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This study provides an adaptive control approach of VSC (voltage source converter) coupled with SPV (solar photovoltaic array), in a 3P3W (three-phase three-wire) system with three single-phase non-linear loads having Distributed Static Compensator (DSTATCOM) abilities using P and O (perturb & observe) methodology. The adaptive control technique converges quickly and has a low mean square error. For the correction of power factor and zero voltage regulation modes, the system is studied and simulated. The system’s great efficacy at high voltages is due to its one-stage structure. Grid current harmonics are significantly below the IEEE-519 norm. The suggested system is modeled and simulated with the available sim power system toolbox in MATLAB/Simulink, and the system’s behavior under different loads and environmental circumstances is confirmed.

In this paper, modified dragonfly algorithm (MDA) with ant lion optimization (ALO) based Load Flow Analysis (LFA) with hybrid power flow controller in comparison with unified power flow controller is established. The proposed technique is exemplify at enhanced search ability, random reduction and decreased complexity. Here, search behavior of dragonflies is updated to ALO algorithm. Hence, the combination of these two optimization algorithm is named as MDA2LO technique. In this paper, load flow studies are suggested with obtain voltage, real and reactive power and load angle in each bus of power system to divide their steady state circumstances utilizing MDA2LO approach. The proposed system is actualized on MATLAB/Simulink working platform. The proposed study is experienced at IEEE 30 and 14 bus system. The performance of proposed system is compared to previous method like salp swarm algorithm-firefly (SSA-FF), combination of modified salp swarm optimization algorithm and moth-flame optimization called CMSSMFO and dragonfly algorithm (DA).The result accuracy (RA) of proposed and existing method is also analyzed. The RA of the proposed hybrid system is 52.7699% and SSA-FF is 9.665%, CMSSMFO is 11.995%, DA is 10.721%. Overall, the comparison outcomes decide the benefit of proposed system and confirm their potential for resolving these issues.

Development of various maximum power point tracking (MPPT) control techniques for proposed systems such as solar photo-voltaic (PV), wind turbine (WT), fuel cell (FC) and hybrid renewable energy system (HRES). HRES is the combination of PV, WT and FC which is connected parallelly by DC link. It is implemented in real-time using OPAL-RT system. In this research article, the MPPT algorithms viz. Perturb and Observe (P&O), Fuzzy Logic (FL), Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been analyzed and compared. Results have been carried out to record tracking performance of MPPT controllers by introducing changes in the radiation, wind speed hydrogen fuel rate. It has been observed that the proposed HRES using ANFIS-based MPPT controller provides better response as compared to other specified MPPT controllers.

High-resolution information is needed for precision agriculture to achieve precise management of inputs. High spatial and temporal resolution is requisite to get the actionable information for the timely response. The objective of the present study is to estimate the leaf chlorophyll Concentration using high-resolution (2 cm) images captured from UAV-mounted multispectral sensors for crop health monitoring. In this study, a hexacopter was flown at an elevation of 25 m to capture the images in green, red, red edge and NIR bands of turmeric plots grown at ICAR Research Complex, Northeast Hilly Region, India. A handheld SVC spectroradiometer having spectral range from 350 to 2500 nm was also used to collect the spectra of sample plants to support the UAV study. We evaluated an advanced machine learning algorithm kernel ridge regression combined with spectral information and ground-truth chlorophyll data to model the chlorophyll estimation. The multivariate analysis was also applied on spectroradiometer and UAV data, which recommended red band for chlorophyll prediction with R2 value greater than 0.6. We also found that kernel ridge regression is a robust method for developing chlorophyll estimation model with lesser training time. The results indicate that kernel ridge regression with a radial basis kernel function with four multispectral input bands can be utilized to evaluate the leaf chlorophyll concentration with an root mean squared error RMSE = 0.10 mg/g and regression coefficient R2 = 0.7452. However, this study is site specific and needs to be practiced in different crop sites in order to generalize this method for precision agriculture.

The power generation demand is increasing day-by-day throughout the world, therefore, the use of hybrid systems becomes a significant solution. The hybrid renewable energy system (HRES) is used for...

