a novel hvdc control strategy to enhance interconnected...
TRANSCRIPT
American Journal of Scientific Research
ISSN 1450-223X Issue 11(2010), pp.35-46
© EuroJournals Publishing, Inc. 2010
http://www.eurojournals.com/ajsr.htm
A Novel HVDC Control Strategy to Enhance Interconnected
Power Systems: A Graphical-Based Solution
A.Srujana
Research Student, JNT University, Hyderabad
E-mail: [email protected]
Tel: 9866152847
S.V.Jayaram Kumar
Professor, Jawaharlal Nehru Technological University, Hyderabad
E-mail: [email protected]
Tel: 9848770581
Abstract
The occurrence of transient stability issues such as synchronism loss and many
more are because of certain severe power system disturbances. The issues can be solved by
the interaction of the High Voltage Direct Current (HVDC) links with the interconnected
systems. However, to improve the transient stability, it is necessary to have an effective
control strategy. Numerous control strategies are available in the literature. Since, they are
all system-based, a graphical-based novel control strategy is proposed in this paper. By
considering pre-developed control strategies, a mathematical model for power flow with
the interaction of HVDC links is developed. From the mathematical model, a control model
is derived by introducing the AC variables such as rotor speeds, voltage phasors and tie-
line power flows. The variables are termed as control parameters and the control strategy is
to determine the optima control parameters using Genetic Algorithm. From the determined
control parameters, the optimal power flow settings can be obtained. With the aid of the
power flow setting, the transient stability as well as the power flow capacity of the system
can be improved. The proposed system is evaluated by adding two HVDC links with the
IEEE 24-Bus reliability test system. The implementation results show the power flow
improvement due to the proposed control strategy.
Keywords: HVDC, control strategy, power flow capacity, Genetic Algorithm (GA),
mathematical model, control model, control parameters, power flow settings,
transient stability
1. Introduction Three phase AC current is utilized in high voltage transmission grids, due to its outstanding technical
properties for instance rotating magnetic fields can be easily produced by it in motors [1]. Many
problems arise if extra high voltage alternative current (EHVAC) interconnections are used between
power systems particularly for long distance transmissions. The overall dynamic performance of the
system is worsened by the key problems connected with these lines which include frequent tripping
caused by large power oscillations, bigger defective current level and disturbances that are passed on
A Novel HVDC Control Strategy to Enhance Interconnected Power Systems: A
Graphical-Based Solution 36
from one system to the other [11]. In rectifiers and inverters, the AC networks that connect the
commutating buses are modeled as infinite sources separated by system impedance [9]. Due to load
disturbances that occur in an an AC power system, severe system frequency oscillations problems may
arise as a result of possible operating frequency disturbances [3]. High voltage dc transmission systems
have emerged in the power scene as a promising alternative to effectively avoid these difficulties [11].
HVDC has made a significant impact in the technical and commercial aspects of electric power
transmission in developing countries [2]. The entrenched HVDC technology has got appealing
characteristics that has made it more suitable than AC transmission for certain applications like long-
distance power transmission, long submarine cable links and interconnection of asynchronous systems
[4]. Moreover HVDC system provides an efficient and reliable solution, for the major technical
challenges faced by traditional AC solutions such as high capacitive cable currents and necessity of
keeping the wind farm frequency same as that of grid frequency [17]. The attractive features of HVDC
transmission lines include quick controllability of line power by means of converter control,
improvement of transient stability in HVAC lines, and economical benefits [5]. In the AC system
interface, decline of effective short-circuit ratio (ESCR) increases the disturbance sensitivity of AC/DC
interaction which makes it much more difficult to get good overall performance by correctly adjusting
the control constants [10]. With the objective of providing a coordinated control of the HVDC link
transmitted power, the off-shore grid ac voltage and HVDC inverter controllers were designed. An
adequate regulation of both HVDC voltage and current is provided by the coordinated control system
[16].
The advantages of the conventional HVDC systems over three-phase AC transmission systems
are. [1]. Firstly, transmission line cost and operating cost are lower for HVDC systems. Secondly,
synchronous operation of HVDC is not necessary between the two AC systems it connects. Thirdly,
controlling and adjusting power flow are easier, etc [12]. Conversion, switching and control are the
disadvantages of HVDC systems. Though static converters are expensive, even for short distances
static converters may be preferable because of the reduced losses associated with it compared to static
inverters for AC transmission [6]. Operating HVDC link in parallel with an EHVAC link for
interconnecting two control areas is one of its major applications [18]. Power transmission capability
can be increased and system stability problems can be avoided by operating a bipolar high-voltage
direct-current (HVDC) power transmission system in parallel with a 400-kV AC power transmission
system [8]. In two area power systems the length of transmission lines in the transmission links are
regarded as long and are greater than the break even lengths of AC and HVDC transmission lines [15].
The three basic parts of HVDC Transmission systems are: 1) AC to DC converter station 2)
transmission line and 3) DC to AC converter station [6]. The important limitation of conventional
HVDC converters is its dependence on AC network voltage to turn-off the thyristor valves [13]. A
destabilizing influence can be exerted by the HVDC converter on the torsional mode of vibration of the
turbine-generator. There are several forms in which the sub-synchronous torsional interaction between
an HVDC converter and a turbine-generator may be manifested: Interaction through the HVDC current
regulator, Interaction through HVDC supplementary controls and HVDC converter mis-operation [19].
Various configurations of HVDC systems identified based on the function and location of the converter
stations are: Back-to-back HVDC system, Monopolar HVDC system, Bipolar HVDC system [20] and
Multi-terminal HVDC system [7].
