Real Time Intelligent Signal Validation in Power Plants

Copyright © IFAC Control of Power Plants and Power Systems. Cancun, Mexico, 1995

REAL TIME INTELLIGENT SIGNAL VALIDATION IN POWER PLANTS P.H. Ibargiiellgoytia' L.E. Suear "S. Vadera .. , *lnstitu to df In llf stiga ciones EI ,'c tricas. Dt"jJa7·tamfnto de Electronica. A.F, 475. (C7a·l"1!avaca. :Uor .. 62000, Mexico. E·mai l pibar(fp. iif. org. m:r **lnstitut o Tecnologico y de Estudios SuperioTts de JUonterrry, Campus Mortlos. DIV. de (;mduados , I nllfstigaci6n. A.P. e . .4.'!. {,utrnallaca, ,U or .. 62050. Mexico. E·mai l esucar (~7·s.(170.mo7·.it,sm.mx '''C'nivrrsity oJ SalJord, D'partmfnt oJ Mathematics and {,omputer ScienCl. SaIJn·d. :\1 .5 ;, WT. c.l\' .. E·mail S, VadfralO;mcs.saIJord.ar.uk

Abstract. The validation of data from sensor" has become an important issue in the operation and control of modern power plants, One approach is t o use kllowledl1;e based techniques t o detect inconsistenc ies in measured data. These techniques involve two challenl1;es:. real time performance and the u se of reasoning methods wlder unce rtainty. A prollusin/1; approach to reasoninl( about lU1 certainty is t o use Bayesian or causal networks. On their own. 't hese networks do not :address the problem uf returning an answer in a limited amowlt of time. This article therefore develops an extension of Bayesian networks so that inconsistencies can be detected in real· time. The extensions proposed involve the addition of temporal relationships to Bayesian networks and the adoption of a layered architecture to facilitate any tim, behaviour. Key Words . s~'stenl:S:

Artificial intelligence: Automation: Electric power systems: Real tim" computer

:;e nst)f faill1re~.

human expert employs to solve problems in a specific application domain. Csing t.his knowledge, the system can rt'ason to infpr the solut.ion:" of a problem . The potential power of s u ch syst.ems is that they can replicate expensiVE' or rare human knowledge. The use of knowledge based systems techniques for detecting inconsistencies poses two special challenges: real time performance and the use of reasoning methods under uncertainty, Real time expert systems ll1ake a continuous trade off be· tween performance and prpcision. That is. the syst.ern tnust provide an answer in the t inw required even if it is riot. tl1f' l)pst. answer. Since the information providpd by the enVlron· tnent is considerpd unreliable . rea':'o ning tnethods rnanaging uncprtainty are spacially required in this application. Ofthp diffprf'nt approache:" for managing uncert.ainty (\g alld Ahrarnson. 1990) . hplit>f np(works (also called BaYP:3ian or causalllet\\'orb) i:" a promising llwth od for thi;; application. However . t.he;;e nwthocls do not address the prohlell! of rt>turning an an;;wt>r ill a limit.ed amount of t.inH' , One way of rneeting titne cOll:,traint" is t c, lIse any timf algorithm:,. That i". algorithnls that can be terminated at any time yiPiding SOlIW rf'suIt. possibly with reduced I)fPcision . contidpncf'. or completeness (Strosnider and PauL 1994). Thi" anic le presents the development of an extension of the Bayesian networks methodology in two irnpor-

1. INTRODUCTION In recent years. the generation of energy has faced ilnp orr.ant problems t.hat. necessitat.e the modernization of current installations. principally in both t.he instrumentation and control systems. A current strategy in the modernization of plants is the use of int.elligent systems in several aspects of the process operation . For example: intelligent ('untrol =-ysterns that maintain the plant in opt.iIllal conditions: int.elligent systems that. manage the high number of variables and alarms that the ope rator must consider: knowl edge based systerns t hat validate the information prov ided by sensors. Even with the most :"opbisticated instrumpnts and control sY:3terns, a decision baspcl on faulty data could lead t.o disast.er. The validation of dat.a from spnsors has therefore become an import ant. is:3uf' in d1P operation and control of modern POWPf plant:'<, The da,:,sical approach ha.« l)E'en to apply rnathelllatical rnodellin}!.;. i.p .. nurnerical colnput.ations that rUll alongside t-lw cuntrol syst.el11. The lflain problems \\' ith thi:; approach haw hef'n the difficu lty of building a cornplete lnodel. and tlw COlT!put.ational cost for "il11ulating the model. An alternatiw approach i:3 to usp knowledge based techniques to dptect inconsistf'ncies in mea..;;urpd data (Henry, 1994) . Knowledge based systems can bp defined as t.he techniques utilized to rnodel the knowlpdge that a 141

