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40220140504009

  1. 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME 96 AN ENHANCED GENETIC ALGORITHM APPROACH FOR OPTIMAL PLACEMENT OF FACTS DEVICES TO ENHANCE ATC Prof. P. Ramanjaneyulu1 , Prof. Dr. V.C. Veera Reddy2 1 Professor, Electrical and Electronics Engineering, KITS, Ramachandrapuram 2 Professor in Electrical and Electronics Engineering Department, SVUCE, Tirupathi ABSTRACT In order to facilitate the electricity market operation and trade in the restructured environment, ample transmission capability should be provided to satisfy the demand of increasing power transactions. The conflict of this requirement and the restrictions on the transmission expansion in the restructured electricity market has motivated the development of methodologies to enhance the available transfer capability (ATC) of existing transmission grids. The insertion of flexible AC transmission System (FACTS) devices in electrical systems seems to be a promising strategy to enhance single area ATC and multi-area ATC. This paper determines optimal location and controlling parameter of TCSC and SVC to maximize Available Transfer Capability (ATC) and improve Contingency simultaneously using Genetic Algorithm for this purpose as the optimization tool. In this paper ATC is defined as varying and objective function of Contingency consists of line congestion alleviation and bus voltage magnitudes enhancement. The Available Transfer Capability (ATC) of a transmission network is the unutilized transfer capabilities for the transfer of further commercial activity, over and above already committed usage. Contingency analysis is performed to detect and rank the severest one-line fault Contingency in a power system. The obtained results show that TCSC and SVC simultaneously are very effective Devices on ATC enhancement and Contingency improvement in a power system. 1. INTRODUCTION Inter-area power transfer can be increased without system security encroachments [2]. Transmission lines contain several physical limits due to thermal capacity, stability, and voltage [1]. Optimization methods have been widely used in conventional power system to solve numerous problems such as market clearing mechanism, bidding decision, and ATC computation [7].The Available Transfer Capability (ATC) denotes the unexploited transfer capabilities of a transmission INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2014): 6.8310 (Calculated by GISI) www.jifactor.com IJEET © I A E M E
  2. 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME 97 network for the transfer of power for further commercial activity, in addition to already committed usage [3]. More precisely, ATC is considered as Total Transfer Capability (TTC) less than Transmission Reliability Margin (TRM), sum of existing transmission commitments (which includes retail (customer service) and Capacity Benefit Margin (CBM) and assuming the other components related to ATC are zero for simplicity [4]. Total transfer capability refers to a gauge of the transfer capability residual in the physical transmission network for further commercial activity in addition to previously committed uses [5]. Using FACTS devices, the power system performance and stability can be improved [8]. Flexible Alternating Current Transmission System (FACTS) is an auspicious technology, which can boost the transmission capacity of the ac lines and can control the power flow over a certain transmission lines [9]. Also, FACTS devices are competent in controlling the voltage magnitude, phase angle, and circuit reactance [6]. The power flow arrangements as well as the reactive power flow in the transmission lines are controlled by means of FACTS technology, such as Static Var Compensator (SVC), Static Synchronous Compensator (STATCOM), Static Synchronous Series Compensator (SSSC), and Unified Power Flow Controller (UPFC) [13]. UPFC is one of the most adaptable and intricate FACTS devices, combining the features of the STATCOM and SSSC [10]. 2. RELATED WORKS Some of the recent works related to ATC enhancement with FACTS controllers are discussed below. Rani et al. [11] have proposed a genetic algorithm based technique to identify the best location for fixing FACTS. Devices for improving the Available Transfer Capability (ATC) of power transactions between source and sink areas in the deregulated power system. Here, two types of FACTS have been simulated: Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC) for improving the ATC of the interconnected power system. A Repeated Power Flow with FACTS devices including ATC has been employed to compute the best possible ATC value within real and reactive power generation limits, line thermal limits, and voltage limits. Venkaiah et al. [12] have proposed a Static Security based ATC computation for real-time applications by means of three artificial intelligent techniques: Back Propagation. Umapathy et al. [13] have presented an application of probabilistic distribution based interval arithmetic approach to compute the ATC in a power network in terms of confidence intervals. The interval arithmetic approach allows integration of the uncertainty in the input parameters and offers strict bounds for the solution. Here, the deviation of the real power load has been represented as a Gaussian distribution function. Moreover, the proposed technique has been tested and validated on IEEE 14 bus test system. An application of complex valued neural network for ATC calculations with and without contingencies have been introduced by Chary et al. [14]. Here, a 9 bus test system has been used to evaluate the performance. The objective function is to increase the load on certain source and sink nodes. Also, the voltage limits of the buses and the line losses have been well considered in this proposed technique. A unified optimization approach has been proposed by Jayashree et al. [15] for computing Available Transfer Capability (ATC) and performing Congestion Management (CM) in a deregulated power system handling both pool and bilateral transactions. Here, a power injection model has been employed for Unified Power Flow Controller (UPFC), DC load flow model for power network, and repeated linear programming method for optimization. The DC model enforces the line operating limits in MW. A computer package has been developed and the efficacy of the proposed unified technique has been validated on 4 bus and an IEEE 30 bus systems.
