fuzzy logic

In islanded microgrids, circulating currents among parallel inverters pose significant challenges to system stability and efficient power distribution. Traditional droop control methods often struggle to manage these currents effectively, leading to inefficiencies and potential system damage. This study introduces an advanced fuzzy-robust droop control strategy that integrates fuzzy logic with robust droop control to address these challenges. By incorporating fuzzy logic, the proposed strategy enhances the adaptability of droop control to varying system conditions, improving the management of circulating currents and ensuring more accurate power sharing among inverters. Comprehensive mathematical modeling and extensive simulation analyses validate the performance of this control strategy. The results show that the fuzzy-robust droop control method significantly outperforms conventional approaches, achieving up to a 70% reduction in circulating currents. This improvement leads to a substantial reduction in power losses and enhances the dynamic response under varying load conditions. Additionally, the strategy improves voltage and frequency regulation, contributing to the overall stability and reliability of the microgrid. The findings provide a robust solution to the longstanding issue of circulating currents, optimizing microgrid operations, and paving the way for more efficient and resilient distributed energy systems. The advanced control strategy presented in this study not only addresses critical challenges but also demonstrates the potential for innovative methodologies to meet the growing demands of future energy infrastructures, where reliability and efficiency are essential.

In this research paper, a multivariable prediction control method based on direct vector control is applied to command the active power and reactive power of a doubly-fed induction generator used into a wind turbine system. To obtain high energy performance, the space vector modulation inverter based on fuzzy logic technique (fuzzy space vector modulation) is used to reduce stator currents harmonics and active power and reactive power ripples. Also the direct vector control model of the doubly-fed induction generator is required to ensure a decoupled control. Then its classic proportional integral regulators are replaced by the multivariable prediction controller in order to adjust the active and reactive power. So, in this work, we implement a new method of control for the doubly-fed induction generator energy. This method is carried out for the first time by combining the MPC strategy with artificial intelligence represented by Fuzzy SVM-based converter in order to overcome the drawbacks of other controllers used in renewable energies. The given simulation results using Matlab software show a good performance of the used strategy, particularly with regard to the quality of the energy supplied.

The Power Quality (PQ) issue refers to the occurrence of irregular voltage, current, or frequency that leads to failure or incorrect functioning of equipment used by end users. The PQ meter is utilized to monitor a diverse range of power supply characteristics, all of which possess the capacity to impact the effectiveness of both operational procedures and machinery. The dynamic voltage restorer (DVR) performs the role of a specialized power device employed to mitigate the voltage drop experienced at the terminal of a sensitive load. DVR can be controlled by various control designs. This work conducts a comparative analysis on a normally managed voltage system and a medium-power DVR controlled by a neural network (NN), fuzzy logic (FL), or adaptive neuro-fuzzy inference system (ANFIS) by utilizing an output voltage regulator. The identification and rapid compensation of voltage perturbations, such as voltage sag, are essential elements in monitoring and controlling DVRs. The conventional PI controller is commonly employed in regulating DVRs. While the traditional controller possesses certain merits, it is not free of limitations. One such downside pertains to its utilization of constant gains, which can impede its capability to provide optimal control performance in instances where system parameters undergo fluctuations. Possible solutions have been proposed to effectively tackle this issue, such as the use of NNs, FL, or ANFIS controllers. Furthermore, to attain both rapid dynamic response and robustness, a modified d-q converted three-phase voltage regulator was adopted. Instead of employing a conventional three-phase regulator, this particular regulator is operated by means of an NN, FL, ANFIS, or PI controller. The suggested voltage regulator offers a prompt solution for rectifying voltage irregularities, such as voltage sag, by promptly restoring the voltage to the nominal magnitude. The primary source of power adopted in this study is a wind turbine unit.

