Optimal Reliability of a Smart Grid (original) (raw)
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Thomas K. DeLillo for his valuable time and support. I want to thank my family members who were always supporting and motivating me to continue my higher studies. I also want to thank my friends whose interminable motivation and help brought me this far. Finally, I would like to thank Wichita State University for giving me this opportunity to do my research work. vi ABSTRACT Reliability standards are followed in power system industries as a series of requirement from planning to operation and this necessitates evaluating, improving and reporting reliability indices of the power systems to the regulators on a regular basis. Eighty percent of the power system outages happen due to disturbances caused in the distribution power system. Recent developments in smart grid technologies demonstrate how communication technologies can be used to improve the reliability of the distribution power system. In this research, a distributed sensor network architecture is projected for monitoring the distribution system. A dedicated communication protocol "ALARM" for distributed sensor monitoring network communication is briefly discussed. Furthermore, a Hidden Markov Model (HMM) based local event detection mechanism is proposed to improve the reliability of the distribution power system. The proposed system has the capability of detecting faults locally with a minimum delay time. It is shown that such a local event detection system can improve the reliability of the distribution power system in many aspects. Further, a novel methodology to evaluate the reliability of cyber physical power system is proposed in this research. This work incorporates power component failure, automation component failure, communication failure, communication delay and cyber-attacks to develop a comprehensive equipment level reliability model. From the 36 possible states, a 12-state model is derived to aid the component level reliability analysis. Furthermore, for large network level reliability evaluation purpose, a reduced 2 state model is also obtained. Depending on the application in the power system, smart component categorized into three groups and corresponding 2 state models are obtained for each category. Finally, sensitivity analysis is carried out to evaluate the impact of cyber-failure and cyber-attacks on the reliability of the smart component. vii
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International Journal of Industrial Electronics, Control and Optimization (IECO), 2019
The advent of DG and SEGs has led to fundamental changes in various fields of power system operation. The current paper is aimed to investigate the reliability of SEGs considering DGRs based on the self-healing concept. Due to the emergence of new uncertainties in the power system resulted from the presence of DGRs, this paper is dedicated to comparing network reliability indices before and after the entry of DGRs and analyzing their effect on improving network reliability. To do so, improving the indices based on customer satisfaction, such as reducing the SAIFI, and SAIDI, is evaluated. More specifically, the improvement of the most important index based on load and energy, namely energy not supplied (ENS), is investigated. To do this, the MCS method is used given the pdf of the samples due to the presence of uncertainty created by the presence of DGRs, demanded load change and network restoration time after the presence of DG. Also, after providing an appropriate model for problem analysis, results of applying this model to the case study system are investigated using reliability indices. Subsequently, in order to improve performance of the system, impacts of the changes of various parameters on the given indices are reported. One of the most important points in this regard is to investigate the impacts of the changes in the system configuration on the results. It is observed that self-healing positively affects the reduction of the electrical energy restoration time as well as the system reliability.
An Overview on Reliability Analysis and Evaluation Methods Applied to Smart Grids
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The reliability can be defined as to carry out predefined requirements in a predefined duration. The importance of reliability analysis increases day by day as the customers become more conscious about a product or service which they buy. The application area of reliability analysis is very wide. Any product\ system or service including smart grids can be subject of the reliability analysis. As power system technology and computer science improving, the concept of smart grids begins to take part in our lives. Therefore, it is very essential to make reliability analysis for smart grids. In this paper, reliability analysis methods applied to smart grids are focused on and the classifications in reliability analysis have been explained. Besides, distribution networks reliability concept and different methods such as simulation and analytical approaches to assess the reliability have been introduced. The applications of these methods on smart grids are well explained. In addition to these, smart grids and conventional grid is compared. Different approaches such as tree analysis, failure mode effect analysis, Markov process and Monte Carlo simulation methods are carried out with wind turbines. The strengths and weaknesses of each method were evaluated.
Distributed Generators (DGs) are now commonly used in distribution systems to reduce the power disruption in the power system network. Due to the installation of DGs in the system, the total power loss can be reduced and voltage profile of the buses and reliability of the system can be improved. The significant process to decrease the total power loss and to improve the power quality of the system is to identify the optimal number of DGs and their suitable locations in the system. To accomplish the aforementioned process and to evaluate the amount of power to be generated, a new method is proposed using Particle Swarm Optimization. The proposed method is tested for IEEE 30 bus system, by connecting optimal number of DGs in the system. The results showed a considerable reduction in the total power loss in the system and improved voltage profiles of the buses and reliability indices.
Reliability Analysis of Smart Grid Networks Incorporating Hardware Failures and Packet Loss
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The developments in communication technologies paved way for the materialization of the envisioned smart grid (SG). The SG is the next generation power grid with enhanced capabilities for monitoring and control. Especially, the development of high-speed digital processing devices known as the phasor measurement units (PMUs) has increased the monitoring capabilities of the grid. The measurements acquired by the PMUs which are known as the synchrophasor are communicated to a central monitoring station for processing and control. The communication networks based on synchrophasor applications, referred to as synchrophasor communication networks (SCN) can also be used for providing connectivity between the control station and the PMUs and thus, this paper discusses different communication architectures for the synchrophasor applications from the perspective of their reliability and cost. The unique contribution of the work is that it considers both the hardware failures as well as the packet delivery ratio (PDR) for estimating the reliability indices for these networks. Three scenarios based on dedicated, shared and hybrid SCNs have been proposed for a practical power grid with the PMUs located at the optimal locations. These networks were simulated using the QualNet network simulator and their performance is analyzed in terms of reliability, end-to-end delay (ETD) and cost.
The Distributed Generators (DGs) are connected in distribution system to reduce the power losses and to improve the reliability of the distribution system. The most important process to decrease the total power loss and to improve the reliability of the system is to identify the proper placement of DG units and also to find the amount of power to be generated by them. A hybrid technique is proposed which includes Particle Swarm Optimization (PSO) and Neural Network. By fixing DGs in suitable optimal locations and by generating power based on the load conditions, the total power loss in the system can be reduced and the system reliability can be improved. The proposed method is tested for different load conditions on IEEE 30 bus system, by connecting one DG, two DGs, three DGs and four DGs in the system. The results obtained show the improved voltage profiles and reliability indices like EENS and ECOST.