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Dipankar Deb
Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India

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Journal article
Published: 21 July 2021 in Applied Sciences
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Robotic manipulators have been widely used in industries, mainly to move tools into different specific positions. Thus, it has become necessary to have accurate knowledge about the tool position using forward kinematics after accessing the angular locations of limbs. This paper presents a simulation study in which an encoder attached to the limbs gathers information about the angular positions. The measured angles are applied to the Kalman Filter (KF) and its variants for state estimation. This work focuses on the use of fractional order controllers with a Two Degree of Freedom Serial Flexible Links (2DSFL) and Two Degree of Freedom Serial Flexible Joint (2DSFJ) and undertakes simulations with noise and a square wave as input. The fractional order controllers fit better with the system properties than integer order controllers. The KF and its variants use an unknown and assumed process and measurement noise matrices to predict the actual data. An optimisation problem is proposed to achieve reasonable estimations with the updated covariance matrices.

ACS Style

Sagar Gupta; Abhaya Singh; Dipankar Deb; Stepan Ozana. Kalman Filter and Variants for Estimation in 2DOF Serial Flexible Link and Joint Using Fractional Order PID Controller. Applied Sciences 2021, 11, 6693 .

AMA Style

Sagar Gupta, Abhaya Singh, Dipankar Deb, Stepan Ozana. Kalman Filter and Variants for Estimation in 2DOF Serial Flexible Link and Joint Using Fractional Order PID Controller. Applied Sciences. 2021; 11 (15):6693.

Chicago/Turabian Style

Sagar Gupta; Abhaya Singh; Dipankar Deb; Stepan Ozana. 2021. "Kalman Filter and Variants for Estimation in 2DOF Serial Flexible Link and Joint Using Fractional Order PID Controller." Applied Sciences 11, no. 15: 6693.

Journal article
Published: 03 June 2021 in Actuators
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The application scope of unmanned aerial vehicles (UAVs) is increasing along with commensurate advancements in performance. The hybrid quadrotor vertical takeoff and landing (VTOL) UAV has the benefits of both rotary-wing aircraft and fixed-wing aircraft. However, the vehicle requires a robust controller for takeoff, landing, transition, and hovering modes because the aerodynamic parameters differ in those modes. We consider a nonlinear observer-based backstepping controller in the control design and provide stability analysis for handling parameter variations and external disturbances. We carry out simulations in MATLAB Simulink which show that the nonlinear observer contributes more to robustness and overall closed-loop stability, considering external disturbances in takeoff, hovering and landing phases. The backstepping controller is capable of decent trajectory-tracking during the transition from hovering to level flight and vice versa with nominal altitude drop.

ACS Style

Nihal Dalwadi; Dipankar Deb; Mangal Kothari; Stepan Ozana. Disturbance Observer-Based Backstepping Control of Tail-Sitter UAVs. Actuators 2021, 10, 119 .

AMA Style

Nihal Dalwadi, Dipankar Deb, Mangal Kothari, Stepan Ozana. Disturbance Observer-Based Backstepping Control of Tail-Sitter UAVs. Actuators. 2021; 10 (6):119.

Chicago/Turabian Style

Nihal Dalwadi; Dipankar Deb; Mangal Kothari; Stepan Ozana. 2021. "Disturbance Observer-Based Backstepping Control of Tail-Sitter UAVs." Actuators 10, no. 6: 119.

Journal article
Published: 27 April 2021 in IEEE Transactions on Energy Conversion
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Data-driven condition monitoring reduces downtime of wind turbines and increases reliability. Wind turbine operation and maintenance (O\&M) cost is a significant factor that calls for automated fault detection systems in wind turbines. In this manuscript, the anomaly detection problem for wind turbine gearbox is formulated based on adaptive threshold and twin support vector machine (TWSVM). In this work, SCADA data from wind farms located in the UK is considered with samples from thirteen months before failure. Gearbox oil and bearing temperatures are used as two univariate time-series for analyzing adaptive threshold. The effectiveness of the proposed method is compared with standard classifiers like support vector machines (SVM), k-nearest neighbors (KNN), multi-layer perceptron neural network (MLPNN), and decision tree (DT). Anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability. The missed failure and false positive rate that indicate the proposed methodology's ability is also investigated to discriminate between false alarms, and comparison with previous studies shows superior performance.