Fire accidents are a disaster that can cause loss of life, property damage and permanent disability to the affected victim. Firefighting is a very important and dangerous job. Firefighters must extinguish the fire quickly and safely to prevent further damage and destruction. Detecting and extinguishing fires is a dangerous task that always puts the lives of firefighters at risk. One of the most effective tools for early fire extinguishing is the firefighting robot. Fire sensing in most industries is absolutely essential to prevent catastrophic losses. Robots with this type of embedded system can save the lives of engineers in industrial sites with hazardous conditions. This project aims to design and implement a solar-powered  with artificial intelligent of mobile fire detection robot to detect fires in disaster-prone areas and thus reduce human work effort and level of destruction. Design a robot capable of moving using a rotary motor, finding a flame using a flame sensor, and extinguishing a fire using a water spray using a pump, all of which is controlled by an Arduino Uno microcontroller and programmed using an artificial intelligence (fuzzy) logic technology) using MATLAB, the inputs It has two variations:: flame and gas with three organic functions, each of which has a gas variable (low, medium, high), flame sensor (small, normal, large), and the output is a pump, (pump off , pump on ) with 9 rules. In addition to the experimental setup of the proposed system which demonstrates the performance of sensors (gas, flame) using fuzzy and implemented logic tools. The performance of the solar panels was first tested using MATLAB software as well as experimentally under different weather conditions. The pump's performance is being tested experimentally, and the robot is also being tested to detect and extinguish fires. The process of designing and implementing robotics involves creating mechanical and electrical systems. The results showed the effect of temperature change on the solar panel, as when it increases, the panel’s production capacity decreases, as well as the effect of decreased solar radiation resulting from clouds and other things, and the extent of its effect. Impact on the performance efficiency of solar panels, and observing the pump performance in terms of flow rate and height. Hence, it can be noted that the robot designed in the project is capable of discovering fire sources and extinguishing them using fire-fighting systems equipped with a water tank and a controllable pump to spray the water necessary for the process. From this study, can be concluded that the designed model is able to work according to its initial design  with artificial intelligence  with the least amount of errors, and therefore it can be applied in industrial applications, avoiding fire damage and extinguishing it when it occurs for the first time.

New technologies have emerged over the last few years, such as IoT and fog computing. IoT devices and the enormous amounts of data generated every minute have led to the vast growth of the Internet of Things (IoT). In order to meet the term "Data Never Sleeps", some IoT applications require real-time services and low bit latency. In order to provide quick processing, storage, and services, Cisco proposed fog computing as an extension of cloud computing. The traditional methods are not capable of addressing the complex scheduling scenarios of fog computing. In this paper, we introduce a novel Fuzzy Reinforcement Learning Scheduling algorithm (FRLS) that enhances schedule accuracy in dynamic computing environments. In order to optimize task scheduling, the FRLS algorithm integrates fuzzy logic with reinforcement learning. In order to prioritize critical tasks, fuzzy logic handles uncertainty and prioritizes tasks according to deadlines, sizes, and file sizes. Then, reinforcement learning schedules the prioritized tasks, continually adjusting to dynamic conditions to ensure the best resource allocation. In addition to improving overall system performance, this combination provides a robust framework that can address the complexity and variability of fog computing environments. FRLS is designed to minimize response time while adhering to resource and deadline constraints in fog-based applications. A comparison of FRLS with existing algorithms shows that it significantly improves load balancing, deadline satisfaction, response time, and waiting time. Combining reinforcement learning and fuzzy logic leads to an efficient scheduling solution. In addition, FRLS outperforms non-prioritized algorithms.

Iran is a vast country rich with energy resources. Perhaps, such unlimited energy resources were the reason to keep us in a dream of abundance, and as a result, neglecting justifiable energy conservation. Buildings are one of the main energy consumption and waste sources, and regretfully, still many of them are constructed violating modern engineering rules and solely through experimental and traditional methods. Air conditioning systems are still calculated and designed using estimation. However, we have to know such methods are obsolete in developed countries since ages ago, and we shall commence with a new determination and effort right away if we would like to reach the Global trend. Fortunately, many of the ways have been taken before us, and have certain instructions. In late 20th century and upon development of smart technologies, development of communication and internet networks, sensor networks and sensors, extensive efforts and studies started to use such group of technologies to present solutions to improve humans lives. Using IOT to control the house smartly has been one of the study fields during recent years. The suggested method tries to improve the house smart control for energy conservation. The used sensors are heat and humidity sensors with the duty of monitoring smart home. In this article we plan to suggest optimized smart method to control smart sustainable common home relays smartly. The sensors data is entered into the sustainable common home smart control system and is on/off considering the house relays smart systems algorithm.

