SHAILESH M. DESHMUKH
@kalingauniversity.ac.in
Assistant Professor, Department of Electrical Engineering
Kalinga University, New Raipur
Working as an Assistant Professor in the department of Electrical Engineering, Kalinga University, New Raipur, Chhattisgarh-India.
15 years of experience in the field of academics. Research area is power quality, electrical machines, energy sources, electrical vehicles.
More than 35 research publication in various journals and conference.
EDUCATION
Diploma in Electrical Engineering
M. Tech in Power System & Electrical Devices.
Ph. D. (Pursuing)
RESEARCH INTERESTS
power quality, electrical machines, electrical vehicles
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Shailesh Madhavrao Deshmukh
EDP Sciences
The problem of voltage scheduling in smart grids has been well studied. There exist number of approaches around the problem which would consider the residual voltage as the major key. The existing approaches suffer to achieve higher performance in voltage scheduling in smart grids. To handle this issue, an Energy Efficient Voltage Scheduling (EEVS) model is presented in this article. The proposed method considers residual energy, average power generation, average voltage supplied as the factors in the selection of grid towards scheduling. The method performs scheduling by computing the voltage scheduling factor (VSF). The method identifies the set of grids available and for any power requirement; the method computes the VSF value for various grids and based on that a suitable grid has been identified to support the power requirement. It has been performed at each time interval and the grids which are supporting in the current and previous cycles are neglected from the selection. The proposed EEVS model introduces higher scheduling performance and power factor maximization performance.
Anu G. Pillai and Shailesh Madhavrao Deshmukh
EDP Sciences
Data streaming has been identified as a key challenge in Mobile networks. Mobile handover is the key factor in maintaining data streaming and there exist number of approaches available which perform mobility handover according to energy, signal strength, and mobility speed. However, the methods suffer to achieve higher performance in data streaming. To handle this issue, an efficient Mobility and Energy based Streaming Model (MESM) is presented in this article. The method maintains the traces of streaming and network conditions like number of base stations with node details like energy, mobility speed and data rate received, signal strength, data rate required. Using all these details, the proposed model estimates Data Rate Factor (DRF) at each interval. The method identifies set of base station and routes to compute DRF factor according to the statistics. Based on the value of DRF value, the method performs handover with different base station. The proposed MESM improves the performance of handover and data streaming.
Anu G. Pillai and Shailesh Madhavrao Deshmukh
EDP Sciences
The data security in smart grid systems is well studied. There exists number of approaches around the problem. But still they suffer to achieve higher performance in data security which degrades the performance of the grid system. To handle this issue, an efficient Service centric blockchain data security model (SBDSM) is presented in this article. Unlike previous methods, the proposed SBDSM model uses different signature of blockchain for different service data. The method use blockchain data towards sharing data between the grids which used to control the mode as well as flow of voltage in the grid. The model has a controller which decides the flow of voltage to be regulated from the micro grid. This information has been communicated from the controller to the micro grid through the blockchain. The micro grid can identify the type of information from the signature of the blockchain data and the hash code. The model monitors the incoming voltage and finds set of microgrids to be switched to discharge mode and informed with the amount of voltage to be regulated from the microgrid. This information is hidden in the block of the chain. By receiving the blockchain, the microgrid would extract the data and decrypt the voltage level to be regulated for the current cycle. Similarly, to perform switching of the microgrid, the controller sends another blockchain which has information about the mode the microgrid to be work on at the current cycle. The proposed method improves the data security and performance of voltage regulation.
Shailesh Madhavrao Deshmukh and Ravi Prakash Mahobia
EDP Sciences
The power stabilization in photovoltaic systems is well studied. There exist number of approaches in stabilizing the output power of PV systems. The most approaches concern about the input power and residual energy of capacitors. Still they suffer to achieve higher performance in power stabilization. To handle this issue, an efficient Fuzzy inference based Power stabilization model (FIPSM) is presented in this paper. The model is focused on utilizing residual energy on different circuits and avoiding higher drain of energy on any of the circuit. The model fabricated with MOFSET device in each circuit which monitors the input and output voltage of any circuit. As the model has number of circuits framed in serial connection, the method generates fuzzy rules based on the conditions of different circuits for different input voltage with output voltage required. At each duty cycle, the method reads the input voltage and identifies set of circuits were in sleep or charging mode. With the list of circuits, the method computes the Fuzzy Inference Voltage Stabilization Support (FIVSS) for various circuits. Based on the value of FIVSS, the method identifies a unique or a subset of circuits to support stabilization. The proposed model improves the performance of power stabilization in photovoltaic systems.
