Planning of Interconnected Powers Systems Considering Security Under Cascading Outages and Catastrophic Events
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Estado de la publicación:
Activo
Año de edición: 2011
ISBN-13: 9789586955546
Páginas: 137
Tamaño(cm): 17 x 24
Peso (kg): 0.2900 kg
SKU (Número de Referencia): 72891
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This dissertation presents a method for assessing the vulnerability of a composite power system. It is based on the modeling of failures and repairs using stochastic point process theory and a procedure of the sequential Monte Carlo simulation to compute the indices of vulnerability. Stochastic point process modeling allows including constant and time-varying rates, a necessity in those scenarios considering aging and diverse maintenance strategies. It also allows representing the repair process performed in the power system as it really is: a queuing system. The sequential Monte Carlo simulation is applied because it can artificially generate all the aspects involved in the operating sequence of a power system and also because it can easily manage non-stationary probabilistic models. The indices of vulnerability are the probability of occurrence of a high-order 1055 of component scenario, its frequency and its duration. A high-order loss of component scenario is that one higher than n - 2. Examples using the IEEE One Area RTS show how the presence of aging and others factors that produce increasing component failure rates dramatically increase the risk of occurrence of high-order loss of component scenarios. On the other hand, the improvement in aspects such as preventive maintenance and repair performance reduces this risk. Although the main focus of this method is composite power systems, its development produced other outcomes, such as procedures for assessment of power distribution systems, protective relaying schemes and power substations.The sequential Monte Carlo simulation is applied because it can artificially generate all the aspects involved in the operating sequence of a power system and also because it can easily manage non-stationary probabilistic models. The indices of vulnerability are the probability of occurrence of a high-order 1055 of component scenario, its frequency and its duration. A high-order loss of component scenario is that one higher than n - 2. Examples using the IEEE One Area RTS show how the presence of aging and others factors that produce increasing component failure rates dramatically increase the risk of occurrence of high-order loss of component scenarios. On the other hand, the improvement in aspects such as preventive maintenance and repair performance reduces this risk. Although the main focus of this method is composite power systems, its development produced other outcomes, such as procedures for assessment of power distribution systems, protective relaying schemes and power substations.On the other hand, the improvement in aspects such as preventive maintenance and repair performance reduces this risk. Although the main focus of this method is composite power systems, its development produced other outcomes, such as procedures for assessment of power distribution systems, protective relaying schemes and power substations.
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Abstract
List of Figures
List of Tables
1. Introduction
2. Stochastic Point Processes
2.1. Definition
2.2. The Concept of Tendency
2.3. SPP Models
2.4. Selection Procedure of an SPP Model
2.5. The Power Law Process
2.6. How to Generate Samples from SPP Models
2.6.1. Renewal Processes
2.6.2. Non-Homogeneous Poisson Processes
2.7. Superposition
3. Some Misconceptions about SPP and the Modeling of Repairable Components
3.1. Review of Basic Concepts
3.1.1. Definitions
3.1.2. How to Select a Model for a Random Process
3.2. Reliability Analysis of Non-Repairable Components
3.3. Reliability Analysis of Repairable Components
3.4. Markov Chain Models
3.4.1. Homogeneous Exponential Markov Chain
3.4.2. General Homogeneous Markov Chain
