Improving Frequency Stabilization in Power System Network using Optimization Method

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ISSN No: 2456
International
Research
Improving Frequency
Network using Optimization M
Aguodoh Patrick C .
Michael Okpara. University of Agriculture
Umudike, Abia State, Nigeria
ABSTRACT
The new concept of Integrated Power Network
Controls transcending Generation, transmission,
Distribution and even Local Consumption, have been
found to leverage on the reliability of the composite
system unit controls if the robustness of the entire
system to faults and interruptions would be optimized.
This paper presents an improvement to frequency
stabilization in power system network by developing
an optimization network - the controller network or
the optimization network controller using matlab code
adjuster to get the desired stable frequency of 50Hz.
The optimization network thus implements a widely
acceptable and a global control scheme applicabl
varied environments and situations.
Keywords: Optimization, Frequency, power System,
Stabilization, Network controller
INTRODUCTION
The primary function of an electrical power system is
to provide a secured, economic and reliable source of
electricity to the consumer; such electric power
supply can only be guaranteed with a stable and
reliable electrical power system. However, several
perturbations tend to inhibit such desired quality and
usable supply of electricity.
Variation in load demand has been identified as one
of the vital causes of frequency fluctuation in power
system. From the point of view of transient
disturbance and system instability, two possibilities of
increasing load are identifiable:
i. Total load exceeds the steady-state l
system,
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 1 | Nov-Dec 2017
ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume
International Journal of Trend in Scientific
Research and Development (IJTSRD)
International Open Access Journal
Frequency Stabilization in Power S
Network using Optimization Method
Udeani Henrietta U.
The new concept of Integrated Power Network
Controls transcending Generation, transmission,
Distribution and even Local Consumption, have been
leverage on the reliability of the composite
system unit controls if the robustness of the entire
system to faults and interruptions would be optimized.
This paper presents an improvement to frequency
stabilization in power system network by developing
the controller network or
the optimization network controller using matlab code
adjuster to get the desired stable frequency of 50Hz.
The optimization network thus implements a widely
acceptable and a global control scheme applicable to
: Optimization, Frequency, power System,
The primary function of an electrical power system is
to provide a secured, economic and reliable source of
electricity to the consumer; such electric power
supply can only be guaranteed with a stable and
reliable electrical power system. However, several
perturbations tend to inhibit such desired quality and
d has been identified as one
of the vital causes of frequency fluctuation in power
From the point of view of transient
disturbance and system instability, two possibilities of
state limit of the
ii. The rate of increase in load is so high as to set
up system oscillations which cross the critical
point.
At constant generation, should the total generation
available be insufficient to meet load demand, the
frequency will fall. This is due to the fact that
increased load forces increased operation on the
generator. Since momentarily, there is no change in
load angle corresponding to power input, the machine
decelerates in speed which translates to
frequency reduction that ofte
compulsory and traditional load shedding via under
frequency relays to save the system from total
collapse.
In such situation as above, load power demand must
be reduced by usual disconnection of loads. This must
continue until the frequency rises to a reasonable
level again where the system is such that can take
more loads.
This dynamic relationship between the generated
power, the load demand and the system frequency,
makes the system frequency an invaluable parameter
that provides easy and direct method of precisely
monitoring the balance between the consumer load
demand power and the generated power.
It is on this basis that this study is undertaken to
determine ways of optimizing electrical power system
by exerting improved control of load frequency to
achieve better and more reliable supply from
electrical power systems.
Dec 2017 Page: 448
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Scientific
(IJTSRD)
International Open Access Journal
System
Udeani Henrietta U.
The rate of increase in load is so high as to set
up system oscillations which cross the critical
At constant generation, should the total generation
available be insufficient to meet load demand, the
is due to the fact that
increased load forces increased operation on the
generator. Since momentarily, there is no change in
load angle corresponding to power input, the machine
decelerates in speed which translates to system
frequency reduction that often necessitates
compulsory and traditional load shedding via under
frequency relays to save the system from total
load power demand must
be reduced by usual disconnection of loads. This must
frequency rises to a reasonable
level again where the system is such that can take
This dynamic relationship between the generated
power, the load demand and the system frequency,
makes the system frequency an invaluable parameter
s easy and direct method of precisely
monitoring the balance between the consumer load
demand power and the generated power.
