Hybrid Technique for Self Tuning PI Controller Parameters in HVDC Systems

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					                                                          (IJCSIS) International Journal of Computer Science and Information Security,
                                                          Vol. 8, No. 9, December 2010

       Hybrid Technique for Self Tuning PI Controller
               Parameters in HVDC Systems

                          A.Srujana                                                           Dr. S.V.Jayaram Kumar
                     Research Scholar                                                                Professor
                 JNT University, Hyderabad                                           Jawaharlal Nehru Technological University

Abstract— Nowadays, due to certain advantages, the HVDC                    HVDC, so it has the advantage of providing more efficient
systems are commonly used in long distance transmissions. The              long distance transmission [21]. System interconnection use of
major drawback associated with HVDC system is that it takes a              HVDC transmission link has not attracted much awareness
longer duration to return to its steady state value after the              [4]. In power transmission systems, HVDC converters have
occurrence of a fault. In a HVDC system, when a fault occurs, the          the unique virtues of large capacity and fast controllability
current and voltage will deviate from their normal range and PI            [18].
controllers are used to maintain its current and voltage at the
normal steady state value. Controller parameter tuning plays a                 In recent years, because of the development of power
significant role in maintaining the steady state current and               electronics, an active role is played by HVDC transmission
voltage of a HVDC system. Here, we propose a hybrid technique              link based Voltage source converters (VSC), using self-
to self tune the PI controller parameters. The proposed hybrid             commutated valves (IGBTs, IGCTs and GTOs) in improving
technique utilizes fuzzy logic and neural network to self tune the         the electricity transmission and distribution system [9]. VSC-
controller parameters. The fuzzy rules are generated using                 HVDC system is one of the most modern HVDC technologies,
different combinations of current error, rate and combined gain.           and it incorporates two VSCs, one function as a rectifier and
To train the neural network, different combinations of fuzzy               the other as an inverter [8].
gain, proportional gain and integral gain are used. The neural
network is trained using a back propagation algorithm. By                      In power distribution and transmission systems, line to
experimentation it is shown that the system that uses this method          ground, line to line, double line to ground, and three-phase to
takes a very short time to return to its normal steady state. The          ground are the possible faults [11]. The literature presents lot
implementation results show that the performance of the                    of fault detection techniques. The method based on the
proposed hybrid technique is superior to that of both the self             sequence components of the fundamental frequency of the
tuning techniques.                                                         post-fault current and voltage is an example for this [14]. A
                                                                           general Fault Detection and Diagnostic scheme consists of two
   Keywords- fuzzy logic; HVDC; neural network; fuzzy rules;               phases, namely symptom generation and diagnosis [1]. So as
proportional and integral gain.                                            to accomplish this, by executing modern control strategies the
                         I. INTRODUCTION                                   power system must be maintained at the preferred operating
                                                                           level [7]. Contemporary controls which are based on Artificial
    Presently, due to economic, environmental, and political               Neural Network, Fuzzy system and Genetic algorithm are
limitations which hinder the erection of large power plants and            found to be quick, and reliable. Hence, they can be employed
high voltage lines, increasing the power system capacity is                for protection against the line faults [13].
often difficult. Hence, to solve the above issues new solutions
are sought. One of the most promising solutions suggests the                   Generally, the controller is tuned adaptively to perform the
replacement of conventional HVAC transmission technologies                 controlling effectively. However, because a single technique is
by targeted deployment of HVDC (High Voltage Direct                        deployed for this purpose, the effectiveness remains a
Current) ones [1]. Of late, there has been a significant increase          challenge as the necessity and complexity of HVDC system
in the HVDC systems that interconnect large power systems                  peaks. To overcome this issue, in this paper, we propose a
offering many technical and economic benefits [2].                         hybrid technique by means of which the PI controller that
                                                                           controls the HVDC system is self tuned whenever a fault
    HVDC is a proven technology and the features presented                 occurs. The rest of the paper is organized as follows. Section
by it have made it more alluring than AC transmission for                  II reviews the related works briefly and section III details the
certain applications for example long submarine cable links                proposed technique with sufficient mathematical models and
and interconnection of asynchronous systems [1]. Fixed gains               illustrations. Section IV discusses implementation results and
PI controllers are commonly used by HVDC systems [3]. The                  Section V concludes the paper.
operating in which a HVDC system can be designed are
bipolar mode; mono-polar metallic return and mono-polar                                           II. RELATED WORKS
ground return modes [5]. Charging the capacitance of a                        Chi-Hshiung Lin [22] has discussed the difference between
transmission line with alternating voltage is not necessary for            two faults in an HVDC link. A misfire fault in the rectifier

