Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient.

No abstract

No abstract

Distributed Generation (DG) based on Renewable Energy Sources (RES) are considered as an effective and economical technology for the advancement of an Electric Power System (EPS) to fulfill the load demand. Mostly, studies pertaining to DG planning are performed while considering constant load demand and DG generation. However, these considerations may provide misleading and inconsistent values for loss reduction, voltage profile, power quality, and other operational parameters. Therefore, this paper proposes a novel framework to determine the impact of different Time Varying Voltage Dependent (TVVD) load models on wind DG planning study. Firstly, wind DG optimal allocation is performed using Salp Swarm Algorithm (SSA) for different TVVD load models. Afterwards, impact of different TVVD load models on wind DG planning is investigated. Comparative evaluation of various impact indices, real and reactive power (losses and intakes), penetration level, and apparent power support provided due to integration of wind DG are discussed for various TVVD loads. The analysis of results indicates that TVVD loads have a significant impact on performance of distribution system and DG planning studies. INDEX TERMS Distributed generation, impact indices, salp swarm algorithm, time varying voltage dependent loads.

No abstract

No abstract

The efficiency of PV systems can be improved by accurate estimation of PV parameters. Parameter estimation of PV cells and modules is a challenging task as it requires accurate operation of PV cells and modules followed by an optimization tool that estimates their associated parameters. Mostly, population-based optimization tools are utilized for PV parameter estimation problems due to their computational intelligent behavior. However, most of them suffer from premature convergence problems, high computational burden, and often fall into local optimum solution. To mitigate these limitations, this paper presents an improved variant of particle swarm optimization (PSO) aiming to reduce shortcomings offered by conventional PSO for estimation of PV parameters. PSO is improved by introducing two strategies to control inertia weight and acceleration coefficients. At first, a sine chaotic inertia weight strategy is employed to attain an appropriate balance between local and global search. Afterward, a tangent chaotic strategy is utilized to guide acceleration coefficients in search of an optimal solution. The proposed algorithm is utilized to estimate the parameters of the PWP201 PV module, RTC France solar cell, and a JKM330P-72 PV module-based practical system. The obtained results indicate that the proposed technique avoids premature convergence and local optima stagnation of conventional PSO. Moreover, a comparison of obtained results with techniques available in the literature proves that the proposed methodology is an efficient, effective, and optimal tool to estimate PV modules and cells’ parameters.

No abstract