There is a sudden fluctuations in wind speed and change of direction that affects the generator speed thus power generation of wind turbine system, so a controller like maximum power point tracking (MPPT) algorithm is essential in tracking the maximum power out of the available wind speed. MPPT algorithm can be classified into two categories such as with and without sensor as well as according to the algorithms used to find the maximum peak. A comparative analysis of different types of MPPT control methods on the basis of different speed responses and ability to obtain maximum power has been conducted. The literature based on the simulation results points out that the optimal torque control is a better MPPT control method for achieving the maximum power from wind turbine energy system against the most frequently used perturb and observe (P&O) method. The P&O method on the other hand is flexible and easy to implement but provide fluctuations about the maximum power point and is less efficient. The different types of MPPT techniques have been studied for wind turbine renewable energy system. The study points out that MPPT may also include of different dc–dc power converter and various control methods, and hybrid renewable energy system can be developed with multi-input energy systems.

Background Though many rehabilitative treatments are available for treatment of spasticity, thus the effectiveness of different robo-rehabilitative devices needs to be evaluated through a systematic review. Objective The objective of this study is to focuses on the efficacy of Robot assistive rehabilitation device for the removal of spasticity from the lower limb of Spastic patients. Data Sourcessources PubMed, Web of Sciences, EMBASE (Excerpta Medical database), CDSR (Cochrane database of systematic reviews), Scopus, IEEE Xplore, Wiley online library, MEDLINE (OvidSP), Science Direct, Springer Link were from January 1980 to September 2017 Data Extractionextraction Seventy-one publications from eleven databases published were selected using keywords Ankle foot, spasticity, robotic rehabilitation, efficacy of robotics and Ankle foot rehabilitation. The review is narrowed down to twenty-six articles which were selected for they focused on effects of Robot assistive rehabilitation device quantitatively. Result A quantitative study from analyzing 26 studies comprising of 786 subjects is carried out. The major outcome of the effectiveness of the robot assistive therapy for the movement of ankle and functioning of gait is deduced. As the used protocols and treatment procedures vary, made comparative study complex or impracticable. Conclusion Robo-rehabilitation possesses an ability to provide unified therapy protocols with greater ease in comparison to conventional therapies. They continuously prove to be irreplaceable assistant devices when it comes to providing excellent treatment in terms of improvement from this study. Though many mechatronic devices are available but the devices for treatment of early stage rehabilitation of stroke patients is very limited.

This present work addresses simulation and hardware implementation of low cost Arduino based Maximum Power Point Tracking (MPPT) that is used with simple monitoring parameters in solar array power systems. To optimise the performance of a photovoltaic device, continuous monitoring of Maximum Power Point is important. In this paper, control mechanism is demonstrated by using Perturb and Observe MPPT technique to implement a buckboost converter for photovoltaic power control. The MPPT algorithm is responsible for detecting and deriving the maximum power from the photovoltaic and, with the help of the buck-boost converter to step up the voltage to the appropriate magnitude. The results involve the difference in the duty cycle with the change in load and thus average output voltage of the buck-boost converter with MPPT algorithm regulated current. The device is designed to be effective, reliable, reliable, economical, compact and simple to handle. Experimental findings show that the device is feasible and has enhanced functionality.

In this manuscript, the design of a five-level inverter with a reduced number of switches suitable for industrial and commercial applications is developed. The multilevel inverter input can easily be integrated with a renewable energy source. Furthermore, the multilevel inverter provides full control for both output power factor reducing the filter weight, volume, and maximum power tracking. A modified version of cascaded H-bridge is used to reduce the switches up to six compared to the conventional model. The inverter switching angles are calculated using the SHE-PWM-GA technique to reduce lower order harmonics which suitable for medium-voltage high-power applications. This model is first tested using (MATLAB/Simulink)-simulation and then implemented on hardware to validate the results. The hardware setup consists of MOSFET switches and is driven by the Arduino controller for generating gate pulse.

This paper proposes a new single phase 9-level inverter topology for electrical loads like R-load and RL-load. The anticipated topology has been configured with only seven semiconductor power switches. A low frequency fundamental switching based Selective Harmonic Elimination(SHE) technique is utilized to reduce the switching losses of the semiconductor. Here, Genetic Algorithm(GA) is employed to determine optimal switching angles of anticipated 9-level Multi-Level Inverter(MLI) by solving the non-linear SHE equations. Here, MATLAB/Simulink software is employed to analyze the performance of the anticipated MLI topology. A real time prototype setup also has been implemented to validate the Simulink analysis. The hardware setup is tested at different loads to confirm that it works as a Voltage Source Inverter(VSI). Finally, the results are compared with available literature for reduction in component count.