  3. 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME 98 3. MODELING OF TCSC AND SVC Available transfer capability is used in power system to identify the ability of power flow between two areas for different system conditions. One of the techniques used to improve the available transfer capability of the transmission line is connecting FACTS controllers in the system. The major problem in connecting FACTS controller in the system is identifying the optimal location for fixing FACTS controllers and also computing the amount of voltage and angle to be injected in the system .Building new transmission lines to meet the increasing electricity demand is always limited economically and by environmental constraints and FACTS devices meet these requirements using the existing transmission systems [12]. Two types of FACTS have been used in this study namely; Thyristor Control TCSC Series Compensator (TCSC) and Static Var Compensator (SVC) Modeling of TCSC Transmission lines are represented by lumped equivalent parameters. The series compensator TCSC is simply a static capacitor/reactor with impedance jxc [13]. Fig. 1 shows a transmission line incorporating a TCSC. where Xij is the reactance of the line, Rij is the resistance of the line, Bio and Bjo are the half-line charging susceptance of the line at bus-i and bus-j. Xnew is the new defined as varying reactance of the line after placing TCSC between bus i and j [13]. Fig. 1: Equivalent circuit of transmission line after placing TCSC Modeling of SVC The SVC is a shunt connected static var generator or absorber. The SVC can be used to control the reactive compensation of a system. BSVC represents the controllable susceptance of SVC. It can be operated as inductive or capcitive compensation. In this study, it is modeled as an ideal reactive power injection at bus i, at where it is connected. Fig. 2 shows the equivalent circuits of SVC at bus i [14]. Fig. 2: Variable shunt susceptance
  4. 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME 99 In this paper, the objective function of ATC is defined as λ varying and is considered in following forms where: PDi , QDi : are real and reactive load demand at bus I and P0 Di , Q0 Di : are original real and reactive load demand at bus i. kDi: is constant to specify the rate of changes in load as λ varies. In this paper the value of kDi has been taken as 1. λ is a scalar parameter representing the increase in load bus and is defined as ATC. 4. CONSTRUCTION OF GENETIC ALGORITHM In genetic algorithms individuals are simplified to a chromosome that codes the control variables of the problem. The strength of an individual is the objective function (fitness) that must be optimized. A random start function might generate the initial population size. After the start, successive populations are generated using the GA iteration process, which contains three basic operators: reproduction, crossover and mutation. Finally, the population stabilizes, because no better individual can be found. When algorithm converges, and most of the individuals in the population are almost identical, it represents a sub-optimal solution. A genetic algorithm has three parameters: the population size, crossover rate and mutation rate. These parameters are important to determine the performance of the algorithm A. Presentation of control variables To apply GA to solve a specific problem, one has to define the solution representation and the coding of control variables. The optimization problem here is to use Continuation Power Flow (CPF) to find the Total Transfer Capability for different FACTS devices locations and compensations. Every individual chromosome should contain B. Initialization The initialization procedure will select the initial population within the range of the control variables with a random number generator. The user can specify the population number in this procedure. C. Fitness evaluation After control variables are coded, the objective function (fitness) will be evaluated. These values are measures of quality, which is used to compare different solutions. The better solution joins the new population and the worse one is discarded. The fitness value of an individual will determine its chance to propagate its features to future generations. Here ATC is used as the fitness in the genetic algorithm. D. Reproduction Reproduction is a process in which individual chromosomes are copied according to their objective function (fitness), This operation is an artificial version of the Darwinian Process of natural selection. The first stage of the reproduction process is to select chromosomes for mating. Two different techniques, roulette wheel selection and stochastic universal sampling are tested here. It is seen that stochastic universal sampling exhibits better convergence.
