Several power utilities have recently designed and implemented advanced wide-area monitoring systems and data processing strategies to enhance grid stability and reliability. These strategies encompass the implementation of situational awareness systems, including disturbance alert, event location triangulation, oscillation detection, early warning systems, data archiving, and other advanced features [1-3].

Wide-area monitoring systems will continue to evolve as software, sensing, and communications technology advance and signal processing tools improve. Various forms of wide-area monitoring systems have been developed to give early warning of power system disturbances [4, 5]. In order to integrate data from multiple sensors, specialized data fusion and communication techniques must be integrated into the existing wide area monitoring system (WAMS) architectures. The design methods must incorporate both fault-tolerant strategies and intelligent data fusion techniques to enhance reliability and safety and also to improve the performance of global monitoring systems.

This chapter gives a broad overview of various wide-area monitoring architectures, including sensor development and conceptualization, data processing and data fusion, and anomaly detection algorithms. Inspired by recent work on data fusion techniques, advanced WAMS architectures are introduced that represent a combination of measuring, monitoring, and analysis architectures. Critical concepts in multisensor modeling, estimation, and fusion are reviewed.

A framework for integrating data from multiple sensors to produce actionable intelligence is identified. Advantages, challenging aspects, and recent advances in the design and implementation of WAMS architectures are reviewed, and areas, where research is needed to advance the use of WAMS data, are highlighted and described.

Challenges posed in developing distributed data fusion algorithms are also outlined.

Chapter Contents:

• 2.1 Introduction
• 2.2 WAMS architectures
• 2.2.1 Centralized WAMS architectures
• 2.2.2 Hierarchical WAMS architectures
• 2.2.3 Hybrid WAMS architectures
• 2.2.4 Distributed WAMS architectures
• 2.3 Issues in data fusion
• 2.3.1 Data
• 2.3.2 Intelligent synchrophasor data fusion
• 2.3.3 Power system data fusion strategies
• 2.3.4 General framework for data assimilation
• 2.3.5 Fusion or integration of multivariate PMU data
• 2.4 Relationship between multiblock and single-block models
• References

Inspec keywords: power system measurement; sensor fusion; wide area networks

Other keywords: fault-tolerant strategies; anomaly detection algorithm; data fusion; early warning systems; situational awareness systems; sensor development; global monitoring systems; wide area monitoring system architectures; multisensor modeling; intelligent data fusion techniques; power system disturbances; data processing; WAMS architectures; distributed data fusion algorithms; sensor conceptualization; data processing strategies

Subjects: Other computer networks; Power system measurement and metering; Sensor fusion; Computer communications