Special
Issue on Intelligent Technologies for Nuclear and Electric Power Systems
Guest Editor: Ephraim
Nissan
FuelGen:
effective evolutionary design of refuellings
for pressurized water reactors
J. Zhao, B.
Knight, E. Nissan, A. Soper
Abstract.
The paper describes the design of an efficient and robust genetic algorithm for
the nuclear fuel loading problem (i.e., refuellings: the in-core fuel management
problem) - a complex combinatorial, multimodal optimisation., Evolutionary
computation as performed by FuelGen
replaces heuristic search of the kind performed by the Fuelcon
expert system (CAI 12/4), to solve the same problem.
In contrast to the
traditional genetic algorithm which makes strong requirements on the
representation used and its parameter setting in order to be efficient, the
results of recent research results on new, robust genetic algorithms show that
representations unsuitable for the traditional genetic algorithm can still be
used to good effect with little parameter adjustment.
The representation presented here is a simple symbolic one with no
linkage attributes, making the genetic algorithm particularly easy to apply to
fuel loading problems with differing core structures and assembly inventories. A
nonlinear fitness function has been constructed to direct the search efficiently
in the presence of the many local optima that result from the constraint on
solutions.
fuzzy-logic control applications to
the belgian reactor 1 (BR1)
Da Ruan,
Xiaozhong Li
Abstract.
Fuzzy-logic control (FLC) applications in nuclear industry present a tremendous
challenge. The main reason for this is the public awareness of the risks of
nuclear industry and the very strict safety regulations in force for nuclear
power plants. The very same regulations prevent a researcher from quickly
introducing novel fuzzy-logic methods into this field.
On the other hand, the application of FLC
has, despite the ominous sound of the word "fuzzy" to nuclear
engineers, a number of very desirable advantages over classical methods, e.g.,
its robustness and the capability to include human experience into the
controller.
In
this paper
we report an on-going R&D project for controlling the power level of
the Belgian Reactor 1 (BR1) at the Belgian
Nuclear Research Centre (SCK·CEN).
The project started in 1995 and aims to investigate the added value of FLC for
nuclear reactors. We first review some relevant literature on FLC in nuclear
reactors, then present the state-of-the-art of the BR1 project. After
experimenting FLC under off-line test cases at the BR1 reactor, we now foresee a
new development for a closed-loop FLC as an on-line operation of the BR1
reactor. Finally, we present the new development for the closed-loop FLC at BR1
with an understanding of the safety requirements for this real FLC application
in nuclear reactors.
sensor data validation for nuclear
power plants through bayesian conditioning and dempster's rule of combination
A.F. Dragoni, P.
Giorgini
Abstract.
Sensor data fusion and interpretation, sensor failure detection, isolation and
identification are extremely important activities for the safety of a nuclear
power plant. In particular, they become critical in case of conflicts among the
data. If the monitored system's description
model is correct and its components work properly, then incompatibilities
among data may only be attributed to temporary deterioration or permanent
breakage of one or more sensors.
This paper introduces and discusses three simple ideas:
1.
classical "model-based diagnosis" can be extended straightforwardly to
encompass the sensors models into the system's description in order to diagnose
even their own faults
2.
from the "log-file" of the diagnosed minimal conflicts among the
sensors, one can draw interesting conclusion regarding their relative
reliability (e.g., through Bayesian conditioning)
3.
the estimated reliability of the sensors is useful when assessing (e.g. through
Dempster's Rule of Combination) the actual state of the monitored physical
system, even in cases of conflicting data.
These ideas lead to the
conception of a distributed monitoring system able to attach to each sensor a
statistically evaluated relative reliability, which is especially useful for
devices situated in dangerous zones or areas, difficult to reach inside huge and
complex power plants.
Neurofuzzy approaches and their
application to nuclear power systems
R.E. Uhrig, L.H.
Tsoukalas
Abstract.
Neurofuzzy approaches (NFA) utilize a variety of neural and fuzzy synergisms
that exploit a measured tolerance for imprecision and uncertainty for the
purpose of enhancing flexibility and tractability in models and systems.
It is theoretically expected
and empirically conformed that neurofuzzy approaches when appropriately
structured allow for improved control over the modeling economy or parsimony
resulting in easier to develop and modify systems.
Hence, they hold considerable promise for significant enhancements in the
control and safety of nuclear plant appurtenances, components and systems. Two
nuclear power system applications are presented in this paper. The first is in
the reactor control area. It uses neural networks to predict power trajectories
and fuzzy rules that incorporate such predictions in proactive or anticipatory
strategies in order to improve power manoeuvres during reactor startup. The
second is in the area of safety, where neural mappings are used to produce fuzzy
values for epistemic variables. The methodology is extending the notion of
measurement to variables with functional or operational significance and hence
is preferred to as virtual measurement; it is applied to flow visualization and
holds considerable promise for improving diagnostics and hence safety in nuclear
reactors.
Gaussian
mixture densities for classification of
nuclear power plant data
Y. Bengio, F.
Gingras, B. Goulard, J.-M. Lina, K. Scott
Abstract.
In this paper we are concerned with the application of learning algorithms to
the classification of reactor states in nuclear plants. Two aspects must be
considered: (1) some types events (e.g., abnormal or rare) will not appear in
the data set, but the system should be able to detect them, (2) not only
classification of signals but also their interpretation are important for
nuclear plant monitoring. We address both issues with a mixture of mixtures of
Gaussians in which some parameters are shared to reflect the similar signals
observed in different states of the reactor. An EM algorithm for these shared
Gaussian mixtures is presented.
Experimental results on nuclear plant data demonstrate
the advantages of the proposed approach with respect to the above two
points.
optimizing
neural network models:
motivation and case studies
S.A. Harp, T.
Samad
Abstract.
Practical successes have been achieved
with neural network models in a variety of domains, including
energy-related industry. The large, complex design space presented by neural
networks is only minimally explored in current practice. The satisfactory
results that nevertheless have been obtained testify that neural networks are a
robust modeling technology; at the same time, however, the lack of a systematic
design approach implies that the best neural network models generally
remain undiscovered for most applications.
This paper first presents an experimental study that demonstrates the
complex interdependencies between various parameters of neural models.
We then present an approach, based on genetic algorithms, for designing
optimized neural networks for specific applications. Two case studies are
discussed n which the benefits of a systematic design method are exemplified.
These studies are on real data sets that are relevant to the power industry. The
flexibility of genetic optimization also permits some novel twists on neural
modeling: input selection, and the synthesis of network architectures well
suited
for problem classes can be directly addressed.
a
hybrid neuro-genetic approach to flow calculation
based on the representation of an
electrical power system by critical switches
D. Arjona
Abstract.
This
paper is intended to present an approach to decision making in the operation of
electrical power systems that will use a simple genetic algorithm as a teacher
for the process of supervised learning of a feedforward, backpropagation
artificial neural network. The fitness function used in the genetic algorithms
is based on a load flow program and used to determine the optimal condition of
the critical switches of the system. Reward and penalty functions are applied to
it in order to emphasize environmental, economic, security, robustness, public
policy and other considerations as they are pre-determined by the philosophy of
operation of the utility. These considerations (policies) become a part of the
training set and operation of the neural network.
The fitness function used
by the genetic algorithm in order to rank the possible solutions is based on a
load flow program. The binary nature of the genetic algorithm is particularly
appropriate for the operation of switches.
The result of the
methodology is the equivalent of an on-line implicit load flow program used to
redesign the configuration of the system in real time by opening and closing
critical switches that are placed along the power system.
Experiments leading towards
the development of this methodology using real data from the Peninsular Control
Area (The Yucatan Peninsula) of the National Mexican Interconnected Power Grid
are presented.
Concepts of electrical
power engineering are presented as a general reference for the reader of this
document who is not a specialist on that field.