Maximum Power Point Estimation Research Articles

Novel reference condition independent method for estimating performance for PV modules based on double-diode model

The traditional methods for performance estimation mainly include two steps: determine the model parameters under reference condition and calculate the model parameters at varying operating conditions through transformation equations. These methods have the disadvantages of being influenced by the selection of reference condition. A novel reference condition independent method is proposed based on double-diode model (DDM) for parameter calculation and performance estimation at varying operating conditions for photovoltaic (PV) modules. In the proposed method, a new set of transformation equations is reconstructed according to the dependence of physical parameters in DDM on irradiance and temperature. All coefficients in the proposed transformation equations are independent of the information under reference condition and applicable to all varying operating conditions, which eliminates the influence of reference condition. The coefficients are optimized by fitting measured I–V data at varying operating conditions using guaranteed convergence particle swarm optimization technology, and used for model parameters calculation at varying operating conditions. Then the output property of PV module under any operating condition can be determined independently, first against the reference condition and then against the model parameters under reference condition. The accuracy and applicability of proposed method is validated by massive experimental data for six different types of PV modules under wide range of operating conditions. Moreover, three traditional methods are selected to compare with the proposed method. The proposed method shows better accuracy in I–V, P–V curve and maximum power point estimation under different irradiance and temperature conditions, which can be further used to predict the output power of PV system.

Frequency regulation algorithm of wind turbines with control gain based on maximum power point estimation

Frequency regulation algorithm of wind turbines with control gain based on maximum power point estimation

Development of the estimation model for the maximum power point of building-applied photovoltaic systems based on machine learning

With increased photovoltaic (PV) applications in buildings, operational and maintenance (O&M) is crucial to preserving initial performance. Faults detection and diagnostics (FDD) methods, such as current and voltage measurement, are used to identify system errors by comparing measured values with calculated maximum power point (MPP) outputs. This method typically uses the One-Diode model to calculate MPP values and considers them as optimal performance outputs. However, the accuracy can inevitably vary based on PV installation conditions and types of solar cells. Data-driven models have been developed to address these complexities but require further exploration for practical building applications. This study developed a data-driven model using an artificial neural network (ANN) to estimate MPP in the context of FDD for various building-applied PV systems. Initial training was conducted with hourly-based simulated data, and the model was validated using real-world building-applied PV system data, including building-attached PV (BAPV) and building-integrated PV (BIPV) types. To improve the performance of the initial MPP estimation model, additional training and re-validation processes were conducted using the measured data of the BIPV system. Initial results showed acceptable performance for power, current, and voltage estimation with less than 30% CvRMSE for building-attached PV systems. Building-integrated PV systems, affected by external factors like shading, yielded less accuracy with over 30% CvRMSE. After additional training, the model showed improved performance, including around 11% CvRMSE for all parameters. This study indicates that the ANN-based MPP estimation model can be practically applied to FDD with acceptable accuracy.

Performance evaluation of single-stage photovoltaic inverters under soiling conditions

Fly ash soiling effects are an air pollution consequence, especially during the heating season, and have considerable influence on the ability of urban photovoltaic (PV) systems to operate as an electricity supply grid. In this paper, an experimental study of soiling influence on PV systems with single-stage inverters was conducted on the hybrid micro-grid example. PV panel field measurements showed that soiling induces not only well-known maximum power point (MPP) tracking problem, but also reduces available power up to 30 %. Laboratory comprehensive tests with PV panels measured and emulated characteristics showed that, due to soiling, grid forming single-stage inverters operate with stability operation problems that can lead to energy production cessation.A reliable MPP estimation providing PV system with sufficient power reserve is necessary for hybrid micro-grid uninterrupted operation. In case of MPP tracking, it is necessary to establish additional limitations for the DC voltage to keep grid inverter functionality.

