The rise in energy demand in the present scenario can be balanced with the help of solar Photovoltaic (PV) systems. But, the nonlinearity in I-V and P-V characteristics makes it very difficult to extract the maximum power of solar PV. Also, the classical Maximum Power Point Tracking Techniques (MPPT) fail to track the global Maximum Power Point (MPP) from the multiple local MPPs under Partial Shading Conditions (PSCs). In this work, Variable Step Size ANN-based MPPT Techniques are studied and are compared in terms of steady-state behavior, settling time of converter power, power point tracing speed, oscillations of MPP, and operating efficiency. The discussed MPPT techniques are Adaptive Perturb & Observe (AP&O), Adaptive Feed Forward Neural Network Controller (AFFNNC), Artificial Neural Network-based P&O (ANN-based P&O), ANN-based Incremental Conductance (ANN-based IC), ANN-based Hill Climb (ANN-based HC), and Radial Basis Functional Controller based Fuzzy (RBFC based Fuzzy). The boost converter is interfaced in middle of the PV system and load to step-up the PV supply voltage. The performance of selected neural networks MPPT techniques is studied by utilizing a MATLAB/Simulink window.

Robot manipulators are widely used in many fields and play a vital role in the assembly, maintenance, and servicing of future complex in-orbit infrastructures. They are also helpful in areas where it is undesirable for humans to go, for instance, during undersea exploration, in radioactive surroundings, and other hazardous places. Robotic manipulators are highly coupled and non-linear multivariable mechanical systems designed to perform one of these specific tasks. Further, the time-varying constraints and uncertainties of robotic manipulators will adversely affect the characteristics and response of these systems. Therefore, these systems require effective modelling and robust controllers to handle such complexities, which is challenging for control engineers. To solve this problem, many researchers have used the fractional-order concept in the modelling and control of robotic manipulators; yet it remains a challenge. This review paper presents comprehensive and significant research on state-of-the-art fractional-order modelling and control strategies for robotic manipulators. It also aims to provide a control engineering community for better understanding and up-to-date knowledge of fractional-order modelling, control trends, and future directions. The main table summarises around 95 works closely related to the mentioned issue. Key areas focused on include modelling, fractional-order modelling type, model order, fractional-order control, controller parameters, comparison controllers, tuning techniques, objective function, fractional-order definitions and approximation techniques, simulation tools and validation type. Trends for existing research have been broadly studied and depicted graphically. Further, future perspective and research gaps have also been discussed comprehensively.

The extensive application of power transfer through high-voltage direct current (HVDC) transmission links in smart grid scenarios is due to many factors such as high-power transfer efficiency, decoupled interconnection, control of AC networks, reliable and flexible operation, integration of large wind and photovoltaic (PV)-based off-shore and on-shore farms, cost-effectiveness, etc. However, it is vital to focus on many other aspects like control, protection, coordinated operation, and power management to acquire the above benefits and make them feasible in real-time applications. HVDC protection is needed to focus further on innovative and devoted research because the HVDC system is more vulnerable to system faults and changes in operational conditions in comparison to AC transmission because of the adverse effects of low DC-side impedances and sensitive semi-conductor-based integrated power electronics devices. This paper provides a comprehensive review of the techniques proposed in the last three decades for HVDC protection, analyzing the advantages and disadvantages of each method. The review also examines critical findings and assesses future research prospects for the development of HVDC protection, particularly from the perspective of smart-grid-based power systems. The focus of the review is on bridging the gap between existing protection schemes and topology and addressing the associated challenges and issues. The aim is to inform power engineers and researchers about potential research avenues to tackle the challenges in HVDC protection in smart-grid-based power systems.

In this paper, a probabilistic load flow analysis technique that combines the cumulant method and Gaussian mixture approximation method is proposed. This technique overcomes the incapability of the existing series expansion methods to approximate multimodal probability distributions. A mix of Gaussian, non-Gaussian, and discrete type probability distributions for input bus powers is considered. Probability distributions of multimodal bus voltages and line power flows pertaining to these inputs are precisely obtained without using any series expansion method. At the same time, multiple input correlations are considered. Performance of the proposed method is demonstrated in IEEE 14 and 57 bus test systems. Results are compared with cumulant and Gram Charlier expansion, cumulant and Cornish Fisher expansion, dependent discrete convolution, and Monte Carlo simulation. Effects of different correlation cases on distribution of bus voltages and line power flows are also studied.