a novel hvdc control strategy to enhance interconnected...

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.



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.


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.



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)


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



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


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



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

[email protected]

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


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


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


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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of Grid-Connected Voltage-Source Converters", IEEE Transactions on Power Systems,

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[28] Aurobinda Routray, P. K. Dash, and Sanjeev K. Panda, “A Fuzzy Self-Tuning PI

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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.

a novel hvdc control strategy to enhance interconnected...

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