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

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

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

Performance analysis of distributed power flow controller with ultra-capacitor for regulating the frequency deviations in restructured power system

This paper presents a novel approach for automatic generation control (AGC) of two-area deregulated system with unequal sources for sustaining the frequency and tie-line power at perturbations. The combination of ultra-capacitor (UC) and various FACTS controllers such as Thyristor-controlled series capacitor (TCSC), Static synchronous series compensator (SSSC), Unified power flow controller (UPFC), and Distributed power flow controller (DPFC) have been investigated in AGC of interconnected system with thermal-wind and hydro-diesel generating units. An innovative metaheuristic method called bat algorithm (BA) is used to ascertain the optimal gain parameters of the two degree of freedom (2DOF) controllers using an integral squared error (ISE) criteria. Furthermore, the productive assessment of the bat tuned 2DOF controllers are also compared with teaching learning-based optimization (TLBO) and cuckoo search (CS) methods optimized 2DOF in distinct contract scenarios of the suggested restructured system. The effect of the coordinated performance of UC and DPFC has been mitigated the oscillatory response of the AGC system at various operating circumstances. The investigations disclose that the bat optimized 2DOF-PID yield the productive outcomes with coordination of DPFC and UC in all contract transactions of the restructured system

An Improved Efficiency of Solar Photo Voltaic System Applications by Using DC-DC Zeta Converter

This study investigates on how a DC-DC Zeta converter act as intermediate among SPV and VSI, in which it drags the maximum power from the solar photo voltaic (SPV) system and to drive a BLDC motor connected to a water pumping system application. Here INC-MPPT (Incremental Conductance Maximum Power Point Tracking) method is utilized smartly to control the zeta converter in order to drive brushless DC (BLDC) motor smoothly. Soft starting current prevents the influence of peak starting current on the BLDC motor windings. The fundamental frequency of Electronic computational from the BLDC motor is used to avoid the voltage source inverter losses. The proposed converter is also suitable to increase the voltage of DC link connected to the VSI. The major benefit of this configuration is designed and modelled in such a way that even under dynamic conditions, the performance of a solar photovoltaic application is not affected. The suggested system is developed by using MATLAB/Simulink softwar

Simulation and experimental design of adaptive-based maximum power point tracking methods for photovoltaic systems

This paper presents a filter-based adaptive fuzzy proportional integral derivative (FPIDN) controller for photovoltaic (PV) systems. The proposed maximum power point tracking (MPPT) method is implemented in two blocks. The first block represents by an adaptive calculation block; to produce a reference voltage for every maximum power point (MPP), whereas the second is the FPIDN controller; utilized to manage duty cycle of the PWM converter. The effectiveness of the proposed MPPT has been evaluated to different MPPT methods. The efficiency of the proposed MPPT recorded at 99.45% and 99.72% with MPP capture time clocks at 0.048s, outperforms the benchmarked traditional MPPT methods under diverse irradiance and temperature conditions