EUROCON 2007 The International Conference on “Computer as a Tool” Warsaw, September 9-12

Neural Network Based Excitation Control of

Synchronous Generator
Neven Bulic*, Edwin Krasser † and Igor Erceg*
* Faculty of Electrical Engineering and Computing/Department of Electric Machines, Drives and Automation, Zagreb,
Croatia
†
Institute of Electronics, Graz University of Technology, Austria

Abstract — usage of Neural Networks' (NN) based excitation y (t −1) . By applying y (t −1) at the input dynamical neural
control on single machine infinite bus, its implementation
and experimental studies have been reported earlier. The
network is created. The transig function (Fig. 2) is used
proposed feed forward neural network integrates a voltage as activation function for neurons in a hidden layer and
regulator and a power system stabilizer. It is trained on-line for a neuron in an output layer. The numerical
from input and output signals of a synchronous generator. representation is described in (1) and its derivation in (2),
A modified error function used for training the neural [6].
network by the back propagation algorithm uses the 1
ψ (v ) = −1 (1)
reference and terminal voltage as controlling voltage and 1 + e − gav
active power deviation to provide stabilization. The
complete control algorithm is implemented on a fixed point 4e −2 g a v
DSP and tested in laboratory environment on an 83 kVA, 50 ψ ′(v) = g a −2 g av 2
= g a ⋅ (1 − ψ 2 ) (2)
Hz synchronous generator connected over one transmission (1 + e )
line to network. The experimental results described in this
In (1) and (2) ψ (v) is the nonlinear activation function
paper show advantages of this method over classic control
method (an excitation current loop with P controller and a and g a has constant value.
terminal voltage loop with PI controller).

Keywords – neural networks, synchronous generator, DSP

I. INTRODUCTION
Different types of Neural Networks (NN) have been
tested for controlling synchronous generator till now. A
simple structure with only one neuron for voltage control
is studied in [1], [2] and [3]. Different types of NN for
improving stability with more then one neuron are studied
in [4] and [5].
The approach proposed in this paper is considered and Fig. 1. Proposed neural network
studied in [4]. Unlike that study, the NN based excitation
controller is placed on the position of the PI voltage
controller, whereas the excitation current controller is
kept. Some modifications are made in the modified error
function to scale every signal. Therefore its influence on
changing weights can be modified. The back propagation
algorithm (BP) is used to update weights online.
The complete control algorithm is implemented on a
TMS320F2812 (32 bit fixed point DSP by Texas
Instruments) and tested in laboratory environment on a 83
kVA, 400 V, 50 Hz synchronous generator weakly
connected with transmissions lines to a power system.
Experimental studies are done by reference voltage step
changes while the synchronous generator is connected to
the power system.

II. PROPOSED NEURAL NETWORK

The NN used in this paper is shown in Fig. 1. The NN
has three inputs, six neurons in hidden layer and one Fig. 2. Tansig activation function and its derivation
neuron in output layer. The inputs of NN are reference The NN uses a procedure to update weights on-line
voltage U ref , terminal voltage U g and previous output and there is no need for any off-line training.

