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# APPLICATION OF FUZZY LOGIC TECHNIQUE FOR POWER LOSS REDUCTION IN THE NIGERIA 330KV SYSTEM

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APPLICATION OF FUZZY LOGIC TECHNIQUE FOR POWER LOSS REDUCTION IN THE NIGERIA 330KV SYSTEM

ABSTRACT

To improve the overall efficiency of the power system, the performance of transmission system must be improved. Some of the vital ways of achieving this objective is by reducing power losses in the system and also improving voltage profile. An important method of controlling bus voltage is by shunt capacitor banks in the transmission substations. The capacitor absorbs reactive power flow in the system, thus improving power factor. When this is done, active power is also improved. In this work, a 10-bus transmission system is taken as model. Newton-Raphson’s power flow program is executed using MATLAB toolbox to obtain p. u nodal voltage ranging from 0.8890 to 1.0564, total real power line losses (0.09438 p.u), and total reactive power line losses (0.36970 p. u). By using power loss reduction, power loss index is evaluated and normalized in the range [0, 1]. These indices, together with the p. u nodal voltage magnitude, is fed as inputs to the Fuzzy Inference System to obtain Capacitor Suitability Index (CSI). The CSIs obtained, ranges from 0.244 to 0.897. The values of the CSIs determine nodes most suitable for capacitor installation. Experimentally, highest values of CSIs are chosen for capacitor installation. As a result, 3 buses (3, 8, and 10) with CSI values of 0.680, 0.750, and 0.897 respectively, are chosen. Capacitor sizes of 50MVar, 85MVar, and 60MVar (obtained from Index Based Method) are installed on the buses. Voltage profile improves by 3.74%, 3.27%, and 3.33% respectively, while total real power loss in the system reduces by 17.55% and total reactive power injection to the network reduces by 8.70% respectively. Overall, system stability and efficiency, hence, reliability, are improved by installation of capacitors at suitable locations in a transmission system.
CHAPTER ONE

INTRODUCTION

1.1 Background of Study

Electrical energy is generated at power stations which are usually located far away from load centres [1]. Thus, a network of conductors between the power stations and the consumers is required in order to harness the power generated. This network of conductors may be divided into two main components, namely, the transmission system and the distribution system [1]. As power flows in the lines, a significant amount is lost. Accurate knowledge of these power losses on transmission lines and their minimization is a critical component for efficient flow of power in an electrical network. Power losses result in lower power availability to final consumers. Hence, adequate measures need to be taken to reduce power losses to the barest minimum.

Power plants' planning in a way to meet the power network load demand is one of the most important and essential issues in power systems. Since transmission lines connect generating plants and substations in power network, the analysis, computation and reduction of transmission losses in these networks are of great concern to scientists and engineers.

Studies have indicated that as much as 9% or more of total power generated is consumed as [1] losses at the transmission level [2]. The losses can be separated to active and reactive component of branch current, where the losses produced by reactive current can be reduced by the installation of shunt capacitors. Capacitors (capacitor banks) are widely used in transmission systems to reduce energy and peak demand losses, release the MVA capacities of transmission apparatus and to maintain a voltage profile within permissible limits [3]. The objective of optimal capacitor placement problem is to determine the size, type, and location of capacitors to be installed on the transmission network to achieve positive economic response. The economic benefits obtained from the loss reduction weighted against capacitors costs while keeping the operational and power quality constraints within required limits. Fuzzy logic provides a remedy for any lack of uncertainty in the data. Fuzzy logic is a subset of conventional (Boolean) logic that has been extended to handle the concepts of partial truth-values, between ‘completely true’ and ‘completely false’.

The ideas of fuzzy logic dates all the way back to Plato who proposed that there is a third region between true and false. Fuzzy logic is a technique of making a choice answer to questions, other than ‘yes’ or ‘no’. It resembles human reasoning in the use of approximate information and uncertainty to generate decision. The fuzzy logic is a designed technique used in providing tools for dealing with imprecision that are intrinsic to many problems. The fuzzy set theory implements clauses of data that are not sharply defined. To that effect, the degree of power loss incurred in transmission lines is evaluated using Fuzzy logic in MATLAB toolbox.

Furthermore fuzzy logic has the advantage of including heuristics and representing engineering judgments into the capacitor allocation optimization process.

1.2 Statement Of The Problem

Presently, many electrical energy companies in a number of countries are experiencing very high losses. Studies show that about 9% of total power generated is wasted in the form of losses at the transmission level in Nigeria [1]. Similarly, voltage drops and/or over-voltages are frequently experienced by TCN in transmitting electricity. To reduce these losses and improve voltage profile, shunt capacitor banks are installed on transmission sub-stations. With active power loss reduction and voltage profile improvement as objectives, the optimal capacitor placement problem aims to determine the optimal capacitor location and capacitor sizes in the transmission systems. Efficient methods are required to determine the best location and sizes.

1.3 Objectives Of The Study

The aim of the research is to reduce loss of electrical power in Nigeria’s 330KV system using Fuzzy logic technique.

The specific objectives of this research include;

i.             Using Newton-Raphson’s method to perform a load flow analysis on the chosen 10-bus transmission model to determine line losses, bus voltages and load angles.

ii.             Applying Fuzzy Inference System to determine the buses suitable for capacitor placement so as to reduce active power losses on the chosen bus system and also improve voltage profile, thus, improving on the stability and efficiency of the power system.

1.4 Significance of the Study

Shunt capacitor installations enhance an improved voltage profile, power factor, and reduction in real power loss in the transmission system. Thus, this work is significant because of the following

i.            It would be a reference material for researchers.

ii.             The work would also be useful to Transmission Company of Nigeria (TCN).

iii.             It could also be a resource material for policy makers in power system, etc.

14

1.5 Scope of the Study

The scope of this work is limited to a 10-bus model transmission network. The bus system (which cut across 2 of the 8 TCN regions), is carefully chosen as a case study for this work. This is because of higher losses and voltage drops experienced in the regions. Power flow analysis of this sub-network is conducted to obtain losses. ‘Capacitor placement method’ is applied using Fuzzy logic technique to determine the sizes and locations of buses to install the capacitors. Sizes of capacitors to be installed are equally evaluated.

1.6 Organization of Thesis

This work is organized in five chapters. Chapter one presents a general introduction of the work. Chapter two presents an overview of power systems, power losses (both technical and non-technical), fuzzy logic technique, power flow analysis etc. Chapter three deals with the modeling of the selected 10-bus system, capacitor placement method, algorithm of the proposed approach, calculation of loss reduction & loss indices, calculation of capacitor sizes using Index Based method etc. Chapter four present fuzzy logic as simulation software used. Various results obtained from load flow analysis, fuzzy logic implementation, calculation of capacitor sizes and improvement recorded in voltage profile and reduction in total power losses were also presented and discussed in this chapter. Conclusion is drawn and necessary recommendation made in chapter five

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