Network Restoration Expert System

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IF .-\C Power S"t e llls and

Power Plant Control. Seoul. l\.ort'.1. I ~I H ~

NETWORK RESTORATION EXPERT SYSTEM G. Krost and D. Rumpel E/~(l ri( PlIll'n

Snln//., ll/.\Iilul~, DlIisb/llg L·llil'I'nif\·, Dui.,lllllg, FRC;

Abstract. Despite preventive measures, system breakdowns cannot entirely be avoided . In the attempt to mlnlmlze the time of outage, the use of intelligent restoration strategies adapted to the special network and breakdown case, plays an important role. For that aim, an expert system will provide a reasonable tool in finding the appropriate proceee4ngs. This tool in principle can be used in real time operation as well as in training the operational staff . The paper describes experiences gained with a (presently) stand-alone expert system for network restoration and its extension to obtain a training simulator for restorative strategies. Keywords. Black out; restoration; expert systems; database management systems; training simulator. INTOODUCI'ION

An

analysis of several severe black-outs which happened during the last 12 years all over the wor Id proved that most of them could not have been forecasted and avoided by the use or-preventive contingency evaluation, because - there were already present, but unknown defects or missettings (hidden contingencies) in the network which caused second and third failures after the first incident, overruling (n-1)-security; the critical situation was known, but the reqired reserve capacity was lacking. Regarding these facts and also account that after the breakdown

taking

into

- the economic damage increases over-proportionally with time ; - most of the network components are available, but extremely disorganized the aim of minimizing restoration time turns up. For that aim an expert system will provide a reasonable and flexible tool. The advantages of using an expert system are - accumulation of the knowledge of several experienced operators. This is especially interesting in developed countries, where breakdowns seldomly occur; - heuristic proceeding in case of vague information keeping up the ability to act; - inclusion of experience gained from analytic computation; - avoidance of the "combinatorial explosion" in the computation; - transparency of reasoning which is especially important in training facilities.

An expert system for power system restoration has been developed at Duisburg University; the present state of which is a stand alone dialog version with powerful explanatory capabilities. Present work is to extend this version to an

"intelligent" training system for restoration strategies by direct coupling to a real time network database. KNOWLEDGE BASE

The expert system's knowledge is based on the restoration strategy plans of two german utilities and on simulation studies which were performed with a dynamic network model (Vorbach et al.,1988). This information has been condensed to a consistent set of about 90 "if. .. then"-rules and about 80 switching advices stored in the knowledge base of the expert system in form of prolog clauses. The rules are predominantly related to effects of the system fault (such as actual frequency, actual power, load shedding, accumulation of fault indications) and to actual states of system devic es or groups of them (breakers, power stations, tie lines etc.). The advices describe single switching operations or sequenc es of them. The set of rules is classified into two types: general rules being valid for any electric power system; specific rules refering to the actual network only . Setting up the rules we tried to substitute specific rules by general ones with subordered specific facts in order to keep the rule base universal and adaptive. (Example: "If then begin restoration with hydro plant P." ==) "If then begin restoration wi th hydro plants." and "Power plant P is a hydro plant.") Besides the "direct" rules the knowledge base also contains about 20 "indirect" rules describing the interlocking or the preference of certain direct rules corresponding to the actual system situation.

G. Krost and D. Rumpel

480

INFERENCE AND EXPLANATION cx:tiroNENTS

The inference engine of the expert system a prolog program consisting of more than 240 statements - controls the linkage of rules, facts and input data; it pennits the choice of forward and backward chaining of rules and provides the ability to handle vague input information. Additionally the inference engine contains a powerful explanation component which, on request, gives the reasons for the expert system's questions, helps answering them by presentation of background information, describes the expert system's decisions and explains why certain rules have not been taken into consideration. Furthermore, it infonns about the actual state of the expert system's dynamic database consisting of input data and results gained by conclusions drawn in between. The natural language explanatory output of the expert system is obtained by using implicit text structures, the notions of which are constituent parts of the rules themselves. Thus, also the rules are expressed in an understandable and transparent fonn. An easy maintenance of the knowledge base is achieved, because there is no necessity for additional text storage. STAND ALONE EXPERT SYSTEM

In the present state the network restoration expert system gathers all input information by user's dialog. The operational advices are presented on the screen in fonn of alphanumeric texts. Structurally the expert system proceeds as follows (Fig.l):