1-4244-0813-X/07/$20.00 2007 IEEE. 1935

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 Furthermore, there is no need for an identifier and neither where wkij is weight of neuron i in layer k of previous
for reference model. In an online mode from the inputs
and outputs of the generator the NN is trained directly neuron j , η is learning rate, δ is local gradient, e is
and there is no need to determine the states of the system. output from error function, ϕ 'j is derivation of activation
Sampled values of the machine parameters are used by
function
NN to calculate the error with a modified error function.
This error is back propagated through the NN to update As error function for the BP algorithm commonly is
its weights using the model shown in Fig. 3 used:
1
After adjusting weights, the output of the NN is ℑ = (tki − yki )2 . (7)
calculated. 2
The desired output of the neuron i in layer k is t ki . If
III. MODIFIED ERROR FUNCTION AND BP ALGORITHM the neuron is in the output layer, the error function is:
The training of the NN with a BP algorithm is briefly ∂ℑ
= t ki − y ki . (8)
described in [6]. Inputs and outputs of one neuron can be ∂y ki
determined by: If the neuron is in the hidden layer, the error function is
⎛ ⎞ calculated recursively:
y ki = ψ ⎜
⎜ ∑w kij ⋅ x kj + b1 ⎟ ,
⎟
(3)
∂ℑ
n ( k +1)
∂ℑ . (9)
⎝ k ⎠
∂yki
= ∑p =1
∂yk +1, p
⋅ψ k′ +1, p ⋅ wk +1,1,i
where y ki is the output of the neuron i in layer k , ψ is
In the NN controlling the synchronous generator
the nonlinear activation function of neuron i , w is the changing weights is not just based on the error between
neuron weight, x is input signal and b is bias. output and desired output, but also on the derivative of the
The BP algorithm is an iterative gradient algorithm error dy ki / dt :
designed to minimize the mean square error between the
actual output and NN desired output. This is a recursive ∂ℑ dy . (10)
= (t ki − y ki ) − ki
algorithm starting at the output neuron and working back ∂y ki dt
to the hidden layer by adjusting the weights according to: Such a modified error function speeds up the BP
wkij (t + 1) = wkij (t ) + Δwkij (t ) , (4) algorithm and gives it a faster convergence. That way the
NN gets appropriate for an on-line learning
Δw ji (n) = η ⋅ δ j (n) ⋅ y i (n) , (5) implementation. The error function is:
∂ℑ dU g , (11)
δ j (n) = e j (n) ⋅ ϕ ′j (v j (n)) , (6) = K (U − U ) − k ref g u
∂y ki dt
W1,Θ1 y1
where K and k u are scaling factors for adjusting the
∑ φ(*)
Two layer feedforward
Neural network influence of changing weights.
∑ -1φ(*) In order to perform power system stabilization, the
X1
active power deviation ΔPel and the derivation of the
v2 W2,Θ2

X2
∑ φ(*) y2 o1
active power in the modified error function Pel were
considered. The complete modified error function is:
∑ φ(*)

∑ φ(*)
Xp
∂ℑ ⎡ dU g ⎤ ⎡ dPel ⎤ (12)
= ⎢ K (U ref − U g ) − k1 ⎥ − k3 (ΔPel ) + k2
∑ φ(*) ∂yki ⎣ dt ⎦ ⎢⎣ dt ⎥⎦

where the coefficients k 2 and k3 are scaling factors.

∑ φ(*)

The modified error function (12) is divided in to two

parts: the voltage control and the power system stabilizer.

φ´(*) φ´(*) IV. CONTROL SCTRUCTURE

X ∑
In Fig. 4 the classic control structure for synchronous
v
e1 generators is shown. There are two control loops: the
X ∑ excitation current loop with P controller and the terminal
voltage loop with PI controller. Usually, a power system
X ∑ stabilizer with its output is added to the summation point
Back
propagation
in front of the voltage controller. In this structure two
X ∑ phase currents and two line voltages are measured. The
output signal of the excitation current controller is a duty
X ∑ cycle for the PWM input of an AC/DC converter.

X ∑

δ(1) δ(2)

Fig. 3. Back propagation paradigm

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 Fig. 7. Generator (in the middle) and DC motors

TABLE I.
SYNCHRONOUS GENERATOR PARAMETERS
Fig. 4. Classical control structure for synchronous generator
Terminal voltage 400 V
The control structure for implementation of the NN is
shown in Fig. 5. The NN replaces the voltage controller in Phase current 120 A
front of the excitation current controller. Everything else Power 83 kVA
in the control structure is not changed. Fervency 50 Hz
Speed 600 r/min
Power factor 0,8
Excitation voltage 100 V
Excitation current 11.8 A

