Fuzzy Logic Membership Functions Research Articles

Adaptive neuro fuzzy technology to enhance PID performances within VCA for grid-connected wind system under nonlinear behaviors: FPGA hardware implementation

Wind Energy Conversion Systems (WECSs) exhibit nonlinear behavior due to internal and external disturbances, which can significantly impact the control performance. Firstly, this paper proposes a Vector Control Approach (VCA) based on Adaptive Neuro Fuzzy (ANF) of Proportional Integral Derivative (PID) controllers for a variable speed Permanent Magnet Synchronous Generator (PMSG)-based WECS connected to the grid to address these issues. The proposed ANF-PID controller combines the robustness of the fuzzy logic with the neural network's performance to ensure high accuracy, excellent tracking, and robustness against the WECS's nonlinear behavior. The neural network automates the placement of fuzzy logic membership functions. Secondly, this paper suggests a real-time hardware implementation of the VCA based on ANF-PID using a Field Programmable Gate Array (FPGA) to reduce the processing time and the WECS's sampling period. The effectiveness of the VCA based on ANF-PID controllers is validated by conducting a numerical simulation within the Matlab-Simulink using the Xilinx System Generator tool, alongside an experimental in-the-loop hardware implementation using FPGA board. Different scenarios are conducted to validate the robustness and stability of the proposed VCA based on ANF-PID control schemes, which is compared to Conventional Fuzzy PID (C-FPID) and PID controllers. The results exhibit that the VCA based on ANF-PID controllers outperforms VCA-C-FPID and VCA-PID controllers in terms of precision, tracking accuracy, and robustness against the WECS's nonlinear behavior and the PMSG parameter variations. The performances of the proposed controllers are tested under some criteria. In fact, the proposed ANF-PID controller provides a Mean Absolute Error (MAE), a Mean Squared Error (MSE) and a Root Mean Squared Error (RMSE) of 8.1911 × 10−04, 8.4181 × 10−05 and 0.0092, respectively. Compared to the C-FPID controller, the proposed controller offers an improvement gain of 98.96 %, 99.61 %, and 93.79 % of MAE, MSE, and RMSE, respectively. When compared to the PID controller, its improvement is even more significant, with a reduction of 99.9919 %, 99.9999 %, and 99.9288 % of MAE, MSE, and RMSE, respectively. In addition, the ANF-PID-based VCA utilizes hardware resources on the FPGA efficiently, requiring only 65.60 % slices LUT, 0.28 % LUTRAM, and 22.99 % FF. This makes it a more cost-effective option compared to the VCA-C-FPID controller for diverse control systems.

Optimal State Estimation via Adaptive Fuzzy Particle Filter

Particle Filters (PF) accomplish nonlinear system estimation and have received high interest from numerous engineering domains over the past decade. The main problem of PF is to degenerate over time due to the loss of particle diversity. One of the essential causes of losing particle diversity is sample impoverishment (most of particle’s weights are insignificant) which affects the result from the particle depletion in the resampling stage and unsuitable prior information of process and measurement noise. To address this problem, a new Adaptive Fuzzy Particle Filter (AFPF) is used to improve the precision and efficiency of the state estimation results. The error in AFPF state is avoided from diverging by using Fuzzy logic. This method is called tuning weighting factor (α) as output membership function of fuzzy logic and input memberships function is the mean and the covariance of residual error. When the motion model is noisier than measurement, the performance of the proposed method (AFPF) is compared with the standard method (PF) at various particles number. The performance of the proposed method can be compared by keeping the noise level acceptable and convergence of the particle will be measured by the standard deviation. The simulation experiment findings are discussed and evaluated.

Evaluation of Drinking Water Quality Using Fuzzy Logic in Sulaimani City, Kurdistan, Iraq

