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Fault Identification in Transformers through a Fuzzy Discrete Event System Approach
Abstract
A new fault detection and identification (FDI) scheme for transformer faults are suggested in this paper. The new method is based on fuzzy discrete event, from now FDES, composed from between a transformer's measured outputs and its faults. In the FDES, events and state membership functions take values between zero and one. All events occur at the same time with different membership degrees. The main advantage of the suggested scheme is that different types of incipient or abrupt faults of transformers can correctly be identified. Principal component analysis (PCA) is mainly used for fuzzy event generation purpose. Event based FDES diagnoser involves fuzzy IF-THEN rules created by an artificial neural network (ANN) based on radial basis functions to identify incipient faults in transformers. It shows single or multiple faults and occurring degrees of these faults. The study is concluded by giving some examples about distinguishability of the single or multiple fault types in transformers. The real-time laboratory experiments verify the effectiveness of the suggested method.
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Introduction to Discrete Event Systems is a comprehensive introduction to the field of discrete event systems, offering a breadth of coverage that makes the material accessible to readers of varied backgrounds. The book emphasizes a unified modeling framework that transcends specific application areas, linking the following topics in a coherent manner: language and automata theory, supervisory control, Petri net theory, Markov chains and queueing theory, discrete-event simulation, and concurrent estimation techniques. Distinctive features of the second edition include: •more detailed treatment of equivalence of automata, event diagnosis, and decentralized event diagnosis •expanded treatment of centralized and decentralized control of partially-observed systems •new sections on timed automata with guards (in the Alur-Dill formalism) and hybrid automata •an introduction to hybrid systems •updated coverage of discrete event simulation, including new software tools available •recent developments in sensitivity analysis for discrete event systems as well as hybrid systems This textbook is valuable to advanced-level students and researchers in a variety of disciplines where the study of discrete event systems is relevant: control, communications, computer engineering, computer science, manufacturing engineering, operations research, and industrial engineering. © 2008 Springer Science+Business Media, LLC. All rights reserved.
In order to make it possible to effectively represent deterministic uncertainties and vagueness as well as the human subjective observation and judgement inherent to many real-world problems, especially those in biomedicine, we introduce, in this paper, fuzzy states and fuzzy events and generalize (crisp) discrete event systems (DES) to fuzzy DES. The largely graph-based current framework of the crisp DES is unsuitable for the expansion, and we have thus reformulated it using state vectors and event transition matrices which can be extended to fuzzy vectors and matrices by allowing their elements to take values between 0 and 1. To measure information related to fuzzy DES, we generalize the crisp DES observability. The new observability allows one to determine whether or not the system output observed is sufficient for decision making. Finally, we extend the optimal control of DES to fuzzy DES. The new fuzzy DES theory is consistent with the existing theory, both at the conceptual and the computation levels, in that the former contains the latter as a special case when the memberships must be either 0 or 1. Numerical examples are provided to illustrate the theoretical development.
The paper investigates specific methodologies for the effective use of neural networks. For this purpose 220/132/11 kV 100 MVA transformer is considered. A transformer model can be viewed as a functional approximator constructing an input-output mapping between specific variables and the terminal behaviors of the transformer. A neural network system is proposed to implement the entire approximating task by taking the advantage of its self learning and highly non-linear mapping capability. Two kinds of neural networks, back propagation feed forward network (BPFN) and radial basis function network (RBFN) were investigated to model the faults in power transformers. A voltage impulse of 1 kV is applied at the HV terminal and different types of fault conditions are simulated using PSPICE (Simulation Program with Integrated Circuit Emphasis) and thus the data generated is used for training and testing the neural network.
Detection and isolation of failures in large, complex systems is a
crucial and challenging task. The increasingly stringent requirements on
performance and reliability of complex technological systems have
necessitated the development of sophisticated and systematic methods for
the timely and accurate diagnosis of system failures. We propose a
discrete-event systems (DES) approach to the failure diagnosis problem.
