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Robert Carl Nikolai Pilawa-podgurski - One of the best experts on this subject based on the ideXlab platform.
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A Distributed Approach to maximum power point tracking for photovoltaic submodule differential power processing
IEEE Transactions on Power Electronics, 2015Co-Authors: Shibin Qin, Stanton T. Cady, Alejandro D. Dominguez-garcia, Robert Carl Nikolai Pilawa-podgurskiAbstract:This paper presents the theory and implementation of a Distributed algorithm for controlling differential power processing converters in photovoltaic (PV) applications. This Distributed algorithm achieves true maximum power point tracking of series-connected PV submodules by relying only on local voltage measurements and neighbor-to-neighbor communication between the differential power converters. Compared to previous solutions, the proposed algorithm achieves reduced number of perturbations at each step and potentially faster tracking without adding extra hardware; all these features make this algorithm well-suited for long submodule strings. The formulation of the algorithm, discussion of its properties, as well as three case studies are presented. The performance of the Distributed tracking algorithm has been verified via experiments, which yielded quantifiable improvements over other techniques that have been implemented in practice. Both simulations and hardware experiments have confirmed the effectiveness of the proposed Distributed algorithm.
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A Distributed Approach to MPPT for PV sub-module differential power processing
2013 IEEE Energy Conversion Congress and Exposition ECCE 2013, 2013Co-Authors: Shibin Qin, Stanton T. Cady, Alejandro D. Dominguez-garcia, Robert Carl Nikolai Pilawa-podgurskiAbstract:This paper presents a Distributed control algorithm for differential power processing in photovoltaic (PV) applications. This Distributed algorithm performs true maximum power point tracking (MPPT) of series-connected PV sub-modules with only neighbor-to-neighbor communication and local measurements of each differential power converter voltages, obviating the need for local current measurements. Reduced number of perturbations at each step and potentially faster tracking can be achieved compared to previous solutions, while no extra hardware is required, all of which make this algorithm well-suited for long sub-module strings. The formulation of the control algorithm as well as three case studies are presented; both simulations and hardware experiments have confirmed the effectiveness of the proposed Distributed algorithm.
Amaratunga Gaj - One of the best experts on this subject based on the ideXlab platform.
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A Distributed Maximum-Likelihood-Based State Estimation Approach for Power Systems
2021Co-Authors: Chen T, Cao Y, Chen X, Sun L, Zhang J, Amaratunga GajAbstract:© 1963-2012 IEEE. The distribution of measurement noise is commonly considered as an assumed Gaussian model in power systems, but this assumption is not always true in reality. This article introduces a Distributed maximum-likelihood-based state estimation Approach for multiarea power systems using the student's $t$ -distribution measurement noise model. The $t$ -distribution has the property of 'thick tail' to better model the occurrence of outliers and is fairly flexible to model different noise statistics. The finite-time average consensus algorithm is utilized in conjunction with an influence function to realize the proposed Distributed Approach within a totally Distributed framework. Based on the local measurement residuals and the limited information exchanged with neighboring areas, each local area can obtain the global optimum system-wide robust state estimates, while the existing Distributed state estimation methods can only get local estimates. Moreover, the communication scheme is more flexible and can be totally different from the transmission lines between local areas. Simulations tested on the IEEE 14-bus and 118-bus systems verify the effectiveness of the proposed Distributed Approach
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A Distributed Maximum-Likelihood-Based State Estimation Approach for Power Systems
2021Co-Authors: Chen T, Cao Y, Chen X, Sun L, Zhang J, Amaratunga GajAbstract:The distribution of measurement noise is commonly considered as an assumed Gaussian model in power systems, but this assumption is not always true in reality. This article introduces a Distributed maximum-likelihood-based state estimation Approach for multiarea power systems using the student's $t$ -distribution measurement noise model. The $t$ -distribution has the property of 'thick tail' to better model the occurrence of outliers and is fairly flexible to model different noise statistics. The finite-time average consensus algorithm is utilized in conjunction with an influence function to realize the proposed Distributed Approach within a totally Distributed framework. Based on the local measurement residuals and the limited information exchanged with neighboring areas, each local area can obtain the global optimum system-wide robust state estimates, while the existing Distributed state estimation methods can only get local estimates. Moreover, the communication scheme is more flexible and can be totally different from the transmission lines between local areas. Simulations tested on the IEEE 14-bus and 118-bus systems verify the effectiveness of the proposed Distributed Approach
Shibin Qin - One of the best experts on this subject based on the ideXlab platform.