The frequently occurring transient stability issues and its resultant, reduced power flow
capacity in interconnected systems makes the interaction of HVDC systems an essential pre-requisite.
Linking HVDC systems with interconnected systems requires effective control strategy to improve the
power flow capacity as well as transient stability. Unfortunately, the environment is in such a way that
all the control strategies are system-based and so the strategy will be ineffective in upcoming days. In
this paper, we propose a graphical-based novel control strategy. The control strategy is derived by
developing a mathematical model from some pre-developed control strategies and then by deriving a
control model. The proposed strategy uses GA to determine the optimal control parameters and the
37 A.Srujana and S.V.Jayaram Kumar
optimal power flow settings. From the determined settings, stability improved and improved power
flow is obtained. The rest of the paper is organized as follows. Section 2 makes a brief review over the
related literary works and Section 3 gives an introduction about GA. Section 4 details the proposed
methodology with required mathematical illustrations. Section 5 discusses about the test system and
implementation results and Section 6 concludes the paper.
2. The Literature: A Brief Review Ibraheem et al. [11] have detailed a comprehensive study about the effect of parametric uncertainties
on the dynamic performance of a two-area power system interconnected via parallel ac/dc transmission
links. Based on the frequency deviation at rectifier end, the dynamic model of incremental power flow
through dc transmission link is derived. Moreover, constant current control mode is considered to be
the operating mode of dc link. To carry out the investigations, the system under consideration is
implemented with optimal automatic generation control (AGC) regulators designed using full state
vector feedback control strategy if ever a 1% load disturbance occurred in one of the areas. The
response plots of frequency deviation of disturbed area (∆F2) with (i) nominal system parameters and
(ii) ± 50% variation in system parameter values were scrutinized to study the effect of ± 50% variation
in system parameters from their nominal parameter values on system dynamic performance.
Kim et al. [21] have detailed about the development of a new type of simulator for studying the
dynamic performance of a High Voltage Direct Control (HVDC) scheme. A digital model of the power
equipment and an analogue model of the HVDC controller were used by the new simulator. The
dynamic performance of the Cheju - Haenam HVDC system was studied using the simulator and the
control characteristics of the HVDC system were verified.
Khatir et al. [9] have discussed the comparison of the transient behaviors of the capacitor
commutated converter (CCC) and conventional inverter in feeding weak AC systems by modeling
them using PSB/Simulink. Many beneficial features are present in the capacitor commutated converter
which makes its use attractive in HVDC transmission systems that is connected to a weak receiving
AC system. For studying the effectiveness of these features steady-state and transient analyses can be
used. In a weak receiving AC network, a better behavior compared to conventional technology is
demonstrated by the CCC-HVDC system in following a single phase-to-ground and a remote single
phase-to-ground fault. Insensitivity to commutation failures is provided by the increased commutation
margin-angle. Though, commutation can be successful when the AC bus voltage is close to zero, on
the recovery of AC bus voltage commutation failure occurs.
Agelidis et al. [7] have discussed an overview of the recent advances in the area of voltage-
source converter (VSC) HVDC technology. Exploitation of the benefits of the four-quadrant static
converter by utilities interlinking two AC systems through HVDC has been assisted by the
development of high-voltage high-power semiconductors. The important advantages include capability
to control active and reactive power independently by controlling the converter using pulse-width
modulation (PWM), fast dynamic response and possibility of connecting AC islands with the grid
where synchronous generation is absent.
Ganapathy et al. [22] have proposed a new design of decentralized load-frequency controllers
for interconnected power systems with AC-DC parallel tie-lines which is based on Multi-objective
Evolutionary Algorithm. For stabilizing the frequency oscillations of AC system HVDC link is
connected in parallel with the existing AC link. The two main objectives accomplished by the proposed
controller are, minimum Integral Squared Error of the system output and maximum closed loop
stability of the system. A trade-off between Integral Squared Error criterion and Maximum Stability
Margin criterion is provided by the optimal Proportional plus Integral controller, obtained by the
proposed design. The implementation result of the proposed design on a two area interconnected
thermal power system with parallel AC-DC tie-lines has revealed that the transient response is
improved considerably in addition to the increased stability margin.
A Novel HVDC Control Strategy to Enhance Interconnected Power Systems: A
Graphical-Based Solution 38
Ramesh et al. [3] have proposed a fuzzy logic controller that stabilizes the frequency oscillation
in a parallel AC – DC interconnected power systems for use in HVDC link applications. Considerable
disturbance occurs in the system frequency which makes it oscillatory when load disturbance occurs in
an interconnected AC power system. For stabilizing the frequency oscillation of AC system, tie line
power modulation of HVDC link through interconnections can be used in which the system
interconnections acts as the control channels of HVDC link. To overcome the inadequate control
performance of the conventional Integral controller Fuzzy Logic Controller (FLC) is utilized with a set
of control rules.
Khatir et al. [14] have proposed a control strategy for steady-state and dynamic performances
of an asynchronous VSC based back-to-back HVDC link while stepping changes of the active and
reactive powers, balanced and unbalanced faults. Fast and satisfactory dynamic responses have been
provided by the proposed control strategy for all the cases. It controls the through power flow and
supplies reactive power in addition to provide independent dynamic voltage control at its two
terminals. The reactive power supply capability of one side or the other can be doubled by connecting
the two converters in parallel. Transmission lines or cables designed for higher voltage can be used to
form point-to-point or multi-terminal transmission links. Additional network benefits can be provided
using more sophisticated controls.
