Photovoltaic sample-and-hold circuit enabling MPPT indoors for low-power systems

Photovoltaic (PV) energy harvesting is commonly used to power autonomous devices, and maximum power point tracking (MPPT) is often used to optimize its efficiency. This paper describes an ultra low-power MPPT circuit with a novel sample-and-hold and cold-start arrangement, enabling MPPT across the range of light intensities found indoors, which has not been reported before. The circuit has been validated in practice and found to cold-start and operate from 100 lux (typical of dim indoor lighting) up to 5000 lux with a 55cm2 amorphous silicon PV module. It is more efficient than non-MPPT circuits, which are the state-of-the-art for indoor PV systems. The proposed circuit maximizes the active time of the PV module by carrying out samples only once per minute. The MPPT control arrangement draws a quiescent current draw of only 8uA, and does not require an additional light sensor as has been required by previously-reported low-power MPPT circuits

Global Maximum Power Point Tracking of PV Array Under Non-Uniform Irradiation Condition Using Adaptive Velocity Particle Swarm Optimization

Non-uniform irradiation condition (NUIC) is a condition of differences irradiation level received by each Photovoltaic (PV) on PV array. NUIC of PV array causes the emergence of several power peaks (consisting of several local peaks and one global peak) in the power-voltage (P-V) characteristic curve. This condition can cause several algorithms (hill-climbing / P&O, IC) that are unable to reach the global peak as they are trapped at a local peak. This paper proposes an Adaptive Velocity Particle Swarm Optimization (AVPSO) algorithm to search the global peaks/Global Maximum Power Point (GMPP) of PV arrays under NUIC. The proposed algorithm is a modification of the PSO algorithm. AVPSO algorithm able to adjust its own weight factor values and cognitive acceleration coefficients depend on the distance of the particle's position now with the global best position during the tracking process. Adaptive weight factors can reduce the level of power or voltage oscillation during the tracking process until convergent, while the cognitive acceleration coefficient can prevent particles trapped at the local peak. Thus, the proposed AVPSO algorithm can reach GMPP with faster tracking time and low oscillation rates. In addition, this paper proposed an algorithm that can work both in static and dynamic NUIC patterns; thus, the proposed algorithm can track again when there is a change in global peak value in the PV array

Modelling and Tracking of the Global Maximum Power Point in Shaded Solar PV Systems Using Computational Intelligence

Solar Photovoltaic (PV) systems are renewable energy sources that are environmentally friendly and are now widely used as a source of power generation. The power produced by solar PV varies with temperature, solar irradiance and load. This variation is nonlinear and it is difficult to predict how much power will be produced by the solar PV system. When the solar panel is directly coupled to the load, the power delivered is not optimal unless the load is properly matched to the PV system. In the case of a matched load the variation of irradiance and temperature will change this matching so a maximum peak power point tracking is therefore necessary for maximum efficiency. The complete PV system with a maximum power point tracking (MPPT) includes the solar panel array, MPPT algorithm and a DC-DC converter topology. Each subsystem is modelled and simulated in MATLAB/Simulink environment. The components are then combined with a DC resistive load to assess the overall performance when the PV panels are subjected to different weather conditions. The PV panel is modelled based on the Shockley diode equation and is used to predict the electrical characteristic curves under different irradiances and temperatures. In this dissertation, five MPPT algorithms were investigated. These algorithms include the standard Perturb and Observe (PnO), Incremental conductance (IC), Fuzzy Logic (FL), Particle Swarm Optimisation (PSO) and the Firefly Optimisation (FA). The algorithms are tested under different weather conditions including partial shading. The Particle Swarm and Firefly algorithm performed relatively the same and were chosen to be the best under all test conditions as they were the most efficient and were able to track the global maximum power point under partial shading. The PnO and IC performed well under static and varying irradiance, the PnO was seen to lose track of the MPP under rapid increasing irradiance. The PnO was tested under partial shaded conditions and it was seen that it is not reliable under these conditions. The Fuzzy logic performed better than the PnO and IC but was not as good as the PSO and FA. Since the fuzzy logic requires extensive tuning to converge it was not tested under partial shaded conditions. A DC-DC boost converter interface study between a DC source and the DC load are performed. This includes the steady state and dynamic analysis of the Boost converter. The converter is linearised about its steady state operating point and the transfer function is obtained using the state space averaged model. The simulation results of the complete PV system show that PSO and Firefly algorithm provided the best results under all weather conditions compared to other algorithms. They provided less oscillations at steady state, high efficiency in tracking (99%), quick convergence time at maximum power point and where able to track global power under partial shaded weather conditions for all partial shaded patterns. The Fuzzy logic performed well for what it was tested for which are static irradiance and rapid varying irradiance. The PnO and IC also performed relatively well but showed a lot of ringing at steady state. The PnO failed to track the MPP at certain instances under rapid increasing irradiance and the IC was shown to be unstable at low irradiance. The PnO was not reliable in tracking the global maximum power point under partial shaded conditions as it converged at local maximum power points for some partial shaded patterns

