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Optimal and Neural Network Control for Renewables and Electric Power and Energy Systems

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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 December 2016) | Viewed by 41433

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Dr. Shuhui Li

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Guest Editor

Department of Center for Advanced Vehicle Technologies, College of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487, USA
Interests: smart grid and smart microgrid; renewable energy; systems power; electronics; electric machines and drives; power systems; artificial intelligence and neural networks modeling; analysis and simulation of dynamic systems; massively parallel processing applications software; engineering measurements and instrumentations
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Dr. Eduardo Alonso

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School of Mathematics, Computer Science & Engineering, City University London, London, UK
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

Dr. Xingang Fu

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Guest Editor

Department of Electrical & Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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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

Published Papers (6 papers)

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Research

7488 KiB

Open AccessArticle

Research on Unstructured Text Data Mining and Fault Classification Based on RNN-LSTM with Malfunction Inspection Report

by Daqian Wei, Bo Wang, Gang Lin, Dichen Liu, Zhaoyang Dong, Hesen Liu and Yilu Liu

Energies 2017, 10(3), 406; https://doi.org/10.3390/en10030406 - 21 Mar 2017

Cited by 66 | Viewed by 6592

Abstract

This paper documents the condition-based maintenance (CBM) of power transformers, the analysis of which relies on two basic data groups: structured (e.g., numeric and categorical) and unstructured (e.g., natural language text narratives) which accounts for 80% of data required. However, unstructured data comprised of malfunction inspection reports, as recorded by operation and maintenance of the power grid, constitutes an abundant untapped source of power insights. This paper proposes a method for malfunction inspection report processing by deep learning, which combines the text data mining–oriented recurrent neural networks (RNN) with long short-term memory (LSTM). In this paper, the effectiveness of the RNN-LSTM network for modeling inspection data is established with a straightforward training strategy in which we replicate targets at each sequence step. Then, the corresponding fault labels are given in datasets, in order to calculate the accuracy of fault classification by comparison with the original data labels and output samples. Experimental results can reflect how key parameters may be selected in the configuration of the key variables to achieve optimal results. The accuracy of the fault recognition demonstrates that the method we proposed can provide a more effective way for grid inspection personnel to deal with unstructured data. Full article

(This article belongs to the Special Issue Optimal and Neural Network Control for Renewables and Electric Power and Energy Systems)

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4597 KiB

Open AccessArticle

Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control

by Deyou Yang, Jiaxin Wen, Ka-wing Chan and Guowei Cai

Energies 2016, 9(8), 629; https://doi.org/10.3390/en9080629 - 11 Aug 2016

Cited by 17 | Viewed by 5141

Abstract

The application of large scale energy storage makes wind farms more dispatchable, which lowers operating risks to the grid from interconnected large scale wind farms. In order to make full use of the flexibility and controllability of energy storage to improve the schedulability of wind farms, this paper presents a rolling and dispatching control strategy with a battery energy storage system (BESS) based on model predictive control (MPC). The proposed control scheme firstly plans expected output, i.e., dispatching order, of a wind/battery energy storage hybrid system based on the predicted output of the wind farm, then calculates the order in the predictive horizon with the receding horizon optimization and the limitations of energy storage such as state of charge and depth of charge/discharge to maintain the combination of active output of the wind farm and the BESS to track dispatching order at the extreme. The paper shows and analyses the effectiveness of the proposed strategy with different sizes of capacity of the BESS based on the actual output of a certain actual wind farm in the northeast of China. The results show that the proposed strategy that controls the BESS could improve the schedulability of the wind farm and maintain smooth output of wind/battery energy storage hybrid system while tracking the dispatching orders. When the capacity of the BESS is 20% or the rated capacity of the wind farm, the mean dispatching error is only 0.153% of the rated capacity of the wind farm. Full article

(This article belongs to the Special Issue Optimal and Neural Network Control for Renewables and Electric Power and Energy Systems)

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5393 KiB

Open AccessArticle

EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments

by Misael Lopez-Ramirez, Luis Ledesma-Carrillo, Eduardo Cabal-Yepez, Carlos Rodriguez-Donate, Homero Miranda-Vidales and Arturo Garcia-Perez

Energies 2016, 9(7), 565; https://doi.org/10.3390/en9070565 - 20 Jul 2016

Cited by 27 | Viewed by 6231

Abstract

In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches. Full article

(This article belongs to the Special Issue Optimal and Neural Network Control for Renewables and Electric Power and Energy Systems)