ACS Style

Harsh S Dhiman; Dipankar Deb; S. M. Muyeen; Innocent Kamwa. Wind Turbine Gearbox Anomaly Detection based on Adaptive Threshold and Twin Support Vector Machines. IEEE Transactions on Energy Conversion 2021, PP, 1 -1.

AMA Style

Harsh S Dhiman, Dipankar Deb, S. M. Muyeen, Innocent Kamwa. Wind Turbine Gearbox Anomaly Detection based on Adaptive Threshold and Twin Support Vector Machines. IEEE Transactions on Energy Conversion. 2021; PP (99):1-1.

Chicago/Turabian Style

Harsh S Dhiman; Dipankar Deb; S. M. Muyeen; Innocent Kamwa. 2021. "Wind Turbine Gearbox Anomaly Detection based on Adaptive Threshold and Twin Support Vector Machines." IEEE Transactions on Energy Conversion PP, no. 99: 1-1.

Journal article
Published: 24 March 2021 in IEEE Transactions on Instrumentation and Measurement
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The existing four linear section location methods are all established based on the three-phase fault current direction. When a single-phase grounding fault occurs in a neutral non-effectively grounded system, these methods are no longer applicable. This study proposes a linear optimization method for section location suitable for single-phase ground fault in the neutral non-effectively grounded system. It presents an improved transient zero-sequence current direction identification algorithm based on line voltage to accurately determine a transient zero-sequence current direction under single-phase grounding faults without zero-sequence voltage information. Secondly, it proposes a suitable linear section location model for single-phase ground faults as per the transient zero-sequence current direction’s distribution characteristics after the single-phase ground fault. Finally, it is verified in different feeders that the proposed method can quickly and accurately locate the fault section after the single-phase ground fault occurs in the neutral point ineffectively grounded distribution system.

ACS Style

Qiujie Wang; Tao Jin; Mohamed A. Mohamed; Dipankar Deb. A Novel Linear Optimization Method for Section Location of Single-Phase Ground Faults in Neutral Noneffectively Grounded Systems. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -10.

AMA Style

Qiujie Wang, Tao Jin, Mohamed A. Mohamed, Dipankar Deb. A Novel Linear Optimization Method for Section Location of Single-Phase Ground Faults in Neutral Noneffectively Grounded Systems. IEEE Transactions on Instrumentation and Measurement. 2021; 70 (99):1-10.

Chicago/Turabian Style

Qiujie Wang; Tao Jin; Mohamed A. Mohamed; Dipankar Deb. 2021. "A Novel Linear Optimization Method for Section Location of Single-Phase Ground Faults in Neutral Noneffectively Grounded Systems." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-10.

Journal article
Published: 01 February 2021 in Electronics
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Maglev transportation system is become a hot topic for researchers because of the distinctive advantages, such as frictionless motion, low power consumption, less noise, and being environmentally friendly. The maglev transportation system’s performance gets sufficiently influenced by the control method and the magnetic levitation system’s dynamic performance, which is a critical component of the maglev transportation system. The Magnetic Levitation System (MLS) is a group of unstable, nonlinear, uncertain, and electromagnetically coupled practical application. Control objective of this study is to design a position stabilizing control strategy for Magnetic Levitation system under extreme uncertain parametric conditions using a reference model governed by a reference stabilizer and nonlinear adaptive control structure. After successful tuning the reference stabilizer with and without time-varying payload disturbance, the tracking-error dynamics are obtained in the presence of both matched and mismatched types of parametric uncertainties. Next, the close-loop stability theorem is formulated for Lyapunov stability analysis to get the design constraints, parameter update laws, and adaptive control law. Numerical simulations performed for a high range of parametric violations check the control design’s efficacy. The performance robustness gets confirmed by comparing the results with the nonlinear control approach. The MLS gets performance recovery and settles within safe limits in few seconds using the proposed methodology. However, the nonlinear controller faces permanent failure in stabilizing the MLS.

