Department of Electrical Engineering

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IIT Jodhpur

This paper proposes a new and simple method based on Pade approximation and Routh-Hurwitz criterion to calculaté communication delay margin for time delayed automatic generation control (AGC) system. In modern power systems, due to extensive use of open and distributed channel communication the stability is a concern as these types of communication systems lead to instability. Although various complex algorithms can be used to compute the delay margin (critical delay), this paper proposes a simpler method which is based on Padé approximation. The accuracy of the method is presented in the results obtained with MATLAB / Simulink simulation. © 2019 IEEE.

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Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019

Computation of Delay Margin in a Power System Having Open Channel Communication Based Automatic Generation Control using Padé Approximation

Significant recent developments in 3D display technology have focused on techniques for converting 2D media into 3D. Depth map is an integral part of 2D-to-3D conversion. Combining multiple depth cues results in a more accurate depth map as it compensates for the errors caused by one depth cue as well as its absence by other depth cues. In this paper, we present a novel framework to generate a more accurate depth map for video using defocus and motion cues. The moving objects present in the scene are the source of errors in both defocus and motion-based depth map estimation. The proposed method rectifies these errors in the depth map by integrating defocus blur and motion cues. In addition, it also corrects the errors in other parts of depth map caused by inaccurate estimation of defocus blur and motion. Since the proposed integration approach relies on the characteristics of point spread functions of defocus and motion blur along with their relations to camera parameters, it is more accurate and reliable. © 2018 IEEE.

IEEE Transactions on Circuits and Systems for Video Technology

Depth Map Estimation Using Defocus and Motion Cues

Distribution system protection against faults is a vital task for reliability of a power system. In the proposed work, three phase current signals obtained at substation of an IEEE 13 bus system are initially decomposed using empirical mode decomposition (EMD). The higher frequency components, Intrinsic mode functions (IMFs) of this decomposition are eliminated leaving behind a residue containing predominantly fundamental frequency. This residue being monotonic in nature, is utilized to generate visually distinct symmetrical dot patterns (SDP) for three phases. These SDPs are then correlated with those obtained under normal conditions to obtain a fault index based on alienation method. The faults are successfully detected and classified when the fault index is above a predetermined threshold. This algorithm has been successfully established for various types of faults with changing fault incidence angle, fault resistance at all the buses of the distribution system. The proposed algorithm is found to be fast in detecting and classifying faults within half cycle. © 2019 IEEE.

IEEE Region 10 Annual International Conference, Proceedings/TENCON

Distribution Feeder Protection Using Uncorrelated Symmetrical Dot Patterns

Nowadays, modern world technologies are rapidly becoming part of the Internet of things (IoT) and 5G networks where the role of power line communication (PLC) technology will be predominant in some of the standard applications of IoT such as smart homes, smart industries, and smart grids by considering its favorable conditions. In this paper, we analyze the physical layer security (PLS) of a low-frequency PLC system in terms of average secrecy capacity (ASC), secure outage probability (SOP), and strictly positive secrecy capacity (SPSC) where the wiretap channel model is employed to measure the degree of data confidentiality between authorized and unauthorized user over low frequency PLC networks. The PLC channel characteristics are characterized by specifically, two different cases, when the main and wiretap channel are independent log- normally distributed and correlated log-normally distributed; concurrently the effect of impulsive noise is modeled by the Bernoulli Gaussian process. Furthermore, the impact of impulsive noise, the distance between the users, path loss factor, and transmitted power is also considered. The validity of the derived analytical results is established through close matching with the Monte Carlo based simulation results. © 2019 IEEE.

IEEE Vehicular Technology Conference

Secrecy analysis of PLC system with channel gain and impulsive noise

In this paper, a novel power normalized kernel least mean fourth algorithm based neural network (NN) control (PNKLMF-NN) technique and learning-based hill climbing (L-HC) maximum power point tracking (MPPT) algorithm are proposed for grid-integrated solar photovoltaic (PV) system. Here three-phase single-stage topology of a grid-integrated PV system is used for feeding the nonlinear/linear load at the point of common coupling. A single layer neuron structure is used for active load component (ALC) extraction from distorted load current. During ALC extraction, PNKLMF-NN control very precisely attenuates harmonics components, noise, dc offset, bias, notches, and distortions from the nonlinear current, which improves the power quality under normal as well as under abnormal conditions. This single layer PNKLMF-NN control has a very simple architecture, which reduces the computational burden and complexity. Therefore, it is easy in implementation. Moreover, proposed L-HC is the improved form of hill climbing (HC) algorithm, where inherent problems of traditional HC algorithm, such as steady-state oscillation, slow dynamic responses, and fixed step size issues, are successfully mitigated. The prime objective of proposed PNKLMF-NN control is to meet the active power requirement of the loads from generated solar PV power and excess power fed into the grid. However, when generated PV power is less than the required load power, then PNKLMF-NN control meets the load by taking extra required power from the grid. During these processes, power quality is maintained at the grid. Moreover, when solar irradiation is zero, voltage source converter (VSC) acts as distribution static compensator (DSTATCOM), which enhances the utilization factor of the system. The proposed techniques are modeled and their performances are verified experimentally on a developed g prototype in adverse conditions, which test results have satisfied the objectives of the proposed system and the IEEE-519 standard. © 2005-2012 IEEE.