Water quality assessment is crucial for maintaining the health and well-being of communities. In this study, Fuzzy Logic Water Quality Index (FWQI) utilized as a tool to assess the water quality in Sulaimani City, Kurdistan, Iraq. FWQI combines the flexibility and interpretability of fuzzy logic with the ability to handle imprecise and uncertain data, making it suitable for assessing complex systems such as water quality. Daily data of eleven water parameters from 14-water tanks location across Sulaimani city were evaluated for the duration of 2011 to 2021. The identified parameters includes pH, TDS, EC, TA, TH, Ca, Mg, Cl, Na, K, and SO4 . Fuzzy logic membership functions used to represent the linguistic variables associated with each parameter, allowing for the conversion of numerical data into fuzzy sets. A set of fuzzy rules established to map the fuzzy sets of the input parameters to an overall water quality index. The formulated rules were based on expert knowledge and existing water quality guidelines. The Fuzzy Logic Inference Index (FLII) outcomes were validated by comparing them with conventional water quality indices and expert judgments, demonstrating the effectiveness and accuracy of the proposed approach. The FWQI values of the study area showed that the drinking water quality of all selected tanks are suitable for drinking. The quality classes of water ranges between acceptable to desirable.

First-of-Its-Kind Frequency Enhancement Methodology Based on an Optimized Combination of FLC and TFOIDFF Controllers Evaluated on EVs, SMES, and UPFC-Integrated Smart Grid

The most recent advancements in renewable energy resources, as well as their broad acceptance in power sectors, have created substantial operational, security, and management concerns. As a result of the continual decrease in power system inertia, it is critical to maintain the normal operating frequency and reduce tie-line power changes. The preceding issues sparked this research, which proposes the Fuzzy Tilted Fractional Order Integral Derivative with Fractional Filter (FTFOIDFF), a unique load frequency controller. The FTFOIDFF controller described here combines the benefits of tilt, fuzzy logic, FOPID, and fractional filter controllers. Furthermore, the prairie dog optimizer (PDO), a newly developed metaheuristic optimization approach, is shown to efficiently tune the suggested controller settings as well as the forms of the fuzzy logic membership functions in the two-area hybrid power grid investigated in this paper. When the PDO results are compared to those of the Seagull Optimization Algorithm, the Runge Kutta optimizer, and the Chaos Game Optimizer for the same hybrid power system, PDO prevails. The system model incorporates physical constraints such as communication time delays and generation rate constraints. In addition, a unified power flow controller (UPFC) is put in the tie-line, and SMES units have been planned in both regions. Furthermore, the contribution of electric vehicles (EVs) is considered in both sections. The proposed PDO-based FTFOIDFF controller outperformed many PDO-based traditional (such as proportional integral derivative (PID), proportional integral derivative acceleration (PIDA), and TFOIDFF) and intelligent (such as Fuzzy PID and Fuzzy PIDA) controllers from the literature. The suggested PDO-based FTFOIDFF controller has excellent performance due to the usage of various load patterns such as step load perturbation, multi-step load perturbation, random load perturbation, random sinusoidal load perturbation, and pulse load perturbation. Furthermore, a variety of scenarios have been implemented to demonstrate the advantageous effects that SMES, UPFC, and EV units have on the overall performance of the system. The sensitivity of a system is ascertained by modifying its parameters from their standard configurations. According to the simulation results, the suggested PDO-based FTFOIDFF controller can improve system stability despite the multiple difficult conditions indicated previously. According to the MATLAB/Simulink data, the proposed method decreased the total fitness function to 0.0875, representing a 97.35% improvement over PID, 95.84% improvement over PIDA, 92.45% improvement over TFOIDFF, 83.43% improvement over Fuzzy PID, and 37.9% improvement over Fuzzy PIDA.

Smart Fall Detection by Enhanced SVM with Fuzzy Logic Membership Function

Falling is a critical issue for disabled people, and it leads to potentially serious injuries and death. Smart fall detection is a technology that depends on sensors and auxiliary devices that seek to improve the quality of life and enhance the lifestyle of disabled people. So far, the most widely used fall prediction methods collect data from inertial measurement unit (IMU) sensors. In addition, they use thresholds to identify falls based on artificial experiences or machine learning (ML) algorithms. Nonetheless, these approaches still require extensive classification and calibration. In this paper, we suggest a new technique to detect falls by combining Fuzzy Logic (FL) and Support Vector Machine (SVM). The FL model is built by using a fuzzy membership function along with the input dataset to obtain the intermediate output. Because combining these two algorithms is not an easy task, we leverage SVM with a kernel comprised of a fuzzy membership function and thus build a new model known as FSVM. Besides, the hyperplane of the SVM is used as the separating plane to replace the traditional threshold method for detecting falling Activities of Daily Living (ADLs) on a comprehensive dataset containing simulated falling ADLs, non-falling ADLs, and scripted ADLs, including falling ADLs and unscripted ADLs performed by volunteers with our designed device. The results show that no false-positive rate had been triggered, and 100% specificity was achieved for ADL. An overall accuracy of about 99.87% in detecting the fall function was obtained. Furthermore, the overall sensitivity of 100% with no false negative rate obtained was achieved by implementing the proposed method. The attained results validate that our introduced method can effectively learn from features extracted from a multiphase fall model. 

