Neural network for contingency ranking dynamic security indices for use under fault conditions in a power distribution system
First Claim
1. A method for real-time evaluation of fault contingencies upon the dynamic security of a power system that includes power generators and power-carrying lines, the method comprising the following steps:
- (a) defining a set of indices describing a fault-induced deviation from a pre-fault steady state condition of said power system;
(b) forming from said set of indices a subset of composite indices for each fault contingency of interest, wherein a fault contingency of interest includes a fault experienced by said power system, for which fault contingency real-time evaluation is desired, wherein said subset of composite indices is formed as follows;
(b-1) calculating a change from pre-fault steady-state condition of said power system for each system variable defined by an index in a said set of indices;
(b-2) segregating changes calculated at step (b-1) into positive-signed values and negative-signed values;
(b-3) normalizing said positive-signed values and said negative-signed values so segregated at step (b-2);
(b-4) raising to a power n, n≧
4, values normalized at step b-3);
(b-5) combine values so power-raised in step (b-4) to yield at least one composite index selected from the group consisting of (a) terms corresponding to at least some of said positive-signed values, (b) terms corresponding to at least some of said negative-signed values, (c) terms corresponding to at least some of said positive-signed values and corresponding to at least some of said negative-signed values, and (d) terms corresponding to a difference between at least some of said positive-signed values and some of said negative-signed values;
(c) providing a computer system including a neural network to classify in terms of at least stability and instability each of said composite indices, said neural network receiving said composite indices as input; and
(d) providing from an output of said neural network at least one indication of relative stability of said power system in response to a said fault contingency of interest.
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Abstract
Analysis and evaluation of outage effects on the dynamic security of power systems is made with a neural network using composite contingency severity indices. A preferably small number of indices describes the power system characteristics immediately post-contingency. These indices are then used as classifiers of the safety of the power system. Using the values of the severity indices, an artificial neural network distinguishes between safe, stable contingencies and potentially unstable contingencies. The severity of the contingency is evaluated based upon a relatively small fixed set of severity indices that are calculated based on a partial time domain simulation. Because a fixed set of severity indices is used, the size and architecture of the neural network is problem independent, thus permitting its use with large scale power systems. Further, the amount of required time domain simulation for the selection of the potentially harmful unstable contingencies is reduced by screening out benign, stable appearing contingencies. The network is trained off-line using training cases that concentrate around the security boundary to reduce the number of cases required to train the neural network.
111 Citations
15 Claims
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1. A method for real-time evaluation of fault contingencies upon the dynamic security of a power system that includes power generators and power-carrying lines, the method comprising the following steps:
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(a) defining a set of indices describing a fault-induced deviation from a pre-fault steady state condition of said power system; (b) forming from said set of indices a subset of composite indices for each fault contingency of interest, wherein a fault contingency of interest includes a fault experienced by said power system, for which fault contingency real-time evaluation is desired, wherein said subset of composite indices is formed as follows; (b-1) calculating a change from pre-fault steady-state condition of said power system for each system variable defined by an index in a said set of indices; (b-2) segregating changes calculated at step (b-1) into positive-signed values and negative-signed values; (b-3) normalizing said positive-signed values and said negative-signed values so segregated at step (b-2); (b-4) raising to a power n, n≧
4, values normalized at step b-3);(b-5) combine values so power-raised in step (b-4) to yield at least one composite index selected from the group consisting of (a) terms corresponding to at least some of said positive-signed values, (b) terms corresponding to at least some of said negative-signed values, (c) terms corresponding to at least some of said positive-signed values and corresponding to at least some of said negative-signed values, and (d) terms corresponding to a difference between at least some of said positive-signed values and some of said negative-signed values; (c) providing a computer system including a neural network to classify in terms of at least stability and instability each of said composite indices, said neural network receiving said composite indices as input; and (d) providing from an output of said neural network at least one indication of relative stability of said power system in response to a said fault contingency of interest. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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Specification