Both control structures (Fig. 4 and Fig. 5) and the BP

paradigm (Fig. 3) are implemented on a TMS320F2812
DSP of Texas Instruments, [7] and [8]. The control
algorithm is written in C with the Code Composer Studio
v3.1 environment and executed at a frequency of 2 kHz.
The sampling frequency of the internal A/D converter is
equal to the executed frequency. PWM output frequency
used in this experiment is 500 Hz. Higher frequencies for
PWM are not needed because the time constant of the
excitation is approximately 0.5 s.
Phase current measurements are done with current
transformers and line voltage measurements are done with
voltage transformers. The excitation current is measured
with a DC current transformer. In order to convert the
Fig. 5. NN control structure for synchronous generator
voltage levels of the signals to the appropriate input range
of the DSP, an adjustment board was built (Fig. 8). As
V. EXPERIMENTAL SETUP AC/DC converter for excitation a single quadrant 50 A,
The synchronous generator parameters are shown in 400 V IGBT converter is used. PWM signal outputs of the
TABLE I. The generator is connected over 0.2 p.u. DSP are connected over a SEMICRON SKHI 10 driver
reactance of transmission lines to the power system. The [9] to the IGBT modules.
synchronous generator connection is shown in Fig. 6.
Two 40 kW DC motors operate at 600 rpm and drive
the synchronous generator (Fig. 7).

Fig. 6. Synchronous generator connection

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 Fig. 8. DSP (on eZdsp) with adjustment board

VI. EXPERIMENTAL RESULTS

Various tests with the generator connection (Fig. 6),
with a step down of 0.1 p.u. of the reference voltage (Fig. Fig. 10. Synchronous generator oscillations influenced by reference
9) were made. step down

NAPON The behavior of the generator voltage and active power

for the classic control structure (Fig. 4) is shown in Fig.
1,1 11. The generator operates with approximately 0.5 p.u. of
1 active power.
U [p.u.]

0,9 TEMINAL VOLTAGE

0,8
0,7 1,2
0 0,4 0,8 1,2 1,6 2 2,4 1,1
U [p.u.]

t[S] 1

0,9

Fig. 9. Reference step down of 0.1 p.u. 0,8

0 0,4 0,8 1,2 1,6 2 2,4
The behavior of the synchronous generator and the t[S]
expected results are shown in Fig. 10. The generator
voltage is nominal and is driven by the mechanical power
Pm where load angle is δ 0 (point ‘a’ in Fig. 10). ACTIVE POWER
Decreasing the voltage reference by 0.1 p.u. at point ‘a’
0,9
the synchronous generator changes its characteristics
Pg (δ ) from Pg1 (δ ) to Pg 2 (δ ) (point ‘b’ in Fig. 10). The 0,7
P [p.u.]

0,5
load angle cannot instantaneously change its value while
the electrical power changes its value in point ‘b’. The 0,3
difference between mechanical and electrical power is the 0,1
power of acceleration. In point ‘b’ the acceleration power 0 0,4 0,8 1,2 1,6 2 2,4
is positive and speeds up the rotor to point ‘c’. The t[S]
electrical power starts to rise while the acceleration
power decreases its value. The load angle changes its
Fig. 11. Synchronous generator terminal voltage and active power
value from δ 0 to δ1 . In point ‘c’ the acceleration power with PI control structure operating at 0.5. p.u. of active power
is equal to zero and rotor speed is maximum. The rotor
continues to move giving more electrical power than The results for the classic control structure (Fig. 4) and
mechanical until it stops in point ‘d’. There the speed of operating conditions of 0.8 p.u. active power are shown
rotor is equal to the synchronous speed ( Δω = 0 ) and in Fig. 12.
maximum negative accelerating power is reached. The
rotor moves to point ‘c’ and negative acceleration power
starts slowing down the rotor until reaching point ‘b’. The
whole process is repeated for some time until the
oscillations stop because of friction and electrical losses,
[10].

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 TERMINAL VOLTAGE TERMINAL VOLTAGE

1,1 1,2
1 1,1
U [p.u.]