3. Genetic Algorithm (GA) Computer programs that simulate the heredity and evolution of living organisms are known as GAs
[23]. Because GAs are multi-point search methods, they can be utilized to obtain an optimum solution
for multi modal objective functions. GAs are also applicable in discrete search space problems. Thus,
GAs are very powerful optimization tools and at the same time they are very easy to use [26]. In GA,
each candidate solution to the problem is known as chromosome and they are represented as strings in
the search space. The fitness value of a chromosome is the value of its objective function. A set of
chromosomes along with their associated fitness is termed as population. Populations generated in an
iteration of GA are termed as generations [24].
Crossover and mutation techniques were utilized to create new generations (offspring).
Crossover creates new chromosomes by splitting two chromosomes and combining their split parts,
taking one split part from each chromosome. A single bit of a chromosome is changed by mutation.
Then by calculating the fitness value of each chromosome for a given fitness criteria, the best
chromosomes are retained while others are discarded. The process is repeated until one chromosome
has best fitness value which is chosen as the solution for the problem [25].
4. The Graphical-based HVDC Control Strategy The proposed methodology for the HVDC interacted AC system is comprised of four main stages,
namely, developing a mathematical model for pre-developed HVDC control strategies, developing a
control model from the mathematical model, determining optimal control parameters and finally
determining the optimal power flow settings for the system. To perform the four stages, a pre-
developed system-based HVDC control strategies proposed in [27] are considered. The optimal power
flow through the HVDC link for four control strategies are obtained and then the different stages of
operation is performed to achieve the optimal power flow settings.
39 A.Srujana and S.V.Jayaram Kumar
4.1. Mathematical Modeling for Pre-developed HVDC Control Strategies
Let 1dcP , 2dcP , 3dcP and 4dcP be the power flow through the HVDC link with strategies 1, 2, 3 and 4
respectively. From each power flow graph, the number of curves are determined by the following
novel curve detection algorithm.
4.1.1. Curve Detection Algorithm
The curve detection algorithm is based on the analysis of the power flow graph and the algorithmic
steps are given below:
• Determine the case selection factor selC from the power flow graph at every incremental
change of time.
• Increase cN by ‘1’, when there is a change of value between recently obtained selC ( selC
at t ) and the previously obtained selC ( selC at tt ∆− ).
• Repeat the process iteratively until the end of the graph.
The above said process is performed by traversing the dcP power flow graph. At every
incremental change of time, the power flow settings are subjected to three criterions and based on the
criterion results, selC is determined. The criterions are tabulated in Table 1.
Table 1: Power flow criterions to decide the case selection factor selC
S.No Csel Criterions
1 0 )()( ttPtP dcdc ∆−=
2 1 )()( ttPtP dcdc ∆−>
3 -1 )()( ttPtP dcdc ∆−<
Once the number of curves are determined from the power flow graph of the control strategies,
quadratic equation for each control strategy is determined as follows:
( ) )1(0
)1(1
2)1(2
2)1(2
1)1(1
)1(1 atatatatatatP
NN
NN
NNdc ++++++= −
−−
− L (1)
( ) )2(0
)2(1
2)2(2
2)2(2
1)2(1
)2(2 atatatatatatP
NN
NN
NNdc ++++++= −
−−
− L (2)
( ) )3(0
)3(1
2)3(2
2)3(2
1)3(1
)3(3 atatatatatatP
NN
NN
NNdc ++++++= −
−−
− L (3)
( ) )4(0
)4(1
2)4(2
2)4(2
1)4(1
)4(4 atatatatatatP
NN
NN
NNdc ++++++= −
−−
− L (4)
The Eqs. (1), (2), (3) and (4) are solved individually and later the values of the coefficients are
determined and applied in the corresponding coefficients’ positions. Then, new consolidated
coefficients and power flow capacity is determined using the Eq. (5) and Eq. (6), respectively and a
mathematical model is developed as given in the Eq. (7).
∑=
==4
1
max)(' ,2 ,1 ;25.0j
j
ii Niaa L (5)
( ) ( )∑=
=4
1
25.0
j
dcjdc tPtP (6)
( ) 0'
1'2
2'2
2'1
1'' max
maxmax
maxmax
max atata tatatatPN
NN
NN
Ndc ++++++= −−
−− L (7)
From the mathematical model, which is obtained from the control strategies, the control model
to be processed is developed.
A Novel HVDC Control Strategy to Enhance Interconnected Power Systems: A
Graphical-Based Solution 40
4.2. Derivation of Control Model
The control model is developed by equating the power flow determined for the mathematical model
and the power flow capacity mentioned in [27]. To accomplish this, a new power flow model is
derived from [27]. The four control strategies and the power flow setting given in [27] are as follows: )(
0set
dcjdcdcj PPP += (8)
where,
∫+= dteKeKP jijpset
dcj .