                                                                                                      ISSN 1947-5500
                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                         Vol. 8, No. 9, December 2010

valve and inverter valve are the two faults that have been                transmission capacity and enhanced stability of the AC
compared. A dynamic simulation analysis has disclosed that                network, when it is linked to a very weak AC system.
the resultant phenomena are not the same. A misfire fault in
the rectifier valve creates a substantial torsional torque in a               Bandarabadi et al. [9] have discussed the fault-ride through
turbine generator adjoining the inverter station whenever the             capability improvement possibility through utilization of VSC-
natural torsional modes are disrupted by the power disturbance            HVDC link to transmission network in connection of 160 MW
which it induces on the rectifier side of system frequency.               wind farm. 80 individual 2 MW permanent magnet
Conversely, a misfire fault in an inverter valve attempts to              synchronous generators that comprise the 160 MW wind farm
create commutation breakdown in converters which in turn                  has been divided into 4 groups with 40 MW nominal powers.
causes HVDC link failure. HVDC link failure if it happens                 At the time of wind speed fluctuations and after repairing the
radically affects the rectifier and inverter sides of the                 grid side faults the voltage at the transmission network
generator.                                                                terminal has to be re-instituted with reduced power losses.
                                                                          Supporting the voltage of transmission network side has also
    Vinod Kumar et al. [23] have presented a HVDC                         been vital for the VSC-HVDC at the time of short circuit
transmission system which operates with speed and precision               faults in the main grid which is also called as fault ride-
in a weak ac system and they have analyzed the control                    through capability improvement. Both uneven speed
strategy and performance of this system, which has been                   operations in wind farm network and fault ride-through
controlled by employing fuzzy. Under oscillations and huge                capability improvement in transmission network have been
deviations of the input power, the system has been capable of             stressed by the proposed technique. By means of simulation
feeding a weak or even dead network. The competence of the                carried out in the PSCAD/EMTDC software, the behavior of
link under a variety of disturbances was optimized with the               the wind farm, transmission voltage and dc voltage for diverse
help of the fuzzy logic-based control of the system.                      changes in wind speed and three-phase short circuit fault have
Fundamental building blocks that exist in a typical HVDC                  been studied. The simulation results have proved the
system have been made available by the proposed model for                 performance of the connection method and the improvement
use by individual users to build their own models. For                    in the fault ride- through capability.
synchronizing the firing pulses to the HVDC converter, the
DQ-type of phase-locked-loop presented has been a specific                    Khatir Mohamed et al. [26] have presented the steady-state
contribution of the proposed method. Supplying a clean                    and dynamic performances obtained during step changes of
sinusoidal synchronizing voltage from a contaminated and                  the active and reactive powers, balanced and unbalanced faults
harmonic imprecise commutation voltage has been made                      in a HVDC transmission system that is based on VSC. It has
possible by this gate-firing unit. The capability of the proposed         been shown that fast and satisfactory dynamic responses of the
fuzzy logic based HVDC system to operate steadily and                     proposed system have been provided by the proposed control
recover steadily in case of short circuit faults, and its obvious         strategies in all cases. It has been evident from the simulation,
merits have been proved by PSCAD/EMTDC based                              that the VSC-HVDC is capable of performing fast and bi-
simulations.                                                              directional power transfer. It has also been evident that, except
                                                                          for a small fluctuation, the transmitted power can be kept
    Mohamed Khatir et al. [24] have discussed that the relative           constant at the time of a single-phase fault. Conversely, at the
strength of the AC system which connects a HVDC link                      time of a three-phase fault, the power flow by the DC link has
considerably affects its functioning. Yet, the relative strength          been significantly reduced by the voltage at the converter
of the AC system compared to the capacity of the DC link has              terminals. There has been a quick recovery to usual operation
a major effect on the interaction between the AC and DC                   after the fault has been cleared.
systems and the problems connected with it. In an HVDC
inverter following AC system fault in line commutated                         Lidong zhang et al.[27] have presented a control method of
thyristor inverter feeding a weak AC system, the effect of the            grid-connected voltage-source converters (VSCs). This
DC control on recovery from AC system fault produced                      method has been expected to be of most significance in high-
commutation failures has been investigated by the proposed                voltage dc (HVDC) applications, though it can be usually
method. The study system has been subjected to the AC                     applied for all grid-connected VSCs. The proposed method
                                                                          has made use of the internal synchronization mechanism in ac
system fault known as Single phase ground fault. Using
MATLAB Simulink, simulation studies have been performed.                  systems, in principle, similar to the operation of a synchronous
                                                                          machine which is different from the preceding control
    Mohamed Khatir et al. [25] have discussed that HVDC                   methods. By employing this type of power-synchronization
converter topology type capacitor commutated converter                    control the instability due to a standard phase-locked loop in a
(CCC) are suitable for utilization in long distance transmission          weak ac-system connection has been prevented by the VSC.
via cables. The proposed method has the potential to be                   Furthermore, a VSC terminal has been capable of providing
employed in HVDC transmission across large bodies of water.               strong voltage support to weak ac system like a normal
The proposed technology of the Capacitor Commutated                       synchronous machine. By analytical models and time
Converters (CCC) has been presented and its advantages in                 simulations the control method has been proved.
high power transmission have been illustrated. By employing
PSCAD/EMTDC the transient performance evaluations has                       III. NEURO-FUZZY SELF TUNING PI CONTROLLER IN HVDC
been presented. From the primary CIGRE HVDC Benchmark                         In this paper, for tuning PI controller parameters in HVDC
model the system has been derived. The results have revealed              system both normal and abnormal conditions are considered.
the enhanced performance of a CCC link in terms of increased              During normal condition the current remains at its reference