  5. 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME 100 E. Crossover Crossover is one of the main distinguishing features of GAs that make them different from other algorithms. Its main aim is to recombine blocks on different individual to make a new one. Convex crossover is used in this work as the following formulation. F. Mutation Mutation is used to introduce some sort of artificial diversification in the population to avoid premature convergence to local optimum. An arithmetic mutation operator that has proved successful in a number of studies is dynamic or non uniform mutation, which is used in this study. G. Population replacement Two population replacement methods, nonoverlapping generations and steady-state replacement are used in this work. When using non-overlapping generations, a generation was entirely replaced by its offspring created through selection, crossover and mutation. It is possible for the offspring to be worse than their parents and some fitter chromosomes may be lost from the evolutionary process. Steady-state replacement is used to overcome this problem. In this process, a number of offspring are created and these replace the same number of the least fit individuals in the population. In this work the steady-state replacement demonstrates better convergence than non- overlapping generations. 5. CASE STUDIES AND RESULTS A two are power system model from (17) is used to implement the proposed method. The PSAT (18) tool box, a matlab based tool for power system analysis is used for analyzing and calculating the ATC. The optimal location of FACTS devices are also checked using this toolbox. The model is a 11 bus system, 8 lines with 4 generators, 4 Transformers and 2 Loads. The simulink model of system used is given in the figure (3) Figure 3: Simulink model of the 2 Area System
  6. 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME 101 The below figure gives voltage profile of the above system when no FACTS device is placed Figure 4: Voltage Magnitude Profile without FACTS Figure (5) depicts. voltage profile of the above system when SVC is placed. Figure 5: Voltage magnitude profile with SVC Figure (6) depicts the voltage profile of the system when TCSC is placed. Figure 6: Voltage magnitude profile with TCSC
  7. 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME 102 The steps involved in calculation of the optimal location using GA approach are mentioned below. This method is implemented using Matlab version 7.1 Step 1: Read the power system bus and line data using NR repeated power flow. Bus data: Bus no, Bus type, Voltage mag, Angle deg, etc.. Step 2: Read data for genetic operations. Step 3: Read no. of control variables i.e. TCSC and SVC locations and XTCSC, QSVC. Step 4: Read line data=[ ] (for calculating Contingency) and Calculate the function that consists Contingency as (SV1). Step 5: Read (for calculating ATC) and calculate the function that consists ATC as (SV2). Step 6: Calculate the function that consists ATC and Contingency simultataneously using NR repeated power flow as Table 1: ATCs and Contingencies without and with FACTS devices State Location ATC value Contingency No FACTS - 1.42 319.13 With SVC Bus-8 1.49 318.6 With TCSC Bus-8 1.44 317.6 6. CONCLUSION ATC enhancement and Contingency improvement are two important issues in power systems. ATC can be usually limited by heavily loaded circuits and buses with relatively low voltage. It is well known that FACTS technology can control voltage magnitude, phase angle and circuit reactance. Using these devices may redistribute the load flow, regulating bus voltages. Therefore, the FACTS utilization enhances the power system security in contingency. This paper has proposed Genetic Algorithm to find optimal location and setting of the combined TCSC and SVC for maximizing ATC and minimizing Contingency of power system. Simulations Test results indicate that optimally placed TCSC and SVC by GA could increase ATC, reduce Contingency in this system. 7. REFERENCES [1] Manoj kumar dinkar and shishir dixit, "Available transfer capability determination using load flow methods", International Journal of Power System Analysis, Vol. 1, No. 1, pp. 5-9, 2012. [2] K. Narasimha Rao, J. Amarnath and K. Arun Kumar, "Voltage Constrained Available Transfer Capability Enhancement With FACTS Devices ", ARPN Journal of Engineering and Applied Sciences, Vol. 2, No. 6, pp. 1-9, Dec 2007. [3] J. Vara Prasad, I. Sai Ram and B. Jayababu, "Genetically Optimized FACTS Controllers for Available Transfer Capability Enhancement", International Journal of Computer Applications, Vol. 19, No.4, pp. 23-27, April 2011. [4] Kiran Seelam, Surender Kumar Yellagoud and Veeranjaneyulu Puppala, "An Improved Evaluation Method for Available Transfer Capability by Incorporating the Reactive Power Flows", International Journal of Engineering Science and Technology, Vol. 2, No. 12, pp. 7572-757, 2010.
  8. 8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 4, April (2014), pp. 96-103 © IAEME 103 [5] Venkatesan Chakkaravartthy and Mary Daniel, "Computation of Total Transfer Capability using Power Producer’s Bidding in the Deregulated Environment", European Journal of Scientific Research, Vol.68 No. 2, pp.172-181, 2012. [6] K. Chandrasekar and N. V. Ramana, "Fast and Efficient Method to Assess and Enhance Total Transfer Capability in Presence of FACTS Device", International Journal of Advances in Engineering & Technology, Vol. 1, Issue 5, pp. 170-180, Nov 2011 [7] Mohamed Shaaban, Yixin Ni and Felix F.Wu, "Transfer Capability Computations in Deregulated Power Systems", International Conference on System Sciences, pp. 1-5, 2000. [8] K. Lokanadham, "Optimal Location of FACTS Devices in Power System by Genetic Algorithm", Global Journal of Researches in Engineering, Vol. 10, Issue. 1, pp. 25-30, Apr 2010. [9] Hossein Farahmand, Masoud Rashidinejad and Ali Akbar GharaveisI, "A Combinatorial Approach of Real GA & Fuzzy to ATC Enhancement", Turk J Elec Engin, Vol. 1, No. 4, pp. 77- 88, 2007. [10] S. Tara Kalyani and G. Tulasiram Das, "Simulation of Real and Reactive Power Flow Control with UPFC Connected to a Transmission Line", Journal of Theoretical and Applied Information Technology, pp. 16-22, 2008. [11] K. Radha Rani, J. Amarnath, and S. Kamakshaiah, "Allocation Of FACTS Devices For ATC Enhancement using Genetic Algorithm", ARPN Journal of Engineering and Applied Sciences, Vol. 6, No. 2, pp.87-93, Feb 2011. [12] Chintham Venkaiah and Dulla Mallesham Vinod Kumar, "Static Security Based Available Transfer Capability (ATC) Computation for Real-Time Power Markets", Serbian Journal of Electrical Engineering, Vol. 7, No. 2, pp. 269-289, November 2010. [13] Prabha Umapathy, C.Venkataseshaiah and M.Senthil Arumugam, "Assessment of Available Transfer Capability Incorporating Probabilistic Distribution of Load Using Interval Arithmetic Method", International Journal of Computer and Electrical Engineering, Vol. 2, No. 4, pp. 692-697, August, 2010. [14] D. Venu Madhava Chary and J. Amarnath, "Computation of Available Transfer Capability Incorporating Effect of Reactive Power and Losses using Complex Neural Network", ARPN Journal of Engineering and Applied Sciences, Vol. 4,No. 8, pp. 45-50, Oct 2009. [15] Ramasubramanian Jayashree and Mohammed Abdullah Khan, "A Unified Optimization Approach for the Enhancement of Available Transfer Capability and Congestion Management using Unified Power Flow Controller", Serbian Journal of Electrical Engineering, Vol. 5, No. 2, pp. 305-324, Nov 2008. [16] Ibraheem and Naresh Kumar Yada, "Implementation of FACTS Device for Enhancement of ATC Using PTDF", International Journal of Computer and Electrical Engineering, Vol. 3, No. 3, pp. 343-348, Jun 2011. [17] Prabha Kundur, “Power system Satbility and Control”. [18] PSAT Tool Box Release Notes, Version 5.1. [19] M.V.Ramesh and Dr. V.C. Veera Reddy, “Optimal Allocation of FACTS Devices in Different Over Load Conditions”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 1, 2013, pp. 208 - 222, ISSN Print: 0976-6545, ISSN Online: 0976-6553. [20] T. Nageswara Prasad, V. Chandra Jagan Mohan and Dr. V.C. Veera Reddy, “Shunt Compensator for Integration of Wind Farm to Polluted Distribution System”, International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 3, 2012, pp. 89 - 101, ISSN Print: 0976-6545, ISSN Online: 0976-6553. [21] M.V.Ramesh and Dr. V.C. Veera Reddy, “Optimal Placement of SVC & IPFC in Different Load & Contingency Conditions using DE”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 3, 2013, pp. 115 - 120, ISSN Print: 0976-6545, ISSN Online: 0976-6553.

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