II. PV system description: Maximum power extraction in PV system using fuzzy logic and dual MPPT control

In this paper introduces a novel Fuzzy Logic Controller (FLC) as a Maximum Power Extracting (MPE) of photovoltaic standalone system is proposed, which can handle the uncertainties of the rules under high deviations in climate conditions. Conventional Perturb and Observe (P & O), hill-climbing MPE structures are examined. The MPE employed boost converter controller. The new controller improves P & O based maximum power tracker search method by rules fuzzifying of such method and eliminates the conventional drawback. A new FLC based improved P & O offers an accurate and fast converging to MPE during steady state and changing climatic situation compared to existing P & O and hill climbing method. The performance of the designed MPE is demonstrated in simulation MATLAB at dissimilar operating circumstances.

Renewable energy sources are the best solution to reduce dependence on conventional and nonrenewable sources that also cause environmental pollution. With the increase in the prices of conventional fuels globally, the increase of gas emissions resulting from its use, and the impact on the environment and the global climate; various renewable energy sources have emerged as an alternative to traditional sources of energy. Ssolar energy is one of the most important renewable energy sources used globally; The technology used is relatively simple and uncomplicated, compared to the technology used in other renewable energy sources. Solar energy is the ideal alternative to conventional energy in the Gaza Strip in Palestine, due to the relatively high solar radiation in the region, which makes its application more practical and economical compared to other parts of the world. Palestine has higher rates of total solar absorption, ranging from 4-8 kWh / m2 per day, which is high compared to other countries. This paper offers a solution to the Gaza Strip, which has suffered from a severe power shortage due to the Israeli blockade, by using solar PV as a backup system and a good alternative to diesel generators. Photovoltaic cells convert the sunlight into DC electric power. Where the major problem of the PV is that with the changing of atmospheric conditions, the voltage is changing, and so the maximum power is changing. We know that PV systems are still very expensive; therefore, the Artificial Neural Network controller is designed for the converter to secure the maximum power to the system to increase the efficiency of it. ANN controller is designed to bring out the maximum power from the solar panel. This paper uses a controller that utilizes MPPT technique to increase the efficiency of converting solar energy into electrical energy by modifying the duty cycle of Puls Width Modulation (PWM) for the boost converter to obtain the MPP energy from solar cells at all times. A solar panel applied and their components are individually modeled in the MATLAB / SIMULINK program to simulate a real PV system behavior, then an MPPT technique, including DC/DC boost converter was designed. Then an ANN controller is designed and then trained to get the maximum power 65 point from the solar panel at different atmospheric conditions. Also, this controller is compared with the direct connected method without an MPPT controller. The system performance is measured by changing solar radiation and temperature of the PV module. The findings indicate that MPPT ANN has a fast response to the variability and is more efficient, which means more power transfer to the system. The outcome shows that the photovoltaic module directly associated without MPPT technique has less efficiency because of the mismatch between the photovoltaic module and the load.

Photovoltaic power supplied to the utility grid is gaining more and more visibility while the world’s powers demand is increases. Growing demand, advancements in semiconductor technology and magnetic materials such as high frequency inductor cores, has a significant impact on PV inverter topologies and their efficiencies, on the improvement of the control circuits on the potential of costs reduction. The user naturally wants to operate the Photovoltaic (PV) array at its highest energy conversion output by continuously utilizing the maximum available solar power of the array. The electrical system PV modules are powered by solar arrays requires special design considerations due to varying nature of the solar power generated resulting from unpredictable and sudden changes in weather conditions which change the solar irradiation level as well as the cell operating temperature. This paper, a mathematical model of a Photovoltaic (PV) cell used matlab-simulink environment, is developed and presented. The model is developed using basic circuit equations of the photovoltaic solar cells including the effects of solar irradiation and temperature changes. The main objective is to find the parameters of the nonlinear I–V equation by adjusting the curve at three points: open circuit, maximum power, and short circuit. the method finds the best I–V equation for the single-diode photovoltaic (PV) model including the effect of the series and parallel resistances – Key words : Photovoltaic system (PV), maximum power, PV array,PV cell DOI: http://dx.doi.org/10.11591/ijece.v2i1.117