                                                                                                     ISSN 1947-5500
                                                         (IJCSIS) International Journal of Computer Science and Information Security,
                                                         Vol. 8, No. 9, December 2010

value and when a fault occurs in the system, the current value            integral gain as the output. By using this proportional and
increases and at that moment the PI controller parameters are             integral gain, the controller parameters are adjusted and makes
tuned and this makes the current to remain at its reference               current to remain stable.
value. Here we use a hybrid technique to tune the PI controller
parameters in HVDC. First the error and rate values are                   A. System Model
calculated from the current value and they are given as input                 HVDC system model considered in our method is shown
to the fuzzy logic and the fuzzy logic produces a combined                in Figure 1. HVDC systems are commonly used for long
gain as the output. The fuzzy gain is given as the input to the           distance transmission and its major problem is due to the fault
neural network which in turn gives the proportional and                   that occurs in the system.

                                                      Figure 1. HVDC system model

   The faults considered in our system are                                    where, I ref is the reference current, I m is the measured
   i.      Single Line to Ground fault                                    current, ∆T is the sampling rate, ∆I pv is the previous value
   ii.     Line to Line fault                                             of error, and G1 , G 2 are the gains for normalization.
i. Single line to Ground fault                                                By using the formulas the error and rate are calculated and
      The single line to ground fault is a very common fault in           these calculated values are given as input to the fuzzy logic.
HVDC systems. During this fault the current value gets
increased and the corresponding voltage decreases.                        B. Obtaining Fuzzy Gain
ii. Line to Line fault                                                        The fuzzy logic is used here, to obtain the combined gain.
      The line to line fault occurs between two transmission              The current error and rate are the inputs given to the fuzzy
lines. This fault is one of the common faults that occur in               logic for rectifier pole controller and inverter controller and its
overhead transmission lines.                                              output is the combined gain.