It is on this basis that this study is undertaken to
determine ways of optimizing electrical power system
trol of load frequency to
achieve better and more reliable supply from

International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 1 | Nov-Dec 2017 Page: 449
BACKGROUND
Electric Power Generation has become an
indispensable and integral part of human existence,
the numerous advantages and applications of Electric
power in our daily operations can really not be over
emphasized. As the range of application of Electric
Power widens, so does the need for adequate, reliable,
effective and efficient control mechanisms.
The particular control mechanism that can be
developed for the respective system units would be
directly dependent on such operational and
interrelated characteristics of the unit which are
identified to include Speed, frequency, voltage,
induced current, generated voltage(E), temperature,
voltage losses, load size and type among others.
Available control techniques based on the above
characteristics includes linear controllers, variable
structure controllers, Proportional Integral (PID)
controllers, gain scheduling controllers etc.
The complexity and non- linear load characteristics of
large electrical power systems, tend to render them
unstable especially in conditions involving non –
linear load characteristics. Their unstable transient
response to abruptness in system parameters imposes
great limitation to their wide application. Besides,
they cannot obtain accurate linear time of variant
models at variable operating points; consequently,
resultant time delays from random disturbances not
only reduces system performance but can even cause
system instability on frequency and other parameters
[Bevani, H., Hiyama, T. 2007].
STATEMENT OF THE PROBLEM
Transient frequency changes (over-frequency and
under-frequency) are unavoidable consequences of
changes in load power demand in an electrical power
system. The unabated propagation of the combined
effects of load frequency variations alter the operating
point parameter of the system and therefore are
adjudged the responsible factor for:
a. High flux density in electrical machines leading to
high magnetizing currents
b. High core loss, overheating of machines and
possible failures
c. Abnormally high and low speed of operations of
equipment
d. Increased fault level
e. Excessive vibration of machines, motors and
plants
f. Low efficiency, poor quality products from
production lines and wastages and
g. Possible total failure and system collapse among
other consequences leading to loss of return on
investment and negative impact on the Nation’s
GDP. Limitations inherent in previous attempts to
control the fluctuation of system frequencies
rendered their control actions inadequate in
handling all forms of system characteristics (linear
and non-linear).
A desirable controller must therefore reduce the
controller’s response time to its barest minimum.
To solve all these problems, the optimization Network
Controller is developed to evaluate relevant dynamic
characteristics during operation, and suitably
adjusting control parameters when they occur to
ensure convergence of the system state vector to a
steady state under a desirable response time and less
overshoot.
OBJECTIVES
i. To develop a reference mathematical model
for the adaptive control configuration that uses
optimization,
ii. Develop optimization networks – the
controller network or the optimization network
controller using matlab code to confirm( i)
iii. To develop optimization matlab code adjuster
to get the desired stable frequency of 50Hz
iv. Develop optimization networks – the model
network or the optimization network plant
model
v. To generate frequency stabilized data plant
iteratively when (iv) is implemented by matlab
simulation
vi. To adaptively optimized network
controller(controller network) using the plant
measurement so as to predict the effect of
controller changes on plant output, and force
the plant output to track the reference model,[
B. Ogbonna, S.N. Ndubisi 2012].
RELEVANCE OF THE STUDY
Constant frequency is a significant feature of a
normally operating system. Realizing a stabilized
system frequency is considered very critical due to the
fact that:

 Most type of AC motors run at speed that are
related to the frequency,
 The operating turbines, particularly steam driven
ones, are designed to operate t a very precise
speed,
 The generator turbines particularly steam driven
ones, are designed to operate at a very precise
speed
 The overall operation of a power system can be
controlled if the frequency error is kept within
strict limits
 A large number of electrically operated clocks are
used. They are all driven by synchronous motors,
and the accuracy of these clocks is a function not
only of the frequency error, but actually of the
integral of this error.
 The overall balance in electrical power system
operating frequency would be responsible for
efficient and reliable operation of industrial
motors and machines which has a far reaching
effect in ensuring:
 Prolonged operating life of the machines,
guaranteeing return on investment(both on the
power system infrastructures as well as on the
industries that run on the product of power
system)
 Flawless and high quality products from the
production lines. The standard of such products
have direct impact on the nations GDP and the
socio- economic wellbeing of the country in
general.