ACS Style

Nihal Dalwadi; Dipankar Deb; S. Muyeen. A Reference Model Assisted Adaptive Control Structure for Maglev Transportation System. Electronics 2021, 10, 332 .

AMA Style

Nihal Dalwadi, Dipankar Deb, S. Muyeen. A Reference Model Assisted Adaptive Control Structure for Maglev Transportation System. Electronics. 2021; 10 (3):332.

Chicago/Turabian Style

Nihal Dalwadi; Dipankar Deb; S. Muyeen. 2021. "A Reference Model Assisted Adaptive Control Structure for Maglev Transportation System." Electronics 10, no. 3: 332.

Journal article
Published: 25 November 2020 in Sensors
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The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models.

ACS Style

Harsh S. Dhiman; Dipankar Deb; James Carroll; Vlad Muresan; Mihaela-Ligia Unguresan. Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis. Sensors 2020, 20, 6742 .

AMA Style

Harsh S. Dhiman, Dipankar Deb, James Carroll, Vlad Muresan, Mihaela-Ligia Unguresan. Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis. Sensors. 2020; 20 (23):6742.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb; James Carroll; Vlad Muresan; Mihaela-Ligia Unguresan. 2020. "Wind Turbine Gearbox Condition Monitoring Based on Class of Support Vector Regression Models and Residual Analysis." Sensors 20, no. 23: 6742.

Journal article
Published: 21 August 2020 in Actuators
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This work explores the extent of jet mixing for a supersonic jet coming out of a Mach 1.8 convergent-divergent nozzle, controlled with two short rectangular vortex-generating actuators located diametrically opposite to each other with an emphasis on numerical methodology. The blockage ratio offered by the tabs is around 0.05. The numerical investigations were carried out by using a commercial computational fluid dynamics (CFD) package and all the simulations were performed by employing steady Reynolds-averaged Navier–Stokes equations and shear-stress transport k-ω turbulence model on a three-dimensional computational space for more accuracy. The numerical calculations are administered at nozzle pressure ratios (NPRs) of 4, 5, 6, 7 and 8, covering the overexpanded, the correctly expanded and the underexpanded conditions. The centerline pressure decay and the pressure profiles are plotted for both uncontrolled and the controlled jets. Numerical schlieren images are used to capture the barrel shock, the expansion fans and the Mach waves present in the flow field. Mach contours are also delineated at varying NPRs indicating the number of shock cells, their length and the variation of the shock cell structure and strength, to substantiate the prominent findings. The outcomes of this research are observed to be in sensible concurrence with the demonstrated exploratory findings. A reduction in the jet core length of 75% is attained with small vortex-generating actuators, compared to an uncontrolled jet, corresponding to nozzle pressure ratio 5. It was also seen that the controlled jet gets bifurcated downstream of the nozzle exit at a distance of about 5 D, where D is the nozzle exit diameter. Furthermore, it was fascinating to observe that the jet spread increases downstream of the nozzle exit for the controlled jet, as compared to the uncontrolled jet at any given NPR.

ACS Style

Abhash Ranjan; Mrinal Kaushik; Dipankar Deb; Vlad Muresan; Mihaela Unguresan. Assessment of Short Rectangular-Tab Actuation of Supersonic Jet Mixing. Actuators 2020, 9, 72 .

AMA Style

Abhash Ranjan, Mrinal Kaushik, Dipankar Deb, Vlad Muresan, Mihaela Unguresan. Assessment of Short Rectangular-Tab Actuation of Supersonic Jet Mixing. Actuators. 2020; 9 (3):72.