U [p.u.]
0,9 1

0,8 0,9
0,7 0,8
0 0,4 0,8 1,2 1,6 2 2,4 0 0,4 0,8 1,2 1,6 2 2,4
t[S] t[S]

ACTIVE POWER ACTIVE POWER

1
1,1
0,9
P [p.u.]

P [p.u.]
0,9
0,8
0,7
0,7
0,5 0,6
0 0,4 0,8 1,2 1,6 2 2,4 0 0,4 0,8 1,2 1,6 2 2,4
t[S] t[S]

Fig. 12. Synchronous generator terminal voltage and active power Fig. 14. Synchronous generator terminal voltage and active power
with PI control structure operating at 0.8. p.u. of active power with NN control structure without stabilization operating at
0.8. p.u. of active power
Decreasing a reference by step down of 0.1 p.u.
influences the active power oscillations (Fig. 12) in the The active power oscillations in the system controlled
classic control structure (Fig. 4). by the NN without stabilization effect are slightly
In Fig. 13 voltage and active power of synchronous damped in comparison with the results when using the
generator are shown for the NN control structure (Fig. 5). classic control structure with the PI voltage controller
The stabilization effect in the modified error function (Fig. 4). The terminal voltage control is good but the
(12) is not implemented. The generator operates with overshooting at load changes is bigger compared to the PI
approximately 0.5 p.u. of active power. voltage controller.
The results with the NN control structure (Fig. 5) In Fig. 15 is shown behavior of the generator’s voltage
without stabilization at operating conditions of 0.8 p.u. and active power for the NN control structure (Fig. 5).
active power are presented in Fig. 14. The stabilization effect in the modified error function
(12) is implemented and the generator operates at
TEMINAL VOLTAGE approximately 0.5 p.u. of active power. The results are
shown in Fig. 16.
1,1
The NN control structure with stabilization effect in
1 the modified error function significantly damps active
U [p.u.]

0,9 power oscillations in comparison with classic control

0,8 structure (Fig. 4). In the NN control structure (Fig. 5)
0,7
without stabilization the terminal voltage control also
0 0,4 0,8 1,2 1,6 2 2,4
remains good.
t[S] The active power oscillations of the generator for all
three control structures are shown in Fig. 17 and Fig. 18
for better view and comparison. The NN control structure
ACTIVE POWER with stabilization effect in the modified error function
provides the best power system stabilization with best
0,65 damping of active power oscillations in comparison with
the other control structures presented in this paper.
0,55
P [p.u.]

0,45

0,35
0 0,4 0,8 1,2 1,6 2 2,4
t[S]

Fig. 13. Synchronous generator terminal voltage and active power

with NN control structure without stabilization operating at
0.5. p.u. of active power

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 ACTIVE POWER
TERMINAL VOLTAGE
NN WITH PSS NN WITHOUT PSS PID
1

1,1 0,95

1 0,9
U [p.u.]

0,85
0,9

P [p.u.]
0,8
0,8
0,75

0,7 0,7

0 0,4 0,8 1,2 1,6 2 2,4 0,65

t[S] 0,6
0 0,5 1 1,5 2 2,5
t [s]

ACTIVE POWER Fig. 18. Synchronous generator active power oscillations with all
three types of control structure operating at 0.8. p.u. of active
0,6 power
0,55 For the quality analysis of active power oscillations
P [p.u.]

0,5 two numerical criteria are used:

0,45 • Integral absolute error IAE: Absolute difference
0,4
integral between stationary active power and
0 0,4 0,8 1,2 1,6 2 2,4 oscillation.
t[S] • Integral absolute error difference derivation IAED:
Absolute difference derivation integral between
stationary active power and oscillation.
Fig. 15. Synchronous generator terminal voltage and active power
with NN control structure with stabilization operating at 0.5. The numerical values of these two criterias are listed in
p.u. of active power TABLE II and TABLE III (the better the response, the
smaller the value).
TERMINAL VOLTAGE
TABLE II.
1,1 NUMERICAL REPRESENTATION OF ACTIVE POWER
DEVIATION FOR PG ≈0.5 P.U.
1
U [p.u.]

