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In general, pitch-angle controller regulates the generator output power when the wind speed exceeds the rated wind turbine speed. Besides this, it can also be employed to stabilize the wind energy system rotor speed during the transient disturbances. In this article, therefore, a logical pitch-angle controller strategy (in power and speed control modes) has been developed and an interval Type-2 fuzzy logic technique is proposed to design the controller. To evaluate the effectiveness of the Type-2 fuzzy logic–based pitch-angle controller, the simulations have been carried out for severe network faults and fluctuating wind conditions, and the results are compared with conventional proportional–integral and fuzzy logic controller (called as Type-1 fuzzy logic controller). Moreover, some key factors that affect the transient stability of wind generator have also been investigated. The electrical torque and mechanical torque versus rotor speed results are obtained under different pitch-angle conditions, and the concept of stable and unstable electrical–mechanical equilibrium points is established.

Summary In this research study, Type‐1 and Type‐2 fuzzy logic controller‐based model reference adaptive system speed estimator for direct torque and flux control with space vector modulation of a speed sensorless induction motor drive is proposed to replace the conventional proportional‐integral controller. Initially, Type‐1 fuzzy logic controller is developed, which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type‐1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties in the system which can be caused by sudden change in speed or different load torque disturbances, process noise, etc. Therefore, a new Type‐2 fuzzy logic controller‐based adaptation scheme is proposed to better handle the higher degree of uncertainties along with enhancing the performance and also robust to various load torque and sudden change in speed conditions respectively. The detailed performances of various control schemes are carried out in MATLAB/SIMULINK with speed sensor and sensorless modes of operation when an induction motor drive is operating under different conditions, such as no‐load, load, sudden change in speed and low speed respectively. To validate the different control approaches, an experimental prototype is developed and adequate results are reported for its validation. Superior performance has been obtained using the Type‐2 fuzzy logic controller scheme in speed sensor and sensorless modes of operation compared with Type‐1 fuzzy logic controller and proportional‐integral controller based adaptation schemes respectively. Copyright © 2016 John Wiley & Sons, Ltd.

SUMMARY This research article presents a novel approach based on an instantaneous active and reactive current component (id‐iq) theory for generating reference currents for shunt active filter (SHAF). Three‐phase reference current waveforms generated by proposed scheme are tracked by the three‐phase voltage source converter in a hysteresis band control scheme. The performance of the proposed control strategy has been evaluated in terms of harmonic mitigation and DC link voltage regulation under various source conditions. In order to maintain DC link voltage constant and to generate the compensating reference currents, we have developed type‐1 fuzzy logic controller (T1FLC) and type‐2 FLC (T2FLC) triangular M.F. The SHAF with proposed type‐1 and type‐2 FLCs using triangular M.F is able to eliminate the uncertainty in the system. By using T2FLC triangular M.F, SHAF gains outstanding compensation abilities. The detailed simulation results using MATLAB/SIMULINK software are presented to support the feasibility of proposed control strategy. To validate the proposed approach, the system is also implemented on OPAL‐RT (real‐time) simulator, and adequate results are reported for its verifications. Copyright © 2012 John Wiley & Sons, Ltd.

This research validates An Adaptive Fuzzy Logic Controller (AFLC) has been developed for grid-connected photovoltaic (PV) systems. The primary objective of this implementation is to enhance the PV system’s power generation efficiency. For achieving this, techniques of Maximum Power Point Tracking (MPPT) are utilized, which are essential to extract the highest possible power outing from PV panels. Recent developments in MPPT methods focus on improving control strategies to ensure efficient operation and smooth integration with the grid. The performance of the AFLC is extensively evaluated and compared with other controllers, like fuzzy-logic controller (FLC) and Proportional Integral (PI). The proposed AFLC controller’s performance is evaluated with other methods to verify its effectiveness. To validate this method, the system is tested using MATLAB/Simulink simulations, along with experimental evaluations conducted on the control strategies are executed in real-time utilizing the DSpace DS1104 control. Experimental results show that the AFLC outperforms both the FLC and PI controllers in several key performance areas. Specifically, the AFLC demonstrates faster response times, higher convergence rates, decreased peak overshoot, minimal undershoot, and lower the error of the mean square. Additionally, the Compared to conventional Fuzzy Logic Control (FLC) and PI controllers, the AFLC delivers superior efficiency and transient response, and oscillation reduction. Compared to the FLC, the AFLC enhances tracking of power by 68.26%, and it achieves 86.25% improvement over the PI controller. These findings highlight the AFLC’s potential as a highly effective and reliable optimization tool for maximizing the output power of the systems of PV. Furthermore, integral absolute error (IAE) is used as a performance metric for the PV system connected to grid to assess the efficiency of the AFLC. The AFLC demonstrated superior performance over other methods, achieving a 20% increase in PV output power compared to traditional FLC and a 30% improvement over PI controllers. The errors of the PI, FLC and AFLC approaches, each utilizing five controllers, are estimated. The error of mean square is reduced by 79.67% in comparison to PI and by 66.5% in comparison to FLC.