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A Distributed Approach to maximum power point tracking for photovoltaic submodule differential power processing
IEEE Transactions on Power Electronics, 2015Co-Authors: Shibin Qin, Stanton T. Cady, Alejandro D. Dominguez-garcia, Robert Carl Nikolai Pilawa-podgurskiAbstract:This paper presents the theory and implementation of a Distributed algorithm for controlling differential power processing converters in photovoltaic (PV) applications. This Distributed algorithm achieves true maximum power point tracking of series-connected PV submodules by relying only on local voltage measurements and neighbor-to-neighbor communication between the differential power converters. Compared to previous solutions, the proposed algorithm achieves reduced number of perturbations at each step and potentially faster tracking without adding extra hardware; all these features make this algorithm well-suited for long submodule strings. The formulation of the algorithm, discussion of its properties, as well as three case studies are presented. The performance of the Distributed tracking algorithm has been verified via experiments, which yielded quantifiable improvements over other techniques that have been implemented in practice. Both simulations and hardware experiments have confirmed the effectiveness of the proposed Distributed algorithm.
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A Distributed Approach to MPPT for PV sub-module differential power processing
2013 IEEE Energy Conversion Congress and Exposition ECCE 2013, 2013Co-Authors: Shibin Qin, Stanton T. Cady, Alejandro D. Dominguez-garcia, Robert Carl Nikolai Pilawa-podgurskiAbstract:This paper presents a Distributed control algorithm for differential power processing in photovoltaic (PV) applications. This Distributed algorithm performs true maximum power point tracking (MPPT) of series-connected PV sub-modules with only neighbor-to-neighbor communication and local measurements of each differential power converter voltages, obviating the need for local current measurements. Reduced number of perturbations at each step and potentially faster tracking can be achieved compared to previous solutions, while no extra hardware is required, all of which make this algorithm well-suited for long sub-module strings. The formulation of the control algorithm as well as three case studies are presented; both simulations and hardware experiments have confirmed the effectiveness of the proposed Distributed algorithm.
Aya Aboudina - One of the best experts on this subject based on the ideXlab platform.
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a bi level Distributed Approach for optimizing time dependent congestion pricing in large networks a simulation based case study in the greater toronto area
Transportation Research Part C-emerging Technologies, 2017Co-Authors: Aya Aboudina, Baher AbdulhaiAbstract:Abstract Congestion pricing is one of the most widely contemplated methods to manage traffic congestion by charging fees for the use of roads, more where and when it is congested, and less where and when it is not. This study presents a bi-level Distributed Approach for optimal time-dependent congestion pricing in large networks. The bi-level procedure involves a theoretical model of dynamic congestion pricing and a Distributed optimization algorithm. The bi-level toll optimization module is integrated into a testbed of hybrid departure time choice and dynamic traffic assignment simulation models for the Greater Toronto Area (GTA). The integrated system provides a unified (location- and time-specific) congestion pricing system that determines optimal tolling and evaluates its impact on road traffic congestion and travellers’ behavioural choices, including departure time and route choices. For the system’s large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel computing cluster. The system is applied to a simulation-based case study of tolling major highways in the GTA while capturing the network-wide regional effects of tolling. The travel demand and drivers’ attributes are extracted from regional household travel survey data that reflect travellers’ heterogeneity. The main results indicate that: (1) optimal variable pricing reflects congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays, (2) optimal tolls intended to manage traffic demand are significantly lower than those intended to maximize toll revenues, (3) tolled routes have different sensitivities to identical toll changes, (4) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (5) toll payers benefit from tolling even before toll revenues are spent, and (6) the optimal tolling policies determined offer a win–win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.
Emilio J. Bueno - One of the best experts on this subject based on the ideXlab platform.
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Distributed Approach for SmartGrids Reconfiguration Based on the OSPF Routing Protocol
IEEE Transactions on Industrial Informatics, 2016Co-Authors: Francisco J. Rodríguez, Susel Fernandez, Ines Sanz, Miguel Moranchel, Emilio J. BuenoAbstract:Smart grids (SG) are essential for efficient management and monitoring of electric power networks. One of the most important tasks in SG focuses on fault detection and automatic network reconfiguration. This process allows minimizing power losses and load balancing in distribution networks. In this paper, an adaptation of the open shortest path first (OSPF) routing protocol to accomplish the network reconfiguration task is proposed. The algorithm is intended to run in secondary substation nodes over an agent-based Distributed architecture. The proposed algorithm has been tested on the IEEE 123 modified node test feeder and on an actual grid deployed by an electrical distribution company. Moreover, a performance comparison with a typical centralized reconfiguration algorithm is carried out.