Power-voltage Curve Research Articles

Maximum Power Point Tracking Implementation under Partial Shading Conditions Using Low-Cost Photovoltaic Emulator

Maximum power point tracking (MPPT) is a pivotal objective for photovoltaic (PV) systems. To test various MPPT techniques, a reliable and effective PV emulator is required. Therefore, this article proposes a low-cost PV emulator for partial shading conditions, in which a cascaded structure of a DC power source with a resistor is constructed to generate the multiple peaks of the power-voltage (P-V) curve. The proposed structure is simple and modular. Consequently, it can be extended to obtain several peaks in the P-V characteristics to emulate more complex partial shading conditions. The partial shading occurrence over the PV source (PV array) causes a significant power loss production from the PV system. To increase the PV system’s efficiency, optimization techniques are employed to harness the global power. Accordingly, the particle swarm optimization (PSO) technique is used to track the global peak. Furthermore, the conventional perturb and observe (P&O) method is applied for comparison and investigation. The proposed PV emulation system is validated under different operating conditions using simulation and experimental hardware-in-the-loop (HIL) results.

Partial shading detection and hotspot prediction in photovoltaic systems based on numerical differentiation and integration of the P − V curves

Hotspot phenomenon is an expected consequence of long-term partial shading condition (PSC), which results in early degradation and permanent damage of the shaded cells in the photovoltaic (PV) systems. Hence, partial shading detection and estimation of its intensity is a significant factor to predict the possibility of hotspotting process and its occurrence time for PV system protection. In this paper, two partial shading detection indices are introduced based on the extracted power-voltage ( P − V $P - V$ ) curves. The first P − V $P - V$ characteristic is related to the real shaded PV system and the second P − V $P - V$ curve is numerically obtained from the equivalent single-diode circuit model. The proposed PSC detection indices are established according to the difference between the numerical differentiation and integration of the P − V $P - V$ curves from the real PV system and the virtual reference model. These techniques have the advantages to distinguish between temporary and permanent PSC. Moreover, it is possible to estimate the intensity of the shading level and predict the hotspot occurrence time, accordingly. The performance of these methods are confirmed using some simulations and an experimental setup. The results verify the capabilities of the methods for partial shading detection without the requirement of an extra physical cell/module as a reference model.

Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking

This paper presents a modified Levy particle swarm optimization (MLPSO) to improve the capability of maximum power point tracking (MPPT) under various partial shading conditions. This method is aimed primarily at resolving the tendency to trap at the local optimum particularly during shading conditions. By applying a Levy search to the particle swarm optimization (PSO), the randomness of the step size is not limited to a specific value, allowing for full exploration throughout the power-voltage (P-V) curve. Therefore, the problem such as immature convergence or being trapped at a local maximum power point can be avoided. The proposed method comes with great advantages in terms of consistent solutions over various environmental changes with a small number of particles. To verify the effectiveness of the proposed idea, the algorithm was tested on a boost converter of a photovoltaic (PV) energy system. Both simulation and experimental results showed that the proposed algorithm has a high efficiency and fast-tracking speed compared to the conventional HC and PSO algorithm under various shading conditions. Based on the results, it was found that the proposed algorithm successfully converges most rapidly to the global maximum power point (GMPP) and that the tracking of GMPP under complex partial shading is guaranteed. Furthermore, the average efficiency for all test conditions was 99% with a tracking speed of 1.5 s to 3.0 s and an average output steady-state oscillation of 0.89%.

Procedure Used to Estimate the Power Production of a Photovoltaic Module Operating under Partial Shading Conditions

This paper presents a methodology used to estimate the energy generated during one year by a photovoltaic module (PVM) operating under partial shading conditions. The methodology starts by calculating the solar paths and contours of nearby objects that produce shadows. Then, a method was proposed to estimate the shading factors of each submodule. Afterwards, the solar resource data and the calculated shading factors were used to feed a detailed PVM model to calculate the power–voltage curves for each hour, which were used to obtain a power profile and estimate the energy generated by the PVM in one year. The procedure was validated through simulation and experimental results. The simulation results consider a case study available in the literature, which was simulated to evaluate the effect on the PVM energy estimation considering and disregarding the partial shading conditions. The experimental results illustrate the capacity of the proposed methodology to predict the shaded and unshaded submodules and the module power–voltage curve. The results show that the proposed method avoids the energy overestimation introduced by classical estimation methods, which affects the sizing of a photovoltaic generator.

