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This paper aims to propose, the multi-objective method for optimal planning and operation of distributed generators (DGs) on distribution system (DS) using hybrid technique is proposed.

The proposed hybrid technique denotes hybrid wrapper of black widow optimization algorithm (BWOA) and bear smell search algorithm (BSSA). BWOA accelerates the convergence speed with combination of the search strategy of BSSA; hence, it is named as improved black widow-bear smell search algorithm (IBWBSA) technique.

The multiple-objective operation denotes reducing generation cost, power loss, voltage deviation with optimally planning and operating the DS. For setting up the DG units on DS, IBWBSA technique is equipped to simultaneously reconfigure and find the optimal areas.

In this planning model, the constraints are power balance, obvious power flow limit, bus voltage, distribution substation’s capacity and cost. Then, proposed multiple-objective hybrid method to plan electrical distribution scheme is executed in the MATLAB/Simulink work site.

The distributed generation (DG) proper placement is an extremely rebellious concern for attaining their extreme potential profits. This paper aims to propose the application of the communal spider optimization algorithm (CSOA) to the performance model of the wind turbine unit (WTU) and photovoltaic (PV) array locating method. It also involves the power loss reduction and voltage stability improvement of the ring main distribution system (DS).

This paper replicates the efficiency of WTU and PV array enactment models in the placement of DG. The effectiveness of the voltage stability factor considered in computing the voltage stability levels of buses in the DS is studied.

The voltage stability levels are augmented, and total losses are diminished for the taken bus system. The accomplished outcomes exposed the number of PV arrays accompanied by the optimal bus location for various penetration situations.

The optimal placement and sizing of wind- and solar-based DGs are tested on the 15- and 69-test bus system.

Moreover, the projected CSOA algorithm outperforms the PSOA, IAPSOA, BBO, ACO and BSO optimization techniques.

The main purpose of this controller is to carryout irrigation by the farmers with renewable energy resources.

The proposed design includes the Deep learning based intelligent stand-alone energy management system used for irrigation purpose. The deep algorithm applied here is Radial basis function neural network which tracks the maximum power, maintains the battery as well as load system.

The Radial Basis Function Neural Network algorithm is used for carrying out the training process. In comparison with other conventional algorithms, this algorithm outperforms by higher efficiency and lower tracking time without oscillation.

It is little complex to implement the hardware setup of neural network in terms of training process but the work is under progress.

The practical hardware implementation is under progress.

If controller are implemented in a real-time environment, definitely it helps the human-less farming and irrigation process.

If this system is implemented in real-time environment, every farmer gets benefitted.

This study aims to investigate the tensile strength and elastic modulus of custom-designed polymer composites developed using voxel-based design. This study also evaluates theoretical models, such as the rule of mixtures, Halpin–Tsai model, Cox–Krenchel model and the Young–Beaumont model and the ability to predict the mechanical properties of particle-reinforced composites based on changes in the design of rigid particles at the microscale within a flexible polymer matrix.

This study leverages the PolyJet process for voxel-printing capabilities and a design of experiments approach to define the microstructural design elements (i.e. aspect ratio, orientation, size and volume fraction) used to create custom-designed composites.

The comparison between the predictions and experimental results helps identify appropriate methods for determining the mechanical properties of custom-designed composites ensuring informed design decisions for improved mechanical properties.

This work centers on multimaterial additive manufacturing leveraging design freedom and material complexity to create a wide range of composite materials. This study highlights the importance of identifying the process, structure and property relationships in material design.