from the expected values given the design working point of the turbine. Conversely. an experienced operator is capable of det.ect.ing such deviat.ions of a variable by direct. observation of the related variables and consequently, avoids false plant trips . Since the blade path temperature is the 11IOSt critical variable. it is obtained through sixteen t.hermocouple sensors all around the turbine. from these sixteen , three sets of averages are taken by analog circuitry. These values are then processed in order to inform the operator a unique value of temperature and it it; also used in control st.rat.egy to protect the engine . Table 1 shows a list of variables that are related to the measure of the teIllperature and t.ht:' namt:' a,.,signed. The value" that. these variabl"" call t.ake are obt.ained by discretizing t.he full rangp of pt:'rmitted values . The nurnher of different. discret.e values vary depending on the precision r!"quired for each variable .

tant aspects. First. the knowledge representation of the dependency relations between the different sip;nals. including t.ime relat.ions . which is structu'red as a set. o'f c.ausal networks. one for each variable. Second. the inclusion of any tunc algori thrns in order t.o perform real time reasoning.

L. THE CASE STt:rry: A C;AS TCRBI~E

In order to demonstrat.e the ideas contained in this article , a module of a combined cyde power plant was chosen: the gas turbine. Figure 1 shows a simplified schema of the gas turbines at. Dus Bocas and Gome:: Palaria power plant.s in Mexico. air entrance

generator

Table 1

Variables in the example

process variable Selected blade path temperature Blade path temp. group 1 avg. Blade path temp. group :2 avg. Blade path temp. group :3 avg. Flow of gas Flow of air Gas fuel pressure supply Real fuel valve position Real inlet. guide vane position Position demand of fuel valve Position demand of inlet guide vanes

Fig. 1. Simplified schema of a gas turbine.

A (;as turbine consists fundamentally of four main parts: the compressor, the combustion chamber, the t.urbine itself and the generator. The compressor feeds air t.o the combustion c.hamber, where t.h!" gas is also fed. Here. the combustion produces high pressure gases at high temperature. The expansion of these gases in the turbine produces the turbine rotation with a torque that is transmit.ted t.o the generator in order to produce electric power output. The air is regulated by means of the mlet guide vanes (I(;V) of the compressor. and a control val VP does t.he sam!" for t.he gas fuel in t.he combustion charnb!"r. Th!" control valve is commanded by the control system or by t.he opprat.or in manual operation mode. and it.s aperture can be read by a position sensor. The temperature at the blade path , which is the most critical variable. is taken along the circumference of t.he turbine. Additionally. the temperature of the exhaust gases is a very important variable for the security and maximum efficiency of the turbine operat.ion. Other important. variables. measured directly through sensors are the mega watts gen!"rat.ed and the t.urbine speed in revolutions per minute. Among all variables that. part.icipate in the gas turbine. only a fe\\" are directly measured by the sensors. However. instrurnent.ation can be subject t.o decalibrat.ion. so t.he quantit.ative informat.ion supplied is not always reliable. l ~ sually. thp control system can not. detect significant deviations

name T t1 t:2 t.:3 fl £'2 ps pr pa dp da

Given such an applicat.ion , t.he problem is how t.o develop a system t.hat. is able to det.ect in valid readings in a real timt:' sit-uat.ion . First. ;;t:'ctiun :3 ignores the tirlJe const.raint." and eXalllillt:':' t ht:' "irnplified problern of deteC't.ing invalid reading::; by using t.echniques for handling uncertainty. St:'ctions 4 and .'i then examine how to consider tirne in two aspects: as the cause of changes in the state of the variables. and as a constraint in the execution of the program. Section 6 then incorporat.es time constraints into causal networks