Extendable Voltage Equalizer Topology With Reduced Switch Count and MPPT With Partial Shading Detection Capability for Long Serially Connected PV Modules

Maximum power point tracking (MPPT) plays a significant role in a photovoltaic (PV) system's optimal performance. Bypass diodes create multiple peaks on the power–voltage curve during partial shading conditions (PSCs), reducing the PVs overall power extraction capability. This article proposed a new hybridized architecture for switched inductor and switched capacitor circuit based voltage equalizer (VE) topology with reduced switch count to prevent the multiple peaks and increase the energy yield during PSCs. The proposed VE operates with a fixed duty cycle and frequency. The MPPT controller with PSC detection is also proposed to operate VE only when the PSC is detected to avoid unwanted switching and conduction losses. The proposed system's effectiveness for ten modules connected in series has been determined compared with other modular state-of-the-art VEs. The proposed topology can be extended with a smaller number of control switches. On average, the experimental results showed a 30.42% improvement in energy yield in comparison with the conventional systems. Furthermore, the proposed controller offers the MPPT efficiency of 99.28% with a tracking time of 0.10 s for uniform shaded conditions and 0.692 s for PSCs on average.

A comprehensive analyses with new findings of different PSO variants for MPPT problem under partial shading

Evolutionary algorithms (EAs) have emerged as powerful maximum power point trackers (MPPTs) for solar photovoltaic (PV) system under partial shading condition (PSC). However, due to stochastic nature of an EA, tracking performance is not similar during its each independent execution. Consequently, the effectiveness of such method cannot be benchmarked merely with single run or through average statistical results. This paper therefore presents a two-fold contribution. The first contribution demonstrates that run length distribution (RLD) is the most suitable methodology to evaluate the explicit success of EA inspired MPPTs during each independent run. For test case, particle swarm optimization (PSO) is taken as an MPPT algorithm. Extensive simulations and experimentations of six PSO variants are carried out against three partially shaded power-voltage ( P - V ) curves, including the complex and experimental curve. Each algorithm is run 50 times and results are evaluated through RLD test, which is never used in most of the research for MPPT rationale. The second contribution involves the development of a generic adaptive PSO (APSO), which clearly highlights the importance of parameters’ improvisation of PSO as an MPPT algorithm. The designed APSO obtains almost 100% success rate with minimum evaluation count to converge at global peak (GP) under PSC.

Hyper-Spherical Search Algorithm for Maximum Power Point Tracking of Solar Photovoltaic Systems under Partial Shading Conditions

Maximum Power Point Tracking (MPPT) for photovoltaic systems has widely been studied. However, studying the impact of partial shading (due to buildings, trees, clouds, adjacent arrays, and so on) on the MPPT and achieving the global maximum is still a challenging topic. This is because not only there is a global maximum power point (GMPP) but also there are several local maximums due to the non-linear nature of the power-voltage curve after partial shading. Therefore, the conventional MPPT methods fail to track the GMPP and are commonly trapped at one of the local maximum power points. This study utilizes the Hyper-Spherical Search (HSS) algorithm in MATLAB to achieve the GMPP while improving the efficiency, convergence speed, dynamic response, and reducing the losses. To track the GMPP using the HSS algorithm, the output power for the array (PPV) is defined as the objective function, and the duty cycle of the DC-DC converter is selected as the particles’ position (control variable). The performance of the proposed algorithm has been studied in three different partial shading patterns, and the simulation results confirm the capability of the algorithm in the rapid tracking of GMPP points. In addition, if the GMPP position changes over time, it will track the new GMPP with minimal oscillations. The proposed method along with PSO, P&O, ABC, and Dragon algorithms has been applied for various scenarios, and the obtained results using HSS have been compared with the four mentioned algorithms, which confirmed the effectiveness of the proposed method. Briefly, the advantages of the HSS algorithm in finding GMPP can be stated as simple implementation with few parameters, strong exploration, and exploitation during the tracking process, fast-tracking and low fluctuations during tracking, low oscillation at steady-state, high dynamic efficiency, and non-convergence to LMPP points.

