Modeling and Simulation of Photovoltaic Fed Drive by Using High Voltage Gain DC-DC Boost Converter

D.C. motors are seldom used in ordinary applications because all electric supply companies furnish alternating current. However, for special applications such as in steel mills, mines and electric trains, it is advantageous to convert low value of DC into high value of DC in order to use D.C. motors controlled by power electronic apparatus. Here the DC motor is controlled power electronic converters through RES system. The renewable energy sources such as PV modules, fuel cells or energy storage devices such as super capacitors or batteries deliver output voltage at the range of around 15 to 40 VDC. A boost converter is used to clamp the voltage stresses of all the switches in the interleaved converters, caused by the leakage inductances present in the practical coupled inductors, to a low voltage level. Overall performance of the renewable energy system is then affected by the efficiency of step-up DC/DC converters with closed loop control action, which are the key parts in the system power chain. This paper presents a dc-dc power converter integrated closed loop system to attain high stability factor in such a way to obtain, in a single stage conversion fed DC motor drive. This review is mainly focused on high efficiency step-up DC/DC converters with high voltage gain. The results are obtained through Matlab/Simulink software package

A new metaheuristic-based MPPT controller for photovoltaic systems under partial shading conditions and complex partial shading conditions

Solar photovoltaic energy is the potential energy in the universe for generating electricity and meeting the required load demand. However, on account of partial shading conditions, the difficult task in the PV system is to track global maxima instead of local maxima and maintain the uninterrupted power supply. To solve this problem, a new metaheuristic algorithm is introduced in this paper such as a heap-based optimizer (HBO). The proposed method is developed in MATLAB/Simulink software. The system is examined under distinct irradiation conditions and compared their performance with other methods. The simulation results reveal that the suggested HBO shows a reliable enhancement as compared to other studied methods with regard to tracking maximum power, convergence time, and settling time. The extracted power efficiencies are 99.85% for case 1, 99.96% for case 2, and 99.92% for case 3. It is found that HBO shows better enrichment than other studied methods

Swarm intelligence-based MPPT design for PV systems under diverse partial shading conditions

The photovoltaic (PV) system has attracted attention in recent years for generating more power and freer from pollution and being eco-friendly to the environment. Nonetheless, the PV system faces many consequences under partial shading (PS) on account of the non-linear nature of the environment. Various traditional methods are used to solve the difficulties of the PV system. However, these methods have oscillations around global maxima peak power (GMPP) and are not able to deliver accurate outcomes when the system becomes complex. Therefore, the combination of teaching-learning (TL) and artificial bee colony (ABC) called TLABC are hybridized in this work for mitigating the oscillations around the GMPP. To find the effectiveness of the proposed method, it can be evaluated with other methods such as PSO, IGWO, MFO, and SSA. As per simulation outcomes, the proposed TLABC shows greater performance in terms of Standard Deviation (SD), Mean Absolute Error (MAE), Successful rate (Suc. Rate), and efficiency are 3.95, 0.13, 98.88 and 99.89% respectively. Furthermore, the suggested system is evolved in the PV laboratory and tested in four different cases for validating the system performance with simulation outcomes. It is found that the suggested TLABC method ensures a greater performance than other studied methods

Frequency stabilization in interconnected power system using bat and harmony search algorithm with coordinated controllers

Modern power system faces excessive frequency aberrations due to the intermittent renewable generations and persistently changing load demands. To avoid any possible blackout, an efficient and robust control strategy is obligatory to minimize deviations in the system frequency and tieline. Hence, to achieve this target, a new two-degree of freedom-tilted integral derivative with filter (2DOF–TIDN) controller is proposed in this work for a two-area wind-hydro-diesel power system. To enhance the outcome of the proposed 2DOF–TIDN controller, its gain parameters are optimized with the use of a newly designed hybrid bat algorithm-harmony search algorithm (hybrid BA–HSA) technique. The effectiveness and superiority of hybrid BA–HSA tuned 2DOF–TIDN is validated over various existing optimization techniques like cuckoo search (CS), particle swarm optimization (PSO),HSA, BA and teaching learning-based optimization (TLBO). To further refine the system outcome in the dynamic conditions, several flexible AC transmission systems (FACTS) and superconducting magnetic energy storage (SMES) units are adopted for enriching the frequency and tie-line responses. The FACTS controllers like static synchronous series compensator (SSSC), thyristor-controlled phase shifter (TCPS), unified power flow controller (UPFC) and interline power flow controller (IPFC) are employed with SMES simultaneously. The simulation results disclose that the hybrid BA–HSA based 2DOF–TIDN shows superior dynamic performance with IPFC–SMES than other studied approaches. A sensitivity analysis is examined to verify the robustness of proposed controller under ±25% changes in loading and system parameters

