Exact maximum power point tracking of partially shaded PV strings based on current equalization concept

A new framework for improving MPPT algorithms through search space reduction

The position of MPP of a solar panel changes with changes in weather conditions such as radiation, temperature and partial Shadow. In such a situation, several local MPPs and one GMPP are created in the P-V curve, and the common methods of MPPT(P&O, IC,…) have converged due to the failure to recognise the local MPP. They cause a lot of losses to the photovoltaic system. Therefore, in this article, it has been tried to provide a new method of particle swarm optimisation with variable coefficients, which in combination with the perturb and observe method can reach the GMPP in the fastest possible time. In this proposed method, we first reach the convergence of the GMPP using the VCPSO method, and after converging to it, we go to the P&O method and use P&O until significant changes are made in the system, and if observed Variations To re-examine the GMPP, we return to the VCPSO algorithm. The results of the simulation of the efficiency of the proposed method show that this method was able to reach the GMPP in the shortest time, and the efficiency of the photovoltaic system increases and the effects of partial shading are reduced.

This paper proposes a Newton-Raphson based parameter extraction method based on experimental analysis for modeling of a PV panel. In this method, pre-knowledge of open-circuit voltage, short-circuit current, maximum power point voltage and current is not necessary. Hence, the proposed parameter extraction method and analysis described in this paper can be suitably employed to obtain the panel dynamics for a PV panel without any information of voltage, current or power rating. Together with the parameter extraction, I-V and P-V characteristics and maximum power point tracking analysis has also been conducted to the PV panel.

PurposeTo analyze the operating performance of a fuzzy logic control (FLC) based solar energy conversion modular system controlled by a digital signal processor (DSP) microcontroller.Design/methodology/approachA range of published works relevant to the solar energy conversion modular systems are evaluated and their limitations are indicated in the first section of the paper. The circuit diagram of the panel‐boost converter system is described in the second section. In the third section, a neural network model is suggested for the photovoltaic panel and the model is created in the MATLAB/SIMULINK and then combined with other blocks existing in the system. The design of the FLC method is described in section 4. The simulation and experimental results corresponding to the control of the duty‐cycle of the converter to set the operating point of the solar panel at the maximum power point (MPP) are given in sections 5 and 6, respectively. Section 7, summarizes the results and conclusions of the study.FindingsThe paper suggests a simple dc‐dc boost converter controlled by FLC method. The proposed converter model can be used to obtain maximum power from a photovoltaic panel.Research limitations/implicationsIn preparing this paper, the resources books existing in the library of our university and the resources relative to the solar energy conversion and FLC published in English language and reachable through the internet were researched.Practical implicationsThe paper suggests a neural network model for a solar panel, which can be used in the simulation of the solar energy panel‐boost converter system. The solar energy panel‐boost converter system proposed in this study can be used by the researchers who are working in the solar energy conversion area.Originality/valueThe suggestion of a neural network model for a solar panel and creation of this model in the MATLAB/SIMULINK environment provides researchers to simulate and to analyze the performance of the solar energy panel‐boost converter system using the MATLAB/SIMULINK simulation program. In addition, since the control approach proposed in this paper does not require the information on temperature and solar irradiance that affect the maximum output power, can effectively find the MPP of the solar panel.

It is possible to give a valuable estimation for an entire solar array IV curve, based only on a single working point (current and voltage) measurement, on panels of solar cells, when several of their parameters are known and environmental parameters also known. The estimations were performed using Matlab/Simulink and the simulation is based on the 10 parameter solar cell model. The estimated curves only slightly deviate from the actual measured curves of real solar panels. The average short circuit current error is less than 5%, when the worst case simulated of just 33% of full lighting is also included in that average. When the open circuit voltage drops in error, the short circuit current grows in error and vice verse, so the MPP error is smaller. It is shown that this approach could be used to evaluate, on line, the Maximum Power Point (MPP) of solar panels, while even two or more local maxima could exist. The average relative MPP errors seem to be less than 2% with a full illumination (1 sun), less than 2.5% for 2/3 illumination and less than 3% for less than 1/3 illumination. After the MPP of solar cells, which are not in their optimal working point, has been evaluated, then by this new MPP, they can be classified for re-meandering. Such a procedure would require evaluation of the entire I/V and I/P curves of the cell/panels. It would effectively eliminate the problem of local maxima on the I/P curve. Once the MPP has been evaluated for each panel, it is possible to evaluate the MPP of an entire solar field after re-meandering. Once this is knows, the inverter could be controlled to project the MPP voltage towards the solar field, thus eliminating the need to use a MPPT module (dc/dc converter).