Solar photovoltaic (PV) system contributes a significant role in meeting power requirements and helps the decentralization of power generation and distribution. The output of the individual photovoltaic module depends on local temperature and irradiance. Since the variation of these parameters depends on the environmental factors, it is difficult to obtain maximum power in all instances. The maximum power point tracking (MPPT) technique is used for tracking the maximum power available at a particular instant. This paper presents the maximum power extraction from a photovoltaic system using Artificial Intelligence (AI) techniques. AI based methods provide a fast dynamic response with high accuracy and mitigate the effects of partial shading. A comparative study is performed for various AI based MPPT techniques such as Fuzzy, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS). PV module output is interfaced with the resistive load through the Z-Source inverter (ZSI). The ZSI boost the input voltage and provides an AC output voltage. The AI based MPPT techniques have been investigated with conventional Z-source Inverter (ZSI) and Switched Inductor Z-Source inverter (SL-ZSI). A simple boost control technique is employed to control, both conventional and SL Z-source inverter. The performance of the system is verified using MATLAB Simulink.

The utilization of renewable energy sources significantly increased in response to the growing global energy demand. Rising concerns about the environment, the photovoltaic (PV) systems emerged as prominent and widely adopted among all other renewable energies. The installation cost of PV systems is more however, the recent advancements in PV technology have made them feasible for a wide range of applications. In addition to module and inverter efficiencies, the overall efficiency of a PV system also depends upon the efficiency of the tracking method. The conventional and intelligent methods are experiencing low tracking efficiency under different irradiation and temperature conditions. PV grid integration can be done in two methods. The single stage grid connected systems can experience problems related to power quality and stability of the DC link voltage at low irradiance levels. To limit this problem, two-stage systems are preferable for maintaining a stable DC-link voltage. This research evaluates several MPPT strategies, including conventional approaches (perturbation and observation, incremental conductivity) and intelligent techniques (fuzzy logic controllers, ANFIS), and proposes a hybrid AGORNN controller, that is based on the Adaptive Grasshopper Optimization Recurrent Neural Network strategy. The performance of each method is assessed through parameters such as PV maximum power (Pmpp), DC link voltage (Vdclink), MPPT tracking efficiency η_MPPT (%), Vdc Ripple Vdcr (%), response time (Tres), overshoot Osh (%), utilization efficiency η_ut (%), THD, cost analysis, and payback period (PB). The results of simulations, which were examined using MATLAB/Simulink (R2023b), and compared with existing hybrid MPPT control methods. It exhibits that the proposed AGORNN controller is more effective than the standard and intelligent MPPT. The novel hybrid MPPT controller was examined under the conditions of STC and different irradiance and temperature, which were referred to as PTC. In STC tracking and conversion efficiencies are 99.86 and 96.55, respectively. The efficiencies under PTC are 96% and 91.50%, which ensures a promising solution to high efficiency grid-connected PV systems. The LCOE and Payback Period are also estimated and compared with other methods.

Remote areas in India are still not connected to the power grid. But they have mobile network connectivity. The people face problems in charging their cell phones. They are forced to travel a long distances to get access to electrical outlets. The use of new efficient photovoltaic solar cells (PVSCs) has emerged as an alternative measure of renewable green power, energy conservation and demand-side management. The manufacturing of solar cells and photovoltaic arrays has advanced dramatically in recent years. But they have two inherent major problems the first is low conversion efficiency (10to16%for commercially available amorphous silicon solar cells), second is high capital cost [1]. Solar powered systems consist of different parts to be controlled separately. Since the generated power is a function of uncontrollable environmental conditions, it requires extra caution to design controllers that handle unpredictable events and maintain efficient load matching power. In this study, a photovoltaic (PV) solar array model was developed and analyzed using Matlab/Simulink. The model includes converter circuits, a controller using fuzzy logic control (FLC), and a maximum Power point (MPP) tracking control which is used to operate the PVA at its maximum power. The MPP point is tracked using four algorithms perturbation & Observation, Incremental conductance method, Constant Voltage method and Constant current method. Later a comparative performance analysis of the model for MPP algorithms is done.

