Accurate diagnosis of system health is a vital step in wide-area monitoring. Advanced event characterization is crucial for improving the detection, identification, and description of system health and the development of corrective measures. Large interconnected power systems and their areas or regional systems are highly complex and variable structures that defy predictions. Monitoring these systems in the face of uncertainty and variability remains a daunting challenge.

The last two decades have borne witness to an explosion of interest in developing power system monitoring and analysis techniques [1]. By monitoring the time evolution of crucial system parameters, monitoring techniques can be used to trigger remedial control actions and alarms and aid in developing situational awareness tools [1-3].

Central to this framework is the diagnostic and prognostic signal processing and measurement techniques used to detect and diagnose power system health [4, 5]. Inappropriate monitoring strategies can lead to irrelevant or poor system characterization, which, in turn, can have profound operational and economic impacts.

Power system monitoring encompasses a variety of activities that involve event detection and classification and assessment of power system health status [6]. The inclusion of spatiotemporal dynamics is needed in order to identify localized and propagating features in measured data as well as to compress system information. It has been realized that these measurements may contain moving patterns and traveling waves of different spatial scales and temporal frequencies [7].

Furthermore, because wide-area measurements are characterized by nonlinearity and high dimensionality, a challenging task is to find ways to reduce system dimensionality to a few modes and link these modes to the underlying dynamical/physical behavior involved.

In this chapter and in Chapter 7, several tools to assess power system health are developed and tested. Methods for evaluating changes in measured oscillatory response are examined, and new approaches for use in wide-area system monitoring are presented.

Issues related to the robustness of the methods in the presence of measurement noise and multiple events are discussed.

Chapter Contents:

• 6.1 Introduction
• 6.2 Power system health monitoring
• 6.3 Disturbance and anomaly detection
• 6.4 Modal-based health monitoring methods
• 6.4.1 Filtering and data conditioning
• 6.4.2 Entropy and energy
• 6.4.3 Entropy-based detection of system changes
• 6.5 Wide-area inter-area oscillation monitoring
• 6.5.1 Case A
• 6.5.2 Case B
• 6.6 High-dimensional pattern recognition-based monitoring
• 6.6.1 Sparse diffusion implementation
• 6.6.2 Data clustering
• 6.6.3 Numerical example
• 6.6.4 Hybrid schemes
• 6.7 Voltage and reactive power monitoring
• 6.7.1 Measured data
• 6.7.2 Statistical approach to voltage monitoring
• 6.7.3 Complex POD/PCA analysis
• References

Inspec keywords: fault diagnosis; spatiotemporal phenomena; power system measurement; power system interconnection

Other keywords: system dimensionality; wide-area monitoring; 5]; prognostic signal processing; advanced event characterization; system information; power system health status; crucial system parameters; measurement techniques; interconnected power systems; poor system characterization; power system monitoring; regional systems; wide-area system monitoring; inappropriate monitoring strategies; monitoring these systems; wide-area measurements; irrelevant system characterization

Subjects: Power system measurement and metering; Biology and medical computing; Data handling techniques; Power system control; Control of electric power systems; Power system management, operation and economics; Information networks