    When a fault occurs in the system the current value                       The error and rate are given as input to the fuzzy logic and
increases and due to this increased current more problems                 the combined gain is obtained as its output. For obtaining this
occur in the system. To control this current we used a hybrid             fuzzy logic, the generation of fuzzy rules and training are
technique which is a combination of fuzzy logic and neural                important processes and these processes are explained in
network. The fuzzy logic is trained by giving error and rate              section III.E and III.F respectively. By giving any value of
values are given as its input.                                            error and rate as input to the fuzzy the related combined gain
                                                                          can be got as its output. When the current value changes the
    The error and rate values always depend on the current. If            combined gain also changes accordingly. Then, the fuzzy
the current value is normal then the error is zero and if current         output is given as an input to the neural network and the
increases the error also increases. The error and rate are                proportional and integral gain are obtained from the neural
calculated by using the equations given below.                            network as outputs. Based on the change in the combined gain
                                                                          that is given as input to the neural network, the proportional
         ∆ I dc = I ref − I m                                 (1)         and integral gain values will change.
           •        (∆I   dc   − ∆I pv )                                  C. Obtaining PI Controller Parameters from Neural Network
         ∆ Ι dc =                                             (2)             Artificial neural networks (ANNs) are excellent tools for
                               ∆T                                         complex manufacturing processes that have many variables
               E = G1 ⋅ (∆I dc )                              (3)         and complex interactions. Basically, neural network consists
               R = G 2 ⋅ (∆I dc )                             (4)         of three layers, namely input layer, hidden layer and output
                                                                          layer. In our model, input layer has one variable, hidden layer
                                                                          has n variables and output layer has two variables.
                                                                               The configuration of the network used is shown in Figure

                                                                                                      ISSN 1947-5500
                                                                   (IJCSIS) International Journal of Computer Science and Information Security,
                                                                   Vol. 8, No. 9, December 2010

                                                                                         In our method the fault that occurs in both rectifier and
                                                                                     inverter sides of the HVDC system are considered. In inverter
                                                                                     side the maximum fault current for line to ground fault is 2
                                                                                     KA and in line to line fault maximum fault current value is
                                                                                     2.5KA. In rectifier side the maximum fault current value is 1.5
                                                                                     KA for both single line to ground fault and line to line fault.
                                                                                     When a fault occurs in the system the current reaches its
                                                                                     maximum value and voltage becomes value. For maintaining
                                                                                     the current at its normal value the current value must be
                                                                                     reduced and voltage value must be increased. By using our
                                                                                     technique K p and K i values are adjusted, to make the
                                                                                     current reach its normal value. By using this method, when a
                                                                                     fault occurs in the system the current can be made to return to
                                                                                     its normal value within a fraction of a second.
Figure 2. Single input and two output neural networks to obtain proportional
                              and integral gain                                      E. Generation of Fuzzy Rules
                                                                                         For training the fuzzy logic, training data set and fuzzy
    The input to the neural network is the fuzzy gain and its
                                                                                     rules are generated. The faults which are considered for
outputs are proportional and integral gain. For operation, the
                                                                                     training fuzzy logic are line to line fault and line to ground
neural network must be trained using a training data set. The
                                                                                     fault in both rectifier and inverter side. By considering these
training of neural network is explained in section III.F. Once
                                                                                     faults the input variables are selected and based on that the
the network is trained a general model is created for the
                                                                                     training data set for fuzzy logic is generated. Inputs are
relationship between its input and output. So, when the
                                                                                     fuzzified in to three sets i.e.; large, medium and small and
combined gain value is given as an input to the network the
                                                                                     outputs are very large, large, medium, small and very small.
related proportional and integral gain will be got as the output.
                                                                                     The membership grades are taken as triangular and
   By using this technique, the PI controller parameters are                         symmetrical. Fuzzy rules are generated by considering both
tuned automatically and the current is made to remain stable,                        normal and abnormal conditions.
even if any fault occurs in the system it returns to its stable
                                                                                         For different combination of input variables the generated
value in a short time.
                                                                                     fuzzy rules are shown in table I. After generating fuzzy rules
D. Fault Clearance                                                                   the next step is to train the fuzzy logic.
    During normal condition, current will remain in its steady
                                                                                                              TABLE 1. FUZZY RULES
state value and so when the function checks the value, it
decides that no change is necessary in the system. When a                                         Fuzzy rules
fault occurs in the system the current will increase suddenly.                             1       if, E=large and R=large, then G=very large
This time when the function checks the current value, it                                   2       if, E=large and R=medium, then G=large
identifies the increase in current and calls our technique. By                             3       if, E=large and R=small, then G=small
using our technique, the error and rate values are calculated                              4       if, E=medium and R=large, then G=large
from the current value and they are given as input to the fuzzy
                                                                                           5       if, E=medium and R=medium, then
logic. By giving error and rate values as an input to the fuzzy
logic we get fuzzy gain as the output. This gain value is given
as input to the neural network and the neural network gives                                6       if, E=medium and R=small, then G=large
proportional and integral gain as output. By using the                                     7       if, E=small and R=large, then G=small
proportional and integral gain from the neural network the                                 8       if, E=small and R=medium, then G=large
current values are calculated using the equation given below.                              9       if, E=small and R=small, then G=very small

