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.

Support Vector Machine Approach to Downscale Precipitation in Climate Change Scenarios

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.

Direct evidence of redox interaction between metal ion and support oxide in Ce(0.98)Pd(0.02)O(2-delta) by a combined electrochemical and XPS study

A combined electrochemical method and X-ray photo electron spectroscopy (XPS) has been utilized to understand the Pd(2+)/CeO(2) interaction in Ce(1-x)Pd(x)O(2-delta) (x = 0.02). A constant positive potential (chronoamperometry) is applied to Ce(0.98)Pd(0.02)O(2-delta) working electrode which causes Ce(4+) to reduce to Ce(3+) to the extent of similar to 35%, while Pd remains in the +2 oxidation state. Electrochemically cycling this electrode between 0.0-1.2 V reverts back to the original state of the catalyst. This reversibility is attributed to the reversible reduction of Ce(4+) to Ce(3+) state. CeO(2) electrode with no metal component reduces to CeO(2-y) (y similar to 0.4) after applying 1.2 V which is not reversible and the original composition of CeO(2) cannot be brought back in any electrochemical condition. During the electro-catalytic oxygen evolution reaction at a constant 1.2 V for 1000 s, Ce(0.98)Pd(0.02)O(2-delta) reaches a steady state composition with Pd in the +2 states and Ce(4+) : Ce(3+) in the ratio of 0.65 : 0.35. This composition can be denoted as Ce(0.63)(4+)Ce(0.35)(4+)Pd(0.02)O(2-delta-y) (y similar to 0.17). When pure CeO(2) is put under similar electrochemical condition, it never reaches the steady state composition and reduces almost to 85%. Thus, Ce(0.98)Pd(0.02)O(2-delta) forms a stable electrode for the electro-oxidation of H(2)O to O(2) unlike CeO(2) due to the metal support interaction.

Spatial Decision Support System for Assessing Micro, Mini and Small Hydel Potential

This study describes the design and implementation of DSS for assessment of Mini, Micro and Small Schemes. The design links a set of modelling, manipulation, spatial analyses and display tools to a structured database that has the facility to store both observed and simulated data. The main hypothesis is that this tool can be used to form a core of practical methodology that will result in more resilient in less time and can be used by decision-making bodies to assess the impacts of various scenarios (e.g.: changes in land use pattern) and to review, cost and benefits of decisions to be made. It also offers means of entering, accessing and interpreting the information for the purpose of sound decision making. Thus, the overall objective of this DSS is the development of set of tools aimed at transforming data into information and aid decisions at different scales.

Decision support system for regional electricity planning

Electricity appears to be the energy carrier of choice for modern economics since growth in electricity has outpaced growth in the demand for fuels. A decision maker (DM) for accurate and efficient decisions in electricity distribution requires the sector wise and location wise electricity consumption information to predict the requirement of electricity. In this regard, an interactive computer-based Decision Support System (DSS) has been developed to compile, analyse and present the data at disaggregated levels for regional energy planning. This helps in providing the precise information needed to make timely decisions related to transmission and distribution planning leading to increased efficiency and productivity. This paper discusses the design and implementation of a DSS, which facilitates to analyse the consumption of electricity at various hierarchical levels (division, taluk, sub division, feeder) for selected periods. This DSS is validated with the data of transmission and distribution systems of Kolar district in Karnataka State, India.

Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals

In this paper, we develop a low-complexity message passing algorithm for joint support and signal recovery of approximately sparse signals. The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable to strictly sparse signal recovery from noisy measurements. The support recovery embedded in the approach makes it suitable for recovery of signals with same sparsity profiles, as in the problem of multiple measurement vectors (MMV). Simulation results show that the proposed algorithm, termed as JSSR-MP (joint support and signal recovery via message passing) algorithm, achieves performance comparable to that of sparse Bayesian learning (M-SBL) algorithm in the literature, at one order less complexity compared to the M-SBL algorithm.

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.

Knowledge-Based Approach Using Support Vector Machine for Transmission Line Distance Relay Co-ordination

In this paper, knowledge-based approach using Support Vector Machines (SVMs) are used for estimating the coordinated zonal settings of a distance relay. The approach depends on the detailed simulation studies of apparent impedance loci as seen by distance relay during disturbance, considering various operating conditions including fault resistance. In a distance relay, the impedance loci given at the relay location is obtained from extensive transient stability studies. SVMs are used as a pattern classifier for obtaining distance relay co-ordination. The scheme utilizes the apparent impedance values observed during a fault as inputs. An improved performance with the use of SVMs, keeping the reach when faced with different fault conditions as well as system power flow changes, are illustrated with an equivalent 265 bus system of a practical Indian Western Grid.

A Durable Graphitic-Carbon Support for Pt and Pt3Co Cathode Catalysts in Polymer Electrolyte Fuel Cells

The high efficiency of fuel-cell-powered electric vehicles makes them a potentially viable option for future transportation. Polymer Electrolyte Fuel Cells (PEFCs) are most promising among various fuel cells for electric traction due to their quick start-up and low-temperature operation. In recent years, the performance of PEFCs has reached the acceptable level both for automotive and stationary applications and efforts are now being expended in increasing their durability, which remains a major concern in their commercialization. To make PEFCs meet automotive targets an understanding of the factors affecting the stability of carbon support and platinum catalyst is critical. Alloying platinum (Pt) with first-row transition metals such as cobalt (Co) is reported to facilitate both higher degree of crystallinity and enhanced activity in relation to pristine Pt. But a major challenge for the application of Pt-transition metal alloys in PEFCs is to improve the stability of these binary catalysts. Dissolution of the non-precious metal in the acidic environment could alleviate the activity of the catalysts and hence cell performance. The use of graphitic carbon as cathode-catalyst support enhances the long-term stability of Pt and its alloys in relation to non-graphitic carbon as the former exhibits higher resistance to carbon corrosion in relation to the latter in PEFC cathodes during accelerated-stress test (AST). Changes in electrochemical surface area (ESA), cell performance and charge-transfer resistance are monitored during AST through cyclic voltammetry, cell polarization and impedance measurements, respectively. Studies on catalytic electrodes with X-ray diffraction, Raman spectroscopy and transmission electron microscopy reflect that graphitic carbon-support resists carbon corrosion and helps mitigating aggregation of Pt and Pt3Co catalyst particles. (C) 2012 The Electrochemical Society. DOI: 10.1149/2.051301jes] All rights reserved.

Infinite push: a new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list

Ranking problems have become increasingly important in machine learning and data mining in recent years, with applications ranging from information retrieval and recommender systems to computational biology and drug discovery. In this paper, we describe a new ranking algorithm that directly maximizes the number of relevant objects retrieved at the absolute top of the list. The algorithm is a support vector style algorithm, but due to the different objective, it no longer leads to a quadratic programming problem. Instead, the dual optimization problem involves l1, ∞ constraints; we solve this dual problem using the recent l1, ∞ projection method of Quattoni et al (2009). Our algorithm can be viewed as an l∞-norm extreme of the lp-norm based algorithm of Rudin (2009) (albeit in a support vector setting rather than a boosting setting); thus we refer to the algorithm as the ‘Infinite Push’. Experiments on real-world data sets confirm the algorithm’s focus on accuracy at the absolute top of the list.