ARTIFICIAL INTELLIGENCE IN POWER SYSTEM SECURITY AND STABILITY
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DECEMBER,2018
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Commonly, artificial intelligence is known to be the intelligence exhibited by machines and software, for example, robots and computer programs. The term is generally used to the project of developing systems equipped with the intellectual processes features and characteristics of humans, like the ability to think, reason, find the meaning, generalize, distinguish, learn from past experience or rectify their mistakes. The quest for an intelligence compliance system to solve power security and stability problems in real-time with high predictive accuracy, and efficiency has led to the discovery of deep learning (DL) techniques. This paper investigates the potency of artificial neural network (ANN) techniques in assessing the steady-state security and stability of a power system. In this study, we consider five neural network algorithms and the effectiveness of these NN algorithms. The simulation results show that the artificial neural network possess a relatively better performance in terms of accuracy and efficiency for the considered power networks.
TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE PROJECT
- PROBLEM STATEMENT
- AIM AND OBJECTIVES OF THE PROJECT
- SIGNIFICANCE OF THE STUDY
- SCOPE OF THE PROJECT
CHAPTER TWO
LITERATURE REVIEW
- REVIEW OF THE STUDY
- REVIEW OF RELATED STUDIES
- APPLICATION OF AI IN POWER SYSTEMS
CHAPTER THREE
METHODOLOGY
- MATHEMATICAL MODELING OF THE NEW VOLTAGE STABILITY POINTER
- NEURAL NETWORK ALGORITHMS
- SECURITY ASSESSMENT (SA)
- APLLICATION OF NEURAL NETWORK BASED PATTERN RECOGNITION (NN-PR) APPROACH TO SECURITY ASSESSMENT
CHAPTER FOUR
- RESULT ANALYSIS
- PERFORMANCE ANALYSIS OF THE REGRESSION LEARNER ALGORITHM
- COMPARATIVE ANALYSIS OF THE NEURAL NETWORKS
- RESULTS OF STATIC SECURITY ASSESSMENT
- RESULTS OF TRANSIENT SECURITY ASSESSMENT
CHAPTER FIVE
- CONCLUSION
REFERENCES
CHAPTER ONE
1.0 INTRODUCTION
1.1 BACKGROUND OF THE STUDY
An electric power system is a network of electrical components used to supply, transmit and use electric power. Power systems engineering is a subdivision of electrical engineering that deals with the generation, transmission, distribution and utilisation of electric power and the electrical devices connected to such systems like generators, motors and transformers.
Nowadays, power systems are forced to operate under stressed operating conditions closer to their security limits. Under such fragile conditions, any small disturbance could endanger system security and may lead to system collapse (Rahim et al., 2022). Fast and accurate security monitoring method has, therefore, become a key issue to ensure secure operation of the system. Power System Security is defined as the system’s ability to withstand credible contingencies without violating normal operating limits.
Security analysis may be broadly classified as (i) Static Security and (ii) Transient Security (Mokred et al., 2023). Static security evaluation detects any potential overload of a system branch or an out-of limit voltage following a given list of contingencies (Mokred et al., 2023). Transient security evaluation pertains to system dynamic behavior in terms of rotor angle stability, when subjected to perturbations. Traditional security assessment involves numerical solution of non-linear load flow equations and transient stability analysis for all credible contingencies (Mokred et al., 2023). Because of the combinatorial nature of problem, this approach requires a huge amount of computation time and hence found infeasible for real time security analysis of large scale power system networks.
The intricacy of an interconnected power network has forced most power systems to be operating close to their stability breakpoint. This is because power systems with much uncertainty are vulnerable to voltage instability, especially when faced with contingency. However, accurate and timely prediction of voltage instability could avert voltage collapse or blackout if properly managed (Mokred et al., 2023). The conventional practice of deploying numerical methods for assessing a power network seems not to yield convincing results (Xie et al., 2022), mostly, for early detection of possible voltage breakdown. Aside from that, numerical methods of solving power stability problems are growing out of phase, as they are less effective in the analysis of a complex interconnected power network (Fikri et al., 2018). The complexity of most power systems could be traced to the adverse effect of matching up with the increasing rate of power demand through the penetration of renewable energy. Conversely, these renewable energy injection schemes have their inherent dynamic characteristics, thereby, altering the stability of the existing power system (Celikel et al., 2021). In such a context, a more reliable and smart controlling mechanism is needed to ensure a secured power system.
