The Experts below are selected from a list of 110889 Experts worldwide ranked by ideXlab platform
Xiaofu Xiong - One of the best experts on this subject based on the ideXlab platform.
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Time-varying failure rate simulation model of transmission lines and its application in power System Risk Assessment considering seasonal alternating meteorological disasters
Iet Generation Transmission & Distribution, 2016Co-Authors: Jian Wang, Xiaofu Xiong, Zhou Ning, Li Zhe, Shijie WengAbstract:Years of operation experience on power Systems reveal that most transmission line fault events are related to seasonal alternating meteorological disasters, which have typical temporal and spatial distribution characteristics. However, power System Risk Assessment lacks basically accurate descriptions of failure rate time distribution for transmission lines. In this study, a method to calculate the time-varying failure rate of transmission lines in a monthly time scale is proposed to reflect the time-dependent fault regulation. However, the failure rate during any time interval cannot be derived directly from limited historical fault samples. Therefore, a simulation method of continuous time distribution function for failure rate is proposed, which adopts Fourier, Gaussian, and Weibull function assumptions. Furthermore, the parameters of these function hypotheses are fitted and compared using the fault samples of a province power grid and an urban power grid in China, respectively. Results show that the proposed simulation model is reasonable. Finally, the time-varying failure rate simulation model is adopted to quantify the Risk of the verified IEEE-RBTS System. The Risk indices also indicate that considering the time distribution characteristics of failure rate has a more significant influence on the System Risk than the conventional methods.
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power System Risk Assessment using a hybrid method of fuzzy set and monte carlo simulation
IEEE Transactions on Power Systems, 2008Co-Authors: Wenyuan Li, Jiaqi Zhou, Xiaofu XiongAbstract:This paper presents fuzzy-probabilistic modeling techniques for System component outage parameters and load curves. The fuzzy membership functions of System component outage parameters are developed using statistical records, whereas the System load is modeled using a combined fuzzy and probabilistic representation. Based on the fuzzy-probabilistic models, a hybrid method of fuzzy set and Monte Carlo simulation for power System Risk Assessment is proposed to capture both randomness and fuzziness of loads and component outage parameters. An actual example using a regional System at the British Columbia Transmission Corpoation is given to demonstrate the application of the presented fuzzy-probabilistic models for System parameters and new System Risk evaluation method.
Yuan Zeng - One of the best experts on this subject based on the ideXlab platform.
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hierarchical Risk Assessment of transmission System considering the influence of active distribution network
IEEE Transactions on Power Systems, 2015Co-Authors: Hongjie Jia, Zhe Liu, Bingdong Wang, Yuan ZengAbstract:In traditional distribution System, power flow was unidirectional; thus loads were usually aggregated into the substation as an equivalent constant bus in the original Risk analysis of transmission System. Detail structures of the distribution System were absolutely ignored. However, distribution System is transformed from passive to active nowadays with more and more distributed generations (DGs) being integrated into it. So, distribution System not only can be considered as loads but also can be considered as virtual power plants to supply power to local loads and even to send power back to the transmission System sometimes. In this paper, a hierarchical method is presented to evaluate the impact of role changing of the distribution System in power System Risk Assessment. In the study, DGs can supply critical loads in distribution System through re-dispatching after some contingency happens. Discrete probability model is employed to simulate the intermittent output of DGs. Available supply capacity (ASC) is used as a general constraint to guarantee the security level of distribution System that needs high reliability. Also, an iterative calculation between transmission System and distribution network is adopted to derive the Risk indices with considering detail structures of the active distribution network. Further, impact of some factors on System Risk calculation, such as DGs' capacities, dispersions, locations, and component outage probabilities, are discussed. Finally, correctness and effectiveness of the proposed method are validated by a modified IEEE RTS and RBTS System.
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study on load shedding model based on improved power flow tracing method in power System Risk Assessment
International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, 2011Co-Authors: Ruixin Niu, Yuan Zeng, Meili Cheng, Xiangwen WangAbstract:An improved method of load-shedding model based on power flow tracing is proposed in this paper. When the electric power System is in the process of over limit because of some faults or in the emergency situation, cutting part of load will make the System back to stability. This improved method provides a strategy for the load shedding procedure. Contribution factors and distribution factors between loads and generators are introduced in this paper. Those nodes with larger contribution factors and distribution factors will be selected to be adjusted. By this means it ensures that the adjustment on the overload lines is the most effective, while having little impact on the non-overload lines. By this way, it decreases the number of the nodes which should be regulated, which can improve the calculation speed and accuracy. The suggested method can be useful in the Risk Assessment of generation and transmission System for regulating power flow. In order to verify the algorithm proposed in this paper, the IEEE24-bus System is used and the result shows its validity, efficiency and accuracy.
