Proceedings of the 36th Ha0-7695-1874-5/03 $17.0

Sensor Fusion and Complex Data Analysis for Predictive Maintenance

Dr. Rahmat Shoureshi, Tim Norick, David Linder, Colorado School of Mines John Work, Paula Kaptain, Western Area Power Administration

rshoures@mines.edu

Abstract An essential step toward the development of an intelligent substation is to provide self-diagnosing capability at the equipment level. Transformers, circuit breakers and other substation equipment should be enabled to detect their potential failures and make life expectancy prediction without human interference. This paper focuses on the development of an on-line equipment diagnostics using artificial intelligence and a nonlinear observer to prevent catastrophic failures in substation equipment, thus providing preventive maintenance. Key elements of the system are a nonlinear observer, system identifier, and fault detector that use a uniquely designed neuro-fuzzy inference engine. Experimental results from application of this system to a distribution transformer are presented. 1. Introduction Presently most maintenance of substation equipment, e.g. transformers and circuit breakers, is done on a pre-scheduled basis. Maintenance crews inspect a transformer at set intervals based on its age and past performance history. As can be expected, this leads to many catastrophic failures of improperly diagnosed transformers and the over-inspection of other healthy transformers. On-line transformer diagnostics is the key to greatly reducing the cost and reliability of providing the needed electrical energy to a growing society [1]. As has been seen recently in California, the United States is already beginning to reach a point where it needs more energy but does not have the capability to transmit and distribute it. One of the major costs of providing electrical energy is the maintenance of substation equipment. The transformer is one of the key components to any substation. There are currently transformers that handle one thousand megavolt-amperes. The savings that would be accrued from the prevention of failures in these large transformers are in the millions of dollars [1].

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waii International Conference on System Sciences (H0 © 2002 IEEE

. Literature Review

Maintenance of substation equipment has taken on any different forms over the past years. Originally, aintenance was almost exclusively done off-line. In the

ast, service crews would measure the effective ransformer turns ratio (TTR) hoping this would indicate f the transformer coils had shorted. These attempts never eemed to provide any useful information [2]. Recently, aintenance crews took a transformer off-line and

xamined the gas content in the oil or took a sample of the nsulation to determine if it was deteriorating by finding ts moisture content and degree of polymerization. There ave even been mathematical equations developed which etermine the life expectancy of the transformer through he insulation results [3]. Many rule base criteria have een developed lately for different types of sensor easurements. For example, in dissolved gas easurements, a high content of acetylene is a sure sign

f arcing. An increase in the ration of carbon dioxide to arbon monoxide indicates overheating. Likewise, a ertain water content in the oil indicates significant ecrease in the dielectric constant of the oil and indicates he insulation is degrading. From these general trends, ets of limits have been formed to indicate when a arning should be issued. These trends have also been

ormed for temperature above reference, which has ecently been determined quickly using thermography [4]. In order to not miss any failures, advancements have ately focused on developing on-line methods for iagnosing a substation equipment. These advances nclude the development of on-line DGA and moisture onitors. The Hydran and Aquaoil by GE are two such

evices. Likewise, there has also been the creation of on-ine SF6 detectors using infrared laser technology for etermining faults and excessive arcing in circuit reakers. Also, Digital Corona Cameras are being used to etect damaged or defective insulators in substations [5]. rending algorithms have been developed for these ensors in the hope of making life expectancy predictions. A relatively new technique has been to monitor the requency of the vibrations on the shell of the transformer.