In the era of globalization, manufacturing industries are facing intense pressure to prevent unexpected breakdowns, lower maintenance costs and improve machine availability. Due to the increasing trend of Condition Monitoring (CM), numerous sensors deployed on industrial apparatus around the world and several monitoring techniques available for fault diagnosis of induction motors are growing at a rapid pace. This article provides a state-of-art review of past and recent developments taking place in wavelet analysis-based CM and failure diagnosis in three-phase induction motors. The utilization of different CM techniques in data processing and application of wavelet-based automatic failure prediction in induction motors for timely scheduling of the maintenance holds great research potential in the future.

Maximum Power Point Tracking (MPPT) is a control technique for the dynamic optimization of power production by a Solar Photo-voltaic (SPV) module. MPPT is useful not only in SPV applications but also whenever the power produced using the power source must be maximized regardless of some unmanageable parameters. In this chapter, different MPPT techniques such as Perturb and Observe (P&O), Fuzzy Logic (FL), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been designed in MATLABTM/SIMULINKTM environment for proposed SPV system. Simulation results have been carried out to record the tracking performance of MPPT controllers by introducing changes in the solar radiations. It is found that the proposed model using ANFIS-based MPPT controller gives better performance as compared to other specified MPPT controllers.

This paper proposes an adaptive maximum power point tracking (MPPT) control method to achieve the maximum power from the wind turbine (WT) power generation system. The MPPT control method has a vit...

The non-conventional energy sources possess a major problem of power reliability due to their dependencies on the environment. Advance research and development in solar, wind and other renewable energy sources are needed to solve the problem of power demand and reliability. The proposed hybrid system incorporates three renewable energy systems consisting of photo-voltaic (PV), wind turbine (WT) and fuel cell (FC) systems working together to provide maximum power at DC-link. A vast deal of novel research is done for improving the efficiency of an overall system using maximum power point tracking (MPPT) method. In this paper, the developed fuzzy logic controller (FLC)-based MPPT method has been used to analyze PV, WT and FC with DC–DC converters. The FLC-based MPPT control method optimizes the output of the proposed hybrid system with variable inputs to extract maximum power. In order to fabricate a reliable hybrid system, there is a massive scope of research to develop multi-input renewable energy systems.

The demand for power generation of the world is increasing day by day so the use of hybrid systems become an important solution. The hybrid systems are used for supplying power in different areas to overcome the intermittence of solar and wind resources. The hybrid system incorporate two or more renewable energy sources so techno economics analysis of different combinations of hybrid systems is necessary for efficient utilization of renewable energy resources. In this study an extensive review of power generation from different hybrid systems are carried out and research gaps are identified. To conduct our studies different hybrid systems such Photovoltaic (PV)-Wind-Diesel-Battery, PV-Diesel-Battery, PV-Wind-Diesel, Wind-Diesel-Battery, Wind-Diesel, PV-Diesel are investigated for different cities Amritsar, Ludhiana, Patiala, and Chandigarh in Punjab, India. The telecommunication load demand is used in HOMER simulation. The results show that the PV-Wind-Diesel-Battery produce more power in comparison to PV-Diesel-Battery, PV-Wind-Diesel, Wind-Diesel-Battery, Wind-Diesel, PV- Diesel system. The cost of energy (COE) is found to be 0.162 $/kW h, 0.210 $/kW h, 0.198 $/kW h, 0.199 $/kW h respective cities for load 1.3 kW peak, providing best combination PV-Wind-Diesel-Battery system are useful for generation of power.

Activity recognition technology is one of the most important technologies for life-logging and for the care of elderly persons. Elderly people prefer to live in their own houses, within their own locality. If, they are capable to do so, several benefits can follow in terms of society and economy. However, living alone may have high risks. Wearable sensors have been developed to overcome these risks and these sensors are supposed to be ready for medical uses. It can help in monitoring the wellness of elderly persons living alone by unobtrusively monitoring their daily activities. The study aims to review the increasing trends of wearable devices and need of multimodal recognition for continuous or discontinuous monitoring of human activity, biological signals such as Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG) and parameters along with other symptoms. This can provide necessary assistance in times of ominous need, which is crucial for the advancement of disease-diagnosis and treatment. Shared control architecture with multimodal interface can be used for application in more complex environment where more number of commands is to be used to control with better results in terms of controlling.