)( (9)
Strategy 1: 01 =e (10)
Strategy 2: ( )ir dcdc
dt
de ωω −=2 (11)
Strategy 3: ( )ir dcdc
dt
de δδ −=3 (12)
Strategy 4: ( )IAPdt
de =4 (13)
From Eq. (8) and from the strategies (given in Eqs. (10), (11), (12) and (13)) the power flow
model can be mathematically modified as,
dteKeKdteKeKdteKeKPtP ipipipdcdc ∫∫∫ ++++++= 4433220)( (14)
dtPdt
dKP
dt
dKdt
dt
dK
dt
dKdtww
dt
dKww
dt
dKPtP
IAiIApdcdci
dcdcpdcdcidcdcpdcdc
ir
iririr
)()( )(
)( )()()( 0
∫∫
∫
++−
+−+−+−+=
δδ
δδ
(15)
Then by simplifying, we can get,
( ) ( )IAdcdcdcdciIAdcdcdcdcpdcdc PwwKPwwKPtPiriririr
+−+−++−+−+= )()()()()('''
0 δδδδ (16)
where,
)()( '
irir dcdcdcdc wwdt
dww −=− (17)
)()( '
irir dcdcdcdcdt
dδδδδ −=− (18)
)('
IAIA Pdt
dP = (19)
By equating Eq. (7) and Eq. (16), control model can be derived as,
)(... 321'1'
1
2'2
'1
'0
max
max
max
max TTTKtatatataa pcN
N
N
N+++=+++++
−
−α (20)
In Eq. (20), the cα is represented as strengthening constant and 1T , 2T and 3T are time
dependent variables. They are given as:
( )'1 ir dcdc wwT −= (21)
( )'2idcrdc
T δδ −= (22)
'3 IAPT = (23)
( ) ( )( )IAdcdcdcdcidcc PwwKP
irir+−+−+= δδα
0 (24)
41 A.Srujana and S.V.Jayaram Kumar
Without any system based descriptions, the control model is solved using GA. To obtain
optimal power flow settings, it is necessary to determine optimal time dependent variables 1T , 2T , 3T
and cα . Hence, in this work, we consider the aforesaid variables as control parameters and GA is used
to determine them.
4.3. Determining Control Parameters Using GA
The control parameters are determined at every incremental change of time and so the process is
initiated at 0=t . The step-by-step procedures for determining the control parameters using GA are
described in this section.
Step 1: Create a population pool of size PN and fill it with
chromosomes ][)(
3)(
2)(
1)(
0aaaa
k x x x xX = ; 1,,2,1,0 −= pNk L . The gene )(
0a
x represents cα ,
)(1
ax represents 1T ,
)(2a
x represents 2T and )(
3a
x represents 3T and they are arbitrarily generated within
the interval ),( maxmincc αα , ),( max
1min
1 TT , ),( max2
min2 TT and ),( max
3min
3 TT respectively.
Step 2: Evaluate the fitness of each chromosome present in the population pool. The fitness
function is defined as,
)(
1max
kPf
dck
∆= (25)
where,
)(max)( 321'2'
2'1
'0
max
TTTKtatataa kP pcN
Ndc ++−−++++=∆ αL (26)
Step 3: Select the best 2/pN chromosomes which have maximum fitness value and place
them in the mating pool.
Step 4: Perform crossover operation between the 2/pN chromosomes at a crossover rate of
RC and obtain a child for every parent chromosomes. The crossover is performed by exchanging RC4
genes between two parent chromosomes. Hence, 2/pN children chromosomes childX which have the
features of both parents are obtained.
Step 5: Perform mutation over the childX at a mutation rate of RM to obtain new 2/pN
chromosomes i.e. newX . To perform mutation, firstly, RM4 genes are selected arbitrarily in the
offspring. Then, the selected genes are replaced by new gene values by arbitrarily generating them in
the corresponding gene value limits.
Step 6: Neglect the population pool chromosomes and fill it up with the mating pool
chromosomes and the new chromosomes newX .
Step 7: Iteratively repeat the process from step 2 until a maximum number of iterations gets
reached, say maxI .
Step 8: Once the iteration gets completed, a best chromosome is selected from the population
pool based on its fitness value and the power flow is determined from the control parameters using Eq.
(16).
Step 9: Repeat the process from step 1 for every incremental change of time as Nt ≤≤0 . So
the process is again started from step 1 with ttt ∆+= .
Step 10: Obtain the determined power flow setting for every time instant that are called as the
optimal power flow settings for the HVDC link.
The power flow settings can be achieved by optimal control parameters determined from the
GA. From the derivation and the GA process, the control model aids in the determination of optimal
A Novel HVDC Control Strategy to Enhance Interconnected Power Systems: A
Graphical-Based Solution 42
control parameters and so the optimal power flow settings are determined. By utilizing the parameters,
optimal power flow can be achieved while an interaction is made between the HVDC link and AC.
5. Results and Discussion The proposed control strategy was implemented in the MATLAB (version 7.10) and tested using IEEE
24-bus reliability system. Two HVDC links were added with the test system, one between bus 18 and
bus 3 and the other between bus 21 and bus 11. The HVDC link added test system is shown in Figure
1. As the control strategy is graphical-based, load flow analysis is not necessary. Hence, we preferred
to use the pre-contingency data [27] for the implementation of our methodology. The load flow data
for pre-contingency are given in Table 2, 3 and 4. The GA parameters utilized in the proposed
methodology are tabulated in Table 5. The control parameters, optimal power flow settings and the
mean power flow obtained from the proposed control strategy are given in Table 6. A comparison of
the power flow graph for the pre-developed control strategy I and the power flow graph for the
proposed control strategy are given in Figure 2.