A Unified Power-Setpoint Tracking Algorithm for Utility-Scale PV Systems With Power Reserves and Fast Frequency Response Capabilities

This paper presents a fast power-setpoint tracking algorithm to enable utility-scale photovoltaic (PV) systems to provide high quality grid services such as power reserves and fast frequency response. The algorithm unites maximum power-point estimation (MPPE) with flexible power-point tracking (FPPT) control to improve the performance of both algorithms, achieving fast and accurate PV power-setpoint tracking even under rapid solar irradiance changes. The MPPE is developed using a real-time, nonlinear curve-fitting approach based on the Levenberg-Marquardt algorithm. A modified adaptive FPPT based on the Perturb and Observe technique is developed for the power-setpoint tracking. By using MPPE to decouple the impact of irradiance changes on the measured PV output power, we develop a fast convergence technique for tracking power-reference changes within three FPPT iterations. Furthermore, to limit the maximum output power ripple, a new design is introduced for the steady-state voltage step size of the adaptive FPPT. The proposed algorithm is implemented on a testbed consisting of a 500 kVA three-phase, single-stage, utility-scale PV system on the OPAL-RT eMEGASIM platform. Results show that the proposed method outperforms the state-of-the-art.

A novel method for performance estimation of photovoltaic module without setting reference condition

In traditional methods, the process of performance estimation of a photovoltaic (PV) module is achieved through two steps. First, the model parameters are determined under some reference condition, and then, the model parameters are determined under varying operating conditions based on the reference values and the dependence of the model parameters on environmental conditions. This paper presents a novel method for the performance estimation of PV modules under varying operating conditions without setting a reference condition. In the proposed method, the dependence of the model parameters on environmental conditions is modeled and modified without setting a reference condition or using reference values. The model parameters are determined from experimental data using guaranteed convergence particle swarm optimization technology, and are suitable for all operating conditions. Thus, the performance of PV modules can be directly determined under varying operating conditions, which simplifies the process and improves the accuracy. The effectiveness and accuracy of the proposed method are validated by large amounts of experimental data for different types of PV modules at different locations. In comparison with the traditional methods, the proposed method exhibits better accuracy in terms of the I-V and P-V curve and maximum power point estimation under different irradiance and temperature conditions. It can be further used to estimate the output power of PV system under varying operating conditions.

High-Performance Photovoltaic Constant Power Generation Control With Rapid Maximum Power Point Estimation

Photovoltaic (PV) constant power generation (CPG) control is regarded as an advanced active power control by limiting the maximum feed-in power in order to avoid the adverse impacts of high-penetration PV systems such as overloading and overvoltage. Conventional CPG strategies show obvious limitations in terms of the dynamics response, oscillations during the steady state, and the estimation speed of limited power points (LPPs). In this article, a novel CPG control with the rapid approach to LPPs is proposed with the estimation of maximum power point (MPP), which guarantees fast converging speed in response to rapid changes in environmental conditions and low power oscillations for the steady state. Specifically, the proposed control allocates the operating point at the left-hand side of the MPP, which can not only avoid the instability issue but also simplify the MPP and LPP estimation according to the single-diode PV equivalent model. Furthermore, based on the estimated available power $P_{\rm avi}$, the proposed CPG control contributes a direct solution in approaching the region around LPP or MPP, which improves the converging speed under environmental-changing conditions. Thus, regardless of the changes in irradiance or temperature, high-performance and reliable operation are ensured by switching the maximum power point tracking mode and CPG mode smoothly. The proposed control can be directly applied for the existing PV system and no additional hardware devices such as irradiance sensors are required, which is cost effective. A systematic comparison of the proposed control with other advanced CPG strategies was made under various scenarios and the advantages of the proposed control were validated experimentally.

A robust experimental-based artificial neural network approach for photovoltaic maximum power point identification considering electrical, thermal and meteorological impact

This paper aims to develop a robust and practical photovoltaic (PV) Maximum Power Point (MPP) identification tool developed using reliable experimental data sets. The correlations between the voltage and the current (Vmp and Imp) at maximum power from one side, and the irradiance information, electrical parameters, thermal parameters and weather parameters from another side, are investigated and compared. A comparative study between a number of input scenarios is conducted to minimize the MPP estimation error. Four scenarios based on a combination of various PV parameters using various Artificial Neural Network (ANN)-based MPP identifiers are presented, evaluated using the most common regression measure (Mean Squared Error (MSE)), improved in terms of the accuracy of the identification of MPP, and then compared. The first scenario is divided into two parts I(a) and I(b) and considers the irradiance information in addition to the highest correlated parameters with Imp and Vmp, which are circuit current (Isc) and open-circuit voltage (Voc), respectively. The second scenario considers irradiance information and the electrical parameters only. The irradiance information, in addition to the electrical, thermal, and weather parameters, are considered in the third scenario using a single layer network, while the irradiance information, in addition to the electrical, thermal, and weather parameters, are considered in the fourth scenario using a two-layer ANN network. Although the correlation study shows that the Vmp and Imp have the best correlation with the open-circuit voltage and the short circuit current (scenario I), respectively. Nonetheless, the consideration of irradiance, electrical, thermal, and weather parameters (scenario IV) yielded higher identification accuracy. The results showed a decrease in the MSE of Vmp by 74.3% (from 1.6 V to 0.411 V), and in the MSE of Imp by 95% (from 4.4e−6 A to 2.16e−7 A), respectively. In comparison to the conventional methods, the proposed concept outperforms their performances and dynamic responses. Moreover, it has the potential to eliminate the oscillations around the MPP in cloudy days. The MPP prediction performance is 99.6%, and the dynamic response is 276 ms.

