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With the penetration of renewable energy sources based distributed generation (RES-DG) in the distribution network (DN), the operating time of protection relays becomes a major concern to avoid the mis-coordination and nuisance tripping of RES-DG. It is due to the bidirectional flow of current, variance in the fault current level, i.e. high fault current in case of Squirrel cage induction generator (SCIG) based RES-DG, and low fault current for an inverter-based RES-DGs. This paper proposes a novel User-defined dual setting direction overcurrent relay with hybrid time current-voltage (UDDOR-TCV) characteristics to intensify the proposed scheme’s flexibility without any communication assistance for radial DN. The proposed model is formulated in a constrained non-linear optimization fashion and solved with the MINLP solver of general algebraic modelling system(GAMS) software to determine the optimal relay settings. The propounded protection scheme is simulated on IEEE-33 bus radial distribution system and a local 40-bus system hosting the multiple SCIG and PV based RES-DGs at optimal locations. Detailed numerical studies are carried out to show the performance of the proposed scheme. 66.848% and 68.26% reduction in relay operation time with zero mis-coordination is achieved with SCIG-WTG case and PV-units case for the IEEE-33 bus system and a similar reduction in operation time is 68.937% and 66.563% for Local-40 bus system. Moreover, the relays numbers are reduced by 32.692% and 39.7% for IEEE-33 and Local-40 bus systems, respectively, due to the forward/reverse characteristics capability of the proposed UDDOR-TCV. The performance of the proposed scheme is also evaluated on IEEE-8 bus meshed DN. The results are compared with the existing protection schemes in recent literature.

This paper investigated the effects of variant lighting conditions on the recognition process. A framework is proposed to improve the performance of gesture recognition under variant illumination using the luminosity method. To prove the concept, a workable testbed has been developed in the laboratory by using a Microsoft Kinect sensor to capture the depth images for the purpose of acquiring diverse resolution data. For this, a case study was formulated to achieve an improved accuracy rate in gesture recognition under diverse illuminated conditions. For data preparation, American Sign Language (ASL) was used to create a dataset of all twenty-six signs, evaluated in real-time under diverse lighting conditions. The proposed method uses a set of symmetric patterns as a feature set in order to identify human hands and recognize gestures extracted through hand perimeter feature-extraction methods. A Scale-Invariant Feature Transform (SIFT) is used in the identification of significant key points of ASL-based images with their relevant features. Finally, an Artificial Neural Network (ANN) trained on symmetric patterns under different lighting environments was used to classify hand gestures utilizing selected features for validation. The experimental results showed that the proposed system performed well in diverse lighting effects with multiple pixel sizes. A total aggregate 97.3% recognition accuracy rate is achieved across 26 alphabet datasets with only a 2.7% error rate, which shows the overall efficiency of the ANN architecture in terms of processing time.

The authors wish to make the following corrections to this paper [...]

The apparent advantages of Multilevel Inverter (MLI) topologies in handling medium and high power with less loss in switching and lower harmonic distortion in an output voltage waveform makes it better than the conventional inverter. However, the MLI topologies utilize a large number of DC power supplies and power semiconductor devices. They also have a higher value of total standing voltage (TSV). Moreover, capacitor voltage balancing problems, self-voltage boosting inability, and complex control techniques require a relook and improvement in their structure. More recently, Switched-Capacitor Multilevel Inverter (SCMLI) topologies have been proposed to overcome the shortcomings of MLIs. In this paper, a generalized structure for a single-phase switched capacitor multilevel inverter (SCMLI) with self-voltage boosting and self-voltage balancing capability is proposed. A detailed analysis of a general structure of SCMLI is presented. The comparative analysis of the structures is carried out with recently reported topologies to demonstrate superiority. An optimized low-frequency modulation controls the output voltage waveform. The simulation and experimental results are included in the paper for single-unit symmetric (9-level voltage) and asymmetric (17-level voltage) configurations.

Clean water scarcity is still an intense, prolonged global issue that needs to be resolved urgently. The solar steam generation has shown great potential with a high energy conversion efficiency for clean water production from seawater and wastewater. However, the high evaporation rate of water cannot be preserved due to the inevitable fouling of solar absorbers. Herein, a self-floatable and super hydrophilic solar-driven steam generator composed of activated carbon coated melamine foam (ACM). The deposited ACM photothermal layer exhibits outstanding solar absorption (92%) and an efficient evaporation rate of 1.27 kg m−2 h−1, along with excellent photothermal conversion efficiency (80%) as compared to commercially available primitive solar stills. The open porous assembly of melamine foam equipped with 80% flexibility (0.8 MPa) enabled smooth water transport and sustain heat accumulation within the matrix. The thermal insulation of ACM is 10 times greater than pure water. Moreover, open porous assembly of designed solar-powered steam generator rejects salt ions as well as volatile organic compounds efficiently. The low-cost and facile fabrication of photothermal based water production presents a potential solution to single step drinking water supply from various resources of the sea, the lakes and mixtures of emulsified oil and industrial wastewater.

Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition.