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Opportunistic IoT networks operate in an intermittent, mobile communication topology, employing peer-to-peer transmission hops on a store-carry-forward basis. Such a network suffers from intermittent connectivity, lack of end-to-end route definition, resource constraints and uncertainties arising from a dynamic topology, given the mobility of participating nodes. Machine learning is an instrumental tool for learning and many histories-based machine learning paradigms like MLPROPH, KNNR and GMMR have been proposed for digital transformations in the field with varying degrees of success. This paper explores the dynamic topology with a plethora of characteristics guiding the node interactions, and consequently, the routing decisions. Further, the study ascertains the need for better representation of the versatility of node characteristics that guide their behavior. The proposed scheme Opportunistic Fuzzy Clustering Routing (OFCR) protocol employs a three-tiered intelligent fuzzy clustering-based paradigm that allows representation of multiple properties of a single entity and the degree of association of the entity with each property group that it is represented by. Such quantification of the extent of association allows OFCR a proper representation of multiple node characteristics, allowing a better judgement for message routing decisions based on these characteristics. OFCR performed 33.77%, 6.07%, 3.69%, 6.88% and 78.14% better than KNNR, GMMR, CAML, MLPRoPH and HBPR respectively across Message Delivery probability. OFCR, not only shows improved performance from the compared protocols but also shows relatively more consistency across the change in simulation time, message TTL and message generation interval across performance metrics. [ABSTRACT FROM AUTHOR]

This paper bestows 3-phase grid interfaced solar-wind hybrid renewable energy system (RES), feeding three-phase loads. The proposed system includes solar photovoltaic, permanent magnet based synchronous generator (PMSG), DC-DC converter, maximum power point tracker (MPPT) based on incremental conductance, three phases IGBT based voltage source converter (VSC), with a third order generalized integrator (TOGI) control technique. This control technique bestows multifunctional capabilities as harmonic mitigations, load balancing, and reactive power compensation. A fundamental component of load current is extracted by TOGI based controller, and further it is utilized to provide switching pulses to VSC for power quality enrichment. The fuzzy logic-based controller is used for loss computation of VSC as well as for maintaining DC link voltage. Moreover, fuzzy logic provides better dynamic performance compared to conventional PI controller. The results are presented in many aspects for linear and nonlinear loads such as, intermittent nature of solar and wind as well as disturbances in the system. A comparative analysis between proposed TOGI based controller and conventional control algorithm has been presented. Test results are performed by using MATLAB/ Simulink environment and demonstrate, AC-grid current is maintained within the IEEE-519 standard. [ABSTRACT FROM AUTHOR]

In this paper, the authors have addressed the modeling and design of the BLDC Motor-Driven E-Rickshaw based on hybrid energy storage system (HESS) for optimum power management using fuzzy logic. In Hybrid energy sources, solar power is used to charge a battery (primary source) that is effectively coupled to supercapacitor (ancillary source) for peak demand supplies. A power-split control strategy is proposed to control the power supply by using the HESS Fuzzy Logic in different engine operating modes. Projected power layering improves the battery life cycle with the proper use of the Supercapacitor. By providing a new switching algorithm, the DC link voltage is boosted to effectively transfer power to the HESS unit. Fuzzy logic-based HESS provides better performance in electric vehicles, such as deep discharge protection of the battery, and faster acceleration. Also, there is a quick comparison of E-rickshaw solar power with traditional E-rickshaw. The planned design model was simulated by MATLAB®/Simulink environment. [ABSTRACT FROM AUTHOR]

In this paper, a solar photovoltaic model integrated with brushless DC motor via DC to DC zeta converter is controlled in two stage. In first stage, a fuzzy rule based maximum power point tracking (PPT) is proposed to generate the pulse for DC to DC zeta converter. It is efficient intelligent control approach to extract maximum power from the solar PV system and enhance the speed to track the maximum power. The basic three process of fuzzy logic controller (FLC) are fuzzifier, inference and defuzzifier where the defuzzification process is used center of gravity (COG) method to convert its original value. The FLC to extract maximum PPT for solar PV based brushless DC motor can be examined the performance under transient and dynamic condition with different solar insolation. Moreover, in second stage a trapezoidal control approach based electronic commutation is chosen to generate the pulses of voltage source inverter (VSI) and it offers the smooth control of the brushless DC motor which can easily applicable for water pumping or irrigation purpose. A second stage, trapezoidal control approach is close loop control algorithm using sensorless drive. The performance of proposed fuzzy rule based control algorithm is shown using simulation results on MATLAB platform. [ABSTRACT FROM AUTHOR]

In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM. [ABSTRACT FROM AUTHOR]

The present work represents the implementation of the various fuzzy controller with robust sliding mode control (SMC) technique on a nonlinear system considering various external disturbances and model uncertainties. The nonlinear system considered here is a single link inverted pendulum. The proposed work combines the advantages of the sliding mode controlling technique and fuzzy logic controller. A set of linguistic rules are designed in fuzzy logic control, which causes the system to be chattering free. Parameters of the nonlinear system are adjusted according to fuzzy adaptive laws, while the uncertainties of the nonlinear system have been approximated using a fuzzy system. Various types of controller based on fuzzy sliding mode, like approximation based sliding mode control technique; equivalent control based fuzzy sliding mode technique, and switch-gain regulation based sliding mode control methods have been implemented here. A comparative analysis of various methods is also have been discussed. [ABSTRACT FROM AUTHOR]