Ashok Pon Kumar

OPRO: Precise emergency preparedness for electric utilities

Electric utilities spend a large amount of their resources and budget on managing unplanned outages, the majority of which are driven by weather. The weather is the largest contributing factor for power outages faced by the population in the United States and several other countries. A major ongoing effort by utilities is to improve their emergency preparedness process, in order to 1) reduce outage time, 2) reduce repair and restoration costs, and 3) improve customer satisfaction. We present an approach called Outage Prediction and Response Optimization (OPRO) to improve emergency preparedness by combining a) localized and highly accurate weather prediction, b) damage prediction, c) infrastructure health-aware damage hotspot analysis, and d) optimal resource planning. The combination of these capabilities can enable utilities to initiate their storm preparation process 1 to 2 days in advance of the storm and precisely plan their resource schedules and escalation stance. This would be a profound change to the business process of utilities, which today tends to be reactionary once the storm hits. We describe these capabilities and their effectiveness in terms of metrics relevant to a utility, the related use cases, and the overall business process that brings them together in the context of a real U.S. utility.

SocketWatch: An autonomous appliance monitoring system

A significant amount of energy is wasted by electrical appliances when they operate inefficiently either due to anomalies and/or incorrect usage. To address this problem, we present SocketWatch - an autonomous appliance monitoring system. SocketWatch is positioned between a wall socket and an appliance. SocketWatch learns the behavioral model of the appliance by analyzing its active and reactive power consumption patterns. It detects appliance malfunctions by observing any marked deviations from these patterns. SocketWatch is inexpensive and is easy to use: it neither requires any enhancement to the appliances nor to the power sockets nor any communication infrastructure. Moreover, the decentralized approach avoids communication latency and costs, and preserves data privacy. Real world experiments with multiple appliances indicate that SocketWatch can be an effective and inexpensive solution for reducing electricity wastage.

Improving efficiency in software maintenance

Efficiency is critical to the profitability of software maintenance and support organizations. Managing such organizations effectively requires suitable measures of efficiency that are sensitive enough to detect significant changes, and accurate and timely in detecting them. Mean time to close problem reports is the most commonly used efficiency measure, but its suitability has not been evaluated carefully. We performed such an evaluation by mining and analyzing many years of support data on multiple IBM products. Our preliminary results suggest that the mean is less sensitive and accurate than another measure, percentiles, in cases that are particularly important in the maintenance and support domain. Using percentiles, we also identified statistical techniques to detect efficiency trends and evaluated their accuracy. Although preliminary, these results may have significant ramifications for effectively measuring and improving software maintenance and support processes.

Optimizing energy costs of commercial buildings in developing countries

Energy cost from hvac is a significant fraction of the overall operational cost of a commercial building. Moreover, in developing countries such as India with inadequate grid connectivity and frequent outages, diesel generators are a common source of backup power. While this may solve the problem of outages, power produced by diesel generators is much more expensive than grid power. This work proposes an optimization framework targeted to minimize energy costs of buildings in the presence of outages and a mix of energy sources such as grid, diesel generators, solar, and battery. Our framework is novel since it exploits the inherent ability of a building to store thermal energy and availability of opportunistic occupancy proxies such as CO2 concentration. The framework when investigated for real world scenarios including office buildings and cell towers, resulted in savings of more than 10% relative to normal operational practices.

A platform for the next generation of smarter energy applications

A number of key technological, social, and business disruptions will drive a new generation of smarter energy applications. The disruptions include the following: 1) large sensor deployments, resulting in a huge increase in data volumes and variety, 2) a move toward clean energy and intermittent renewable energy sources, and 3) a move to highly distributed energy resources. To enable resilient and efficient power delivery, with these disruptions, will require a host of new applications that analyze large amounts and varieties of data in the context of the connected grid and perform analysis, visualization, and control in real-time with very low latency. In this paper, we present a set of capabilities that enable such applications, and a software and hardware platform that combines these capabilities to enable rapid development of a wide array of high-performance and analytics-rich applications. These capabilities include: 1) high-performance time-series ingestion, 2) a flexible data model that spans multiple contexts, 3) high-performance, in-memory analysis of time-varying, hierarchical graphs, 4) data service for co-presenting real-time and static spatiotemporal data for real-time web-based visualization, and 5) a seamless combination of event-based and service-oriented programming models.

