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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 srujanaphd@gmail.com Hyderabad 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 139 http://sites.google.com/site/ijcsis/ 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 140 http://sites.google.com/site/ijcsis/ 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 2. 141 http://sites.google.com/site/ijcsis/ 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 Sl.no 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 G=medium 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. 142 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, December 2010 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 1.c 143 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, December 2010 2.a 2.b 2.c 3.a 3.b 144 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, December 2010 3.c 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 1.c 145 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, December 2010 2.a 2.b 2.c 3.a 3.b 146 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 9, December 2010 3.c 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 147 http://sites.google.com/site/ijcsis/ 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 148 http://sites.google.com/site/ijcsis/ 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. 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Fulli, “Modeling and Application of VSC-HVDC in Vol.7, No.6, pp.812-821, June 2008 the European Transmission System”, International Journal of [14] Pan Zhencun,Wang Chengshan,Cong Wei and Zhang Fan, "Single Innovations in Energy Systems and Power, Vol.5, No.1, pp. 8-16, phase-to-ground fault line identification and section location method for April 2010. non-effectively grounded distribution systems based on signal injection", [2] N.M.Tabatabaei and N.Taheri, ”Damping Function of Back to Back Transaction of Tianjin University, Vol.14, No.2, pp.92-96, 2008. HVDC Based Voltage Source Converter”, International Journal on [15] C.H.K. Chui, Z. Litifu and B. Kermanshahi, "Value-based HVDC Technical and Physical Problems of Engineering, Vol.1, No.2, P.p.1-7, Enhancement Alternative Selection Using System Well-being Analysis", 2010. Iranian Journal of Science & Technology, Transaction B, Engineering, [3] Narendra Bawane, Anil G. Kothari and Dwarkadas P Kothari, "ANFIS Vol.32, No.B3, pp 223-234, 2008. Based HVDC control and Fault Identification of HVDC converter", [16] Nita R. Patne and Krishna L. Thakre, "Factor Affecting Characteristic of HAIT Journal of Science and Engineering, Vol.2, No.5-6, pp.673-689, Voltage Sag Due to Fault in the Power System", Serbian Journal of 2005 Electrical Engineering, Vol. 5, No. 1, pp.171-182, May 2008. [4] H.D. Mathur and H.V. Manjunath, ”Study of Dynamic Performance of [17] Arthit Sode-Yome, "System and Network Performance Indicators for the Thermal Units with Asynchronous Tie lines using Fuzzy Based Electricity Generating Authority of Thailand: Current and Future Ones", Controller”, J. Electrical Systems, Vol.3, No.3, pp.124-130, 2007. Vol.1, No.1, pp.8-20, 2009. 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[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 Dynamics. 150 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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