Finally the study will serve as a reference for both the
academia and professionals in the power industry,
contributing to the body of knowledge existing on the
subject matter.
METHODOLGY
The control of a system is made possible if a
mathematical model of the system under study is
already known. If the system has un-modeled
dynamic behaviour, or is totally unknown, then a
system model of some form is required. The system
model can be calculated fully, or more normally
estimated to a
given degree. Once a system model has been
estimated, it can be used to predict the future
behaviour of the plant under study, and hence used as
a basis for the calculation of control commands. The
type and accuracy of the estimated system model is
dependent on the type of system being considered
and the required application for the model.[ Birch,
Alan Philip(1988]. Generally a model of a complex
system can be obtained by reducing the system under
study into smaller, more easily defined blocks. Each
block, may then in turn be broken down, and itself
modeled as a system, if the internal
Characteristics of the system are known. The
complexity of each model can be decided upon by
considering the use to which the model will be put,
and the required accuracy of the result. Also, the
complexity of the model will have implications on the
time taken to calculate the model, and hence this must
be be considered when time is also a constraint on the
modeling process. Often a complete description of a
system cannot be achieved because of unknown
parameters or variables which cannot, or are not
measured. Also often the system which is to be
described by the model is an extremely complimented
and detailed process. (Francoise B., Youssef A. M.,
Bernard W., 1994)
Any system can be modeled to a given degree by
carrying out a set of detailed experiments on the plant.
The unknown variables in the model can be
determined using an exact mathematical description
of the system (if known) and this will then represent
the plant under all operating conditions. However, in
some cases this can be too precise, as what may
actually be required is a general model of the system
under study. Also, when the plant is in normal
operating mode, it may only be subject to a small
proportion of the conditions modeled, and certain
operations included in the model may not have any
effect in this particular operating range. It is known
that for most controller designs, -the control is found
to be satisfactory, if it is based on a very simple
system model which can be obtained over one
particular set of operating conditions. The most
commonly used approach to find any model is to use
data from the actual plant inputs and outputs, when it
is operating dynamically within its normal range.
However, this on its own causes problems due to the
nature of the system. The raw plant data is noisy
because of random fluctuations, disturbances and
noise introduced by the measuring and telemetry
systems used. This process of using data directly
gathered from the system under study, including the
added noise, to obtain estimates of the parameters of
the model of the plant is called System Identification.
System identification is therefore, the procedure
carried out to model system mathematically, taking

into consideration the external physical intractable
characteristics which are inherent in the system.
In realizing the control of frequency in electrical
power system, it is of interest therefore:
i. To develop a reference mathematical model
for the adaptive control configuration that uses
optimization,
ii. Develop optimization networks – the
controller network or the optimization network
controller using matlab code to confirm( i)
iii. To develop optimization matlab code adjuster
to get the desired stable frequency of 50Hz
iv. Develop optimization networks – the model
network or the optimization network plant
model
v. V. To generate frequency stabilized data
plant iteratively when (iv) is implemented by
matlab simulation
vi. To adaptively optimized network
controller(controller network) using the plant
measurement so as to predict the effect of
controller changes on plant output, and force
the plant output to track the reference model, [
B. Ogbonna, S.N. Ndubisi 2012].
.
2 To develop a reference model for the adaptive
control configuration that uses optimization.
An EEDC company produces two types of power
supply A and B that require frequencies F1 and F2.
Each unit of type A require 1Hz of F1 and 2Hz of
F2.Type B requires 2Hz of F1 and 1Hz of F2 (Each
unit).The company has only 100Hz of F1 and 80Hz of
F2. Each unit of type A brings a profit of #500 and
each unit of type B brings a profit of #400. Formulate
the optimization problem to maximize profit that will
stabilize the frequency.
SOLUTION
Table 1: To give the information in a tabular form
as shown below
Frequencies F1 (Hz) F2 (Hz) Profit
(#)
A 1 2 500
B 2 1 400
100 80
Decision variable: The decision variable are
frequencies A and B. Thus let the number of
frequency A be x while that of B is y.
Objective function: The given problem is aimed at
maximizing profit.
Let Z be the objective function profit of each unit
of type A = #500, That is profit of x unit of type A
= #500x.
Profit of each unit of type B = #400, That is profit
of y unit of type B = #400y.