This concept results in an adaptive dialog controlled by the already acquired information, limiting the number of questions to those closing-in on the actual fault status. Schematic patterns of questioning are avoided. Nevertheless, the number of remaining questions (8 to 25 for the diagnostic part and further 10-20 including advices for each restoration strategy type) would still put an unreasonable demand on the operator in case of a real application. The fault type diagnosis - as far as conclusive results in the selection of a general restoration strategy for the actual fault situation. These strategies consist of dedicated sequences of switching operations which in their turn are modified by the actual states of the system components (breakers, power plants, tie lines etc.). In the present state of the expert system, these state-informations again are gathered by user's dialog. The particular steps are proposed on the screen and may be confirmed or revoked by the user. The stand alone version is suitable to study the performance of the expert system in general, to improve the knowledge base and to demonstrate its facilities in feasibility studies based on practical examples. It proves that its structure is appropriate in principle and that the response time required for the computations in total does not exceed a few seconds an a workstation. From this point of view, there seems to be no problem for real time application.

generation of suspicions

In order to identify the actual fault situation, as a first step, the expert system asks for a few signficant symptoms (such as violation of boundaries in frequency and load or local accumulation of alanns); evaluation of these answers arranges an order of suspected preclassified "fault types". Regarding this order, the expert system tries to verify the occurence of a suspected fault type, by checking further expected symptoms in more detail (load shedding, tripping of lines or power plants etc.). In case of a non-fulfilment of the suspicion, the next probable fault type is checked until a close-in is achieved.

order of assumptions

The proceeding in phase three is a first step to handle vague information and may be refined by assigning "confidence factors" to rules and input information. Doing so, the confidence can be evaluated arithmetically and the resolution of rules can be broken off, if the confidence drops below a set limit.

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fault type verification

The check is performed in three phases: During the first phase the expert system tries to conclude from existing information contained in its actual dynamic database; in case it does not succeed, during the second phase it tries to obtain missing information by user's dialog; if this information is negative or insufficient ("don't know"), the next fault type is tested. If the fault types run out, during a third phase the expert system sets the information collected as "unknown" to a preselected default value. If a result can be gained under these conditions, the output information is relativated by words like "probably", "possibly" e.g ..

evident symptoms

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selection of restoration strategy

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predominantly substituable by database query

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diagnosis of sys tern s ta te

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deduction of f-restoration steps

Fig.

effects of di sturbance

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status of system devices

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Structural flow chart of the stand alone restoration expert system

'\et\,'ork Restoration Expert S\'stCIll RESTORATION TRAINING SIMULATOR

DATABASE COUPLING

Practical application, as a matter of course, is hampered by the tiresome and time consuming dialog. The major part of the answers can be derived from a power system database. Thus, a coupling of the expert system to such a database is the logical next step. The information required by the expert and presently acquired by questioning, classified as follows:

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system, can be

1.)

Questions on the state of single pieces of equipment (breakers, synchronizing equipment etc.) . 2. ) Questions on the global state of functional groups. These functional groups usually cent~r on one piece of equipment (transformer, lIne, power plant), but also comprise stateinformations of the substructure making this piece of equipment operable (e.g. bays attached to the transformer, cooling units etc.) . 3. ) Questions on the global state of the network ("Is the network broken in multiple islands ? "). 4. ) Questions on the performance of the network in the past time ("Were there major frequency swings ?"). 5. ) Questions on the state and performance of resources outside of the network under regard ("How much power c an be imported via interconnection X ?"). Class is the type of information readily retrieved from power system databases. unfortunately, only a minority of the expert system's questions are for this type of information, and an explosive increment of network-specific rules would result if one intended to base an expert system on this level. Class 2, naturally, is the level of most questions of the expert system. In bridging the gap between class 1 and class 2 data, the concept of a "network-component" proposed by Tietze (1987) may become a useful tool (Rumpel, 1987). Class 3-questions require an interface to analvtical programs for evaluation of the answer. . Class 4-information is not present in the data base at all. To procure it, an access from the expert system to the event-bookkeeping, probably further auxiliary programs tracking continous processes and preparing and storing answers for expected questions, are required. Class 5 comprises a small number of questions left over to the dialog system in any case. This classification of questions and information presupposes an expert system comprising general rules as well as specific rules prepared for the special network under study. The intricate task remains, to derive and prepare the special rules using a set of metarules inquiring into any given network description filled into the database. Further, a stop-down of the expertise, in case when expected information is not available in the database, has to be avoided. For that case a default path using the dialog is foreseen.