An Experimental and Case Study on the Evaluation of the Partial Shading Impact on PV Module Performance Operating Under the Sudano-Sahelian Climate of Cameroon

Partial shading is a factor that influences the performance of a PV module. The study sought to evaluate the impact of partial shading on PV module performance in the Sudano-Sahelian climate conditions of Cameroon. The behavior of the PV module was simulated using MATLAB/Simulink for 12 months with data from the town of Yagoua. The power, current, and voltage losses of the PV module were estimated by varying the partial shading rate from 5.0% to 95.0%, with an increase factor of 5.0%. The results show that, when the shading ranges from 5.0% to 55.0%, the power and current losses are very significant and vary from 3.0% to 52.0% and 3.0%–53.0%, respectively. The voltage in this shading range remains almost invariant. For shading from 60.0% to 95.0%, the power losses increase slightly and reach approximately 60.0%. A very small current loss is observed, varying from 1.0% to 3.0%. Significant voltage losses are noted and vary from 55.0% to 59.0%. From 40.0% shading rate onwards, a mismatch is observed on the power-voltage characteristics curve by the presence of two maximum power points. This method can be used to evaluate the efficiency of different PV array topologies under partial shading. The results show the importance of paying attention to partial shading, however small its occurrence.

Enhanced energy output from a PV system under partial shaded conditions through grey wolf optimizer

Due to its several powerful characteristics, the Grey Wolf Optimization (GWO) algorithm has recently become widely used in numerous optimization problems. In photovoltaic (PV) systems, power-voltage (P–V) curves present multiple maxima in case of partial shadow or modules mismatch which can be viewed as an optimization problem. In this paper, a GWO-based maximum power tracking (MPPT) algorithm for PV systems operating under partial shading conditions (PSCs) is proposed. With a direct control method, the proposed GWO-based MPPT allows successfully catching the global maximum power point (GMPP) of the PV array in case of uniform and non-uniform solar irradiation. To evaluate the effectiveness of the proposed MPPT scheme, a co-simulation combining Matlab/Simulink™ and Cadence/Pspice™ software environments is carried out and tested under various static and dynamic shading conditions. Furthermore, the tracking performance is compared with that of the particle swarm optimization (PSO) and classical perturb and observe (P&O) based MPPT methods under transient PSCs and also under inhomogeneous real conditions of Algerian sky. The simulation results have demonstrated the satisfactory performances of the proposed approach in terms of tracking efficiency and tracking speed over the other MPPT methods.

Research on Maximum Power Tracking of Grey Wolf Optimization Algorithm Introducing Levy Flight

In the case of partial shadow, the power-voltage characteristic curve of photovoltaic array output will show multi-peak phenomenon, the traditional maximum power point tracking method is easy to fall into the local maximum power point, resulting in the loss of output power. Aiming at the common problems of traditional swarm intelligence optimization algorithms, such as slow convergence speed, large oscillation amplitude, and easy to fall into local optimum, a control method of gray wolf optimization algorithm with Levy flight was proposed. The algorithm uses GWO’s excellent ability of fast convergence speed and high solution accuracy to quickly converge to the vicinity of the maximum power point, and then uses CSA to achieve a stable state near the maximum power point. The power fluctuation is smaller. The simulation statistics show that compared with the traditional The improved gray wolf optimization algorithm has higher solution accuracy, shorter tracking time, and improves the output efficiency of the photovoltaic array.