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The cellulose insulation on the coils of the transformer shrinks with aging. The shrinkage causes a loosening in the clamping pressure. If the coils are not retightened, the decreased pressure can often lead to catastrophic failure in transformers [2]. When the coils loosen, there is an increase in the presence of the lower odd harmonics. In particular, the 3rd, 5th, 7th, and 9th harmonics are excellent indicators of coil loosening [6]. Thus, through monitoring the shell vibration frequency content it is possible to determine if the coils are loosening. Lately, there has been more intensive focus on developing models for both the transformer and circuit breaker. The state of the art currently in thermal detection comes in the form of these types of models. Mathematical models have been used to accurately predict oil and winding temperature for varying conditions, exclusively using external thermal detectors. The parameters for these models include main tank temperature, hot spot temperature, and ambient temperature. The IEEE had developed a model, but it did not properly account for changes in the ambient temperature and thus set off alarms when there was no pending fault. In the past few years, researchers at MIT have developed a model that accurately determines the thermal state of a transformer. As noted earlier, here at CSM we have also developed a thermal model, which has been used to determine failures with the help of a neural network. These modern models adapt to each unique transformer through parameter estimation [7]. This allows for differences between individual transformers and enables the detection of numerous failures. In addition to models, there has been emphasis on developing an observer that predicts the expected output of a system without having to develop a precise mathematical model. This is the technique we use here. Many have tried to use fuzzy logic, neural networks, petri nets, genetic algorithms, or a combination of these to identify a equipment system, e.g. a transformer, and use this to diagnose when a failure is present. With most of these techniques, the observer trains itself to the piece of equipment and uses this training to estimate what the output should be under normal operating conditions. The training times and number of required parameters vary for the different methods that are used. The fuzzy logic is often implemented in combination with the training algorithms in order to define regions and failure modes [8-12]. 3. Research Objective The research objective of the Intelligent Substation Project is to develop a combined sensor and data acquisition system, artificial intelligence, automatic control techniques, expert experience and software design that will enable on-line diagnostics and predictive

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aintenance for all equipment in the substation. This ntelligent monitoring system will prevent catastrophic ailures of large power transformers and will eliminate the resent unneeded/scheduled maintenance. The greater eliability this system will provide will also lessen the urden on the power grid, thus resulting in increased ower reliability.

Another objective of this project is to develop an utomated analysis system that will convert the large olume of data received from the sensors into useful nformation that can be easily used by the maintenance rew to detect failures, to predict the life expectancy of he equipment and whether it needs to be serviced. The onitoring system will use a neural network to enable it

o adapt to changes in the state of equipment. The ombination of the sensors and intelligent diagnostic ystem will alert service crew only if a failure mode is etected. The combined nonlinear observer, neural etwork, and expert system resulting from this study has he potential to revolutionize the maintenance procedure n the power industry and create a vastly different echnique for health assessment of substation equipment.

. Nonlinear System Identification In order to predict failure in a complex system, e.g. a igh power transformer, it is important to develop ccurate dynamic models that can predict the state of peration under any conditions. For real life systems this odel would need to be almost invariably nonlinear. In

raditional modeling, mathematical equations are defined o relate the states of the systems to the outputs [7,13].

ith nonlinear systems this quickly becomes omputationally too complex even for modern computing. he question then is how to easily form a nonlinear model

hat can be applied to these systems. The approach taken or this project is to create an artificial neural network ANN) that can accurately represent the system. The NN is trained using a set of input patterns and the nown output patterns [14-17]. Assuming that all cases re represented in the training sets, the network can eproduce the appropriate output based on a given input. herefore, in the case of the transformer, it will be ecessary to instrument many transformers in different ealth states such that the neural network would be able to ind the pattern between sensor inputs and the health of he transformer (output). Our prior research has resulted in a neuro-fuzzy etwork (TNFIN) that has several unique features ncluding low number of parameters, fast training, and onvergence that would retain the ability to accurately stimate system response.

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4.1 TNFIN Neuro-Fuzzy Network

As shown in Figure 1, the TNFIN network is made up of four layers: fuzzification, normalization, defuzzification, and summation layers. For a network with N inputs the first layer, fuzzification, transforms each input into a fuzzy set. This layer consists of a number of nodes equal to the number of membership functions in the fuzzy set times N. The second layer, normalization, normalizes the weighted outputs form each node in layer one. This layer contains a number of nodes equal to the number of nodes in layer 1. When k is the number of nodes, the output of this layer is given as (1).