Present work, in order not to aim at all goals at the same time, is aligned to an expert system based training simulator for network restoration. The expert system's knowledge base contains the set of rules representing the diagnostic part and the restoration strategy plans. It is coupled to a database of the grid data language type (Rumpel, Zaluk, Post, 1987) mapping the stock of objects and ~e potential topology of the network in operator's terminology. The actual system states are initialized and changed using a query language developed for this database (Rumpel, Post, Zaluk, 1988). The information is also indicated on a graphic screen. This part of the software utilizes already existent programs. The hardware of the training system is based on two workstations, being coupled by L~, fig.2. The database and the graphic representation of the network are implemented on workstation 1, which is also equipped with a functional keyboard consisting of a digipad bearing a network specific keyboard sheet. This sheet is automatically plotted by evaluation of the database contents (Rumpel, Post, Zaluk, 1988). State inputs and questions to the database can be formulated using the query language. Workstation 2 houses the existing expert system with knowledge base and dynamic database, and is equipped with an alphanumeric keyboard. The user's communication takes place via the functional keyboard of workstation 1 (for the network operation part) and (for the inquiries of and answers to the expert system) via the alphanumeric keyboard of workstation 2. Beginning from an initial state entered in the database the trainee autodidactically performs the restoration strategy. Two operational modes can be applied in doing so: a)

The expert system proposes the strategy and delivers corresponding sequences of switching operations; the trainee has the opportunity to ask for the reasons of the expert system's decisions b~- making use of the explanatory component.

b)

The trainee restores the system by his own considerations; only in cases of heavy faults the eA~rt system inte rvenes in the procedure.

Presently being developed is the expert system interface to the database. The tasks of this interface are to transform expert system questions into statements of the query language; to handle the queries as external fortran routines called from the expert system written in prolog; to transform and retransfer the answers of the database to the expert system; to fulfill further special requirements resulting from the question classes (see above) ; to check the database for spontaneous state changes; to fulfill the control beween ex~rt system and database during the data exchange processes.

(;. Krost alld D. RUlIlpel

EXPERT SYSTEM

NETWORK DATA MANAGEMENT

network description

workstation 1

workstation 2

dynamic database

knowledge base

network database

query

activation

comments of expert system

alphanumeric keyboard

D Fig. 2

functional keyboard

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data

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single-line diagram echo-line--

programs / routines

Restoration strategy training system

CONCLUSIOI\ Knowledge based systems represent a new technique to give help in operating electric power systems. Especially in the actual case of a disturbance, a breakdown and in restoration, analytical tools alone are of very restricted usefulness. Here the application of expert systems seems to open a way to handle complex facts in a straightforward manner in order to reach feasible results. On the other hand, nontrivial logical implications ensue from the requirement to achieve a solution in reasonable time (Liu, Tomsovic, Zhang, 1987) and to keep the flexibility of the eA~rt system for application with different networks. ACKNOWLEDGEMENT

The authors wish to express their gratitude to the German Research Council (DFG) and the State Ministry for Science and Research of NordrheinWestfalen (MWF) for their financial support of the expert system (DFG) and database (MWF) research. Further thanks have to be given to the utilities Bayernwerk and TWS Stuttgart for their cooperation in the development.

REFERENCES Liu,

C.C., Tomsovic, K., Zhang, S. (1987). Efficency of eA~rt systems as on-line operation aids. Proe. 9th PSCC, Cascais 1987 Rumpel D. (1987). Das Konzept der Netzkomponente in BetriebsfUhrung und Datenaufbau. Elektrizitatswirtschaft 86(1987) pp. 971-973 Rumpel, D., Zaluk, R., Post, U. (1987). Concept of an on-line data base supporting grid data language. Proe. 9th PSCC, Cascais 1987 Rumpel, D., Post, U. and Zaluk, R. (1988). Application of a grid data language for power system database definition and query. IFAC-Symposium Power Systems, Bruxelles 1988, paper 2.3.1 Tietze, E.-G. (1987). Die Bedeutung der Netzkomponenten in der zentralen NetzfUhrung. Elektrizitatswirtschaft 86(1987) pp. 210-217 Vorbach, A. et al. (1988). Power system restoration - Methods and model-simulations. IFAC-Symposium Power Systems, Bruxelles 1988, paper 9.4.1