This paper presents the main integration of wind turbine and hydraulic turbine generation to improve the power quality. The voltage is maintained at PCC with the help of STATCOM designed for reactive power compensation for better performance. The generation of both power plant real and reactive powers is connected at PCC. The STATCOM controller has been used for controlling the reactive power, through PI controller to maintain the voltage at PCC in the wind connected power system. To observe the effect of STATCOM on voltage profile of various nodes of the system, the proposed system is verified through MATLAB/ SIMULINK.

Induction motors are widely used as a workhorse in modern industry. Rolling element bearings constitute an important mechanical component in these motors. Fault detection and diagnosis of bearings are of outmost concerns for prevention of machinery malfunction and abrupt failures. Vibration monitoring is considered as most effective technique for finding bearing-related faults. In this paper, frequency domain features are computed from experimentally collected data from three-phase induction motor and used to classify bearing conditions. Feed-forward back-propagation neural network is used to classify different conditions of bearings with high accuracy. Different number of hidden layer neurons and training algorithms are evaluated for their performance. The proposed procedure requires no pre-processing of vibration signal and proves its effectiveness for bearing fault detection.

This paper presents a framework of surface electromyography signals based robotic arm prototype control using discrete wavelet transform, principle component analysis, ensemble algorithms and Arduino Uno controller. In this context, the sequential floating forward selection algorithm is used for sorting out the features based on their relevance. The performance of different ensemble algorithms is evaluated with various parameters like classification accuracy, sensitivity, specificity, false descriptive rate, positive predictive rate and speed. Among the all ensemble algorithm, the subspace discriminate ensemble was found the best method with the 100% accuracy, specificity, and sensitivity using 35 base classifiers. Subspace ensemble algorithm with principle component analysis and 4th scaling daubechies 4 wavelet filters produced the best performance. The main contribution of this work is that method has the potency of best classification of sEMG signal for elbow movement which can be beneficial for assistive robotic device development.

This paper presents multiple fault diagnosis and detection using artificial neural feed forward network. In this work analysis is done on induction motor, as these motor are widely used in industries because of their robustness, easy maintenance etc. The current and vibration responses of healthy motor, motor with bearing, rotor and stator defects are analysed. The feature extraction process is done in time domain only. From the results it is cleared that among various transfer functions in ANN the trainlm performs best and traingdm performs worst for fault detection.

Spasticity is a neurological disorder which results in disordered sensorimotor control owing to an upper motor neuron lesion. The muscles are continuously contracted which causes stiffness in the muscle which hinders the movement of muscle from their natural movement. It is mainly caused due to an injury to the central nervous system. Commonly used assessment methods of spasticity like the Ashworth and modified Ashworth scales do not quantify the degree of spasticity in the patients as they simply make available a semi quantitative degree of the force applied by the foot as resistance to passive movement with restricted inter-rater reliability. Electromechanical methods like isokinetic dynamometers can be used only when an objective quantitative weigh is available for the resistance to passive motion. Electrophysiological methods are valuable for the understanding of the pathophysiological procedures tangled in spasticity. But none of the methods are easy and reliable.

In past few years, unmanned aerial vehicles (UAV) or drones has been a hot topic encompassing technology, security issues, rules and regulations globally due to its remarkable advancements and uses in remote sensing and photogrammetry applications. This review paper highlights the evolution and development of UAV, classification and comparison of UAVs along with Hardware and software design challenges with diverse capabilities in civil and military applications. Further, safety and security issues with drones, existing regulations and guidelines to fly the drone, limitations and possible solutions have also been discussed.

Three-phase induction motors are considered the backbone of the modern industrial processes. Incipient stage detection of deformities in induction motors is very essential to prevent them from catastrophic failures. The detection and diagnosis of broken rotor bar fault has been receiving prominence amid modern industrial era. An irregularity in operation of motor produces harmonic content in motor supply current. Thus, motor current analysis can provide requisite useful information about occurrence of machine faults in near future. In this paper, a comparative analysis of broken rotor bar fault diagnosis is carried out under different motor loading conditions and fault severity. The detection of fault related components is done with the help of steady state current analysis using Fast Fourier Transformation (FFT). This method is load dependent and capable of identifying the fault using magnitude of frequencies in current spectra.