Assessment of energy storage and renewable energy sources-based two-area microgrid system using optimized fractional order controllers

The stability of modern power systems faces significant challenges due to intermittent renewable generation and fluctuating load demands, resulting in excessive frequency variations. To address this issue and prevent potential blackouts, implementing a robust control strategy is crucial. This study introduces a cascaded control system, combining a novel fractional order integral derivative (FOID) with a fractional order proportional integral derivative with filter (FOPIDN) controllers, denoted as CFOID-FOPIDN. The gain parameters for CFOID-FOPIDN are determined using the Archimedes optimization algorithm (AOA). The proposed two-area microgrid system incorporates various power-generating sources, including solar, wind turbine generators, fuel cells, micro-turbines, and battery energy storage technologies. To assess the AOA-tuned CFOID-FOPIDN controller's effectiveness, the system is tested under five different conditions: load fluctuations, changes in wind speed, variations in irradiance, combined wind and irradiance variations, and comprehensive changes across all conditions. Simulation results reveal that the AOA-based CFOID-FOPIDN outperforms other existing algorithms, such as particle swarm optimization (PSO), bat algorithm (BAT), moth flame optimization (MFO), and whale optimization algorithm (WOA). In dynamic variations across all sources, the AOA-tuned strategy demonstrates shorter settling times in area-1 (2.5 s), area-2 (2.2 s), and tie-line power (2.1 s) compared to PSO (3.5 s, 3.8 s, 3.4 s), WOA (3.2 s, 3.4 s, 3.1 s), BA (3.0 s, 3.1 s, 3.0 s), and MFO (2.9 s, 2.8 s, 2.9 s). Across all five scenarios, the AOA-tuned CFOID-FOPIDN strategy consistently demonstrates superior performance compared to the other strategies under consideration

Optimized controllers for stabilizing the frequency changes in hybrid wind-photovoltaic-wave energy-based maritime microgrid systems

Reducing the dependence on traditional energy sources and shifting towards the utilization of renewable energy sources (RES) of energy in the maritime sector is imperative for reducing greenhouse gas emissions. Inherently, RES sources like solar and wind are intermittent and variable, resulting in inconsistent power availability and hence leading to energy supply fluctuations and potential shortages. In this respect, an efficient control strategy to maintain system stability and address intermittency effectively is essential. This work considers a hybrid marine microgrid with various energy sources like photovoltaics (PV), wind energy conversion system (WECS), marine biodiesel generator, Archimedes wave power generation, solid oxide fuel cell, and batteries. A 2-degree of freedom (2DOF) structure is designed and implemented with the tilt-integral-derivative filter (TIDN) to address frequency variations. Furthermore, an Archimedes optimization algorithm (AOA) is used to optimize the 2DOF-TIDN controller. The stability of the proposed microgrid system is assessed under various combinations of RES availabilities, including real-time data from WECS and PV. The AOA-based 2DOF-TIDN performance is compared to the following algorithms: genetic, Jaya, bat, grasshopper optimization, particle swarm optimization, and moth flame optimization. Simulation results obtained show that the AOA-based 2DOF-TIDN control strategy achieves shorter settling times in mitigating the changes of marine microgrid systems under different dynamic conditions as compared to the other algorithms. Finally, the controller being proposed in this paper was tested for robustness with parameter deviations of +25%, −20%, and − 40% from the nominal values, and proved to be the proposed 2DOF-TIDN controller parameters demonstrate significant robustness in effectively managing the uncertainties and parametric variations

