Innovative two-stage thermal control of DC-DC converter for hybrid PV-battery system

A DC-DC converter plays a crucial role in a photovoltaic (PV) system. Power generated by the PV system is a function of solar irradiance and temperature. Power voltage (P-V) characteristic of a PV module exhibits a single power peak at uniform irradiance and temperature. To operate the PV array at its maximum power point, a maximum power (MP) point (MPP) tracking (MPPT) algorithm is required. The DC-DC converter placed in between the PV array and load, works as an impedance matching circuit. Depending on the application, a suitable selection of DC-DC converter is an important decision. In this study, a comparative simulation analysis of different buck-boost derived non-isolated DC-DC converters are discussed in terms of power conversion efficiency, output power ripple, and tracking speed. The converters studied are buck-boost, Cuk, single-ended primary-inductor converter (SEPIC), Zeta. To evaluate the performance of each DC-DC converter, a widely used Perturb and Observe (PO) MPPT algorithm is modeled and simulated in MATLAB Simulink.

<span>The main components of a Stand-Alone Photovoltaic (SAPV) system consists of PV array, DC-DC converter, load and the maximum power point tracking (MPPT) control algorithm. MPPT algorithm was used for extracting maximum available power from PV module under a particular environmental condition by controlling the duty ratio of DC-DC converter. Based on maximum power transfer theorem, by changing the duty cycle, the load resistance as seen by the source is varied and matched with the internal resistance of PV module at maximum power point (MPP) so as to transfer the maximum power. Under sudden changes in solar irradiance, the selection of MPPT algorithm’s sampling time (T<sub>S_MPPT</sub>) is very much depends on two main components of the converter circuit namely; inductor and capacitor. As the value of these components increases, the settling time of the transient response for PV voltage and current will also increase linearly. Consequently, T<sub>S_MPPT </sub>needs to be increased for accurate MPPT and therefore reduce the tracking speed. This work presents a design considerations of DC-DC Boost Converter used in SAPV system for fast and accurate MPPT algorithm. The conventional Hill Climbing (HC) algorithm has been applied to track the MPP when subjected to sudden changes in solar irradiance. By selecting the optimum value of the converter circuit components, a fast and accurate MPPT especially during sudden changes in irradiance has been realized.</span>

PV power plants encounter varying levels of irradiance, temperature fluctuations, and partial shading because of the differences in sunlight conditions. Partial shading can cause an increase in the power loss, leading to a reduction in efficiency. Maximum Power Point Tracking (MPPT) is of utmost importance in the functioning of photovoltaic (PV) systems for electricity generation because it is indispensable for maximizing power extraction from PV modules, thereby increasing the overall power output. In situations where partial shading is present, the utilization of MPPT algorithms to achieve maximum power output becomes complex because of the existence of multiple distinct peak power points, each having a unique local optimum. To overcome this issue, a method is proposed that uses Darts Game Optimization (DGO), a game-based optimization process, to efficiently determine and extract the maximum power from various local optimal peaks. A population-based optimization method known as the Darts Game Optimization algorithm exists. In this approach, the optimization process begins by creating a population of random players. Then, the algorithm iteratively updates and improves the population to search for the best player or solution. In this study, the DGO algorithm was applied to the MPPT process for voltage optimization in the PV procedure. The DC-DC converter is utilized to capture the maximum available power, and the findings demonstrate that the DGO algorithm efficiently identifies the global maximum, resulting in accelerated convergence, reduced settling time, and minimized power oscillation. Through simulations, the feasibility and effectiveness of the DGO centered MPPT approach was confirmed and compared with MPPT algorithms relying on perturb and observe (P&O) and Particle Swarm Optimization (PSO). The simulation results offer compelling evidence that the DGO algorithm, as proposed in this study, proficiently traces the global maximum, thereby substantiating its practicality and efficiency.