Development of Wind and Solar Based AC Microgrid With Power Quality Improvement for Local Nonlinear Load Using MLMS

This work proposes a microgrid ( μ -grid) integrating wind and solar photovoltaic (PV) resources, along with the battery energy storage (BES) to the three-phase grid feeding the nonlinear load. The μ -grid disconcerted by probabilistic nonlinear time dependent parameters and their effects are compensated by cohesive controllers used for utility grid side voltage source converter (GVSC) and machine side voltage source converter (MVSC). The switching controls and the reconfigurability of the μ -grid are addressed on imperative aspects of improving power quality (PQ), power reliability, nonlinear load compensation, and economic utilization of resources. The nonlinear load compensation and PQ enhancement are achieved by executing modified version of the adaptive filtering technique including “momentum”-based least mean square (MLMS) control technique, utilized for providing the switching control signals to the GVSC. It utilizes two preceding gradient weights for obtaining updated weight thereby improving the convergence rate and overcoming the limitation of conventional control of the same family. The MVSC acquires its switching signals from conventional vector control scheme and the encoderless estimation of speed and rotor position of the synchronous generator driven by wind turbine through back electromotive force control technique. The external environmental disturbances are overcome by utilizing perturb and observe (P&O) maximum power point (MPP) for wind optimal power extraction and adaptive P&O with variable perturbation step size for solar MPP estimation. Test results are obtained from the laboratory prototype under steady-state and dynamic conditions, including altering wind speed, intermittent solar insolation, and variable load conditions. The PQ issues are addressed and investigated successfully.

Enhanced MPPT method based on ANN‐assisted sequential Monte–Carlo and quickest change detection

The performance of a photovoltaic system is subject to varying environmental conditions, and it becomes more challenging to track the maximum power point (MPP) and maintain the optimal performance when partial shading occurs. In this study, an enhanced MPP tracking (MPPT) method is proposed utilising the state estimation by the sequential Monte–Carlo (SMC) filtering, which is assisted by the prediction of MPP via an artificial neural network (ANN). A state-space model for the sequential estimation of MPP is proposed in the framework of incremental conductance MPPT approach, and the ANN model based on the observed voltage and current or irradiance data predicts the global MPP to refine the estimation by SMC. Moreover, a quick irradiance change detection method is applied, such that the SMC-based MPPT method resorts to the assistance from ANN only when partial shading is detected. Simulation results show that the proposed enhanced MPPT method achieves high efficiency and is robust to rapid irradiance change.

Estimation of MPP of a Double Diode Model PV Module From Explicit I–V Characteristic

This paper presents a novel approach for the conversion of an implicit current–voltage (I–V) expression of a double diode model (DDM) photovoltaic (PV) module to an explicit expression of current in terms of voltage. A new method is proposed for the estimation of maximum power point (MPP) of a DDM PV module using the explicit I–V expression of a PV module under uniform irradiance condition. Further, a new mathematical model of a partially shaded PV array is developed using the proposed explicit expression. Since the explicit I–V expression is used for the estimation of MPP of a DDM PV module under uniform and nonuniform irradiance conditions, the estimation of MPP is simple, accurate, and efficient. The accuracy of the explicit expression is verified considering three different PV modules. A comparison of MPP of a DDM PV module extracted using the proposed method with different existing methods under different environmental conditions (DECs) is presented. The results obtained using the proposed method are validated with the experimental results under DEC. Also, the accuracy of the estimated global MPP of a PV array under partial shading conditions is verified.