Grey wolf optimization and differential evolution-based maximum power point tracking controller for photovoltaic systems under partial shading conditions

Photovoltaic (PV) energy is one of the most abundant energy in the world for generating huge electrical power to meet the desired load. However, the arduous task in the electrical industry is to contribute to the uninterrupted power supply by the PV system as a result of partial shading conditions (PSC). To track the global maximum peak power (GMPP) instead of local maxima peak power (LMPP), the combination of gray wolf optimization (GWO) and differential evolution (DE) algorithm is hybridized (GWO-DE) in this work. Furthermore, the proposed system is developed in the MATLAB/Simulink software. The system is investigated under distinct atmospheric conditions and compared its performance with other studied approaches. The simulation results disclose that the hybrid GWO-DE approach shows a greater performance as compared to other studied methods with respect to convergence time, accuracy, extracted power, and efficiency. Moreover, the proposed system is developed experimentally and tested in four different cases. The outcomes of the GMPP are 984.65 W at 0.08 sec for case 1, 630.39 W at 0.08 sec for case 2, 602.56 W at 0.07 sec for case 3, and 650.08 W at 0.05 sec for case 4. It is found that the suggested hybrid GWO-DE method ensures a greater performance than other studied methods

An improved grey wolf optimization based MPPT algorithm for photovoltaic systems under diverse partial shading conditions

The photovoltaic (PV) systems are performing a substantial role in electric power systems for generating electrical power in various uncertain circumstances. Nonetheless, the PV systems face numerous challenges for power production in the event of partial conditions. Moreover, different types of multiple peak power points (MPPP) are generated in the characteristics of the PV system under diverse partial patterns. The MPPP's having only one global maximum peak power (GMPP) and the remaining are local peak PowerPoints (LPPP), in which LPPP are interrupted to grab maximum power. Hence, improved grey wolf optimization (I-GWO) approach is developed in this work for enriching the required power generation at partial conditions. The proposed system has been designed in the MATLAB/Simulink environment. As per the simulation findings, the suggested I-GWO demonstrates great performance with regards to tracking time, accuracy, and efficiency as compared with other studied algorithms

Ultra-short-term PV power forecasting based on a support vector machine with improved dragonfly algorithm

Photo-voltaic (PV) is one of the most abundant sources on the earth for the generation of electricity. Although, due to the stochastic nature of PV characteristics to sustain constant power, an accurate PV power prediction is needed for a grid-connected PV system. The proposed model of support vector machine (SVM) with improved dragonfly algorithm(IDA) is used to forecast the PV power. Previously, Theexecution can be done by dragonfly algorithm (DA) through adaptive learning factor along with the differential evolution technique. The IDA is used to select the best support vector machine parameters. Eventually, the suggested model provides better performance as compared to the other algorithm such as SVM with dragonfly algorithm(SVM-DA). It is suitable for forecasting ultra-short-term PV power

Cost regulation and power quality enhancement for PV-wind-battery system using grasshopper optimisation approach

Renewable energy sources perform a potential role in the electrical industry for meeting the required load demand. However, the difficult aspect is to be reduced the entire cost including initial cost, operational cost, replacement cost and maintenance cost. Hence, to achieve this target, a grasshopper optimisation algorithm (GOA) is suggested in this work for optimum sizing of the off-grid. In this study, various power-generating renewable sources such as photovoltaic (PV), wind turbines (WTs) and batteries are integrated into the off-grid system. Moreover, solar irradiance, wind speed and required load are simulated by the HOMER software for 12 months of a year. Further, the performance of the suggested GOA is compared by hybrid genetic algorithm (GA) with particle swarm optimisation (PSO) (GA-PSO) for optimum sizing of the WTs and PV. As per the simulation outcome, the suggested GOA shows better performance and contributes the less levelised cost of energy factor (LFC = 0.502) as compared to studied GA-PSO