Hand gestures recognition system has massive applications which are mainly utilized in robotics and computer vision specially to control Unmanned Aerial Vehicles (UAV). These methods bypass the presence of electronic control to UAVs and provide an ease to the operators. In this paper, we present a method for 3D hand gestures segmentation and classification by combining MASK-RCNN with Grass Hopper Optimization. We created a private 3D and RGB hand gestures dataset using Intel Kinetic and Intel Real sense d435i camera, then proposed a model for RGB hand gestures to estimate the key points using human kinematics, the key points later then utilize to get the best degree of freedom (DoF). The grass hopper optimization besides minimum distance function was applied to achieve the finest deep features from the 3D hand gestures dataset. The ResNet50 network is used as the backbone to calculate the Overlap Coefficient (OC) for segmentation and the ResNet50, ResNet101 networks to calculate the classification for 3D hand gestures. The classification accuracy achieved on the private dataset is 99.05% and 99.29% on public Microsoft Kinect and Leap Motion dataset where the OC are 88.16%. and 88.19% respectively.

This paper primarily focuses on an advance control strategy to enhance the low voltage ride through (LVRT) capability in doubly fed induction generator (DFIG) based wind energy conversion system (WCES). In the proposed control strategy, the captured wind energy during grid faults circumstances is stored timidly in the rotor’s inertia kinetic energy. Though a minimal amount of energy is available in the grid, stator current and DC-link voltage are set beneath the perilous levels. However, both the required stator voltage and stator current are kept within a tolerable range of rotor side converter (RSC), through state feedback linearization technique for maintaining the accurate control to suppress the overvoltage and overcurrent. Furthermore, stator current oscillations are significantly suppressed during fault transient. The input mechanical energy from the wind turbine can be resumed after the fault clearance. In spite of being dissipated in the resistors of crowbar circuit, as in the conventional LVRT assemblies, torque balancing among electrical and mechanical measures is attained; DC-link voltage instabilities and rotor speed inconsistencies are substantially reduced. As a result, a noticeable reduction in the requirement of reactive power and swift restoration of terminal voltage on fault clearance is acquired successfully. Correspondingly, several tests are conducted to validate the effectiveness and enhancement in the performance of the DFIG based wind farms, when the proposed control strategy is implemented over it during numerous fault ride-through circumstances.

The growing energy demand and the emerging environmental concerns across the globe caused increasing penetration of renewable energy sources (RESs) in distribution systems. One of the major issues faced by modern distribution systems with high RESs penetration is voltage instability. Generally, voltage instability can lead to voltage collapse, which is one of the key drivers for blackout incidents around the world. Under voltage load shedding (UVLS) schemes are usually executed as the final option to prevent a large-scale blackout or voltage collapse. But ever-increasing integration of RESs in distribution systems creates an additional threat to the existing UVLS schemes. Therefore, new UVLS methods are necessary to provide the appropriate corrective scheme for distribution systems, particularly when connected with RESs. This paper reviews and updates the recent UVLS methods emphasizing on distribution network application. In addition, the contributions and limitations of the conventional, adaptive, and computational intelligence techniques of UVLS are highlighted. Moreover, this paper provides an overview of the voltage stability indices applied for UVLS methods. Also, at the end of this review article, the further research directions to improve the safety of modern distribution systems during emergencies using UVLS are drawn as the conclusion.

Due to the growth of electric power demand and the intricacy of modern distribution system structure, the voltage stability issue is evolving as a critical problem in distribution grids. Therefore, it is imperative to investigate the corrective measures. In this paper, artificial neural network (ANN) based voltage stability online monitoring approach for distribution systems with distribution generators (DGs) is proposed. The proposed technique employs a local voltage stability index known as the stability index (SI) to identify the weak bus information, which is more effective compared to the conventional load margin techniques. Furthermore, the nonlinear relationship of the distribution grid control status and the resultant SI is mapped using ANN. From the installed distribution-level phasor measurement units (PMUs), the state parameters of buses can be obtained, and the resultant values of SI can be estimated. This approach can significantly enhance the computational speed of SI and evaluate the voltage stability measurement of distribution network in real-time, which assist the operator of the network in order to determine the operational condition and execute actions quickly. The proposed approach is applied on the modified IEEE 33 and IEEE 69-bus system with DGs. It is found that the computation time needed for assessment of voltage stability by CPF method is 16.2500 s and 21.8872 s whilst the computation time needed for the proposed method for the same assessment is 0.0677 s and 0.0749 s respectively for modified IEEE 33 and IEEE 69-bus system. This demonstrates that the proposed method has high accuracy and efficacy.

In this paper, we have developed an automatic brush-based PV cleaning system to control and synchronize the 3 motors together with a smooth periodic of cleaning while moving it horizontally over the PV surface. The mechanical design involved installing linear guides at the top and bottom of the rail to support the aluminium plate that holds the carrier motors and rotating brush. Two different movements of translational and rotational motion of the motors are managed by an algorithm programmed in Arduino Mega. In investigating the performance of motor parameters and dust removal rate, we conducted an experiment by spreading dry sand over the PV surface. Results showed that the torque of the cleaning brush motor increases with the increase in load. The obtained torque of the carrier motor was found to be 9.167 Nm ( stall torque, 9.8 Nm) with a full load of 18 brushes. The torque is inversely proportional to the speed but directly proportional to power. The required power to move the 2.93 kg of cleaning system was 19.20 W with 3.015 Nm of torque. The system achieved 86.8% of the dust removal rate from the four cycles of cleaning operations.