Optimal Power Flow with Static VAR Compensator Using Galaxy ...

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In this paper, Galaxy based search algorithm (GbSA) is used to solve multi-objective problem of optimization in power ... =function Next Chaos in each call. 2.2.
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ScienceDirect Procedia Computer Science 92 (2016) 42 – 47

2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC-2016) Srikanta Patnaik, Editor in Chief Conference Organized by Interscience Institute of Management and Technology Bhubaneswar, Odisha, India

Optimal Power Flow with Static VAR Compensator Using Galaxy based Search Algorithm to minimize Real Power Losses B.Sravan Kumara, M.Suryakalavathib and G.V.Nagesh Kumara* a

Department of EEE, GITAM University, Visakhapatnam-530045,INDIA b Department of EEE, JNT University, Hyderabad, INDIA

Abstract In this paper, Galaxy based search algorithm (GbSA) is used to solve multi-objective problem of optimization in power systems. The proposed GbSA resembles the spiral arms of some galaxies to search for the optimal solutions. The GbSA also uses a modified Hill Climbing algorithm as a local search. Simulation results show that the GbSA finds the optimal or very near optimal values in all runs of the algorithm. The weighted sum technique with equal weights has been chosen to solve the multi-objective function. The functions considered are to minimize the power losses in transmission line, cost of the real power generation and voltage deviation. Static VAR Compensator (SVC) is used for the purpose of optimal power flow. L-index is used to identify the optimal location to place SVC. The results have been compared with Genetic algorithm (GA) for IEEE-14 System. © 2014 Published by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license 2016The TheAuthors. Authors. Published by Elsevier Selection and peer-review under responsibility of scientific committee of Missouri University of Science and Technology. (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of the Organizing Committee of ICCC 2016

* Corresponding author. Tel.: +91-9000573759 E-mail address:[email protected]

1877-0509 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Organizing Committee of ICCC 2016 doi:10.1016/j.procs.2016.07.321

B. Sravan Kumar et al. / Procedia Computer Science 92 (2016) 42 – 47

Keywords: Flexible AC Transmission System, Galaxy based Search algorithm , Optimal Power Flow, Static VAR Compensator, L-index.

1. INTRODUCTION Modern electric power systems have to face many difficulties due to their complex structure and operation. Power system instability is one of the major problems faced by power engineers [1]. Power system instability is majorly due to deficiency of new transmission lines and over usage of the existing lines. Mechanical control is used in conventional power systems. However control using mechanical procedures is not as reliable as the devices tend to wear out fast in comparison to their static counterparts. This necessitates power flow control to shift from mechanical devices to static devices. The power electronic based FACTS introduced in 1980’s, provided a highly efficient and economical means in solving various problems related to power systems. Improved utilization of the existing electrical network with the employment of FACTS devices has become mandatory [2-5]. Out of all FACTS devices, static VAR compensator (SVC) has been the most extensively used in power systems. This device can deliver a very quick control of the susceptance and thus the reactive power supplied to transmission lines, which maintains the node voltage at or near a constant value thereby enhancing the power system performance [6, 7]. SVC offers many prospects for improvement of performance of the power system. This paper offers Galaxy based Search Algorithm for Generation Reallocation of generator buses in the power system, with SVC and without SVC device to reduce real power losses and cost of real power generation and its performance is compared with Genetic Algorithm (GA). GbSA is a recent metaheuristic method with many advantages in comparison the existing methods [8, 9]. The obtained results show that SVC is a very efficient shunt compensation device and it can minimize the system real power losses very efficiently. Simulation is carried out in MATLAB for IEEE14 test bus system. 2. GALAXY BASED SEARCH ALGORITHM In 2011, Hamed Shah-Hosseini introduced the concept of Galaxy based Search Algorithm(GbSA).It is a nature inspired metaheuristic algorithm which is positioned on a variable neighborhood search algorithm. GbSA is based on two main components. Spiral chaotic move: by using spiral movement, the Spiral chaotic moves searches around the current best solution This movement uses some chaotic variables around the current best solution. If it obtains a better solution than the current solution, it immediately updates and goes for the local search to obtain more suitable solution around the newly updated solution [3,7]. Local search: This component is activated to search locally around the newly updated solution. The local search ensures the exploitation of search space and spiral chaotic move provides exploration of the search space ensuring towards the global optimum solution. 2.1. Parameters of GbSA: L = the number of components for candidate solution. S = the current solution with L components. SNext = the output of the local search. = the step size which is set by the function Next Chaos. Kmax = the maximum iteration that the local search has to search around a component to find a better solution. Max Rep = is the maximum iteration that the spiral chaotic move searches. = is an initial parameter. =function Next Chaos in each call. 2.2. Algorithm of GbSA Implementation of Galaxy based Search Algorithm in Generation reallocation STEP 1: Generate the initial solution. F1 = [0.25