Shailesh Madhavrao Deshmukh
EDP Sciences
The problem of power stability in electrical systems has been well analyzed. There exist number of approaches to handle the issue which consider the factors like residual energy in various power grids of the system. However, they suffer to achieve higher performance in power stability. To handle this issue, an efficient deep learning based voltage switching model (DLVSM) is presented in this article. The proposed model adapts deep neural network towards the selection of electrical circuit from the list of serially connected grids. As the grids have their own residual energy and varying voltage production, the proposed model trains the network with different features like average voltage generation, average output voltage and residual energy. Using all these factors, the neurons are designed to measure the Optimized Power Stability Factor (OPSF) for various patterns of grid circuit. The network is designed with number of intermediate layers where each layer has set of neurons which estimates the Power stability factor (PSF) for specific grid unit. The output layer neurons estimates OPSF value for various sequences of grids. Based on the value of OPSF, the proposed model identifies a specific sequence and performs voltage switching to maintain the power stability in the electrical systems. The proposed model improves the performance of power stability and reduces voltage loss.
Shailesh Madhavrao Deshmukh and Ravi Prakash Mahobia
EDP Sciences
Towards effective voltage regulation and power stability, there exist number of approaches available in literature. The methods consider the residual voltage in various power grids in maximizing the power stability. However, the methods suffer to achieve higher performance in power stability and voltage regulation. To handle this issue, an efficient Electric Patter Based Voltage regulation model (EPVRM) is presented in this article. The method maintains the voltage trace belongs to various grids of the power system. Using the traces maintained, the method preprocesses the traces to remove the noisy records. The preprocessed trace has been used to generate the electrical pattern which contains residual voltage of various grid units. Using the electric pattern the method computes Electric Pattern Stability Support (EPSS) towards the required voltage. Based on the EPSS value, the method identifies the most efficient pattern to be scheduled for current cycle. The selected pattern has been scheduled to maintain the power stability. The proposed method improves the performance of voltage regulation and power stability.
Shailesh Madhavrao Deshmukh and Sumit Ramswami Punam
Uphills Publishers LLC
According to statistics, Alzheimer's is the fourth leading cause of mortality in the US and its annual death toll is steadily rising. Additionally, it has been seen to be developing moderately in Asian nations; by 2050, half of all Alzheimer sufferers would come from these countries. While there are currently no known medications that can entirely cure Alzheimer's, early detection of the disease can help doctors treat patients appropriately, extending their lives and improving their quality of life. A computerised detection system may be employed to help physicians identify Alzheimer's and to detect the disease early. Many CAD systems were created in the past, mostly for the benefit of public health, however the majority of them had poorer accuracy rates. Therefore, it is necessary to design an effective CAD system that can accurately anticipate the presence of AD. In this paper, a CAD system for accurately detecting if a patient has Alzheimer's disease or not was established. Medical professionals may now reliably and swiftly identify a wide range of diseases and ailments with the aid of computer algorithms, artificial intelligence, and medical imaging thanks to the rapidly expanding field of computer-aided diagnosis (CAD).
Dr. Shailesh Madhavrao Deshmukh and Mrunal Salwadkar
Uphills Publishers LLC
The process of assessing patient data that is taken from various medical devices is known as health care analytics. The patient's condition is projected by the analytics, which helps the physician support the diagnosis. The data created are referred to as Electronic Health Records (EHR) / Electronic Medical Records (EMR). Large volumes of electronic health and medical records being produced, giving rise to the idea of medical big data. Medical data may be in its early stages of development or fully developed and organised. The data research community now has a new way to experiment with medical data and access the results of intelligent algorithms through testing or historical experiences. The vast amount of medical big data used in this study project served as the impetus for an attempt to handle the data using supervised classifiers and predictors from clever machine learning techniques. In healthcare, this analytical model is used to forecast illnesses. The goal of this project is to develop an intelligent framework for mining large amounts of medical data in order to assess public health issues and forecast illness occurrences.