Parameter analysis of thermoelectric generator/dc-dc converter system with maximum power point tracking

PV irrigation systems have begun to be used intensively today, as energy needs increase. In Partially Shaded Conditions (PSC), the efficiency of the PV system decreases significantly, and traditional Maximum Power Point Tracking (MPPT) algorithms become insufficient. On the other hand, traditional MPPT algorithms require sensors to measure the current and voltage of the PV system. In this study, a sensorless hybrid MPPT algorithm is proposed to reduce system costs and enable operation without the need for PV system data. A simulation study was conducted in the MATLAB/Simulink environment to examine the PV system. The proposed algorithm has been tested under four different PSC scenarios. PV system power, motor speed, and currents were examined under each condition. The high maximum power tracking performance of the proposed algorithm is demonstrated through simulation results. In the steady state, the lowest MPPT efficiency was 95.66%, whereas the highest MPPT efficiency was 99.9%. The MPPT algorithm completed in less than 2 seconds, with the first stage taking 1.3 seconds to reach most of the maximum PV system power. The second stage of the MPPT algorithm was used to achieve maximum power in a narrower area.

Due to the Randomness and fluctuation of the photovoltaic (PV) system, Maximum Power Point Tracking (MPPT) algorithm is employed to ensure the maximum power extraction from PV arrays, when it is connected to the grid. The previous studies have indicated that the MPPT algorithm is one of the main causes of interharmonics emission in PV systems. The MPPT algorithm parameters such as perturbation step size inevitably have a strong impact on the interharmonics in the current injected to the grid. In general, a smaller MPPT perturbation step size will reduce the interharmonics emission level, but it will result in poor tracking performance of the MPPT algorithm. Thus, there is a trade-off between the MPPT efficiency and the interharmonics emission in PV system. In this paper, an interharmonics suppression scheme with variable step size MPPT algorithm was proposed. In the suppression scheme, two voltage values near by the MPPT voltage and two tracking step size are preset. When the dc-link voltage of PV is between the two preset voltage values, it means that the PV power near its MPP and the smaller perturbation step size implemented in the MPPT algorithm. Otherwise, the bigger perturbation step size is employed. It can greatly reduce interharmonics emission level and maintain the MPPT efficiency. Simulations have been carried out to verify the effectiveness of the suppression scheme.

Renewable power plant has more advantageous over conventional power plant. Specifically, Solar energy is available everywhere, free of cost but challenge is to extract maximum power from the available energy. Solar cell is a non-linear source of electrical energy, hence various algorithms like Perturb & Observe, Incremental Conductance etc. are implemented using DC-DC converter to extract maximum power from it. These algorithms create oscillations in output. The proposed algorithm takes feedback of irradiance of sunlight and temperature to avoid oscillations. Internal resistance of the solar panel is depends on factors like temperature, irradiance etc. At a particular instant, internal resistance of solar panel is calculated by sensing irradiance and temperature values from sensor. On that operating condition, all dependent parameters of solar panel are calculated and internal resistance of solar panel is calculated. DC-DC converter transfers the load resistance to its input depending upon its duty cycle. The proposed control strategy will set the duty cycle of converter so as to match transferred load resistance with internal resistance of solar panel. Hence by the maximum power transfer theorem, maximum power will extracts from the solar panel when transferred load resistance is equal to the internal resistance. This condition is achieved by this proposed maximum power point tracking (MPPT) algorithm. Control System is continuously monitoring irradiance and temperature, if there is change in it then only it will try to track MPPT. This paper discusses about the design, simulation and practical implementation of MPPT algorithm using DC-DC Converter.

Maximum power point trackers are efficient enough to enhance an efficiency of the photovoltaic system. So many methodologies have been proposed up to this date to achieve maximum power. PV modules are capable of generating maximum power under diverse atmospheric conditions. The base paper proposed an MPPT (Maximum Power Point Tracking) algorithm that based on fuzzy logic for a solar system. The solar panel is analyzed and then simulated by using SIMULINK or MATLAB. The PV system is bound to DC-DC BUCK BOOST converter. The MPP (Maximum Power Point) is an operating point where Solar panel used to generate maximum power. In order to gain maximum efficiency and the power, it is required that entire system should operate at MPP. The MPP of PV panel maintains similar on changing by changing cell temperature and solar irradiance. So to achieve maximum power with PV system, the MPPT algorithms are put into the operation. In this paper, we are enhancing voltage, current and power performance of solar output. Hence, further, the application of PI controller helps to enhance performance that we are going to implement. PI controller is efficient enough to enhance solar output power performance.Solar Photovoltaic (PV) power generation system is comprised several elements like solar panel, DC-DC converter, MPPT circuit, and load. These components are designed in MATLAB software. Proportional plus Integral (PI) is used for tracking maximum power from the photovoltaic panel in the case of varying irradiation. Keywords: Solar Panel, proportional plus integral control. maximum power point tracking