Autism spectrum disorder (ASD) is a collection of inconstant characteristics. Anomalies in reciprocal social communications and disabilities in perceiving communication patterns characterize These features. Also, exclusive repeated interests and actions identify ASD. Computer games have affirmative effects on autistic children. Serious games have been widely used to elevate the ability to communicate with other individuals in these children. In this paper, we propose an adaptive serious game to rate the social skills of autistic children. The proposed serious game employs a reinforcement learning mechanism to learn such ratings adaptively for the players. It uses fuzzy logic to estimate the communication skills of autistic children. The game adapts itself to the level of the child with autism. For that matter, it uses an intelligent agent to tune the challenges through playtime. To dynamically evaluate the communication skills of these children, the game challenges may grow harder based on the development of a child's skills through playtime. We also employ fuzzy logic to estimate the playing abilities of the player periodically. Fifteen autistic children participated in experiments to evaluate the presented serious game. The experimental results show that the proposed method is effective in the communication skill of autistic children.

The use of solar energy has begun to be developed in PLTS, but the photovoltaic module electricity produced is not at maximum output power. To increase the efficiency of the photovoltaic module, Maximum PowerPoint Tracking (MPPT) technology is used. Differences in the level of solar energy irradiation can cause the output power of the solar panels to vary and will not be maximized. Changing temperature and irradiation can be maintained with a maximum voltage of 40Volt and according to what is desired. In this study, MPPT consists of a Boost Converter whose function is to regulate the output voltage, while the algorithm used is the fuzzy logic which works based on Error (E) and Change Error (CE) from changes in the voltage and current of the photovoltaic module. The results showed that after using MPPT when input was given a change in load resistance with irradiation of 1000 W/m2 and a temperature of 25 ℃ resulted in a difference in power under different conditions compared to a system without MPPT. The power generated without the use of MPPT has a significant change with the results of 227.7W, 114.4W, 76.5W, 57W, and 45.5W. Testing the system after installing the MPPT when given an input change in irradiation with a load resistance of 20 Ω and a temperature of 25°C, a more stable power is produced with a value of 0.008W. Then when the input changes to irradiation with a load resistance of 20 Ω and a temperature of 25 ℃, the maximum power produced for each of the highest irrigation is 746.9 W/m2, 779.4 W/m2, and 839.4 W/m2 of 38.88 W, 42.07 W, and 47.8 W and compared to the system without MPPT only 21.76W, 21.96W and 22.28W.

Globally, aluminium alloys are being used in many industries. Application of aluminium alloys is realized by many manufacturing processes in which joining processes are inevitable. Joining of aluminium alloys is achieved by various welding processes. One of the appropriate welding processes used to join aluminium alloy is Gas Metal Arc Welding (GMAW). This paper investigates the effect of process parameters of the GMAW process while welding AA 6351 aluminium alloy weldment with the help of an integrated Taguchi–Grey–Fuzzy approach. Taguchi L-16 array was designed by using an orthogonal method to conduct the experiments. From the experimental results, Signal-to-Noise Ratios (S/N ratio) were calculated from which Grey Relational Grades (GRG) were computed. These computed Grey Relational Grades were used as input for the fuzzy controller to find the Grey Fuzzy Relational Grades (GFRG), by which optimized process parameters were found and validated. Furthermore, Analysis of Variance (ANOVA) was used to identify the contributions of the GMAW process parameters over the responses. Subsequently, the effects of process parameters on the weldments were also discussed in detail. By identifying the optimized process parameters and contributing process parameters, the quality of weld joints is improved.

Fuzzy logic can be arranged concerning practical experiences and blended with conventional control techniques. Even though it is not the replacement of conventional control methods in many cases, a fuzzy control system eases the implementation and design process. It has also been put in use in other matters like traffic control. Increasing vehicles and insufficiency of passages capacity have led to widespread traffic emergence. While it is very difficult to widen existing roads, optimizing traffic like control schemas is still possible. This paper assumes a common four-directional crossroad where vehicles can move in a bidirectional way from each direction. A Sugeno fuzzy logic set of rules is presented to regulate the timing schedule of green lights for the crossroad concerning the vehicle accumulation at each line.