supply power to the island after failure, this paper adds the load loss rate to the constraint conditions to study the influence of fault reconstruction on the placement scheme. A new two-step SE method considering mixed measurements was used in this paper to improve the computational efficiency of real-time SE of large-scale system while ensuring the accuracy of the calculation results. We have conducted tests in IEEE-33 and IEEE-123 systems. After preliminary verification, compared with the single fault scenario, we find that the placement scheme considering multiple fault scenarios can effectively improve the accuracy of state estimation and reduce the load loss rate of each fault scenario and only increase the cost a little. The increase in the number of DG and contact lines in the network will also effectively improve the feasibility of the placement scheme. The increase of the number of FTU configurations can effectively reduce the configuration of DULR by adding a small amount of µPMU, reduce the total cost as a result. In future studies, we will further consider the integrated configuration of µPMU, DULR and FTU, to meet more failure scenarios while reducing costs.

A fault-tolerant sensor fusion in mobile robots using multiple model Kalman filters

Accurate localization is crucial in the navigation of mobile robots. However, in other circumstances, single-sensor localization faces different challenges, including software and hardware problems or data outages. Sensor fusion is used in most autonomous vehicles (including aerial and ground vehicles) to overcome such challenges. In this paper, the localization of a mobile robot is studied in the presence of sensor faults. The mobile robot has two sensors: two Inertial Measurement Units (IMU) and wheel encoders. Regarding the fault-tolerant scheme, measurements of both sets of sensors are fused using an Interacting Multiple Model (IMM) Kalman filter based on both unscented and extended Kalman filters (UKF and EKF). UKF and EKF-based IMM are chosen for this study since the dynamic model of the localization is highly nonlinear. Regarding contributions, it should be noted that this scheme eliminates the need to model every single fault scenario and use an additional sensor to oversee the performance of the sensing system. Also, comparing this method with similar approaches adopted by other studies shows better performance regarding the cost of computations and RMSE. To evaluate performance, the outputs of the proposed filters are simulated and compared for different trajectories where the data of each sensor is intentionally corrupted to observe the fault detection capability. Simulations are performed for different trajectories and noises to demonstrate this method’s efficiency in different situations. In addition, the results of unscented and extended Kalman filter-based IMM are compared in terms of error and computational costs to evaluate their performance. Overall, simulation and experiments indicate accurate 3D estimations in all cases. Moreover, designated weights vividly show that sensor fault detection is achieved by both unscented and extended IMM Kalman filters, which enable complete fault isolation consequently. This approach provides mobile robots with a reliable and straightforward sensor fault detection and localization solution.

On the analysis of spectrum based fault localization using hitting sets

Combining spectrum-based fault localization (SBFL) with other techniques is generally regarded as a feasible approach as advantages from both techniques would be preserved. Sendys which combines SBFL with slicing-hitting-set-computation is one of the promising techniques. However, all current evaluations on Sendys were obtained via empirical studies, which have inevitable threats to validity. Besides, purely empirical studies cannot reveal the essential reason that why Sendys performs well or badly, and whether all the complicated computations are necessary. Therefore, in this paper, we provide an in-depth theoretical analysis on Sendys, which can give definite and convincing conclusions. We generalize our previous theoretical framework on SBFL, to make it applicable to combined techniques like Sendys. We first provide a variant of current Sendys by patching its loophole of ignoring “zero or negative risk values” in normalization. This variant plays as a substitution of the original Sendys, as well as one of the baselines in our analysis. Then, by modifying a few steps of this variant, we propose an enhanced Sendys and theoretically prove its superiority over several other methods in single-fault scenario. Moreover, we provide a short-cut reformulation of the enhanced Sendys by preserving its performance, but only requiring very simple computations. And it is proved to be even better than traditional SBFL maximal formulas. As a complementary, our empirical studies with 13 subject programs demonstrate the obvious superiority of the enhanced Sendys, as well as its stability across different formulas in single-fault scenario. For multiple-fault cases, the variant of Sendys is observed to have the best performance. Besides, this variant has shown great helpfulness in improving the bad performance of the original Sendys when encountering the NOR problem.

A vector table model-based systematic analysis of spectral fault localization techniques

Spectral fault localization (SFL) is an automatic fault-localization technique, which uses risk evaluation formula to rank the risk of fault existence in each program entity after collecting the testing information dynamically. To provide insight into SFL techniques, the evaluation method is an important research topic. In this paper, we present a uniformly systematic investigation framework to evaluate and compare SFL techniques with fixed formulas considering both single-fault and multiple-fault scenarios. Particularly, we design a generic vector table model called VTM as a novel measurement model to thoroughly understand various SFL techniques. By defining different types of faulty statements and investigating suspiciousness factors’ mathematical expression of statements on the basis of VTM, the effectiveness of different SFL techniques could be systematically analyzed and compared. Under the VTM-based evaluation framework, the latest formula D* and the optimal formula O considered before as examples are explored: (1) under a single-fault scenario, O has a better performance than D*, and O is the best SFL technique at present; (2) O shows stable performance and the performance of D* fluctuates in a range under double-fault scenarios: O outperforms D* in three cases, and the performance of O is between the worst performance and the best performance of D* in the other three cases that are less likely to happen; and (3) sample programs are presented to explain such observations. The VTM-based method overcomes the limitations of existing empirical and systematic approaches, which enables a systematic evaluation for SFL techniques with fixed formulas under both single-fault and multiple-fault cases.