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B. Sravan Kumar et al. / Procedia Computer Science 92 (2016) 42 – 47 Where, abs is absolute value. SM is security margin. VD is voltage deviation. PQ is capacity of installed SVC. STEP 2: Local search (SG) searches the space around the given solution S with small step sizes. Then it gradually increases the step sizes to faster explore the search spaces. At the end, it returns the locally best solution found around the given solution S. STEP 3: While the condition is not satisfied, then the flag is set to false. STEP 4: Spiral chaotic move is the first component in the loop, which globally searches around the solution SG. It stops searching whenever it reaches a solution better than SG (or) it exceeds Max Rep. = = STEP 5: If flag is set to true, then the local search is called to search locally around the newly updated solution SG STEP 6: The above process is repeated until a stopping condition is satisfied. 3. PROBLEM FORMULATION For a given system load, the best configuration of SVC device minimizing the following objective function Min F = Min (W1* FC + W2* FPloss+ W3*FVD) (1) Where w1, w2 are the weighting factors. W1+W2+W3=1 (2) W1=0.7 W2=0.15 W3=0.15 Cost of Real power generation:

Where ng = number of generator bus Real Power Loss It consists of reducing the loss in the system lines in terms of real power. The formula is given in eq(10)

Where ntl = number of transmission lines,Sij is the total complex power flow from bus i to bus j in line k. Voltage Deviation To attain standard voltage profile it is necessary that the voltage deviation should be minimum at all buses. The voltage deviation (VD) can be formulated as: = (5) Where, represents the voltage magnitude at bus represents the reference voltage magnitude at bus L-index Based on the load flow solution of power flow equations, Kessel et al [17] developed a voltage stability index model. To determine the distance between the actual position of the system and the desired state (stability limit), Lindex is quantitatively used. The stability of the system characterized by L-index is given by: (6)

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B. Sravan Kumar et al. / Procedia Computer Science 92 (2016) 42 – 47 Where, Lj represents local indicator Fji represents complex elements The limits of L-index lies between the range 0(close to no load) and 1(close to voltage collapse) 4. RESULTS AND DISCUSSIONS In IEEE 14 bus system, the first bus is the slack bus. Bus numbers 2,3,6,8 are the generator buses. All other buses are considered as load buses. This system has 20 transmission lines. An OPF program using Flower pollination algorithm approach is written using MATLAB without the SVC, which was further extended with the SVC. The parameters of Flower pollination algorithm for the 14 bus power system have been shown in the Table 1 and the GA parameters have been given in Table 2. Table 1 Specification of Galaxy based Search Algorithm Parameters a) Spiral Chaotic Move Parameters Value Max Rep 150 0.001 (dr max) 0.01 (dt max) A 4

Parameters

b) Local Search Value

Kmax (ds max) S step size I step size

100 10 0.0001 0.05

Table 2 Specification of Genetic Algorithm Parameters S.No Parameters Value 1 2 3 4 5

Size of population Maximum number of total Generations Crossover Fraction Migration Fraction Migration Interval

20 50 0.8 0.2 20

Table 3 Power Flow in 14 Bus System without SVC and after SVC located at Bus number 14 Power Flow Solution

GA-OPF

Cost of real power generation ($/hr) 1216.6

Total ‘P’ gen (MW)

Total ‘P’ loss(MW)

Voltage deviation (p.u)