0,9 Voltage
reference
0,8 Maximal Minimal
change
value MAX value MIN IAE IAED
0,7 1-0.9-1 p.u.
(p.u.) (p.u.)
0 0,4 0,8 1,2 1,6 2 2,4 Pg ≈ 0.5
t[S] P.U.
PI voltage
0.7 0.25 0.3012 0.5358
control
ACTIVE POWER
NN without
stabilization 0.6 0.4 0.1543 0.2645
0,9
effect
0,85
P [p.u.]

NN with
0,8
stabilization 0.54 0.45 0.07143 0.1019
0,75 effect
0,7
0 0,4 0,8 1,2 1,6 2 2,4
TABLE III.
t[S] NUMERICAL REPRESENTATION OF ACTIVE POWER
DEVIATION FOR PG ≈ 0.8 P.U.

Fig. 16. Synchronous generator terminal voltage and active power Voltage
with NN control structure with stabilization operating at 0.8. reference
Maximal Minimal
p.u. of active power change
value MAX value MIN IAE IAED
1-0.9-1 p.u.
(p.u.) (p.u.)
ACTIVE POWER
Pg ≈ 0.8
NN WITHOUT PSS NN WITH PSS PID
P.U.
0,8
PI voltage
0.97 0.68 0.3142 0.5541
0,7
control
0,6

NN without
P [p.u.]

0,5
stabilization 0.9 0.71 0.1582 0.2563
0,4
effect
0,3

0,2
0 0,5 1 1,5 2 2,5 NN with
t [s]
stabilization 0.85 0.75 0.07143 0.1019
Fig. 17. Synchronous generator active power oscillations with all effect
three types of control structure operating at 0.5. p.u. of active
power

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 The numerical IAE and IAED criteria are
approximately five times smaller for NN with
stabilization in comparison to the classic control structure
and approximately two times smaller in comparison to
the NN without stabilization effect.

VII. CONCLUSIONS
The classic control structure (Fig. 4) with PI control
algorithm and NN control structure (Fig. 5) were
implemented on a DSP and tested on a synchronous
generator connected via thin lines to the power system. A
back propagation algorithm with modified error function
is used to train the neural network online. The modified
error function uses sampled input and output values of the
synchronous generator. Therefore is no need to determine
the states of the system. Test results show that the NN
with stabilization effect effectively damp down active
power oscillations and provide a good terminal voltage
control.

REFERENCES
[1] O.P.Malik,.M.M.Salem, A.M. Zaki, O.A. Mahgoub and E. Abu
El-Zahab, "Experimental studies with simple neuro-controller
based excitation controller", IEEE Proc.-Gener. Transm. Distrib.
Vol. 149. No I. January 2002
[2] Bulic N., Erceg G., Idzotic T, Comparison of different methods of
excitation control for a synchronous generator, EPE-PEMC, Riga,
2004
[3] M.M.Salem, O.P.Malik,. A.M. Zaki, O.A. Mahgoub and E. Abu
El-Zahab "Simple neuro-controller with modified error function
for a synchronous generator", Electrical Power and Energy
Systems 25 (2003) 759-771
[4] M.M.Selem, A.M. Zaki, O.P. Mahgoub, E. Abu El-Zahab, O.P.
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Meeting 2000, Singapure
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Malik, "Studies on Multi-Machine Power System With a Neural
Network Based Excitation Controller" Proc. IEEE Power
Engineering Society, 2000
[6] Symon Haykin , “Neural Networks: A Comprehensive
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[8] Spectrum Digital:“eZdspTM F2812 Technical Reference”,
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[9] Semikron: “SKHI10/12“,22-08-2003, Driver electronic-PCB
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[10] Sumina, D.; Idzotic, T.; Erceg, G., "The appliance of the estimated
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