The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jovica V. Milanovic - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Framework for transient stability assessment of power systems with high penetration of renewable generation
IEEE Transactions on Power Systems, 2017Co-Authors: Panagiotis N Papadopoulos, Jovica V. MilanovicAbstract:This paper introduces a Probabilistic Framework for transient stability assessment (TSA) of power systems with high penetration of renewable generation. The critical generators and areas of the system are identified using a method based on hierarchical clustering. Furthermore, statistical analysis of several transient stability indices is performed to assess their suitability for TSA of reduced inertia systems. The proposed Framework facilitates robust assessment of transient stability of uncertain power systems with reduced inertia.
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Probabilistic Framework for assessing the accuracy of data mining tool for online prediction of transient stability
IEEE Transactions on Power Systems, 2014Co-Authors: Tingyan Guo, Jovica V. MilanovicAbstract:The paper presents a generic Probabilistic Framework for assessing the accuracy of online prediction of power system transient stability based on phasor measurement unit (PMU) measurements and data mining techniques. It allows fair comparison of different data mining models in terms of the accuracy of the prediction. To illustrate the concept, a decision tree (DT) method is used as an example of a data mining technique. It is implemented in a 16-machine, 68-bus test power system. Generator rotor angles and speeds provided by PMUs during post-fault condition are chosen as predictors. The performance of the DT based prediction method is tested using a wide variety of disturbances with Probabilistically modeled locations, durations, types of fault and the system loading levels. The accuracy of prediction is approximately 98.5% immediately following the fault clearance and can increase to almost 100% if the prediction is made 2.5 s after the fault clearance.
Thomas S Huang - One of the best experts on this subject based on the ideXlab platform.
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a Probabilistic Framework for semantic video indexing filtering and retrieval
IEEE Transactions on Multimedia, 2001Co-Authors: H R Naphide, Thomas S HuangAbstract:Semantic filtering and retrieval of multimedia content is crucial for efficient use of the multimedia data repositories. Video query by semantic keywords is one of the most difficult problems in multimedia data retrieval. The difficulty lies in the mapping between low-level video representation and high-level semantics. We therefore formulate the multimedia content access problem as a multimedia pattern recognition problem. We propose a Probabilistic Framework for semantic video indexing, which call support filtering and retrieval and facilitate efficient content-based access. To map low-level features to high-level semantics we propose Probabilistic multimedia objects (multijects). Examples of multijects in movies include explosion, mountain, beach, outdoor, music etc. Semantic concepts in videos interact and to model this interaction explicitly, we propose a network of multijects (multinet). Using Probabilistic models for six site multijects, rocks, sky, snow, water-body forestry/greenery and outdoor and using a Bayesian belief network as the multinet we demonstrate the application of this Framework to semantic indexing. We demonstrate how detection performance can be significantly improved using the multinet to take interconceptual relationships into account. We also show how the multinet can fuse heterogeneous features to support detection based on inference and reasoning.
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a Probabilistic Framework for semantic indexing and retrieval in video
International Conference on Multimedia and Expo, 2000Co-Authors: Milind Naphade, Thomas S HuangAbstract:This paper proposes a novel Probabilistic Framework for semantic indexing and retrieval in digital video. The components of the Framework are multijects and multinets. Multijects are Probabilistic multimedia objects (Naphade et al., 1998) representing semantic features or concepts. A multinet is a Probabilistic network of multijects which accounts for the interaction between concepts. The main contribution of this paper is a Bayesian multinet which enhances the detection probability of individual multijects, provides a unified Framework for integrating multiple modalities and supports inference of unobservable concepts based on their relation with observable concepts. We develop multijects for detecting sites (locations) in video and integrate the multijects using a multinet in the form of a Bayesian network. Experiments reveal significant performance improvement using the multinet.
Goran Andersson - One of the best experts on this subject based on the ideXlab platform.
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a Probabilistic Framework for reserve scheduling and rm n 1 security assessment of systems with high wind power penetration
IEEE Transactions on Power Systems, 2013Co-Authors: Maria Vrakopoulou, Kostas Margellos, John Lygeros, Goran AnderssonAbstract:We propose a Probabilistic Framework to design an N-1 secure day-ahead dispatch and determine the minimum cost reserves for power systems with wind power generation. We also identify a reserve strategy according to which we deploy the reserves in real-time operation, which serves as a corrective control action. To achieve this, we formulate a stochastic optimization program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines and the generation limits. To incorporate a reserve decision scheme, we take into account the steady-state behavior of the secondary frequency controller and, hence, consider the deployed reserves to be a linear function of the total generation-load mismatch. The overall problem results in a chance constrained bilinear program. To achieve tractability, we propose a convex reformulation and a heuristic algorithm, whereas to deal with the chance constraint we use a scenario-based-approach and an approach that considers only the quantiles of the stationary distribution of the wind power error. To quantify the effectiveness of the proposed methodologies and compare them in terms of cost and performance, we use the IEEE 30-bus network and carry out Monte Carlo simulations, corresponding to different wind power realizations generated by a Markov chain-based model.