Design of Control Algorithm for Renewable Energy Resources

The need for renewable energy sources is on the rise because of the subtle energy crisis in the world today. By the year 2020, India plans to produce atleast a minimum of 20 Gigawatts of Solar power, whereas we have only tapped less than half a Gigawatt of our potential as of March 2010. Solar energy is an important untapped resource in a tropical country like ours. The main obstruction for the penetration and reach of solar PV systems is their high capital cost and low efficiency. In this thesis, we examine a schematic to extract maximum obtainable solar power from a PV module and use the energy for DC and AC application also tackling with the problem of partial shading in PV. This project also uses the concept of Maximum PowerPoint Tracking (MPPT) which significantly increases the efficiency of the solar photovoltaic system. But in this project our main intention is to interface the PV array with the MPP tracker and process power for dc and ac loads. All simulations are carried under MATLAB/Simulink environment

ANALYSIS AND SIMULATION OF PHOTOVOLTAIC SYSTEMS INCORPORATING BATTERY ENERGY STORAGE

Solar energy is an abundant renewable source, which is expected to play an increasing role in the grid\u27s future infrastructure for distributed generation. The research described in the thesis focuses on the analysis of integrating multi-megawatt photovoltaics (PV) systems with battery energy storage into the existing grid and on the theory supporting the electrical operation of components and systems. The PV system is divided into several sections, each having its own DC-DC converter for maximum power point tracking and a two-level grid connected inverter with different control strategies. The functions of the battery are explored by connecting it to the system in order to prevent possible voltage fluctuations and as a buffer storage in order to eliminate the power mismatch between PV array generation and load demand. Computer models of the system are developed and implemented using the PSCADTM/EMTDCTM software

Dynamic Characteristics of PV System in Shaded/Un-shaded Portions Using MPPT Algorithm

In this paper, a combined low cost high efficiency converter and maximum peak power tracker has been presented. The maximum power point tracker system consists of DC-DC boost converter and PWM. PWM generates high quality sinusoidal line current. The suitable duty ratio for the boost converter will force the PV to work around the optimum voltage. The power generated by a PV cell depends on the operating voltage of the array, its voltage-current and voltage-power characteristic curves specify a unique operating point at which maximum possible power is delivered and the array is operated at its highest efficiency. One of the problems in designing efficient PV systems is to track the maximum power operating point for varying solar irradiance levels and ambient conditions. The output power produced by the PV panel is non-linear and changes with the solar irradiation and the ambient temperature. Therefore, a maximum power point tracking controller is needed to optimize the photovoltaic output power. A dc-dc converter is used to match the PV system to the load and to operate solar array at maximum power point .The perturbation and observation algorithm which is often employed to track the maximum power point This algorithm is selected due to its ability to withstand against any parameter variation and having a very high efficiency. As a result, by variation of the temperature and the insolation, the algorithm still managed to track the MPP successfully

Direct usage of photovoltaic solar panels to supply a freezer motor with variable DC input voltage

