Fuzzy Logic Techniques Research Articles

Fuzzy sliding mode power control for wind power generation systems connected to the grid

In recent years we have witnessed a real increase in the production of wind turbines and wind farm installations around the world, in order to improve this own energy, several studies have focused on the interest of controlling the active and reactive power of the system. Wind power, and at the same time on the quality of the energy produced and its connection in order to ingest suitable electrical energy into the distribution network. This article studies a new control technology to meet the various constraints in the field. The objective for work is to develop and study the sliding mode control method applied to a wind power system based on doubly fed induction generation (DFIG), as well as an optimization using the fuzzy logic technique. Ensuring the stability of the system is one of the objectives of using the Lyapunov nonlinear technique in the sliding mode control strategy which will be applied to the two converters (machine side and network side). The proposed solution was to validate a simulation on MATLAB/Simulink, tracking test (true wind speed) and also the robustness of the system.

Fuzzy Logic and Deep Learning Integration in Likert Type Data

Deep learning networks have many modern applications and demonstrate a high-performance level. As the applications of deep learning networks to real-world problems continues to spread, the reason why they are effective remains unknown. However, it is possible to make some judgments by examining the behaviour of the network in experiments. The main aim of this study is to analyse the performance of deep learning techniques in the form of a 5-point Likert-type scale by converting the artificial data sets into a fuzzy form using triangular or trapezium fuzzy numbers. To test the performance of the proposed model, which is the integration of deep learning and fuzzy logic techniques, the satisfaction estimation problem was chosen. Data sets consisting of fuzzy numbers which reach at least three or four times more parameters than normal data sets. Thus, it decreases the possibility of falling into the local optimum trap in optimization studies with big data. In the analysis conducted with deep learning, in accordance with the fuzzification examples in the literature, the defuzzification was carried out with separate results for peak, maximum, and minimum values. In contrast to the literature, the performances of the deep learning model were investigated by suggesting that fuzzy numbers produce a single result series.

A Proposal for the Diagnosis of Incipient Faults in Power Transformers Using Fuzzy Logic Techniques

The availability of power transformers is essential for the safety and continuity of electrical service. Today's fault diagnosis methods use intelligent techniques such as neural networks, support machines, hybrid techniques, among others. Although they present good results, these techniques find restrictions in the ability to determine the precise moment in the event of multiple and small-magnitude faults. The proposal includes a new algorithm based on fuzzy rules that incorporates the daily increase of dissolved gases in the transformer oil that improves the classification of incipient faults. With reliable samples of gas dissolved in oil, the method proposed in the research can obtain a total precision rate of 91.4%. In contrast, this degree of precision is lower in other conventional methods reported in the bibliography. In addition, its performance in the classification of multiple failures is 97.5%. The method uses fuzzy logic tools to suggest actions aimed at preventive maintenance by monitoring the total of combustible gases dissolved in the oil. The proposal is a simple and easy solution to implement in practice that allows determining the status of the transformer in service without affecting the continuity of the electricity supply.

Application of artificial intelligence for the prediction of plain journal bearings performance

Artificial intelligence techniques are applied to predict the performance of a plain journal bearing instead of classical methods. AI techniques are known to be superior for prediction; they are accurate and fast compared to finite difference, finite element, and finite volume methods. To obtain the data needed for the AI prediction, the finite difference method is used to solve the dimensionless Reynolds equation at various aspect ratios. The bearing performance characteristics, such as load-carrying capacity, attitude angle, friction variable, and maximum-film-pressure ratio, are determined considering isothermal conditions. Four aspect ratios are considered from 0.25 to 4, with eccentricity ratios varying between 0.2 and 0.8. Three artificial neural networks (Feed-forward, Radial basis, and Generalized regression networks) and fuzzy logic techniques were applied to the obtained data from FDM simulation to predict the performance parameters. The three trained neural networks and the fuzzy system were tested to obtain the performance characteristics for aspect ratios and eccentricity ratios that were not included in the FDM study. The current response of the trained ANN models and the fuzzy logic technique is found to be very fast and precise, with a prediction computational time of less than one second and an error of less than 2.5 percent.

Automatic Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Improved Discrete Particle Swarm Optimization.

Diabetic retinopathy (DR) is one of the most important microvascular complications associated with diabetes mellitus. The early signs of DR are microaneurysms, which can lead to complete vision loss. The detection of DR at an early stage can help to avoid non-reversible blindness. To do this, we incorporated fuzzy logic techniques into digital image processing to conduct effective detection. The digital fundus images were segmented using particle swarm optimization to identify microaneurysms. The particle swarm optimization clustering combined the membership functions by grouping the high similarity data into clusters. Model testing was conducted on the publicly available dataset called DIARETDB0, and image segmentation was done by probability-based (PBPSO) clustering algorithms. Different fuzzy models were applied and the outcomes were compared with our probability discrete particle swarm optimization algorithm. The results revealed that the proposed PSO algorithm achieved an accuracy of 99.9% in the early detection of DR.

Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: A comprehensive and systematic study.

To overcome the need of the world for energy consumption, we have to find some better and stable alternate ways of renewable energy with advanced technology. The most readily available source of energy is solar energy but solar energy has nonlinear nature due to the random nature of climate conditions. So, one way to solve is solar radiation prediction and solar energy prediction using more accurate techniques. Also, energy business and power system control units require more accuracy along with very short to large duration prediction in advance. So, to complete the requirement many prediction techniques are used and among them, Artificial Neural Network (ANN) and Fuzzy are more accurate and reliable techniques. In this paper basically, a literature study for solar radiation and energy prediction using ANN and Fuzzy logic techniques has been carried out. Many studies are reviewed and then selected some most accurate, reliable, and relevant studies for further study. ANN models with different algorithms such as feed-forward back-propagation-based ANN, Multi-layer feed-forward-based ANN model, Linear regression with ANN model, GNN-based model are reviewed in the study. ANN models with different input parameters combinations and the different number of neurons were also reviewed. Fuzzy logic-based and Adaptive Neuro-Fuzzy interface (ANFIS)-based different models have been reviewed and observed that the ANFIS technique performs better. From the study, it has been noted that ANN and Fuzzy logic employed models are most effective for estimation than any other empirical models. It is found that solar radiation and energy prediction models are dependent on input parameters more. At last, highlighted some possible research opportunities and areas for better efficiency of the results.

A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique

Cloud security vulnerabilities have recently become more prevalent around the world, posing a threat to cloud service providers' (CSPs) ability to respond to client demands. In cloud market, the requests are announced by the client nodes to their CSP. A malicious node can alter a client's request, resulting in the next cloud market collapse, decreased reliability, and data leaking.To identify malicious nodes in the cloud market, a novel fuzzy multiple criterion decision making scheme is suggested. Authentication test, trust level, traffic size, and node activity levels are all taken into consideration simultaneously as the major criteria for identifying malicious nodes. For each node, the CSP uses fuzzy Integral to generate a composite value based on these criteria. The malicious node is then removed from the cloud market using this composite value. The simulation results demonstrated the potential of the proposed method to prevent nodes in the cloud market from running malware or software that can be used to degrade quality of service by exhausting resources in the cloud market.

Design and Analysis of Renewable Energy Microgrids for Operations in Different Latitudes by Applying Fuzzy Logic Modeling

The modeling results presented in this work used fuzzy logic techniques to design renewable energy microgrids for operation in six cities worldwide: Dwarka, India; Shanghai, China; Milwaukee, United States; Rostock, Germany; Copenhagen, Denmark, and Kamaishi, Japan. A meticulous study on solar photovoltaic, wind potential, power demand, and population density was executed as part of this study. The results reveal that for a microgrids operative scenario comprising 50% photovoltaic solar energy and 50% wind power energy, the amount of energy that must be supplied to the six cities varies from 17,031.46 to 160,971.25 kWh/month, and the number of photovoltaic panels varies between 126 and 6,444. The number of wind turbines required varies between 3 and 11. The most significant amount of photovoltaic solar energy (546,366.41 kWh/month) generated from the microgrids was reported in Rostock, Germany. The most significant amount of generated wind power energy (277,012.15 kWh/month) was reported in Milwaukee, United States. Environmental assessments revealed that the highest amount of reduction in CO2 emission (217.88 tonnes) was obtained from the Rostock (Germany) microgrid. The implementation of such microgrids costs ~US$ 3,903,724.

Application of Soft Computing Techniques to Estimate Cutter Life Index Using Mechanical Properties of Rocks

The wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear could be expressed in terms of the life of the tool used to excavate rocks in hours or cutter per unit volume of excavated materials. The aim of this study is to estimate disc cutter wear as a function of common mechanical rock properties including uniaxial compressive strength, Brazilian tensile strength, brittleness, and density. To achieve this goal, a database of cutter life was established by analyzing data from 80 tunneling projects. The data were then utilized for evaluating the relationship between rock properties and cutter consumption by means of cutter life index. The analysis was based on artificial intelligence techniques, namely artificial neural networks (ANN) and fuzzy logic (FL). Furthermore, linear and non-linear regression methods were also used to investigate the relationship between these parameters using a statistical software package. Several alternative models are introduced with different input variables for each model, to identify the best model with the highest accuracy. To develop these models, 70% of the dataset was used for training and the rest, for testing. The estimated cutter life by various models was compared with each other to identify the most reliable model. It appears that the ANN and FL techniques are superior to standard linear and non-linear multiple regression analysis, based on the higher correlation coefficient (R2) and lower Mean square error (MSE).

