ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & ENVIRONMENT AZOJETE March 2024. Vol. 20(1):133-150 Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria. Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 133 APPLICATION OF GENETIC ALGORITHM BASED OPTIMAL PLACEMENT OF STATIC VAR COMPENSATOR FOR THE NIGERIAN 330 KV ELECTRICITY GRID PERFORMANCE ENHANCEMENT O. I. Adebisi,1* A. B. Ogundare,2 B. J. Olajuwon3 and A. Adebeshin4 1,3Department of Electrical and Electronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria 2Department of Electrical and Electronics Engineering, Lagos State University of Science and Technology, Nigeria 4Mantrac Nigeria Limited, Lagos State, Nigeria *Corresponding author's email address: adebisioluwaseun@funaab.edu.ng ARTICLE INFORMATION Submitted 9 January, 2024 Revised 15 February, 2024 Accepted 20 February, 2024 Keywords: FACTS GA Line loss Newton-Raphson SVC Voltage profile ABSTRACT Power supply-demand imbalance is one of the critical challenges bedeviling the electricity grid operation of a third world nation such as Nigeria. This has consequently led to the system’s poor voltage profile with its associated high- power losses. Many potent solutions have been proposed to address these problems; however, technological advancements favour the adoption and widespread use of a class of economic and fast acting solid-state controllers called flexible alternating current transmission systems (FACTS). This study, therefore, assessed the potential of an optimally placed static var compensator (SVC) via genetic algorithm (GA) in enhancing the performance of the Nigerian 330 kV, 28-bus electricity grid. The static power flow model of the system with and without SVC was analyzed using Newton-Raphson method and simulated in MATLAB R2020a environment. The system performance with SVC placed optimally using GA was compared with the first principle approach. The simulation results revealed that before compensation, the test grid had five buses namely Ayede, New-Haven, Gombe, Kano and Makurdi with voltage magnitudes of 0.926, 1.058, 0.906, 0.859 and 0.944 p.u., respectively, violating the acceptable voltage tolerance limit of 0.95≤Vi≤1.05p.u. The first principle-based compensation improved the voltage magnitudes of the critical buses on the test grid to 1.015, 1.008, 0.958, 0.995 and 0.975 p.u., respectively, while compensation via GA enhanced the voltage magnitudes to 1.05, 0.985, 1.038, 1.016 and 0.996 p.u. respectively. The grid’s total active and reactive line losses reduced from 181.479 to 149.786 MW and 145.323 to 125.161 MVAr, respectively with compensation from the first principle approach, whereas the values were minimized to 115.675 MW and 109.336 MVAr, respectively with the GA based compensation. The GA based optimal placement of SVC exhibited better enhancement characteristics on the considered Nigerian electricity grid than the first principle method. 1.0 Introduction Electrical energy is one of the available and most widely used forms of energy. It is a key ingredient in measuring the development of a nation. In a third world country such as Nigeria, increasing population and technological advancements have placed enormous demand on electrical power usage without sufficient energy generation to address it. Hence, creating electricity supply-demand imbalance (Dahunsi et al., 2022; Akpojedje and Ogujor, 2021; Hussaini et al., 2021; Ayamolowo et al., 2019; Ogbonnaya et al., 2019; Yakuba, 2019; Emodi and http://www.azojete.com.ng/ mailto:%20salami.lukman@adelekeuniversity.edu.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Arid Zone Journal of Engineering, Technology and Environment, March 2024; Vol. 20(1):133-150. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 134 Yusuf, 2015). The imbalance between electricity supply and demand along with poor allocation of loads and overloading of transmission lines has affected the performance of the Nigerian power system negatively resulting into problems such as poor voltage profile, increased power losses, unreliable and poor-quality power supply among others (Ahmad and Sirjan, 2020; Idoniboyeobu et al., 2018). Over the years, many efforts have been put forward generally to solve the key operational problems hampering power systems from attaining full potential without the desired goal achieved. Conventional methods including construction of new generating stations, upgrading of existing transmission lines, expansion of distribution networks, the use of mechanical switches and capacitor banks have all been proposed in the past for improving the power system networks to enhance service delivery, however, some major limitations such as high investment cost, possible wear and tear of the mechanical components and time delay over- shadowed the merits offered by these techniques (Ahmad and Sirjan, 2020; Muhammad et al., 2020; Adebayo and Sun, 2017). This has, therefore, led to the emergence and adoption of FACTS for compensating power network performance. FACTS are solid state devices with capacity to improve power system performance in a prompt manner, increase voltage profile, reduce power losses, increase system reliability, prevent cascading outages through emergency control and damp oscillations that can threaten system security (Shehata et al., 2020; Rana et al., 2019; Adebisi et al., 2018; Simeon et al., 2018; Adebisi et al., 2017). Members of this family include but not limited to unified power flow controller, static