An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production

The stochastic and erratic behavior of solar photovoltaic (SPV) is a challenge, especially due to changing meteorological conditions. During a partially irradiated SPV system, the performance of traditional maximum power point tracking (MPPT) controllers is unsatisfactory because of multiple peaks in the Power-Voltage curve. This work is an attempt to understand the performance uncertainties of the SPV system under different shading conditions and its mitigation. Here, a novel hybrid metaheuristic algorithm is proposed for the effective and efficient tracking of power. The algorithm is inspired by the movement of grey wolves and the swarming action of birds, and is thus known as the hybrid grey wolf optimizer (HGWO). The study focuses on the transient and steady-state performance of the proposed controller during different conditions. A comparative analysis of the proposed technique with incremental conductance and a particle swarm optimizer for different configurations is presented. Thus, the results are presented based on power extracted, shading loss, convergence factor and efficiency. The proposed HGWO–MPPT is found to be better as it has a maximum efficiency of 94.30% and a minimum convergence factor of 0.20 when compared with other techniques under varying conditions for different topologies. Furthermore, a practical assessment of the proposed controller on a 6.3 kWp rooftop SPV system is also presented in the paper. Energy production is increased by 8.55% using the proposed approach to the practical system.

Magnetic Flux Switching Type Permanent Magnet Generator for Bicycles: Modelling and an Experimental Study

Abstract The article introduces two new designs of flux switching permanent magnet alternators with a radial and an axial arrangement of the inductor relative to the magnetic flux switch. The study presents the results of 3D magnetic flux modelling along with the outcomes of physical experiments. In the course of laboratory experiments, the results of theoretical estimations are compared with actual physical performance parameters. The results are presented in the form of power and output voltage curves in relation to the frequency and load current. The study proposes the practical application of the alternators based on the magnetic flux switching principle in the development of a bicycle generator. Laboratory test models are made of a laminated steel using a waste-free technology.

Fast Artificial Neural Network Based Method for Estimation of the Global Maximum Power Point in Photovoltaic Systems

In partially shaded photovoltaic (PV) arrays, the power-voltage curve displays multiple maxima, one of which is the global maximum power point (GMPP). One of the major concerns related to PV systems is to locate and track the GMPP in all circumstances to boost efficiency. Combining conventional hill climbing (HC) algorithm and artificial neural networks (ANNs), a new two-stage GMPP tracking method is introduced in this article that aims to be fast and accurate. Moreover, it does not require irradiance or temperature sensors. In the first stage, the current-voltage (I-V) curve is sampled at specific points determined based on the array I-V curve analysis with the objective to be the minimum samples possible that can reflect the changes of irradiance and temperature. Then a simple feedforward ANN is employed to estimate the neighborhood of the GMPP using these samples. In the second stage, the HC algorithm is adopted to ensure the GMPP is tracked precisely. The proposed method is validated by simulations in MATLAB/Simulink environment and experimental tests under uniform irradiance condition, partial shading conditions, and a wide range of temperatures.

Intelligent series inducing switching scheme (ISISS) to enhance power delivery in shaded photovoltaic array

PurposeThe aim of this research study is to mitigate shading impact on solar photovoltaic array. Photovoltaic (PV) array when getting shaded not only results in appreciable power loss but also exhibits multiple power peaks. Due to these multiple power peaks, the maximum power point tracking (MPPT) controllers’ performance will be affected, as most of the times it ends up in tracking the local maximum power peak and not the global power peak.Design/methodology/approachThe PV panels in an PV array when getting shaded even partially would result in huge power loss. The pattern of shading also plays a crucial role, as it renders a cascaded impact on the overall power output because the cells/panels are connected in series and are parallel. Therefore, during shading, intelligent schemes are needed to appropriately connect and discard the unhealthy and healthy panels in right place with right combination. This research proposes one such scheme to mitigate the shading impact.FindingsTo mitigate the shading impact and also to have a smooth power-voltage (P-V) curve, a new series inducing switching scheme is introduced. The proposed scheme not only mitigates the shading impact and enhances the output power but also smoothens the P-V curve that facilitates the MPPTs to track the P-V appropriately.Originality/valueThe research findings are inventive in nature and not copied work. The reference works and the inspirations have been duly cited and credited.