Total profit = 500x + 400y.--------------------------- 1
Constraints (i): Company has only 100Hz of F1
Unit of A requires 1Hz of F1, That is x-unit
requires x-Hz of F1.
1 unit of B requires 2Hz of F1.
Thus y-unit requires 2y-Hz of F1
Thus total available quantity of F1 for A and B =
100Hz
Therefore x + 2y <= 100--------------------------- 2
Constraint (ii): Company has only 80Hz of F2.
1 Unit of A requires 2Hz of F2.
Thus x- unit requires 2x – Hz
1 Unit of B requires 1Hz of F2.
Thus y – units requires y – Hz.
Total available quantity of F2 for A and B = 80 Hz.
That is 2x + y <= 80.----------------------------------- 3
Constraint (iii): Supply of A and B cannot be
negative, That is x >= 0 and y >= 0
The mathematical model formulation for
improving frequency stabilization in power system
network using optimization method becomes
Maximize Z = 500x + 400y--------------------------- 4
Subject to x + 2y <= 100------------------------------ 5
2x + y <= 80------------------------------ 6
x >= 0 and y >= 0
Then use simplex method to solve the
mathematical model of equations 4, 5 and 6
z = 500x + 400y------------------------ 7
x + 2y <= 100-------------------------- 8
2x + y <= 80---------------------------- 9
Equate equation 3.2.7 to zero and remove all the
constraints in equations 8 and 9 respectively by
introducing slacks
Z – 500x – 400y = 0------------------ 10
x + 2y + S1 = 100--------------------- 11
2x + y + S2 = 80---------------------- 12

No of
iterations
Basic Z X y S1 S2 Sol
iter 1 z 1 -500 -400 0 0 0
S1 0 1 2 1 0 100
S2 leaves x
enters
S2 0 2 1 0 1 80
Iter 2 z 1 0 -150 0 250 20000
S1 0 0 3/2 1 -1/2 60
x 0 1 ½ 0 ½ 40
Iter 3 z 1 0 0 100 200 26000
y 0 0 1 2/3 -1/3 40
x 0 1 0 -1/3 2/3 20
Power z = 26000
frequency F1 = 20 Hz
Frequency F2 = 40 Hz
 Step2 To find the key column go for the most negative in the z row which is -500
 Step 3 To find the key row use the positive numbers in the key column to divide their respective
solutions which ever gives the smaller answer is where the key row falls 100/1 = 100, 80/2 = 40
 Step 4: To fill the pivot row x use the number that is at the point of intersection in the key row and
key column of iteration 1 which is 2 to divide all the numbers in S2 row of iteration1.
z = 0/2 = 0, x = 2/2 = 1,y=1/2 = 1/2,S1 = 0/2 = 0,S2 = 1/2= 1/2,
Sol = 80/2 = 40
 Step 5; To fill the 2 row of iteration 2.Recall new No = old NO – (pivot No x Constant) z= 1 –(0 x -500)
= 1
x = -500 –(1x-500) = -500+500 =0
y = -400 –(1/2x-500) = -400+250 = -150
s1 = 0 – (0x-500) = 0
s2 = 0 – (1/2 x -500) = 0 + 250 =250
sol = 0 – (40 x -500) = 20000
 Step 6 To fill s1 row of iteration 2.
z = 0 – (0x1) = 0
x = 1 – (1x1) = 0
y = 2- (1/2x1) = 2 -1/2 = 4-1/2 =3/2
s1 = 1 – (0x1) = 1
s2 = 0 – (1/2x1) = -1/2
sol = 100 – (40x1) = 60
 Step 7 To find the key row in iteration 2
60/3/2= 60/1 x2/3 = 40
40/1/2 = 40/1 x 2/1 = 80
 Step 8 To fill the pivot row of iteration 3
z = 0/3/2 = 0
x = 0/3/2 = 0
y = 3/2/3/2 =3/2 x 2/3 =1
s1 = 1/3/2 = 1/1 x 2/3 =2/3
sol = 60/3/2 = 60/1 x 2/3 = 40
 Step 9 To fill the 2 row of iteration3
z = 1 –(0 x -150) = 1

x = 0 –(0 x -150) = 0
y = -150 – (1 x -150) = -150+150 = 0
s1 = 0 – (2/3 x – 150) = 0 + 100 = 100
s2 = 250 – (-1/3 x -150) 250 – 50 = 200
Sol = 20000 – ( 40 x 150)
20000 + 6000 = 26000
 Step 10 To fill x row of iteration 3
z = 0 –(0 x 1/2) = 0
x = 1 – (0 x 1/2) = 1
y = 1/2 –(1 x 1/2) = ½ - ½ = 0
s1 = 0 – (2/3 x ½) = - 1/3
s2 = ½ - ( -1/3 x ½) = ½ +1/6 =2/8 = 3+1/6 =4/6 =2/3
Sol = 40 –(40 x 1/2) = 40 -20 = 20
3. Develop optimization networks – the controller network or the optimization network controller using matlab
code to confirm( i)
Table 2. values of frequency(Hz) vs Power(mw) for constant R.
