Voltage Stability Improvement Of 33kV PHED Power Supply To NAOC , Port Harcourt, Using Artificial Neutral Network

ABSTRACT

The demand for clean, reliable and affordable energy is growing at unprecedented rates across the world. As we seek to increase the quantity of energy produced, the supply quality challenges will grow in tandem. The growing long distances between generation and load centers only serve to compound the voltage stability challenge. This paper aims at addressing these power quality problems in the distribution network using artificial neural network (ANN) controller based dynamic voltage restorer (DVR). The artificial neural networks controller engaged to controlling the dynamic voltage restorer were trained with input and output data of proportional integral (PI) controller and of unit amplitude generator obtained during simulation. All simulations and modeling were carried out in MathLab/Simulink. Proposed dynamic voltage restorer was tested with replicated model of substation by simulating with sample of average voltage. Simulation results showed that DVR is effective in compensating for under-voltage and over-voltage in Distribution network Port Harcourt, Rivers State.

TABLE OF CONTENTS

COVER PAGE

TITLE PAGE

APPROVAL PAGE

DEDICATION

ACKNOWELDGEMENT

ABSTRACT

CHAPTER ONE

• INTRODUCTION
• BACKGROUND OF THE PROJECT
• PROBLEM STATEMENT
• AIM AND OBJECTIVE OF THE PROJECT
• SCOPE OF THE PROJECT
• LIMITATION OF STUDY
• SIGNIFICANCE OF THE STUDY
• PROJECT JUSTIFICATION
• THESIS ORGANIZATION

CHAPTER TWO

LITERATURE REVIEW

• INTRODUCTION
• OVERVIEW OF THE STUDY
• ANALYSIS ANDMETHODS FOR VOLTAGE STABILITY
• OVERVIEW OF AN ARTIFICIALINTELLIGENCE
• ARTIFICIAL NEURAL NETWORKS
• APPLICATION OF ARTIFICIAL NEURAL NETWORK IN POWER SYSTEM
• CAUSES OF VOLTAGE STABILITY PROBLEMS
• TYPES OF VOLTAGE STABILITY
• OPERATING MEASURES TO PREVENT VOLTAGE COLLAPSE
• CONTROL TECHNIQUES BASED ON SENSITIVITY ANALYSIS
• CONTROL TECHNIQUES BASED ON STRUCTURAL CHARACTERISTICS OF POWER SYSTEM
• CONTROL TECHNIQUES BASED ON SECONDARY VOLTAGE CONTROL

CHAPTER THREE

METHODOLOGY

• AREA OF STUDY
• SOFTWARE
• DATA COLLECTION
• RUMUOLA DISTRIBUTION SYSTEM
• MODELING OF ANN BASED CONTROLLER

CHAPTER FOUR

• SIMULATION
• POWER QUALITY ASSESSMENT OF RUMUOLA DISTRIBUTION SYSTEM
• OVER-VOLTAGE AND   UNDER-VOLTAGE COMPENSATION IN RUMUOLA DISTRIBUTION NETWORK

CHAPTER FIVE

• CONCLUSION AND RECOMMENDATION
• REFERENCES

CHAPTER ONE

1.0                                                      INTRODUCTION

1.1                                       BACKGROUND OF THE STUDY

Until early 20th century, the quality of power supply was not considered as important factor in power delivery. Utility companies only focused on achieving a power delivery state with little or no interruption. But with improvement in technology, paving way for development of very sensitive loads, and coupled with customer awareness, increased in electricity demand in homes, offices and industries, and inter-connection of electrical utility into complex grid etc, power system engineers were implored to consider power quality. Power quality as a term, defines a set of electrical boundaries within which a piece of equipment can function as intended without significant loss of performance or life expectancy [Sankaran, 2002]. It entails delivering electric power with minimal distortions, and therefore, maintaining a near sinusoidal signal waveform at a frequency of 50Hz and at required load voltage.

Power Quality problems are manifested in voltage, current or frequency [Zahir, 2011]. Examples include: voltage swell and sag, voltage fluctuation, harmonic distortions etc. Aside factors like power system faults, start up and shutdown of heavy equipment, switching operations etc, non-linear loads are identified as major cause of power quality problems. Power quality problems are global issues and exist in distribution systems of several countries of the world, including Nigeria [4. The effects of power quality problems are enormous, ranging from equipment failure to equipment damage which can result in huge financial losses in process and automation industries. In Rumuola distribution system network, under-voltage and over- voltage are identified as major power quality problems [Irfan, 2013].

The need to mitigate power quality problems and maintain power of good quality has brought power system engineers, equipment manufacturers, researchers and statutory bodies to a focal point of methodology development. Today, several methods exist to improve the quality of power to sustain the ever increasing applications of sensitive and non-linear loads in distribution network. Conventionally, Synchronous condenser, capacitor banks, static VAR compensators (SVCs), self-commutated VAR compensators etc. are used to control reactive power and improve power factor, though with drawbacks such as instability problems, generation of high transient during connection and disconnection etc. [9]. More recently, Custom power devices such as distribution static compensator (DSTATCOM), unified power quality conditioner (UPQC), dynamic voltage restorer (DVR) etc were found to be improved methods for power quality control. Onohaebi and Omorogiuwa considered the relevance of custom devices in tackling power efficiency problems in Nigeria distribution network stating that such devices have been widely used in distribution network of developed countries. However, the performance of custom power devices is dependent on the type of controller employed. Proportional integral (PI), proportional integral differentiators etc are effective but slow in response and perform poorly under parameter variations. Artificial intelligent (AI) controllers such as Artificial Neural Network (ANN), fuzzy logic etc are proposed by researchers as they offer better performance in terms of response time and operation under dynamic loads.