:3. CATSAL \ETV\'ORKS A causal network (C\) is a graphical reprpsentat ion of dependencies for probabilistic reasoning in expert systems (Pearl. 1988). Figure 2 depict.s a simplified causal network representation of the variables. and t.heirrelat.ionships. for t.he gas turbint:' example. In a C\. each node represent.s a discrete random variable and each arc a probabilistic dependency. The variable at the end of a 142

link is dependent on the variable at its origin, e.g. ps is dependent on fl in the eN of Fig. :2. The graph in Fig. :2 can be taken as representing the joint probability distribution of the variables T. fl.····daas: P(T.fl.f2,p$.pr.pa.dp.dal P(rip P(ps

bility theory, based on the application of Bayesian calculus and the derendencies represented in the network. For example . in the eN in Fig. '2. if fl and f2 are measured and T is unknown , their effect can be propagated to obtain the posterior probability of T given fl and f'2. The simplified probabilistic model in Fig. '2 ha.;; a tree structure. i.e. each node has only one iucoming link or one parent. For singly connect.ed net.works , such as t.rees. there is an efficient. algorithm for probability propagation (Pearl. 1g86). It consists on propagating the effects of the inst.antiated variables through the links, and combining them in each unknown variable. This can be done by local operations and a message pa.:;sing rnechanism, in a time which is linearly proportional to the diameter of the network. A more complete causal network representation of the turbine variables is given in Fig. ;3. In this case, the three blade path temperatures and their dependencies are also considered. This network has a more complex struct.ure, i.e. it is a multiply connected network. For multiply connected networks there are alternative techniques for probability propagation', such as clustering , conditioning. and stochastic simulation (Pearl , H)88). These algorithms are less efficient t.hat the one for singly connected networks. and in the worst ca.;;e , their time complexity is exponential. So. in general , t.he time required for probabilistic reasoning lllcreases in proportion to the complexity of t.hf' net.work.

= P(da i pa)

i pr)P(pa ! fl.)P(pr I jl)

I fJ )p(fJ i T}f'(fJ I T}P(T}

(1)

Equation (1) is obtained by applying the chain rule and using the dependency information represented in the network.

(T ( f1 \

/ .1'. /

>- \

',

' PS )

" /

\ :., "

( pr \

,<\ -'

\

\

(dp)

Fig. 1. Example of a tree representing the causal relation between variables of the gas turbine.

The topology of aCN gives direct. inforrrlat.ion about t.hf' dependency relationships between t.lw variables involved. In partic.ular , it represents which variables are conditionally independent given another variable. By definition, X is conditionally independent of Y , given Z. if:

P(X

I y, Z)

= P(X I Z)

(:2) /

This is represented graphically by node Z "separating" X from Y in t.he network. In general , Z will be a subset of nodes from the network that if removed will make the subsets of nodes X and }' disconnected. For example , in the C;\ of Fig. :2. {T} is conditionally independent of {ps, pr. dp} given {J I}. To completly specify a (':'-I. the conditional probability of each node given its parents. and the prior probability of the root nodes. are required. That is the terms in equation 1 for the f'xample . (;iven a knowledge hase represented as a prohabilistic network . it can be used to reason about tlH" consequences of specific input dat.a, by what is called probabilistic 7·rasoning. This consist.s in instantiating the input variables. and propagating their effect through the net.work t.o update the probabili ty of t.he hypothesis variables. In contrast with previous approaches. the updating of the cert.ainty rneasures is consistent with proba-

Fi~.

:3. Example of a multiple connected network representing the causal relation between variable;; of the 1i\as turbine.

Causal networks can be used t.o represent the dependency relations between the measurelllents, and obtain their posterior probabilities giVf'1l Ilew evidence. Next. the descript.ion of how they can be used in an algorithln for detecting invalid readings is gi veIl. The algori thin assumes that. there is a checklist consist.ing of t.he variables to be validated and that. the causal networks for each variable are already defined in the knowledge base. 143

The algorithm can be expressed as follows: I. Select. the variable t.o Lw validated. 2. Read the valup of thE' variablE' providE'd by thE' SE'llSor. :). Read the valup of all variables that appear in t.he network of the selected variable. 4. Propagate the probabilities and obtain the posterior probabilit.y of the value t.hat t.he selected variable has . .1. If the probability (obtained in step 4) of the value acquired in step 2 is lower than a specified value. activate an alarm. 6. Repeat this process for all the variables in the checking list

ti-1

ti

(a)

ti+1 (b)

Fig. 4. Causal relations between a variable at different times.