Dr. Shailesh Madhavrao Deshmukh and Nisha Milind Shrirao
Uphills Publishers LLC
The present paper proposes personalised remote healthcare based on soft computing. The primary goal of the thesis is to develop an intelligent, personalised RHM that uses personalised monitoring to create alerts in almost real-time settings and detect abnormalities in the status of human wellness. Since the activities have a significant impact on the vital sign values, the range of linguistic severity class labels in this research work is fixed based on the same, which results in a good level of personalisation of health data. The suggested study project can be very helpful to those under home quarantine or in specially designated quarantine areas, such as hotels, etc. Soft computing techniques offer a chance to create a RHM that can reliably detect changes in the population health state and accurately monitor many or all metrics of interest.
Shailesh M Deshmukh, Mahesh Singh, Shashank Kumar, and Gautam Kumar Rana
IEEE
The integration of microgrid capabilities into PHEV charging stations enables a versatile and robust energy supply, mitigating grid dependency and enhancing overall system resilience. Leveraging the adaptive optimization technique, the system dynamically adjusts parameters, optimizing charging processes based on real-time conditions, thereby ensuring efficient and dependable service. Specifically, the study integrates Adaptive Harris' Hawks Optimization (HHO) to enhance the efficiency of simultaneous energy distribution within these stations. A meta-heuristic solving tool is created for controlling the PHEV charging sequence. This programming model's primary contribution is its capacity to schedule the cars in both areas concurrently. With reference to the IEEE 33-bus and IEEE 69 bus, the effectiveness of the suggested energy management framework is assessed. The results were obtained for renewable DG integration of Constant Power (CP), Constant Impedance (CZ), and Constant Current (CI) load models respectively. The stochastic nature of the proposed Harris' Hawks Optimization algorithm is tested by running 100 trails with RDG placements. The result comprises RDG size, ENS, power loss, time taken to complete the simulation, and the convergence point of iteration at all load models. In comparison to FFA and PSO, Harris' Hawks Optimization has the least optimum ENS in DG placement. The findings demonstrate that the lowest operating costs are possible when charging during periods of low electricity prices and discharging during periods of high electricity prices.
Manoj Kumar Nigam, Shailesh M. Deshmukh, Ankit Mishra, and Rahul Mishra
IEEE
In this we discussed about the impact of mutual inductance on the dynamic modeling of Switched Reluctance (SR) motors and their performance. COMSOL software is used to simulate the performance of an 8/6 pole SR motor. Here, we also examine how well the redesigned stator pole SR motor performs in comparison, taking into account the impact of mutual inductance. The inductance profile and magnetic flux profile at various rotor positions have been displayed by the simulation results.
Sonali Khorge, S. P. Gawande, Pradyumn Chaturvedi, Akshaya Bonde, Sajid Sheikh, and Shailesh Deshmukh
IEEE
Now-a-days due to rising concerns about environmental degradation majorly caused by GHG emissions, emitted by ICE powered engines, a new “sustainability mantra” is emerging at center stage. This is to electrify automotive transportation industry As a result, demand for Electric Vehicle (EV) is at pace. EVs are considered to be green as they have no tailpipe emissions exhausted from them. Since EVs are powered by electricity to charge the battery from grid. The major segment of EV is charging. Amongst the various battery charging technologies, two types of charging methods are enlisted depending upon power level and sizing of battery ie. ON Board and OFF Board charging. Accordingly, it is more preferential and beneficial to charge battery through grid. This will also facilitate the bi-directional power flow between the grid and vehicle which introduces the concept of Grid-to-Vehicle(G2V) and Vehicle-to-Grid(V2G). To emphasize this concept, this paper proposes ON Board EV charging station. The converters and their suitable controls have been implemented to analyze the system. The system is simulated in MATLAB/SIMULINK environment and fruitful results are obtained to validate the system performance.