Several authors have researched on mitigating power quality problems in distribution system network using DVR. Harmonics and undervoltage compensation using DVR was studied by Sundarabalan C. K. and Selvi K. [12] using ANN controller based on park’s transformation strategy. The ANN controller was trained off-line with data from a proportional integral controller. In another study, Shairul, et al simulated the performance of DVR using PSCAD [13]. Raunak, et al [14] in their study of DVR performance on sag and swell mitigation applied PI controller and unit vector extraction control scheme. In these studies, a simple distribution network composed of two feeders fed from a substation was employed, and voltage sag was simulated by different conditions. The results obtained showed the effectiveness of DVR in under-voltage and over-voltage mitigation [14]. This paper seeks to improve power quality by mitigating under-voltage and over-voltage using ANN based controller model of dynamic voltage restorer (DVR). The distribution system adopted for this study is the Rumuola distribution sub-station (RDS) network, Port Harcourt, Nigeria.

1.2                                    PROBLEM STATEMENT

Current civilization is susceptible to case of power system blackout, the consequences of systems failure are social and economic as well. Even short disturbance can be harmful for industrial companies, because restarting of process might take several hours. In recent years, voltage instability has been responsible for several major network collapses. There are many method that has been used in the past for the improvement.

Conventional voltage stability improvement methods such as capacitor banks, reactors and transformers can be used to provide steady state voltage control. However, these devices are based on electro-mechanical control among other drawbacks explained later thus impeding high speed voltage control.

Using artificial neural network provide a better adaptation to varying operational conditions and improvement on the usage of existing installations in power systems by using power electronic controllers. Their main advantages over the conventional methods are that the devices are both dynamic-fast controllability using power electronics- and static-no moving parts to perform the dynamic controllability.

This research sought to come up with a new way of voltage stability improvement using artificial neural network by solving the challenge of reactive power absorption and generation for real-time control.

1.3                             AIM AND OBJECTIVES OF THE STUDY

Voltage stability analysis is important in power system in order to maintain the power system equilibrium. The main aim of this work is to discuss how improve voltage stability by using artificial neutral network of 33kv PHED power supply to NAOC, Port Harcourt.

The main objective of this research is to analyze the static voltage stability improvement of a power system using artificial neural networks.

Specific Objectives

i.        To perform a security constrained load flow solution on the system

• To develop artificial neural networks trained control system for the voltage stability
• To perform a cost-benefit analysis of the developed voltage control

1.4                                         SCOPE OF THE PROJECT

Power systems are complex systems consisting of large number of generating units and interconnected network of transmission lines. The voltage stability is an issue of prime importance in this complex power system network since the demand for electric power is increasing drastically. The control of reactive power in the transmission lines will enhance the voltage stability of the power system network.

1.5                                    LIMITATION OF STUDY

As we all know that no human effort to achieve a set of goals goes without difficulties, certain constraints were encountered in the course of carrying out this project and they are as follows:-

1. Difficulty in information collection: I found it too difficult in laying hands of useful information regarding this work and this course me to visit different libraries and internet for solution.
2. Financial Constraint: Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).

• Time Constraint: The researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted for the research work.

1.6                                            SIGNIFICANCE OF THE STUDY

This research work will throw more light on how Artificial Neural Networks was developed in Simulink in other to show it can be used to improve power stability in a 33kv power system.

This research sought to teach the reader on how to come up with a new way of voltage stability improvement thereby solving the challenge of reactive power absorption and generation using artificial neural network trained control system for real-time control.

1.7                                             PROJECT JUSTIFICATION

Power systems are increasingly becoming more overloaded and constantly being operated close to their voltage stability limits. Voltage stability is the ability of a power system to maintain acceptable voltage levels under normal operating conditions and after being subjected to disturbances such as a sudden increase in load or loss of a major generation plant. Major national power outages have been documented in the recent past in countries in Africa such as in Nigeria.

The rise in the use of artificial intelligence methods for voltage stability improvement has been a growing trend due to the huge capital outlay required to construct new transmission and distribution lines, pressure on land as well as environmental concerns worldwide.

An artificial intelligence method will go a long way in voltage stability improvement. This research uses artificial neural networks which mimic biological nervous systems as a way of trying to replace human operators who are at times slow to relay and act on information on system voltage profiles thus leading cascaded system voltage collapse.

1.8                                              THESIS ORGANIZATION

Chapter 1

This chapter presents an introduction of the research work, outlines the problem statement, gives a justification for the research work and finally the goals of the work.

Chapter 2

This chapter presents a literature review on the conventional methods of voltage-reactive power control, , a review of recent works related to the use of UPFC in reactive power control, ANN and finally a discussion on investment appraisal methodology.

Chapter 3

This chapter gives the methodology followed in carrying out this research work.

Chapter 4

This chapter presents the results obtained and an analysis and discussion of the same against the objectives of the research.

Chapter 5

This chapter presents a conclusion of the work and gives recommendations and/or gaps for future research.