Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

Journal Browser

Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 38661

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids book cover image

Share This Special Issue

Special Issue Editors

Prof. Dr. Marcelo Godoy Simões

E-Mail Website
Guest Editor

Colorado School of Mines, Electrical Engineering Department, Golden, CO 80401, USA
Interests: power electronics; smart-grid; power systems; power quality; renewable energy systems; artificial intelligence; motor drives

Prof. Dr. Helmo Kelis Morales Paredes

E-Mail Website
Guest Editor

Institute of Science and Technology of Sorocaba, São Paulo State University–UNESP, Sorocaba, São Paulo 18087-180, Brazil
Interests: novel power theories, power quality, power electronics, renewable energy, smart-grid

Special Issue Information

Dear colleagues,

The objective of this Special Issue is to address and disseminate the latest results on various aspects of artificial intelligent techniques, such as expert systems, fuzzy logic, and artificial neural networks in important areas in advanced high-tech electronics, such as applications in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids. There is a multidisciplinary relevant to all of those. Fuzzy, neural, and neuro-fuzzy techniques have become efficient tools in modeling and control applications. There are several benefits in optimizing cost-effectiveness, because fuzzy logic is a methodology for the handling of inexact, imprecise, qualitative, fuzzy, and verbal information systematically and rigorously. A neuro-fuzzy controller generates or tunes the rules or membership functions of a fuzzy controller with an artificial neural network approach. There are new instantaneous power theories that may address several challenges in power quality, power electronics, and the integration of renewable energy when associated with artificial intelligence. We invite original and unpublished submissions discussing innovative approaches to enhancenced artificial intelligence techniques in all relevant applications in power electronics and power systems.

Keywords

neural networks, fuzzy logic, genetic algorithms, deep learning, expert systems; machine learning: artificial intelligence techniques for power quality problems; AI, fuzzy logic and neural networks techniques for power-quality conditioners in smart grids; power-quality measurement and assessment in smart grids using artificial intelligence techniques; fuzzy logic, neural networks in power electronics, motor drives and industrial automation, artificial intelligence techniques for renewable energy systems; artificial intelligence techniques for power electronics in smart grids; artificial intelligence techniques for energy storage in smart grids; artificial intelligence techniques for load curve and generation prediction in smart grids; artificial intelligence techniques for energy management in smart grids; artificial intelligence techniques for smart microgrids and distributed generators with ancillary services; artificial intelligence techniques for the control, protection, and monitoring of smart grids

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

20 pages, 3826 KiB

Open AccessArticle

Optimal Site Selection for a Solar Power Plant in the Mekong Delta Region of Vietnam

by Chia-Nan Wang, Van Tran Hoang Viet, Thanh Phong Ho, Van Thanh Nguyen and Syed Tam Husain

Energies 2020, 13(16), 4066; https://doi.org/10.3390/en13164066 - 6 Aug 2020

Cited by 8 | Viewed by 3414

Abstract

Following the recent development trend in the struggle for cleaning the earth’s environment, solar is the one of most promising area that can partially be used as a replaceable energy from non-renewable fuel sources. As such, it plays a significant role in protecting the environment from global warming. As solar power does not emit harmful gases into the atmosphere, its production, distribution, setup, and operation are vital should the production remain constant. Even solar energy waste emissions are small; when compared to current energy sources, the amount of harmful gases is negligible. This paper presented an integrated approach for site of solar plants by using data envelopment analysis (DEA) and Fuzzy Analytical Network Process (FANP). Furthermore, these integrated methodologies, incorporated with the most relevant parameters of requirements for solar plants, are introduced. First, the paper considers an integrated hierarchical DEA and FANP model for the optimal geographical location of solar plants in Mekong Delta Region, Vietnam. Using the proposed model for implementation would allow the renewable energy policy makers to select and control the optimal location for allocating and constructing a solar energy power plant in Vietnam. This is the preferred strategy for location optimization problems associated with solar plant units in Vietnam and around the world. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Figure 1

20 pages, 5383 KiB

Open AccessArticle

Vector Speed Regulation of an Asynchronous Motor Based on Improved First-Order Linear Active Disturbance Rejection Technology