A novel PV array reconfiguration approach to mitigate non-uniform irradiation effect

The extraction of power at global maximum power point (GMPP) has been affected due to non-uniform row-current generation leading to the formation of multiple peaks in power-voltage (P-V) curve. This issue booms out due to set of factors referred to as partial shading condition (PSC). To root-out this issue, puzzle-game based strategies were developed based on physical relocation of shaded modules with the unshaded one. This has a limitation when considering the size as a parameter which is non-effective for larger shade-dispersion. Thus, to alleviate this issue, this research paper intends to execute a novel reconfiguration topology based on idea of permutation and combination (P-C). This novel metaheuristic approach is focussed on building a fitness function in terms of efficiency (Ƞ), fill-factor (FF) and power loss (PLOSS). A connection list has been prepared to evaluate for optimal combination of solar modules. Hence, this approach excels in providing an optimum solar array attenuating the complexity of the photovoltaic (PV) system. The effectiveness of this approach has been validated through MATLAB tool on 4 × 4, 6 × 4 and 9 × 9 solar array size under various shading patterns. To bring out more efficacy, this suggested novel approach has been elaborately compared with traditional Total cross tied (TCT) topology and two other puzzle-based topology Ken-Ken (K-K) and L-shape (L-S) topology for 4 × 4 solar array size on the basis of 16 performance parameters; maximum voltage (VMAX), maximum current (IMAX), GMPP, local maximum power point (LMPP; its values and number of peaks generated on the P-V curve), misleading power loss (MLPL), mismatch loss (MLOSS), current and voltage loss (ILOSS, VLOSS), PLOSS, execution ratio (ER), Ƞ, FF, %power improvement (%PI), thermal voltage (TV) and power enhancement ratio (PER). Results for 4 × 4 solar array size portray that P-C has enhanced GMPP by 5.64 W, 5.64 W and 6.61 W reducing PLOSS by 7%, 6% and 8.26% respectively over L-S, K-K and TCT under border shading. Validation of the novel P-C for 6 × 4 solar array size has been done with TCT and L-S. Its results conclude that GMPP of P-C is incremented by 12.99 W and 0.70 W reducing the PLOSS by 11.16% and 0.99% respectively over TCT and L-S under corner shading. Further, the novel P-C for 9 × 9 solar array size has been validated with TCT, K-K, L-S, Ancient Chinese (A-C) and Skyscraper (S-S) topologies. GMPP of the novel P-C is improved by 16.19 W, 12.34 W, 12.34 W, 12.11 W and 2.75 W minimizing the PLOSS by 5.80%, 5.75%, 5.75%, 5.73% and 4.23% over TCT, K-K, L-S, A-C and S-S under zigzag shading. The comparative results under all the three considered solar array size strongly suggests the increased ability of the proposed novel P-C approach in enhancing the performance parameters and an effective shade dispersion over the entire PV array under PSC as compared to other considered reconfigurations.

Hardware-In-the-Loop Validation of Direct MPPT Based Cuckoo Search Optimization for Partially Shaded Photovoltaic System

During partial shading conditions (PSCs), the power-voltage curve becomes more complex, having one global maximum power (GMP) and many local peaks. Traditional maximum power point tracking (MPPT) algorithms are unable to track the GMP under PSCs. Therefore, several optimization tactics based on metaheuristics or artificial intelligence have been applied to deal with GMP tracking effectively. This paper details how a direct control cuckoo search optimizer (CSO) is used to track the GMP for a photovoltaic (PV) system. The proposed CSO addresses the limitations of traditional MPPT algorithms to deal with the PSCs and the shortcomings of the particle swarm optimization (PSO) algorithm, such as low tracking efficiency, steady-state fluctuations, and tracking time. The CSO was implemented using MATLAB/Simulink for a PV array operating under PSCs and its tracking performance was compared to that of the PSO-MPPT. Experimental validation of the CSO-MPPT was performed on a boost DC/DC converter using a real-time Hardware-In-the-Loop (HIL) simulator (OPAL-RT OP4510) and dSPACE 1104. The results show that CSO is capable of tracking GMP within 0.99–1.32 s under various shading patterns. Both the simulation and experimental findings revealed that the CSO outperformed the PSO in terms of steady-state fluctuations and tracking time.