The model represellt.ing dIP knowlpdge about the blade path ternpE'rat.ure is showll in Fig . :). Howe\'Pr , t.his nJOdel neE'ds to consider the history of the variable. and thE' inclusion of techniques to eXE'cuk this algorithm in rE'al t.ime. The next sect.ions discuss these t.opics.

hibit a real tirrJf' behaviollr. This ability i" lwcoming increasingly critical in syst.ems that lllonitor and control complex processes. In this cont.ext , real time can be defined as a existence of strict time limit by which the system must have produced a response. Consequently. the system must produce the response when it is needed . even if it is not the best answer. In many cases. an approximate result , that is on time , is preferred over a precise numerical response that is too late. This section introduces the analysis of the trade off between performance and precision that can be applied in the probabilistic inference process of causal networks. Among several mechanisms proposed for real tirne reasoning (e .g ., see (Strosnider and Paul , 1994)). any time algorithms represent an appropriate mechanism to obtain a guaranteed response time . This kind of algorithm returns some answer for any allocation of computation time and is expected t.o return better answers when given Inore time. In fact, any time algorithms appear more to be an attempt to define a property than a "lwcific technique. Therefore. an implementation of an algorithm, in order to be considered any tl1I1C must possesses the following charact.eristics (Dean and Boddy. 1980):

4. TE:vtPORAL I:'-lFORMATION An important characteristic of this application dorllain is its dynamic behaviour - i.e .. t.llt:' information provided by the sensors changes constantly as the pown generation process procE'eds. This signifies that. besides the analysis of the dependency relationships between the variables , one needs to consider the history of the variable with respect to time. The approach followed in this article considers that. the value of a variable at time li-l causes the value of the same variablE' at time ti. with an analogous relation between I; and li+ 1. This can be represE'nted graphically by the network in Fig. 4a. For E'val uating t.he lletwork at timE' t; . t.o consider only the nodE' for thE' variable at time li_ l (V') is rE'quired . obt.aining thE' st.ructurE' shown in Fig. 4b. This simplification is basE'd on t.he a,,sumption that knowing the value of thE> variable at I, _ ! makes t.h e previous observations irrelE'vant. for V at t ; . Thus. thE' posterior probabilities for a variable in the previolls sample will influence the prior probability of the corresponding variable in the current sample (Sucar and C;illies. 1994). For example. if a temperature reading changes drastically between one sample and the next. it indicat.es t.hat the sensor suffered an obvious damage . In a like mannE'r. IE'sS drastic changes in the variables can be detected with this t.ime reasoning together with the relation with the rest of the variables

1. it can be suspended and resUlned with negli-

gible overhead. :2. it can be terminated at any t.ime and will return sorne answer. :). the answers returned improve in some well behaved manner as a function of time. One of the difficulties in irnplerIJf'nting any Lw/( algori thms is generating performance profiles. i.E' .. this approach requires an initial solut.ion whoo:
5. REAL TIME PERFORMAi"CE The utilization of artificial intelligence in domains that involve industrial applications requires to ex144

sion. and 4. exponential precision.

Finally, the fourth level considers the most. complicated model obt.ained with the complete network (Fig. :3) and time dependencies. This model considers all the information pa.st and present but. obviously is the most time consuming. In t.hi" case. probabilistic inference is performed with the algorithm for multiply connected networks.

(1)

(3)

(2)

(4)

Fig ..'i. Exa.mples of performance profiles.

\Yith class (1) algorithms , the system produces no answer until time r, and then produces the answer with a bounded precision. Class (2) produces an answer with increasing precision. In class (:3), the system produces a linear improvement until time 1'. and then produces the answer with a bounded precision. Finally, class (4) algorithms present an exponential , bounded, precision function of time . Clearly, all performance profiles classes are special case of a superclass that can be defined as monotonic improvement, i.e. , the accuracy of its intermediate results is non decreasing as more time is spent. t.o produce the result.

Fig. 6. Different probabilistic inference methods.

f1 6. REAL TIME INFEREl\'CE IN CACSAL l\ETWORKS

!

( f2

i

Fig. I. Reduced tree.

(;iven the different methods for making probabilistic inference in causal networks described in section :3. and the explanation of t.he characteristics of any timf algorithms, the problem now is to propose a mechanism that will carry out probabilistic inferences within a guarant.eed response t.ime. Figure 6 describes four progressive levels of inference. In the first level, it responds with the information provided by the expert (or data) in the form of prior probabilities. This corresponds to making a question to an expert. and expecting the answer immediately. The second level corresponds to the decomposition of the network into trees. where the probabilist.ic inference is perforrned by t he propagation algori thin proposed by Pearl (1986) . This is achieved by eliminating some dependencies in the network for the relations which are less important. (Fig. I). This is equivalent to the reasoning process t.hat. a human expert. realizes under t.irne pressure. i.e .. he/she takes the 11Iost irnportant dat.a into consideration to provide all answer. The third level considers t.llt:' same rnodel as lewl two but indudes t.ime dependencies as descri bed in senion 4 (Fig. 8).