Chicago/Turabian Style

Abhash Ranjan; Mrinal Kaushik; Dipankar Deb; Vlad Muresan; Mihaela Unguresan. 2020. "Assessment of Short Rectangular-Tab Actuation of Supersonic Jet Mixing." Actuators 9, no. 3: 72.

Journal article
Published: 17 August 2020 in Electronics
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A robotic navigation system operates flawlessly under an adequate GPS signal range, whereas indoor navigation systems use the simultaneous localization and mapping system or other vision-based localization systems. The sensor used in indoor navigation systems is not suitable for low power and small scale robotic systems. The wireless area network transmitting devices have fixed transmission power, and the receivers get the different values of signal strength based on their surrounding environments. In the proposed method, the received signal strength index (RSSI) values of three fixed transmitter units are measured every 1.6 m in mesh format and analyzed by the classifiers, and robot position can be mapped in the indoor area. After navigation, the robot analyzes objects and detects and recognize human faces with the help of object recognition and facial recognition-based classification methods respectively. This robot detects the intruder with the current position in an indoor environment.

ACS Style

Jatin Upadhyay; Abhishek Rawat; Dipankar Deb; Vlad Muresan; Mihaela-Ligia Unguresan. An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System. Electronics 2020, 9, 1326 .

AMA Style

Jatin Upadhyay, Abhishek Rawat, Dipankar Deb, Vlad Muresan, Mihaela-Ligia Unguresan. An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System. Electronics. 2020; 9 (8):1326.

Chicago/Turabian Style

Jatin Upadhyay; Abhishek Rawat; Dipankar Deb; Vlad Muresan; Mihaela-Ligia Unguresan. 2020. "An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System." Electronics 9, no. 8: 1326.

Journal article
Published: 03 July 2020 in Renewable and Sustainable Energy Reviews
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One way of setting up hybrid wind farms is through augmentation of turbines with battery energy storage systems (BESS). Due to variation in wind speed in such wind farms, the cost of BESS increases and reduction in battery life takes place. This paper proposes a wake management technique to reduce the operational cost of a hybrid wind farm equipped with BESS. The battery charging and discharging powers are ascertained considering error in the wind speed prediction. This investigation considers three different conditions for a wind farm, namely, (i) without wake (ii) without wake management and (iii) with wake management. An ageing model is utilized for the lead-acid battery while accounting for temperature and depth of discharge changes to assess the battery operational cost and lifecycle count. Simulation analysis for a two-turbine layout with the upstream turbine yawed from 0∘to 5∘, presents a 44.37% savings in operational cost and 79.74% increase in battery life evaluated for a dataset obtained from Challicum hills in Australia. Additionally, the battery operational cost is minimized at a yaw angle of 15°. Uncertainty analysis is done to study the effect of prediction technique on the lifecycle count. The proposed methodology is extended to a 5-turbine layout where along with lifecycle count and operational cost, the horizontal shear on the turbine blades is also analyzed.

ACS Style

Harsh S. Dhiman; Dipankar Deb. Wake management based life enhancement of battery energy storage system for hybrid wind farms. Renewable and Sustainable Energy Reviews 2020, 130, 109912 .

AMA Style

Harsh S. Dhiman, Dipankar Deb. Wake management based life enhancement of battery energy storage system for hybrid wind farms. Renewable and Sustainable Energy Reviews. 2020; 130 ():109912.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb. 2020. "Wake management based life enhancement of battery energy storage system for hybrid wind farms." Renewable and Sustainable Energy Reviews 130, no. : 109912.

