An improved optimization technique for estimation of solar photovoltaic parameters

The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV

Identification the internal parameters for mono-crystalline solar module using Matlab-simulation and experimental ascertainment

The research studies the effects of some weather parameters for Baghdad city on the output of the solar module of the type monocrystalline. The experimental part measures the electrical parameters of the photo-voltaic (PV) module for three levels of radiation rate 500, 750, and 1000 W/m2. The theoretical part includes the modeled and simulation of the PV panel, via the proposed mathematical single–diode model (SDM, 5 parameters), and Matlab-simulation. The Newton Raphson method was applied to find the output current of the solar panel and the plotting P-V, I-V curves.  The work involves preparing a simple mathematical model to estimate the optimal ambient conditions to give the highest output of the solar module. The validation of the model was verified by the practical testing of the cell for 6 months. The best results were obtained at standard testing conditions (25℃, 1000 W/m2). The output power calculated by the mathematical model was 30.1 W while from experimental work was 30.45 W. The relative error is 1.15%. The converge between experimental and modeling results for the same conditions is about 98.9% that proves the validity of the proposed model and the possibility of using it for all types of photovoltaic

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO

Optimal Parameter Estimation of Solar PV Panel Based on Hybrid Particle Swarm and Grey Wolf Optimization Algorithms

The performance of a solar photovoltaic (PV) panel is examined through determining its internal parameters based on single and double diode models. The environmental conditions such as temperature and the level of radiation also influence the output characteristics of solar panel. In this research work, the parameters of solar PV panel are identified for the first time, as far as the authors know, using hybrid particle swarm optimization (PSO) and grey wolf optimizer (WGO) based on experimental datasets of I-V curves. The main advantage of hybrid PSOGWO is combining the exploitation ability of the PSO with the exploration ability of the GWO. During the optimization process, the main target is minimizing the root mean square error (RMSE) between the original experimental data and the estimated data. Three different solar PV modules are considered to prove the superiority of the proposed strategy. Three different solar PV panels are used during the evaluation of the proposed strategy. A comparison of PSOGWO with other state-of-the-art methods is made. The obtained results confirmed that the least RMSE values are achieved using PSOGWO for all case studies compared with PSO and GWO optimizers. Almost a perfect agreement between the estimated data and experimental data set is achieved by PSOGWO

Comparison of two PV array models for the simulation of PV systems using five different algorithms for the parameters identification

Simulation is of primal importance in the prediction of the produced power and automatic fault detection in PV grid-connected systems (PVGCS). The accuracy of simulation results depends on the models used for main components of the PV system, especially for the PV module. The present paper compares two PV array models, the five-parameter model (5PM) and the Sandia Array Performance Model (SAPM). Five different algorithms are used for estimating the unknown parameters of both PV models in order to see how they affect the accuracy of simulations in reproducing the outdoor behavior of three PVGCS. The arrays of the PVGCS are of three different PV module technologies: Crystalline silicon (c-Si), amorphous silicon (a-Si:H) and micromorph silicon (a-Si:H/µc-Si:H). The accuracy of PV module models based on the five algorithms is evaluated by means of the Route Mean Square Error (RMSE) and the Normalized Mean Absolute Error (NMAE), calculated for different weather conditions (clear sky, semi-cloudy and cloudy days). For both models considered in this study, the best accuracy is obtained from simulations using the estimated values of unknown parameters delivered by the ABC algorithm. Where, the maximum error values of RMSE and NMAE stay below 6.61% and 2.66% respectively.Peer ReviewedPostprint (author's final draft