A novel hybrid numerical with analytical approach for parameter extraction of photovoltaic modules

• A Novel hybrid approach analytical/Numerical technique is proposed and validated. • The minimum value of ARE is obtained using our model and equal to 1.14%. • The obtained RMSE is 0.0031 A , it’s considered as the smallest among the values found by other model. • Low computational time is carried using our Hybrid approach compared with the others models. • The parameter extraction method is superior in terms of accuracy and convergence. Building an accurate mathematical model of photovoltaic modules is an essential issue for providing reasonable analysis, control and optimization of photovoltaic energy systems. Therefore, this study provides a new accurate model of photovoltaic Panels based on single diode Model. In this case, the proposed model is the link between two models which are the ideal model and the resistance network. All parameters are estimated based on hybrid Analytical/Numerical approach: three parameters photocurrent, reverse saturation current and ideality factor are obtained using an Analytical approach based on the datasheet provided by the manufacturer under Standard Test Conditions. The series and shunt resistances are obtained by using a Numerical approach similar to the Villalva's method in order to achieve the purpose of modeling the resistance network part. Our model is tested with data from the manufacturer of three different technologies namely polycrystalline, Mono-crystalline silicon modules and thin-film based on Copper Indium Diselenide, and for more accurate performance evaluation we are introducing the Average Relative Error and the Root Mean Square Error. The simulated Current-Voltage and Power-Voltage curves are in accordance with experimental characteristics, and there is a strong agreement between the proposed model and the experimental characteristics. The computation time is 0.23 s lower than those obtained using others approach, and all obtained results under real environment conditions are also compared with different models and indicated that the proposed model outperforms the others approach such as villalva’s and kashif’s method.

A novel strategy for implementation of intelligent techniques in solar photovoltaic arrays to improve the performance and various comparison of partial shading mitigating techniques

ABSTRACT This paper proposes a new reconfigure technique to improve efficiency by reconfiguring the photovoltaic array during the different partial shading conditions and comparative analysis with various partial shading mitigating techniques. Normally, the photovoltaic array efficiency is less even if the shading occurs in parts on the PV panels in an array. To extract maximum output from solar PV panels, the different modulation and MPPT techniques are utilized. Due to continuous and discontinuous partial shading, this MPPT technique also fails to perform a uniform power voltage curve. Therefore, to improve the efficiency and performance of the PV array, a prudent solution is needed to dispersing the shading by reconfiguring the solar panel array. Most of the present reconfiguration schemes are quite complex, succumb to erroneous execution, and not suitable for practical implementations. The proposed Intelligent Reconfiguration Technique (IRT) technique is well suited for extension of power ranges and it offers smooth power vs voltage graph there by the algorithm struck up in local maxima can be averted. Also the superiority of the IRT method quantitatively proven in Table 6 by comparing other methods with various shading patterns.

Designing a Generic Multi-Modality Processing Adapter for the Practical Implementation of the Photovoltaic Regulated Power Point Tracking under the Partial Shading

There is a growing trend around the globe to shift from the maximum power point tracking (MPPT) to the regulated power point tracking (RPPT) while harvesting power from a photovoltaic (PV) array. Unlike the MPPT control, the RPPT control makes it possible to flexibly adjust the PV power output at different values as long as the power requirement does not exceed the maximum power availability from the PV array. The objective of this paper is to design a generic multi-modality processing adapter (MMPA) to enable the RPPT operation under the partial shading. Practically, the RPPT is to be implemented via a quantum-mode iterative procedure on the basis of a discretised power-voltage curve. The necessity of the quantum-mode iterations arises because of a natural duty ratio quantisation phenomenon that happens in power electronic converters. There is an oscillation-guided adaptive-step enumeration (OGASE) algorithm to perform the RPPT in the quantum mode. However, a standalone OGASE controller may, as usual, be deceived by a local power peak under the partial shading. In order to help the OGASE controller overcome such local peak traps, the above MMPA module is introduced. The proposed MMPA module is suitably verified via both MATLAB simulations and hardware results.

Study on Electrical Characteristics of Thin Flexible Crystalline Silicon Solar Cells

Flexibility makes output voltage-current curve of thin crystalline silicon solar cell’s photovoltaic (PV) array stair-stepping and corresponding power-voltage curve possessing non-uniform illumination characteristics, so existing conventional PV array model is no more suitable and it is necessary to build a new mathematical model of PV array that is suitable for flexibility condition. Based on engineering model of PV array and combining with series-parallel connection of electric circuit, the PV array under flexibility is analyzed in detail and it is derived theoretically that the mathematical model of PV array under flexibility can be expressed by piecewis function.