                                         T                                               For training fuzzy logic the first step is to generate the
                 I out = K p e + K I   ∫0edt                             (5)         training data set. This training data set is generated by
                                                                                     calculating error and rate values for different current values.
                                                                                     To perform the process, a current dataset I is generated within
    where, I out is the output of the PI controller. After                           the current limit [ I max , I min ] . The elements of current
calculating the current values the function checks whether the                       dataset                   are                     given              by
steady state value of current is reached or not. If the current
                                                                                      I = {I min , I min + I T , I min + 2 I T , L , I max } where, I T is a
has reached the steady state value then the function stops its
process. If the current has not reached the steady state value                       threshold to generate elements in a periodic interval. For every
then the function will repeat the process by calculating the                         current value the error and rate values are calculated. By using
error and rate values again and give them as input to the fuzzy.                     the calculated values fuzzy data set is generated. By using the
This process continues until the current reaches the steady                          generated data set the fuzzy logic training process is
state values i.e., until error value reaches zero.                                   performed.

                                                                                                                   ISSN 1947-5500
                                                         (IJCSIS) International Journal of Computer Science and Information Security,
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F. Neural Network Training                                                  Step 2: Apply a training sample to the network.
    The first process for training the neural network is the                Step 3: Determine the output at the output layer of the
generation of training data set. Training dataset is generated           network.
for training neural network with different combinations of
fuzzy gain, proportional and integral gain. For generating                  Step 4: Determine the value of        and        using the actual
training dataset set of fuzzy gain, proportional gain and                output of the network.
integral gain are selected and this dataset is used for training
the neural network. After generating the training data set, the               Step 5: Repeat the iteration process till the output reaches
network is trained using a back propagation algorithm.                   its least value.
    The neural network is trained using generated data set. For             Once the training is completed, the network is ready to
training neural network back propagation algorithm is used               tune the control parameters of PI controller and when fuzzy
and steps for training neural network are explained below in             gain changes the network output also changes to maintain the
detail.                                                                  current within the normal range.
   The training steps are given as follows:                                                IV. RESULTS AND DISCUSSION
   Step 1: Initialize the input weight of each neuron.                       The proposed technique was implemented in the working
                                                                         platform of MATLAB 7.10 and its operation was simulated.

                                        1.a                                                   1.b


                                                                                                    ISSN 1947-5500
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            Vol. 8, No. 9, December 2010

      2.a                                         2.b


3.a                                                     3.b

                                                         ISSN 1947-5500
                                                                   (IJCSIS) International Journal of Computer Science and Information Security,
                                                                   Vol. 8, No. 9, December 2010

Figure 3. 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


                                                                                                                      ISSN 1947-5500
            (IJCSIS) International Journal of Computer Science and Information Security,
            Vol. 8, No. 9, December 2010