Currently, Photovoltaic (PV) energy is the most important energy resource in electrical power applications around the world since it is inexhaustible, clean and pollution free. As the output power from the PV arrays changing with solar irradiance and temperature, so it is necessary to operate the photovoltaic (PV) system at the maximum power point (MPP) to increase the power extraction from the solar panel. For this effective operation and maximum power extraction from a PV system, Maximum power point tracking (MPPT) algorithm plays a vital role by operating the PV energy system near the MPP. In this work, amongst several existing well established MPPT algorithms, the open voltage (OV) based algorithm is chosen for simulation of a PV system using Matlab / Simscape. As maximum power point (MPP) can be attained with a certain amount of load from the PV power system, so load matching is an important concept of MPPT algorithms. Here, the performances of this open voltage (OV) based algorithm method is evaluated for different values of solar radiation and battery was used as load to see the charging performance for the algorithm.

Energy crises and the growth of the energy demand have increased the interest in utilizing unconventional power sources. Thus, renewable energy sources have become a topic of interest to mitigate rising energy concerns and cope with increased electricity demand. With remarkable merits including cleanness and abundance, photovoltaic (PV) solar energy systems are a key to solving these issues. The employed inverters should effectively utilize the maximum available power from the PV solar system and transfer this power to the utility grid without posing any further limitations. However, the unequal power generation of different PV systems caused by partial shading (PS) and other PV panel degradation factors leads to a reduction in generation capacity. One of the relatively new solutions to mitigate the mismatch concerns between the PV modules and sub-modules is to extract the maximum power of each sub-module individually. The main objective of this paper is to present a comprehensive review of such PV grid-connected inverters topologies associated with sub-module connection and control. It will classify the PV grid-tied inverters in accordance with the level where the maximum power point tracking (MPPT) system is implemented. A special focus has been placed on sub-module microinverters (MI) in terms of circuit topologies, conversion efficiency, and controller design. This paper provides a comprehensive analysis of employing the distributed MPPT (DMPPT) approach to maximize the power generation of PV systems by mitigating the mismatch issues inside the PV module. The circuit topology, PV system configuration, and MPPT algorithms used for applying DMPPT solutions in PV SMs are discussed in detail in this study.

This paper presents an adaptive load-frequency control scheme for a grid-connected solar PV system which supplies a load locally. The grid is meant to complement the solar Photo-Voltaic (PV) system ideally, but because of different loading conditions and intermittent nature of the solar PV system grid support becomes necessary. The PV system is integrated to the synchronous generator based grid via power electronic converters which extracts maximum power from solar PV based using Perturb and Observe (P&O) algorithm to get maximum benefit from the PV system. Integration of renewable energy sources tends to reduce the overall inertia of the grid and hence threaten its stability. Also, perturbations on the load side are known to affect the frequency negatively and now with increased penetration of intermittent renewable energy sources, the frequency problem is expected to be aggravated. The proposed frequency control scheme incorporates a Model Reference Adaptive Controller (MRAC) on the grid side to ensure that the grid frequency is restored within a much smaller time post small disturbances on either the load or the generation side. The proposed system is verified using MATLAB-SIMULINK under wide variation in system conditions.

