Optimal and Neural Network Control for Renewables and Electric Power and Energy Systems
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 December 2016) | Viewed by 44289
Special Issue Editors
Interests: artificial intelligence; power electronics; power systems; renewable energy systems; electric machines and drives; smart grid
Special Issues, Collections and Topics in MDPI journals
Interests: adaptive control and optimization; time series analysis; data science; big data; machine learning and deep learning; neural and evolutionary computation; complex systems; data mining and visualization; computational modeling and scientific software
Interests: power electronics; renewable energy; power system; smart grid; motor control; electrical vehicle; reinforcement learning; deep learning; recurrent neural networks; cloud computing
Special Issue Information
Dear Colleagues,
In today's electric power and energy systems, power electronic converters play an increasingly important role in smart grids/microgrids, renewable energy systems, energy storage devices, and traction systems. Power converters are key components that physically connect wind power, solar panels, and batteries to the grid and for energy conversion in electric vehicles and trains. A critical issue for energy generation from renewable sources, for smart grid integration, and for energy conversion in electric vehicles is the control of the power converters. Traditionally, these power converters are mainly controlled using standard control methods in power and energy industry.
In recent years, significant research has been conducted in developing optimization-based control technologies, including neural network control based on dynamic programming, H∞ and μ synthesis control techniques, and model predictive control. This Special Issue focuses on recent advances in optimal and artificial neural network control in power and energy system applications. From a control perspective, the special issue is interested in optimization-based control methods including neural network control based on approximate dynamic programming, adaptive critic designs, H∞ and μ synthesis control techniques, model predictive control, etc. In terms of applications, the special issue is interested in optimal control applied in renewable energy systems (including wind, solar, and energy storage), smart grid/microgrid, power transmission and distribution systems, and electric machines, drives and traction systems (including electric vehicles and trains).
Dr. Shuhui Li
Dr. Eduardo Alonso
Dr. Xingang Fu
Guest Editors
Manuscript Submission Information
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Keywords
artificial neural networks
optimization, dynamic programming
adaptive critic designs
model predictive control
H2, H∞ and μ synthesis control
wind power
solar photovoltaics
battery storages
charging stations
smart grids
microgrids
HVDC
FACTs
power distribution
electric machines and drives
electric vehicles
electric trains
traction power systems
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