Objective Function value

270.4

11.11

1.0

613.89

With SVC

1108.3

268.1

8.871

0.763

Without SVC

1187

267.74

8.75

0.964

184.04

With SVC

1080

264.63

5.63

0.2959

164.96

Without SVC

558.60 GBSA-OPF

PV bus NO

Table 4 Comparison of The Real Power Generated at the PV Busses specified system conditions GbSA-OPF NR Method GA-OPF GbSA-OPF with SVC Generation limits With SVC With SVC Without SVC

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B. Sravan Kumar et al. / Procedia Computer Science 92 (2016) 42 – 47

1 2 3 6 8

Min 10 0 10 20 30

Max 300 20 30 80 100

209.85 20.000 20.00 10.000 10.000

188.3 20.00 20.00 17.54 22.32

139.74 32

136.63 32

32 32 32

32 32 32

Voltage Magnitude in p.u

The active power generated and real power loss for the transmission system without SVC and with SVC has been shown in Table 3. From Table 3, it is observed that the total active power required to be generated is reduced from 267.74 MW to 264.63 MW and power loss is reduced from 8.75 MW to 5.63 MW due to SVC in Galaxy based search algorithm. Table 4 represents the active power generated by PV buses for various conditions those are GA method, GA method with SVC and Galaxy based Search algorithm without SVC and with SVC. By using Galaxy based search Algorithm Generation reallocation has been done properly resulting in less power loss. Table 5 indicates the magnitude of voltage of all the buses of the system after Optimal Power Flow using GbSA without SVC and with SVC. It indicates that by incorporating the SVC at bus number 14 in GbSA based OPF, the voltage profile has been enhanced. Fig 1 shows the comparison of voltage profile. 1.2 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4

NR Method

FPA-OPF without SVC

FPA-OPF with SVC 0

5

10

15

Bus Number Fig.1 .Comparison of the Voltage Magnitudes with and without SVC

6. CONCLUSION In this paper, Galaxy based Search Algorithm is introduced to solve optimal power flow in the presence of SVC. The results demonstrate the robustness and effectiveness of the proposed method with SVC. L-index is used to identify the weakest bus, critical line in the entire system for optimal location of SVC. By using simulation of standard IEEE 14 bus, the proposed method has been verified for without and with placing of SVC. The results show that by placing of SVC the real power generation cost, real power losses and voltage deviation are reduced. It is also observed that GbSA is effective optimization method when compared to GA. REFERENCES 1. 2. 3. 4.

P. Kundur, Power System Stability and Control. New York: McGraw-Hill, Inc., 1993. N. G. Hingorani and L. Gyugyi, “Understanding FACTS: Concepts and Technology of Flexible AC Transmission System”, IEEE Press, 2000. Hamed Shah-Hosseini “Otsu’s Criterion-based Multilevel Thresholding by a Nature-inspired Metaheuristic called Galaxy-based Search Algorithm”. Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress, IEEE conference,pages383-388. Praing Ch, Tran-Quoc T, Feuillet R, Sabonnadiere J C, Nicolas J, Nguyen-Bio K and Nguyen-Van L (2000), “Impact of FACTS Devices on Voltage and Transient Stability of a Power System Including Long Transmission Lines”, IEEE Power Engineering Society Meeting, Vol. 3, pp. 1906-1911.

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Gerbex R Cherkaoui and Germond A J (2001), “Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms”, IEEE Transaction Power Systems, Vol. 16, August, pp. 537-544. Yuryevich Janson, Wong Po kit. Evolutionary programming based optimal power flow algorithm. IEEE Trans Power System 1999;14(4): 1245–50. X. S. Yang, Nature-Inspired Meta-Heuristic Algorithms, Luniver Press, Beckington, UK, 2008. B.Venkateswara Rao, G.V.Nagesh Kumar, “Sensitivity Analysis based Optimal Location and Tuning of Static VAR Compensator using Firefly Algorithm to enhance Power System Security”, Indian Journal of Science & Technology, Volume 7, Issue 8, August 2014, pp. 12011210. B.Venkateswara Rao, G.V.Nagesh Kumar, “Voltage Collapse Proximity Indicator based Placement and Sizing of Static VAR Compensator using BAT Algorithm to improve Power System Performance”, Bonfring International Journal of Power Systems and Integrated Circuits, 2014, Volume: 4, Issue: 3, pp 31-38.

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