In modern years due to rising environmental issues such as energy cost and greenhouse gas emission have motivated new research into alternative methods of generation of electrical power. A vast deal of new research and enlargement for the renewable energy photovoltaic (PV) system. The PV module is conducted to search out non-polluting and renewable sources. New inventions are in development and exploring the perfection of solar cells to increase the efficiency and reduce the cost of power in per peak watt. The analysis of different kinds of control methods in PV system according to reviewed previous studies, shows that the most useful method is a hybrid technique as compared to other maximum power point tracking (MPPT) control methods. MPPT control method used to optimize the output of solar PV system with variable inputs such as solar radiations and temperature. The MPPT may include the use of a different DC–DC converter and also some different MPPT algorithms such as current based MPPT. Multi-input energy systems for the hybrid wind/solar energy systems need to be developed.

For upcoming smart grid one of the most essential topics regarding the implementation of Superconducting Fault Current Limiters (SFCL) is associated to its possible outcome on the reduction of abnormal fault Current. Due to the enlarge fault currents level, SFCL is more likely to infiltrate into a medium voltage and low voltage transmission network to lower the capacity of electric devices and improve their stability. Excessive fault current is a serious problem when grid connection of the micro grids with the current power grids, to be solved after implementation of SFCL in the network. SFCL is irresistible solution to limit fault current. Quick protection to the power system is provided by resistive type SFCL model. At present no Simulink model of SFCL has been introduced in the MATLAB library software. In this paper a Matlab/Simulink model for a resistive type SFCL is proposed. This paper first introduces a resistive type SFCL model in Matlab/Simulink and then simulates different types of fault and analysis is done without SFCL and with SFCL. The study shows that SFCL not only reduce the magnitude of fault current to a satisfactory level, but also damp transient recovery voltage. SFCL also improves the power system transient stability, power quality and reliability by reducing the fault current instantaneously.

FACTS (Flexible Alternating Current Transmission System) has proved to be very effective and viable in alleviating the problems of power transmission systems. FACTS play an important role to enhance the transient stability and maintain the related parameters such as voltage sag, harmonics, frequency changes, reactive power, active power. It also decreased the generation cost and reduce the transmission line losses. The Unified Power Flow Controller (UPFC) is a mostly used FACTS device due to its reliability and flexibility. It realizes lines parameters adjusting such as node voltages, line impedance, and phase angle. This paper is focused on the recent developed technologies and real time simulators for UPFC in establishing transient stability of electric power system.

To meet the global standards of food production and quality there is a strong need to remain update with the crop’s spatial and temporal variability from sowing to harvesting season. High spatial and spectral resolution images from satellite have made this task quick, easier and cheaper to some extent. The availability of new generation sensors on Landsat-8 and sentinel-2 satellites with onboard OLI and MSI sensors respectively are designated to study the land cover worldwide, and offers remarkable opportunities for monitoring applications with high temporal resolution. This paper presents the results of an ongoing research on chlorophyll estimation of vegetation with high accuracy from Landsat and sentinel satellites. Results with more than 0.75 correlation point out that in both cases the two sensors can be well used for remote estimation of chlorophyll concentration in vegetation.

Induction motors are an indispensible commodity in modern manufacturing and service industries. These motors are reliable, rugged, and require negligible maintenance. However, due to undue thermal mechanical and electrical stresses, these may fail prematurely. An induction motor applied for critical application, if fails may cause loss of productivity and huge amount of capital loss to company. Over the last 20 years, a number of condition monitoring methods and systems have evolved to reduce their abrupt failure. This paper provides a comprehensive insight into various popularly known techniques for fault diagnosis of induction motors. Review of state of the art techniques for condition monitoring, evolving techniques and recent advancements are discussed along with some case studies.

The rolling element bearings are essential components in rotating machines. Their condition can directly affect the machine operations. In this paper a new methodology for bearing fault diagnosis is proposed using weighted K nearest neighbor Classifier (WKNN). In WKNN, a squared inverse feature weighting technique is used to improve the performance of K-NN classifier. The proposed method uses simple time domain features to classify bearing condition using vibration signal. Multiple WKNN based classifiers are tested for optimizing the classification accuracy and computation complexity. Principle component analysis (PCA) is used to reduce the dimensionality of data set. Performance of WKNN for different K values and different distance metrics are compared with PCA and without PCA. Three bearing conditions, namely healthy, inner race fault and outer race fault were classified. The experimental results indicate that this method enables the fault detection in bearings with high accuracy. An accuracy of 100% was achieved using Mahalanobis distance metric.