Photovoltaic Cell (PV) is an environment friendly source for electric power generation. This is relatively costly as compared to the other available sources of energy. The maximum power point tracking (MPPT) for the PV system application provides PV generated power for all irradiation conditions to have the maximum output power. It results in low per unit cost of power for efficient PV system. MPPT is important unit in photovoltaic power systems because it increases the system efficiency by increasing the output power. Normally, PV panel generates nonlinear Voltage-Current characteristics and its unique point varies with solar irradiation where the maximum power is produced. To extract maximum power from the solar panel, it needs to operate the photovoltaic (PV) system at the maximum power point (MPP) or unique point. MPPT consists of DC-DC Converter and MPPT algorithm. This paper presents a Hardware implementation of DC-DC push pull converter based on TL598 control circuit to track maximum power point. TL598 is a fixed frequency and variable duty cycle control IC for charge controller purpose. However, here we have used this converter for different purpose to maintain MPP PV voltage. We have achieved this with the help of MPPT algorithm by simply applying adjustable external voltage at Pin 4 of Dead Time Control (DTCON) of IC TL598. The adjustment of voltage depends on the algorithm. This can improve the tracking speed and stability of output power at unique point. This is suitable for various maximum power point tracker algorithms. The resultant DC-DC converter extracts power directly from MPP algorithm and is capable of tracking MPPs accurately, rapidly.

The IU and PU characteristic curves of PCs output in photovoltaic generation (PVG) were analyzed according to the working principle of photovoltaic cells (PCs) in the study, so as to provide a theoretical basis for the further improvement of the maximum power point tracking (MPPT) algorithm. The three-stage charging method was used to charge the battery to improve the energy conversion efficiency in the charging process of the system. The variable step incremental conductance algorithm (ICA) was proposed, which can effectively increase the speed of MPPT in the system and enhance its stability. By setting up a test platform, verification experiments of the tracking algorithm were performed on sunny (higher light intensity), cloudy (medium light intensity), and rainy (lower light intensity) days. The results showed that the adoption of MPPT algorithm can effectively increase the output power (OP) of PCs, the OP in sunny days was higher than in cloudy days and OP in cloudy days was higher than in rainy days compared with the system without MPPT algorithm. Compared with the traditional incremental conductance algorithm, the output power of the PCs using the MPPT algorithm improved by the incremental conductance algorithm had increased by 5.86%. The feasibility and effectiveness of the energy storage system in the study were verified.

A Photo Voltaic (PV) system turns Solar Energy (SE) into electricity that needs efficient converter topology. One significant obstacle associated with solar systems is the fluctuation in energy production from solar PV modules due to meteorological factors like temperature and irradiance. The advancement of Maximum Power Point Tracking (MPPT) algorithms has enhanced the effectiveness of solar panels. Implementing an effective operational control approach may optimize the performance of PV panels, while utilizing proper semiconductor materials can maximize the efficiency of converters. This work introduces a MPPT method based on Perturb and Observe (P&O) algorithm. The PV system performs with optimal efficiency when it is near the IV curve, where it produces the highest amount of electricity due to its non-linear voltage-current characteristics. The power output is determined by the degree of irradiance and the temperature of the cells, resulting in the maximum achievable power output. The conditions vary according on the season, time of day, and environmental factors. Rapid variations in light can occur due to factors like clouds. Thus, it is imperative to precisely monitor the maximum power point (MPP) in all settings to optimize power generation. This study proposes a method to optimize the power output of a PV system by integrating MPPT with a DC-DC energy converter. This approach ensures that the PV generator operates at its maximum power level, irrespective of external factors such as sunshine intensity and temperature. MPPT algorithms exhibit variations in their implementation and performance, with a range of MPPT control algorithms being accessible. The P&O algorithms are widely used due to their ability to regulate the MPP of PV panels. The method is validated in real-time using Typhoon Hill Simulation.