Review
Published: 14 May 2020 in Processes
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A microbial fuel cell (MFC) is a potentially viable renewable energy option which promises effective and commercial harvesting of electrical power by bacterial movement and at the same time also treats wastewater. Microbial fuel cells are complicated devices and therefore research in this field needs interdisciplinary knowledge and involves diverse areas such as biological, chemical, electrical, etc. In recent decades, rapid strides have taken place in fuel cell research and this technology has become more efficient. For effective usage, such devices need advanced control techniques for maintaining a balance between substrate supply, mass, charge, and external load. Most of the research work in this area focuses on experimental work and have been described from the design perspective. Recently, the development in mathematical modeling of such cells has taken place which has provided a few mathematical models. Mathematical modeling provides a better understanding of the operations and the dynamics of MFCs, which will help to develop control and optimization strategies. Control-oriented bio-electrochemical models with mass and charge balance of MFCs facilitate the development of advanced nonlinear controllers. This work reviews the different mathematical models of such cells available in the literature and then presents suitable parametrization to develop control-oriented bio-electrochemical models of three different types of cells with their uncertain parameters.

ACS Style

Dipankar Deb; Ravi Patel; Valentina E. Balas. A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells. Processes 2020, 8, 583 .

AMA Style

Dipankar Deb, Ravi Patel, Valentina E. Balas. A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells. Processes. 2020; 8 (5):583.

Chicago/Turabian Style

Dipankar Deb; Ravi Patel; Valentina E. Balas. 2020. "A Review of Control-Oriented Bioelectrochemical Mathematical Models of Microbial Fuel Cells." Processes 8, no. 5: 583.

Journal article
Published: 07 May 2020 in Energy
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Wind energy installation numbers have witnessed a sharp increase in the recent past. Additionally, wind farms are seen as an effective and potent part of the interconnected power system. Significant variations in the wind speed pose a challenge for wind farm operators to provide accurate forecasts. In this manuscript, three hybrid wind farms, each comprising of wind turbines and battery energy storage systems, are located in the vicinity of each other and are assumed to deliver power to a utility grid. Fuzzy-based Multi-criteria decision-making techniques are applied to this cluster of three hybrid wind farms to determine the best strategy. Machine learning-based LSSVR method is utilized for wind speed forecasting and penalty cost estimation. Fuzzy TOPSIS and Fuzzy COPRAS evaluated and potential reversal of rankings is also explored. With a cumulative priority score of 0.4573 and 99.3 for dataset X1, both, fuzzy TOPSIS and fuzzy COPRAS respectively indicate that A3, that is, paying penalty for power borrowed from a neighboring wind farm is the best alternative for a hybrid wind farm. This study gives new insights into decision-making, specifically for hybrid wind farms.

ACS Style

Harsh S. Dhiman; Dipankar Deb. Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms. Energy 2020, 202, 117755 .

AMA Style

Harsh S. Dhiman, Dipankar Deb. Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms. Energy. 2020; 202 ():117755.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb. 2020. "Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms." Energy 202, no. : 117755.

Journal article
Published: 03 May 2020 in Renewable and Sustainable Energy Reviews
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Optimal placement of turbines in a wind farm is a major challenge where the wake effect reduces the effective wind power capture. Wind speed prediction is essential from a reliability point of view. In this article, a bilateral wake model which is derived from two benchmark models, namely, Jensen's and Frandsen's variation is used for studying the performance of far-end wakes. A prediction based approach is formulated wherein the inputs to the classical SVR model are based on the two benchmark models and the proposed bilateral Gaussian wake model. Wind speed is predicted for upstream turbines of two wind farm layouts (5-turbine and 15-turbine). Further, to observe the impact of input dimensionality, two techniques: (i) Grey relational analysis (GRA) and (ii) Neighborhood component analysis (NCA), are considered. Results reveal that for a wind site WBZ tower, NCA outperforms GRA by 36.48%, 34.0% and 7.03% for Jensen's, Frandsen's and bilateral wake model respectively. When compared to the two benchmark models for both the techniques (GRA and NCA), the prediction performance of bilateral wake model is superior. Overall, it is observed that the feature selection tools like GRA and NCA improve the wind speed prediction accuracy in the presence of wind wakes.

