(247r) Power Plant Abnormal Condition Detection Using the Artificial Immune System Paradigm

Power
Plant Abnormal Condition Detection Using the Artificial Immune System Paradigm

Ghassan Al-Sinbol, Mario G. Perhinschi, Debangsu
Bhattacharyya

            Modern power plants are expected to
function safely and efficiently under both normal and abnormal operating
conditions.  Due to the complexity, multi-dimensionality,
and strong interaction among various sections of these plants, health
monitoring requires comprehensive and integrated methodologies.  In this paper, the artificial immune system (AIS)
paradigm is used to develop a detection scheme for abnormal operation of
advanced power plants.

            The functionality of the biological
immune system and its capability to discriminate between “self” (normal
conditions) and “non-self” (abnormal conditions) inspired the AIS paradigm as a
novel artificial intelligence technique with promising capabilities for
abnormal condition detection.  An abnormal situation or failure affecting a
dynamic system is considered similar to an antigen invasion.  Using a positive selection-type of algorithm,
failures are declared or detected when the current configuration of “features”
does not match with any configuration from an exhaustive set known to
correspond to normal situations or the self. 
Building the self requires a substantial amount of data collected at
normal operational conditions; however, it does not require modeling of the
system. 

            In
this paper, a novel approach, denoted as ‘the partition of the universe’
approach, for self/non-self generation is presented.  In this approach, the n-dimensional feature
space is divided into uniform partitions with predefined centers, shape, and
size.  The raw self points are then
tested against the universe partitions. 
Self partitions are then identified and saved as integer strings
that can be produced and used with reduced computational effort. 

            An abnormal
condition detection scheme is designed based on a positive-selection-type of
algorithm in conjunction with a dendritic cell (DC) mechanism that is expected
to allow extension towards abnormal condition identification and
evaluation.  The artificial DC is a
computational module inspired from the interaction between the innate and
adaptive immune systems.  The proposed
artificial DC mechanism is expected to provide the detection outcome based on
the current and past discrimination results and overcome any imperfections in
the definition and generation of the self. 
The general flowchart of the detection process is presented in Figure 1
and the flowchart of the proposed abnormal condition detection scheme using the
partition of the universe approach is presented in Figure 2.

            The proposed
approach is demonstrated with promising results using a rigorous model of an
acid gas removal (AGR) unit as part of the integrated gasification combined cycle
power plant developed in Dynsim^® environment.  A
total of 150 features was selected to build the self/non-self of the AGR unit,
including pressure, temperature, flow rate, and composition measurements across
the unit.  The AGR unit is divided into
22 subsystems and lower dimensional projections of the self are considered for
each subsystem within the hierarchical multi-self strategy.  For the purpose of demonstrating the
operation of the proposed detection scheme, a limited number of abnormal
conditions that include deposition of solids, such as flyash,
and leakages in the pipes or equipment items has been considered.  These abnormal conditions affect five main
sub-systems characterized by sixty features. 

            The proposed detection scheme
provides excellent performance with high detection rates and zero false alarms
for all cases considered.  The detection
rates vary between 94.6% and 99.3% with detection times between 2.5 and 10.5
seconds.

Figure 1.  AIS-based Abnormal Condition Detection

Figure 2.  Abnormal Condition Detection Using the
Partition of the Universe Approach