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An Investigation on Pruned NNARX Identification Model of Hydropower Plant

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Abstract

The aim of this paper is to determine an accurate nonlinear system model for identification of dynamics. A small hydropower plant connected as single machine infinite bus (SMIB) system is considered in the study. It is modeled by a neural network configured as a feedforward multilayer perceptron neural network (MLPNN). An investigation is conducted on various NN structures to determine the optimally pruned neural network nonlinear autoregressive with exogenous signal (NNARX) identification model. The structure selection is based on validation tests performed on these network models. The proposed structure identifies the model characteristics, which represent the dynamics of a power plant accurately. The results show an improved performance in identification of power plant dynamics by optimal brain surgeon (OBS) pruned network as compared to un-pruned (i.e., fully connected) network.

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Abbreviations

m :

mechanical torque

m l :

load torque

w 0 :

base angular speed (377.16 rad/s)

w :

rotor angular speed (p.u.)

δl :

load angle (rad)

D :

damping torque

T a :

mechanical time constant

e′ q :

internal transient voltage in the q-axis (p.u.)

V t :

terminal voltage (p.u.)

x d :

d-axis synchronous reactance (p.u.)

x′ d :

d-axis transient reactance (p.u.)

r e +jx e :

transmission line impedance (p.u.)

x q :

q-axis synchronous reactance (p.u.)

K A, K E :

voltage regulator gains

T A, T E :

voltage regulator time constants

K F, T F :

stabilizing transformer gain, time constant

K 1–K 6 :

constants of the linearized model of synchronous machine

P e , Q e :

active and reactive power output from synchronous machine

V f :

stabilizing transformer voltage

E FD :

field voltage

V ref :

reference voltage

V a :

regulator voltage

τ ′ d0 :

d-axis open circuit field time constant

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Authors and Affiliations

  1. Department of Electrical Engineering, Royal Bhutan Institute of Technology, Phuentsholing, Bhutan

    Nand Kishor, P. R. Sharma & A. S. Raghuvanshi

Authors
  1. Nand Kishor
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  2. P. R. Sharma
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  3. A. S. Raghuvanshi
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Correspondence to Nand Kishor.

Appendix

Appendix

The plant data are as follows:

S n =131 MVA; V n =13.8 kV; x′′ d =0.330; x′ d =0.360; x d =1.010; x′′ q =0.330; x′ q =0.57; x q =0.57; r a=0.004; τ ′′ d =0.030 s; τ ′ d =2.7 s; τ ′′ d0=0.030 s; τ ′ d0=7.6 s; D=0.0; K E=− 0.17; T A=0.05; T E=0.95; K A=400; K F=0.025; T F=1.0; T w =2.23 s.

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Kishor, N., Sharma, P.R. & Raghuvanshi, A.S. An Investigation on Pruned NNARX Identification Model of Hydropower Plant. Engineering with Computers 21, 272–281 (2006). https://doi.org/10.1007/s00366-006-0016-z

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