ACS Style

Harsh S. Dhiman; Dipankar Deb; Aoife M. Foley. Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach. Renewable and Sustainable Energy Reviews 2020, 127, 109873 .

AMA Style

Harsh S. Dhiman, Dipankar Deb, Aoife M. Foley. Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach. Renewable and Sustainable Energy Reviews. 2020; 127 ():109873.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb; Aoife M. Foley. 2020. "Bilateral Gaussian Wake Model Formulation for Wind Farms: A Forecasting based approach." Renewable and Sustainable Energy Reviews 127, no. : 109873.

Journal article
Published: 03 April 2020 in Processes
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Both cold and flame jets find numerous applications in different fields, ranging from domestic applications to aerospace and space technology. Indeed, the applications of isothermal and non-isothermal jets in the flame heating industry fascinated the researchers to gain an in-depth understanding. Nevertheless, these benefits are not standalone, rather, they are associated with major disadvantages such as improper jet mixing and flame instabilities that require careful remedies. In the present investigation, three-inline jets, having methane jet at the center and two oxygen jets at the periphery, are studied computationally in a three-dimensional domain, with and without considering the effects of combustion. To study the mixing characteristics of cold jets, the radial velocity distributions at different streamwise locations have been analyzed at the jet inlet velocity of 27 m/s. However, for oxygen and methane flame jets, inlet velocities are varied as 27 m/s and 54 m/s. Moreover, to investigate the effects of temperature variation on mixing characteristics at a particular jet velocity, the inlet temperatures of reactants are varied as 300 K, 500 K, and 700 K, at the jet inlet velocity of 27 m/s. Combustion is found to have a profound impact on the mixing characteristics. At the inlet temperature of 300 K, a higher centerline velocity decay is observed for non-reactive jets as compared to reactive flame jets. Moreover, the turbulent kinetic energy distribution is relatively higher in the case of non-reactive jets, which is the direct evidence of an augmented mixing. As is obvious, the turbulent kinetic energy at the jet shear layer is maximum due to the formation of large-scale coherent eddies. The decay in centerline velocity is found to be increasing with an increase of inlet temperature. Additionally, with an increase of jet velocity, the radial velocity profiles become more asymmetrical, consequently yielding an unstable flame.

ACS Style

Tamal Jana; Mrinal Kaushik; Dipankar Deb; Vlad Mureşan; Mihaela Ungureşan. Aerodynamic Studies on Non-Premixed Oxy-Methane Flames and Separated Oxy-Methane Cold Jets. Processes 2020, 8, 429 .

AMA Style

Tamal Jana, Mrinal Kaushik, Dipankar Deb, Vlad Mureşan, Mihaela Ungureşan. Aerodynamic Studies on Non-Premixed Oxy-Methane Flames and Separated Oxy-Methane Cold Jets. Processes. 2020; 8 (4):429.

Chicago/Turabian Style

Tamal Jana; Mrinal Kaushik; Dipankar Deb; Vlad Mureşan; Mihaela Ungureşan. 2020. "Aerodynamic Studies on Non-Premixed Oxy-Methane Flames and Separated Oxy-Methane Cold Jets." Processes 8, no. 4: 429.

Journal article
Published: 13 January 2020 in Renewable Energy
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Lidar based wind measurement is an integral part of wind farm control. The major issues and challenges in power maximization include the potential losses due to wake effect observed among wind turbines. This manuscript presents a wake management technique that utilizes lidar simulations for wake redirection. The proposed methodology is validated for 2-turbine and 15-turbine wind farm layouts involving a PI control based yaw angle correction. Yaw angle misalignment using wake center tracking of the upstream turbines is used to increase the power generation levels. Results of wake center estimation are compared with a Kalman filter based method. Further, the velocity deficit and overall farm power improvement by yaw angle correction is calculated. Results reveal a 1.7% and 0.675% increase in total wind farm power for two different wind speed cases.