A new hybrid risk assessment process for cyber security design of smart grids using fuzzy analytic hierarchy processes

Abstract IT vulnerabilities, cyber threats, and resulting risks significantly impact the stability of current and future power grids. The results of a Risk Assessment process contribute to a better understanding of the causes and nature of the associated risks. The risks assessed by experts are available in both numerical and linguistic representations – this makes it beneficial to include a combination of linguistic and numerical analyses. In this paper, we propose a new Hybrid Risk Assessment method based on fuzzy logic, leading to more precise results. The presented approach specifies the variables and membership functions of fuzzy logic with reference to Smart Grids. For this propose, a case study with five risk events in a small-scale Smart Grid is carried out as an example. The results can then support decision-makers in ensuring grid stability.

A new on-line self-adapting fuzzy controller design using unidimensional input-output with dynamical membership functions

Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation. The initial limits of the fuzzy logic membership functions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membership functions online using the kth and k - 1th outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membership functions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems.

Improvements in Classical Proportional Navigation Guidance Using Fuzzy Logic

The performance of classical Proportional Navigation guidance is limited against highly maneuvering targets and in presence of measurements noise resulting into large miss distances and lower hit probability. Also, the advanced Augmented PN guidance technique requires target state estimation. In this paper, fuzzy logic has been used to implement PN guidance and Membership Functions of fuzzy logic has been tuned using genetic algorithm. The proposed Fuzzy Based PN (FBPN) guidance gives superior performance than PN and APN in terms of lower miss distance, higher hit ratio and better ability to handle measurement noise at all target attack aspects. Deterministic simulations have proved the superior performance and robustness of proposed scheme for various conditions such as step target maneuver upto 9g, weaving maneuver and presence of measurement noise. Further, Monte Carlo simulations have proved that proposed scheme enhances interception performance in realistic scenarios by improving the hit ratio upto 48% and rms miss distance upto 41%. Limitations of APN scheme in terms of target state estimation is also mitigated in the proposed guidance scheme. Finally, global asymptotic stability of fuzzy homing guidance loop is proved using circle criterion.

Adaptive Motion Cueing Algorithm Using Optimized Fuzzy Control System for Motion Simulators

The Motion Cueing Algorithm (MCA) is the main unit of motion simulators responsible for transforming the vehicle motions to generate driving motion sensation for the simulator users within the motion simulator's physical workspace through washout filters. In this study, we design and provide a new framework by developing a set of novel washout filters using optimized fuzzy control systems to solve the drawbacks associated with the existing optimal MCAs of the motion simulators. These drawbacks include constant washout filter parameters, inefficient simulator movement in the workspace, lack of considering the simulator's physical constraints and driver sensation-related factors, and sub-optimal settings which causes false motion cues and subsequently simulator sickness. As a pioneering framework, the fuzzy logic controllers generate restitution signals for the optimal washout filters based on the motion sensation error between the real vehicle and simulator divers as well as the platform's position in the workspace aiming to correct the filtered signals, reduce error, generate realistic motions, and increase motion fidelity online. The GA is applied to optimize the fuzzy logic membership functions and control rules. The optimization procedure takes into account a number of important factors that are normally ignored in the current MCAs including the sensed motion error between the drivers of simulator and real car; the platform physical constraints; the motion threshold limiter effects; and signal shape following. The new adaptive MCA is designed, implemented and tested using the MATLAB/Simulink. The simulation results confirm the effectiveness of the developed MCA in maximizing motion fidelity and the reference sensation shape following, and enhancing the workspace usage of the simulator.