synchronous compensator, interline power flow controller, static synchronous series compensator, static var compensator and thyristor-controlled series capacitor (Bharambe, 2021; Shelke and Bhole, 2021; Kaur and Gupta, 2021; Muhammad et al., 2020; Siddique et al., 2019; Movahedi et al., 2019; Adebisi et al., 2018). Usually, FACTS are incorporated into power system networks for performance improvement either through the use of first principle or optimization approach. While first principle involves manual installation of FACTS devices in some defective buses where voltage tolerance limit has been violated, optimization approach involves the integration of FACTS controllers at the best node or branch where its impact can generally be felt by the entire system using versatile techniques such as artificial intelligence (AI). Placement of FACTS devices via first principle which is inherently characterised by slow response is unsuitable for today’s large-scale power system operation considering the complexity of different networks and the data structure. This, therefore, paves way for optimal placement of FACTS controllers using efficient optimization techniques such as AI based methods that can handle the complexity involved in the analysis of power system networks with spontaneous response. Artificial intelligence, in a simple term, is a field of study concerned with the creation of human- like thought processes such as reasoning, perceiving, learning, interaction and self-correction to facilitate robust problem solving (Ergen, 2019; Grewal, 2014). Widespread adoption of AI based applications in recent times in various aspects of human endeavours including power system engineering has been attributed to massive data, advancements in algorithms as well as growing economical computational power and storage (Ergen, 2019). Different AI techniques have been deployed for optimal placement of FACTS for enhancing power system network performance. Among the commonly used techniques are artificial neural network (ANN), fuzzy logic (FL), differential evolution algorithm, ant colony optimization, genetic algorithm (GA), file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Adebisi et al.: Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement. AZOJETE, 20(1):133-150. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 135 particle swarm optimization (PSO) and harmony search algorithm (Singh and Swami, 2020; Gaur and Mathew, 2018; Nabavi et al., 2011). Each of these techniques offers varying degree of dynamisms which adapts them suitably to the problems being addressed. Therefore, in this work, an optimally placed SVC via GA was employed to enhance the performance of the Nigerian 330 kV electricity grid. SVC possesses an excellent capability to compensate reactive power and regulate voltages at various buses in power system networks (Ćalasan et al., 2020; Biswas and Das, 2011). Due to these reasons, several investigations have been conducted on potential application of SVC for varieties of performance improvement related issues in power system; voltage stability control (Muthukumar et al., 2023; Al-Saidi et al., 2023), power loss minimization (Osman et al., 2023; Lakshmi, 2021), transient stability improvement (Ikonwa et al., 2023; Sahare and Bonde, 2020) and dynamic stability enhancement (Essien et al., 2023; Himaja et al., 2022). The choice of GA for optimal placement of SVC to improve the performance of a power system network in the present work was due to its high flexibility, efficiency, positive outcomes and user-friendliness (Kothai and Jayapal, 2021; Adebanji et al., 2020). 1.1 Static Var Compensator An SVC is a shunt compensator capable of generating or drawing var for power system voltage stabilization. SVC will consume reactive power from the power network if such system is characterized by a capacitive or leading reactive load and hence, reduces the system voltage. It will, however, increase the system voltage through reactive power injection if the network is characterized by an inductive or lagging condition (Kumar and Dash, 2016). Basically, SVC is composed of air core reactors in series with thyristor which is connected in parallel to filter banks as shown in Figure 1. With proper adjustment of the thyristor firing angle, the current of air-core reactors can be controlled to produce voltage within acceptable limit at the bus. Generally, SVC is an efficient FACTS device that can regulate bus voltage magnitude, improve power flow along transmission lines, reduce power losses, increase system load ability among other (Anand, 2017; Kumar and Dash, 2016). Figure 1: An SVC configuration (Adebisi et al., 2018) http://www.azojete.com.ng/ adebisioluwaseun@funaab.edu.ng Arid Zone Journal of Engineering, Technology and Environment, March 2024; Vol. 20(1):133-150. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 136 2 Materials and Methods 2.1 Power Flow Analysis Power flow is an essential mathematical tool for obtaining insightful information about power system performance under steady state operating conditions (Gupta, 2011; Hadi, 2008; Kothari and Nagrath, 2008). For this work, a typical power system bus arrangement depicted in Figure 2 was considered for the theoretical analysis of power flow. The resultant current with KCL applied to the n-bus arrangement in Figure 2 and the complex apparent power delivered at bus i are respectively expressed by equations (1) and (2): (1) 𝑆𝑖 = 𝑃𝑖 + 𝑗𝑄𝑖 = 𝑉𝑖𝐼𝑖 ∗ (2) where: 𝑉𝑖, 𝐼𝑖, 𝑃𝑖 , 𝑄𝑖 and 𝑆𝑖 respectively denote voltage (V), current (A), active power (W), reactive power (VAr) and apparent power (VA) associated with bus i;𝐼𝑖 ∗, 𝑉𝑗 and 𝑌𝑖𝑗 denote complex conjugate of bus i current (A), bus j voltage (V) and transfer admittance of bus i relative to bus j respectively (Ʊ). Vi V1 V2 Vn Ii Ii1 Ii2 Iin yi1 yi2 yin Iio yio Figure 2: A typical power system n-bus arrangement (Gupta, 2011; Kothari and Nagrath, 2008; Hadi, 2008) The use of equation (1) in equation (2) yields equation (3) which was decoupled into real and imaginary parts expressed by equations (4) and (5) respectively: (3) 𝑃𝑖 = 𝑅𝑒{𝑉𝑖 ∗ ? 