      2.a                                       2.b


3.a                                                     3.b

                                                       ISSN 1947-5500
                                                                     (IJCSIS) International Journal of Computer Science and Information Security,
                                                                     Vol. 8, No. 9, December 2010

Figure 4. 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

                                                                                                                        ISSN 1947-5500
                                                                    (IJCSIS) International Journal of Computer Science and Information Security,
                                                                    Vol. 8, No. 9, December 2010

                                          3.a                                                                      3.b
Figure 5. 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

                                                                                                                      ISSN 1947-5500
                                                                     (IJCSIS) International Journal of Computer Science and Information Security,
                                                                     Vol. 8, No. 9, December 2010

                                            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 dc line-to-line
                                                                       fault at rectifier.

    Only the technique was implemented by MATLAB coding                                        HVDC Transmiissiion Systems: An example from NE India", Journal
and the model and its operation were considered from [28].                                     Earth Science India, Vol.2, No.4, pp.249 - 257, Oct.2009.
                                                                                        [6]    C.Srinivasa Rao, Z.Naghizadeh and S.Mahdavi, ”Improvement of
The performance of the proposed technique was compared                                         dynamic performance of hydrothermal system under open market
with the conventional self tuning technique and fuzzy-based                                    scenario using asynchronous tie-lines”, World Journal of Modeling and
self tuning technique. From the results, it is evident that the                                Simulation, Vol.4, No.2, pp.153-160, 2008.
proposed technique takes considerably less time to stabilize                            [7]    S.Ramesh and A.Krishnan, "Fuzzy Rule Based Load Frequency Control
                                                                                               in a Parallel AC – DC Interconnected Power Systems through HVDC
the system than the other existing techniques with which it                                    Link", International Journal of Computer Applications, Vol.1, No.4, pp.
was compared.                                                                                  62-69, 2010.
                                                                                        [8]    Mohamed Khatir,Sid Ahmed Zidi, Samir Hadjeri ,Mohammed Karim
                               V. CONCLUSION                                                   Fellah, ”Dynamic Performance Of a Back–To–Back HVDC Station
                                                                                               Based on Voltage Source Converters”, Journal of Electrical Engineering,
    In this paper, a neuro-fuzzy hybrid technique to self tune                                 Vol.61, No.1, pp.29–36, 2010.
the parameters of the PI controller in a HVDC system, was                               [9]    Hanif. Livani, Mohsen. Bandarabadi, Yosef. Alinejad, Saeed. Lesan and
proposed. Faults which are considered in our system are line                                   Hossein. Karimi-Davijani, "Improvement of Fault Ride-Through
to line fault and line to ground fault of both rectifier and                                   Capability in Wind Farms Using VSC-HVDC", European Journal of
inverter sides. When a fault occurs in the system the current                                  Scientific Research, Vol.28, No.3, pp.328-337, 2009
                                                                                        [10]   Uma Vani M, "Damping effects of Supplementary Control Signals for
and voltage increases and by using this neuro-fuzzy hybrid                                     Enhancement of Transient Stability in AC-DC Power Systems",
technique, the system voltage and current can be made to                                       International Journal of Engineering Science and Technology, Vol.2,
return to their stable values within a fraction of a second. The                               No.7, pp.084-3092, 2010.
performance of the system was evaluated from the                                        [11]   Rasli A Ghani,Azah Mohamed and Hussain Shareef, "ANFIS Approach
implementation results. The implementation results showed                                      for Locating Precise Fault Points with Co-ordinated Geometries in a Test