Now-a-days Renewable Energy Sources became an alternative to meet the increasing load demand because they are environmental friendly and also available abundant in nature. Among the Renewable Energy Sources, the Photo Voltaic (PV) System is gaining more attention due abundant availability of solar energy. The Maximum Power Point Tracking Technique is used to extract maximum power from the Photo Voltaic (PV) Array. When there is a need to transfer bulk amount of power from PV Array to Power Grid, the power quality issues, especially the real and reactive power flow problems, are a major concern. In this paper a novel control technique was proposed to control the power flow and to deal with power quality issues that arise when PV Array is integrated with power grid. It consists of a PI controller and Fuzzy Logic Controller (FLC) fed Flexible AC Transmission System device, namely STATCOM, for effective control of real and reactive power flow in grid connected photovoltaic system. The proposed system was simulated by using MATLAB-SIMULINK Tool Box and the results are compared.

there is an increased need for analysing the effect of atmospheric variables on photovoltaic (PV) production and performance. The outputs from the different PV cells in different atmospheric conditions, such as irradiation and temperature , differ from each other evidencing knowledge deficiency in PV systems [14]. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). Among all MPPT methods existing in the literature, perturb and observe (PO however, it presents drawbacks such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions. In order to allow a functioning around the optimal point Mopt, we have inserted a DC-DC converter (Buck–Boost) for a better matching between the PV and the load . This paper, we study the Maximum power point tracking using adaptive Intelligent fuzzy logic and conventional (P&O) control for stande-alone photovoltaic Array system .In particular, the performances of the controllers are analyzed under variation weather conditions with are constant temperature and variable irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, fuzzy logic controller has shown better performance during the optimization.

In this paper, a solar photovoltaic (PV) array powered scalar control of the induction motor (IM) drive for the water pumping application is presented. This control scheme involves the standalone operation of the IM drives. Solar PV array has nonlinear I- V characteristics, it requires additional control technique for Maximum Power Point (MPP) tracking. The efficiency is improved with the help MPP tracking (MPPT) technique which extracts maximum power available to the PV array. Perturb and observe (P&O) control technique offers simple control, ease of implementation and low cost is utilized along with DC/DC converter. This Stage extracts maximum power available from the PV array and fed power to the next stage of the DC/AC converter. The switching of DC/AC converter is generated with the help of open-loop V/F control of IM. The performance of the system is examined with the help of MATLAB/Simulink and corresponding results are present to support the suitability of the control scheme.

Today, Photovoltaic (PV) systems are very popular for power generation because they enjoy the advantage of being pollution free abundant natural resource, low maintenance cost and noiseless operation. Photovoltaic (PV) systems needs maximum power point tracking (MPPT) to maximize the PV array power output which is independent of the temperature, irradiation coPIC18F452nditions and electrical characteristics of the load. The Research Work introduces photovoltaic MPPT system based on PIC18F452 to achieve maximum power tracking. Moreover, the combination of boost circuit and PIC18F452 microcontroller is used for tracking of the maximum power generation from PV system. Moreover, Research presents a practical implementation of Perturbation and Observation (P&O), Incremental Conductance (IncCon) and Fuzzy Logic algorithms to achieve MPPT control. These algorithms are widely used because of its low-cost and ease of realization. Proposed P&O, Incremental Conductance and Fuzzy Logic algorithms are implemented and tested under different loads, and the test results are analyzed and compared. The results show the performance of the Fuzzy Logic and Incremental Conductance algorithm in tracking MPP is better than the P&O algorithm and the experimental results of Fuzzy Logic and Incremental Conductance algorithm indicate the feasibility and improved system functionality with high-efficiency. Also, results concludes that the design can effectively accomplish the purpose of the maximum power tracking.

This paper presents a control for a three-phase an eleven-level cascaded H-bridge inverter for photovoltaic (PV) system. The maximum power point tracking (MPPT) is capable of extracting maximum power from the PV array connected to each DC link voltage level. The MPPT algorithm is solved by fuzzy logic controller. Space vector pulse width modulation (SVPWM) algorithm uses a simple mapping to generate gate signals for the inverter. This is done to achieve high dynamic performance with low total harmonic distortion (THD). The validity of the system is verified through MATLAB/Simulink and results are compared with three-phase three-level, five-level, seven-level and nine-level cascaded H-bridge inverter for PV system in terms of THD. Finally, simulation results are presented to verify the effectiveness and accuracy of the proposed system.

Chapter 6 - Flexible Power Control of Photovoltaic Systems