1

12

kk

kk O

OOΣ

= )21( Nk ≤≤ (1)

The third layer, defuzzification, transforms the normalized output of each node of layer three back into a non-fuzzy value. It contains nodes equal to the number of nodes in layers one and two times the number of outputs. The defuzzified values for the output of this layer is given by (2). Here l denotes a column number that has a maximum equal to the number of membership functions. {ckl ,dkl }represent a set of parameters which control the shape of the membership functions of the consequent part.

=−+

=−−

=even k if)11(

odd k if)11(

22

22

3

kklklk

kklklk

m

OdcO

OdcO

O (2)

where

2) l 1 4, k (1 1 1)-(k * 2 m ≤≤≤≤+= The last layer, summation, sums the outputs of layer three together into an output. There are as many nodes in this layer as there are outputs, as defined by (3). ∑ +−=

klkl OO 3

)1*(24 (3)

Training of this network is done via a hybrid learning algorithm that combines error minimization and supervised learning. p is the number of training sets and l is the number of inputs, training is preformed by (4).

eedings of the 36th Hawaii International Conference on System Sciences (H95-1874-5/03 $17.00 © 2002 IEEE

24,

*,,

24,

*,

)(21

)(21min

lplplp

plp

llp

OTEand

OTE

−=

−= ∑∑ (4)

where O4

p,l is the lth output of the network for the pth training set, and T*

p,l is the measured target [14-17]. Figure 1: Architecture of the TNFIN For the Intelligent Substation project the TNFIN network is utilized as a nonlinear state estimator that can predict the response of the system under any conditions. The output of the network is compared with the output of the normal system in order to develop a residual. This residual represents the difference between the system performance and its expected value. Any change in this value shows an alteration in the system mechanics such as a fault. The magnitude and rate of change in this value can be used to detect faults and identify faults. 4.2 Neural-based Nonlinear Observer In this project, the TNFIN will serve as a neural-based non-linear observer. The class of systems to be considered can be described by:

))(),(()(

))(),(()1(kukxgky

kukxfkx=

=+

where x ∈ Rn, u ∈ Rm, y ∈ Rp.

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An alternative representation for the system is as a predictor equation of the following form:

),...,,,,...,,(

)1(......

)1()1(

2121

2

1

mp

n

uuuyyyF

ky

kyky

=

+

++

where: (5)

)(),...,1(),(

)(),...,1(),(

ilkukukuu

jlkykykyy

iiij

jjjj

−−=

−−=∆

∆

The model representation given by (5) has been discussed in [14]. The system identification of a nonlinear system involves training the neural network to approximate the mapping (5). Let the spaces defining the input and output sequences of the system be represented by U ∈ Rr, Y ∈ Rp, and Yin ∈ Rq, where:

=

=

−−−

−−−=

=

−−

−−

−−=

∆

∆

∆

∆

∆

in

delayin

Tpp

delay

Tp

Tmm

m

YU

Z

kYkY

kY

plkykylky

kylkykykY

kykykykY

mlkuku

kulkuku

kulkukukukU

)()(

)(

)](),...,1(),...,2(

),...,1(),1(),...,1([)(

)](),...,(),([)(

)](),...,1(

),(),...,2(),...,1(

),(),1(),...,1(),([)(

2

211

21

22

2111

Let pqr RZNNf →+: be a neural network representing the mapping given by (5). Let )1())1(),1((: +→−+−+ kYlkUlkYNN infl

Let one output iy be measurable for all time k.

L

f

D

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eedings of the 36th Hawaii International Conference on System Sciences (H95-1874-5/03 $17.00 © 2002 IEEE

et ]1[ pC ×=∆

where 1=iC and 0=jC where

ij ≠

A sequence of l observations can be described by the ollowing set of equations:

−+−+∗=

++∗=+∗=+

))1(),1((......