Improving the performance of industrial mixers that are used in agricultural technologies via chaotic systems and artificial intelligence techniques

In this study, it is aimed to show how important to apply chaotic systems and Fuzzy Logic artificial intelligence technique to increase the production performance of industrial mixers used in agriculture in terms of important criteria such as product quality, homogeneity, time, and energy saving by using. A PLC (Programmable Logic Controller) controlled mixer whose all functions can be controlled by the HMI (Human Machine Interface) operator panel is designed and manufactured for experimental studies. Water, leonardite and potassium hydroxide (KOH) mixture components are mixed in a newly designed mixer in three different ways by using traditional, chaos, and artificial intelligence techniques in order to obtain liquid humic acid which is used as plant nutrition and soil modulator in agriculture. For the chaotic mixing option, chaotic systems with different dynamic features are chosen from the literature. After converting the time series values of chaotic systems to the frequency values with the program written on the PLC device, it is provided to rotate at variable speeds according to different chaotic systems of the mixer motor connected to the frequency inverter. Fuzzy Logic is chosen for the artificial intelligence mixing option. Fuzzy Logic membership functions and rules are established in accordance with the results of the chaotic systems mixing experiments and the data obtained from experts in humic acid production. Mixing experiments are carried out by using an interface program that makes the choice of chaotic systems and motor rotation speed range. By mixing humic acid components with traditional methods, the comparison is done in terms of product quality, solute ratio, pH values and total energy consumption. When the developed mixer is provided to operate with chaos and artificial intelligence methods, it is observed that more efficient results are obtained in terms of criteria such as product quality, homogeneity, time, and energy savings compared to the traditional mixing methods.

Optimum fuzzy logic controller applied to a hybrid hydraulic vehicle to minimize fuel consumption and emissions

This paper aims to combine the hydraulic hybrid vehicle (HHV) architecture with optimum fuzzy logic control. Besides the expressive fuel savings of the HHV in urban routes, the engine intermittent operation with on–off control may delay the catalyst system warm-up, resulting in a higher level of emissions. Therefore, this study presents a multi-objective optimization in which the HHV drivetrain parameters are optimized, simultaneously with the fuzzy logic membership functions, defuzzification method, rules and weights, aiming to reach a trade-off among fuel savings and the minimization of the tailpipe emissions. To ensure feasible solutions, the engine simulation was based on the ADVISOR™ fuel converter block, which is a well-known tool applied in several similar works. The optimization procedure was carried out under the FTP-72 standard driving cycle, in which the HHV was able to reduce 13.07% HC, 10.55% CO, and 4.74% NOx emissions with 35.67% fuel savings, as compared to a conventional vehicle with similar characteristics and engine. Moreover, the optimized HHV was also evaluated under 3 different real-world driving cycles, resulting in fuel savings up to 43.06% and emissions below the conventional vehicle in all analyzed scenarios.

Comparative study of energy management systems for a hybrid fuel cell electric vehicle - A novel mutative fuzzy logic controller to prolong fuel cell lifetime

Hybrid fuel cell battery electric vehicles require complex energy management systems (EMS) in order to operate effectively. Poor EMS can result in a hybrid system that has low efficiency and a high rate of degradation of the fuel cell and battery pack. Many different types of EMS have been reported in the literature, such as equivalent consumption minimisation strategy and fuzzy logic controllers, which typically focus on a single objective optimisations, such as minimisation of H2 usage. Different vehicle and system specifications make the comparison of EMSs difficult and can often lead to misleading claims about system performance. This paper aims to compare different EMSs, against a range of performance metrics such as charge sustaining ability and fuel cell degradation, using a common modelling framework developed in MATLAB/Simulink - the Electric Vehicle Simulation tool-Kit (EV-SimKit). A novel fuzzy logic controller is also presented which mutates the output membership function depending on fuel cell degradation to prolong fuel cell lifetime – the Mutative Fuzzy Logic Controller (MFLC). It was found that while certain EMSs may perform well at reducing H2 consumption, this may have a significant impact on fuel cell degradation, dramatically reducing the fuel cell lifetime. How the behaviour of common EMS results in fuel cell degradation is also explored. Finally, by mutating the fuzzy logic membership functions, the MFLC was predicted to extend fuel cell lifetime by up to 32.8%.