Table 2: Pre-Contingency data: Generator outputs in the test system
Bus number Active power(MW) Reactive power (MVAR) Bus voltage (p.u)
1 161 31 1.025
2 161 24 1.025
7 257 61 1.025
13 525 102 1.025
15 187 -2 1.000
16 133 -50 1.000
18 363 145 1.025
21 363 122 1.025
22 280 31 1.025
23 446 87 1.035
Table 3: Pre-Contingency data: Power flows in the test power system
From bus To bus Active power MW) Reactive power (MVAR)
16 17 -144 -49
15 21 -111 -40
15 24 98 -2
11 14 44 10
23 12 114 16
23 13 40 16
Table 4: Pre-Contingency data: Power flows through the HVDC links
Label From bus To bus Active power (MW)
HDVC1 21 11 180
HDVC2 18 3 90
43 A.Srujana and S.V.Jayaram Kumar
Table 5: GA parameters in determining the control parameters
S.No Parameters Values
1 pN 10
2 maxmincc αα 0/100
3 max1
min1 TT 0/5
4 max2
min2 TT 0/5000
5 max3
min3 TT 0/100
6 RC 0.5
7 RM 0.5
8 maxI 50
Table 6: The control parameters and optimal power flow settings for time instants (at t = 1, 2, 3, 4 and 5
secs) and the mean power flow through the HVDC link
HVDC link Control parameters
Optimal power
flow setting (MW)
Mean power flow
(MW) α T1 T2 T3
1
1.7012 2.6157 2.262 2 178.9866
130.1128
77.4252 7.0775 494.4256 878 240.5251
77.4252 9 494.4256 877 240.6173
77.4252 8.9049 494.4256 943 247.2078
77.4252 9.1332 494.4256 995 252.4306
2
0.6044 7.7557 2.8878 9 91.3600
219.2212
66.6511 3.5605 497.4741 679 141.3524
66.6511 6.2834 4.5447 644 88.8317
66.6511 7.9206 5.2645 863 110.9674
66.6511 2.0698 495.0128 605 133.5572
Figure 1: IEEE 24-bus RTS-96 system linked with two bipolar HVDC systems
A Novel HVDC Control Strategy to Enhance Interconnected Power Systems: A
Graphical-Based Solution 44
Figure 2: Power flow through HVDC links 1 and 2 obtained from the proposed control strategy and control
strategy I
The obtained results show that the proposed control strategy is capable of improving the power
flow capacity of the system. The power flow graph illustrates that the transient stability of the system
can also be improved by the proposed control strategy.
6. Conclusion In this paper, we have proposed a novel control strategy through HVDC interactions for transient
stability improvement in AC systems. The strategy has been tested in the IEEE 24-bus systems with
two HVDC links. As the proposed strategy is graphical-based and not system-based, our control
strategies have been derived by considering some pre-developed control strategies based on rotor
speeds, voltage phasors and tie-line powers. The control strategy has performed well in improving the
transient stability as well as the power flow capacity of the system. The results illustrate that the
proposed control strategy offers improved transient stability as well as improved power flow capacity
compared to other system-based control strategies. This can pave the way for effective utilization of
generated power and satisfy the load efficiently.
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10.1109/PTC.2009.5281816
An Effective PI Controller Self Tuning Technique for HVDC
Links: A Hybridization by Blending of GA and ANN
1A.Srujana and
2 Dr. S.V.Jayaram Kumar
1Research Scholar, JNT University, Hyderabad
2Professor, Jawaharlal Nehru Technological University, Hyderabad
Abstract: Today, HVDC transmission system has emerged as the only promising alternative
available to handle large bulk of power. However, controlling and operating the HVDC
system remains a challenging task, especially due to the complexities involved in maintaining
the system stability when a fault occurs. This can be accomplished by incorporating
controllers and utilizing proper techniques to tune them. Hence, controlling effectiveness of
the HVDC system relies on the effectiveness of the self tuning mechanism used by the
controllers. In this paper, a hybrid technique is proposed to tune the PI controller-
incorporated HVDC system. The technique automatically tunes the PI controller parameters
to effectively stabilize the HVDC operation. Basically, the technique is a blend of the two
renowned Artificial Intelligence techniques, Artificial Neural Network and Genetic
Algorithm. The Genetic Algorithm is used to generate the training dataset for the neural
network. The neural network continually provides suitable controller parameters from the
time of fault until the fault is corrected. The technique is simulated and compared with
conventional and fuzzy-based self tuning techniques. The implementation results show that
the performance of the proposed hybrid technique is superior to that of both the self tuning
techniques.
Keywords: HVDC, fault clearance, Genetic Algorithm (GA), neural network, PI controller
1. Introduction
Today, increasing the capacity of power systems is often a taxing choice because erection of
large power plants and high voltage lines are hindered by economic, environmental, and
political constraints. Therefore, possible new solutions to deal with the above mentioned
issue are explored. One such highly promising technique recommends the change over of
existing and planned conventional HVAC (High Voltage Direct Current) transmission
technologies with HVDC (High Voltage Direct Current) ones[1]. In recent times, HVDC
systems that interconnect large power systems providing several technical and economic
advantages have increased significantly.
In comparison with AC transmission, the features of the proven HVDC technology are
universally more appealing for certain applications like long submarine cable links and
interconnection of asynchronous systems [1]. Bipolar, mono-polar metallic return and mono-
polar ground return modes are the three types in which HVDC systems are designed for
operation [5]. Since charging the capacitance of a transmission line with the alternating
voltage is not necessary, HVDC has the important advantage of more efficient long distance
transmission [21]. Due attention has not been paid to the system interconnection use of
HVDC transmission link [4]. The good features unique to HVDC converters in power
transmission systems are huge capacity and rapid controllability [18].
In recent years, due to the improvements made in the power electronics sector, electricity
transmission and distribution has been significantly improved by HVDC based Voltage
source converters (VSC-HVDC) transmission link that employs self-commutated valves
(IGBTs, IGCTs and GTOs) [9]. VSC-HVDC system is one of the latest HVDC technologies
and it employs two VSCs one each for the rectifier and the inverter [8]. However, the fault
occurrence remains an open challenge in the system. The faults that commonly occur in
power distribution and transmission systems are line to ground fault , line to line fault, double
line to ground fault, and three-phase to ground fault [11]. Hence, controllers are incorporated
in the system to clear the fault. Conventionally, fixed gains PI controllers are used by HVDC
systems [3]. But this is replaced by self tuning controllers.