))1(),1(()1())(),(()1(

2

1

lkUlkYNNCy

kUkYNNCkykUkYNNCky

infli

infi

infi

(6)

ue to the forward prediction of the fNN the only

nknowns in (6) is the vector )(kYin . If there exists a nique solution to this set of equations for some value of l e say the system is output observable, and the vector

)(kin can be recovered through a nonlinear LMS lgorithm. The existence of the solution to (6) is ensured if the utput observability condition is satisfied. For a onlinear system the concept of output observability ecomes a function of the system trajectory, and hence, he existence of a solution to (6) changes with time. Even f a solution exists, finding the inverse of the nonlinear MS problem can in many cases result in a very ill onditioned solution. This is undesirable because many mall disturbances, or modeling errors can create large hanges in the estimation of the unknown outputs. An alternative approach to the nonlinear neural etwork observer was presented in [14] where the inverse apping is directly identified through an ANN. This

liminates any physical relation to the real system. hysical insight into the dynamics might make the orward mapping more attractive, especially where the ystem is represented as a combination of a known linear erms and an ANN. Identifying the system dynamics llows any output to be considered the known easurement. This is a desirable feature in fault detection here the dynamic model could be the basis for fault etection in several scenarios.

Newton’s method [14] was implemented to the solve he set of equations (6) as a root finding problem, and was mplemented on a system with very little noise, and small odeling errors. Under these ideal circumstances the

lgorithm converged fast. However, noise in the system, r a poor initial guess had a tendency to make the lgorithm diverge. A more robust approach is the evenberg-Marquard algorithm.

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As a general nonlinear least squares equation solver the Levenberg-Marquard algorithm [14] has demonstrated good performance. The algorithm will always take a step in a descent direction, and will not diverge, but finding an acceptable local minima or the global minimum is dependant on the initial starting point. It is desirable to make the observer perform well in the presence of noise. Equation (6) was first solved without regularization, and then with a regularization term added to make it less sensitive to disturbances.

Let T

infmminf

infin

UYNNCyUYNN

CyUYNNCyYR

)],(),...,,(

),,([)(

2

211

∧∗

∧

∗∧

∗∆∧

∗−

∗−∗−=

qin

m

iini

inT

inin

RY

Yr

YRYRYfimize

∈

=

=

∧

=

∧

∧∧∧

∑1

2)(21

)()(21)(min

(7)

where )(, inYRqm∧

> is nonlinear in inY∧

and

),()( UYNNCyYr infiiini

∧∗

∧∗−=

where ∗iy is the observed value and ),( UYNN infi

∧is

estimated by the model. If a solution exists the global minimum = 0. However, modeling errors and noisy measurements will

often result in a solution > 0. Let ∆∧=inY best estimate of

)(kYin as a starting point. The recursive update of inY∧

then becomes:

)()(])()([ 1in

Tincin

Tinin YRYJIYJYJYY

∧∧−

∧∧∧

+

∧+−= µ (8)

where 0=cµ if

21 ||)()())()((|| in

Tinin

Tinc YRYJYJYJ

∧∧−

∧∧≥δ and

0>cµ otherwise. Here, cδ can change from iteration, and will adjust

the step size, +

∧∧= YY in for the next iteration. The

ceedings of the 36th Hawaii International Conference on System Sciences (695-1874-5/03 $17.00 © 2002 IEEE

solution to the minimization problem (7) represent the mapping:

)()](),...,1(),(),(),...,2(),1([

kYlkUkUkUlkykyky

in

lii

→+++++ ∗∗∗

(9)

Under these noisy conditions, and if the solution is poorly output observable, hence ill conditioned, small deviations in ∗

iy can cause large deviations in the estimate

of inY . To stabilize the solution around possible poorly observable regions, a regularization term was added:, and I is the measured output.

Let ]1[ pD ×=∆

where 0=iD and 1=jD where ij ≠

Let ))1(),1((1 −−∗=∆∗∗∧

kUkYNNDY inf

Let ))(())((∗∗∧∧∆∧

−= YkYkYG inin α , where α is a weighting factor.

Here, ))(( kYG in∧

represent the regularization term. The updated minimization problem becomes:

)])(),([)](),(([21)(min inin

Tininin YGYRYGYRYfimize

∧∧∧∧∧= (10)

The regularization term has the effect of shifting the weight from an observer problem and an inverse mapping to a forward predictor. When the observer problem is ill

conditioned, that is, the jacobian of )()( inT

in YRYR∧∧

is not full rank or poorly conditioned, then there are some

terms of inY∧

that cannot be uniquely solved for, or that are sensitive to noise in the data. In these cases, the regularization term becomes dominant in the

minimization algorithm for the associated terms of inY∧

, and the forward prediction is used instead. 5. Experimental Results Implementation and experimental verification of our research results included the testing of a 10 kVA distribution transformer located in the Power Research Center laboratory (see Figure 2). Non-intrusive sensors were used to measure the temperature on the shell of the transformer, as well as the ambient temperature. Temperature sensors were also placed on the inside of the

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transformer in the mineral oil for verification of the nonlinear observer estimates. By having the actual inside temperature measurements, it was possible to compare the predictions made by the neural network to the actual values. Figure 2: Experimental setup with 10 kVA transformer The TNFIN neural network was then trained and allowed to analyze incoming data. In order to simulate a fault, insulation was wrapped around the transformer in order to abnormally increase its temperature causing an overheating of the transformer. Figure 3 shows how the neural network estimation closely follows the actual values. It also shows how the measured values deviate from the neural network predictions when a fault is encountered. A unique feature of this neuro-fuzzy network is that the network retrains itself to the new faulted state. By doing so, the neural network will be able to detect future failures as well. Likewise, Figure 4 shows how the transformer paprameters change before and after the fault is induced, thus illustrating the network ability as a nonlinear observer. Through this example, it can be seen how the TNFIN neural network can be used to successfully detect failures on a transformer. 6. Portable Diagnostics System With the neuro-fuzzy software developed for the nonlinear system identification, using the TNFIN network, the research progress has focused on field implementation and acquisition of the actual field data. The immediate plans are to develop a portable diagnostic system that would be used to test several large transformers. This portable system would include a sensor package, a data acquisition (DAQ) system, and a lab top computer with the TNFIN neural network. As noted earlier, the TNFIN has previously only been used to diagnose failures on a small 10 kVA distribution transformer. In order for the full potential of the TNFIN

ceedings of the 36th Hawaii International Conference on System Sciences (695-1874-5/03 $17.00 © 2002 IEEE

to be realized, it must receive large amounts of data from several transformers, which have operated under normal conditions and under various failure conditions. This goal can be achieved with the proposed system. 7. Field Testing Field testing and implementation of the proposed system is currently underway on three single-phase, 166 MVA transformers at the Ault Substation (shown in Figure 5) from Figure 3: Measured temperature(dark) versus TNFIN model prediction(light) Figure 4: System estimated parameters before and after failure

0 1 2 3 4 5 6

x 105

0

50

100Wall State

0 1 2 3 4 5 6

x 105

0

50

100Oil State

0 1 2 3 4 5 6

x 105

0

50

100Core State

Time (Seconds)

Te m p e r a t u r e C

Faulted Conditions

Normal Conditions Faulted

Conditions

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Figure 5: 3 single-phase 166MVA transformers to be used for field testing Western Area Power Administration (WAPA) in northern Colorado. These three transformers will serve as a testbed. This test system will consist of three main components. These three components will be essentially the same components that will be implemented on the portable system as well. The sensor package will consist of temperature, current, vibration, and acoustic sensors all externally mounted. These sensors have been found to provide information about the condition of the oil and insulation, the coil clamping pressure, and any faults in the cooling, pumping, or bushing systems [4-6]. 8. Conclusion In summary, the system uses a combination of neural networks and fuzzy logic to determine the state of substation equipment, e.g. a transformer, using only a few external sensor measurements. By training the network to many transformers of varying health state, the system learns a pattern between inputs and outputs and can therefore, once trained, indicate the health of the equipment. Thus, the system is able to prevent unneeded maintenance and can mitigate catastrophic failures that would otherwise go undetected. Furthermore, when the system is trained to all different types and models of transformers, it will be able to determine the trends and behavior of various transformers over their life span. In this way, the system would be able to provide life expectancy predictions, the most coveted piece of information. Acknowledgement This research has been supported by the National Science Foundation and Western Area Power Administration through the CSM-NSF IU/CRC for Power System Engineering Research Center (PSERC).

ceedings of the 36th Hawaii International Conference on System Sciences (695-1874-5/03 $17.00 © 2002 IEEE

References [1] J.S. Cournoyer, A.D. Little, and C. Newton, “A market view of substation on-line monitoring,” EPRI-Substation Equip. Diag.Conf. (1999). [2] V.R. Garcia-Colon, G.A. Nava, and E.R. Pimental, “Maintenance of transformer based on diagnostic testing techniques in Mexico,” EPRI-Substation Diag. Conf. VI 123-132 (1999). [3] S Pensuwan, P.K. Sen, and J.P. Nelson, “Overloading, loss-of-life, and assessment of remaining life expectancy of oil cooled transformers,” Proc. Amer. Power Conf. 62, 249-255 (2000). [4] J.L. Kirtley, Jr., W.H. Hagman, B.C. Lesieutre, M.J. Boyd, E.P. Warren, H.P. Chou, and R.D. Tabors, “Monitoring the health of transformers,” IEEE Comp. Appl. Power 63, 18-23 (1996). [5] “The Western States Power Crisis: Imperatives and Opportunities,” EPRI White Paper, June 25, 2001. [6] B. Ward, “Integrated monitoring and diagnostics: Maintenance ranking and diagnostics algorithms for transformers,” EPRI Tech. Rep. #1001951 (2001). [7] S.A. Ottele, “Parameter estimator and observer design for power transformer diagnostics,” M.S. Thesis, Colorado School of Mines Engineering Dept. (1999). [8] P.S. Szczepaniak, “Fuzzy and genetic approach to diagnosis of power transformers,” Proc. 4th IFAC Symp., 2001. [9] W. Xu, D. Wang, Z. Zhou, and H. Chen, “Application of artificial neural networks combined by genetic algorithm in fault diagnosis of power transformer,” Proc. CSEE (China), 1997. [10] H. Yann-Chang, Y. Hong-Tzer, and H. Ching-Lien, “Developing a new transformer fault diagnosis system through evolutionary fuzzy logic,” IEEE Trans. Power Deliv. (USA), 1997. [11] T. Bi, Y. Ni, and Q. Yang, “Evaluation of artificial intelligent technologies for fault diagnosis in power network,” Dianli Xitong Zidonghue, 2000. [12] C.N. Hadjicostis and G.C. Verghese, “ Power system monitoring using Petri net embeddings,” IEEE Proc: Generation, Transmission and Distribution, 2000. [13] R. Shoureshi, T Fretheim, and T.L. Vincent, “Optimization based fault detection for nonlinear systems,” Proc. Amer. Control Conf., 2001. [14] R. Shoureshi, T. Fretheim, T. Vincent, D. Torgerson, and J. Work, “General approach to non-linear output observer design using neural network models,” Proc. of the Amer. Control Conf., 2000. [15] R. Shoureshi, A. Ottele, D.Torgerson, and J. Work, “Neural network-based adaptive monitoring system for power transformer,” Amer. Soc. Mechanical Engineers, Dynamic Systems Control Div.(Publication) DSC, 1999. [16] T. Fretheim, “Fault detection and non-linear output observer design using neural network models,” Ph.D. Phil. Thesis, Colorado School of Mines Engineering Dept. (2000). [17] R. Shoureshi and Z. Hu, “Tsukamoto-type neural fuzzy inference network,” Proc. Amer. Control Conf., 2000.

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