Membership Function Optimization of Fuzzy Inference System Using Cuckoo Search Algorithm for Peak Load Forecasting in National Holiday

This study aims to analysis peak load prediction of Indonesian national holidays for Jawa-Bali electricity system. Forecasting applied using the Fuzzy Logic System (FLS) method combined with the Cuckoo Search Algorithm (CSA). CSA is used to determine the optimal membership function in fuzzy logic. Cuckoo search algorithm has a very good performance in terms of optimization. This method is applied for short-term load estimates on holidays/special days on the Jawa-Bali electricity system, Indonesia. The study used data from daily peak loads during Indonesian national holidays in 2014 on the Jawa-Bali electricity system. The data analyzed is the daily peak load documentation data for 4 days before national holidays and during national holidays in 2014. Testing the simulation results, it was found that the Fuzzy Logic System - Cuckoo Search Algorithm (FLS-CSA) method gives good forecasting results, this is evidenced by using the mean absolute percentage error (MAPE). Forecasting results using the Cuckoo Search Algorithm (CSA) optimization method on fuzzy logic membership functions for peak loads on national holidays on the Java-Bali 500kV electrical system give satisfactory results with an average forecasting error of 1.511314562%.

Design of Anfis based pacemaker controller having improved transient response and its FPGA implementation

This paper presents the design and hardware implementation of an Adaptive Neuro-Fuzzy Inference System (Anfis) based controller for a cardiac pacemaker. Cardiac pacemakers are used for the patients who suffer from bradycardia: heart rate below 60 beats per minute, and pacemaker controllers are required to provide stability between the heart rate and the desired preset profile of the pacemaker. The proposed work aims to design a controller to improve the parameters of cardiovascular system response in terms of stability, steady-state error, transients, etc. The settling time is the major constraint in the design of a pacemaker controller; ideally, it is required to have no delay between the need of heartbeat and the stimulus delivery by the pacemaker. The combination of neural network with fuzzy logic makes the proposed controller more effective concerning ‘learning’ and speed. The membership function of fuzzy logic plays an important role in controller design; the optimal number of membership functions are selected, so as to avoid the controller design complexities. In the proposed scheme, we have designed a pacemaker controller with fifteen membership functions on MATLAB/Simulink. To validate the theoretical results of the proposed methodology, the proposed controller is implemented on the Artix 7 Field Programmable Gate Array (FPGA) board. The simulation results show that the control strategy of the proposed design is more suitable in terms of timing response and the hardware implementation shows that the proposed controller will consume less power. The settling time and the power consumption for the proposed pacemaker controller are found to be 1.3813sec and 8.04 mW respectively.

A Takagi Sugeno based reactor power control of VVER-1000 using linear parameter varying identification of two-point kinetic model

This study aims to design Takagi Sugeno (TS) Fuzzy logic based power control for VVER-1000 PWR to achieve smooth and precise load following situations under various disturbances and model uncertainties. To achieve the purpose, a two-point kinetic model with consideration of neutron diffusion, thermal hydraulics and poison concentration is developed. Model identification for the equivalent linear parameter varying (LPV) system is performed using the fixed functional parameters and reproducing the corresponding LPV subsystems for each of those functional points in the form of SISO transfer function synthesis. Linear heft factors are introduced to compute the weighted output based on the triangular and trapezoidal membership functions of fuzzy logic. Linear interpolation is used to find the controlled power based on heft factors and operational specifications of the nuclear reactor over the entire power range. A TS fuzzy based controller with fixed gain and parameters is designed to efficiently control the reactor power under multiple transient conditions. The LPV subsystems are to be controlled with the fuzzy controller comprising two inputs with effective utilization of unsymmetrical, nonlinear and optimized membership functions and scaled output linear function of fuzzy variables. Stability analysis is performed using the Lyapunov like function and the condition is demonstrated for the two-point kinetic model of the reactor. The performance of the proposed framework is evaluated for load following scenarios with fast and slow ramp changes; along with accident handling with positive reactivity insertion tests. The proposed TS fuzzy power control model found preferably efficient in load tracking applications and disturbance rejection conditions.

Vibration control of structure by optimising the placement of semi-active dampers and fuzzy logic controllers

ABSTRACT This study concentrates on applying semi-active dampers to control structures. In this paper, fuzzy algorithm types 1 and 2 have been used as controllers to evaluate the voltage of semi-active dampers. To design a proper fuzzy logic controller, membership functions and suitable rule sets need to be tuned and extracted. The location of a semi-active damper is very significant to reduce the responses of structure. In this paper, a 15-story building’s responses for both uncontrolled and controlled (with MR dampers) situations subject to different ground motions are evaluated. Type 1 and type 2 fuzzy logic controllers, and the location of three MR dampers are optimised by Grey Wolf Optimiser, and also dissipating energies are investigated. The results show that although both optimised controllers can successfully reduce the seismic response of the building, the performance of the fuzzy type 2 controller is slightly superior than type 1. It is confirmed that the best location for dampers would be in the upper stories.