Comparative performance analysis of Fuzzy Logic and Particle Swarm Optimization (PSO) techniques for image quality improvement: With special emphasis to old and distorted folk paintings

Cultural artwork has always held an important place in the society. These artworks hold within themselves a treasure trove of information about one’s society/community, their way of living and their history and many more things. However such artworks are not properly maintained and need to be carefully preserved. Preserving them digitally can give them a long life and the digital image processing techniques can be used to restore or enhance the quality of degraded artworks. This paper presents a comparison of two such techniques, PSO (Particle Swarm Optimization) and Fuzzy logic technique for enhancement of the distorted images. Image parameters like PSNR (Peak-to-Signal Noise Ratio), SSIM (Structure Similarity Index), AMBE (Absolute Mean Brightness Error), r (Pearson Correlation Coefficient), NMSE (Normalized Mean Square Error), IEF (Image Enhancement Factor) and CPP (Contrast per Pixel) have been tabulated to compare the performance of both the techniques. MATLAB software has been used for implementation of the algorithms and calculations of the parameters.

FUZZY-LOGIC-BASED ERGONOMIC ASSESSMENT IN AN AUTOMOTIVE INDUSTRY

The objective of this study was to develop and evaluate a tool for measuring an organisation’s ergonomic level. The study was carried out to fill a research gap in which no attempt had been made to design an index for evaluating a company’s ergonomics. An ergonomic index measuring model with 15 criteria using a fuzzy logic technique was developed during this study. The fuzzy ergonomic index (FEI), which represents the ergonomic level of the company, and the fuzzy performance importance index, which assists in resolving ergonomics barriers, were then calculated. The findings show that the model is capable of analysing ergonomics adequately and has practical application. Application of the methodology presented in this paper will reveal the measures necessary to improve an organisation’s ergonomic level. Keywords Fuzzy Ergonomic Index; Manufacturing; Fuzzy Logic; Ergonomic Assessment;

Application of fuzzy logic technique to track maximum power point in photovoltaic systems

The use of photovoltaic (PV) systems for generating electric power is increasing in our everyday lives but since the generated voltage and current vary non linearly it has been very difficult to trace the maximum power point (MPP) of the PV systems so to overcome with this problem many power tracking methods were introduced out of which fuzzy logic technique was found to be one of the easy and efficient maximum power point tracking (MPPT) method. In this paper, various MPPT algorithms are observed how they help in improving the efficiency of PV systems by adjusting the duty ratio of the power interface, and also understand why the fuzzy logic control (FLC) technique is preferred over other algorithms. The system was established using MATLAB/Simulink.

SAFRank: Multi-Agent based Approach for Internet Services Selection

In the era of modern world, organization are preferring to adopt smart solutions for their business tasks and managing huge and complex transactions. These solutions are provided through online application infrastructures of Internet of Things (IoT), cloud, fog, and edge computing. In the presence of numerous prospects, the selection benchmark for such offers becomes vibrant, especially, when there is no supportive platform available. Prevailing approaches provide services by evaluating the quality of service parameters, K-Nearest Neighbours (KNN) classifications, k-mean clustering, assigning scores, trustworthiness and fuzzy logic techniques on customer's feedback. However, these approaches classically depend on seeker’ feedback and do ‘not consider interrelationship between the services. Secondly, these techniques do not follow standards derived by well-known organizations like National Institute of Standards and Technology (NIST), International Organization for Standards (ISO), and IEEE. Feedback may be self-generated or biased and leading to inappropriate recommendation to end users. To resolve the issue, we propose multi agent based approach using service association factor that computes interrelationship values among services appearing together in a package as SAFRank and evaluates it on standards along with dynamically defined quality of service parameters. It assists seekers to select the best services on their preferences from pool of IoT and internet services. The technique is tested on leading cloud vendors and results show that it meets the desires of service seekers in all service models in an efficient manner.

Optimal Tuning of FOPID-Like Fuzzy Controller for High-Performance Fractional-Order Systems

This paper addresses improvements in fractional order (FO) system performance. Although the classical proportional–integral–derivative (PID)-like fuzzy controller can provide adequate results for both transient and steady-state responses in both linear and nonlinear systems, the FOPID fuzzy controller has been proven to provide better results. This high performance was obtained thanks to the combinative benefits of FO and fuzzy-logic techniques. This paper describes how the optimal gains and FO parameters of the FOPID controller were obtained by the use of a modern optimizer, social spider optimization, in order to improve the response of fractional dynamical systems. This group of systems had usually produced multimodal error surfaces/functions that occasionally had many variant local minima. The integral time of absolute error (ITAE) used in this study was the error function. The results showed that the strategy adopted produced superior performance regarding the lowest ITAE value. It reached a value of 88.22 while the best value obtained in previous work was 98.87. A further comparison between the current work and previous studies concerning transient-analysis factors of the model’s response showed that the strategy proposed was the only one that was able to produce fast rise time, low-percentage overshoot, and very small steady-state error. However, the other strategies were good for one factor, but not for the others.

