John Alan Interrante

Applying Cluster Computing to Enable a Large-scale Smart Grid Stability Monitoring Application

The real-time execution of grid stability monitoring algorithms are critical to enabling a truly smart grid. However, the combination of a high sampling rate for grid monitoring devices, combined with a large number of devices scattered across a grid, result in very high throughput requirements for the execution of these algorithms. Here we define a centralized hardware and software infrastructure to enable the real-time execution of a small signal oscillation detection algorithm using a cluster of commodity nodes. Our research has demonstrated that readings from up to 500 phasor measurement units (PMUs) sampling at 60Hz can be analyzed in real-time by a single 8-core, 2.53GHz machine with 8GB of RAM, and that a cluster of four of these machines can be used to monitor up to 2,000 PMUs in parallel.

Bridging high velocity and high volume industrial big data through distributed in-memory storage & analytics

With an exponential increase in time series sensor data generated by an ever-growing number of sensors on industrial equipment, new systems are required to efficiently store and analyze this “Industrial Big Data.” To actively monitor industrial equipment there is a need to process large streams of high velocity time series sensor data as it arrives, and then store that data for subsequent analysis. Historically, separate systems would meet these needs, with neither system having the ability to perform fast analytics incorporating both just-arrived and historical data. In-memory data grids are a promising technology that can support both near real-time analysis and mid-term storage of big datasets, bridging the gap between high velocity and high volume big time series sensor data. This paper describes the development of a prototype infrastructure with an in-memory data grid at its core to analyze high velocity (>100,000 points per second), high volume (TBs) time series data produced by a fleet of gas turbines monitored at GE Power & Waters Remote Monitoring & Diagnostics Center.

Agent Learning to Manage Costs for Event Detection

Recent scandals around manipulated financial filings have caused investors and analysts to search for alternative ways to study the financial health of companies. The use of news events such as CEO or auditor changes has proven valuable at providing insights into the status of a companys financial health. However, this information can be extremely difficult and expensive to gather in practice. An intelligent multi-agent system was designed and developed to simulate the collection of news events in an efficient, cost-effective manner. Results show that a multi-agent system is an effective tool for collecting critical business intelligence while minimizing cost

Monitoring key company events through deliberative learning

Recent scandals concerning the discovery of fraud committed by a few high profile companies has reinforced a need for innovative approaches to detecting fraudulent company behavior. Fraud detection experts agree that many of the critical clues to fraud, such as frequent management and auditor changes, can be found in qualitative sources such as news articles, press releases, and footnotes accompanying financial statements. This paper presents a simulated multi-agent system that learns how to collect valuable events from textual sources with pinpoint precision, utilizing the best content providers for each event type while minimizing the overall cost.