∆ ƒ 15.8 15.5 14.0 11.95 9.6 9.2 8.1 7.3 6.5 6.1 5.7 5.3 4.9
PL(MW) 6.15 6.43 6.96 7.77 8.74 8.91 9.39 9.72 10.04 10.20 10.36 10.53 10.69
For a linear situation at R = constant say = 0.04% 0r 20
4 pu (H2) mw, the plot of ∆L was called out using the
following mat lab codes, while the figure below shows the straight line graph with a negative slope.
%-- 10/3/15 2:40 PM --%
>> % plot of static frequency vs power respone under constant R;
>>pl=[6.15 6.96 7.77 8.74 8.91 9.39 9.72 10.04 10.20 10.36 10.53];
>> f=[15.8 14.0 11.95 9.6 9.2 8.1 7.3 6.5 6.1 5.7 5.3];
>> plot(pl,f); grid on; xlabel('Power(p)');ylabel('Frequency(Hz)');title('F-Pl response at constant R'
Fig 1: System response under variable regulation Parameter.
Also a non linear situation (non constant regulation parameter) where R is variable, different values of R were
generated from say 0-2 putted/mw insteps of 0.05 and used to compute corresponding values of ∆f from the
relation ∆f0 = R∆PL with PL (mw) values as in table in table 9. The new table of table of non-linear values is
shown in table 4.2. Again the graph of the non-linear values of ∆f and PL is plotted, the downward parabolic
response in shown in figure 4.2

Table 3: values of frequency(Hz) vs Power(mw) for variable R
∆ ƒ 3.38 3.48 3.497 3.1185 2.817 2.43 2.008 1.53 1.036 0.526
PL(MW) 6.15 6.96 8.74 8.91 9.39 9.72 10.04 10.20 10.36 10.53
% 10/3/15 7:47 PM --%
>> % plot of frequency vs power respone under variable R;
>>pl=[6.15 6.96 8.74 8.91 9.39 9.72 10.04 10.20 10.36 10.53];
>> f=[3.38 3.48 3.4965 3.1185 2.817 2.43 2.008 1.53 1.036 0.526];
>>plot(pl,f); grid on; xlabel('Power(p)'); ylabel('Frequency(Hz)'); title('F-Pl response at variablr R')
Optimization network controller action is founded upon the basis of non-linear regulation parameter (R) or
factor of the turbine power in this circumstance. This parameter is as noted earlier is obtained from the
observed relationship between the system frequency ∆f(0) and the turbine output power, also drawing from the
relationship between the frequency and load power taken from the system during load variation.
100% to + 100%. Fig.1 shows the frequency fluctuation at the output of the power unit when a 10% step load
increase occurs on the bus.
Table 4: SIMULATED DATA
Frequency Time
0 0
82 0.3
25 0.8
69 1
38 1.4
60 1.8
43 2
55 2.3
50 5
50 10
Figure 2 Frequency – power response under variable Regulation Parameter R

Fig 3 SIMULATED FREQUENCY VS TIME RESULT
Fig 3 Shows the simulated frequency vs Time .It shows that the frequency becomes stable at a coordinated
frequency vs time of (50,5) to (50,10).This indicates that there will be stable power supply to the consumers.
CONCLUSION
Optimization networks have been successfully applied
to control the turbine reference power of a computer –
simulated generator unit. The optimization networks
have been adapted using back propagation. Through –
time algorithm. By the adaptive method (where
control action is based on decisions taken by the
remembering information already stored or learnt in
the system control unit) employed in this paper,
frequency variations in a single – area system was
inputted into the optimization network controller. The
optimization network thus implements a widely
acceptable and a global control scheme applicable to
varied environments and situations. It is found very
suitable for controlling the plant dynamics in
relatively less time.