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a Probabilistic Framework for security constrained reserve scheduling of networks with wind power generation
IEEE International Energy Conference, 2012Co-Authors: Maria Vrakopoulou, Kostas Margellos, John Lygeros, Goran AnderssonAbstract:This paper proposes a novel Probabilistic Framework to design an N-1 secure day-ahead dispatch, while determining the minimum cost reserves for power systems with high wind penetration. To achieve this, we build on previous work, and formulate a stochastic optimization program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines. To incorporate then a reserve decision scheme, we take into account the steady state behavior of the secondary frequency controller, and hence consider the reserves to be a linear function of the total generation-load mismatch. The overall problem results in a chance constrained bilinear program; to achieve tractability, two alternative convex reformulations are proposed, and the so called scenario approach is employed. This approach is based on sampling the uncertain parameter (in this paper the wind power) while keeping the desired Probabilistic guarantees. To illustrate the effectiveness of the proposed technique we apply it to the IEEE 30-bus network, and compare the alternative reformulations in terms of cost and performance by means of Monte Carlo simulations, corresponding to different wind power realizations generated by a Markov chain based model.
Maria Vrakopoulou - One of the best experts on this subject based on the ideXlab platform.
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a Probabilistic Framework for reserve scheduling and rm n 1 security assessment of systems with high wind power penetration
IEEE Transactions on Power Systems, 2013Co-Authors: Maria Vrakopoulou, Kostas Margellos, John Lygeros, Goran AnderssonAbstract:We propose a Probabilistic Framework to design an N-1 secure day-ahead dispatch and determine the minimum cost reserves for power systems with wind power generation. We also identify a reserve strategy according to which we deploy the reserves in real-time operation, which serves as a corrective control action. To achieve this, we formulate a stochastic optimization program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines and the generation limits. To incorporate a reserve decision scheme, we take into account the steady-state behavior of the secondary frequency controller and, hence, consider the deployed reserves to be a linear function of the total generation-load mismatch. The overall problem results in a chance constrained bilinear program. To achieve tractability, we propose a convex reformulation and a heuristic algorithm, whereas to deal with the chance constraint we use a scenario-based-approach and an approach that considers only the quantiles of the stationary distribution of the wind power error. To quantify the effectiveness of the proposed methodologies and compare them in terms of cost and performance, we use the IEEE 30-bus network and carry out Monte Carlo simulations, corresponding to different wind power realizations generated by a Markov chain-based model.
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a Probabilistic Framework for security constrained reserve scheduling of networks with wind power generation
IEEE International Energy Conference, 2012Co-Authors: Maria Vrakopoulou, Kostas Margellos, John Lygeros, Goran AnderssonAbstract:This paper proposes a novel Probabilistic Framework to design an N-1 secure day-ahead dispatch, while determining the minimum cost reserves for power systems with high wind penetration. To achieve this, we build on previous work, and formulate a stochastic optimization program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines. To incorporate then a reserve decision scheme, we take into account the steady state behavior of the secondary frequency controller, and hence consider the reserves to be a linear function of the total generation-load mismatch. The overall problem results in a chance constrained bilinear program; to achieve tractability, two alternative convex reformulations are proposed, and the so called scenario approach is employed. This approach is based on sampling the uncertain parameter (in this paper the wind power) while keeping the desired Probabilistic guarantees. To illustrate the effectiveness of the proposed technique we apply it to the IEEE 30-bus network, and compare the alternative reformulations in terms of cost and performance by means of Monte Carlo simulations, corresponding to different wind power realizations generated by a Markov chain based model.
Panagiotis N Papadopoulos - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Framework for transient stability assessment of power systems with high penetration of renewable generation
IEEE Transactions on Power Systems, 2017Co-Authors: Panagiotis N Papadopoulos, Jovica V. MilanovicAbstract:This paper introduces a Probabilistic Framework for transient stability assessment (TSA) of power systems with high penetration of renewable generation. The critical generators and areas of the system are identified using a method based on hierarchical clustering. Furthermore, statistical analysis of several transient stability indices is performed to assess their suitability for TSA of reduced inertia systems. The proposed Framework facilitates robust assessment of transient stability of uncertain power systems with reduced inertia.