A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN

Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested method’s purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA-CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA-CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs.

Performance Improvement of Grid Interfaced Hybrid System Using Distributed Power Flow Controller Optimization Techniques

The main aim of this paper is to introduce a framework for the design and modelling of a photovoltaic (PV)-wind hybrid system and its control strategies. The purpose of these control techniques is to regulate continuous changes in the operational requirements of the hybrid system;currently, in power system networks, the distribution of energy plays a major role in maintaining power reliability in distribution systems. In this study, the proposed hybrid system was incorporated with a combined PV and wind energy system. Maximum power point tracking (MPPT) methods have been proposed to achieve maximum efficiency from the designed system. In addition, this study focused on improving the stability of the hybrid system. To improve the power quality and transient stability of the proposed system, we introduce a novel control strategy called the distributed power flow controller (DPFC) implementation with an optimization technique called the lion optimization algorithm(LOA)technique. This LOA control technique was developed for the first time in the application of a DPFC controller in a grid-connected system. The control technique was developed using signals from the system parameters, that is, voltage and current. To tune these parameters, this study used fuzzy logic and lion optimization techniques. The proposed system with controllers was tested in MATLAB/Simulink and the results were compared.

Artificial intelligence in mobile communication: A Survey

In this paper, we elaborate on artificial intelligence (AI) techniques used to improve the performance of mobile communication. This article describes brief AI approaches in mobile communication, several classics AI techniques, and the current AI approaches in wireless communication. The techniques contain fuzzy logic, neural networks, reinforcement learning, and AI techniques implemented on mobile communication. Some keys or terms challenges between AI and future mobile communication, not only 5G generation issues but also how the sixth generation (6G) of mobile networks will be driven to give stable networks and service types on huge mobile devices and data.

Duty cycle modulation - fuzzy logic technique to track the maximum power point of a solar-wind hybrid Source

In this article, we propose a strategy for finding the maximum power point (MPP) of a hybrid plant (solar and wind), in order to maximize the power extracted from this production plant. This strategy exploits the perturbation and observation method, based on fuzzy logic coupled with the Duty Cycle Modulator (DCM). The main objective of this study is to extract the maximum power from this hybrid power plant, while ensuring the precision and speed of convergence towards this point of maximum power. This method, tested under the Matlab / Simulink environment for a 160 kW hybrid power plant, gave results that we compared to those obtained with the Fuzzy - PWM (Pulse Width Modulator) strategy. It emerges that the Fuzzy-DCM strategy gives better precision (around 2.6 times) and a speed (around 2 times) of convergence compared to the Fuzzy-PWM strategy.

Modeling and analysis of flexural strength with fuzzy logic technique for a fused deposition modeling ABS components

Fused Deposition Modeling (FDM) is an additive manufacturing technique used for prototype, physical modeling, and end-of-line production. Proper parameter selection is essential for producing high-quality items with suitable mechanical properties, such as flexural strength. Four critical process variables, such as layer thickness, raster width, raster angle, and orientation angle, are investigated in this study. The link between these factors and the flexural strength of the Acrylonitrile Butadiene Styrene (ABS) printed components are analysed using analysis of variance. The fuzzy logic (FL) approaches were used to develop a prediction model. The test samples are made on a 3D forge dreamer II printing machine of the FDM type. In Minitab software, the Taguchi mixed L18 orthogonal design technique was used to organize the trials. The result showed that the raster angle has the most significant parameter affecting flexural strength when compere with layer thickness, raster width and orientation angle. The fuzzy logic models provided high accuracy prediction of flexural strength with the maximum percentage error of 3.62%, and this revealed that the model and the experimental data were in good agreement.

High Voltage Device Design and Predictive Maintenance Software based on Fuzzy-Logic

The goal of this study is to examine some important and vital issues regarding the preventative upkeep of high-voltage equipment (switches, surge arresters, etc.). Real data was principally gathered from the industrial environment in order to develop a monitoring and diagnostic digital-based technique and accurately analyze the technical status of their major HV connections. All of the techniques described here may be used with other high voltage components, such as separators and surge arresters. A simple fuzzy-logic technique is used to identify the essential elements of predictive maintenance. This method, which could be put into practice as software, may be used for any high voltage equipment maintenance. It might be used as an example of how cutting-edge software and mathematics can be used to update high-voltage apparatus that is already in place.