The performance of the transmission system must be increased in order to improve the overall efficiency of the power system. Reducing power losses in the system and optimizing the voltage profile are two important strategies to achieve this goal. Shunt capacitor banks in transmission substations are a significant means of managing bus voltage. The capacitor improves power factor by absorbing reactive power flow in the system. Active power is increased as a result of this. A 10-bus transmission system is used as a model in this study. The MATLAB toolbox is used to run Newton-power Raphson’s flow algorithm to produce p. u nodal voltages ranging from 0.8890 to 1.0564, total real power line losses (0.09438 p.u), and total reactive power line losses (0.09438 p.u) (0.36970 p. u) These indices, along with the magnitude of the p. u nodal voltage, are supplied into the Fuzzy Inference System to produce the Capacitor Suitability Index (CSI). The CSIs obtained vary from 0.244 and 0.897. The CSI values decide which nodes are best for capacitor installation. For capacitor installation, the highest CSI values are chosen experimentally. As a result, three buses (3, 8, and 10) are picked, with CSI values of 0.680, 0.750, and 0.897, respectively. The buses have capacitors with capacities of 50MVar, 85MVar, and 60MVar (as determined by the Index Based Method). The voltage profile improves by 3.74 percent, 3.27 percent, and 3.33 percent, respectively, while the total actual power loss in the system decreases by 17.55 percent and the total reactive power injection into the network decreases by 8.70 percent.




Power stations, which are often positioned far away from load centers, provide electrical energy [1]. To harness the power generated, a network of wires between the power stations and the customers is required. The transmission system and the distribution system are the two fundamental components of this network of wires [1]. A large proportion of power is lost as it passes through the lines. For effective power flow in an electrical network, accurate knowledge of these power losses on transmission lines and their mitigation is essential. The availability of power to ultimate customers is reduced as a result of power losses. As a result, appropriate precautions must be made to keep power losses to a minimal minimum.

Because transmission lines connect generating plants and substations in a power network, scientists and engineers are concerned about the investigation, computation, and reduction of transmission losses in these networks.

According to studies, as much as 9% or more of all power generated is consumed as [1].

At the transmission level, there are losses [2]. The active and reactive components of branch current can be separated, and the losses caused by reactive current can be decreased by installing shunt capacitors. Capacitors (capacitor banks) are commonly employed in transmission systems to reduce energy and peak demand losses, release transmission apparatus MVA capacities, and keep a voltage profile within acceptable ranges [3]. The goal of the problem of optimal capacitor placement is to find the best possible solution.

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benefits achieved from loss reduction weighted against capacitor prices while maintaining required operational and power quality restrictions Any absence of uncertainty in the data can be remedied with fuzzy logic. Fuzzy logic is a subset of traditional (Boolean) logic that has been extended to handle the concepts of partial truth-values, which fall somewhere between “completely true” and “completely false.”

The origins of fuzzy logic can be traced back to Plato, who posited the existence of a third state between truth and untrue. Fuzzy logic is a technique for deciding on a response to a question that isn’t “yes” or “no.” It uses approximation knowledge and uncertainty to generate decisions, similar to human reasoning.

Fuzzy logic is a technique for providing tools for dealing with imprecision, which is inherent in many problems. The fuzzy set theory is used to implement data clauses that aren’t clearly defined. To that end, Fuzzy logic in the MATLAB toolbox is used to assess the degree of power loss in transmission lines.

Fuzzy logic also offers the benefit of incorporating heuristics and engineering judgments into the capacitor allocation optimization process.


Many electrical energy firms in a number of countries are currently losing a lot of money. According to studies, around 9% of total power generated in Nigeria is lost due to transmission losses [1]. TCN, too, commonly experiences voltage drops and/or over-voltages while transmitting electricity. Shunt capacitor banks are installed on transmission substations to eliminate these losses and improve voltage profile. The optimal capacitor placement problem tries to determine the optimal capacitor position and capacitor sizes in transmission networks with active power loss reduction and voltage profile enhancement as objectives. To establish the ideal location and sizes, efficient procedures are required.


The goal of the study is to use fuzzy logic to reduce electrical power loss in Nigeria’s 330KV grid.

The following are the research’s precise goals:

i. Performing a load flow analysis on the specified 10-bus transmission model using Newton-approach Raphson’s to obtain line losses, bus voltages, and load angles.

ii. Using a fuzzy inference system to evaluate which buses are suitable for capacitor placement in order to reduce active power losses on the selected bus system and enhance voltage profile, hence enhancing the power system’s stability and efficiency.


Installation of shunt capacitors improves the voltage profile, power factor, and real power loss in the transmission system. As a result, this work is relevant for the following reasons:

i. It would serve as a source of information for researchers.

ii. The work would also be beneficial to the Nigerian Transmission Company (TCN).

iii. It could also serve as a resource for policymakers in the power sector, among other things.


The scope of this project is restricted to a 10-bus transmission network model. The bus system was chosen as a case study for this project since it crosses two of the eight TCN zones. This is due to greater losses and voltage decreases in the affected areas. To determine losses, a power flow analysis of this sub-network is performed. The ‘capacitor placement method’ use Fuzzy logic to decide the sizes and locations of buses on which the capacitors will be installed. Capacitor sizes to be placed are all weighed in the same way.


This work is divided into five sections. The first chapter serves as a broad overview of the work. The second chapter covers power systems, power losses (both technical and non-technical), fuzzy logic, power flow analysis, and other topics. The modeling of the selected 10-bus system, the capacitor placement method, the proposed approach’s algorithm, the calculation of loss reduction and loss indices, and the computation of capacitor sizes using the Index Based method are all covered in Chapter 3. The fourth chapter introduces fuzzy logic as a simulation tool. In this chapter, various results from load flow analysis, fuzzy logic implementation, capacitor size calculations, and improvements in voltage profile and total power losses were also presented and analyzed. In chapter five, a conclusion is established and required recommendations are presented.

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