Fault Detection and Isolation in Power Systems Using Unknown Input Observer

Fault Detection and Isolation in Power Systems Using Unknown Input Observer

Hassan Haes Alhelou (Tishreen University, Syria)
DOI: 10.4018/978-1-5225-6989-3.ch002
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Unknown input observers (UIO) find application in many cases for successful fault detection and isolation (FDI). In this chapter, a scenario where the unknown input observer is applied to load frequency control loops in interconnected power systems is analyzed. A UIO was chosen because load demand is not always constant and it can be considered to introduce an unknown disturbance to the system. Mathematical formulations on how to detect and isolate sensor faults are presented which are then implemented in MATLAB Simulink for simulations. Based on this historical survey on the application of UIO, a thesis on UIO application in FDI in distributed generation is done.
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Generally, FDI methods can be classified in two broad categories the model based methods and the process history/ data based methods. Figure 1 shows how these methods can then be further divided into different categories. The model-based methods can be broadly classified as qualitative or quantitative. The model is usually developed based on some fundamental understanding of the physics of the process or system. In quantitative models this knowledge is expressed as mathematical functional relationships between the inputs and outputs of the system while in qualitative model based methods these relationships are expressed in terms of qualitative functions centered on different units in a process.

In contrast to the model-based approaches in process history based methods only the availability of large amount of historical process data is assumed. This data is transformed and presented as a prior knowledge for fault diagnosis by a process called feature extraction. It should be noted however that within these methods there are overlaps.

The most commonly used FDI approaches include diagnostic observers, parity relations, Kalman filters and parameter estimation. Under observer methods there are unknown input observers (UIO), functional observers, Lueneberg observer, and proportional integral observer, sliding mode observers (SMO), state observers and many others. The motivation behind the development of model based methods is replacing direct redundancy with analytical redundancy. Most of the work on quantitative model-based approaches have been based on general input/output and state/space models. The main concern of observer-based FDI is the generation of a set of residuals which detect and uniquely identify different faults. These residuals should be robust that is they are not sensitive to unknown inputs as unstructured uncertainties like process and measurement noise and modelling uncertainties. Observers can be designed in such a way that it is sensitive to one fault and insensitive to the rest.

Figure 1.

The different types of methods


Generally, a good fault detection and isolation system (FDI) must at least have all or most the properties explained as below.

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