This paper presents a reduced component count power converter for the medium voltage induction motor drive (MVIMD). For critical applications, a compact size, less complex drive system is developed, which satisfies the power quality standard at the ends of the drive system. The proposed drive system is designed such that it has the least number of windings in the multi-winding transformer for a 36-pulse AC-DC converter and the least number of semiconductor switches for the five-level multilevel inverter as compared to the conventional topologies. Hence, the main concerns of this drive system are the reduction in the component count, weight, and losses of the system with excellent harmonics performance. The operational aspects of the topology such as circuit structure, design, and indirect field-oriented control (IFOC), and modulation technique (modified pulse width modulation technique) are explained. To guarantee the high reliability of the presented drive system, the drive model is developed in the Matlab/Simulink 2018b, whereas the simulated results are analyzed in steady-state and in dynamic states for a 298.4kW, 4kV medium-voltage induction motor.
The technological developments in the control strategy cause optimal coordination of conventional energy resources and renewable energy sources and increase the popularity of the AC microgrid (MG). This paper proposes a seamless mode transfer capability of MG to achieve optimal coordination between solar photovoltaic (PV)/battery/utility grid/diesel energy sources. The MG is controlled in such a way that the maximum power demand is fulfilled from a solar PV array; thereby, the optimum use of a clean energy source is targeted. Battery storage at the DC link manages the power variations from the solar PV array during the varying solar irradiations. In GC mode, the MG is synced to the three-phase power grid and thereby, the excess power generation is being fed to it, or the power deficiency is taken from it. Moreover, the MG is synchronized to a diesel engine generator (DEG) set as a standby energy source. The MG is synchronized to it only when the utility grid is not available, the battery state of charge (SOC) is under the lower limit and where PV array power is lower than the load power demand. In this way, the MG is controlled such that optimal coordination of various energy sources is performed in this paper. An improved second-order generalized integrator-frequency locked loop with DC offset rejection capability (SOGI-FLL-WDCRC) is developed in this paper for enhancing the quality of power injected to the utility grid even under abnormal grid conditions and mitigating the power quality (PQ) issues due to the connected nonlinear loads.
This paper presents the grid interactive wind-solar-diesel generator (DG) based microgrid at unpredictable weather conditions with improved reliability of power supply. With the enhanced reliability, the loads are powered continuously during both on-grid and off-grid modes. In view of sensitive loads, the DG is used in the system on the grounds of its dispatchability, which is lacking in the wind-solar power generation. However, the DG is controlled to consume minimum fuel during its operation. The wind power is captured by using a doubly fed induction generator (DFIG), which is coupled to the wind turbine. Moreover, a battery bank is connected in the system for energy storage and to deliver when its need arises. Modified controls are presented for converter on rotor side (CRS) and converter on load side (CLS) of DFIG to make the microgrid flexible for on-grid and off-grid operations. An adaptive step change incremental conductance based maximum power point tracking (MPPT) technique is utilized for acquiring the peak power from the solar photovoltaic (PV) array. Moreover, a modified adaptive step change based perturb and observe (P&O) wind MPPT strategy is incorporated in the control of CRS for improved power capturing as compared to the conventional fixed step methods. Results are presented to evidence the microgrid performance during on-grid and off-grid modes, at fluctuating wind speeds and solar irradiances. Moreover, the comparison of wind turbine efficiency is presented between the modified and conventional wind MPPT techniques.
High Voltage (HV) Pulsed Electric Field (PEF) has been extensively used for the treatment dairy products and juices to inactivate microbes. The present work suggests HV PEF to disinfect the effect of microbes commonly found in water and milk. The PEF of the order of 1.2 microsecond rise time to 50 microsecond tail time in parallel plate static chamber has been applied to get microbes’ free water and milk. For microbial inactivation by PEF the field intensity was varied from zero to 180kV/cm and number of pulses has been varied from zero to 100. In first case field intensity was kept constant and numbers of pulses were varied while in another case numbers of pulses are kept constant and field intensity was varied. In both the cases survival ratio of microbes has been calculated. This experiment's objective was to find out the survival ratio of five kinds of bacteria namely Enterobaracter Aerogenes, Escherichia coli, Listeria Monocytogene, Staphylococcus Aurous, and Acetobacter commonly found in water and milk. The survival ratio of some of microbes were observed very low 0.000001, at 40kV and 40 pulses and some other microbes has also very low survival ratio at more 40kV and number of pulses more than 40. E. coli was completely inactivated at 80kV whereas L.monocytogenes reduced by only two orders of magnitudes at 140kV.It has been found that the bacteria were destroyed due to field-induced at the protecting wall of microbes and not due to ohmic heating.