𝑌𝑖𝑗𝑉𝑗 𝑛 𝑗 =1 } (4) 𝑄𝑖 = −𝐼𝑚{𝑉𝑖 ∗ ? 𝑌𝑖𝑗𝑉𝑗 𝑛 𝑗 =1 } (5) The use of polar coordinates form of 𝑉𝑖, 𝑉𝑖 ∗, 𝑉𝑗, and 𝑌𝑖𝑗 given by equation (6) in equations (4) and (5) results in equations (7) and (8) respectively: file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Adebisi et al.: Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement. AZOJETE, 20(1):133-150. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 137 𝑉𝑖 = |𝑉𝑖|𝑒 𝑗𝑑𝑖 𝑉𝑖 ∗ = |𝑉𝑖|𝑒 −𝑗𝑑𝑖 𝑉𝑗 = |𝑉𝑗|𝑒 𝑗𝑑𝑗 𝑌𝑖𝑗 = |𝑌𝑖𝑗|𝑒 𝑗?𝑖𝑗 𝑒𝑗𝑓 = cos 𝑓 + 𝑗 sin 𝑓 } (6) where: 𝑑𝑖 and ?𝑖𝑗 respectively denotes bus i voltage angle (o) and line admittance angle (o). 𝑃𝑖 = ? 𝑌𝑖𝑗𝑉𝑖𝑉𝑗 𝑛 𝑗 =1 cos(?𝑖𝑗+ 𝑑𝑗 − 𝑑𝑖) (7) 𝑄𝑖 = −? 𝑌𝑖𝑗𝑉𝑖𝑉𝑗 𝑛 𝑗 =1 sin(?𝑖𝑗+ 𝑑𝑗 − 𝑑𝑖) (8) The expressions of equations (7) and (8) together constitute the static power flow equations. They are non-linear equations whose solutions are obtained through numerical iterative method (Gupta, 2011; Hadi, 2008; Kothari and Nagrath, 2008). Newton-Raphson iterative technique was employed in this work to linearize equations (7) and (8) because of its faster convergence, accuracy, reduced iteration number and reliability. [ ? 𝑃2 ? 𝑃3 ? ? 𝑃𝑛 ? 𝑄2 ? 𝑄3 ? ? 𝑄𝑛] = [ ?𝑃2 ?𝑑2 ?𝑃2 ?𝑑3 ? ?𝑃2 ?𝑑𝑛 | ?𝑃2 ?𝑉2 ?𝑃2 ?𝑉3 ? ?𝑃2 ?𝑉𝑛 ?𝑃3 ?𝑑2 ?𝑃3 ?𝑑3 ? ?𝑃3 ?𝑑𝑛 | ?𝑃3 ?𝑉2 ?𝑃3 ?𝑉3 ? ?𝑃3 ?𝑉𝑛 ? ? ? ? | ? ? ? ? ?𝑃𝑛 ?𝑑2 ?𝑃𝑛 ?𝑑3 ? ?𝑃𝑛 ?𝑑𝑛 | ?𝑃𝑛 ?𝑉2 ?𝑃𝑛 ?𝑉3 ? ?𝑃𝑛 ?𝑉𝑛 − − − − − − − − − ?𝑄2 ?𝑑2 ?𝑄2 ?𝑑3 ? ?𝑄2 ?𝑑𝑛 | ?𝑄2 ?𝑉2 ?𝑄2 ?𝑉3 ? ?𝑄2 ?𝑉𝑛 ?𝑄3 ?𝑑2 ?𝑄3 ?𝑑3 ? ?𝑄3 ?𝑑𝑛 | ?𝑄3 ?𝑉2 ?𝑄3 ?𝑉3 ? ?𝑄3 ?𝑉𝑛 ? ? ? ? | ? ? ? ? ?𝑄𝑛 ?𝑑2 ?𝑄𝑛 ?𝑑3 ? ?𝑄𝑛 ?𝑑𝑛 | ?𝑄𝑛 ?𝑉2 ?𝑄𝑛 ?𝑉3 ? ?𝑄𝑛 ?𝑉𝑛] [ ? 𝑑2 ? 𝑑3 ? ? 𝑑𝑛 ? 𝑉2 ? 𝑉3 ? ? 𝑉𝑛 ] (9) The linearized set of Newton-Raphson power flow equations is given by equation (9) with modified variant in equation (10). [ ? 𝑃 ?𝑄 ] = [ 𝐽1 𝐽2 𝐽3 𝐽4 ] [ ? 𝑑 ? 𝑉 ] (10) where: ? 𝑃, ? 𝑄, ? 𝑑 and? 𝑉 respectively denotes mismatch in active power (W), reactive power (VAr), phase angle (o) and voltage (V) and 𝐽1 , 𝐽2, 𝐽3and 𝐽4 are the Jacobian matrix elements obtained as partial derivatives of equations (7) and (8) with 𝑑 and 𝑉 as independent variables. At each level of iteration, the mismatch in active and reactive power with updated bus voltage phase angle and magnitude are given equations (11) to (14): ? 𝑃𝑖 𝑘 = 𝑃𝑖 𝑠𝑝𝑒𝑐 − 𝑃𝑖 𝑘 (11) ? 𝑄𝑖 𝑘 = 𝑄𝑖 𝑠𝑝𝑒𝑐 − 𝑄𝑖 𝑘 (12) 𝑑𝑖 𝑘+1 = 𝑑𝑖 𝑘 +?𝑑𝑖 𝑘 (13) 𝑉𝑖 𝑘+1 = 𝑉𝑖 𝑘 +?𝑉𝑖 𝑘 (14) where: k is the iteration count, ? 𝑃𝑖 𝑘, ? 𝑄𝑖 𝑘, ? 𝑑𝑖 𝑘 and ? 𝑉𝑖 𝑘 respectively denotes mismatch in bus i active power (W), reactive power (VAr), voltage angle (o) and voltage magnitude (V) at http://www.azojete.com.ng/ adebisioluwaseun@funaab.edu.ng Arid Zone Journal of Engineering, Technology and Environment, March 2024; Vol. 20(1):133-150. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 138 iteration 𝑘, 𝑃𝑖 𝑠𝑝𝑒𝑐 and 𝑄𝑖 𝑠𝑝𝑒𝑐 denote bus i specified active power (W) and reactive power (VAr) respectively, 𝑃𝑖 𝑘 , 𝑄𝑖 𝑘, 𝑑𝑖 𝑘 and 𝑉𝑖 𝑘 respectively denotes bus i calculated active power (W), reactive power (VAr), voltage angle (o) and voltage magnitude (V) at iteration 𝑘 and 𝑑𝑖 𝑘+1 and 𝑉𝑖 𝑘+1 denote updated bus i voltage angle (o) and voltage magnitude (V) at iteration 𝑘 + 1 respsectively. The voltage and reactive power constraints imposed at bus i are expressed by equations (15) and (16) respectively: 𝑉𝑖𝑚𝑖𝑛 = 𝑉𝑖 = 𝑉𝑖𝑚𝑎𝑥 (15) 𝑄𝑖𝑚𝑖𝑛 = 𝑄𝑖 = 𝑄𝑖𝑚𝑎𝑥 (16) where: 𝑉𝑖𝑚𝑖𝑛, 𝑉𝑖𝑚𝑎𝑥, 𝑄𝑖𝑚𝑖𝑛 and 𝑄𝑖𝑚𝑎𝑥 respectively denotes minimum voltage magnitude (V), maximum voltage magnitude (V), minimum reactive power supply (VAr) and maximum reactive power supply (VAr) at bus i. 2.2 Power Flow Modeling of an SVC Referring to Figure 2, an SVC placed at bus i acted or served as reactive power 𝑄𝑆𝑉𝐶𝑖 generator or absorber. Hence, the effective var at bus i is obtained by equation (17) (Auchariyamet and Sirisumrannukul, 2010): 𝑄𝑖 = 𝑄𝑔𝑖 − 𝑄𝑆𝑉𝐶𝑖 = 𝑄𝑙𝑖 (17) where: 𝑄𝑔𝑖, 𝑄𝑙𝑖 and 𝑄𝑆𝑉𝐶𝑖 respectively represents reactive power generation (VAr), reactive power demand (VAr) and SVC reactive power (VAr) at bus i. Considering the SVC representation in Figure 3, the controller was modelled as a variable susceptance with the right constraint imposed on the susceptance or the firing angle. The current taken by the SVC and the equivalent reactive power absorbed are respectively expressed by equations (18) and (19) (Adebisi et al., 2018; Adebisi et al., 2017): 𝐼𝑆𝑉𝐶 = 𝐵𝑆𝑉𝐶𝑉𝑖 (18) 𝑄𝑆𝑉𝐶 = 𝑄𝑖 = −𝑉𝑖 2𝐵𝑆𝑉𝐶 (19) where: 𝐼𝑆𝑉𝐶 , 𝐵𝑆𝑉𝐶and 𝑄𝑆𝑉𝐶 represents SVC current (A), susceptance (Ʊ) and reactive power (VAr) respectively. Equation (19) gives an expression for the reactive power supplied when an SVC is installed at bus i. The linearized set of equations modeling the SVC at bus i where BSVC is considered as a state variable are expressed by equation (20) (Adebisi et al., 2018; Adebisi et al., 2017): [ ? 𝑃𝑖 ? 𝑄𝑖 ] (𝑘) = [ 0 0 0 𝑄𝑖 ] (𝑘) [ ? ?𝑖 ? 𝐵𝑆𝑉𝐶 𝐵𝑆𝑉𝐶⁄ ] (𝑘) (20) file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Adebisi et al.: Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement. AZOJETE, 20(1):133-150. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 139 ISVC i BSVC Figure 3: A variable shunt susceptance model of an SVC At the end of iteration k, the updated BSVC is expressed as equation (21): BSVC (k) = BSVC (k−1) + ( ?BSVC BSVC ) (k) BSVC (k−1) (21) The susceptance of the SVC, 𝐵𝑆𝑉𝐶 , is related to its firing angle 𝑎𝑆𝑉𝐶by equation (22): 𝐵𝑆𝑉𝐶 = −1 𝑋𝐶𝑋𝐿 {𝑋𝐿 − 𝑋𝐶 𝑝 [2(𝑝 − 𝑎𝑆𝑉𝐶) + sin(2𝑎𝑆𝑉𝐶)]} (22) where: 𝑋𝐶and 𝑋𝐿are respectively the SVC capacitive and inductive reactances in Ω. The Newton-Raphson power flow equations with firing angle 𝑎 in degrees (o) as the new state variable is expressed as equation (23) (Adebisi et al., 2018; Adebisi et al., 2017): [ ? 𝑃𝑖 ? 𝑄𝑖 ] (𝑘) = [ 0 0 0 𝑑𝑄𝑖 𝑑𝑎 ] (𝑘) [ ? ?𝑖 ? 𝑎𝑆𝑉𝐶 ] (𝑘) (23) Where: 𝑑𝑄𝑖 𝑑𝑎 = 2𝑉𝑖 2 𝑝𝑋𝐿 [cos(2𝑎𝑆𝑉𝐶) − 1] (24) The updated 𝑎SVCafter iteration k is given by equation (25): 𝑎SVC (k) = 𝑎SVC (k −1) +?𝑎SVC (k) (25) 2.3 Genetic Algorithm Based Optimal Placement of SVC With the inherent shortcomings of the conventional methods, FACTS devices today play crucial roles in enhancing the power system performance so that the wide margin between supply and demand for electrical energy could be minimized. The deployment of FACTS controllers for power system performance improvement usually involves a huge initial capital investment. Therefore, to ensure these devices operate at maximum efficiency and produce satisfactory effect, their placement and sizing in power networks needs to be optimised. Various approaches are available for optimizing the location or placement of FACTS devices. However, for this study, an artificial intelligence-based technique called genetic algorithm was adopted for optimizing the location of SVC. http://www.azojete.com.ng/ adebisioluwaseun@funaab.edu.ng Arid Zone Journal of Engineering, Technology and Environment, March 2024; Vol. 20(1):133-150. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 140 Genetic algorithm is a heuristic optimization approach inspired by the principle of genetics and natural selection. It is commonly employed tool for solving constrained and unconstrained problems arising in many fields of study. It is fast, efficient and has a good parallel capability in addition to its ability to produce multiple of good solutions. The flowchart of the implementation process of the genetic algorithm based optimal placement of SVC in this work is shown in Figure 4. Typically, GA architecture is segmented into three distinct phases which are initial population generation, objective function evaluation and genetic operations. Initial population generation phase involves random generation of possible solutions of the problem. This is followed by the computation of the objective function on every individual of the population to evaluate their fitness. New population sets are then generated via genetic operation involving reproduction, crossover and mutation until the maximum number of iterations is reached with each iteration equating to a generation. The cycle represented by these three phases of GA architecture is repeated severally until final improved solution is achieved. For the GA optimization in this work, the SVC was configured based on two control parameters which are location and size setting of the device. To take these two parameters into account in the optimization process, each individual was represented with two strings during implementation. The first string corresponds to the location of the SVC which comprises the number of buses where the device is to be installed while the second string corresponds to the size setting of the controller. The strings have discrete values between 0 and 1, with 0 representing the minimum value that the controller can take and 1, the maximum value. The creation of an individual was done by randomly generating all the possible buses or lines of the power network that could be a suitable location for the SVC within the range of the number of bus data under consideration. file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Adebisi et al.: Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement. AZOJETE, 20(1):133-150. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 141 Start Increase number of generation Stop Input network data including bus number and type Generate the initial Population (size and location of SVC) Initialize best result to zero Compute objective function (voltage deviation and active power loss) for individuals in this generation Select best result in this generation Best result equals best result between initial best result and best result in this generation Is maximum number of iteration reached? Final output equals best result Selection (Roulette wheel) Output the final result Crossover (Arithmetic) Mutation (Non-uniform) NoYes Figure 4: GA flowchart for the implementation of optimal placement of SVC This was then followed by the characterization of the controller. Finally, the size of the device was randomly chosen among the possible values and the objective functions which are voltage deviation minimization and active power loss reduction were determined for every individual of the population sets and subjected to further optimization procedures involving reproduction, crossover and mutation to produce new and improved population sets which are the final solutions. The two components of the objective function in the flowchart of Figure 4, that is, voltage deviation and active power loss are respectively defined by equations (32) and (33): 𝑉𝐷 = ? |𝑉𝑗 − 𝑉𝑗 𝑟𝑒𝑓 |𝑛 𝑗=1 (32) 𝑃𝐿 = ? (𝑔𝑗(𝑉𝑖 2 + 𝑉𝑗 2 − 2𝑉𝑖𝑉𝑗 cos(𝑑𝑖 − 𝑑𝑗))) 𝑛 𝑗=1 (33) http://www.azojete.com.ng/ adebisioluwaseun@funaab.edu.ng Arid Zone Journal of Engineering, Technology and Environment, March 2024; Vol. 20(1):133-150. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 142 where: 𝑉𝐷 is the voltage deviation (V), 𝑉𝑗 𝑟𝑒𝑓 is the reference of the bus j voltage (V),𝑃𝐿 is the real power loss (W) and 𝑔𝑗 is the conductance of the connecting line between buses i and j (Ω). For power system analysis, unless specified, it is usual to maintain the bus voltages within ±5% of the nominal value (Ezeruigbo et al., 2021). Hence, the voltage tolerance of 0.95 = 𝑉𝑖 = 1.05 𝑝. 𝑢. was adopted in this work. 2.4 Test Network The test network considered in this work to assess the performance enhancement capability of SVC was the Nigerian 28-bus power grid. The grid whose one-line diagram is presented in Figure 5 comprised of twenty-eight buses, fifty-two transmission lines and nine generating stations. The network and generator data of the system are respectively presented in Tables 1 to 3. B/Kebbi Kainji GS Kano Kaduna Shiroro GS Jos Gombe Makurdi Mambila Jebba GS Jebba Abuja Ajaokuta Osogbo Ayede Sapele GS Benin Ikeja West Delta GS Akangba Onitsha Aladja Alaoje New Haven Aja Egbin GS Papalanto Afam GS Figure 5: The Nigerian 28-bus power network (NCC, 2012) file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Adebisi et al.: Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement. AZOJETE, 20(1):133-150. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 143 Table 1: Bus data of the Nigerian 28-bus power network Bus Identification Bus Loads Name No MW MVAr Egbin 1 68.90 51.70 Delta 2 0.00 0.00 Aja 3 274.40 205.80 Akangba 4 244.70 258.50 Ikeja-West 5 633.20 474.90 Ajaokuta 6 13.80 10.30 Aladja 7 96.50 72.40 Benin 8 383.30 287.50 Ayede 9 275.80 206.8 Osogbo 10 201.20 150.90 Afam 11 52.50 39.40 Alaoji 12 427.00 320.20 New-Heaven 13 177.90 133.40 Onitsha 14 184.60 138.40 B/Kebbi 15 114.50 85.90 Gombe 16 130.60 97.90 Jebba 17 11.00 8.20 Jebba G 18 0.00 0.00 Jos 19 70.30 52.70 Kaduna 20 193.00 144.70 Kanji 21 7.00 5.20 Kano 22 220.60 142.90 Shiroro 23 70.30 36.10 Sapele 24 20.60 15.40 Abuja 25 110.00 89.00 Makurdi 26 290.10 145.00 Mambila 27 0.00 0.00 Papalanto 28 0.00 0.00 Table 2: Transmission Line of the Nigerian 28-Bus Power System Transmission Lines Data Bus Resistance R(pu) Reactance X(pu) From To 1 3 0.0006 0.0044 4 5 0.0007 0.0050 1 5 0.0023 0.0176 5 8 0.0110 0.0828 5 9 0.0054 0.0405 5 10 0.0099 0.0745 6 8 0.0077 0.0576 2 8 0.0043 0.0317 2 7 0.0012 0.0089 7 24 0.0025 0.0186 8 14 0.0054 0.0405 8 10 0.0098 0.0742 8 24 0.0020 0.0148 9 10 0.0045 0.0340 http://www.azojete.com.ng/ adebisioluwaseun@funaab.edu.ng Arid Zone Journal of Engineering, Technology and Environment, March 2024; Vol. 20(1):133-150. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 144 15 21 0.0122 0.0916 10 17 0.0061 0.0461 11 12 0.0010 0.0074 12 14 0.0060 0.0455 13 14 0.0036 0.0272 16 19 0.0118 0.0887 17 18 0.0002 0.0020 17 23 0.0096 0.0271 17 21 0.0032 0.0239 19 20 0.0081 0.0609 20 22 0.0090 0.0680 20 23 0.0038 0.0284 23 25 0.0038 0.0284 12 26 0.0071 0.0532 19 26 0.0059 0.0443 26 27 0.0079 0.0591 5 28 0.0016 0.0118 Table 3: Generator Data of the Nigerian 28-Bus Power System Bus Identification Voltage Magnitude Generator Reactive Limits Name No MW MVAR Qmin Qmax Egbin 1 1.05 0.00 0.00 -1006 1006 Delta 2 1.05 670.00 0.00 -1030 1000 Afam 11 1.05 431.00 0.00 -1000 1000 Jebba G 18 1.05 495.00 0.00 -1050 1050 Kanji 21 1.05 624.70 0.00 -1010 1010 Shiroro 23 1.05 388.90 0.00 -1010 1010 Sapele 24 1.05 190.30 0.00 -1010 1010 Mambila 27 1.05 750.00 0.00 -1010 1010 2.5 Simulation Software In power system analyses, computer programs or software play a very vital role. They allow the performance of complicated system to be simulated or verified within a short period of time so that insightful information about the real time behaviour of the system can be obtained. In this work, the GA steps in the flowchart of Figure 4 were implemented on the network of Figure 5 through codes written using a MATLAB programming language, R2020a version. MATLAB was adopted because it provides an interactive environment for design and manipulation of algorithms in addition to data visualization, data analysis and numerical computation capabilities (Nasiruzzaman, 2010). 3 Results and Discussion The voltage profile and power losses of the test power grid before and after optimally placed SVC with the bus, branch and generator data in Tables 1 to 3 as the input variables during simulation to fully characterize the network are shown in Figures 6 and 7. The results in Figure 6 showed that GA based optimal placement of SVC on the Nigerian 28-bus power grid improved the voltage profile of the overall network much better than the first principle procedure. When no compensation was applied on the grid, the voltage magnitudes of Ayede, New-Haven, Gombe, Kano and Makurdi (buses 9, 13, 16, 22 and 26) which were 0.926, 1.058, file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Adebisi et al.: Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement. AZOJETE, 20(1):133-150. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 145 0.906, 0.859 and 0.944 p.u. respectively fell out of the voltage tolerance limit of 0.95≤Vi≤1.05 p.u. Optimal placement of the SVC via first principle on Makurdi where the size of the compensator was 10.35 MVAr improved the voltage magnitude of the node itself and Ayede, New Haven and Gombe to 1.015, 1.008, 0.958 and 0.995 p.u. respectively, which are now within the voltage tolerance limit. However, this process produced a voltage magnitude of 1.065 p.u. at Kano which was still outside the statutory limit. This drawback was overcome by GA based optimization technique. Figure 6. Voltage profile of the Nigerian 28-bus power grid with and without compensation A 9.65 MVAr rated SVC optimally placed on Kano via genetic algorithm improved the voltage magnitude of the node and Ayede, New-Haven, Gombe and Makurdi to 1.05, 0.985, 1.038, 1.016 and 0.996 p.u. respectively; the values which are all within the statutory limit. This result revealed that with the GA based optimization approach, the compensator performed better with a reduced size unlike the first principle approach where the size was a bit higher. The enhancement of the Nigerian 28-bus power grid voltage profile through optimal location of SVC impacted positively on power losses of the network as presented in Figure 7. Figure 7. Total active and reactive power losses on the Nigerian 28-bus power grid with and without compensation While the grid’s total active and reactive line losses respectively reduced by 17.46 and 13.87 % from 181.479 to 149.786 MW and 145.323 to 125.161 MVAr respectively through first principle, GA based placement of the SVC minimized the grid’s total active and reactive line losses from 181.479 to 115.675 MW and 145.323 to 109.336 MVAr, producing a percentage reduction of 36.26 and 24.76 % in the respective total active and reactive line losses of the grid. These results indicated the suitability of SVC placed by appropriate optimization techniques such as genetic algorithm for enhancing the performance of power system network. http://www.azojete.com.ng/ adebisioluwaseun@funaab.edu.ng Arid Zone Journal of Engineering, Technology and Environment, March 2024; Vol. 20(1):133-150. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 146 The results obtained are in agreement with the outcomes of the work of Aryal and Gynawali (2020), Nassar et al. (2019), Mulyadi et al. (2018) and Duraisamy and Ponnusamy (2017), who all conducted investigations on the GA based placement of SVC considering different types of power networks. Aryal and Gynawali (2020) noted that GA based optimal placement of SVC was a better approach for enhancing power system voltage profile and line loss, although stressing that the performance of the compensator could be improved further when used in combination with other FACTS controller. While Nassar et al. (2019) observed that GA based optimal location of SVC offered the best results in terms improved voltage profile and well minimized power losses of the four scenarios examined for power system voltage performance enhancement, Mulyadi et al. (2018) showed that optimal placement of SVC via GA on 150 kV electricity transmission grid enhanced the system’s overall performance such that voltage magnitude of each bus was raised to standard tolerance limit and line losses were significantly reduced. Duraisamy and Ponnusamy (2017) reported that voltage profile and total lines losses of the considered network after SVC was optimally placed SVC using GA improved appreciably. Hence, all these previous works are suggestions that optimal placement of SVC via artificial intelligence technique such as genetic algorithm is a potential candidate for enhancing the quality of electric power transmitted through grid system which was equally confirmed by the present study. 4 Conclusion One of the key solutions presently gaining widespread attentions of power system engineers and researchers globally is the use of artificial intelligence based techniques for optimal FACTS placement for performance improvement of electricity grids. Hence, this work employed an SVC optimally placed by genetic algorithm for improving line losses and voltage profile on the Nigerian 28-bus power grid. Findings from the work revealed that optimal placement of static var compensator via GA in addition to exhibiting superior positive effects on the system’ s voltage profile produced a much more reduced total active and reactive power losses compared to the manual process and the initial load flow analysis. This, therefore, suggests that adoption of this approach for the Nigerian power system grid will not only go a long way towards facilitating the transmission of a better quality electric power but also will increase transfer capability of the grid, thereby, making more electrical energy that can serve the end- users available. As a means to validating the results obtained in this study, further work is on- going to comparatively evaluate the performance of GA based siting of the SVC on the Nigerian electricity with other optimization methods including FL, ANN and PSO to provide useful insights on the appropriate approach to locate the device since this is decisive in enhancing its effectiveness and efficiency for the performance improvement of power system networks where it is deployed. Acknowledgements Authors wish to appreciate the enormous support received from Al-Aleem Engineering Limited, Lagos State, Nigeria during the implementation phase of the study. References Adebanji, B., Adepoju, GA., Olulope, P., Fasina, T. and Adetan, O. 2020. Feasibility and Optimal Design of a Hybrid Power System for Rural Electrification for a Small Village. International Journal of Electrical and Computer Engineering, 10(6): 6214-6224. file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Adebisi et al.: Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement. AZOJETE, 20(1):133-150. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 147 Adebayo, I. and Sun, Y. 2017. New Performance Indices for Voltage Stability Analysis in a Power System. Energies, 10: 2042. Adebisi, OI., Adejumobi, IA., Ogunbowale, PE. and Ade-Ikuesan, OO. 2018. Performance Improvement of Power System Networks Using Flexible Alternating Current Transmission Systems Devices: The Nigerian 330 kV Electricity Grid as a Case Study. LAUTECH Journal of Engineering and Technology, 12(2): 46-55. Adebisi, OI., Adejumobi, IA., Ogunbowale, PE. and Ade-Ikuesan, OO. 2017. Application of Static Var Compensator for Voltage Stability Enhancement and Power Loss Reduction in Power System Networks. LAUTECH Journal of Engineering and Technology, 11(2): 46-58. Al-Saidi, M., Al-Badi, A., Onen, A. and Elhaffar, A. 2023. Optimal Location and Size of Static Var Compensators (SVC) to Enhance the Voltage Profile on the Main Interconnected System in Oman. Energies, 16(19): 6802. Anand, G. 2017. Voltage Control in a Power System with the Help of Reactive Power Control. Global Research and Development Journal for Engineering, 2(5): 278-292. Ahmad, AAL. and Sirjani, R. 2020. Optimal Placement and Sizing of Multi-Type FACTS Devices in Power Systems Using Metaheuristic Optimisation Techniques: An Updated Review. Ain Shams Engineering Journal, 11(3): 611-628. Akpojedje, FO. and Ogujor, EA. 2021. Demand Side Management Strategy for Alleviating Power Shortages in Nigerian Power System: A Case Study. Nigerian Journal of Technology, 40(5): 927- 937. Aryal, S. and Gynawali, N. 2020. Optimal Setting and Siting of TCSC and SVC to Enhance Power System Performance. Proceedings of 8th IOE Graduate Conference, Nepal, 8: 15-23. Auchariyamet, S. and Sirisumrannukul, S. 2010. Optimal Allocation of Static VAr Compensator for Active Power Loss Reduction by Different Decision Variables. GMSARN International Journal, 4: 57-66. Ayamolowo, OJ., Buraimoh, E., Salau, AO. and Dada, JO. 2019. Nigeria Electricity Power Supply System: The Past, Present and the Future. Proceedings of IEEE PES/IAS Power Africa Conference, Abuja, Nigeria, 64-69. Bharambe, V. 2021. A Review: Capability of FACTS Device for Performance Improvement of Power System. Journal Publication of International Research for Engineering and Management, 10(5): 1-6. Biswas, MM. and Das, KK. 2011. Voltage Level Improving by Using Static VAR Compensator (SVC). Global Journals of Research in Engineering, 11(5): 13-18. Ćalasan, M., Konjić, T., Kecojević, K. and Nikitović, L. 2020. Optimal Allocation of Static Var Compensators in Electric Power Systems. Energies, 13: 3219. Dahunsi, FM., Abdul-Lateef, AO., Melodi, AO., Ponnle, AA., Sarumi, OA. and Adedeji, KA. 2022. Smart Grid Systems in Nigeria: Prospects, Issues, Challenges and Way Forward. FUOYE http://www.azojete.com.ng/ adebisioluwaseun@funaab.edu.ng Arid Zone Journal of Engineering, Technology and Environment, March 2024; Vol. 20(1):133-150. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 148 Journal of Engineering and Technology, 7(2): 183-192. Duraisamy, P. and Ponnusamy, A. 2017. Power System Performance Improvement by Optimal Placement and Sizing of SVC using Genetic Algorithm. International Journal of Applied Power Engineering, 6(2): 55-62. Emodi, NV. and Yusuf, SD. 2015. Improving Electricity Access in Nigeria: Obstacles and the Way Forward. International Journal of Energy Economics and Policy, 5(1): 335-351. Ergen, M. 2019. What is Artificial Intelligence? Technical Considerations and Future Perception. The Anatolian Journal of Cardiology, 22: 5-7. Essien, UJ., Odion, J. and Chukwu, NF. 2023. Performance Evaluation of Optimally Placed SVC for Dynamic Stability of the Power System. International Journal of Multi-Disciplinary Research and Analysis, 6(11): 5120-5129. Ezeruigbo, EN., Ekwue, AO. and Anih, LU. 2021. Voltage Stability Analysis of Nigerian 330 kV Power Grid Using Static P-V Plots. Nigerian Journal of Technology, 40(1): 70-80. Gaur, D. and Mathew, L. 2018. Optimal Placement of FACTS Devices Using Optimization Techniques: A Review. IOP Conf. Series: Materials Science and Engineering 331: 012023. Grewal, PDS. 2014. A Critical Conceptual Analysis of Definitions of Artificial Intelligence as Applicable to Computer Engineering. International Organisation of Scientific Research Journal of Computer Engineering, 16(2): 9-13. Gupta, JB. 2011. A Course in Power Systems. New Delhi, India: S.K. Kataria & Sons Publisher. Hadi, S. 2008. Power System Analysis. New York, USA: McGraw-Hill Companies, Inc. Himaja, K., Kumar, TA. and Kalyani, ST. 2022. Dynamic Stability Enhancement of SMIB Power System with PSS-SVC with LQR Optimal Control. Technological Innovation in Engineering Research, 3: 61-69. Hussaini, IU., Abubakar, SK., Danmaraya, MA. and Ibrahim, SK. 2021. Framework of Sustainable Energy Development in a Bereft Power Supply Economy of Nigeria. Journal of Energy Research and Reviews, 7(2): 32-42. Idoniboyeobu, DC., Ogunsakin, AJ. and Wokoma, BA. 2018. Forecasting of Electrical Energy Demand in Nigeria using Modified Form of Exponential Model. American Journal of Engineering Research, 7(1): 122-135. Ikonwa, W., Obuah, EC. and Wokoma, B. 2023. Impact of Static Var Compensator (SVC) on Transient Stability of Power Network in Nigeria. International Research Journal of Innovations in Engineering and Technology, 7(4): 37-44. Kaur, S. and Gupta, S. 2021. Importance of FACTS Devices in Power System. International Journal of Scientific Research, 10(12): 529-533. Kothai, AC. and Jayapal, R. 2021. Improved GA Based Power and Cost Management System in file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng Adebisi et al.: Application of Genetic Algorithm Based Optimal Placement of Static Var Compensator for the Nigerian 330 kV Electricity Grid Performance Enhancement. AZOJETE, 20(1):133-150. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 149 a Grid-Associated PV-Wind System. International Journal of Power Electronics and Drive Systems, 12(4): 2531-2544. Kothari, DP. and Nagrath, IJ. 2008. Power System EngineeringTata McGraw-Hill Publishing Company. Kumar, S. and Dash, P. 2016. A Study on TSCS, SSSC, SVC FACTS Device. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 5(6): 5713- 5721. Lakshmi, P., Rao, BV., Devarapalli, R. and Rai, P. 2020. Optimal Power Flow with BAT Algorithm for a Power System to Reduce Transmission Line Losses using SVC. IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, Patna, India,1-5. Movahedi, A., Halvaei, A. and Gharehpetian, GB. 2019. Designing SSSC, TCSC and STATCOM Controllers Using AVURPSO, GSA, and GA for Transient Stability Improvement of a Multi- Machine Power System with PV and Wind farms. International Journal of Electricaland Power Systems, 106: 455-466. Muhammad, Y., Khan, R., Asif, M., Raja, Z. and Ullah, F. 2020. Solution of Optimal Reactive Power Dispatch with FACTS Devices: A Survey. Energy Reports, 6: 2211-2229. Mulyadi, HY., Febriana, Y. and Abdullah, AG. 2018. Optimization Placement of Static Var Compensator (SVC) on Electrical Transmission System 150 kV Based on Smart Computation. IOP Conf. Series: Materials Science and Engineering, 306: 012056. Muthukumar, P., Ramesh, MV., Babu, PV., Rohinikumar, P. and Satyanarayana, SV. 2023. Optimal Integration of Multiple D-SVCs for Voltage Stability Enhancement in Radial Electrical Distribution System Using Adaptive Firefly Algorithm. International Journal of Intelligent Engineering and Systems, 16(3): 378-387. Nabavi, SMH., Khafafi, K., Sakhavati, A. and Nahi, S. 2011. Optimal Locating and Sizing of SSSC using Genetic Algorithm in Deregulated Power Market. International Journal of Computer Applications, 22(4): 37-41. Nasiruzzaman, ABM. 2010. A Student Friendly Toolbox for Power System Analysis Using MATLAB. Matlab-Modelling, Programming and Simulations, 67: 86. Nassar, IA. Omara, MA. and Abdella, MM. 2019. Enhancement of Voltage Profile in Power Systems by Using Genetic Algorithm. 21st IEEE International Middle East Power Systems Conference, Tanta University, Egypt, 459-464. NCC. 2012. Single Line Diagram of the Nigerian 330 kV, 28-bus Electricity Grid. Transmission Company of Nigeria, Osogbo, Nigeria. Ogbonnaya, C., Abeykoon, C., Damo, UM. and Turan, A. 2019. The Current and Emerging Renewable Energy Technologies for Power Generation in Nigeria: A Review. ThermalScience and Engineering Progress, 13: 100390. Osman, E., Ahmed, M., Ibrahim, SASS. and Qahtan, H. 2023. Reducing Losses in the Sudanese http://www.azojete.com.ng/ adebisioluwaseun@funaab.edu.ng Arid Zone Journal of Engineering, Technology and Environment, March 2024; Vol. 20(1):133-150. ISSN 1596-2490; e-ISSN 2545- 5818; www.azojete.com.ng Corresponding author’s e-mail address: adebisioluwaseun@funaab.edu.ng 150 Power Transmission System 66 kV Sector. Open Access Library Journal, 10(1): 1-13. Rana, J., Shoaib, M. and Shafiullah, S. 2019. Levenberg-Marquardt Neural Network to Estimate UPFC- Coordinated PSS Parameters to Enhance Power System Stability. Neural Computing and Applications, 31(4): 1237-1248. Sahare, SB. and Bonde, UG. 2020. Transient Stability Enhancement in Multi-Machine Power System by using Power System Stabilizer (PSS) and Static Var Compensator (SVC). International Journal of Engineering Research and Technology, 9(2): 94-103. Shehata, AA., Refaat, A. and Korovkin, NV. 2020. Optimal Allocation of FACTS Devices based on Multi-Objective Multi-Verse Optimizer Algorithm for Multi-Objective Power System Optimization Problems. IEEE International Multi-Conference on Industrial Engineering and Modern Technologies, Vladivostok, Russia, 1-7. Shelke, AS. and Bhole, AA. 2021. A Review on Different FACTS Devices used in Electrical Power System. International Journal of Engineering Research and Technology, 10(4): 309-312. Siddique, A., Xu, Y., Aslam, W. and Rasheed, M. 2019. A Comprehensive Study on FACTS Devices to Improve the Stability and Power Flow Capability in Power System. IEEE Asia Power and Energy Engineering Conference, Chengdu, China, 199–205. Simeon, M., Tita, WS., Adejumobi, IA. and Elizabeth, A. 2018. Minimization of Active Power Loss in Power Systems using SVC Minimization of Active Transmission Loss in Power Systems using Static Var Compensator. International Journal of Applied Engineering Research, 13(7): 4951-4959. Singh, HP. and Swami, AK. 2020. A Review of Power Quality Improvements by using FACTS devices A Review of Power Quality Improvements by using FACTS devices. International Journal of Engineering Science, 8(6): 53-64. Yakuba, AA. 2019. Water and Energy Resources: Development for Renewable Energy in Nigeria. Journal of Agriculture and Environment, 15(1): 61-75. file:///C:/user/Downloads/azojete143/www.azojete.com.ng mailto:%20salami.lukman@adelekeuniversity.edu.ng