This article describes the application of a variant of the shark smell optimization (VSSO) biological inspired algorithm in the optimal design of a type-2 fuzzy controller. We show how the performance of VSSO is based on the frontal and rotational movement of the shark when navigating a dimensional search domain, which is based on the food-seeking behavior of sharks. The optimization of the design of a Mamdani interval type-2 fuzzy controller (IT2-FLC) applying VSSO is also described. The optimized controller is tested with the navigation of an autonomous mobile robot (AMR) in an unknown and changing environment. This work was developed as follows: first, the VSSO algorithm is improved by adjusting its main alpha and beta parameters with a fuzzy system, later the parameter values of the fuzzy controller input/output membership functions are optimized. Finally, a comparison is made between the results of type-1 (T1) and interval type-2 fuzzy controllers applying the proposed methodology. When comparing the T1 and IT2-FLC controllers, the application of the VSSO algorithm in T1-FLC shows good performance in robot navigation; however, IT2-FLC presents better performance due to its ability to handle higher levels of uncertainty. The performance evaluation of the proposed method and its application in different navigation problems has been carried out through computer simulations using Matlab-Simulink.

The intensifying progression in the transformation of the power grid towards a new generation power system introduces frequency regulation challenges in operations which can be overcome with substantial benefits from the latest robust control approaches along with suitable smart optimizing tools. Additionally, with the adoption of smart and intelligent control schemes, the latest communication technologies with the extensive placement of smart devices, there will be significant growth in real-time system measurements. Under this trend, an adaptive fuzzy PID (A-FLC-PID) system will play a vital role in reducing transient time. In this article, a novel adaptive fuzzy control architecture intended for load frequency control with a supplementary hybrid moth flame optimization pattern search (h-MFO-PS) algorithm is presented. The proposed approach is designed to optimize the controller gains for the eventual fitness of the objective function. The hybrid algorithm smartly updates the scaling factors of the adaptive fuzzy controller by considering various proportional and integral thresholds. Simulations are performed on two areas hydrothermal system with the gas unit. To validate the research outcomes and measure the effectiveness of the proposed architecture, the outcomes are compared with recent research approaches and practicability is tested using an OPAL-RT setup. The sturdiness of the concerned approach is studied with a wide range of parameterization and loading conditions. It is observed that the h-MFO-PS scaled A-FLC-PID controller establishes superior improvement in frequency regulation.

The wind energy system (WES) has an undesirable characteristic in which its power output varies with wind speed, resulting in fluctuations in the grid frequency and voltage. Especially, in partial load region where the wind speed is below rated, there is a concern in regards to WES output power. This part of the work initially employed exponential moving average (EMA) concept to generate reference power. Later on, an interval type-2 fuzzy logic (advanced fuzzy logic) based pitch-angle controller is implemented and designed for good reference tracking and therefore, it can smoothen out the WES output power more effectively. Real time simulations are also developed to show the applicability of the proposed controller using the OPAL-RT digital simulator. Below rated wind speed pattern has considered for real time simulations and results are obtained with different control techniques. The results show that the proposed interval type-2 fuzzy logic (advanced fuzzy logic) based pitch-angle controller offers better performance in tracking reference power and hence, it offers good smoothing of output power fluctuations than conventional proportional-integral (PI) and traditional fuzzy logic (Type-1) controllers. The performance of the proposed controller is also estimated using power smoothing and energy loss functions in terms of performance indices.

Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

This paper presents several non-singleton general type-2 fuzzy logic controllers (NGT2FLCs) for an under-actuated mobile two-wheeled self-balancing robot to improve the anti-interference capability of the system. Four kinds of fuzzifiers, including singleton fuzzifier, type-1 non-singleton fuzzifier, interval type-2 non-singleton fuzzifier and general type-2 non-singleton fuzzifier, are considered to construct different general type-2 fuzzy logic controllers (GT2FLCs). In order to show the superiority of the GT2FLCs, three kinds of interval type-2 fuzzy logic controllers (IT2FLCs), including singleton IT2FLCs, type-1 non-singleton IT2FLCs (N1IT2FLCs) and interval type-2 non-singleton IT2FLCs (N2IT2FLCs), are also presented. A comparative study between singleton fuzzy controllers and non-singleton fuzzy controllers, and IT2FLCs and GT2FLCs is also shown. All simulation results show that the performance of non-singleton fuzzy logic controllers is better than that of singleton fuzzy logic controllers. The NGT2FLCs get the best performance in the presence of uncertainties and external disturbances.

INTRODUCTION: Hybrid stand-alone Small Wind Solar Energy System offers a feasible solution in remote areas where grid connectivity is either financially or physically unavailable. A small wind turbine (SWT) and a solar photovoltaic system are part of the hybrid energy system, which is effectively employed to meet the energy needs of rural household loads. OBJECTIVE: This research suggests an effective analysis of wind solar hybrid system controllers taking energy demands into account. The controller should be designed in such a way as to intelligently monitor the availability of wind energy and solar energy and store the energy without spilling it out. METHODS: In order to cope with the challenging factors involved in designing the controller, intelligent power tracking with an artificially intelligent neural network (AI-NN) is designed. Added to that, the whole process has been designed and analysed with the MATLAB SIMULINK tool. RESUSTS: The results of the simulation, infer that AI-NN achieved the regression value of   0.99 when compared with the Perturb & Observe algorithm (P&O), and the Fuzzy Logic Control (FLC) algorithm, and has a higher tracking speed. Also, the AI-NN attained 2.62kW whereas the P&O has attained 2.52kW and Fuzzy logic has attained 2.43W of power which is 3.89% higher than P&O algorithm and 7.52% higher than fuzzy MPPT algorithm. CONCLUSION: The designed controller module enhances the system by artificially intelligent algorithm. The AI-NN attains the better power performance with lesser tracking time and higher efficiency. Thus, it is evident that AI-NN MPPT suits well for the hybrid system.