KEURP. Topeka, Kansas. The Kansas Electric Utilities Research Program (KEURP) is a unique joint venture between six electric utilities in the state of Kansas to ...
the OP will be declared to belong to the class of this node. Note that although extremely straightforward, the above way of using a DT is not unique [4]. The proposed 11 method has been applied to a 14-machine system. DTs relative to a given disturbance have been constructed in three different classifications, corresponding to 2-class (where the OPs are merely labelled stable or unstable) 3-class and 4-class patterns, the latter providing a more refined stability assessment. Among the many results obtained, we briefly observe that: * .he complexity of the DTs in terms of number of nodes and hierarchical levels is overall rather low; for the 2-, 3and 4-class DTs constructed, the total number of nodes is respectively 15, 21 and 27; * the reliability is very satisfactory; moreover the larger the number of classes the better the stability appraisal; note that increasing the number of classes scarcely affects the overall computing time, which anyhow is spent offline; * hence, from the user's point of view the multi-class DTs should be preferred: they ensure very good reliability, without affecting on-line computational performances. Although at an early stage of its development, the 11 method proposed in this paper provides extremely encouraging results. Overall, the artificial intelligence methodology we propose for the transient stability analysis and preventive control of power systems, differs in many respects from other approaches. From the artificial intelligence point of view, it relies much on expertise acquired via numerical methods appropriately exploited, unlike other logic- or heuristic-based methods. From the domain application standpoint, of particular interest is the possibility offered by our method to combine freely the generally contradictory objectives of arbitrary modelling sophistication and of on-line performances. Another essential merit of the method is to replace the "black box" type approaches traditionally used in transient stability studies, by a "transparent box": the DT, which relates explicitly the characteristics of the power system and its stability. Hence, the very electrical process can be followed and better understood. In turn, for the first time, this paves the way towards transient stability control. Discussers: M. A. Pai, S. S. Ahmed, C. Liu, S. Wang, A. Debs, E. Nascimento and B. J. Cory
88 SM 727-0 May 1989
A Method for Identifying Weak Nodes in Nonconvergent Load Flows M. Dehnel and H. W. Dommel, Fellow, IEEE
This paper presents the "Weak Node Method" for identifying weak areas in power system scenarios for which the Newton-Raphson load flow method does not converge. Nonconvergent load flows often present program users with the task of implementing corrective measures with little or no information as to the source of nonconvergence, especially for cases which diverge outright. For slow-converging load flows modifications of the basic Newton-Raphson load flow method [11, better initial guess estimates [21, and modifications in the data specification [31 have been used to achieve convergence. For divergent cases, the Weak Node Method provides meaningful trouble indicators which enable load flow program users to more easily identify problem areas, as well as the nature of the weakness (active or reactive power problem). The Weak Node Method requires a converged minimum mismatch solution [41 for load flows that would normally diverge. This is obtained by a damped Newton-Raphson method which calculates a damping multiplier Xmin to be used in arriving at the next iteration's approximate solution i+ 1x+XminA
where x is the approximate solution to the nonlinear load flow equations, Ax is the Newton correction vector, and i is the iteration count. Xmin is found in such a way that a mismatch function is minimized in the direction of A' from xi [5]. This mismatch function is defined as f=
where Pk and qk
are
the mismatches
Pk=Re
qk=
-I
(1p2p+Zq2q)1/2
m
{
V {
Vg
Ykm Vm }
Ykm
Vm }
Ppecified
Qspecified
Xmin approaches zero as the minimum of the mismatch function is reached. It can therefore be used to indicate that the minimum mismatch solution has been found. The Weak Node Method processes the information at the minimum mismatch solution in three procedures:
References [11 J. R. Quinlan, "Induction of decision trees," Machine Learning, Vol. 1, No. 1, pp. 81-106, 1986. [21 L. Wehenkel, Th. Van Cutsem, M. Ribbens-Pavella, 1 ) List the highest and lowest voltage magnitudes VkI. "Artificial intelligence applied to on-line transient stability 2) List the largest active (Pk) and reactive (qk) power assessment of electric power systems," Proc. of the mismatches. 10th IFAC World Congress, pp. 308-313, Munich, July 3) Obtain sensitivity vectors of the form AX/AUk [61 with 1987. the largest Euclidean norm, where the scalar uk is either [31 L. Wehenkel, Th. Van Cutsem, M. Ribbens-Pavella, the specified active (pspecified) or reactive (Qspecified) "Inductive Inference applied to on-line transient stability power at node k. assessment of electric power systems," Submitted for publication on AUTOMATICA, 1988. (41 L. Wehenkel, Th. Van Cutsem, M. Ribbens-Pavella, The nodes k which are associated with the quantities listed in "Decision trees applied to on-line transient stability procedures 1 to 3 are the weak nodes of the nonconvergent assessment of power systems," IEEE Int. Symp. on scenario. To test the Weak Node Method, a weak node or a number Circuits and Systems, Helsinki, Finland, June 1988. of weak nodes were artificially created in power system scenarios to see whether they could be identified. This was achieved by increasing specified active or reactive power loads at a single node or a number of nodes until the NewtonRaphson method no longer converged. At this point, the minimum mismatch solution vector was obtained. In each the Weak Node Method identified the altered specified loads as being the weak nodes of the nonconvergent system. The Weak Node Method was used to analyze an actual nonconvergent scenario from industry. This scenario was provided by West Kootenay Power and Light Co., Trail, B.C. Canada. The Weak Node Method successfully identified the case
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IEEE Power Engineering Engineering Review, Review, May May 1989
area of this scenario which engineers familiar with the port or extra generation. The Weak Node Method is still unrefined in some respects, and further research should be performed to determine whether the method can be expanded to analyze more information at the minimum mismatch solution. As well, the method may be capable of suggesting corrective measures that would enable nonconvergent load flows to converge. In conclusion, the Weak Node Method identifies weak nodes in power system scenarios which make the NewtonRaphson Method nonconvergent. This information enables engineers to quickly pinpoint the area where system modifications or data corrections should be made. Discusser: S. Iwamoto
References [11 S. C. Tripathy, G. D. Prasad, 0. P. Malik and G. S. Hope, "Load-flow Solutions for III-Conditioned Power Systems by a Newton-Like Method", IEEE Transactions on Power
[21 [3]
[41 [51 [61
Apparatus and Systems, vol. PAS- 1 01, pp. 3648-3657, 1982. S. Abe, N. Hamada, A. Isono and K. Okuda, "Load Flow Convergence in the Vicinity of a Voltage Stability Limit", IEEE Transactions on Power Apparatus and Systems, vol. PAS-97, pp. 1983-1993, 1978. H. W. Dommel, W. F. Tinney and W. L. Powell, "Further Developments in Newton's Method for Power System Applications", IEEE Winter Power Meeting, Paper No. 70 CP 161-PWR, New York, 1970. S. Iwamoto and Y. Tamura, "A Load Flow Calculation Method for Ill-Conditioned Power Systems", IEEE Transactions on Power Apparatus and Systems, vol. PAS 1 00, pp. 1736-1743, 1981. U. M. Ascher, R. M. M. Mattheij and R. D. Russell, "Numerical Solution of Boundary Value Problems for Ordinary Differential Equations", Chapter VIII, PrenticeHall, 1987 (in Press). W. F. Tinney and H. W. Dommel, "Steady-State Sensitivity Analysis", 4th Power Systems Computation Conference, Report No. 3.1/10, Grenoble (France), 1972.
Power Engineering Education 88 SM 536-5 May 1989
88 SM 537-3 May 1989
Closing the Generation Gap
The Kansas Electric Utilities Research Program: A Model of Utility/University Cooperation
A. Chandrasekaran and R. P. Broadwater Center for Electric Power Tennessee Technological University Cookeville, Tennessee
Summary Power system engineers have been using computers ever since the first generation computers (so named much later) came into existence. Automatic power system computations have been reported as early as 1947 [11. According to recent statistics, the power industry is the third largest user of computers in the U.S.A. The explosive developments during the last decade have required the power engineer to be much more computer-literate and the power engineering curriculum has undergone drastic revisions to accommodate this requirement.
Recent software concepts recommend a move towards using fourth generation languages for large application program development. Utilizing a fourth generation language can result in applications built in a fraction of the time with a fraction of the expenditure as compared to using third generation languages. The present day power student is going to face challenges of much greater magnitudes when he joins the power industry and the time is ripe for the introduction of fourth generation languages (4GL) and 'closing' the generation gap. Many salient features of 4GLs that make them useful as effective tools for presenting power system problems to the students are first described. The beneficial aspects of getting exposed to these new trends in software development are illustrated through a distribution analysis program being developed at the Tennessee Technological University. Specific examples of database application, line impedance calculation and very flexible load modeling are included. The use of 4GLs greatly increases the student's programming accomplishments and places the student on the leading edge of computer applications in power systems.
IEEE Power Engineering
Review, May 19899
Robert I. Egbert, Senior Member, IEEE and Gary C. Thomann, Senior Member, IEEE Department of Electrical Engineering The Wichita State University Wichita, Kansas Douglas J. Henry, Member, IEEE Chief Engineer Kansas Gas & Electric Co. Wichita, Kansas R. C. "Pete" Loux Program Director Kansas Electric Utilities Research Program KEURP Topeka, Kansas The Kansas Electric Utilities Research Program (KEURP) is a unique joint venture between six electric utilities in the state of Kansas to undertake and encourage applied research and development projects which may enhance reliability and minimize cost of electric service in Kansas. KEURP was established in 1981 to address a perceived need for applied electric utility related research and development focused on unique conditions in the state of Kansas. The six electric utility companies participating in KEURP are:
Kansas Gas and Electric Company (KG&E), Wichita, Kansas * KPL Gas Service (KPL), Topeka, Kansas * Kansas City Power and Light Company (KCPL), Kansas City, Missouri * Western Power Division, Centel Corporation, (Centel), Great Bend, Kansas * The Empire District Electric Company, (Empire), Joplin, Missouri * Midwest Energy, Incorporated (Midwest), Hays, Kansas Each of these companies serves customers in Kansas, although some serve customers in neighboring states as well. *
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