For this reason, machine learning (ML) is now being predominantly used in the analysis of power systems because of its high accuracy and efficiency. Moreover, with the advent of the smart grid, an intelligent compliance system is desirable (Roy et al., 2022); essentially for the sustainable delivery of qualitative and quantitative power to end users. Similarly, ML techniques are found to be promising in applications where fast and smart decisions are required without compromising the accuracy of the output result (Roy et al., 2022). ML is often regarded as artificial intelligence (AI) and deep learning (DL). These approaches are somewhat efficient and reliable tools for the assessment of power system stability and security (Sanghami et al., 2022). As a branch of ML, artificial neural networks (ANN) have gained application in power systems due to their flexibility (Sanghami et al., 2022), easy adaptability to nonlinear variables, and better performance. The advantage of ANN algorithms lies in their ability to accurately define the security status, (Sanghami et al., 2022)and control the network through the optimal placement of flexible AC transmission system (FACTS) devices (Sanghami et al., 2022). This is to ensure a responsive, spontaneous, and smart power system capable of taking intelligent decisions with little or no supervision.
The aim of this research is to evaluate the application of artificial intelligence in power system security and stability.
1.2 PROBLEM STATEMENT
Power system analysis by conventional techniques (such as simulation-based approaches) becomes more difficult because of: (i) Complex, versatile and large amount of information which is used in calculation, diagnosis and learning. (ii) Increase in the computational time period and accuracy due to extensive and vast system data handling (Khalil et al., 2022). The modern power system operates close to the limits due to the ever increasing energy consumption and the extension of currently existing electrical transmission networks and lines. This situation requires a less conservative power system operation and control operation which is possible only by continuously checking the system states in a much more detail manner than it was necessary. Sophisticated computer tools are now the primary tools in solving the difficult problems that arise in the areas of power system planning, operation, diagnosis and design. Among these computer tools, Artificial Intelligence has grown predominantly in recent years and has been applied to various areas of power systems (Sarker et al., 2021). Artificial intelligence provides a convenient route for power grid stability assessment. Compared with simulation-based approaches, artificial intelligence can potentially save time on model development and numerical computation in stability assessment.
1.3 AIM AND OBJECTIVES OF THE STUDY
The main aim of this work is to carry out a study on the application of artificial intelligence into power system security and stability assessment. The objectives are:
- To assess power system security and transient stability using artificial intelligence tool
- To assess the application of artificial intelligence tool in power system
- To carry out a review on the previous works on power system security and stability solutions.
- To evaluate the voltage stability set-points of the power network buses using a new voltage stability pointer
- To assess the steady-state stability of the NN techniques and
1.4 SIGNIFICANCE OF THE STUDY
This study will provide a means of solving Several problems in power systems which cannot be solved by conventional techniques are based on several requirements which may not feasible all the time. In these situations, artificial intelligence techniques are the obvious and the only option.
1.5 SCOPE OF THE STUDY
This study covers the study of application of artificial intelligence in power system security and stability for a continuous and reliable supply of electricity which is necessary for the functioning of today’s modern and advanced society.
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CHAPTER THREE: The complete chapter three of "artificial intelligence in power system security and stability" is available. Order full work to download. Chapter three of "artificial intelligence in power system security and stability" consists of the methodology. In this chapter all the method used in carrying out this work was discussed.
CHAPTER FOUR: The complete chapter four of "artificial intelligence in power system security and stability" is available. Order full work to download. Chapter four of "artificial intelligence in power system security and stability" consists of all the test conducted during the work and the result gotten after the whole work
CHAPTER FIVE: The complete chapter five of "artificial intelligence in power system security and stability" is available. Order full work to download. Chapter five of "artificial intelligence in power system security and stability" consist of conclusion, recommendation and references.
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