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This paper covers the self‐sharing analysis of dc‐dc nonisolated converters with input parallel–output parallel (IPOP) configuration and operating in discontinuous conduction mode (DCM). The main contribution of the proposed system is its capability of providing self‐sharing of the currents on both sides of each individual converter, without average current sharing control, even in the face of parametric variations. This self‐balance only occurs for DCM. When the addressed converters operate in continuous conduction mode (CCM), the self‐sharing does not occur naturally under parametric differences among them, requiring the use of additional control loops. The use of self‐sharing converters in nonisolated converters simplifies the control system, it makes the modular solution being attractive for many applications, and it increases the power range that the DCM converters may be applied. This paper brings the theoretical study of self‐sharing of the current mechanism to six basic nonisolated converters operating in DCM. The self‐sharing is verified by experimental results, which are obtained from three modules of dc‐dc SEPIC converters. Both converters were designed to operate with 200‐V input voltage, 125‐V output voltage, 1500‐W rated power (500 W each module), and switching frequency at 30 kHz. [ABSTRACT FROM AUTHOR]

Summary: DC–DC boost converters are extensively used in wide range of solar photovoltaic (PV) applications. A common problem in PV regulator is the presence of ripples in output voltage and output current, due to the oscillations in PV power, inaccurate switching, and inadmissible inductor peak currents. In general, conventional boost converters are widely used in PV regulator; however, conventional PWM‐controlled boost converters are upgraded to sliding mode controller (SMC)‐based PWM controllers, which has been elaborated in the literature. However, SMC‐based boost converter experiences the following problems: (i) presence of ripples in the output current and (ii) dynamic instability during sudden change in load current. This paper addresses the aforementioned problems and proposes a boomerang trajectory‐based SMC (BT‐SMC) control strategy for ripple mitigation in conventional DC–DC boost converters specifically used in PV regulator. BT‐SMC tracker tracks the reference wave shape and controls the duty cycle, with respect to the switching frequency to have a ripple mitigated output. This self‐adapting system provides an adequate reduction in ripples and rapid convergence of error nearest to zero. A detailed analysis on switching frequency transition, load variation, trajectory control, and its stability existence is conducted, and the proposed system is implemented using dSPACE‐MicroLabBox‐DS1202. [ABSTRACT FROM AUTHOR]

Summary: Microgrids are the localized network to support electricity consumers either in an islanded manner or in integration with macrogrid. Microgrids are generally vulnerable to major issues like improper voltage regulation, circulating current, and unequal power sharing. In this paper, conventional droop controller for current sharing is upgraded with a Boomerang Trajectory based Sliding Mode Controller (BT‐SMC). In the proposed BT‐SMC technique, the error trajectories are traced and controlled toward the equilibrium point accomplishing the system to reach the stable state. Also, the dynamic changes in the load are governed and controlled by adjusting the droop resistance in the parallel converters. The BT‐SMC generates an equivalent control signal based on the control law and maintains the PWM pulses on par with the droop value and duty ratio. Additionally, this paper concerns the precondition for power sharing and power management among the different RES at different availability conditions by adopting a Source Shifting Algorithm (SSA). The hand‐in‐hand process of BT‐SMC with SSA provides a proper load sharing with a wide stability region in the phase plane. The proposed self‐adapting and self‐tracking system is evaluated in Matlab/Simulink and the results were validated in real time by using dSPACE MicroLabBox—DS‐1202. [ABSTRACT FROM AUTHOR]

The enduring life span of the machine insulation will be decided based on degradation level in motor and generator stator windings. The non-destructive diagnostic tools like dielectric loss and capacitance test and partial discharge (PD) analysis, recognized to access the deterioration in the insulation system of rotating machines. The experiments reveal various characteristic parameters such as leakage current, dielectric dissipation factor, the capacitance value, and PD magnitude. The integrity of the rotating machine can be find out by analyzing these parameters. This research study shows the hybrid method for prediction of insulation condition in the stator winding by utilizing the artificial neural network (ANN) with gravitational search algorithm in comparison with ANN and ANN-genetic algorithm. The advent of expert systems ensures the quality assurance and service life assessment of the high-voltage assets. It offers a predictive maintenance solution to personnel dealt with power utilities thereby increasing the uptime, reliability, and productivity, which in turn reducing the operating costs, downtime and unplanned outages. For testing and predicting the insulation status, several 11 kV machines are considered. The predicted results using hybrid techniques extend a close agreement with reference to the data obtained from the experiments performed. The proposed method indicate the competent and trustworthy, by the presented test results. [ABSTRACT FROM AUTHOR]