Maximum Power Point Tracking Algorithm for Grid-connected Photo Voltaic System

The Photo-Voltaic (PV) system is designed to supply solar power through photo-voltaic. The PV generator exhibits non-linear voltage-current characteristics and its Maximum Power Point (MPP) varies with cell temperature and solar radiation. The MPP Tracking (MPPT) technique gains more attraction in the field of PV systems and it is considered as a vital part in photovoltaic system design to maximize the output power of a photovoltaic array. The conversion efficiency of PV arrays is typically low; it requires MPPT control techniques to enhance the efficiency. The MPPT technique has been proposed for PV systems to reach the MPP. The most common issues associated with MPPTs are low tracking efficiency, need more transient time and steady state oscillation. This paper presents a comprehensive review of the MPPT techniques and analyzed the merits and demerits with different factors such as MPP, tracking error and power estimation error.

Maximum Power Point Estimation for Photovoltaic Strings Subjected to Partial Shading Scenarios

Partial shading is an unavoidable complication in the field of photovoltaic (PV) generation. Bypass diodes have become a standard feature of solar cell arrays to improve array performance under partial shading scenarios (PSS). However, the current–voltage and power–voltage characteristic data vary with different shading patterns. In this paper, a shading pattern detection algorithm is first proposed to estimate the number of shaded modules in a PV string. The result is then fed to a field-support vector regression (F-SVR) model, in which the measured features are transferred into a style-free high-dimension space prior to data training. The process of style-normalized transformation enables the features to be independent and identically distributed. Both simulations and experiments are conducted to evaluate the F-SVR model's ability to estimate the voltage at maximum power points. The results show that the proposed maximum power point estimation method can evidently reduce prediction errors.

Active power control of a photovoltaic system without energy storage using neural network‐based estimator and modified P&O algorithm

This study proposes a variable step size modified P&O algorithm for active power control (APC) that ensures that a predetermined amount of power, which is less than the maximum power, is extracted from the photovoltaic system without any energy storage. Under varying environmental conditions, the maximum available power varies and care should be taken to ensure that the desired operating point is below and on the right side of the current maximum power point (MPP). This requires real-time estimation of MPP. A neural network (NN)-based MPP estimator is proposed. With NN-based MPP estimator and modified P&O algorithm, a novel APC scheme is shown to perform well under varying environmental and demand conditions. This method of APC has been validated through simulation and experimental results.

A New Approach for Photovoltaic Arrays Modeling and Maximum Power Point Estimation in Real Operating Conditions

In this paper, a new approach for photovoltaic (PV) arrays modeling and maximum power point estimation (MPPE) in real operating conditions is presented. The PV array polynomial model is developed from its single-diode equivalent circuit, and the parameters of the proposed model are estimated by measuring the voltage and current of three points around the maximum power point (MPP). The maximum power point voltage (MPPV) is then calculated from the proposed model, and the PV array voltage is fixed at MPPV. Therefore, unlike the maximum power point tracking (MPPT) algorithms, there is no oscillation around the MPP. Using the PV array polynomial model with only three measurements around the operating point, results in less calculation burden, increases the estimation speed and decreases the I-V curve estimation process loss. In order to validate the proposed MPPE algorithm, we have compared it with the most commonly used perturb and observe (P&O) MPPT algorithm, a zero oscillation adaptive P&O technique, and a fast MPPT method by simulation and experimental results.

Rapid Active Power Control of Photovoltaic Systems for Grid Frequency Support

As deployment of power electronic coupled generation such as photovoltaic (PV) systems increases, grid operators have shown increasing interest in calling on inverter-coupled generation to help mitigate frequency contingency events by rapidly surging active power into the grid. When responding to contingency events, the faster the active power is provided, the more effective it may be for arresting the frequency event. This paper proposes a predictive PV inverter control method for very fast and accurate control of active power. This rapid active power control (RAPC) method will increase the effectiveness of various higher-level controls designed to mitigate grid frequency contingency events, including fast power-frequency droop, inertia emulation, and fast frequency response, without the need for energy storage. The RAPC method, coupled with a maximum power point estimation method, is implemented in a prototype PV inverter connected to a PV array. The prototype inverter’s response to various frequency events is experimentally confirmed to be fast (beginning within 2 line cycles and completing within 4.5 line cycles of a severe test event) and accurate (below 2% steady-state error).

