arXiv subjects

Swasti R. Khuntia

Publications and source records attributed to Swasti R. Khuntia.

6 recordsLinked to original sources

arXiv2023

Assessment of Different Health Index Aggregation Techniques for Electric Utilities

The Health Index is a value (in terms of color or score) which describes the technical condition of an asset. Using the Health Index of various assets, the so called aggregated Health Index of a system can be calculated. For electric utilities, the different levels of aggregation can be at asset type (primary, secondary, etc.) or at bay or at substation level. This paper assesses the feasibility and methodology of aggregating Health Index scores with the help of different techniques. For validation, real values from five substations of the Dutch transmission system operator are used. The different aggregation techniques are compared by showing the effectiveness of aggregated methodology both in terms of quantity and quality. The aggregated Health Index score of all assets in a bay are integrated into bay Health Index.

stat.AP↗

arXiv2023

Use of survival analysis and simulation to improve maintenance planning of high voltage instrument transformers in the Dutch transmission system

This paper describes the use of survival analysis and simulation to model the lifetime of high voltage instrument transformers in the Dutch transmission sys-tem. To represent asset aging, the non-parametric Kaplan-Meier method is used to enable the fitting of Weibull distribution. Such an approach is implemented on three different voltage levels, namely 110kV, 150kV, and 220/380kV. Real failure and inspection data is used to achieve a realistic failure model of the instrument trans-formers. Failure and maintenance data occurring between 1989 and 2021 have been used for this study. In spite of missing and low-quality data, a rich failure database could still be prepared. This study also offers insights into factors (i.e., voltage level, in-service age) influencing the remaining life from both graphical survival function and parametric Weibull distribution analysis. Based on the derived statistics, future possible maintenance planning scenarios are simulated under a complex system modelling framework in a digital twin enabled platform. Eventually, the scenarios are evaluated in terms of replacement costs (CAPEX), inspection hours, and unavailability hours.

stat.AP↗

arXiv2018

Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.

stat.AP↗

arXiv2018

Smart Asset Management for Electric Utilities: Big Data and Future

This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it. Condition monitoring of assets collects large amounts of data during daily operations. The question arises "How to extract information from large chunk of data?" The concept of "rich data and poor information" is being challenged by big data analytics with advent of machine learning techniques. Along with technological advancements like Internet of Things (IoT), big data analytics will play an important role for electric utilities. In this paper, challenges are answered by pathways and guidelines to make the current asset management practices smarter for the future.

cs.OH↗

arXiv2017

Mutual Information based Bayesian Analysis of Power System Reliability

This paper aims at assessing the power system reliability by estimating loss of load (LOL) index using mutual information based Bayesian approach. Reliability analysis is a key component in the design, analysis and tuning of complex structure like electrical power system. Consideration is given to rare events while constructing the Bayesian network, which provides reliable estimates of probability distribution function of LOL with lesser computing effort. Also, the ranking of load components due to loss of load is evaluated. The RBTS and IEEE RTS-24 systems are used as test cases.

stat.AP↗

arXiv2017

Pathway for Multivariate Dependence Modeling in Long-Term Horizon of Electrical Power System

Future electricity consumption is fundamentally uncertain and dependent on many variables such as economic activity, weather, electricity rates and demand side management. The stochasticity of system load as well as power generation from renewable energy sources (RES) (i.e., wind and solar) poses special challenges to power system planners. Increasing penetration levels of wind and solar exacerbate the uncertainty and variability that must be addressed in coming years, and can be extremely relevant to power system planners. With this paper, pathways for multivariate dependence modeling using vine copula is proposed which includes both electrical load and power generation from RES.

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