REFERENCES
1. Bevani H., Hiyama T. Robust load frequency
regulation: A real-time laboratory experiment.
Optimum Control Application, 28, 2007, 419-433.
2. Unbehaven, H., Kocaarslan, I. Experimental
modeling and adaptive power control of a 750MW
once-through boiler. Proceeding of 11th
IFAC
World Congress. Talin, SU, 13-17 August 1990,
vol. 4, pp 226-231.
3. Change C.S., Fu W.,Wen F. Load frequency
controller using generic algorithm based fuzzy
gain scheduling of P1 controller. Electric
machines and power systems, 26, 1998, 39-52.
4. Chaturvedi D.K. Modeling and simulation of
power system: an alternative approach. Thesis for
the Degree of Doctor of philosophy, Dayaibagh
Educational Institute (Deemed University),
Dayalhagh, Agra, India. 1997.
5. Chaturveli D. K., Satsagi P.S., Kara P.K. Short
term load forecasting using generalized neural
network approach. Journal of the Institution of
Engineers (India), 78(2), 1997, 83-87.
6. Weedy, B.M, Electric Power Systems, Courier
International limited, Tiptree, Essex; 1989
7. M.F. Hassan, A.A. Abouelsoud, H.M. Soliman.
Constrained load frequency control. Electric
power components and system, 36, 2008, pp.
266279.
8. H. Shayeghi, SHAYANFAR. Application of ANN
technique based on l-synthesis to load frequency
control of interconnected power system. Electrical
power and Energy System, 28, 2006, pp 503511.
9. Birch, Alan Philip(1988)Adaptive load frequency
control of electrical power systems, Durham
thesis, Durham University online E-Theses.
http://etheses.dur.ac.uk/6448/
10. B. Ogbonna, S.N. Ndubisi. Neural Network Based
Load Frequency Control For Restructuring Power
Industry, Nigerian Journal of Technology
(NIJOTECH) Vol. 31, No. 1, March, 2012
11. GUO, SONG (2014) Adaptive Parameter
Estimation of Power System Dynamic Models

Using Modal Information, Durham theses,
Durham University. Durham on-line E-
Theses.http://etheses.dur.ac.uk/10576/
12. P.M. and Fouad, A.A. (1997). Power System
Control and Stability, Ames Iowa State university
Press. HadiSaadal. Power System Analysis.
McGraw-Hill 2002.
13. Chaturvedi D.K. Modeling and Simulation of
Power System, an Alternative Approach. Thesis
for the Degree of Doctor of Philosophy,
Dayaibagh Educational Institute, Redeemed
University, DayalHagh, Agra. India 1997.
14. Werbos, P.J. (1990) Back Propagation through
Time; what it does and how to do it (special issue
on neural networks). Proceedings of IEEE, 2,
1550 – 1560.
15. Hung T. Nguyeu; Nadipuram R. Prasad Carol L.
Walker, Elbert, A. Walker; A First Course in
Fuzzy and Neural Control; Chapman and Hall
C.R.C press Company, New York, 2003.
16. Change C.S., Fu W.,Wen F. Load frequency
controller using gene lie algorithm based fuzzy
gain scheduling of P1 controller. Electric
machines and power systems, 26, 1998, 39-52.
17. T. Söderström and P. Stoica, System
Identification, Englewood Cliffs, NJ, Prentice
Hall, 1989.
18. L. Ljung and T. Glad, Modeling of Dynamic
Systems, Englewood Cliffs, NJ, Prentice Hall,
1994.
19. R. Johansson, System Modeling and Identification,
Englewood Cliffs, NJ, Prentice Hall, 1993.
20. L. Ljung, System Identification: Theory for the
User, 2nd ed., Englewood Cliffs, NJ, Prentice
Hall, 1999.
21. Roadmap to Power sector Reforms; presidential
Policy Publication on Power sector Reforms in
Nigeria, 2010.
22. J. Sjöberg et al., “Non-Linear Black-Box
Modeling in System Identification: A Unified
Overview”, Automatica, (12), 1995, pp. 1691–
1724.

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