ACS Style

Harsh S. Dhiman; Dipankar Deb; Aoife M. Foley. Lidar assisted wake redirection in wind farms: A data driven approach. Renewable Energy 2020, 152, 484 -493.

AMA Style

Harsh S. Dhiman, Dipankar Deb, Aoife M. Foley. Lidar assisted wake redirection in wind farms: A data driven approach. Renewable Energy. 2020; 152 ():484-493.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb; Aoife M. Foley. 2020. "Lidar assisted wake redirection in wind farms: A data driven approach." Renewable Energy 152, no. : 484-493.

Journal article
Published: 02 January 2020 in Journal of Process Control
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In the present work, an augmented subcutaneous (SC) model of type 1 diabetic patients (T1DP) is proposed first by estimating the model parameters with the aid of nonlinear least square method using the physiological data. Next, a nonlinear adaptive controller is proposed to tackle two important issues of intra-patient variability (IPV) and uncertain meal disturbance (MD). The proposed patient model agrees quite well with the responses of one of the most popular existing nonlinear model used in the research of artificial pancreas. Further, the developed adaptive control is shown to be capable of providing desired glycemic control without feed-forward action for meal compensation or safety algorithms to avoid hypoglycemia. Due to the simple structure and capability of handling intra-patient variability of the adaptive controller, it can find immediate applicability in the development of the in-silico artificial pancreas.

ACS Style

Anirudh Nath; Dipankar Deb; Rajeeb Dey. An augmented subcutaneous type 1 diabetic patient modelling and design of adaptive glucose control. Journal of Process Control 2020, 86, 94 -105.

AMA Style

Anirudh Nath, Dipankar Deb, Rajeeb Dey. An augmented subcutaneous type 1 diabetic patient modelling and design of adaptive glucose control. Journal of Process Control. 2020; 86 ():94-105.

Chicago/Turabian Style

Anirudh Nath; Dipankar Deb; Rajeeb Dey. 2020. "An augmented subcutaneous type 1 diabetic patient modelling and design of adaptive glucose control." Journal of Process Control 86, no. : 94-105.

Chapter
Published: 29 September 2019 in Developments in Advanced Control and Intelligent Automation for Complex Systems
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Having dominated as primary energy source, fossil fuel consumption has seen a significant spurt in the twentieth century. However, with increased carbon footprints from fossil fuels globally, renewable energy technologies have been reinforced as energy source in developing and developed countries. Increased penetration of wind power in utility grid questions stable power system operation due to random wind speed causing erratic dispatch of generated power. The concern of forecasting challenges among wind practitioners has gathered industrial limelight over the years in order to minimize their financial losses. An accurate wind power forecast ensures system reliability and reduces auxiliary equipment cost. Traditionally, wind forecasting techniques are categorized into two broad models: statistical models and machine learning models. With wind being stochastic on temporal scale, nonlinearity induces forecasting challenges for statistical models. Machine learning models in tandem with signal decomposition techniques (wavelet transform and empirical model decomposition) form the bulwark for accurate forecasting methods.

ACS Style

Harsh S. Dhiman; Dipankar Deb. Decision-Making in Hybrid Wind Farms. Developments in Advanced Control and Intelligent Automation for Complex Systems 2019, 37 -57.

AMA Style

Harsh S. Dhiman, Dipankar Deb. Decision-Making in Hybrid Wind Farms. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2019; ():37-57.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb. 2019. "Decision-Making in Hybrid Wind Farms." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 37-57.

Chapter
Published: 29 September 2019 in Developments in Advanced Control and Intelligent Automation for Complex Systems
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Wind is a randomly varying resource that needs to be appropriately tapped using wind turbines typically anchored to the ground and subjected to different torques and loads with changing atmospheric conditions. There are significant challenges in modeling such behaviors, and such issues become further complex in the case of offshore wind turbines, hilly terrains, and during ramp events which are extensively dealt with in this book. In this chapter, we present fundamental aspects of wind turbine blade-pitching control, wake control, and also wind reserve power maximization strategy. Micro-siting is an issue in wind farms that affect the total power generated from the farm, and is interrelated to turbine control and wake effect.