by Xuesong Zhou, Chenglong Wang and Youjie Ma

Energies 2020, 13(9), 2168; https://doi.org/10.3390/en13092168 - 1 May 2020

Cited by 16 | Viewed by 2617

Abstract

Asynchronous motors are widely used in industry and agriculture because of their simple structure, low cost, and easy maintenance. However, due to the coupling and uncertain factors of the actual operation of the motor, a traditional controller cannot achieve a satisfactory control effect. A linear active disturbance rejection controller (LADRC), featuring good robustness and adaptability, was proposed to improve the control efficiency of a nonlinear, uncertain plant. A linear extended state observer (LESO) is the core part of a L. The accuracy of the observation of state variables and unknown disturbances is related to the control effect of the controller. The performance of a traditional LESO is not high enough, and thus an error differential is introduced by analyzing the principle of LESO to improve its observation performance. The improved LADRC applies to the vector speed control of the induction motor. Additionally, low-speed and high-speed no-load starting, sudden load, electromagnetic torque, and three-phase stator current of the induction motor was simulated using MATLAB (Developed by MathWorks in Natick, MA, USA, and dealt by MathWorks Software (Beijing) Co., Ltd. in Beijing, China). Theoretical analysis and simulation results show that the ADRC based on the improved linear expansion observer was better than the traditional linear ADRC in terms of the dynamic and static performance and robustness. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Graphical abstract

22 pages, 2525 KiB

Open AccessArticle

Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN–LSTM

by Xiaorui Shao, Chang-Soo Kim and Palash Sontakke

Energies 2020, 13(8), 1881; https://doi.org/10.3390/en13081881 - 12 Apr 2020

Cited by 51 | Viewed by 5087

Abstract

Electricity consumption forecasting is a vital task for smart grid building regarding the supply and demand of electric power. Many pieces of research focused on the factors of weather, holidays, and temperatures for electricity forecasting that requires to collect those data by using kinds of sensors, which raises the cost of time and resources. Besides, most of the existing methods only focused on one or two types of forecasts, which cannot satisfy the actual needs of decision-making. This paper proposes a novel hybrid deep model for multiple forecasts by combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) algorithm without additional sensor data, and also considers the corresponding statistics. Different from the conventional stacked CNN–LSTM, in the proposed hybrid model, CNN and LSTM extracted features in parallel, which can obtain more robust features with less loss of original information. Chiefly, CNN extracts multi-scale robust features by various filters at three levels and wide convolution technology. LSTM extracts the features which think about the impact of different time-steps. The features extracted by CNN and LSTM are combined with six statistical components as comprehensive features. Therefore, comprehensive features are the fusion of multi-scale, multi-domain (time and statistic domain) and robust due to the utilization of wide convolution technology. We validate the effectiveness of the proposed method on three natural subsets associated with electricity consumption. The comparative study shows the state-of-the-art performance of the proposed hybrid deep model with good robustness for very short-term, short-term, medium-term, and long-term electricity consumption forecasting. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Graphical abstract

13 pages, 4810 KiB

Open AccessArticle

Current Balancing Algorithm for Three-Phase Multilevel Current Source Inverters

by Faleh Alskran and Marcelo Godoy Simões

Energies 2020, 13(4), 860; https://doi.org/10.3390/en13040860 - 16 Feb 2020

Cited by 8 | Viewed by 2253

Abstract

In high power, medium voltage applications, Current Source Inverters CSIs are connected in parallel to accommodate high DC currents. Using a proper multilevel modulation technique, parallel-connected CSIs can operate as a Multilevel CSI (MCSI). The most common modulation technique for MCSIs is the Phase-Shifted Carrier SPWM (PSC-SPWM). The proper operation of the MCSI requires each CSI modules to have the same average current flowing through its sharing inductors. In practice, the average currents of the CSI modules deviate from their nominal values. Therefore, current balancing mechanisms must be implemented. In the literature, several solutions have been proposed to tackle the current imbalance problem. Most of these solutions are based on altering the phase-shift or magnitude of the carrier waveforms of the PSC-SPWM. They require dedicated PI controllers and they are applicable to MCSIs with specific numbers of levels. This paper proposes a Current Balancing Algorithm (CBA) that can be implemented in any MCSI with any number of levels. The proposed CBA does not require any PI controllers, nor does it require any alteration to the PWM carrier waveforms. The CBA is implemented using a modified Level-Shifted SPWM (LS-PWM). The modified LS-SPWM is shown to produce lower THD and lower di/dt when compared to the PSC-SPWM. The CBA and modified LS-SPWM where implemented in a proof-of-concept lab prototype. The experimental results are presented for the five-level and seven-level cases. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Figure 1

13 pages, 429 KiB

Open AccessArticle

An Assessment of the Condition of Distribution Network Equipment Based on Large Data Fuzzy Decision-Making

by Ning Wang and Fei Zhao

Energies 2020, 13(1), 197; https://doi.org/10.3390/en13010197 - 1 Jan 2020

Cited by 17 | Viewed by 2396

Abstract

As one of the most important components of power grid, a distribution network is the most vulnerable part in the face of various uncertainties, and influences the stability and economy of a power system. In this paper, the operational information, hardware information and human factors were considered, and a state evaluation model of multi-source information fusion was established. Based on big data fuzzy iteration method and a weighted expert library, a weighted distribution of multi-source information was obtained, and an equipment condition assessment was carried out reasonably. Taking the distribution transformer as an example, the assessment showed that fusion of multi-source information presented in this paper is more comprehensive, and has the ability to reflect the state of equipment. The method proposed in this paper can accurately judge the running state of distribution equipment based on all kinds of information, and provides a reference for the follow-up power marketing for the status assessment of the user equipment. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Figure 1