Several techniques are proposed in the literature for fault detection. One such method is
based on the sequence components present in the fundamental frequency of the post-fault
current and voltage [14]. Generally a Fault Detection and Diagnostic system carries out the
task in two stages; they are symptom generation and diagnosis [1]. This is achieved by
maintaining the power system at the preferred operating level through the employment of
latest control techniques [7]. Protection against line faults can be achieved using Artificial
Neural Network, Fuzzy system and Genetic algorithm based latest controls which are fast and
reliable [13].
Generally they employ adaptive tuning of the controller for effective control. However,
because a single technique is deployed for this purpose, the effectiveness remains a challenge
as the necessity and complexity of HVDC system peaks. To overcome this issue, we propose
a hybrid technique in this paper to self tune the PI controller which controls the HVDC
system whenever a fault occurs. The rest of the paper is organized as follows. Section 2
reviews the related works briefly and section 3 details the proposed technique with sufficient
mathematical models and illustrations. Section 4 discusses implementation results and
Section 5 concludes the paper.
2. Recent Research Works: A Brief Review
Chi-Hshiung Lin [22] has compared the misfire fault in the rectifier valve and the misfire
fault in the inverter valve of an HVDC link. A dynamic simulation analysis has revealed that
the resultant phenomena are not identical. A misfire fault in the rectifier valve induces a
power disturbance on the rectifier side of system frequency which if any of the natural
torsional modes are disrupted induces a considerable torsional torque in a turbine generator
adjacent to the inverter station. But a misfire fault in an inverter valve is likely to breakdown
the HVDC link by creating commutation failure in converters. The rectifier and the inverter
sides of the generator have been affected quite severely if a collapse occurred in the HVDC
link.
Vinod Kumar et al. [23] have modeled a high speed high precision HVDC transmission
system that works with weak ac system and analyzed the fuzzy controlled control strategy &
performance of the system. In spite of unsteadiness and big discrepancies of the input power,
the system has been capable of feeding weak or even dead networks. Optimization of the link
efficiency under diverse disturbances has been achieved by fuzzy logic-based control of the
system. Models can be built for individual users own models using the basic building blocks
found in a typical HVDC systems that has been provided by the proposed model. A DQ- type
of phase-locked-loop that has been presented for synchronizing the firing pulses to the
HVDC converter are specific contributions of the proposed method. In spite of polluted and
harmonic distorted commutation voltage, this gate-firing unit has been capable of supplying a
pure sinusoidal synchronizing voltage. The capability of the proposed fuzzy logic based
HVDC system to operate steadily, restore steadily in the event of a short circuit fault and its
obvious advantages have been confirmed by PSCAD/EMTDC based simulations.
Mohamed Khatir et al. [24] have stated that the relative strength of the AC system
considerably affects the performance of an HVDC link that is connected to it. However, the
strength of the AC system relative to the capacity of the DC link has significant influence on
the interaction between AC and DC systems and the related problems. In a HVDC inverter,
they have investigated the effect of the DC control on recovery from AC system fault
produced commutation failures, in line commutated thyristor inverter feeding a weak AC
system. The study system has focused on the AC system fault, single phase ground fault. For
simulation studies, MATLAB Simulink has been used.
Mohamed Khatir et al. [25] have discussed that HVDC converter of capacitor commutated
converter (CCC) type of topology has the potential for employment in long distance
transmission via cables. Therefore for HVDC transmission across large bodies of water, this
proposed method can be employed. They have presented the Capacitor Commutated
Converters (CCC) technology and demonstrated its advantages for high power transmission.
For presenting the transient performance evaluations PSCAD/EMTDC has been used. The
system has been obtained from the earliest CIGRE HVDC Benchmark model. The superior
performance of a very weak AC system connected CCC link with regard to improved
transmission capacity and better stability of the AC network has been confirmed by results.
Bandarabadi et al. [9] have discussed the use of VSC-HVDC link based transmission
network for possible improvement of fault-ride through capability in 160 MW wind farm
connection. The 80 numbers of 2 MW permanent magnet synchronous generators that
constituted the 160 MW wind farm have been separated into 4 groups with 40 MW nominal
powers. In the course of wind speed variations and after the removal of grid side faults the
power losses for re-establishing the voltage at the transmission network terminal has to be
minimized. It is important for the VSC-HVDC to support the voltage of the transmission
network side in the course of short circuit faults in main grid which has been termed as fault
ride-through capability improvement. Variable speed operation and fault ride-through
capability improvement has been recommended by the proposed method for wind farm
network and transmission network respectively. The behavior of wind farm, transmission
voltage and dc voltage for diverse changes in wind speed and three-phase short circuit fault
has been studied by performing simulation in PSCAD/EMTDC software. Through simulation
results the improvement achieved by the connection method in performance and fault ride-
through capability has been verified.
Khatir Mohamed et al. [26] have applied step changes of the active and reactive powers
and balanced and unbalanced faults to VSC based HVDC transmission system and
investigated its steady-state and dynamic performances. For all cases, the obtained results
have revealed the capability of the proposed control strategy to provide quick and satisfactory
dynamic responses to the proposed system. The capability of VSC-HVDC to perform quick
and bi-directional power transfer has been made evident by the simulation results. It has been
evident that except for a small oscillation constant transmitted power can be maintained
during single line fault. But, during a three-phase fault, power flow by the DC link has been
reduced considerably because of the reduced voltage at the converter terminals. Rapid
recovery of normal operation has been possible after the fault is removed.