Solar panels are a renewable energy power plant that uses sunlight as its main energy source. The power generated by solar panels are determined by the size of the solar panels, solar radiation and temperature. The power of the solar panels is also determined by the output voltage of the solar panels. To get the maximum output power at any time, it is necessary to adjust the output voltage of the solar panel. This study proposes controlling the maximum output power of solar panels, also known as maximum power point tracking (MPPT) by adjusting the output voltage of the solar panels using a buck converter. The buck converter output voltage regulation at the maximum power point of the solar panel is designed with the Perturbation and Observation (PO) algorithm which is implemented using an Arduino Mega 2560. This MPPT control system is applied to 4x50 Watt-Peak (WP) solar panels which are connected in parallel. The experimental results show that the proposed MPPT control system with the PO algorithm has worked well as expected. This can be seen from the output power generated by the solar panels already around the maximum power point at any change in solar radiation and temperature.

Maximum power point trackers (MPPT) are used to operate a solar photovoltaic (PV) panel at its maximum power point (MPP). In this paper a microcontroller based MPPT is realized. Two tracking algorithms, namely, perturb and observation (PAO) algorithm and maximum power point voltage (MPPV) algorithm have been implemented. The MPPT is tested under varying insolation and load conditions. It has been shown that with PAO algorithm, the controller takes significant time to track the MPP. Moreover, the operating point oscillates around the MPP. The large tracking time and oscillations around the MPP result in significant energy loss during the tracking process. The wastage of power is more significant in rapidly changing atmospheric condition, as the controller has to search for new operating point more frequently. On the other hand, with the MPPV algorithm, the MPP is tracked very quickly with no oscillations around the MPP.

Photovoltaic (PV) systems have gained significant attention due to environmental pollution and the increasing depletion of fossil fuels. Maximum Power Point Tracking (MPPT) algorithm was utilized to make the PV systems get the optimal efficiency. In this paper, a Neural Network (NN) based MPPT algorithm was proposed, which is appropriate for almost all environmental conditions especially under partially shaded conditions. Genetic Algorithm (GA) was introduced to advance the NN for its global optimization ability. The NN was then trained by 75,000 sets of data from MATLAB/Simulink. Finally, the exact Maximum Power Point (MPP) was reached by Boost Converter. The proposed algorithm has been verified by simulation for various partially shaded conditions. As the result shown, the relative error of the NN after training was under 2%, and the output power of the whole system raised 10%-30% compared with that using traditional methods.

A maximum power point tracking (MPPT) algorithm optimizes the power output of a solar panel by continuously adjusting the duty cycle based on real-time voltage and current measurements. This process ensures the system operates at the panel’s maximum power point without overloading it. However, maintaining converter stability and fast convergence under varying solar irradiance conditions poses a challenge, as output power can oscillate before stabilizing at the maximum power point. Traditional non-synchronous converters use fastswitching diodes to block reverse current, but in highcurrent applications, power loss increases significantly due to the diode’s internal resistance. Synchronous converters, by using the MOSFET’s intrinsic body diode, address this issue and enhance efficiency. This paper focuses on comparing the performance and power conversion efficiency of the two converter types, highlighting the improvements in power extraction and efficiency at the maximum power point.

Implementation of the novel temperature controller and incremental conductance MPPT algorithm for indoor photovoltaic system

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.

The right choice of a solar panel model is essential while developing a software for a solar panel complex testing system. Currently, there are many versions of solar panel mod-els, but not all of them give accurate results and reflect the performance of the solar panel. Different models of solar panels are considered. Perhaps the most famous of them are the circuits with single diode, with one diode - one resistor, and with one diode - two resistors. In this work, research and analysis of the mentioned schemes are carried out. Simu-lations have been carried out for all the mentioned schemes. For the comparative analysis, the standard test condition (STC) - 250C temperature was chosen for all models and the comparison was made with the results presented for the corresponding temperature in the BP-MSX120 and BP 4180T solar panel datasheets. Of the 11 parameters that exist in solar panels, perhaps the most important are three parameters, short circuit current (Isc), open circuit voltage (Voc), maximum power point (MPP). Thus, we performed modeling for the three selected schemes (one-diode, one-diode and one resistor, one-diode and two resistor models), calculated the specified param-eters, obtained the curves and performed a comparative analysis taking into account all the three parameters. According to the principle of compromise, considering the most im-portant parameter, MPP. We compared those parameters, and considered the model whose MPP is closer to the real results to be the best.