IEEE Transactions on Industrial Informatics

Pnklmf-based neural network control and learning-based hc mppt technique for multiobjective grid integrated solar pv based distributed generating system

This paper proposes a novel gradient descent least squares regression (GDLSR)-based neural network (NN) structure for the control of grid-integrated solar photovoltaic (PV) system with improved power quality. Here, a single-layer neuron structure is used for the extraction of fundamental component (FC) from the load current. During FC extraction, GDLSR-based NN structure attenuates harmonic components, noise, dc offsets, bias, notches and distortions from the nonlinear current, which improves the power quality under normal as well as under abnormal grid conditions. This single-layer GDLSR-based NN structure has a very simple architecture, which decreases the computational burden and algorithm complexity. Therefore, it is easy in implementation. In this work, the GDLSR-based control technique is tested on a single-phase single-stage grid-integrated PV topology with the nonlinear loads. The prime objective of the GDLSR-based NN structure is to provide reactive power compensation, power factor correction, harmonics filtering and mitigation of other power quality issues. Moreover, when solar irradiation is zero, the dc-link capacitor and voltage-source converter act as a distribution static compensator, which enhances the utilization factor of the system. The proposed system is modeled, and its performances are verified experimentally on a developed prototype in different grid disturbances as well as solar insolation variation conditions, which performances have satisfied the motive of the proposed technique as well as the IEEE-519 standard. © 1982-2012 IEEE.

IEEE Transactions on Industrial Electronics

Framework of Gradient Descent Least Squares Regression-Based NN Structure for Power Quality Improvement in PV-Integrated Low-Voltage Weak Grid System

The posture stabilization of a unicycle mobile robot is useful in executing parking and docking maneuvers. It becomes more advantageous to guarantee the posture stabilization in finite time for a battery operated robot, especially in an application involving multiple robots. This paper addresses the posture stabilization of the unicycle mobile robot in finite time, especially when only the position information is available. We adopt the reaching law approach and design a discrete-time sliding mode (DSM) controller by finitely discretizing the chained form of the unicycle, using the notion of multirate input sampling. Finite-time stabilization of the equilibria is achieved by using multirate piecewise continuous inputs. Furthermore, the control inputs are realized using multirate output-feedback (MROF) technique, wherein the states are estimated using the knowledge of the past fast output samples and immediate past control inputs. The proposed MROF-DSM strategy avoids undesirable chatter in the system response owing to the nonswitching-type controller and guarantees the stabilization in finite time. Simulations demonstrate the efficacy of the proposed method. © 2019 John Wiley & Sons, Ltd.

International Journal of Robust and Nonlinear Control

Finite-time posture stabilization of the unicycle mobile robot using only position information: A discrete-time sliding mode approach

A robust tracking control is proposed for the fractional-order systems (FOSs) to achieve a tracking response with no overshoot, even in the presence of a class of disturbances. The control proposed makes use of a newly designed integral sliding mode technique for FOSs, which is capable of rejecting the bounded disturbances acting through the input channel. The proposed integral sliding mode control design has two components: a nominal control component and a discontinuous control component. The overshoot in the system response is avoided by the nominal control designed with the use of Moore's eigenstructure assignment algorithm. The sliding mode technique is used for the design of discontinuous part of the control that imparts the desired robustness properties. © 2019 John Wiley & Sons, Ltd.

Fulltext

Robust nonovershooting tracking control for fractional-order systems

Negative-capacitance transistors use ferroelectric (FE) material in the gate-stack to improve the transistor performance. The extent of the improvement depends on the capacitance matching between the FE capacitance {C}-{\textsf {fe}} and the underlying MOS transistor {C}-{\textsf {MOS}}. Since both {C}-{\textsf {MOS}} and {C}-{\textsf {fe}} have strong non-linearity, it is difficult to achieve a good matching for the entire operating gate voltage range. In this letter, we discuss a new approach using multi-layer FE to engineer the shape of {C}-{\textsf {fe}} The proposed method is validated using the TCAD simulation of negative-capacitance FDSOI transistor, and the results show that it leads to better sub-Threshold swing as well as lower power supply {V}-{\textsf {dd}} compared with a prototype single-layer negative-capacitance field-effect transistor. © 1980-2012 IEEE.

IEEE Electron Device Letters

Proposal for capacitance matching in negative capacitance field-effect transistors

Noise-aided stochastic resonance has been explored in recent literature as a powerful tool that enhances the performance of non-linear systems, particularly in image enhancement and image watermarking. In this paper, we extend the application of stochastic resonance to improve the performance of the conventional non-local means (NLM) filtering for edge-preserving image denoising. The NLM algorithm typically involves computation of weights denoting similarity of a pixel with all other pixels in the image. In the proposed algorithm, these similarity weights are iteratively processed using the concept of dynamic stochastic resonance. The results indicate a significant improvement in sharpness of edges in the denoised images in comparison with the conventional NLM approach both visually and quantitatively in terms of full-reference and no-reference image quality metrics. © 2018 IEEE.