Lidong Zhang et al. [27] have proposed a control method of grid-connected voltage-source
converters (VSCs). The method has been highly significant in high-voltage dc (HVDC)
applications, though it can be commonly employed for all grid-connected VSCs. The
principle of the proposed method resembles the operation of a synchronous machine and
utilizes the internal synchronization mechanism in ac systems unlike earlier control methods.
In a weak ac-system, utilization of this type of power-synchronization control in the VSC has
enabled prevention of instability caused by the standard phase-locked loop. In addition,
similar to a normal synchronous machine, the VSC terminal has been capable of providing
strong voltage support to the weak ac system. Analytical models and time simulations have
validated the proposed control method.
3. The Hybrid Technique for Self Tuning PI controller in HVDC
The proposed technique for self tuning the PI controller of HVDC links is a hybridization of
the two thriving techniques, genetic algorithm and artificial neural network. Self tuning of the
PI controller mainly involves the automatic determination of the proportional and integral
gains of the controller PK and IK , respectively. Self tuning has to be performed in the
controller enabled HVDC links, whenever a fault occurs in it. As mentioned earlier, only
single line-to-ground fault and line-to-line fault are considered. Because of these faults, the
current lose its stability and the fault current dominates. According to the fault current, the
controller has to be tuned to give a stable output in spite of the fault current. The technique is
comprised of three stages, namely, GA-based training dataset generation, network training
and fault clearance. The first two stages can be collectively called as the training phase of the
technique, because it is performed before the fault occurs. Once the training process is
completed, the controller will become capable of self tuning in the event of the occurrence of
any of the two types of faults for which it is trained. This is performed with the aid of the
optimal PK and IK controller gains obtained by the trained network at periodic time
intervals.
3.1. GA-Based Training Dataset Generation
The process of GA-based training dataset generation is depicted in Figure 1. The training
dataset consists of different possible error values and the corresponding optimal values of PK
and IK can be obtained from GA. To perform the process, an error dataset E is generated
within the error limit ],[ maxmin ee . The elements of error dataset are given
by maxminminmin ,,2,, eeeeeeE TT , where, Te is a threshold to generate elements
in a periodic interval. For every element of E , optimal PK and IK are determined using GA,
as described below.
Figure 1: The proposed GA-ANN hybrid PI controller self tuning technique for HVDC
systems.
(i) Chromosome Generation: Generate a population pool of size pN with pN number of
arbitrary chromosomes, )(1
)(0
pp
p xxX ; 1,,1,0 pNp , where, )(
0p
x and )(
1p
x are the
two genes of the thp chromosome that are generated arbitrarily in the interval maxmin , PP KK
and maxmin , II KK respectively. i.e. maxmin)(0
, PPp
KKx and maxmin)(1
, IIp
KKx . Here,
pN is the size of the population pool, minPK and max
PK are the minimum and maximum range
of PK and, minIk and max
Ik are the minimum and maximum range of IK respectively.
(ii) Fitness evaluation: Determine the fitness for every chromosome present in the
population pool, using the following fitness function
T
Npp dtIF
p 0]1,0[
||minarg
(1)
where,
Tppref dtexexII
0
)(1
)(0
(2)
In Eq. (1), pF is the fitness of the thp chromosome, I is the change in current due to
the thp chromosome andT is the time maxima. In Eq. (2), refI is the reference current and
e is the error element of E . .
(iii) Selection: Select the best 2pN chromosomes based on fitness value and place it in
the mating pool.
(iv) Crossover: Crossover the chromosomes in the mating pool at a crossover rate of rC
to obtain a child childX for every parent chromosomes.
(v) Mutation: Mutate the chromosomes by randomly selecting the genes at a mutation
rate of rM . Replace the gene values by arbitrarily selecting the corresponding range of values
to obtain 2pN new children for the 2pN parent chromosomes.
(vi) Termination criteria: Refill the population chromosomes by the 2pN mating pool
chromosomes and new 2pN children chromosomes. Go to step 2 and iteratively repeat the
process until it reaches a maximum number of iterations maxI . Once the iteration reaches
maxI , terminate the process and select the chromosome, which has best fitness in the mating
pool, as the best chromosome
The obtained best chromosome has an optimal PK and IK for the particular element of E .
Similarly, optimal PK and IK are obtained for all the elements of E and the dataset is
generated as follows
)()(
)3()3(
)2()2(
)1()1(
max
min
min
min
2
IEII
IEIp
pp
Ip
Ip
T
T
KK
KK
KK
KK
e
ee
ee
e
D
(3)
where, D is the training dataset generated from the GA. The obtained training dataset is used
to train the neural network in the upcoming phase of network training.
3.2. Training of Neural Network
Multilayer feed forward neural network is selected for our technique, and it is trained using
the dataset given in the Eq. (3). The network structure with parameters is depicted in Figure
2. In order to train the network, Back Propagation (BP) algorithm is used. The network
training process is described below.
Figure 2: The structure of multi-layer feed forward neural network utilized in the proposed
technique.
Step 1: Generate arbitrary weights within the interval 1,0 and assign it to the hidden
layer neurons as well as the output layer neurons. Maintain a unity value weight for all
neurons of the input layer.