James D Mccalley - One of the best experts on this subject based on the ideXlab platform.
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power System Risk Assessment and control in a multiobjective framework
IEEE Transactions on Power Systems, 2009Co-Authors: Fei Xiao, James D MccalleyAbstract:Traditional online security Assessment determines whether the System is secure or not, but how secure or insecure is not explicitly indicated. This paper develops probabilistic indices, Risk, to assess real-time power System security level. Risk captures not only event likelihood, but also consequence. System security level associated with low voltage and overload can be optimally controlled, using the NSGA multiobjective optimization method. A security diagram is used to visualize operating conditions in a way that enables both Risk-based and traditional deterministic views. An index for cascading overloads is used to evaluate the Pareto optimal solutions. This paper shows that the multiobjective approach results in less Risky and less costly operating conditions, and it provides a practical algorithm for implementation. The IEEE 24-bus RTS-1996 System is analyzed to show that Risk-based System security control results in lower Risk, lower cost, and less exposure to cascading outages.
Wenyuan Li - One of the best experts on this subject based on the ideXlab platform.
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power System Risk Assessment using a hybrid method of fuzzy set and monte carlo simulation
IEEE Transactions on Power Systems, 2008Co-Authors: Wenyuan Li, Jiaqi Zhou, Xiaofu XiongAbstract:This paper presents fuzzy-probabilistic modeling techniques for System component outage parameters and load curves. The fuzzy membership functions of System component outage parameters are developed using statistical records, whereas the System load is modeled using a combined fuzzy and probabilistic representation. Based on the fuzzy-probabilistic models, a hybrid method of fuzzy set and Monte Carlo simulation for power System Risk Assessment is proposed to capture both randomness and fuzziness of loads and component outage parameters. An actual example using a regional System at the British Columbia Transmission Corpoation is given to demonstrate the application of the presented fuzzy-probabilistic models for System parameters and new System Risk evaluation method.
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Risk Assessment of power Systems models methods and applications
2004Co-Authors: Wenyuan LiAbstract:Preface. 1 Introduction. 1.1 Risk in Power Systems. 1.2 Basic Concepts of Power System Risk Assessment. 1.3 Outline of the Book. 2 Outage Models of System Components. 2.1 Introduction. 2.2 Models of Independent Outages. 2.3 Models of Dependent Outages. 2.4 Conclusions. 3 Parameter Estimation in Outage Models. 3.1 Introduction. 3.2 Point Estimation of Mean and Variance of Failure Data. 3.3 Interval Estimation of Mean and Variance of Failure Data. 3.4 Estimating Failure Frequency of Individual Components. 3.5 Estimating Probability from a Binomial Distribution. 3.6 Experimental Distribution of Failure Data and Its Test. 3.7 Estimating Parameters in Aging Failure Models. 3.8 Conclusions. 4 Elements of Risk Evaluation Methods. 4.1 Introduction. 4.2 Methods for Simple Systems. 4.3 Methods for Complex Systems. 4.4. Conclusions. 5 Risk Evaluation Techniques for Power Systems. 5.1 Introduction. 5.2 Techniques Used in Generation--Demand Systems. 5.3 Techniques Used in Radial Distribution Systems. 5.4 Techniques Used in Substation Configurations. 5.5 Techniques Used in Composite Generation and Transmission Systems. 5.6 Conclusions. 6 Application of Risk Evaluation to Transmission Development Planning. 6.1 Introduction. 6.2 Concept of Probabilistic Planning. 6.3 Risk Evaluation Approach. 6.4 Example 1: Selecting the Lowest--Cost Planning Alternative. 6.5 Example 2: Applying Different Planning Criteria. 6.6 Conclusions. 7 Application of Risk Evaluation to Transmission Operation Planning. 7.1 Introduction. 7.2 Concept of Risk Evaluation in Operation Planning. 7.3 Risk Evaluation Method. 7.4 Example 1: Determining the Lowest--Risk Operation Mode. 7.5 Example 2: A Simple Case by Hand Calculations. 7.6 Conclusions. 8 Application of Risk Evaluation to Generation Source Planning. 