ACS Style

Harsh S. Dhiman; Dipankar Deb. Fundamentals of Wind Turbine and Wind Farm Control Systems. Developments in Advanced Control and Intelligent Automation for Complex Systems 2019, 1 -18.

AMA Style

Harsh S. Dhiman, Dipankar Deb. Fundamentals of Wind Turbine and Wind Farm Control Systems. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2019; ():1-18.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb. 2019. "Fundamentals of Wind Turbine and Wind Farm Control Systems." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 1-18.

Chapter
Published: 29 September 2019 in Developments in Advanced Control and Intelligent Automation for Complex Systems
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A battery energy storage system (BESS) is a system that stores energy via the use of a battery technology for it to be used when needed later on. Intermittent wind power not only increases the cost of specifically constructed BESS needed in stochastic wind power generation but also leads to degraded battery life. This chapter deals with battery optimization by a wind wake management technique aimed at reducing the operational cost. A two-turbine wind farm is studied, and battery charging and discharging events are identified based on the forecast error of wind speed. The life cycle count is determined based on an empirical relationship between the counts of charging and discharging cycles and depth of discharge of battery.

ACS Style

Harsh S. Dhiman; Dipankar Deb. BESS Life Enhancement for Hybrid Wind Farms. Developments in Advanced Control and Intelligent Automation for Complex Systems 2019, 109 -130.

AMA Style

Harsh S. Dhiman, Dipankar Deb. BESS Life Enhancement for Hybrid Wind Farms. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2019; ():109-130.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb. 2019. "BESS Life Enhancement for Hybrid Wind Farms." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 109-130.

Chapter
Published: 29 September 2019 in Developments in Advanced Control and Intelligent Automation for Complex Systems
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Growing energy demands have heightened the concern for renewables in recent times. A hybrid wind farm operation is often taken into consideration while interconnecting large power systems with the decision being dependent on the wind farm operator. The choice of best strategy results in an optimal market scenario. The current work deals with multi-criteria decision-making for hybrid wind farms. A tangible and non-tangible effect of wind phenomenon is considered to obtain the cumulative priority of each alternative based on a set of criteria.

ACS Style

Harsh S. Dhiman; Dipankar Deb. Fuzzy-Based Decision-Making in Hybrid Wind Farms. Developments in Advanced Control and Intelligent Automation for Complex Systems 2019, 59 -76.

AMA Style

Harsh S. Dhiman, Dipankar Deb. Fuzzy-Based Decision-Making in Hybrid Wind Farms. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2019; ():59-76.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb. 2019. "Fuzzy-Based Decision-Making in Hybrid Wind Farms." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 59-76.

Chapter
Published: 29 September 2019 in Developments in Advanced Control and Intelligent Automation for Complex Systems
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As understood by now, wind turbines extract energy from the randomly varying wind. However, downstream of the turbine, a wake is created where wind speed is reduced. As per the basic definition in fluid dynamics, a wake is the region of recirculating flow behind a moving or stationary blunt object, caused by viscosity, possibly accompanied by flow separation and turbulence. As the flow further proceeds downstream, this wake spreads and finally recovers toward free stream conditions. Such a wake effect is the aggregated influence on the energy produced in the wind farm resulting from effective wind speed changes caused by the impact of the turbines on each other.

ACS Style

Harsh S. Dhiman; Dipankar Deb. Control Applications in Hybrid Wind Farms. Developments in Advanced Control and Intelligent Automation for Complex Systems 2019, 77 -108.

AMA Style

Harsh S. Dhiman, Dipankar Deb. Control Applications in Hybrid Wind Farms. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2019; ():77-108.

Chicago/Turabian Style

Harsh S. Dhiman; Dipankar Deb. 2019. "Control Applications in Hybrid Wind Farms." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 77-108.