T '

Fig. 8. Reduced tree considering time dependencies.

Based on the progressive levels of reasoning illustrat.ed in Fig. 6. the performance profile shown in Fig. 9 is obtained . At time U. the algorithln provides an answer \vith the lowest degree of precision: the prior probabilit.ies (the time ronsidered to provide them is negligible). At. time t 1 • the syst.em had the time to calculate posterior probabilities in the reduced tree. i.e .. t.he algorithm needed a t 1 t.ime t.o conclude the propagat.ion. Aft.er t 1 and if t.here is time rernaining. the systern initiat.f'S t.he propagation algorit.hnl for t.he model with time dependencies. which \\'ill t.ake the time from 11 t.o 12 to provide the answer . Finally. t.ht> rnax145

duee the eause of the sensor deviation and suggest a correetive action. This will permit. in thf> future. t.o provide a complete intelligent validat.ion and diagnosis system. The final st.age in t.hp project will be the adaptat.ion of t.he syst.enl to intPlligent sensors that are linked t.hrough a fidd bll~.

Imum preCisIOn will be reached until t3 and will remain stable f>ven if there is more t.ime available.

K. ACK\O\"'LEDC;ErvlEl\TS

t1

t2

Special thanks to Miguel Angel Delgadillo and Gerardo Torres Toledano , the experts in gas turbines, who provided all the information about the application in this research project . This research is supported by a grant from CONACYT and liE under the In-House IIE/SALFORD/CO\A( :YT doctoral programme.

t3

Pi!!,. 9. Performance profile of the proposed reasonin!!, process.

Ome that the performance profile is obtained , the following analysis can be ac("omplished:

9. R-EFERE!\CES

1. The probabilistic inference process is preemptive , i.e., it can be suspended and resumed without affecting the computation. :2. The probabilistic inference process can be terminated at any time and will return an answer, i.e., thf> result of the last finished reasoning level. :). The probabilisti(" inferen("e process improve its precision as a function of time , as shown in Fig. 9

Dean, T ., and M. Boddy (1988). An analysis of time dependent planning. In: Proc. Seventh Nail. Conf. on A I. Henry, M. (1994). Validating data from smart sensors. Control engineering 41 , (j:)-(j(j. Maiocchi . R., and B. Pernici (1991). Ternporal data managernent systfrns: a cornparatiw view. IEEE transactIOns on knowledge and data engincenng 3 , ·')04-.'):24.

Ng, K .C., and B. Abramson (1990). Uncertainty management in expert. syst.ems. IEEE Expert 5 , 29-48. Pearl , .J. (1986). On evidential reasoning in a hierarchy of hypothesis. Artificial intelligena 28 , 9-1.,). Pearl , .1. (1988). Probabih5tic reasoning in intelligent systems. Morgan Kaufmann . Palo Alto. CaliL U 5.A. Strosnider . .J .K. and C..J. Paul (1994). A structured view of reai time problem solving. AI magazine pp . 4·')-66. Sucar. L.E .. and D.F. C;illies (1994). Probabilistic reasoning in high level vision .. Jmagf and nSIOll

7. CONCLUSIONS AND FUTURE WORK

Sensor validation in power plants involves solving the problem of managing uncertainty in real time. The technique presented in this article proposes the use of causal networks to ("apture the knowledge that an experienced operator possesses about the relationships between variables and the history of the signals examined. Probabilisti(" inferenn:' is then performed in several progressive levels in order to guarant.ee a response time, which is essential in real time applicat.ions. Improvements to this technique include a study of different me("hanisms for time reasoning that allow longer periods of the hist.ory of a variable to be considered (Maio("chi and Pernici. 1991). Also. an ernpiri("al evaluation of the approach is needed to determine the performance profile along both. the time and t.he precision axis. \Vith this information. different levels of probabilistic inference Inay be detf>ct.ed in order to Inake better predictions with lower comput.ation times. This research project includes the ("onstrun.ion of a prototype in a real time platform. In the first stage , the system will be installed in the control room of a therrnoele("tric power plant. with an interface that reports an alarm when a deviation of t he normal flow of signals is detected. The second stage will be to design a higher level knowledge base for interpreting these alarms , which can de-

computing 12 , 4:2-60.

146