Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Data mining approaches to software fault diagnosis

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.

mRegistry: a registry representation for fault diagnosis

Microsoft Windows uses the notion of registry to store all configuration information. The registry entries have associations and dependencies. For example, the paths to executables may be relative to some home directories. The registry being designed with faster access as one of the objectives does not explicitly capture these relations. In this paper, we explore a representation that captures the dependencies more explicitly using shared and unifying variables. This representation, called mRegistry exploits the tree-structured hierarchical nature of the registry, is concept-based and obtained in multiple stages. mRegistry captures intra-block, inter-block and ancestor-children dependencies (all leaf entries of a parent key in a registry put together as an entity constitute a block thereby making the block as the only child of the parent). In addition, it learns the generalized concepts of dependencies in the form of rules. We show that mRegistry has several applications: fault diagnosis, prediction, comparison, compression etc.

On the stacking fault energies of some close-packed hexagonal metals

It is now realised (1,2,3) that a knowledge of stacking fault energy is fundamental for an understanding of the mechanical behaviour of metals. There are several processes in which the imperfect dislocations have to recombine locally to form an unextended dislocation . For intersection of two dislocations it is, for example, necessary to form 'constrictions'. Cross slip of extended dislocations also involves constriction. The onset of stage llI work hardening in a crystal with close-packed structure is attributed to cross slip and hence is controlled by the stacking fault energy (SPE). Methods of estimation of SFE are based on either the direct observation of stacking faults in an electron microscope or their effects on the deformation processes.

Diagnosis of dominant forcing factors for large-scale vertical velocities during active and break phases of the monsoon

An attempt to diagnose the dominant forcings which drive the large-scale vertical velocities over the monsoon region has been made by computing the forcings like diabatic heating fields,etc. and the large-scale vertical velocities driven by these forcings for the contrasting periods of active and break monsoon situations; in order to understand the rainfall variability associated with them. Computation of diabatic heating fields show us that among different components of diabatic heating it is the convective heating that dominates at mid-tropospheric levels during an active monsoon period; whereas it is the sensible heating at the surface that is important during a break period. From vertical velocity calculations we infer that the prime differences in the large-scale vertical velocities seen throughout the depth of the atmosphere are due to the differences in the orders of convective heating; the maximum rate of latent heating being more than 10 degrees Kelvin per day during an active monsoon period; whereas during a break monsoon period it is of the order of 2 degrees Kelvin per day at mid-tropospheric levels. At low levels of the atmosphere, computations show that there is large-scale ascent occurring over a large spatial region, driven only by the dynamic forcing associated with vorticity and temperature advection during an active monsoon period. However, during a break monsoon period such large-scale spatial organization in rising motion is not seen. It is speculated that these differences in the low-level large-scale ascent might be causing differences in convective heating because the weaker the low level ascent, the lesser the convective instability which produces deep cumulus clouds and hence lesser the associated latent heat release. The forcings due to other components of diabatic heating, namely, the sensible heating and long wave radiative cooling do not influence the large-scale vertical velocities significantly.

The use of murine polyclonal anti-idiotypic antibodies as surrogate allergens in the diagnosis of Parthenium hypersensitivity

Background: Anti-idiotypic antibodies (Ab-2), which are the mirror images of idiotypic antibodies (Ab-1), may be useful as diagnostic reagents and for use as immunogen to induce antigen-specific immune responses. Methods and Results: To explore the biologic potential of Ab-2 as diagnostic reagents in allergic diseases, murine mouse (m) Ab-2 were raised by immunizing Balb/c mice with affinity purified rabbit (r) Ab-1 specific for the pollen of Parthenium hysterophorus, an allergenic weed that grows wild on the Indian subcontinent and in Australia, Mexico, and the southern United States. Affinity purified Parthenium-specific human (h)AB-1 could successfully inhibit the binding of mAb-2 to immobilized rAb-1. Further, Balb/c mice immunized with mAb-2 induced Parthenium-specific anti-anti-idiotypic IgE and IgG antibodies. Specificity of the Ab-2 was confirmed by the ability of Parthenium pollen extracts to inhibit the binding of allergen-specific IgE and IgG Ab-1 in the sera of patients with rhinitis to immobilized mAb-2. Parthenium-sensitive patients with rhinitis who had positive results on skin prick tests to Parthenium pollen extracts also responded with a positive skin reaction to mAb-2. Conclusion: Our data demonstrate that Parthenium-specific mAb-2 may be of value as surrogate allergens in allergen standardization and for in vitro diagnosis.

