Rajaiah Karanam

A dependency model-based approach for identifying and evaluating power quality problems

The purpose of this paper is to present a diagnostic system that will not only monitor sensor data streams, but also classify power conditions, and diagnose power quality problems both in real-time and off-line. Signal processing techniques are applied to extract features from monitored data for event detection and classification. A cause-effect relationship model is used to trace the power quality related events to particular equipment of a system under consideration. The methodology has been implemented in a software tool. Results obtained from the application of this tool on monitored data collected from a facility validate the utility of this approach.

Capacity and reliability analyses with applications to power quality

The deregulation of energy markets, the ongoing advances in communication networks, the proliferation of intelligent metering and protective power devices, and the standardization of software/hardware interfaces are creating a dramatic shift in the way facilities acquire and utilize information about their power usage. The currently available power management systems gather a vast amount of information in the form of power usage, voltages, currents, and their time-dependent waveforms from a variety of devices (for example, circuit breakers, transformers, energy and power quality meters, protective relays, programmable logic controllers, motor control centers). What is lacking is an information processing and decision support infrastructure to harness this voluminous information into usable operational and management knowledge to handle the health of their equipment and power quality, minimize downtime and outages, and to optimize operations to improve productivity. This paper considers the problem of evaluating the capacity and reliability analyses of power systems with very high availability requirements (e.g., systems providing energy to data centers and communication networks with desired availability of up to 0.9999999). The real-time capacity and margin analysis helps operators to plan for additional loads and to schedule repair/replacement activities. The reliability analysis, based on computationally efficient sum of disjoint products, enables analysts to decide the optimum levels of redundancy, aids operators in prioritizing the maintenance options for a given budget and monitoring the system for capacity margin. The resulting analytical and software tool is demonstrated on a sample data center.

Integrated reliability analysis, diagnostics and prognostics for critical power systems

Critical power systems, such as data centers and communication switching facilities, have very high availability requirements (5 min./year downtime). A data center that consumes electricity at a rate of 3 MW can have a downtime cost of

Method and apparatus for monitoring electrical usage

300,000 an hour. Even a momentary interruption of two seconds may cause a loss of two hours of data processing. Consequently, power quality has emerged as an issue of significant importance in the operation of these systems. In this paper, we address three issues of power quality: real-time detection and diagnosis of power quality problems, reliability and availability evaluation, and capacity margin analysis. The objective of real-time detection and diagnosis is to provide a seamless on-line monitoring and off-line maintenance process. The techniques are being applied to monitor the power quality of a few facilities at the University of Connecticut. Reliability analysis, based on a computationally efficient sum of disjoint products, enables analysts to decide on the optimum levels of redundancy, aids operators in prioritizing the maintenance options within a given budget, and in monitoring the system for capacity margin. Capacity margin analysis helps operators to plan for additional loads and to schedule repair/replacement activities. The resulting analytical and software tool is demonstrated on a sample data center.