T. R. Jyothsna's research while affiliated with Andhra University and other places

This paper presented a concise summary of equivalent circuit models for photovoltaic modules and verifications. Existing equivalent circuit models are single and double diode models. Single diode model includes ideal diode, Rs, Rp, and semi-empirical (SE Rp) models among them the Rp model is commonly used as a compromise in simplicity and accuracy. But Rp-model has draw back of deviation from actual data at low irradiance level. Semi-empirical Rp (SE Rp) model improved this drawback to some extent. However, this model has drawback of strong dependency on I-V characteristic curve from datasheet. The more accurate counterpart is double diode model that considers the recombination loss in the space charge region. The main focus of this paper is to provide brief review of PV modules models and verification. Accordingly, verification was done for shell S115 multi-crystalline PV module. The I-V and absolute error plots show accuracy level of each model from which double diode model has improved accuracy over single diode models. And from single diode models SE Rp-model showed significant improvement over Rp and Rs models.

This paper proposes a new hybrid method for the islanding detection and minimizes the Non-Detection Zone (NDZ), for an inverter-based grid connected distributed generation (DG) system. The proposed hybrid method is an integration of the modified active Sandia Frequency Shift (SFS) method and the passive reactive power variation technique. The modified active SFS method is obtained by adaptive tuning of the gain parameter Ksfs, using the Adaptive Particle Swarm Optimization (APSO) technique. The proposed hybrid method produces a significant reduction of NDZ and suitable for a more accurate detection of islanding condition. The effect of NDZ for the proposed hybrid method has been derived analytically and the system performance has been assessed, under various power mismatch conditions for a DG-grid interface. The overall system performance has been found to be superior under the presence of constant power control strategy. The effect of frequency and voltage variations at the common coupling point during islanding have also been considered. Finally, the performance of the proposed method under the IEEE standard 1547 islanding test condition has been investigated for different load factors and load variations.

In this paper effectiveness of newly introduced ‘teaching learning based optimization’ (TLBO) is evaluated against different benchmark optimization problems. The effectiveness, then, is compared with the performance of genetic algorithm (GA) using the same parameters as used with TLBO. The functions on which the two algorithms applied in this work are rastrigin function, quartic, rosenbrock, six hump camel back. And the results are validated. Both the algorithms are applied on these functions using the same software (MATLAB) on the same platform also with the same number of population and elite group. The effectiveness of the TLBO is compared with that of GA based on the speed, average solution.

Efficient extraction of available power from photovoltaic system is a mandatory task that needs extraction techniques. A number of maximum power point tracking (MPPT) techniques has been presented of which conventional techniques like perturb and observe (PO however they fail to track the right perturbation direction for rapid change atmospheric conditions and oscillations during steady-sate operation. Different methods has been devised to improve the drawbacks, among them fuzzy logic based technique is the one that works well for systems with no accurate mathematical. Thus, in this paper fuzzy logic controller (FLC) based MPPT technique using PV voltage and power variations as input and reference voltage change as output has been proposed and implemented along with P&O method. The simulation results of MPPT trajectory tracking capability, dynamic and steady state responses for variable irradiance revealed superior performance of fuzzy based technique over P&O method.

Solar photovoltaic (PV) energy has witnessed double digit growth in the past decade. Solar energy is one of the most promising renewable resources that can be used to produce electric energy through photovoltaic process. A significant advantage of photovoltaic systems is that the use of the abundant and free energy from the sun. Large penetration levels of distributed renewable resources in power distribution systems, especially photovoltaic generators, can potentially impose a paradigm shift in the design, operation, protection and control of the distribution systems(DS). The penetration of PV systems as distributed generators in low-voltage grids has also seen significant attention. In addition, the need for higher overall grid efficiency and reliability has boosted the interest in the microgrid concept. High-efficiency PV-based microgrids require maximum power point tracking (MPPT) controllers to maximize the harvested energy due to the nonlinearity in PV module characteristics. Maximum power transfer in solar microgrid applications is achieved by impedance matching with a dc-dc converter with maximum power point tracking by the incremental conductance method(ICM). Perturb and observe (P&O) techniques, although thoroughly investigated in previous research, still suffer from several disadvantages, such as sustained oscillation around the MPP and microgrid.