This approach is applicable to systems that fall naturally in the class
of DES; moreover, for the purpose of diagnosis, continuous-variable
dynamic systems can often be viewed as DES at a higher level of
abstraction. We present a methodology for modeling physical systems in a
DES framework and illustrate this method with examples. We discuss the
notion of diagnosability, the construction procedure of the diagnoser,
and necessary and sufficient conditions for diagnosability. Finally, we
illustrate our approach using realistic models of two different heating,
ventilation, and air conditioning (HVAC) systems, one diagnosable and
the other not diagnosable. While the modeling methodology presented here
has been developed for the purpose of failure diagnosis, its scope is
not restricted to this problem; it can also be used to develop DES
models for other purposes such as control
In the paper the fuzzy logic based multi-criteria protective relay
for a three-phase power transformer is presented. Twelve criteria used
to stabilize the relay are introduced and integrated by means of
multi-objective decision-making methods. The introduced protection
scheme contains several internal functions and coefficients which are
subject to off-line pre-installation self-setting. Three unique
procedures are delivered for self-adjusting the relay, and thus, making
it self-organized. The results of testing show significant gains in both
sensitivity and selectivity of the self-organized relay compared with
traditional approaches
What to do after a transformer is tripped out is a difficult question, and it is desirable to make certain that the transformer is not faulted before getting it into service in order to avoid additional consequential damage. In this paper, a novel analysis of the currents arising during a turn-to-turn fault in transformer in which a winding is delta-connected is done, so that data acquisition pre- and post-fault conditions may lead to a correct diagnosis. The approach given in this paper is based in the analysis of current sequences which appear in the fault state, and mainly in the nature of a zero sequence current (ZSC) in a delta winding, which is thoroughly discussed. The paper examines a faulted transformer performance by analyzing the influence of four variables: load, fault location, fault severity and load power factor. A set of rules is given to determine the currents unbalance in coils as a function of such variables. Results are obtained from finite element analysis and validated in laboratory by means of a specially built transformer. The conclusion obtained is that, even under unbalanced conditions, the comparison of currents in phases identifies the existence of an interne transformer fault, thus warranting a correct switch back and/or disconnection.
We introduce the notion of fuzzy sets as a tool for modeling sets with ill-defined or flexible boundaries. Fuzzy sets naturally appear when describing the meaning of natural language words pertaining to quantitative scales, or when modelling the notion of typicality. The three main semantics for fuzzy sets are recalled: similarity, preference and uncertainty. Each semantics underlies a particular class of applications. Similarity notions are exploited in clustering analysis and fuzzy controllers. Uncertainty is captured by fuzzy sets in the framework of possibility theory. The membership function of a fuzzy set is also sometimes a kind of utility function that represents flexible constraints in decision problems. Fuzzy sets are acknowledged as a major tool in information engineering for the purpose of bridging the gap between human-originated formalized knowledge, and numerical data.
Fuzzy discrete event systems (DESs) were proposed recently by Lin and Ying [19], which may better cope with the real-world problems of fuzziness, impreciseness, and subjectivity such as those in biomedicine. As a continuation of [19], in this paper, we further develop fuzzy DESs by dealing with supervisory control of fuzzy DESs. More specifically: 1) we reformulate the parallel composition of crisp DESs, and then define the parallel composition of fuzzy DESs that is equivalent to that in [19]. Max-product and max-min automata for modeling fuzzy DESs are considered, 2) we deal with a number of fundamental problems regarding supervisory control of fuzzy DESs, particularly demonstrate controllability theorem and nonblocking controllability theorem of fuzzy DESs, and thus, present the conditions for the existence of supervisors in fuzzy DESs; 3) we analyze the complexity for presenting a uniform criterion to test the fuzzy controllability condition of fuzzy DESs modeled by max-product automata; in particular, we present in detail a general computing method for checking whether or not the fuzzy controllability condition holds, if max-min automata are used to model fuzzy DESs, and by means of this method we can search for all possible fuzzy states reachable from initial fuzzy state in max-min automata. Also, we introduce the fuzzy n-controllability condition for some practical problems, and 4) a number of examples serving to illustrate the applications of the derived results and methods are described; some basic properties related to supervisory control of fuzzy DESs are investigated. To conclude, some related issues are raised for further consideration.
