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Accurate magnetic-signature prediction is the most challenging task in the path of active magnetic-signature minimization. This article discusses the effect of the change in hull thickness, relative permeability, and radius of the object, considering the detailed understanding of the magnetic signatures. The traditional magnetic experiment-based method using a 12-coil structure is implemented for a double hull vessel. However, the time required and effort involved in predicting the magnetic signature is much higher. Hence, a multiple linear regression-based statistical machine-learning (ML) technique for the magnetic signature prediction is proposed. The nonlinear magnetic behavior of the naval vessel is assumed to be linear, because the hysteresis curve in the range of the earth’s ambient field is linear. Hence, a linear relationship is developed between the magnetic signatures and each component of the ambient earth’s magnetic field. The ML-based model also considers the crosstalk among the longitudinal, athwartship, and vertical signatures. The predicted magnetic signatures using the ML technique and the experiment-based techniques are compared. The ML method is found to be accurate, fast, and precise compared with the laboratory-based method, which has strict and continuous requirements for hardware magnetic facility. This method is very simple to implement and directly contributes to the development of the real-time degaussing current calculation algorithm.

This paper proposes a fast and efficient method for magnetic signature reduction of underwater vessels. The magnetic signature reduction is required to protect the ferromagnetic vessels from magnetic anomaly detectors and mines. We propose a novel machine learning-based approach for degaussing of the vessel. This method adds a degree of bias to the evaluated coefficients in order to handle inherent multicollinearity issues. The proposed algorithm is efficient in terms of computational efforts, speed, and accuracy. More than 90% of signature reduction is achieved, assuming that the signature predicted is accurate. The proposed method is validated for a simulated model of prototype submarine.

Fuel cell (FC) is emerging as a clean and nonpollutant energy source and is being used widely in most of the transport and microgrid applications. However, certain applications demanding fast dynamic response are restricting usage of FC on account of its lifetime issues. Slow dynamic response of FC is not suitable to provide transient load demands, typically arising in electrical vehicles during acceleration and in braking. This paper presents a novel control strategy and associated power electronics for such applications. We propose a Lure–Lyapunov-based controller executed in cascade mode for the control of hybrid energy management system of FC and ultracapacitor (UC). In the proposed scheme, UC responds to transients in load demand whereas FC takes care of the nominal load bus requirements. This combination helps in improving lifetime of FC as auxiliary storage device such as UC provides a cushion for sudden transients. The proposed control strategy also helps in maintaining state of charge of UC for better performance. The inherent nonlinearities in DC–DC converter are efficiently handled by the outer-loop Lure–Lyapunov framework. Mathematical modeling and a proof for absolute stability of the system is presented. The proposed controller strategy is robust by virtue of being passivity based control methodology. The paper also presents analysis of the required power electronics involving DC–DC converters and inner-loop current control action. The synthesized controller is experimentally validated and the results are presented with their analysis.

Contemporary ships, made of ferromagnetic materials, under the influence of earth’s magnetic field produce an underwater magnetic anomaly. This anomaly, often called the magnetic signature, compromises the stealth and safety of the ship and needs to be minimized. This paper presents an efficient methodology for the design of optimal tri-axial degaussing system and estimation of coil currents in ships to minimize the magnetic signature of the ship. Designing such a system is a complex optimization problem, and we address this problem with high-dimensional variable selection technique along with linear regression techniques.

The objective of this paper is to report findings based on the particle-in-cell (PIC) modeling of the axial and coaxial virtual cathode oscillators in order to validate the experiments already performed on these devices over the years. In addition to this, observations regarding validation and similarities between the simulations and the experiments have been reported, with an emphasis on analysis of device parameters such as e-beam voltage, current density, and microwave power. Theoretical aspects of a virtual cathode oscillator (vircator) and some of the key experimental works have been revisited. Investigation is done on the power generated in a coaxial vircator, and various cavity modifications have been tested for the coaxial vircator using the MAGIC3D, PIC tools suite, in order to search for improvement in the device efficiencies. Also, the axial vircator has been modeled to comment on the particle energies, device design, and beam quality associated with it.

In the past two decades the frequent occurrences of large scale blackouts and catastrophic events has reduced the reliability of power grid to a great extent. A thorough analysis of the propagation of failures, in terms of line outages, combined with the topological characteristics of the grid aids, has been done to take corrective actions to save the system from complete collapse. It also helps to investigate the progress and understand the nature and intensity of blackouts. This motivates to establish the relationship between the network topological characteristics and cascading failure in the power grid. In this paper, the basic topological characteristics of the power network are studied in detail and the average propagation of failure under varying topological conditions is calculated as a branching process parameter. The variation in the mean propagation is studied in detail using a number of test bed networks with the alternate realistic networks derived from the standard IEEE networks imitating the real network conditions. The results confirm a qualitative agreement between the variations in topological parameter and the failure propagation rate in the cascading regime. Based on the analysis mentioned above, data clearly shows that the average failure propagation factor varies linearly with the variations in the statistical metrics.

Major power grid blackouts in various parts of the world are characterized by voltage collapse. This paper studies the blackout case in the Indian power grid due to voltage collapse in the inter-regional corridor. First, the computer simulation models are tuned to match responses from the phasor measurement units at various locations in the grid. The inter-area mode with the frequency of 0.3 and 0.5 Hz with reduced damping were observed and resulted due to switching actions that occurred during the disturbance. This paper simulates the complex power grid network for various loading conditions of Northern region using power system simulator for engineering. Further, the system variables are analyzed using Prony method to estimate the inter-area mode for different NR loading conditions. The eigen values associated with the 0.3 Hz mode depict the movement from open left half plane into open right half plane with a slight increase in the parameter indicating “hopf bifurcation.” This paper also discusses the maximum loading capacity for the inter-regional lines in-order avoid loss of small signal stability, and highlights the use of real time phasor measurement unit measurements in-order to estimate and track the oscillatory modes.

The worldwide major blackout events of power network are highlighting the need for technology upgradation in traditional grid. One of the major upgradations required is in the area of early warning generation in case of any grid disturbances such as line contingency leading to cascade failure. This paper proposes a proactive blackout prediction model for a smart grid early warning system. The proposed model evaluates system performance probabilistically, in steady state and under dynamical (line contingency) state, and prepares a historical database for normal and cascade failure states. A support vector machine (SVM) has been trained with this historical database and is used to predict blackout events in advance. The key contribution of this paper is to capture the essence of the cascading failure using probabilistic framework and integration of SVM machine learning tool to build a prediction rule, which would be able to predict the scenarios of the blackout as early as possible. The proposed model is validated using the IEEE 30-bus test-bed system. Proactive prediction of cascade failure using the proposed model may help in realizing the grid resilience feature of smart grid.

An investigation of the reflex triode virtual cathode oscillator based on simulations with 3-D particle-in-cell code developed by ATK Mission Systems is done. The device model is based on an actual experimental setup with 15-mm anode–cathode gap. The obtained simulation results use a single-shot 200-kV, 300-ns pulse at anode. Numerical analysis is done to validate the results and to gain insight into the dynamics of the electrons emitted from the cathode surface and their movement between the cathode and the virtual cathode. The obtained single dominant microwave frequency for the model is 2.35 GHz with virtually no competing frequencies.