8.1 Introduction. 8.2 Procedure for Reliability Planning. 8.3 Simulation of Generation and Risk Costs. 8.4 Example 1: Selecting Location and Size of Cogenerators. 8.5 Example 2: Making a Decision to Retire a Local Generation Plant. 8.6 Conclusions. 9 Selection of Substation Configurations. 9.1 Introduction. 9.2 Load Curtailment Model. 9.3 Risk Evaluation Approach. 9.4 Example 1: Selecting Substation Configuration. 9.5 Example 2: Selecting Transmission Line Arrangement Associated with Substations. 9.6 Conclusions. 10 Reliability--Centered Maintenance. 10.1 Introduction. 10.2 Basic Tasks in RCM. 10.3 Example 1: Transmission Maintenance Scheduling. 10.4 Example 2: Workforce Planning in Maintenance. 10.5 Example 3: A Simple Case Performed by Hand Calculations. 10.6 Conclusions. 11 Probabilistic Spare--Equipment Analysis. 11.1 Introduction. 11.2 Spare--Equipment Analysis Based on Reliability Criteria. 11.3 Spare--Equipment Analysis Using the Probabilistic Cost Method. 11.4 Example 1: Determining Number and Timing of Spare Transformers. 11.5 Example 2: Determining Redundancy Level of 500 kV Reactors. 11.6 Conclusions. 12 Reliability--Based Transmission--Service Pricing. 12.1 Introduction. 12.2 Basic Concept. 12.3 Calculation Methods. 12.4 Rate Design. 12.5 Application Example. 12.6 Conclusions. 13 Probabilistic Transient Stability Assessment. 13.1 Introduction. 13.2 Probabilistic Modeling and Simulation Methods. 13.3 Procedure. 13.4 Examples. 13.5 Conclusions. Appendix A Basic Probability Concepts. A.1 Probability Calculation Rules. A.2 Random Variable and its Distribution. A.3 Important Distributions in Risk Evaluation. A.4 Numerical Characteristics. Appendix B Elements of Monte Carlo Simulation. B.1 General Concept. B.2 Random Number Generators. B.3 Inverse Transform Method of Generating Random Variates. B.4 Important Random Variates in Risk Evaluation. Appendix C Power--Flow Models. C.1 AC Power--Flow Models. C.2 DC Power--Flow Models. Appendix D Optimization Algorithms. D.1 Simplex Methods for Linear Programming. D.2 Interior Point Method for Nonlinear Programming. Appendix E Three Probability Distribution Tables. References. Index. About the Author.
Patrick Panciatici - One of the best experts on this subject based on the ideXlab platform.
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optimization of computational budget for power System Risk Assessment
arXiv: Machine Learning, 2018Co-Authors: Benjamin Donnot, Isabelle Guyon, Antoine Marot, Marc Schoenauer, Patrick PanciaticiAbstract:We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators. New conceptual frameworks are calling for a probabilistic Risk-based security criterion. However, these approaches suffer from high requirements in terms of tractability. Here, we propose a new method to assess the Risk. This method uses both machine learning techniques (artificial neural networks) and more standard simulators based on physical laws. More specifically we train neural networks to estimate the overall dangerousness of a grid state. A classical benchmark problem (manpower 118 buses test case) is used to show the strengths of the proposed method.
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ISGT Europe - Optimization of computational budget for power System Risk Assessment
2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2018Co-Authors: Benjamin Donnot, Isabelle Guyon, Antoine Marot, Marc Schoenauer, Patrick PanciaticiAbstract:We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators. New conceptual frameworks are calling for a probabilistic Risk-based security criterion. However, these approaches suffer from high requirements in terms of computational resources. Here, we propose a new method to assess this Risk while not increasing too much the computational cost. The proposed method uses both machine learning techniques (artificial neural networks) and more standard simulators based on physical laws (eg. Kirchoff's laws). More specifically we train neural networks to estimate the overall dangerousness of a grid state, and use only the slow but accurate simulator on the most dangerous detected event. A classical benchmark problem (matpower 118 buses test case) is used to show the strengths of the proposed method in the evaluation of the global Risk of the grid.