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

An intelligent energy management and traffic predictive model for autonomous vehicle systems

In recent times, the utilization of autonomous vehicles (AVs) has been significantly increased over the globe. It is because of the tremendous rise in familiarity and the usage of artificial intelligence approaches in distinct application areas. Though AVs offer several benefits like congestion control, accident prevention, and so on, energy management and traffic flow prediction (TFP) remain a challenging issue. This paper concentrates on the design of intelligent energy management and TFP (IEMTFP) technique for AVs using multi-objective reinforced whale optimization algorithm (RWOA) and deep learning (DL). The proposed model involves an energy management module using fuzzy logic system to reach the specified engine torque with respect to different measures. For optimal tuning of the variables involved in the fuzzy logic membership functions (MFs), RWOA is employed to further reduce the energy utilization. Besides, the proposed model uses a DL-based bidirectional long short-term memory (Bi-LSTM) technique to perform TFP. For validating the efficacy of the IEMTFP technique, an extensive experimental validation is carried out. The resultant values ensured the goodness of the IEMTFP model in terms of energy management and TFP.

Upwind Horizontal Axis Wind Turbine Output Power Optimization via Artificial Intelligent Control System

Power capturing capacity is one of the key performance indicators of wind turbines. This article presents a study done on the optimization of output power of upwind horizontal axis wind turbine using artificially intelligent control system. The study shows how blade tip speed ratio (λ) and pitch angle (β) are optimized to increase wind turbines power conversion coefficient (C_p) which increases the output power. An artificial intelligence system named Mandani fuzzy inference system (MFIS) was applied to optimize the power conversion coefficient in combination with blade pitch actuator control. To this end, a novel optimization technique is designed that maximizes the power harvesting ability of wind turbines by updating the parameters of the membership functions of fuzzy logic found in the MFIS. With the application of this optimization method, a power conversion coefficient C_p of 0.5608 value is achieved at optimal values of λ and β. As a result, the energy harvesting ability of the wind turbine considered is improved by 16.74%. This study clearly shows that the wind energy harvesting capacity of wind turbines can be enhanced via optimization techniques that could be further implemented in wind turbine blade pitch drive system. Thus, this novel optimization method creates further insights for the wind energy industry in reducing the cost of energy generation.

Comparative Analysis of Coal Miner’s Fatalities by Fuzzy Logic

In this work, we employ the fuzzy logic technique to achieve and present, for the first time, a proper analysis of the actual intensity of the increase in the coal miners’ fatality rates in Pakistan from 2010 to 2018, compared with China and India, with an objective to minimize the impact of incidents on the miners’ fatalities. The average and yearwise fatality rates in Pakistan, compared with China and India, are used for the fuzzy logic technique in order to calculate the actual degree of flexibility for the surging fatalities. The findings show that both the average (2010-2018) and yearwise fatality rates in 2011, 2015, and 2018 are 2.44, 1.74, and 1.6, respectively. In the fuzzy logic technique, the variables whose membership function (µ) values are ≥ 1 represent the absolute truth. The membership function values for the years 2011, 2015, and 2018 are alarmingly high for the fatalities of coal miners. Similarly, except for 2014 and 2010, where 0 represents the absolute falseness, the results for the remaining years indicate high fatality rates with a flexibility (or small extent) of its corresponding membership function (µ) values such as 0.623, 0.739, 0.219, 0.173 and 0.115, and 0.714, 0.24, 0.01, 0.324 and 0.317 using the average and yearwise analysis, respectively, compared with China. Likewise, the fuzzy logic membership function (µ) values compared with India in the remaining years are 0.704, 0.795, 0.386, 0.159, 0.352 and 0.306, and 0.675, 0.795, 0.386, 0.186, 0.321 and 0.322, respectively. The proposed fuzzy logic analysis has been founded based on the theory of fuzzy sets to properly identify the miners’ fatalities, and also to suggest the implementation of appropriate recommendations to reduce the fatalities in the coal mines in Pakistan.