Energy and Power Engineering, 2013, 5, 999-1004

doi:10.4236/epe.2013.54B191 Published Online July 2013 (http://www.scirp.org/journal/epe)

Accuracy Improvement in CCT Estimation of Power

Systems by iRprop-RAN Hybrid Neural Network

Teruhisa Kumano, Shinjiro Netsu

Department of Electronics and Bioinformatics, Meiji University, Kawasaki, Japan

Email: kumano@isc.meiji.ac.jp

Received March, 2013

ABSTRACT

This paper proposes a new Initial CCT (Critical Clearing Time) estimation method using a hybrid neural network com-

posed of iRprop (Improving the Resilient back PROPation Algorithm) and RAN (Resource Allocation Network). In

transient stability study, CCT evaluation is very important bu t time consuming due to the fact it needs many iteration of

time domain simulations gradu ally increasing the fault clearing time. The key to reduce th e required computing time in

this process is to find accurate initial estimation of CCT by a certain handy method before going to the iterative stage.

As one of the strongest candidates of this handy method is the utilization of the pattern recognition ability of neural

networks, which enable us to jump to a close estimation of the real CCT without any heavy computing burden. This

paper proposes a new hybrid neural network which is a combination of the well-known iRprop and RAN. In the pro-

posed method, the outputs of the hidden units of RAN are modified by multiplying the contribution factors calculated

by an additional iRprop network. Numerical studies are done using two different test systems for the purpose of con-

firming the validity of the proposal. The result of the proposed method is the best. Properly evaluating the contribution

of each input to the hidden units, the estimation error obtained by the proposed method is improved further than the

original RAN based estimation.

Keywords: Critical Clearing Time; Estimation; Power System; iRprop; Resource Allocation Network

1. Introduction

In large power systems operation, stability check in the

contingency analysis is always important. In particular,

in these two years, we, Japanese electric power engineers,

cannot depend upon the strong power supply from the

nuclear power plants and the basic power flow pattern is

different from the original schedule. It implies that every

daily grid operation needs more careful study, in which

stability check should be done. The long and narrow

power corridor is, in a sense, inevitable due to the geo-

graphical restriction of our country, which makes the

stability the main restricting factor for the long and heavy

power transmission.

One of the biggest problems in the transient stability

constrained contingency analysis is the long computing

time required. Transient stability study itself is a typical

time consuming calculation. Here, we need to iterate

dynamic simulation many times gradually increasing

power flow or fault clearing time to reach their critical

value. Comparing these two, the critical clearing time

(CCT) is easy to calculate and is often used. For the fast

evaluation of CCT, we need accurate initial guess of the

clearing time. If we can start from close guess, the re-

quired time to get the true CCT can be shorter. Since the

power flow pattern in the whole system gives a substan-

tial effects on the resultant CCT, it can be expected that

we can accurately estimate CCT if the important vari-

ables such as the specified values of the active and reac-

tive power at each node is given.

Many methods based on so called artificial neural

network (ANN) techniques have been studied for this

initial guess. Ikenono et al proposed to use BP (back

propagation) based ANN [1]. Bettiol et al proposed to

use RBF (Radial Basis Function) network for this pur-

pose [2]. The authors ourselves studied this problem and

proposed to use support vector machine [3] and rele-

vance vector machine [4]. Once properly trained, ANN

can recognize the given input pattern and make classifi-

cation or give a regression in a short computing time.

Because of its nonlinear knowledge representation ability,

it has been a strong candidate for this initial estimator.

However, even after the above mentioned research ef-

forts, it still remains as a research theme and not a real

field application.

In this paper, a new ANN method is proposed for the

above stated purpose. In this proposal so called RAN

(Resource Allocation Network) is coupled with iRprop

(Improving the Rprop Learning Algorithm, in which

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