Shailesh M. Deshmukh, Vijayalaxmi Biradar, and S. P. Gawande
IEEE
DC and AC electricity charge EVs at the charging station. DC charges quickly, but AC charges slowly. The microgrid has few AC loads but lots of DC quick charging. High harmonic current from several AC and DC conversions will increase power usage and decrease microgrid solidity and richness. Hence, the traditional hybrid AC/DC microgrid that mainly relies on an AC microgrid fails in such conditions. Charging stations powered by a hybrid microgrid may help regulate power flow and reduce transmission losses in today's power grid. Nevertheless, when battery electric vehicles (BEVs) are charged without coordination with the hybrid microgrid, the associated renewable energy sources are not used to their full potential. In addition, a multiport charging facility is part of the growth of new charging stations that is expected to strain the power grid. Our unique hybrid microgrid system for electric car charging stations is proposed as a solution to these problems. To better accommodate electric vehicles (EVs) on the grid, this research proposes and evaluates a novel form of photovoltaics (PV) hybrid DC/AC microgrid for EV charging stations. Components of the proposed model include renewable energy sources, a diesel generator, a PV model, storage devices, linear loads, and non-linear loads (RESs). The effectiveness of the suggested model for electrical car charging stations is shown by the simulation results.
Pranjali M. Meshram, S. P. Gawande, S. Deshmukh, and Amrita Chaudhury
IEEE
The most significant electromechanical power- conversion technology over a very long period of time has been the synchronous machine, which is essential for both the generation of electricity and some unique drive applications. This paper explains the synchronization between the synchronous generator and an infinite bus. A real time synchronous generator (SG) with steady field current linked to an infinite bus is investigated to identify verifiable requirements for stability of the equilibria. To carry out this analysis synchroscope method is proposed to establish synchronization. The comprehensive mathematical modelling of SG is produced for analysis purpose. Additionally, detail discussion has been carried out to focus advance methods as an alternative for SG. The simulation and hardware analysis are performed intensively to validate the system performance.
Shailesh M. Deshmukh, Vijayalaxmi Biradar, and S. P. Gawande
IEEE
Eco-friendly automotive technologies like electric cars have risen in popularity due to the need for a cleaner environment. In addition to the growing number of EVs, charging infrastructure is becoming more crucial. Options for increasing operation and efficiency of the EV charging infrastructure, understanding current EV customers' charging habits are some of the concerns. With RFID (radio frequency identification), users may be identified automatically. This technique employs electromagnetic waves to send and receive data. This study reports on the development of distributed renewable power charging stations for EVs. IEC 61851, 62196, and ISO 15118 are used to develop this. As controller and EV user identification, the designed electric charging station employed ATMEGA8535 microcontroller and RFID. Solar, wind, and battery storage charging stations successfully designed for electric vehicles.
Manish Kurre, Shailesh Deshmukh, Rajdeep Tandekar, Pratikanta Mishra, and Atanu Banerjee
Trans Tech Publications, Ltd.
In this paper, a novel low-cost digital controller to drive a buck converter fed voltage source inverter (VSI) based brushless DC (BLDC) machine has been proposed. This controller is designed to be implemented solely in digital platforms and has multiple numbers of predefined discrete duty ratios. In conjunction with buck converter fed VSI, the controller is beneficial to enhance the speed and resultant torque profile of BLDC motor drive as compared to conventional bang-bang or on-off controller. The controller also incorporates a current limiter to avoid the over-current loading of the motor. The sampling time and the design of the current limiter are mathematically derived in the paper. The working efficiency of the developed controller for the BLDC motor drive has been examined for various vital and steady-state conditions. The speed ripples, settling time, tolerance to the commanded speed, and load variations are validated and presented in this paper. The proposed controller has been performed and implemented in the field-programmable gate array platform and compared to generic pre-existing controllers to validate the cost-effectiveness of the controller.