ANJAN KUMAR SAHOO
@outr.ac.in
Electrical Engineering
Odisha University of Technology and Research
RESEARCH INTERESTS
Electric Vehicle
Electric Drives
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
Anjan Kumar Sahoo and Ranjan Kumar Jena
Elsevier BV
Anjan Kumar Sahoo and Ranjan Kumar Jena
Springer Science and Business Media LLC
Sridhar Palo, Anjan Kumar Sahoo, and Subrat Kumar Dash
IEEE
Because of their ease of use, dependability, and relatively low cost, single-phase induction motors find widespread use in a variety of applications across a range of industries as well as in the home. On the other hand, they have a low power factor and significant levels of harmonic distortion, which contribute to their poor performance. Single-phase induction motors can be driven by hybrid H-bridge multilevel inverters, which are an effective solution for the problems described above. The performance and utilization factor of single-phase induction motors with hybrid H-bridge multilevel inverters are the primary subjects of this paper. According to the findings, the application of hybrid H-bridge multilevel inverters has the potential to significantly enhance the performance of single-phase induction motors. This is accomplished through the reduction of harmonic distortion, the increase of power factor, and the increase of utilization factor. The improved performance of single-phase induction motors with hybrid H-bridge multilevel inverters has the potential to reduce energy consumption, reduce the common mode voltage, increase efficiency, and provide a solution that is more sustainable for a wide range of applications. These benefits can be realized in a variety of different contexts.
Subrat Kumar Dash, Anjan Kumar Sahoo, Shruti Ray, Tamanna Samantaray, and Niranjan Nayak
IEEE
This manuscript presents a novel teamwork optimization algorithm based simultaneous allocation of active devices (biomass DGs and DSTATCOMs) for three different voltage dependent loading scenarios namely constant power, constant current, and constant impedance in power distribution network. Both the placements and sizes of these active devices are optimized concurrently for different numbers of devices in a multi-objective framework that includes real power loss reduction, reactive power loss reduction, voltage deviation reduction, and voltage stability index enhancement. The proposed technique has been successfully validated on a standard 33 bus distribution network, and simulation results show that the performance of the power distribution network improves significantly in the presence of optimally allocated biomass DGs and DSTATCOMs for the studied loading scenarios.
ANJAN KUMAR SAHOO and RANJAN KUMAR JENA
The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
Anjan Kumar Sahoo and Ranjan Kumar Jena
VSB - Technical University of Ostrava
Anjan Kumar Sahoo and Ranjan Kumar Jena
Informa UK Limited
Ronak Mohanty, Samarjit Patnaik, Rabindra Kumar Behera, Anjan Kumar Sahoo, Rajat Kumar Muduli, Saroj Kumar Pradhan, and Madhurya Sarangi
Elsevier BV
Anjan Kumar Sahoo, Subrat Kumar Dash, and Ranjan Kumar Jena
IEEE
Globally, the trend is toward reducing CO2 emissions, and one way to do so is to switch from internal combustion to electric vehicles. To this aim, the electric drive system, which is the most critical component of an electric vehicle, has gained prominence and developed into a significant research issue. The induction motor is an excellent contender for electric vehicle applications because of its simplicity, robustness, and low maintenance. However, the conventional direct torque control scheme has a torque ripple issue, due to the use of hysteresis comparators and the availability of restricted voltage vectors. In this paper, the hysteresis comparator along with the switching table is replaced by an artificial neural network-based controller. The results indicate a significant reduction in torque ripple and improvement in dynamic performance.
Rajat Kumar Muduli, Ronak Mohanty, Samarjit Patnaik, Anjan Kumar Sahoo, Saroj Kumar Pradhan, and Rabindra Kumar Behera
IEEE
In the last few decades, oceanic exploration, especially deep-sea exploration, has gained the attention of many explorers, business tycoons, and environmentalists.71% of the Earth is covered by the ocean, but the human race is heading towards space exploration, disregarding the abundant resources present in the ocean. Indeed, cutting-edge sensing technologies cannot meet all underwater observational requirements due to the exceedingly novel and complex undersea environment. Computer vision and navigation are essential technologies for autonomous underwater operations. For a feasible outcome of computer vision, we need optimum image processing meet the Insufficient light and low-quality picture augmentation required in an underwater environment as a prerequisite underwater vision. However, if the vision and navigation are apparent and subtle, it can result in many applications. Therefore, this review paper mainly focuses on the latest existing technologies that can applied for advanced underwater imaging, videography, mapping, and their integration for 3D image reconstruction, detection, and tracking by taking the influence of a variety of underwater parameters turbidity, alkalinity, density, refractive index, salinity, temperature, particulates, lighting, scattering effect, etc.) into account. AUVs are the most necessary and sufficient systems for underwater exploration and research.