Antigen detection assay with parasite specific monoclonal antibodies for diagnosis of lymphatic filariasis

Background: Lymphatic filariasis is a painful and profoundly disfiguring disease. Infection is usually acquired in childhood but its visible manifestations occur later in life, causing temporary or permanent disability. The importance of developing effective assays to diagnose, monitor and evaluate human lymphatic filariasis has been emphasized by the WHO. Methods: High-affinity monoclonal antibodies (mAbs) specific for recombinant filarial antigen WbSXP-1 were developed. An ELISA based capture assay using monoclonal and polyclonal antibodies for WbSXP-1 was used for detection of circulating filarial antigen. Results: High-affinity monoclonal antibodies (mAbs) were developed that specifically binds both W. bancrofti and B. malayi mf antigens. Two mAbs (1F6H3 and 2E12E3) of subclass IgG2a and IgM showed high affinity, avidity and reactivity to recombinant and mf native antigen. Both the mAbs were used in combination as capture antibodies and polyclonal as detection antibody to develop the assay. The assay showed very high sensitivity towards W. bancrofti mf positive samples compared to endemic normal samples (P<0.0001). Conclusion: A capture assay using high-affinity monoclonal antibodies for WbSXP-1 was developed for the detection of filarial circulating antigen in clinical samples from bancroftian infection. Besides, this would also help in epidemiological studies in endemic areas of filarial infections. (C) 2011 Elsevier B.V. All rights reserved.

Fault-Diagnostic System using Support Vector Machines for Power Transmission Systems

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Identification of fault location in power distribution system with distributed generation using support vector machines

This paper presents a multi-class support vector machine (SVMs) approach for locating and diagnosing faults in electric power distribution feeders with the penetration of Distributed Generations (DGs). The proposed approach is based on the three phase voltage and current measurements which are available at all the sources i.e. substation and at the connection points of DG. To illustrate the proposed methodology, a practical distribution feeder emanating from 132/11kV-grid substation in India with loads and suitable number of DGs at different locations is considered. To show the effectiveness of the proposed methodology, practical situations in distribution systems (DS) such as all types of faults with a wide range of varying fault locations, source short circuit (SSC) levels and fault impedances are considered for studies. The proposed fault location scheme is capable of accurately identify the fault type, location of faulted feeder section and the fault impedance. The results demonstrate the feasibility of applying the proposed method in practical in smart grid distribution automation (DA) for fault diagnosis.

Fault tolerance in multiprocessor systems

Multiprocessor systems which afford a high degree of parallelism are used in a variety of applications. The extremely stringent reliability requirement has made the provision of fault-tolerance an important aspect in the design of such systems. This paper presents a review of the various approaches towards tolerating hardware faults in multiprocessor systems. It. emphasizes the basic concepts of fault tolerant design and the various problems to be taken care of by the designer. An indepth survey of the various models, techniques and methods for fault diagnosis is given. Further, we consider the strategies for fault-tolerance in specialized multiprocessor architectures which have the ability of dynamic reconfiguration and are suited to VLSI implementation. An analysis of the state-óf-the-art is given which points out the major aspects of fault-tolerance in such architectures.

Study of corrugations in motor bearings

Corrugation formation is investigated in bearing components in squirrelcage induction motors. The study, conducted on site, measured shaft voltage and analysed motor bearing vibrations from 48 motors on nine sites. The on-site frequency data was compared with the measured natural frequency of the motors. Detailed profilometric, optical and SEM studies were carried out on the surface of failed bearings to aid discussion on the formation of corrugations in bearings used in squirrelcage induction motors.

Polynomial Coefficient Based Multi-Tone Testing of Analog Circuits

Abstract—A method of testing for parametric faults of analog circuits based on a polynomial representaion of fault-free function of the circuit is presented. The response of the circuit under test (CUT) is estimated as a polynomial in the applied input voltage at relevant frequencies apart from DC. Classification of CUT is based on a comparison of the estimated polynomial coefficients with those of the fault free circuit. The method needs very little augmentation of circuit to make it testable as only output parameters are used for classification. This procedure is shown to uncover several parametric faults causing smaller than 5 % deviations the nominal values. Fault diagnosis based upon sensitivity of polynomial coefficients at relevant frequencies is also proposed.

Robust Observer Design with Application to Fault Detection

A new scheme for robust estimation of the partial state of linear time-invariant multivariable systems is presented, and it is shown how this may be used for the detection of sensor faults in such systems. We consider an observer to be robust if it generates a faithful estimate of the plant state in the face of modelling uncertainty or plant perturbations. Using the Stable Factorization approach we formulate the problem of optimal robust observer design by minimizing an appropriate norm on the estimation error. A logical candidate is the 2-norm, corresponding to an H�¿ optimization problem, for which solutions are readily available. In the special case of a stable plant, the optimal fault diagnosis scheme reduces to an internal model control architecture.