FDIR Strategy for Sensor Faults During Re-entry Flight

In this study, a Fault Detection, Isolation and Reconfiguration (FDIR) strategy is proposed for dealing with the problem of single sensor faults during a re-entry flight. The proposed algorithms rely on a model based Fault Detection and Isolation. After the generation of a residual through a Kalman filter observer, detection is obtained via the estimation of residual signal statistics while isolation is obtained thorough the analysis of either residual vector direction or the residual covariance matrix, depending on which fault type has been detected. Finally, for some failure conditions, an adaptive reconfiguration strategy of the control laws is also developed. The effectiveness of the FDIR strategy has been shown through numerical simulations of single fault scenarios.

A theoretical analysis on cloning the failed test cases to improve spectrum-based fault localization

Abstract Fault localization is the activity to locate faults in programs. Spectrum-based fault localization (SBFL) is a class of techniques for it. It contrasts the code coverage achieved by passed runs and that by failed runs, and estimates program entities responsible for the latter. Although previous work has empirically shown that the effectiveness of typical SBFL techniques can be improved by incorporating more failed runs, debugging often takes place when there are very few of them. In this paper, we report a comprehensive study to investigate the impact of cloning the failed test cases on the effectiveness of SBFL techniques. We include 33 popular such techniques, and examine the accuracy of their formulas on twelve benchmark programs, using four accuracy metrics and in three scenarios. The empirical results show that on 22, 21, and 23 of them the fault-localization accuracy can be significantly improved, when the failed test cases are cloned in the single-fault, double-fault, and triple-fault scenarios, respectively. We also analytically show that on 19 of them the improvements are provable for an arbitrary program and an arbitrary test suite, in the single-fault scenario; and moreover, for ten of the rest formulas, their accuracy are proved unaffected in all scenarios.

Human Competitiveness of Genetic Programming in Spectrum-Based Fault Localisation

We report on the application of Genetic Programming to Software Fault Localisation, a problem in the area of Search-Based Software Engineering (SBSE). We give both empirical and theoretical evidence for the human competitiveness of the evolved fault localisation formulæ under the single fault scenario, compared to those generated by human ingenuity and reported in many papers, published over more than a decade. Though there have been previous human competitive results claimed for SBSE problems, this is the first time that evolved solutions have been formally proved to be human competitive. We further prove that no future human investigation could outperform the evolved solutions. We complement these proofs with an empirical analysis of both human and evolved solutions, which indicates that the evolved solutions are not only theoretically human competitive, but also convey similar practical benefits to human-evolved counterparts.

A two-level intelligent alarm management framework for process safety

Alarm management, which alerts process operators to deviations of variables from designed limits, has been a key safety issue in a petrochemical plant. Petrochemical processes frequently operate upon a multitude of steady states, transitions and states in abnormal events. Some single-fault scenarios producing similar symptoms usually cause false alarms, while fault propagation scenarios produce alarm floods which will overwhelm critical alarms. A two-level intelligent alarm management framework (IAMF) is proposed considering fault interdependence in plant operation, including alarm filtering first and root-cause diagnosis second. The two-level IAMF with different strategies are applicable to various hazard scenarios involving multi-fault with similar symptoms and propagation scenarios. Two cases are studied applying the IAMF to FCCU, in which the root causes of alarm floods can be identified. Redundant alarms, false alarms and alarm failures cut down effectively, thus ensuring the safety of the petrochemical process and reducing economic losses caused by improper alarms.

Resource efficient network design and traffic grooming strategy with guaranteed survivability

In WDM networks, path protection has emerged as a widely accepted technique for providing guaranteed survivability of network traffic. However, it requires allocating resources for backup lightpaths, which remain idle under normal fault-free conditions. In this paper, we introduce a new design strategy for survivable network design, which guarantees survivability of all ongoing connections that requires significantly fewer network resources than protection based techniques. In survivable routing, the goal is to find a Route and Wavelength Assignment (RWA) such that the logical topology remains connected for all single link failures. However, even if the logical topology remains connected after any single link fault, it may not have sufficient capacity to support all the requests for data communication, for all single fault scenarios. To address this deficiency, we have proposed two independent but related problem formulations. To handle our first formulation, we have presented an Integer Linear Program (ILP) that augments the concept of survivable routing by allowing rerouting of sub-wavelength traffic carried on each lightpath and finding an RWA that maximizes the amount of traffic that can be supported by the network in the presence of any single link failure. To handle our second formulation, we have proposed a new design approach that integrates the topology design and the RWA in such a way that the resulting logical topology is able to handle the entire set of traffic requests after any single link failure. For the second problem, we have first presented an ILP formulation for optimally designing a survivable logical topology, and then proposed a heuristic for larger networks. Experimental results demonstrate that this new approach is able to provide guaranteed bandwidth, and is much more efficient in terms of resource utilization, compared to both dedicated and shared path protection schemes.

A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis

In this article a combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis has been proposed. The SDG is the first level in this framework and provides a possible candidate set of faults based on the incipient response of the process. The search for the actual fault is performed based on a QTA (level 2), which uses the temporal evolution of the sensors for further resolution. Thus, this framework combines the completeness property of SDG with the high diagnostic resolution property of QTA. Methods to address the problem of incorrect diagnosis arising due to incorrect measurement of initial response have also been presented. The proposed approach is tested on the Tennessee Eastman (TE) case study. Correct fault diagnosis is performed in all possible single fault scenarios. It is shown that this framework provides fast, reliable and accurate incipient fault diagnosis.

On-Line Fault Isolation Algorithm for Industrial Processes

The paper presents the solutions of main problems that appear in diagnostics of large scale processes in chemical, petrochemical, pharmaceutical, power and other industry utilised in applied diagnostic software. In addition to description of reasoning rules based on fuzzy logic and taking into account the dynamics of symptoms forming the following topics where raised: decomposition of diagnostic system, dynamical creation of FI threads and multiple fault isolation assuming single fault scenarios.