Anjan Ku. Sahoo and Ranjan Ku. Jena
American Institute of Mathematical Sciences (AIMS)
<abstract><p>In the near future, zero-emission transportation is anticipated to be implemented in an effort to reduce the major pollutants caused by road transportation. This enormous endeavor will be impossible until all modes of transport are electrified. The induction motor-fed direct torque controller is widely used for EV applications due to its fast torque response and simplicity. However, ripples in torque and flux and current harmonics are the major issues related to DTC. The fuzzy-based DTC replaces the hysteresis comparators and the switching table with fuzzy logic blocks to realize fuzzy DTC control, which improves the system's performance. This paper presents an enhanced fuzzy logic control strategy of induction motor for electric vehicle applications. The main objective is to enhance the system's performance by reducing torque and flux ripples. Both the conventional and fuzzy-based DTC are simulated with MATLAB/SIMULINK, followed by a comparative assessment to validate the effectiveness of the proposed approach for both steady-state and transient operations. The results indicate improvements in torque ripple, flux ripple, and speed ripples by 69%, 10%, and 85%, respectively. Due to the reduction in ripples, there is also an improvement in the THD of the stator current by 17%. During transient, an average improvement of integral square error for torque and speed is 8% and 12%, respectively. Further, the proposed method is validated using EUDC and HWFET drive cycles, demonstrating a reduction in battery energy demand.</p></abstract>
Sambit Pradhan, Anjan Kumar Sahoo, and Ranjan Kumar Jena
IEEE
Direct Torque Control (DTC) is the most frequently employed approach in electric vehicles due to its numerous benefits. However, torque ripples reduce the system's efficiency due to the hysteresis comparators. This results in variable frequency operation, while the finite frequency sampling produces a pseudorandom overshoot of the hysteresis band. Thus, operation at low speeds, mainly when motor resistances vary, affects the machine's behavior. In this paper, a switching scheme for speed control of induction motor driven EV is assessed in light of Space Vector Modulation to improve the performance. The findings of the simulation study represent a reduction in torque, flux ripple, and current harmonics. A comparative analysis of the proposed technique with the conventional DTC shows improved performance.
Anjan Kumar Sahoo and Ranjan Kumar Jena
IEEE
Road transportation is very crucial for socioeconomic growth because they empower cities and enterprises to operate globally. However, a significant portion of the transportation sector is dependent on reciprocating engines, causing an increase in greenhouse gas emissions and air contamination. Electric vehicles offer a viable solution to the transportation sector's problems. Control techniques such as direct torque control and indirect field-oriented control are frequently used. Most vehicles use DTC for controlling the induction motor due to its fast torque response and ease of implementation. However, DTC suffers from low switching frequency and high torque and flux ripples. Various improvements have been made to conventional DTC schemes to reduce ripple and improve performance. As a result, this article provides an overview of recent enhancements in DTC for induction motor control applied to an electric vehicle.
Anjan Kumar Sahoo and Ranjan K. Jena
Informa UK Limited
Saswat Pati, Ritwik Ghosal, Sreetam Pattnaik, and Anjan Kumar Sahoo
IEEE
The faults in the power network affect the operational performance of the Induction Motor. This paper deals with the detection of external faults using soft computing techniques that reduces the workability of the Induction Machine. A Wavelet and Neuro-Fuzzy based algorithm is used here for detection of external faults in Induction Motors.
Saswat Pati, Ritwik Ghosal, Sreetam Pattnaik, and Anjan Kumar Sahoo
IEEE
Prevention of Induction Motors against internal and external faults is highly essential for uninterruptable industrial operation. It has an adverse effect on the performance and functionality of Induction Motor. This paper manages the detection and identification of faults at the terminal which descends the capability of Induction Motor in an Industry. A comparative approach among FFT analysis, Wavelet analysis and ANFIS for detection of external fault is presented here. The effect of each approach is described in detail. The faults are examined by wavelet transform in both space and time domain and the Neuro-fuzzy technique is validated by training an ANFIS model at each load to classify the faults most clearly among all the methods. The comparative study and advantage of one method over another are also described.