This paper presents the analysis and operation of a grid connected photovoltaic (PV)energy conversion system with an Adaptive Neuro-Fuzzy Inference System (ANFIS)based maximum power point tracking (MPPT)algorithm. Particle swarm optimization is used to train the membership functions while the least squares algorithm is used to update the consequent parameters of the ANFIS with changing operating condition of PV solar system. The MPPT algorithm maximizes conversion efficiency by adjusting the duty cycle of the buck boost converter to change the output voltage of the solar panel and hence achieving the maximum panel output power for a given set of environmental conditions. The ANFIS is trained by using a hybrid algorithm implementing least squares estimator and particle swarm optimization with data obtained by operating the system using the Perturb and Observe (P&O)MPPT algorithm. The performance of the proposed ANFIS based MPPT algorithm is validated in simulation using MATLAB/Simulink at different operating conditions. It is proven that the designed ANFIS based MPPT scheme achieves a very fast response with little oscillations while transferring maximum power from solar panel to the grid line as compared to the conventional P&O based MPPT scheme.

Due to the nature of unpredicted wind speed, determining the optimal generator speed to extract the maximum available wind power at any wind speed is essential. Therefore, it is significant to include an intelligent controller that can track the maximum peak regardless of wind speed. This paper describes the design and development of particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm to variable-speed fixed-pitch wind turbines. Other than the electrical power subjected to maximization, the proposed algorithm does not need any additional sensor. In addition, the MPPT algorithm does not require any prior knowledge of the wind energy system. Unlike the conventional search optimization method, PSO-based MPPT algorithm produces almost negligible oscillations at the maximum power once the true peak is located. In short, the proposed MPPT is simple, flexible, accurate and efficient in maximum wind power tracking. In this work, MATLAB/Simulink simulation package is used to simulate the performance of the proposed MPPT algorithm.

Evaluation of loss effect on optimum operation of variable speed micro-hydropower energy conversion systems

Renewable energy (RE) is an ultimate energy source, which plays an important role nowadays due to its advantages over non-RE. Compared to other RE resources, the PV array or module can only convert about 30%–40% of the solar radiation into electrical energy due to the nonlinear behavior of the irradiation and temperature. Many types of research have been conducted in recent years to improve the effectiveness of PV systems and address the issues related to solar PV systems, one of which is the Maximum Power Point Tracking (MPPT) control algorithm. The MPPT helps in extracting the maximum capable power of the modules having irradiation and temperature as its main parameters. The parameters that help in tracking the maximum power point are maximum power (P m), maximum power voltage (V mp), open-circuit voltage (V oc), maximum power current (I mp), and short-circuit current (I sc). The output of the MPPT is generally in terms of duty cycle that changes with the variation with environmental conditions. Various MPPT control algorithms are used nowadays to control the operating point to maximum mower point at higher efficiency. Still, due to nonlinear behavior and shading effect on PV array, photovoltaic arrays have complicated multiple peak (local minima) patterns in output I–V and P–V. The maximum power point shifts with time, which reduces the effectiveness of the system. So, to track the maximum power (global maxima), MPPT is used that has less settling time and transients in the output terminals of converters. MPPT controller algorithms are divided into three categories in this chapter: classical, soft computing, and hybrid-based MPPT controller algorithms. Classical-based MPPT algorithms are used due to their simplicity and directly controlling the PV system array/module parameters. Still, with time new techniques came into existence, i.e., soft computing-based control algorithm with hybrid methods. These controllers employ different algorithms with improved efficiency, performance, modernity, complexity, tracking speed, handling capability, self-learning approach, and less dependency on the system, which act as an advantage over classical algorithms. Hybrid methods combine classical and artificial intelligence (AI) methods, which have the benefit of both classical and AI techniques. The different soft-computing MPPT control algorithms are highlighted in this. Soft-computing algorithm comprises AI-based and Nature-inspired MPPT algorithms. These methods or strategies are used to control and vary the duty cycle for the converter section based on the performance of the PV array and to optimize the output performance in less amount of time. Nature-inspired methods and algorithms are more efficient in all conditions. This review of different MPPT will help the researcher in selecting the suitable MPPT algorithm for enhancing the performance of the PV system; the advantages and disadvantages of the different MPPT algorithms are also highlighted.