Novel fast and efficient sequential learning algorithms are proposed for direct-link radial basis function (DRBF) networks. The dynamic DRBF network is trained using the recently proposed decomposed/parallel recursive Levenberg Marquardt (PRLM) algorithm by neglecting the interneuron weight interactions. The resulting sequential learning approach enables weights to be updated in an efficient parallel manner and facilitates a minimal update extension for real-time applications. Simulation results for two benchmark problems show the feasibility of the new training algorithms.
This paper presents an alternative approach using the differential logic associated to artificial neural networks (ANNs) in order to distinguish between inrush currents and internal faults for the protection of power transformers. The study of the current distortion originated from current transformer (CTs) saturation is one of the main aims of the work. The alternative transients program (ATP) has been chosen as the computational tool to simulate a power transformer under fault and energization situations. The radius basis function (RBF) neural network is proposed as an alternative approach in order to distinguish the situations described, using a smaller amount of data for the training purpose if compared with networks such as the multilayer perceptron (MLP). The MLP neural network with the backpropagation method is also implemented for comparison purposes. A wide range of architectures is evaluated and the work shows the best net configurations obtained. The ANN results are then compared to those obtained by the traditional differential protection algorithm. Encouraging results related to the application of the new method are presented.
Conventional principal component analysis (PCA) is ideally suited
for monitoring steady state processes based on the assumption that the
measurements are time independent (uncorrelated) and normally
distributed. Typically, most of the processes are in dynamic state, with
various events occurring such as abrupt process changes, low drifts, bad
measurements due to sensor failures, human errors, etc. Data from these
processes are not only cross-correlated, but also auto-correlated.
Applying conventional PCA directly to dynamic systems can raise false
alarms, making it insensitive to detect and discriminate different kinds
of events. Every event is associated with a certain frequency band
according to its power spectrum. Wavelets are emerging tools to
decompose a signal into various frequency bands providing simultaneous
time-frequency domain analysis. We combine the potential of wavelets
with the congeniality of PCA to monitor dynamic multivariate processes
at different scales (frequencies). This multiscale monitoring strategy
extends the suitability of PCA to statistically monitor processes based
on auto-correlated measurements. Additionally, the resulting PCA models
are more sensitive in detecting changes in a process. These ideas are
illustrated by a suitable example
This paper describes in detail, field experiments performed on a
single-phase, distribution transformer to study the behavior of
transformer terminal parameters during internal winding faults. A
custom-built transformer provided with external taps was used for these
tests. The taps were used to stage various internal winding faults in
the transformer. Terminal values of voltages and currents were monitored
and the results presented. A comparison of these results with simulation
results is also presented
The paper describes a multi-criteria self-organized fuzzy logic
based protective relay for three-phase power transformers. The employed
protection criteria and the overall relay organization are presented as
well as the novel algorithms for off-line pre-installation self-setting
of the relay. These algorithms are based on statistical information
obtained by mass-simulation using ATP-EMTP package and enable a kind of
learning phase similar to that known from artificial neural network
applications to power system protection. The example and results of
testing demonstrate high stability and sensitivity of the presented
relay which operates with average tripping time below half a cycle.
Robustness of the relay has been also confirmed
The degree of vagueness of variables, process description, and
automation functions is considered and is shown. Where quantitative and
qualitative knowledge is available for design and information processing
within automation systems. Fuzzy-rule-based systems with several levels
of rules form the basis for different automation functions. Fuzzy
control can be used in many ways, for normal and for special operating
conditions. Experience with the design of fuzzy controllers in the basic
level is summarized, as well as criteria for efficient applications.
Different fuzzy control schemes are considered, including cascade,
feedforward, variable structure, self-tuning, adaptive and quality
control leading to hybrid classical/fuzzy control systems. It is then
shown how fuzzy logic approaches can be applied to process supervision
and to fault diagnosis with approximate reasoning on observed symptoms.
Based on the properties of fuzzy logic approaches the contribution gives
a review and classification of the potentials of fuzzy logic in process
automation
This paper presents an application of a finite impulse response
artificial neural network (FIRANN) as differential protection for a real
three phase power transformer. Three FIRANNs are designed, trained and
tested. The first one has an output which identifies internal faults
from any other cases like inrush current and external faults. The two
others FIRANNs, each have two outputs that classify between internal and
external faults, so that a backup protection is included. These FIRANNs
have six inputs, one for each sampled current signal from both
transformer sides. The sample rate selected is 2 kHz for a 50 Hz power
frequency. All FIRANNs were trained to have a 3.5 ms fault detection
time, which is considered as a very fast protection. The test results
show very good behavior of the FIRANN as a differential protection and
it is planned to build a prototype
This paper proposes a new technique for transformer protection.