In this paper, a single-phase photovoltaic (PV) inverter fed by a boost converter to supply a freezer motor with variable DC input is investigated. The proposed circuit has two stages. Firstly, the DC output of the PV panel that varies between 150 and 300 V will be applied to the boost converter. The boost converter will boost the input voltage to a fixed 300 V DC. Next, this voltage is supplied to the single-phase full-bridge inverter to obtain 230 V AC. In the end, The output of the inverter will feed a freezer motor. The PV panels can be stand-alone or grid-connected. The grid-connected PV is divided into two categories, such as with a transformer and without a transformer, a transformer type has galvanic isolation resulting in increasing the security and also provides no further DC current toward the grid, but it is expensive, heavy and bulky. The transformerless type holds high efficiency and it is cheaper, but it suffers from leakage current between PV and the grid. This paper proposes a stand-alone direct use of PV to supply a freezer; therefore, no grid connection will result in no leakage current between the PV and Grid. The proposed circuit has some features such as no filtering circuit at the output of the inverter, no battery in the system, DC-link instead of AC link that reduces no-loads, having a higher efficiency, and holding enough energy in the DC-link capacitor to get the motor started. The circuit uses no transformers, thus, it is cheaper and has a smaller size. In addition, the system does not require a complex pulse width modulation (PWM) technique, because the motor can operate with a pulsed waveform. The control strategy uses the PWM signal with the desired timing. With this type of square wave, the harmonics (5th and 7th) of the voltage are reduced. The experimental and simulation results are presented to verify the feasibility of the proposed strategy

Roach Infestation Optimization MPPT Algorithm of PV Systems for Adaptive to Fast-Changing Irradiation and Partial Shading Conditions

Of all the renewable energy sources, solar photovoltaic (PV) power is considered to be a popular source owing to several advantages such as its free availability, absence of rotating parts, integration to building such as roof tops and less maintenance cost. The nonlinear current–voltage (I–V) characteristics and power generated from a PV array primarily depends on solar insolation/irradiation and panel temperature. The power output depends on the accuracy with which the nonlinear power–voltage (P–V) characteristics curve is traced by the maximum power point tracking (MPPT) controller. A DC-DC converter is commonly used in PV systems as an interface between the PV panel and the load, allowing the follow-up of the maximum power point (MPP). The objective of an efficient MPPT controller is to meet the following characteristics such as accuracy, robustness and faster tracking speed under partial shading conditions (PSCs) and climatic variations. To realize these objectives, numerous traditional techniques to artificial intelligence and bio-inspired techniques/algorithms have been recommended. Each technique has its own advantage and disadvantage. In view of that, in this thesis, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). In addition, the mathematical formulations and operation of the boost converter is investigated. To validate the effectiveness of the proposed RIO MPPT algorithm, MATLAB/Simulink simulations are carried out under varying environmental conditions, for example step changes in solar irradiance, and partial shading of the PV array. The obtained results are examined and compared with the particle swam optimization (PSO). The results demonstrated that the RIO MPPT performs remarkably in tracking with high accuracy as PSO based MPPT. Last but not the least, I am very grateful to the Arctic Centre for Sustainable Energy (ARC), UiT The Arctic University of Norway, Norway for providing an environment to d