A significant challenge in the progress and development of Building-Integrated-Photovoltaic (B-I-PV) systems is the extraction of maximum power from PV modules. The PV system architecture is an essential feature to extract the maximum power. The conventional PV-central-inverter architecture consists of various connections among the PV modules, which are sensitive to shading effects and produces mismatching power loss under partial shading conditions (PSCs). Hence, photovoltaic-distributed-maximum power point tracking (PV-D-MPPT) architecture has been proposed to extract the maximum power. In PV-D-MPPT architecture, the output terminals of DC-DC converters are connected either in series or parallel configuration. The main limitation of the series configuration in open-loop MPPT control is the cross-coupling effect. Because of cross-coupling effects, the maximum-powerpoint (M-P-P) operation of shaded PV modules is lost under PSCs. The lost in M-P-P operation of shaded PV module also affects the unshaded modules M-P-P operation. Under cross-coupling effects, the DC-DC converters are consuming the power instead of delivering to the load. Despite the research activity, there are hardly any papers presenting a clear, comprehensive and mathematical analysis on the existence of cross-couplings in PV string-integrated-converters (S-I-Cs). This article presents a mathematical analysis and also explains the conditions for the existent of cross-coupling effects. The experimental results also validate with the mathematically analysed results. This article also discusses the modeling of the two-diode model of PV module, design of boost type S-I-C, and the Perturb& Observe (P&O) MPPT algorithm implementation.

The photovoltaic generator exhibits a nonlinear v - i characteristic and its maximum power point varies with solar insolation. An intermediate converter is required to match the photovoltaic generator to the load and to operate the solar cell array at maximum power point. In this paper, a feedforward current sensorless maximum power point tracking scheme is developed for the interleaved dual boost converter-supplied photovoltaic system using only solar cell array information. For a given solar insolation, the tracking algorithm adjusts the solar cell array voltage to a value corresponding to the maximum power point. This is done by the feedforward loop, by comparing the instantaneous array voltage and a reference voltage corresponding to the maximum power point. The reference voltage for the feedforward loop is obtained by an off-line trained neural network. Experimental data is used for off-line training of the neural network, employing a back-propagation algorithm. A PI controller generates the control signal in real time for the PWM modulator and adjusts the duty ratio of the converter. The proposed current sensorless peak power tracking effectiveness is demonstrated through simulation and experimental results.

One of the current technologies widely used to extract the earth’s renewable energy is solar modules, which harness energy from the sun; however, their operating conditions and their energy storage capacities vary greatly under different weather conditions. The high-speed Flywheel Energy Storage System (FESS) presents a promising prospective solution for solar energy storage and utilization. Accurately modeling FESS can lead to its efficient design and control. In this paper, a fully integrated FESS, with comprehensive photovoltaic (PV) system, has been modeled to simulate the overall energy harvesting capabilities, power output, and efficiency. The solar module has been modeled using the one-diode solar cell model technique to predict the overall system efficiency and determine the most efficient time of day for switching between the FESS’s charging (morning time) and discharging mode (night time). To validate the developed model using experimental data, four 250 Wp Ankara Solar PV modules and a Balance of System (BOS) were installed. The solar irradiance, wind speed, cell temperature, solar module, FESS output voltage and current were logged during the system’s operation. The theoretical model predicted that the energy output for the test day was 4.80 kWh, while the experimental analysis showed that the solar modules produced 4.68 kWh, only 2.5% percentage difference. The theoretical and experimental power curves followed the same trends throughout the day, which assures that the model could accurately predict the daily energy output of the solar array. The efficiency of the solar module was determined to be 15.3%. The solar module simulation serves as a repeatable replication of the actual solar module source, which enables convenient, low-cost estimation of the solar module-FESS system under different environmental conditions. The developed solar module was integrated with a brushless DC motor and flywheel models to simulate the FESS response. Relative to the input solar energy input of 4.68 kWh, the daily energy stored in a flywheel was 3.51 kWh, giving the overall solar-module-FESS system an efficiency of 74.7%. After the experimental setup completion of FESS, the integrated solar module-FESS model will be tested for its overall power output and efficiency against the traditional solar module-battery-system.