The technique is concerned with the detection of the fault generated
high frequency current transients by means of a specially designed relay
unit. The relay, tuned to a band of high frequencies, is used to capture
the transient currents from both sides of the transformer; the
differential and average currents between the two sides are then
calculated. The spectral energies of these current signals are extracted
to produce the operate and restraint signals; a comparison between the
levels of the two signals determines whether the fault is internal or
external to the protected zone. A new technique for inrush detection has
also been proposed in this paper. The technique detects inrush current
by using the high frequency components contained in its current
transient signal. The restraint signal is derived by computing the ratio
of the spectral energy of the transient signal to the fundamental
current. A comparison between the level of restraint signal and a
pre-defined threshold determines whether a magnetizing inrush is in
process. Simulation studies with respect to different fault and inrush
conditions have been conducted, and the results prove that the proposed
technique is able to offer fast responses in protection and accurately
discriminate between inrush magnetizing current and internal
faults
The modeling of power transformer faults and its application to
performance evaluation of a commercial digital power transformer relay
are the objectives of this study. A new method to build an EMTP/ATP
power transformer model is proposed in this paper. Detailed modeling of
the transformer relay is also discussed. The transient waveforms
generated by ATP under different operating conditions are utilized to
evaluate the performance of the transformer relay. The computer
simulation results presented in this paper are consistent with the
laboratory test results obtained using an analog power system
model
In this paper the performance of a new transformer diagnosis
method is tested experimentally. The method is called model-based
diagnosis because the diagnosis is based on an identified model of the
component-the transfer function. Two test series are carried out to
evaluate the method on transformers, a sensitivity test series and an
ageing test series. The purpose of the sensitivity test series is to
determine how close the identified transformer model is connected to the
physical condition of the modelled transformer. The purpose of the
ageing test series is to determine if it is possible to identify a
development in the transfer function of the transformer towards
breakdown. Based on the two test series it is concluded that the
detection of failures and ageing phenomena in transformers by transfer
functions is dependent on detectable changes in relatively few
characteristic transformer parameters-basically lumped winding
capacitances, total losses and core reluctance
This paper deals with a method of modeling internal faults in a
power transformer. The method leads to a model which is entirely
compatible with EMTP software. It enables simulation of faults between
any turn and the earth or between any two turns of the transformer
windings. Implementation of the proposed method assumes knowledge of how
to evaluate the leakage factors between the various coils of the
transformer. A very simple method is proposed to evaluate these leakage
factors. At last, an experimental validation of the model allows the
estimation of its accuracy
A model to simulate the high-frequency behavior of a power transformer is presented. This model is based on the frequency characteristics of the transformer admittance matrix between its terminals over a given range of frequencies. The transformer admittance characteristics can be obtained from measurements or from detailed internal models based on the physical layout of the transformer. The elements of the nodal admittance matrix are approximated with rational functions consisting of real as well as complex conjugate poles and zeroes. These approximations are realized in the form of an RLC network in a format suitable for direct use with the ElectroMagnetics Transient Program (EMTP). The high-frequency transformer model can be used as a stand-alone linear model or as an add-on module of a more comprehensive model where iron core nonlinearities are represented in detail.< >
The increasing use of power system devices that cause harmonic distortion has stimulated interest in predicting transformer performance in such environments. Of specific interest is the harmonic content of the transformer primary and secondary voltages and currents, under a variety of load conditions. The inherent nonlinearities of the transformer also play an important role in such studies. The performance of classical transformer models under nonsinusoidal excitation is investigated and two new models for such analysis, both of which account for the transformer core magnetic nonlinearities, are presented. All analytical results are compared with experimental data.
Acustic-based real time partial discharge location in model transformer
- A Noro
- K Nakamura
- T Watanabe
- T Morita
A. Noro, K. Nakamura, T. Watanabe, T. Morita, "Acustic-based real time partial discharge location in model transformer", in Proceedings ICSPAT'94, pp. 1077-1082.