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1740 KiB

Open AccessArticle

A Novel Neural Network Vector Control for Single-Phase Grid-Connected Converters with L, LC and LCL Filters

by Xingang Fu and Shuhui Li

Energies 2016, 9(5), 328; https://doi.org/10.3390/en9050328 - 29 Apr 2016

Cited by 20 | Viewed by 8077

Abstract

This paper investigates a novel recurrent neural network (NN)-based vector control approach for single-phase grid-connected converters (GCCs) with L (inductor), LC (inductor-capacitor) and LCL (inductor-capacitor-inductor) filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI controllers, and the NN training approximates the optimal control for the single-phase GCC system. The Levenberg–Marquardt (LM) algorithm was used to train the NN controller based on the complete system equations without any decoupling policies. The proposed NN approach can solve the decoupling problem associated with the conventional vector control methods for L, LC and LCL-filter-based single-phase GCCs. Both simulation study and hardware experiments demonstrate that the neural network vector controller shows much more improved performance than that of conventional vector controllers, including faster response speed and lower overshoot. Especially, NN vector control could achieve very good performance using low switch frequency. More importantly, the neural network vector controller is a damping free controller, which is generally required by a conventional vector controller for an LCL-filter-based single-phase grid-connected converter and, therefore, can overcome the inefficiency problem caused by damping policies. Full article

(This article belongs to the Special Issue Optimal and Neural Network Control for Renewables and Electric Power and Energy Systems)

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1302 KiB

Open AccessArticle

On Scalability and Replicability of Smart Grid Projects—A Case Study

by Lukas Sigrist, Kristof May, Andrei Morch, Peter Verboven, Pieter Vingerhoets and Luis Rouco

Energies 2016, 9(3), 195; https://doi.org/10.3390/en9030195 - 14 Mar 2016

Cited by 26 | Viewed by 6770

Abstract

This paper studies the scalability and replicability of smart grid projects. Currently, most smart grid projects are still in the R&D or demonstration phases. The full roll-out of the tested solutions requires a suitable degree of scalability and replicability to prevent project demonstrators from remaining local experimental exercises. Scalability and replicability are the preliminary requisites to perform scaling-up and replication successfully; therefore, scalability and replicability allow for or at least reduce barriers for the growth and reuse of the results of project demonstrators. The paper proposes factors that influence and condition a project’s scalability and replicability. These factors involve technical, economic, regulatory and stakeholder acceptance related aspects, and they describe requirements for scalability and replicability. In order to assess and evaluate the identified scalability and replicability factors, data has been collected from European and national smart grid projects by means of a survey, reflecting the projects’ view and results. The evaluation of the factors allows quantifying the status quo of on-going projects with respect to the scalability and replicability, i.e., they provide a feedback on to what extent projects take into account these factors and on whether the projects’ results and solutions are actually scalable and replicable. Full article

(This article belongs to the Special Issue Optimal and Neural Network Control for Renewables and Electric Power and Energy Systems)

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12397 KiB

Open AccessArticle

An Improved Current Control Strategy for a Grid-Connected Inverter under Distorted Grid Conditions

by Ngoc Bao Lai and Kyeong-Hwa Kim

Energies 2016, 9(3), 190; https://doi.org/10.3390/en9030190 - 12 Mar 2016

Cited by 32 | Viewed by 7396

Abstract

This paper presents an improved current control strategy for a three-phase grid-connected inverter under distorted grid conditions. The main challenge associated with the grid-connected inverter in distributed generation (DG) systems is to maintain the harmonic contents in output current below the specified values even when the grid is subject to uncertain disturbances such as harmonic distortion. To overcome such a challenge, an improved current control scheme is proposed for a grid-connected inverter, in which the fundamental and harmonic currents are independently controlled by a proportional-integral (PI) decoupling controller and a predictive basis controller, respectively. The controller design approach is based on the model decomposition method, where the measured inverter currents and grid voltages are divided into the fundamental and harmonic components by means of moving average filters (MAFs). Moreover, to detect the angular displacement and angular frequency with better accuracy, even in the presence of the grid disturbance, the MAF is also introduced to implement an enhanced phase-lock loop (PLL) structure. Theoretical analyses as well as comparative simulation results demonstrate that the proposed control scheme can effectively compensate the uncertainties caused by the grid voltages with fast transient response. To validate the feasibility of the proposed scheme, the whole control algorithms are implemented on 2 kVA three-phase grid-connected inverter system using 32-bit floating-point DSP TMS320F28335. As a result, the proposed scheme is an attractive way to control a grid-connected inverter under adverse grid conditions. Full article

(This article belongs to the Special Issue Optimal and Neural Network Control for Renewables and Electric Power and Energy Systems)

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