This paper presents a photovoltaic (PV) system interfaced to a non-ideal grid. The grid is susceptible to have unequal and non-sinusoidal voltages. The control of the system makes it robust against these maladies, and the waveforms of the grid currents are controlled to be sinusoidal with equal magnitude in each phase. The control also prevents the ill-effects of unbalanced nonlinear load currents from deteriorating the grid currents. The current in the grid neutral feeder is also suppressed, thereby reducing the losses. For linking a battery energy storage (BES) to the system DC link, the system uses a bi-directional DC-DC converter. The BES is used as a buffer to maintain the desired power flow to the grid. This removes the unpredictable power injection associated with the PV generation units, thereby helping the utility with the power management and keeping the distribution network stable. Moreover, the BES is utilized to increase monetary returns by keeping the power flow dependent on the utility tariff rates. The continuous revision of the DC link voltage ensures that the peak power is drawn from the PV array. The behavior of the system at several testing circumstances is presented.
This paper deals with the position estimation of a switched reluctance motor (SRM) drive with a simplified sensor-less control. The electric vehicle needs reliable position estimation algorithm. The encoder increases cost and size of overall drive system and is prone to electromagnetic disturbances. Here, a third order sliding mode observer (SMO) based position estimation methodology is employed based on flux-linkage error. The rotor position, angular speed and angular acceleration are estimated from the model based observer. Estimated angular acceleration is used to estimate the load torque input for aforesaid application. The estimated position is used to generate converter gate signals. The designed observer estimates the variables over a wide range and response has less disturbance and noises at steady state. Simulated results show the quite satisfactory results when compares to position and speed measured from the motor.
This paper presents an islanded operation of wind turbine driven doubly fed induction generator (DFIG), photovoltaic (PV) array and battery based microgrid. Here local grid (LG) is formed by a PV-battery-based grid forming converter. Depending upon the wind availability the doubly fed wind turbine driven induction generator (DFIG) is synchronized with LG. The maximum PV power is extracted through the incremental conductance (INC) algorithm. The battery is connected across the DC-link of the grid forming converter. The LG terminal voltage is maintained by grid forming converter. The field-oriented control (FOC) is used to control the rotor side converter (RSC). Moreover, it is used to extract the maximum wind power and provides reactive power to the DFIG. A Discrete Fourth-Order Generalized Integrator-Frequency Locked (DFOGI-FLL) control is used to maintain the DC-link of the grid side converter (GSC). Moreover, it mitigates the reactive power to the LG and calculates the stator angle (Ɵstator), After matching the vstator, fstator and Ɵstator of the DFIG, it synchronizes to LG. Simulated results show performance of the PV-wind-battery based grid forming converter with DFIG synchronization in different conditions.
Brushless permanent magnet (PM) motors are gaining popularity in the appliance industry due to their improved efficiency and high power density. This paper presents the finite element analysis (FEA) based design of a low cost ferrite magnet surface permanent magnet synchronous motor (SPMSM). The objective here is to improve the efficiency and torque ripple profile of the existing motor for an industrial exhaust fan. The single-phase induction motor (SPIM) utilized in the available industrial exhaust fan is modified to an SPMSM. The main dimensions and electrical performance of commercially available SPIM for an industrial exhaust fan are analyzed. The data obtained from an analytical design of SPMSM is validated against FEA. FFT analysis of radial flux density in the air gap and line back emf for different slot-pole combinations of the motor are analyzed, and the best suitable topology is selected. The improved efficiency results in the reduction in energy consumption of the appliance and hence its operating cost.
Activity recognition from human action data is quite a challenging task in the biomedical data science community. The main challenge in dealing with human activity recognition (HAR) datasets is their high cardinality. Therefore, reducing cardinality is a cardinal area of research in the HAR field. In this research, reducing the data dimensionality by utilizing future selection methods has been used. This research work has extracted features using wavelet packet transform (WPT) and the cardinality of the feature set has been reduced by using the Genetic Algorithm (GA) technique. The selected features also have been ranked according to their importance based on their SHAP values. In the venture, an interesting inspection has been found. That is in HAR datasets, signal values lay into lower frequency regions mostly. The highest accuracy and f1-score which have been got are 94.74%, 94.73%, and 89.98%, 89.67% for the feature extracted and feature selected dataset respectively.
This paper analyzes the influence of zero voltage vector injection in the implementation Direct Torque Control(DTC) for Permanent Magnet Synchronous Motors(PMSM). The impact on torque ripples and switching frequencies are particularly focused on. This paper speed and torque control of the PMSM based on a 2 Level switching table incorporating the Zero Voltage vectors to decrease torque ripples while maintaining the dynamic response of the system. Taking percentage torque demand as a criterion, the switching states are switched between non zero voltage vector and zero voltage vector switching tables. This enables the system to keep its dynamic behavior all the while decreasing the torque ripples inherent to DTC implementation. The speed control scheme is setup to test the method in variable speed conditions. A Software in Loop (SIL) verification of the proposed method was performed with Xilinx System Generator in MATLAB environment to test the system in fully loaded conditions. The proposed method resulted in a 14 percent reduction in the torque ripples measured while it resulted in a 25 percent reduction in the average switching frequency.