18 pages, 4179 KiB

Open AccessArticle

Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid

by Xiaoquan Lu, Yu Zhou, Zhongdong Wang, Yongxian Yi, Longji Feng and Fei Wang

Energies 2019, 12(18), 3452; https://doi.org/10.3390/en12183452 - 6 Sep 2019

Cited by 54 | Viewed by 3425

Abstract

Non-technical losses (NTL) caused by fault or electricity theft is greatly harmful to the power grid. Industrial customers consume most of the power energy, and it is important to reduce this part of NTL. Currently, most work concentrates on analyzing characteristic of electricity consumption to detect NTL among residential customers. However, the related feature models cannot be adapted to industrial customers because they do not have a fixed electricity consumption pattern. Therefore, this paper starts from the principle of electricity measurement, and proposes a deep learning-based method to extract advanced features from massive smart meter data rather than artificial features. Firstly, we organize electricity magnitudes as one-dimensional sample data and embed the knowledge of electricity measurement in channels. Then, this paper proposes a semi-supervised deep learning model which uses a large number of unlabeled data and adversarial module to avoid overfitting. The experiment results show that our approach can achieve satisfactory performance even when trained by very small samples. Compared with the state-of-the-art methods, our method has achieved obvious improvement in all metrics. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Graphical abstract

18 pages, 2831 KiB

Open AccessArticle

Load Disaggregation Using Microscopic Power Features and Pattern Recognition

by Wesley Angelino de Souza, Fernando Deluno Garcia, Fernando Pinhabel Marafão, Luiz Carlos Pereira da Silva and Marcelo Godoy Simões

Energies 2019, 12(14), 2641; https://doi.org/10.3390/en12142641 - 10 Jul 2019

Cited by 30 | Viewed by 5299

Abstract

A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Graphical abstract

15 pages, 941 KiB

Open AccessArticle

Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

by Zhengwei Qu, Chenglin Xu, Kai Ma and Zongxu Jiao

Energies 2019, 12(13), 2463; https://doi.org/10.3390/en12132463 - 26 Jun 2019

Cited by 14 | Viewed by 2491

Abstract

In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Figure 1

14 pages, 1494 KiB

Open AccessArticle

Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model

by Aydin Jadidi, Raimundo Menezes, Nilmar de Souza and Antonio Cezar de Castro Lima

Energies 2019, 12(10), 1891; https://doi.org/10.3390/en12101891 - 17 May 2019

Cited by 21 | Viewed by 3234

Abstract

Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test the model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy. Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063, 65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Figure 1

17 pages, 6282 KiB

Open AccessArticle

Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

by Junwei Cao, Wanlu Zhang, Zeqing Xiao and Haochen Hua

Energies 2019, 12(8), 1556; https://doi.org/10.3390/en12081556 - 24 Apr 2019

Cited by 32 | Viewed by 3850

Abstract

The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of great significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Figure 1

15 pages, 7549 KiB

Open AccessArticle

Frequency Support of Smart Grid Using Fuzzy Logic-Based Controller for Wind Energy Systems

by Marcelo Godoy Simões and Abdullah Bubshait

Energies 2019, 12(8), 1550; https://doi.org/10.3390/en12081550 - 24 Apr 2019

Cited by 8 | Viewed by 3283

Abstract

This paper proposes a fuzzy logic-based controller for a wind turbine system to provide frequency support for a smart grid. The designed controller is aimed to provide an appropriate dynamic droop rate depending on the local measurements of each wind turbine of a wind farm such as the maximum power available and the amount of power reserve. The designed fuzzy controller depends on the rate of change of frequency (ROCOF) at the point of common coupling (PCC). The main advantage of the proposed fuzzy controller is to provide frequency support by the wind turbine system connected to a smart grid. The dynamic rate of the controller is defined by the fuzzy sets considering the change in the grid’s frequency and the available reserve power. First, the response of static droop curves is investigated for different scenarios of wind turbines connected to a smart grid. Then, the proposed fuzzy logic-based droop controller is integrated into the system, and its performance and response are evaluated, and the results are compared with static-droop based controller. The proposed controller is tested using Matlab\Simulink. Full article

(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)

► Show Figures

Journal Menu

Journal Browser

Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI