Design and Investigation of Solar PV Fed Single-Source Voltage-Lift Multilevel Inverter Using Intelligent Controllers

Optimization structures are mostly considered for resolving multi-objective difficulties similar to cost, emission, and financial load dispatch in various energy sources. Non-renewable energy sources (NRES) emit harmful gases like CO2, and methane. which results in air pollutants, so various techniques are used in survey papers. By considering optimization techniques, the multi-objective problems are reduced in renewable energy sources (RES) and NRES. Implementing these techniques in RES and NRES will define the proper objective function. Hybrid algorithms are used for solving multi-objective problems like cost, pollutant emission, price penalty factor, valve point, ramp rates, and constraints like generator, power flow, power balance, and heat balance. A fuzzy system is used in numerous surveys for controlling purpose, superiority, and efficiency over other controllers. Subsequently summarized three types of sources like RES, RES-NRES, and NRES for easy identification of techniques and problems. This study reviews various techniques and mathematical modeling of algorithms for future research.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/22179/14046

Various PSO methods investigation in renewable and nonrenewable sources

Testing and performance evaluation of water pump irrigation system using voltage-lift multilevel inverter

In the context of this paper a three phase grid connected Photo-Voltaic (PV) system that is used to design with MPPT and developed Grey Wolf optimization (GWO) algorithm for analyzing the power quality issues in the grid system. The proposed Grey Wolf optimization (GWO) algorithm is incorporated in the prototype model and compared with other related optimization algorithms namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The various loading conditions as well as solar irradiances are modeled by using MATLAB simulation and experimentally validated by a DSPIC (DS 1104) based prototype model. A three phase PV grid connected non-linear load is observed in different operating environmental conditions. The optimization control algorithms was developed and implemented in Super-Lift Inverter (SLI) grid connected system. The findings of this work are, grid reactive power demand is compensated using DSTATCOM, and also from the real power of renewable energy system. But, majority of the active power is provided or absorbed by DSTATCOM component. The objective of this proposed work is that the three optimization control algorithms are examined, and the PV integrated grid tied system maintains a compensation power at Unity Power Factor (UPF). The proposed optimization methods produce load output power factor values such as 0.89 (GWO), 0.88, (PSO) and 0.86 (GA). 

An experimental simulation testing of single-diode PV integrated mppt grid-tied optimized control using grey wolf algorithm

Due to the shortage of fossil fuel usage, the solar Photovoltaic (PV) energy has increased grownup over the last decade. Most conventional applications of renewable energy are being phased out in order to reduce costs and save the environment. PV plants undergo numerous failures in faults detection and ultimate power developments. These consequences demonstrate in the environmental field and internal components. Even when internal standards are followed, the faults are unavoidable and undetectable. Due to this performance of manufacturing plants are not predictable. As a result, a proper fault detection mechanism is required for a PV system to detect faults and avoid energy losses. To address these issues, this research work proposed Internet of Things (IoT) sensor-based fault identification in a solar PV system. The PV panel status is monitored using pressure, light intensity, voltage, and current sensors. These sensor data’s are stored in the cloud for further analysis using a web-based control server. To classify the sensor data, models of Support Vector Machine (SVM), and Extreme Learning Machine (ELM) are utilized. The experimental results indicate that ELM achieves a classification accuracy of 96.32%. Which is higher than SVM and other optimization control techniques. The proposed model uses the IoT cloud to provide real-time monitoring and fault detection in plant environmental and electrical parameters.

IoT based fault identification in solar photovoltaic systems using an extreme learning machine technique

To minimize real-time errors in a Photovoltaic (PV) system performances must be forecasted through precise simulation design before continuing into a practical application. However, due to the scarcity of data in datasheets and the inherent transcendental connections are between PV current and PV voltage, to determining the Single Diode Model (SDM) parameters becomes a more challenging problems. This paper offers a simulated study of a SDM and Double Diode Model (DDM) solar PV system under various irradiation represents, and the performance was developed by incorporating an optimization-based Maximum Power Point (MPP) tracking techniques. According to the present simulation presented in this article, a mathematical model for a SDM/DDM as well as optimization methodologies has been estimated MATLAB platform. The present MPP circuit model designed and compared with BAT optimization algorithms. The nonlinear relationship between Voltage (V) - Current (I) and Voltage (V) –Power (W) acknowledged as characteristic curves for different temperature (∘c) and irradiance (W/m2) values are verified in numerical simulation results. MPP tracking power and efficiency are examined for maximum power (Pmax) to test the optimization based system. The simulation results show that the BAT optimization model was achieved the highest tracking efficiency better than other heuristic algorithms.

Design and development of extract maximum power from single-double diode PV model for different environmental condition using BAT optimization algorithm

Kalaivanan Chinnanan

Papers

Design and development of extract maximum power from single-double diode PV model for different environmental condition using BAT optimization algorithm