Hybrid Evolutionary Algorithm Using Optimal Placement of FACTS Devices for Total Transfer Capability

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1. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 83 Hybrid Evolutionary Algorithm Using Optimal Placement of FACTS Devices for Total Transfer Capability Gujjula Ganesh1 , Ch.Narendrakumar2 & Ezhil Vignesh.K3 1-3 Department of Electrical & Electronics Engineering, Malla Reddy Engineering College (A), Hyderabad, Telangana, India. Email: [email protected] , [email protected] & [email protected] DOI: 10.46382/MJBAS.2020.4208 Article Received: 27 February 2020 Article Accepted: 25 May 2020 Article Published: 30 June 2020 Introduction In recent power systems, the applications concerned with power transmission techniques are developed repetitively. Flexible AC transmission systems (FACTS), have been used for controlling the flow of power, enhance stability of transmission, and increase the safety in power transmission system [1]. Additionally, these devices could be maximized power transfer capability & minimized power loss of the transmission systems, which lead for the effective applications compared to the conventional power system. In physical transmission, Available transfer capability (ATC) is used for transferring the capacity in a transmission network. This needs to be measured for every control region which is given in societal problems for enhancing the open acceptance in a power system. ATC is known as the total transfer capability (TTC) which reduces the transmission reliability margin (TRM), capacity benefit margin (CBM) and the conventional transmission commitments (CTC). The TTC is an important part at ATC calculation. TTC is called the number electrical power which could reassign connected transmission network in a dependable. Huge categories of techniques, like continuation power flow (CPF) [4], linear ATC (LATC) [3], and repetitive power flow (RPF) [5] strategies are introduced to manipulate TTC. Moreover, optimal power flow (OPF) techniques that are developed using several optimizing strategies [6, 7], are introduced for TTC controls several degrees. The technique which needs to find the operations to identify correct solution. Therefore, OPF is a nonlinear function & non-convex optimizing method which results in many solutions that exist the special function in power networks with FACTS devices [8]. The device factors have extra control enables which could not be solved using existing OPF methods due to the factors will ABSTRACT The paper discusses about a hybrid model with an evolutionary algorithm (HEA) for identifying the multi-type flexible AC transmission systems (FACTS) procedures to improve the total transfer capability (TTC). To reduce the loss of power this transferences among various control regions. FACTS devices with Multi objective optimal power flow (OPF) which include TTC to determine a reasonable value without violating system limitations. The results are simulated for FACTS devices with the HEA algorithm which emerges TTC value using an efficient methods using conventional transmission system. The simulation results are obtained by MATLAB/SIMULINK environment.

2. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 84 alter the impedance. However, existing optimizing techniques are used for local optimal solutions. Recently, the power transfer capability improvement [10] & power loss minimization technique by multi-type FACTS devices. Due to these improve competition, minimize operating costs, and effectively apply the conventional power transmission systems. In addition to the advantages the compatibility of the system how they are controlled with other systems. Therefore, the techniques lead to the correct solution due to the loading conditions. GA is searching the exact location of TCSC and CPF is involved to examine the FACTS devices relevant to thermal limits and voltage limits. Problem Statement The devices includes TTC, loss of power in the transmission system and along with Multi-objective OPF FACTS devices are designed to find TTC value which will be moved through a range of generator set for connecting loads. And it has various limits such as reactive power voltage limits, generation limits, thermal limits, FACTS operation limits and steady state stability limits. FACTS devices are categorized into 4 parts as follows: 1. TCSC 2. Unified power flow controller (UPFC) 3. Thyristor-controlled phase shifter (TCPS) 4. SVC. Maximize ∑ ∑ ( ) ( ) Subject to ∑ ( ) ( ) ∑ ( ( ) ) ( ) ∑ ( ) ( ( ) ) Where T objective function PF penalty function

3. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 85 Pmin Gi , Pmax Gi real power generation in lower and upper limits at bus i Qmin Gi , Pmax Gi reactive power generation in lower and upper limits at bus i V min i , V max i voltage magnitude in lower and upper limits of bus i Smax Li i th line or transformer loading limit Icrit ij critical angle difference between bus i–j Xmin Si , Xmax Si lower and upper limits of TCSC at line i αmax Pi , αmax Pi TCPS lower and upper limits in line i V min Ui , V max Ui UPFC of lower and upper limits at line i αmin Ui , αmax Ui UPFC lower and upper limits at line i Qmin V i , Qmax V i reactive power injected in SVC at bus i N, NL amount buses and branches NG, ND amount load buses NG_SCE amount of source area ND_SNK amount of load buses in a sink area | | L | | Variable series reactance model is subdivided into TCPS, UPFC, and SVC by power model of injected described in Appendix A [19].

4. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 86 In order to enhance EC methods, Hybrid Evolutionary Algorithm (HEA) is introduced along with TS, EP, and SA strategies. Advantages of the HEA algorithm are stated as follows: Multiple populations along with modification operatives are developed for improving search and increase population update, providing greater caliber of provisions when compared to conventional searching methods. The procedure is completed for transform and fuse the information contains sub-data assembly brought about by consistency of people in a solitary populace will be eased. Choice of probabilistic changing method dependent on annealing schedule of SA and TS is given to remove need of operating functions & to get local optimal explanations. The procedure will certainly simplify similar execution of parallel computers for minimizing the lapsed time foregoing caliber of results. Four categories in FACTS devices of reasonable every type, that allocates the input information. Position of the system is considered as 3 boundaries: nCF k, „k‟ location, and „k‟ parameter are considered as load (21). FACTs devices category is l € {1, 2, 3, 4} involving placement of TCSC, TCPS, UPFC, and SVC, separately, the quantity of FACTs device category l, nCF l 2 f0; 1g. Obviously, none of the FACTs device category l is if nCF l D O or just a single FACTs devices category l if nCF k D l. Along these lines, plenty of FACTs parts, areas, & boundaries of every class FACTs devices at the same time are used with the HEA calculation. All boundaries in FACTs device category k is legitimate just if nCF l D l. This system is shown in Figure. It is divided as three regions, each has 2 generators. Updated system datasets are specified in [25]. A bilateral transaction has double transactions including from 1 bus to 21 bus & a multilateral transaction starting at region 1 and 2 with the following objectives: Increase TTC, decrease power loss, increase TTC and low loss Through 1 bus to 21 by not considering FACTs devices Table 1, in bilateral transaction the load values is 17.50 MW in bus-21. To increase TTC by the proposed strategy, TTC value is taken as 40.447 MW by not altering limits, are 0.84%, 1.29%, 0.58%, and 0.31% through TS, EP, IEP and TS/SA respectively. For minimizing the power loss the current productions and load, bus voltages in generators are improved by adapting methods like TTC, HEA, and power losses is 2.045 and 17.50 Mega Watt, as expected. TTC increase and diminish loss by HEA, TTC is 40.449 MW, that are greater than through EP-0.85%, TS-0.55%, TS/SA-0.38%,

5. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 87 Figure.1 Modified IEEE-30 bus and IEP-0.58%. The proposed devices are used to concurrently increase TTC and reduce the loss. TTC has the value of 154.061Mega Watt by not considering the limits that can be maximized at 280.88% when comparatively with FACTS devices. Moreover, TTC is from EP-22.25%, TS-21.54%, TS/SA-20.91%, and IEP-15.04% methods. To TTC increase or lossless, generally these are developed for improving the TTC and to eliminate power comparatively OPF when not considering the FACTs devices. In Parallel, improve the TTC and decrease the power by HEA technique, TTC is 125.930 Mega Watt, that are more from EP0.21%, TS-0.12%, TS/SA-0.10%, and IEP-0.17% methods. In HEA algorithm also use a source region, growth of output power, and novel improvement in production bus voltages. In parallel HEA placed every category of FACTs devices are improving TTC and minimizing loss. TTC has 191.379 Mega Watt, which increases 51.97% comparatively than not considering FACTS devices. Additionally, the TTC value is, more EP-40.68%, TS-20.60%, TS/SA-18.40%, and IEP-15.61% methods. CPU execution period is the over-all computation time for HEA algorithm beginning to final includes the NR power flow is shown in Figure 4. Using HEA method, results are obtained on the optimized values through various methods due to the selection mechanism of HEA algorithm with an updating approach depends on SA and TS algorithms to reduce the operational set-up corrected values. Hence, the variations of HEA is best solution as small as demonstrated in solutions, which leads for higher stability of HEA method. The Modified IEEE 118-Bus System It has 54 numbers of bus generators and 186 branches. It is divided as nine regions, as stated in Figure 5. Thermal limits are specified in [25] and [26], correspondingly. The dataset is improved as follows. Power

6. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 88 production has the upper limit in 69 bus is 1,000 Mega Watt. Power production for reactive is upper limits starting from bus 34, 70, and ends in 103 are 80 MVAr. Power production minimum limit is bus 19, 32, 34, 102, and 105 is 22 MVAr. The Thermal limit at line 65–66 is 300 VA. Figure.2 Characteristics of solutions Figure.3 Control areas of the modified IEEE 118-bus system The ML from region 6 to 3 with contingency constraints is considered. Output of the largest generators for each region and the output are included in the contingency list. Load in region 6 is 406.00 MegaWatt and the system real-power loss is 132.863 MegaWatt. TTC when not considering FACTS devices using HEA method is 710.57 MegaWatt. To find the pre-specified contingency controls are as shown in Table 5, TTC value using HEA algorithm is 461.03 MegaWatt without using network components, which is, from EP-4.89%, TS-5.25%, TS/SA-0.91%, and IEP-0.57%. Additionally, TTC value is minimized by 35.12% comparatively greater by not considering contingency components. Factor has the connected line from 42–49 among two regions are output. It is explained that rejecting the impact of given constraints on TTC is found insecure system operation. In parallel, to improve the TTC and lossless system in HEA has optimally placed every category of FACTS devices. TTC value FACTs devices is 725.17 MW, which is maximized by 2.05% compared to that without FACTs devices. TTC value using HEA is 513.6 MegaWatt that increases 11.41% comparatively not considering FACTS devices. The interconnected line is 38–65 output among these regions. However, the TTC value is more than from EP-6.77%, TS-7.93%, TS/SA-5.26%, and

7. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 89 IEP-4.08%. Table 4 states the corrected placement of multi-type FACTS devices for the TTC values. Simulations are indicated in Table 4 that of EP, TS/SA, and TS, is low efficient than population search of HEA and IEP techniques. Since, the HEA calculation needs greater operation period, for scheduling horizon, the calibre of solutions is high significant. Table 1: Simulation Results (The Modified IEEE 30-Bus System) Without FACTS devices Maximize TTC Minimize loss min. loss Method TTC Loss TTC Loss TTC Loss EP 124.994 6.421 56.200 2.029 125.663 6.035 TS 125.553 6.140 56.200 2.029 125.781 5.916 TS/SA 125.808 6.287 56.200 2.029 125.806 5.793 IEP 125.451 6.248 56.200 2.029 125.716 5.967 HEA 125.629 6.043 56.200 2.029 125.930 5.738 Conclusion In this paper, the HEA algorithm is designed to find the optimal placement of FACTs device in multi-type by paralleled increasing TTC value and decreasing the power loss in power transactions among different control regions. The results are obtained for the placement OPF FACTs devices through HEA algorithm. This will increase the TTC value based on the normal and contingency conditions in the proposed system. With FACTS devices Maximize TTC Minimize loss min. loss Method TTC Loss TTC Loss TTC Loss EP 133.694 6.001 56.200 1.144 136.040 3.980 TS 157.054 6.438 56.200 1.105 157.389 6.449 TS/SA 158.482 6.465 56.200 1.101 161.642 6.971 IEP 158.904 7.057 56.200 0.998 165.545 6.351 HEA 185.095 7.426 56.200 0.968 191.379 6.474

8. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 90 Table 2 Simulation results of multilateral transaction from area based on area 1 to 2 on the modified IEEE 30-bus system Without FACTS devices Maximize TTC Minimize loss Max. TTC &min loss Method TTC Loss TTC Loss TTC Loss EP 39.902 4.584 17.500 2.045 40.111 4.612 TS 40.101 4.624 17.500 2.045 40.295 4.686 TS/SA 40.312 4.775 17.500 2.045 40.293 4.684 IEP 40.203 4.645 17.500 2.045 40.216 4.657 HEA 40.437 4.734 17.500 2.045 40.448 4.731 Method With FACTS devices Maximize TTC Minimize loss Max. TTC &min loss TTC Loss TTC Loss TTC Loss EP 125.531 3.921 17.500 1.296 126.021 3.914 TS 126.274 3.725 17.500 1.281 126.755 3.793 TS/SA 127.113 3.880 17.500 1.258 127.415 3.715 IEP 128.675 3.176 17.500 1.154 133.919 2.827 HEA 147.322 4.152 17.500 1.096 154.061 3.607 Table 3 TTC level & TTC value of multilateral transaction on the modified IEEE 118-bus system TTC level (MW) without FACTS devices Case EP TS TS/SA IEP HEA Normal 701.61 703.68 706.17 707.27 710.57 Largest gen. in area 6 outage 656.24 663.68 673.95 669.84 677.84

9. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 91 Largest gen. in area 3 outage 694.29 694.98 703.40 706.12 708.50 Line 38–65 outage 481.08 483.31 483.38 483.68 487.13 Line 42–49 outage 439.55 438.05 456.87 458.40 461.03 Line 44–45 outage 664.59 651.42 655.80 661.85 666.56 Contingency TTC value 439.55 438.05 456.87 458.40 461.03 TTC level (MW) with FACTS devices Case EP EP TS TS/SA IEP HEA Normal 701.61 706.81 718.21 721.27 720.01 725.17 Largest gen. in area 6 outage 656.24 674.11 687.29 687.29 690.45 695.08 Largest gen. in area 3 outage 694.29 708.67 705.20 712.88 723.36 733.64 Line 38–65 outage 481.08 486.75 484.96 487.94 498.87 513.62 Line 42–49 outage 439.55 481.07 475.87 497.45 493.48 520.76 Line 44–45 outage 664.59 671.73 661.08 668.70 683.75 688.79 Contingency TTC value 439.55 481.07 475.87 487.94 493.48 513.62 Table 4 TTC results & CPU times of multilateral transaction on the modified IEEE 30-bus system Without FACTS devices TTC (MW) Method Best Average Worst deviation CPU time (min) EP 125.663 124.205 121.891 1.48 0.71 TS 125.781 125.339 124.796 0.31 0.62 TS/SA 125.781 125.339 124.796 0.31 0.62 IEP 125.716 125.349 124.840 0.32 0.77 HEA 125.930 125.351 124.923 0.31 0.75

10. Mediterranean Journal of Basic and Applied Sciences (MJBAS) Volume 4, Issue 2, Pages 83-92, April-June 2020 ISSN: 2581-5059 www.mjbas.com 92 With FACTS devices TTC (MW) Method Best Average Worst Standard deviation CPU time (min) EP 136.040 129.790 121.937 5.46 3.11 TS 157.389 142.263 125.554 12.68 2.58 TS/SA 157.389 142.263 125.554 12.68 2.58 IEP 165.545 142.758 130.716 10.55 4.26 HEA 191.379 170.497 156.352 9.83 4.17 References [1] T. Backlash, W. Schiff, & S. Backlash, “Prognostic & enhancing management of energy of photovoltaic fuel cell hybrid schemes with short time energy loading,” in Proc. 4th Eur. Conf. PV-Hybrid & Mini- Grid, 2016, pp. 7–13. [2] J. Larine & A. Dicks, Fuel Cell Arrangements Described. New York: Wiley, 2013. [3] A. Capel, W. Xiao, & W. Dun ford “A different displaying technique for photo voltaic cells,” in IEEE 36th Annul. PE Professionals Conf., Jun. 2014, vol. 3, pp. 1952–1958. [4] P. Rodriguez, & D. Sera, R. Teodorescu, “PV board ideal founded on datasheet standards,” in Proc. IEEE Int. Sump. IE, Jun. 5–8, 2008, pp. 2394–2398. [5] C. Wang, M. H. Nehru, & S. R. Shaw, “Active prototypes & ideal authentication for PEM fuel cells utilizing electrical paths,” IEEE Trans. EC., vol. 21, no. 3, pp. 443–452, Jun. 2007. [6] C. Hua & C. Shen, “Reasonable training of peak power tracing methods for solar storage arrangement,” in Proc. 14th Annul. APE conference 1999, vol. 3, pp. 680–686. [7] A. Hajizadeh & M. A. Glomar, “Power flow mechanism of grid-associated fuel cell circulated group schemes,” J. Elect. Eng. Technol., vol. 4, no. 3, pp. 144–152, 2010. [8] C. Hua & J. R. Lin, “DSP-founded supervisor presentation in battery storage of photovoltaic arrangement,” in Proc.22nd IEEE Int. Conf. IEs, Control, & Instrumentation, Aug. 6–11, 1997, vol. 4, pp. 1751–1811. [9] C. Hua, J. Lin, & C. Shen, “Employment of DSP-measured photovoltaic arrangement with peak power tracking,” IEEE Trans. IE., vol. 46, no. 2, pp. 100–108, Feb. 1999. [10] E. Koutroulism & K. Kaalitzakis, “Improvement of microcontroller- constructed, photovoltaic MPPT control structure,” IEEE Trans. PE., vol. 17, no. 2, pp. 47–55, Jan. 2002.

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