                                                                                               Distribution System", European Journal of Scientific Research, Vol.35,
that the fault clearance time of the hybrid technique is very                                  No.3, pp.461-473, 2009.
low compared to conventional methods and fuzzy based self                               [12]   Alberto Borghtti,Mauro Bosetti,Mauro Disilvestro,Carlo Alberto
tuning methods. Thus it was proved the proposed technique                                      Nucci,Mario Pauloni,Lorenzo Peretto,Elisa Scalla and Roberto
makes the controlling of HVDC systems significantly more                                       Tinnarelli, ”Assesment of Fault Location In Power Distribution
                                                                                               Networks”, Electrical Power Quality and Utilization Journal, Vol.8,
effective than other conventional self tuning techniques.                                      No.1, pp. 33-41, 2007.
                                                                                        [13]   Ioana Fagarasan and S.St.Iliescu, "Applications of Fault Detection
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     pp.1723-1729, Oct. 1997.                                                     [26] Khatir Mohamed, Zidi Sid Ahmed, Hadjeri Samir, Fellah Mohammed
[19] S.Ganapathy and S.Velusami, "Design of MOEA based Decentralized                   Karim and Amiri Rabie, “Performance Analysis of a Voltage Source
     Load-Frequency Controllers for Interconnected Power Systems with                  Converter (VSC) based HVDC Transmission System under Faulted
     AC-DC Parallel Tie-lines", International Journal of Recent Trends in              Conditions”, Leonardo Journal of Sciences, Vol.8, No.15, pp.33-46,
     Engineering, Vol. 2, No. 5, pp.357-361, Nov 2009.                                 July-December 2009.
[20] S. B. Warkad,Dr. M. K. Khedkar and Dr. G. M. Dhole, "Impact of
     HVDC Transmission on Optimal Electricity Nodal Prices: A study in            [27] Lidong zhang, Lennart Harnefors and Hans-Peter Nee, "Power-
     India", International Journal of Engineering and Technology, Vol.2,               Synchronization     Control      of   Grid-Connected Voltage-Source
     No.1, March 2009.                                                                 Converters", IEEE Transactions on Power Systems, Vol.25, No.2,
                                                                                       pp.809-820, May 2010.
[21] Asplund, "Sustainable energy systems with HVDC transmission", in
     proceedings of IEEE General Meeting of Power Engineering Society,            [28] Aurobinda Routray, P. K. Dash, and Sanjeev K. Panda, “A Fuzzy Self-
     Vol.2, pp.2299-2303, June 2004.                                                   Tuning PI Controller for HVDC Links”, IEEE Transactions on Power
                                                                                       Electronics, Vol. 11, No. 5, p.p. 669-679, 1996.
[22] Chi-Hshiung Lin, “Phenomena Caused by a Misfire Fault in an HVDC
     Converter Valve and the Impact on a Turbine Generator”, Journal of
     Technology, Vol. 23, No. 2, pp. 93-100, 2008.                                A.Srujana received the B.Tech Degree in Electrical Engineering from
[23] Vinod Kumar, Joshi, Garg and Bansal, “Intelligent Controller Based           Kakatiya University ,Warangal,India in 1998.She received her M.Tech Degree
     Improved Fault Ride- through Capability of HVDC System Connected             in Electrical Engineering from Jawaharlal Nehru Technological University
     to Weak ac Grid”, Journal of Theoretical and Applied Information             Hyderabad in 2002 .Currently she is persuing Ph.D from the same University
     Technology, Vol.4, No.3, pp.203-211, March 2008                              under the guidance of Dr S.V.Jayaram Kumar. Her research interests include
                                                                                  Power Electronics and HVDC.
[24] Mohamed Khatir, Sid Ahmed Zidi, Samir Hadjeri and Mohammed
     Karim Fellah, “Analysis Of Recovery from Commutation Failures in an
     HVDC Inverter Connected to a Weak Receiving ac System”, Acta                 Dr S.V.Jayaram Kumar received the M.E degree in Electrical Engineering
     Electrotechnica et Informatica, Vol. 8, No. 1, pp. 44–50, 2008.              from Andhra University ,Vishakapatnam ,India in 1979.He received the Ph.D
[25] Mohamed Khatir, Sid-Ahmed Zidi, Mohammed-Karim Fellah, Samir                 degree in Electrical Engineering from Indian Institute of Technology ,Kanpur
     Hadjeri and Rabie Amiri, “Performance Evaluation of an HVDC Link             ,in 2000Currently ,he is a Professor at Jawaharlal Nehru Technological
     with a Capacitor Commutated Inverter Connected to a Very Weak                University Hyderabad .His research interests include FACTS & Power System

                                                                                                                  ISSN 1947-5500

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