Understanding events for wide-area situational awareness

With synchrophasor-based wide area situational awareness systems, the number of data signals that an operator must process at any given time, especially during disturbances, can be overwhelming. To assist both the operations team as well other teams monitoring and studying the state of the power system, we propose an event understanding framework that processes raw PMU data, generates and represents pertinent event metadata that can be searched and browsed, and derives inferences that can be used to automatically generate reports on important grid behaviors. In this paper, we describe how we detect basic events on the grid and describe an event ontology that provides a vocabulary to categorize these events. We extend this ontology by introducing spatial and temporal relations. As a first use case from post-mortem analysis, we demonstrate how an end user can search for and retrieve event episodes as part of “what if” scenario analysis. As a second use case, we show how to “screen” fault locations with voltage profiles, a base model of the domain, an inference model, and application of a rule-based reasoner. Based on our initial results, we conclude that this is a promising step towards fault localization, and consequently, automatic, post-disturbance report generation.

Optimal utilization of power transformers through virtual sensing

Power transformers are the most critical and expensive assets for utility companies and are expected to last for at-least 30-40 years. Unfortunately, many of them have failed before reaching their rated life. In order to prevent such premature failures, utility companies usually deploy real time asset management system by installing thermal sensors (e.g. fiber-optical sensor) inside the tank of large transformers (~500MVA). However, being expensive, sensor deployment may not be commercially viable for small and medium size (<;=250MVA) transformers. This paper proposes an asset management scheme of such power transformers through virtual (sensor-less) sensing. It simulates transformer internal heating phenomena (like hot-spot temperature, insulation aging, loss of life, etc.) using easily available SCADA measurements, ambient temperature from on-line weather forecast, and transformer assets data. It predicts future load and incentive and optimizes transformer operation by analyzing the economic incentive for carrying power and payoff for the loss of life calculated from virtual sensing. Proposed scheme is evaluated and implemented in Fortums transformers in Finland and experimental results are presented.

Software-only occupancy inference in a workplace findings from a field trial

HVAC and lighting loads contribute a significant fraction of total energy consumed in office buildings. These loads vary as a function of occupancy and therefore inferring occupancy is vital to optimizing energy efficiency within these buildings. This work presents evaluation and comparison results from a field trial conducted in a large office building, which involved estimating occupancy with the help of existing opportunistic context sources versus instrumented hardware sensors. Our results show that opportunistic sensing yielded an accuracy of 80% in comparison with expensive hardware sensors and may be used to continuously estimate fine-grained workplace occupancy in an inexpensive manner. Moreover the inferred occupancy information may also be used to identify anomalies in thermal management and space utilization within the building.

Unlocking the hidden potential of data towards efficient buildings: Findings from a pilot study in India

Energy cost is one of the significant contributors to the operational expenses of commercial buildings. In developing countries facing problems of frequent power outages and deficient grid connectivity, diesel generators are used as backup power source which significantly increase the costs incurred in management of commercial establishments. Integration of information and communication technologies to building management systems provides a reliable platform to analyze various aspects of the building such as energy consumption trends and occupancy inferences thereby proposing reactive or pro-active strategies directed towards efficient and cost-effective building management. Usually, this potential of data available to building management agencies stays untapped in developing countries. In this paper, we take a data-driven approach to understand various operational aspects of a commercial establishment. To demonstrate the scope for optimization of building operations by exploiting the energy consumption data, a pilot study was conducted in an IT office building in India.

People and entity retrieval in implicit social networks

Online social networks can be viewed as implicit real world networks, that manage to capture a wealth of information about heterogeneous nodes and edges, which are highly interconnected. Such abundant data can be beneficial in finding and retrieving relevant people and entities within these networks. Effective methods of achieving this can be useful in systems ranging from recommender systems to people and entity discovery systems. Our main contribution in this paper is the proposal of a novel localized algorithm that operates on the sub graph of the social graph and retrieves relevant people or entities. We also demonstrate how such an algorithm can be used in large real world social networks and graphs to efficiently retrieve relevant people/entities.