3.4.3. Non-Homogeneous Markov Chain
3.4.4. SPP Models
3.5. The Misconceptions
3.5.1. The Meaning of the Term "Failure Rate"
3.5.2. The Use of a Life Model for a Repairable Component
3.5.3. A Distribution Can Represent a Non-Stationary Random Process
3.5.4. Equation (3.2) Generates a Random Process Whose Model is the Weibull Distribution
3.5.5. A General Homogeneous Markov Chain Can Represent a Non-Stationary Process
3.5.6. The PLP is the Same Thing as a Weibull Distribution
3.5.7. The PLP is the Same Thing as a Weibull RP
3.5.8. The Only Model for a Stationary Failure Process is the HPP
3.6. Relationship Between SPP and Markov Chains
3.7. Conclusions
4. The Repair Process in a Power System
4.1. Introduction
4.2. Traditional Methods for Studying the Repair Process
4.2.1. By Means of Statistical Analysis of Outage
Times
4.2.2. As Part of the Componet Reliability Models
4.3. Modeling of the Repair Process
4.4. Assessment of the Repair Process Performance
4.4.1. Obtaining the Zone Failure Process
4.4.2. Obtaining the Zone Service Process
4.4.3. Assessing the Repair Process Performance
4.4.4. Iteration Procedure
4.5. Examples
4.6. Conclusions
5. Reliability Assessment of a Power Distribution System
5.1. Introduction
5.2. Traditional Component Modeling
5.3. Methods in Widespread Use for Reliability Assessment of Distribution Networks
5.3.1. The Homogeneous Markov Process
5.3.2. Device of Stages
5.3.3. Simplified Method of Blocks
5.3.4. Analytical Simulation
5.3.5. The Monte Carlo Simulation
5.4. Methods for System Reliability Assessment that Can Include Aging
5.4.1. Manual Approach
5.4.2. The Non-Homogeneous Markov Process
5.4.3. Stochastic Point Processes
5.5. Proposed Methodology
5.5.1. Modeling of Component Failure Processes
5.5.2. Modeling of Repair Process
5.6. System Reliability Assessment
5.6.1. Iteration Procedure
5.6.2. Repair Process Indices
5.6.3. Load Point Indices
5.7. Example
5.8. Conclusions
6. Reliability Assessment of a Protective Scheme
6.1. Introduction
6.2. Problem Statement
6.3. Failure Modes of a Protective System
6.4. Protective System Reliability Indices
6.4.1. Reliability
6.4.2. Dependency
6.4.3. Security
6.5. Protection Zone Reliability Index
6.6. Proposed Method
6.6.1. Modeling
6.6.2. Reliability Assessment Procedure
6.6.3. Procedure Inside a Realization
6.6.4. Detection of Failures by Preventive Maintenance
6.7. Example
6.7.1. Test System
6.7.2. Study Cases
6.7.3. Results
6.7.4. Analysis of the Results
6.8. Conclusions
7. Reliability Assessment of a Substation
7.1. Introduction
7.2. Motivation
7.2.1. The Necessity of Considering Time-Varying Rates
7.2.2. The Necessity of Including the Effect of Protective Systems
7.3. Concept of Protection Zones
7.4. Failure Modes of Protected Zones
7.5. Common Mode Failures
7.6. Tie Sets
7.7. Proposed Method
7.7.1. Modeling
7.7.2. Reliability Assessment Procedure
7.7.3. Procedure inside a Realization
7.7.4. Detection of Failures by Preventive Maintenance
7.7.5. Reliability Indices
7.8. Example
7.8.1. Test System
7.8.2. Study Cases
7.8.3. Results
7.8.4. Analysis of the Results
7.9. Conclusions
8. Modeling of Failures to Operate of Protective Systems for Reliability Studies at the Power System Level
8.1. Power System Reliability Assessments Considering PS Failures to Operate 8.2. Problem Statement
8.3. Proposed Method
8.4. Example
8.5. How to Use this Model
8.6. Conclusion
9. The Loss of Component Scenario Method to Analyze the Vulnerability of a Composite Power System
9.1. Definition of Loss of Component Scenario
9.2. Objective of an LCS Study
9.3. Traditional Modeling for Reliability Studies
9.4. What Do the Assumptions of Traditional Modeling Imply?
9.4.1. Stationary Failure and Repair Processes
9.4.2. Independent Component Repair Processes
9.4.3. Proposal
9.5. Proposed Method
9.5.1. Failure Process Modeling
9.5.2. Repair Process Modeling
9.5.3. Algorithm of the Proposed Method
9.6. Examples
9.6.1. Example 1: Constant Rates and Diverse Repair Logistics
9.6.2. Example 2: Increasing Failure Rates - Constant Repair Rates
9.7. Conclusions
10. Main Conclusion
References