Our study shows that to increase help seeking behaviour of adolescents, apart from health and life skill education, their medical screening with a focus on reproductive health by trained physicians, parental involvement, supported by adolescent friendly centers (AFC) for counseling, referral and follow up are essential.

Accurate parameter identification plays an integral role in the modeling of an optimized solar module which in turn helps in the error-free prediction of its output. Five important parameters: the photoelectric current (Iph), ideality factor(α), saturation current (Io), series resistance (Rs), and shunt resistance (Rsh) are required for the accurate modeling of the PV cells/modules and need to be extracted as these parameters are not provided by the manufacturer in the datasheet. This paper proposes a new metaheuristic jellyfish optimization (JFO) algorithm for the parameter extraction of a solar module. The JFO algorithm achieves the optimal solution without being trapped in local solutions in less time. The parameter extraction using the JFO algorithm is done on two different solar modules i.e., Soltech-1STH-215P and PWP-201. The results are compared in terms of extracted parameters (Iph, α, Io, Rs, and Rsh) with the well-known optimization techniques like PSO, GA, and others available in the literature and with the manufacturer I–V and P–V characteristics. The proposed technique I–V and P–V characteristics are validated at different environmental conditions and are found to be similar to that of PSO and GA. It is also observed that the extracted parameters obtained using JFO are comparable with the other twenty-two techniques, and the proposed technique is one of the highly efficient techniques that can be utilized for parameter extraction of PV modules and to predict solar cell characteristics for all commercial modules without setting up any experimental measurements. MATLAB/simulation software is used for implementation and performance validation.

This paper investigates the electrical performance of serial-parallel (SP) electric connections under artificial arrangement for partial shading conditions (PSCs) in a photovoltaic (PV) array. The concept of bypass diode (BPD) is a very attractive solution to reduce the shading effect on the PV module and therefore gives better performance in such shading environment types. Various bypass diode integration (BPD) placement topologies with the solar photovoltaic (PV) modules are being investigated to demonstrate the improved performance under PSTCs in the current work. The seventeen PV modules arranged in SP configuration are associated with the BPD Topologies such as (a) W-BPD (b), SS-SBPD (c) SS-DBPD (d) Series G-BPD (e) SG-BPD (f) ML-OBPD for performance investigation. The performance assessment for all PV array supported by BPD topologies has been investigated using current-voltage (I-V) and power-voltage (P-V) characteristics and comparing to show better power and voltage results at global maximum power point (GMPP), improved fill factor and minimized power losses etc. The results presented may be recommended for the appropriate PV array configuration interconnection of BPD. Overall, this article reports that the ML-OBPD based SP configured PV array is superior among the BPD topologies under the considered PSTCs.

The efficiency of the low-cost renewable energy source i.e. solar is very poor due to inadequate extraction of maximum power. By employing the proper maximum power point tracking algorithm, the efficiency can be increased. An innovative adaptive backstepping neural network controller is proposed in this paper to extract the maximum power from the solar panels by tracking the desired photovoltaic array voltage in real-time. The maximum power is extracted indirectly by tuning the PV voltage to the desired PV voltage where the maximum power is attained at the desired PV voltage point. The desired photovoltaic array voltage is obtained from the linear regression method. The change in photovoltaic current caused by varying irradiance and temperature is approximated using the Chebyshev polynomials. The quicker steady-state and transient responses are accomplished and the computational burden of the photovoltaic system control law is reduced because of the orthogonal property of Chebyshev polynomials. The asymptotically stable system is obtained by tuning the weights of the neurons in accordance with the Lyapunov stability analysis. Also, Lyapunov control function of the backstepping control design procedure finds a control law by an innovative cubic equation interpretation, instead of resolving the first derivative of the control law, that diminishes the ripples in the duty cycle to make its appropriateness in real-time. A prototype is developed to validate the robustness of this controller in maximum power extraction at a faster time and the results confirm that adaptive backstepping neural network controller surpasses the performances of conventional backstepping controller and constant voltage PID controller.

This paper mainly dealt with the technical and economic feasibility of an off-grid hybrid power generation system for a remote rural Turtuk village of Ladakh, located in the northern part of India. The study showed that the proposed configured renewable integrated hybrid system, using Hybrid Optimization of Multiple Energy Resources (HOMER) software, efficiently met the energy demand, exhibiting optimum performance with low investment. The proposed PV(115 kW)/Wind(1 kW)/Battery(164 strings of 6 V each)/DG(50 kW) hybrid system was a highly commendable, feasible solution preferred from a total of 133,156 available solutions resulting from HOMER simulations. The net present cost and energy cost of the proposed configuration were $278,176 and $0.29/kWh, respectively. The proposed hybrid configuration fulfilled local load, with 95.97% reduced dominant harmful carbon dioxide emission, as compared to the sole us of a diesel generator power supply system. The technical performance of the hybrid system was ensured, with advantages including the highest renewable penetration and least unmet load. Furthermore, the analysis exclusively evaluated the impact of the system’s economic parameters (namely, its expected inflation rate, nominal discount rate, and project lifetime) on the net present cost and cost of energy of the system using a noble single fix duo vary approach.

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An increase in the share of distributed generation (DG) in the global generation system is a direct indication of the development of available technologies. The extraction of natural energy resources and their use as DG has several advantages, such as the reduction in line losses, improved voltage profile and reliability, etc., but the incorrect installation of these power plants can also have some negative effects. The innovation in technology has motivated to extract the maximum benefit of natural energy resources. Due to this, the capacity and location of these energy resources should be carefully identified. The optimal placement of a distributed generation power plant, in the existing network, is analyzed in this article. The proposed methodology is inspired by the human immune system. In this methodology clonal selection principle of immune system is combined with particle swarm optimization. For checking the validity of the proposed method two test systems, IEEE 33-node radial distribution system and IEEE 14-node loop distribution system, are considered.Results show the validity of the proposed algorithm in radial as well as in loop distribution system.

This paper presents atechnical and economic analysis of the proposed solar PV/diesel generator smart hybrid power plant for a part of SRM IST, Delhi-NCR campus. The analysis was performed using five battery storage technologies: lead-acid, lithium-ion, vanadium flow, zinc bromide and nickel-iron. The analysis also used the HOMER Pro software. The analysis was conducted to assess performance parameters such as initial cost, simple payback period, return on investment, energy produced, renewable penetration and emission of air pollutants. The optimal solution was obtained as SPP(200 kW)/DG(82 kW)/ZB(2000 kWh), with cycle charging dispatch strategy. The initial cost of this configuration is estimated to be USD163,445, and the operating cost is USD534 per year. The net present cost is estimated to be USD170,348, and the estimated cost of energy with this configuration has been obtained as USD0.090 per kWh. It is estimated that with this optimal solution, the diesel generator may consume only 110 L/year of diesel, which is the minimum of all configurations. Sensitivity analysis was performed between the size of the solar PV array and the size of the battery, along with variations in the battery’s nominal capacity and renewable fraction.