Maximum power point tracking and control of grid interfacing PV systems

Grid interfacing of PV systems is very crucial for their future deployment. To address some drawbacks of model-based maximum power point tracking (MPPT) techniques, new optimum proportionality constant values based on the variation of temperature and irradiance are proposed for fractional open circuit voltage (FOCV) and fraction short circuit current (FSCC) MPPT. The two MPPT controllers return their optimum proportionality values to gain high tracking efficiency when a change occurred to temperature and/or irradiance. A modified variable step-size incremental conductance MPPT technique for PV system is proposed. In the new MPPT technique, a new autonomous scaling factor based on the PV module voltage in a restricted search range to replace the fixed scaling factor in the conventional variable step-size algorithm is proposed. Additionally, a slope angle variation algorithm is also developed. The proposed MPPT technique demonstrates faster tracking speed with minimum oscillations around MPP both at steady-state and dynamic conditions with overall efficiency of about 99.70%. The merits of the proposed MPPT technique are verified using simulation and practical experimentation. A new 0.8Voc model technique to estimate the peak global voltage under partial shading condition for medium voltage megawatt photovoltaic system integration is proposed. The proposed technique consists of two main components; namely, peak voltage and peak voltage deviation correction factor. The proposed 0.8Voc model is validated by using MATLAB simulation. The results show high tracking efficiency with minimum deviations compared to the conventional counterpart. The efficiency of the conventional 0.8 model is about 93% while that of the proposed is 99.6%. Control issues confronting grid interfacing PV system is investigated. The proposed modified 0.8Voc model is utilized to optimise the active power level in the grid interfacing of multimegawatt photovoltaic system under normal and partial shading conditions. The active power from the PV arrays is 5 MW, while the injected power into the ac is 4.73 MW, which represents 95% of the PV arrays power at normal condition. Similarly, during partial shading conditions, the active power of PV module is 2 MW and the injected power is 1.89 MW, which represents 95% of PV array power at partial shading conditions. The technique demonstrated the capability of saving high amount of grid power.Grid interfacing of PV systems is very crucial for their future deployment. To address some drawbacks of model-based maximum power point tracking (MPPT) techniques, new optimum proportionality constant values based on the variation of temperature and irradiance are proposed for fractional open circuit voltage (FOCV) and fraction short circuit current (FSCC) MPPT. The two MPPT controllers return their optimum proportionality values to gain high tracking efficiency when a change occurred to temperature and/or irradiance. A modified variable step-size incremental conductance MPPT technique for PV system is proposed. In the new MPPT technique, a new autonomous scaling factor based on the PV module voltage in a restricted search range to replace the fixed scaling factor in the conventional variable step-size algorithm is proposed. Additionally, a slope angle variation algorithm is also developed. The proposed MPPT technique demonstrates faster tracking speed with minimum oscillations around MPP both at steady-state and dynamic conditions with overall efficiency of about 99.70%. The merits of the proposed MPPT technique are verified using simulation and practical experimentation. A new 0.8Voc model technique to estimate the peak global voltage under partial shading condition for medium voltage megawatt photovoltaic system integration is proposed. The proposed technique consists of two main components; namely, peak voltage and peak voltage deviation correction factor. The proposed 0.8Voc model is validated by using MATLAB simulation. The results show high tracking efficiency with minimum deviations compared to the conventional counterpart. The efficiency of the conventional 0.8 model is about 93% while that of the proposed is 99.6%. Control issues confronting grid interfacing PV system is investigated. The proposed modified 0.8Voc model is utilized to optimise the active power level in the grid interfacing of multimegawatt photovoltaic system under normal and partial shading conditions. The active power from the PV arrays is 5 MW, while the injected power into the ac is 4.73 MW, which represents 95% of the PV arrays power at normal condition. Similarly, during partial shading conditions, the active power of PV module is 2 MW and the injected power is 1.89 MW, which represents 95% of PV array power at partial shading conditions. The technique demonstrated the capability of saving high amount of grid power

Optimization of PV Model using Fuzzy- Neural Network for DC-DC converter systems

Abstract: Due to the large demand on energy, energy sources, as well as the problems of the environment such as the dynamic weather conditions. Hence the world researchers nowadays are moving toward using solar energy because it gives different advantages over the traditional energy sources such as low maintenance costs, eternal sun energy, and the lack of revival of the gases of green houses. As a result, the photo- voltaic (PV) systems' power will be reduced. Under different weather conditions, maximizing the power point tracking (MPPT) is an important part to improve the solar systems power. In this paper, we introduce the neural network approaches for the PV systems. This paper also presents a novel application of Fuzzy Neural Network (FNN) in modeling a PV. The photovoltaic system model is designed with the use of MATLAB/SIMULINK software program with the connection of a DC-DC boost converter, a Maximum Power Point Tracking (MPPT) controller, a one-phase Voltage Source Converter (VSC) and a three-level bridge. The MPPT controller is used to cover the need for advanced controller that can detect the maximum power point in solar cell systems that have unstable current and voltage and keep the resultant power per cost low