Tapan Kumar Saha
Other affiliations: Indian Institute of Technology Delhi, Central Queensland University, Queensland University of Technology ...read more
Bio: Tapan Kumar Saha is an academic researcher from University of Queensland. The author has contributed to research in topics: Electric power system & Transformer. The author has an hindex of 52, co-authored 590 publications receiving 10809 citations. Previous affiliations of Tapan Kumar Saha include Indian Institute of Technology Delhi & Central Queensland University.
Papers published on a yearly basis
TL;DR: A description of commonly used chemical diagnostics techniques along with their interpretation schemes for transformer insulation condition assessment is presented.
Abstract: Cellulosic paper and oil insulation in a transformer degrade at higher operating temperatures. Degradation is accelerated in the presence of oxygen and moisture. Power transformers being expensive items need to be carefully monitored throughout their operation. Well established time-based maintenance and conservative replacement planning is not feasible in a current market driven electricity industry. Condition based maintenance and online monitoring are now gaining importance. Currently there are varieties of chemical and electrical diagnostic techniques available for insulation condition monitoring of power transformers. This paper presents a description of commonly used chemical diagnostics techniques along with their interpretation schemes. A number of new chemical techniques are also described in this paper. A number of electrical diagnostic techniques have gained exceptional importance to the utility professionals. Among these techniques polarisation/depolarisation current measurement, return voltage measurement and frequency domain dielectric spectroscopy at low frequencies are the most widely used. This paper describes analyses and interpretation of these techniques for transformer insulation condition assessment.
TL;DR: In this article, the polarization and depolarization current (PDC) analysis is used for determining the conductivity and moisture content of insulation materials in a transformer, which is a non-destructive dielectric testing method.
Abstract: Moisture and ageing strongly influence the dielectric properties of oil/paper insulation system of power transformer. Moisture measurement in oil sample generally gives inconclusive information since oil/paper moisture equilibrium is temperature dependent and takes a long time to be in equilibrium. Direct moisture measurement of paper sample is not practicable for in-service transformers. The measurement and evaluation of the "dielectric response" and conductivity is one possible way of diagnosing a transformer insulation condition. In a recent research project, polarization and depolarization current measurement has been used for assessing the condition of oil/paper insulation. The polarization and depolarization current (PDC) analysis is a nondestructive dielectric testing method for determining the conductivity and moisture content of insulation materials in a transformer. On the basis of this analysis it is possible to take further actions like oil-refurbishment, drying or replacement of the winding of the transformer. This paper presents a description of the PDC technique with the physical and mathematical background and some results of PDC measurements on several transformers. Analyses and interpretation of the field test data are also presented in this paper.
TL;DR: In this article, the IEEE 13 bus system has been modified and extended to explore network stability impacts of variable PV generation, and the results show that a voltage stability issue with PV integration does exist in distribution networks.
Abstract: Several studies on voltage stability analysis of electric systems with high photovoltaic (PV) penetration have been conducted at a power-transmission level, but very few have focused on small-area networks of low voltage. As a distribution system has its special characteristics-high R/X ratio, long tap switching delay, small PV units, and so on-PV integration impacts also need to be investigated thoroughly at a distribution level. In this paper, the IEEE 13 bus system has been modified and extended to explore network stability impacts of variable PV generation, and the results show that a voltage stability issue with PV integration does exist in distribution networks. Simulation comparisons demonstrate that distribution networks are traditionally designed for heavily loaded situations exclusive of PVs, but they can still operate under low PV penetration levels without cloud-induced voltage-stability problems. It is also demonstrated that voltage instability can effectively be solved by PV inverter reactive power support if this scheme is allowed by the standards in the near future.
TL;DR: In this article, a circuit model, which describes the dielectric behavior of the transformer's main insulation system, has been parameterized in order to identify the values of the parameters of the model and the correlation has been developed between the physical condition of the insulation and the equivalent model parameters that enable a clear and transparent interpretation of the test results.
Abstract: Preventive diagnosis and maintenance of transformers have become more and more popular in recent times in order to improve the reliability of electric power systems. Dielectric testing techniques such as return voltage measurement (RVM) and polarization-depolarization current (PDC) measurement are being investigated as potential tools for condition assessment of transformer insulation. A better understanding and analysis of the dielectric test results are only possible with a clear understanding of the physical behavior of the insulation system in response to moisture and aging. A circuit model, which describes the dielectric behavior of the transformer's main insulation system, has been parameterized in this paper. The values of the parameters of the model have been identified from the dielectric tests. A correlation has been developed between the physical condition of the insulation and the equivalent model parameters that enable a clear and transparent interpretation of the dielectric test results.
TL;DR: In this paper, the authors provide an overview of the use of game-theoretic approaches for peer-to-peer energy trading as a feasible and effective means of energy management.
Abstract: Peer-to-peer (P2P) energy trading has emerged as a next-generation energy-management mechanism for the smart grid that enables each prosumer (i.e., an energy consumer who also produces electricity) of the network to participate in energy trading with other prosumers and the grid. This poses a significant challenge in terms of modeling the decisionmaking process of the participants' conflicting interests and motivating prosumers to participate in energy trading and cooperate, if necessary, in achieving different energy-management goals. Therefore, such a decisionmaking process needs to be built on solid mathematical and signal processing principles that can ensure an efficient operation of the electric power grid. This article provides an overview of the use of game-theoretic approaches for P2P energy trading as a feasible and effective means of energy management. Various game- and auction-theoretic approaches are discussed by following a systematic classification to provide information on the importance of game theory for smart energy research. This article also focuses on the key features of P2P energy trading and gives an introduction to an existing P2P testbed. Furthermore, the article gives specific game- and auction-theoretic models that have recently been used in P2P energy trading and discusses important findings arising from these approaches.
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
31 Oct 2001
TL;DR: The American Society for Testing and Materials (ASTM) as mentioned in this paper is an independent organization devoted to the development of standards for testing and materials, and is a member of IEEE 802.11.
Abstract: The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
Abstract: Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. It consists of three main steps: data acquisition, data processing and maintenance decision-making. Diagnostics and prognostics are two important aspects of a CBM program. Research in the CBM area grows rapidly. Hundreds of papers in this area, including theory and practical applications, appear every year in academic journals, conference proceedings and technical reports. This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Realising the increasing trend of using multiple sensors in condition monitoring, the authors also discuss different techniques for multiple sensor data fusion. The paper concludes with a brief discussion on current practices and possible future trends of CBM.