The use of big data has grown so faster that if we will look around, we are sending data, receiving data; that means we can find data everywhere. In the past few years, a sudden growth of population has been observed and for handling this large amount of data, big data is being used. For making our cities smarter we need to work on big data and not only big data but the emerging technologies that is; internet of things (IoT) and artificial intelligence. IoT gadgets that may include sensors, actuators, smart phones and smart machines that work on artificial intelligence to get the response we want, and big data obviously, to extract the data from the devices and systems. While observing our surroundings, we can see IoT and big data everywhere like as in healthcare, transportation, cities, banks, schools, universities, commercial industries and everywhere. To meet these all things, lot of researchers have proposed a complete system for IoT based big data systems that may include smart buildings, smart transportation networks, smart irrigation systems and what not! In this paper we will look how artificial intelligence and IoT based big data systems can help the cities smarter to the high-level next gen smart cities. For this we will be looking into the architecture and the proposed model to meet all these requirements.
Microgrid comprising a hydro generator faces the problems of voltage and frequency variation in the standalone mode of microgrid (SMM) as well as in the resynchronizing mode. A robust control technique is, therefore, required to minimize the deviation of the system attributes from their set quantities. This work harnesses the power extracted from the PV array and hydro generator and it supplies quality power to the loads. Besides, a battery storage is also utilized to handle the deviations, provides frequency regulation and acts as a backup storage. An M-estimate normalized sub-band adaptive filter (MENSA) is utilized to extricate the fundamental constituents of the loads as well as hydro-generator current in order to prevent the harmonics entering the system. Moreover, a quadrature signal filter (QSF) is used to extricate positive sequence voltages in order to synchronize the system with the utility grid harmoniously. In the standalone mode of microgrid, a cascaded controller employing proportional and resonant (PR) controller is used to generate the switching pulses for the power conversion unit to operate in voltage control mode. Simulated results are presented and illustrated the synchronization, which occurs within 3 cycles upon restoration of the grid. The performance of microgrid is simulated under various conditions and the results are presented, which comply with the IEEE 1547 std.
The enormous volume of data which is generated by healthcare industries needs to be managed and analyzed properly in order to derive meaningful information. These decisions are more accurate than intuition. Big data houses various hidden knowledge's or patterns which are required for decision making. Exploratory data analysis (EDA) gains insights into the data: discovers errors, locates proper data, verifies assumptions, extracts key variables, and examines correlations between factors. EDA is a data analytics method which excludes mathematical modelling and inferences. Data analytics is a lowcost technology and has a vital role in health care industries, various sources include emergency situations, biomedical research, epidemics, pandemics etc. In present work, we took Cleveland cardiopathy dataset and then utilized K-means method to identify risk variables that cause cardiopathy. Age, hypertension, sugarlevel, chest discomfort, Electrocardiogram at relaxation, heart palpitation, and three forms of angina are among the 209 records in the collection and it was found that Kmeans method is the most effective one because of its speed and the proficiency of its output, it gives output in about 8sec so, for the prediction of cardiopathy K-means clustering method is employed by analyzing data in association with a visualization dashboard, and the data that is visualized in tableau demonstrates that the forecast is correct.
In smart cities applications (i.e. intelligent transport systems, traffic management) cellular traffic load prediction is playing an essential role. The cellular data consumption can help to understand the road traffic patterns. In this context, the employment of predictive Machine Learning (ML)techniques can be useful for approximating the possible resource demands in cell towers. Therefore, cellular traffic data may very useful for finding trends and patterns of load of human activities in city traffic. In this paper, the main aim is to identify the suitable machine learning techniques, which can be used for traffic load prediction in a smart city application. The paper includes three main contributions, first providing an overview of 5G technology and their applications, second, a review on existing traffic load prediction techniques and finally, a comparative experimental study is performed among popular supervised and unsupervised learning approaches. In order to compare the performance of supervised learning algorithms, Support Vector Machine (SVM), Artificial Neural Network (ANN), Bays classifier, Linear Regression (LR), and Decision Tree (DT) are implemented. On the other hand, for comparing the performance of unsupervised learning algorithms the Self Organizing Map (SOM), Fuzzy C Means (FCM), and k-Means clustering algorithms have been involved. The experiments on publically available data on Kaggle for 4G (LTE Traffic Prediction) were used. According to the experimental analysis, SVM and ANN are accurate algorithms in the supervised learning algorithms. On the other side in unsupervised learning models, SOM shows superior accuracy. But after summarizing the results we found that the SVM and ANN algorithms are beneficial for the proposed application.