Step 2: Input the training dataset D to the classifier and determine the BP error as follows
outtarerr DDBP (4)
In Eq. (4), tarD is the target output and outD is the network output, which can be
determined as ][)2(
2)1(
2 y yDout , where )1(2y and )2(
2y are the network outputs which
directly represent PK and IK respectively. The network outputs can be determined as
HN
1r
11r2)1(
2 )r(ywy (5)
HN
1r
22r2)2(
2 )r(ywy (6)
where,
)exp(1
1)(
11 inr Dwry
(7)
Eq. (5) and Eq. (6) represents the activation function performed in the output layer and
hidden layer respectively.
Step 3: Adjust the weights of all neurons as www , where, w is the change in
weight which can be determined as
PB yw err.. (8)
In Eq. (8), is the learning rate, usually it ranges from 0.2 to 0.5.
Step 4: Repeat the process from step 2, until BP error gets minimized to a least value.
Practically, the criterion to be satisfied is 0.1 PB err .
Once the process gets completed, the network is well-trained and it would be suitable for
providing optimal PK and IK values for any error.
3.3. Fault Clearance
The fault clearance process is ultimately performed by the PI controller which is auto-tuned
by the proposed technique. When either of the aforesaid faults occur in the link, the technique
gets activated and determines the error teste from the line as follows
testreftest IIe (9)
where, refI is the reference current that needs to be maintained in the link and testI is the
measured current from the link. The measured teste is given as input to the well-trained
network. The network provides a best PK and IK value, termed as bestpK and best
IK
respectively, to the PI controller for the corresponding teste . For the obtained PK and IK
value, the PI controller controls the HVDC fault voltage and current using the traditional PI
calculation,
T
testbestItest
bestpntest dteKeKI
0 (10)
where, ntestI is the output of the PI controller. For the controlled voltage/current, again
teste is measured and the process is repeated. Iterative repetition of the process is performed
until the HVDC voltage/current reaches a stable value. Once the voltage/current reaches the
stable state, the technique is disabled and voltage/current monitoring is continued. Hence, if
any fault occurs, the technique is activated and the fault is cleared in a very short time
because of the hybridization and adaptiveness of the proposed technique.
4. Results and Discussion
The proposed technique was implemented in the working platform of MATLAB 7.10 and its
operation was simulated. For this, the reference HVDC model, which was taken from [28], is
given in Figure 3.
Figure 3: The Model of an HVDC System
The two aforesaid faults were considered in the model and the different parameters
obtained were plotted. The methodology parameters are tabulated in Table I and the results
are depicted in the following figures.
Table I: The parametric values used in the proposed technique
S.No
Technique
Parameters
Values
1 maxmin / ee 0.1/5
2 Te 0.1
3 maxminpp KK 0/10
4 maxminII KK 0/10
5 rC 0.5
6 rM 0.5
7 pN 10
8 maxI 50
9 HN 2
1.a 1.b
1.c
2.a 2.b
2.c
3.a 3.b
3.c
Figure 4: Performance comparison between (1) conventional, (2) the fuzzy-based and (3) the
hybrid PI controller self tuning technique in clearing single line to ground fault at inverter.
1.a 1.b
1.c
2.a 2.b
2.c
3.a 3.b
3.c
Figure 5: Performance comparison between (1) conventional, (2) the fuzzy-based and (3) the
hybrid PI controller self tuning technique in clearing line-to-line fault at inverter.
1.a 1.b
2.a 2.b
3.a 3.b
Figure 6: Performance comparison between (1) conventional, (2) the fuzzy-based and (3) the
hybrid PI controller self tuning technique in clearing single line-to-ground fault at rectifier.
1.a 1.b
2.a 2.b
3.a 3.b
Figure 7: Performance comparison between (1) conventional, (2) the fuzzy-based and (3) the
hybrid PI controller self tuning technique in clearing dc line-to-line fault at rectifier.
Only the technique was implemented by MATLAB coding and the model and its operation
were considered from [28]. The performance of the proposed technique was compared with
the conventional self tuning technique and fuzzy-based self tuning technique. From the
results, it is evident that the proposed technique takes considerably less time to stabilize the
system than the other existing techniques with which it was compared.
5. Conclusion
In this paper, a hybrid technique to self tune the PI controllers used in HVDC systems was
proposed. The technique was proposed with the intention of supporting the PI controller
during the fault clearance process. This has been accomplished by offering optimum PI
controller parameters at every instant of time during the fault clearance process which
stabilizes the system in a shorter time. The performance of the system has been evaluated
from the implementation results. Also, the system was validated by comparing the hybrid
technique with the conventional and fuzzy-based self tuning techniques. The comparison
results proved that the hybrid technique consumes considerably less time to clear the fault
voltage and current and hence to stabilize the system. Therefore, it was evident that the
proposed technique makes the controlling of HVDC systems significantly more effective than
other conventional self tuning techniques.
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A.Srujana received the B.Tech Degree in Electrical Engineering from Kakatiya University
,Warangal,India in 1998.She received her M.Tech Degree in Electrical Engineering from
Jawaharlal Nehru Technological University Hyderabad in 2002 .Currently she is persuing
Ph.D from the same University under the guidance of Dr S.V.Jayaram Kumar. Her research
interests include Power Electronics and HVDC.
Dr S.V.Jayaram Kumar received the M.E degree in Electrical Engineering from Andhra
University ,Vishakapatnam ,India in 1979.He received the Ph.D degree in Electrical
Engineering from Indian Institute of Technology , Kanpur ,in 2000Currently ,he is a
Professor at Jawaharlal Nehru Technological University Hyderabad .His research interests
include FACTS & Power System Dynamics.