Integrated energy management converter based on maximum power point tracking for photovoltaic solar system

This paper presents an integrated power control system for photovoltaic systems based on maximum power point tracking (MPPT). The architecture presented in this paper is designed to extract more power from photovoltaic panels under different partial obscuring conditions. To control the MPPT block, the integrated system used the ripple correlation control algorithm (RCC), as well as a high-efficiency synchronous direct current (DC-DC) boost power converter. Using 180 nm complementary metal-oxide-semiconductor (CMOS) technology, the proposed MPPT was designed, simulated, and layout in virtuoso cadence. The system is attached to a two-cell in series that generates a 5.2 V average output voltage, 656.6 mA average output current, and power efficiency of 95%. The final design occupies only 1.68 mm2.

Comparison between P&O and SSO techniques based MPPT algorithm for photovoltaic systems

Solar photovoltaic (SPV) systems are a renewable source of energy that are environmentally friendly and recyclable nature. When the solar panel is connected directly to the load, the power delivered to the load is not the optimal power. It is therefore important to obtain maximum power from SPV systems for enhancing efficiency. Various maximum power point tracking (MPPT) techniques of SPV systems were proposed. Traditional MPPT techniques are commonly limited to uniform weather conditions. This paper presents a study of MPPT for photovoltaic (PV) systems. The study includes a discussion of different MPPT techniques and performs comparison for the performance of the two MPPT techniques, the P&O algorithm, and salp swarm optimization (SSO) algorithm. MATLAB simulations are performed under step changes in irradiation. The results of SSO show that the search time of maximum power point (MPP) is significantly decreased and the MPP is obtained in the shortest time with high accuracy and minimum oscillations in the generated power when compared with P&O.

Optimal Operation of Automotive Electrical System with Photovoltaic Generation and Three-level Battery Management Scheme

For many years, the electrical power requirements in Automotive Electrical System (AES) have been quickly increasing and are predicted to continue to climb. This trend is being pushed by the introduction of a slew of new vehicle features. The constant growth in power needs is stretching the limitations of current automotive power generation and control technologies, stimulating the development of higher-power and higher-voltage electrical systems and components. Electrical power on a vehicle is not free. It comes as a direct result of consuming fuel within the engine to drive the alternator. With a typical engine efficiency of 44% and with present fuel costs this leads to onboard electrical power costs 4 times more than a typical household utility rate. Global oil and gas resource depletion, as well as environmental concerns, have prompted the automobile industry to build more efficient and eco-friendly cars in order to minimize fuel use and safeguard the environment. In our proposed Automotive Electrical system configuration, we have an AES system which is powered by an automotive alternator and battery combination where the alternator is driven by an IC engine and we have a hybrid energy system using a Rooftop PV array with a battery management system (BMS). We discovered that during the off state, the whole load of the automobile is dependent on the 12Vlead acid battery for power, which causes the SOC to drop dramatically. As a result, the suggested model will include a flexible thin-film solar PV module positioned on the rooftop, which will be supported by a Maximum Power Point (MPPT) Tracking charge controller and will deliver energy to recharge the extra battery and meet the electrical requirements when the vehicle is stationary. When the vehicle is in motion, the existing alternator in the car's electrical system takes over the battery charging requirements, by this way, we can meet the electrical requirements of AES without running the engine for a long time by consuming fuel. The proposed model specialty is investigated using MATLAB/Simulink and compared with existing methods. Keywords: Automotive Electrical System (AES), Internal Combustion Engine (ICE), Hybrid Energy System, Rooftop PV array, Maximum Power Point Tracking (MPPT).

Power Maximization in Standalone Photovoltaic System: An Adaptive PSO Approach

A control strategy for power maximization which is an important mechanism to extract maximum power under changing environmental conditions using Adaptive Particle Swarm Optimization (APSO) is proposed in this paper. An Adaptive Inertia Weighting Factor (AIWF) is utilised in the velocity update equation of traditional PSO for the improvement in speed of convergence and precision in tracking Maximum Power Point (MPP) in standalone Photovoltaic system. Adaptation of weights based on the success rate of particles towards maximum power extraction is the most promising feature of AIWF. The inertia weight is kept constant in traditional PSO for the complete duration of optimization process. The MPPT in PV system poses a dynamic optimization problem and the proposed APSO approach paves way not only to track MPP under uniform irradiation conditions, but also to track MPP under non uniform irradiation conditions. Simulations are done in MATLAB/Simulink environment to verify the effectiveness of proposed technique in comparison with the existing PSO technique. With change in irradiation and temperature, the APSO technique is found to provide better results in terms of tracking speed and efficiency. Hardware utilizing dSPACE DS1104 controller board is developed in the laboratory to verify the effectiveness of APSO method in real time.

Modeling and Control of Grid-Connected Solar-Wind Hybrid Micro-Grid System with Multiple-Input Ćuk DC-DC Converter for Household & High Power Applications

There is a continuous global need for more energy, which must be cleaner than energy produced from the conventional generation technologies. As such, this need has necessitated the increasing penetration of distributed generation technologies and primarily on renewable energy sources. This paper presents a dynamic modeling and control strategy for a sustainable micro-grid, principally powered by multiple renewable energy sources (solar energy, wind energy and Fuel cell), micro sources (such as diesel generator, micro-gas turbine etc.) and energy storage scheme. More importantly, a current-source-interface, multiple-input dc-dc converter is utilized to coordinate the sustainable power sources to the main dc bus. Thus, for tracking maximum power available in solar energy, maximum power point tracking algorithm is applied. The proposed system is designed to meet load demand, manage power flow from various sources, inject excess power into the grid, and charge the battery from the grid as needed. More so, the proposed converter architecture has reduced number of power conversion stages with less component count, and reduced losses compared to existing grid-connected hybrid systems. This improves the efficiency and reliability of the system. The utilization of energy storage is essential owing to the intermittent nature of the renewable energy sources and the consequent peak power shift between the sources and the load. Following this further, a supervisory control system is designed to handle various changes in power supply and power demand by managing power intermittency, power peak shaving, and long-term energy storage. The entire hybrid system is described given along with comprehensive simulation results that reveal the feasibility of the whole scheme. The system model is designed and simulated in MATLAB SimPowerSystem in order to verify the effectiveness of the proposed scheme.