This paper deals with the design and development of observer-based position sensorless scheme of brushless DC motor (BLDCM) drive with optimal torque and current control for electric vehicle application. Conventional back EMF based rotor position detection algorithm fails in lower speed range as magnitude of back EMF is too small to measure directly. As, back EMF of the BLDCM is one of the few parameters, which gives accurate commutation instants, an indirect method of sensing back EMF using an observer is introduced here which estimates line back EMF even during starting with great accuracy. Secondary inverted polarity Cuk (SEPIC) converter is used for efficient maximum power point tracking (MPPT) operation with less input current ripple, ensuring soft starting of the motor. Non- inverted property of the SEPIC eliminates the need of sensing negative voltage at DC link. Coupled inductor is introduced with SEPIC, which reduces cost and space on PCB than two separate inductors. This BLDCM drive is capable of energy regeneration as a provision of having extended driving range. This system topology is designed and analyzed using the simulated results in MATLAB/SIMULINK environment.
This paper deals with the implementation of Delta method using Recurrent Neural Network (RNN) for estimation of stability and control derivatives in lateral-directional mode. The proposed method is implemented on simulated flight data and then on real flight data. The generation of data is done using the parameters of the research aircraft, ATTAS. The results obtained using RNN are further compared to results obtained by using Feed Forward Back propagation algorithm (FFBP) in tabular and graphical formats for both simulated as well as real flight data. It is found that the derivatives obtained using RNN are very close to true values of derivatives with lesser standard deviation as compared to derivatives obtained using FFBP algorithm. The results increase level of confidence and suggest that the RNN can be used advantageously to estimate aerodynamic derivatives of an aircraft from real flight data.
This paper proposes a profound learning approach in Whole-slide images of breast cancer (WSI) for automatic detection and visual study of invasive ductal cancer (IDC) tissue regions. Deep learning techniques are strategies for learning from data including the computer simulation of the learning process. The diagnosis of invasive breast cancer is a time-contracting process, particularly when a pathologist scans vast sections of benign areas to find malignancy areas. Accurate line-up IDC in WSI is critical for subsequent tumor grading and patient outcomes assessment. Artificial Intelligence approaches are especially suitable for managing such problems, particularly if large numbers of samples for training are available, thereby ensuring that the main differentiator and classifier are generalized. The system for visual functional study of tumor regions in this article includes a range of Deep Learning algorithms for instance Artificial Neural Networks (ANNs) and Convolution Neural Networks (CNNs) to facilitate the diagnosis. The procedure has been tested using a WSI sample of 162 IDC patients among the images 113 teaching slides and 49 independent examination slides were chosen. The experimental assessment was planned to detect IDC tissue areas in WSI with classificatory precision. 10-fold cross-validation was used to evaluate how the effects of our classificatory are generalized on our data package, of which 80% of data are used for preparation and 20% for checking in each fold. We have obtained an 81.56 percent average sensitivity for detecting invasive ducal carcinoma in whole-slide images before using some regularization techniques. The exactness of our model efficiency has been increased to 86.24 percent, respectively after application of image increase and regularization. The findings show how successful our custom architecture is to classify invasive dual carcinoma detection in whole-slide pictures.
The influence of transition metal (TM) atoms (Au, Ag, and Cu) and alkaline metal (AM) atoms (Na, Li) doping on the structural and electronic properties of MoS2 bulk layers was studied based on the first-principles of DFT calculations. The density of states (DOS), band structures, and structural parameters of five differently doped MoS2 bulk layers were analyzed. The results show that doping of AM atoms further narrows the bandgap of MoS2 bulk layer than the TM doped atoms. The least bandgap of 0.609eV was observed for Li-MoS2 layer whereas the highest bandgap of 1.42eV for undoped MoS2 was recorded. It can be concluded that in applications of MoS2 based photodiode/phototransistor sensors, doping of AM atoms may prove an effective alternative to conventionally used TM (Au) doped arrays.
Electric vehicles are a vast, dynamic, and fast-growing topic that covers the reduced emissions in the environment, rising energy demand and consumption, ensuring the usage of green energy sources, and so on. With the growing research and development of electric vehicles on a global scale, regenerative braking of those vehicles is becoming increasingly important. The usage of the battery for covering a longer range is aimed here by utilizing this wasted heat energy of the vehicle, adding an extra source of advantage. This manuscript aims to present the proposed design of a Thermoelectric Generator (TEG) analyzing the relation between current and temperature for storing the electrical output in the battery for proliferating the driving range of the Hybrid Electrical Vehicles (HEVs). Following the concept of the regenerative braking system, the wasted heat energy generated from the kinetic energy of the vehicles is converted into electrical energy in this process.