Estimation of maximum power point of a double diode model photovoltaic module

This study presents a robust technique to estimate the maximum power point (MPP) of a double-diode model (DDM) photovoltaic (PV) module. The MPP of the DDM PV module under different environmental conditions (DECs) are estimated by using the simple and computationally efficient Levenberg-Marquardt method. The variation of double diode PV module parameters with the change of temperature and irradiation is analysed. The MPP of the DDM PV array at non-standard test condition obtained from the proposed method is verified with the MPP obtained experimentally and by MATLAB simulation. A comparative study of MPP of a DDM PV module estimated from the proposed method with different existing methods is performed for different operating conditions. The maximum measurement error in power generation of a DDM PV module for different methods under DEC is presented which indicates better performance of the proposed method for the estimation of MPP of a DDM PV module.

Numerical approach to estimate the maximum power point of a photovoltaic array

This study presents a novel approach to estimate the maximum power point (MPP) of a photovoltaic (PV) array on an hourly basis using meteorological data. The estimation of MPP of a PV array based on its characteristic equation is done by using Levenberg–Marquardt (LM) method. The monthly averaged daily and hourly global solar irradiation during sunshine hours of Guwahati city is estimated by taking relative humidity and ambient temperature data using empirical relations. The actual and estimated maximum powers of a PV array of monthly averaged daily and hourly basis is calculated using MATLAB. It is demonstrated that each of the estimated maximum power of a PV array on an hourly basis is nearly equal to each of the actual maximum power. The MPP of a PV module estimated by the LM method is validated with the actual and experimental results. A comparative study of MPP of a PV module estimated from the proposed method with perturb-and-observe based on proportional–integral, genetic algorithm and Gauss–Seidel is performed. The performance of the proposed algorithm for estimation of MPP of a PV array ensures the accuracy and faster convergence.

A performance evaluation model of a high concentration photovoltaic module with a fractional open circuit voltage-based maximum power point tracking algorithm

High concentration photovoltaic (HCPV) modules employing high-efficiency III–V solar cells promise greater system-level efficiency than conventional photovoltaic (PV) systems. Nevertheless, the output power of an HCPV system is very sensitive to rapidly fluctuating tracking errors and weather patterns. The fractional open circuit voltage (FOCV) based maximum power point (MPP) tracking technique benefits from simplified processing circuits with speed response. To investigate the feasibility of using the FOCV technique for MPP estimation on HCPV modules, a theoretical model and simulation are presented in this study. A MATLAB-based MJSC circuit model of an HCPV module with buck-type converter and load is proposed and validated. In addition, the magnitude of the optical loss caused by Fresnel lens shape deformation and air mass (AM) ratio is modeled and quantized. The FOCV technique is then employed and compared with the conventional perturb and observe (P&O) method on the HCPV module under varying irradiance and temperature conditions to study its effectiveness. The results suggest that the FOCV technique could help an HCPV module to attain greater power efficiency.

Innovative and precise MPP estimation using P–V curve geometry for photovoltaics

This paper elaborates a direct maximum power point (MPP) finding method for solar photovoltaics (PV) based on quadratic regression analysis of the geometry of the power–voltage (P–V) curve of a typical PV cell or module. This method works in two stages for determination of the MPP parameters such as voltage (Vmp), power (Pmp) and fill factor with high level of accuracy. At first, it determines the approximate MPP parameters using a few data collected from the open-circuit and short-circuit regions of a current–voltage (I–V) characteristic, and further it refines the obtained parameters using quadratic regression analysis. This method is non-iterative and requires no prior knowledge of the physical and electrical parameters of the cell. Besides high accuracy, the method is also very precise in handling the noise level (up to 20%) in the data. The method was tested on a wide range of PV cells reported in the literature including silicon, copper indium gallium selenide (CIGS), copper zinc tin sulphide selenide (CZTSSe) and organic cells. The estimated MPP parameters are in excellent agreement with those of reported for the cells. The method is also employed for an experimental P–V characteristic of a 10 Wp silicon module and a synthesized P–V characteristic of a 120 Wp silicon module. The estimated fill factor for silicon modules exactly matches with those available in the datasheet specifications. Experimental evaluation of the method as compared to perturb-and-observe (P&O) in the passive mode exhibits quick response. Thus, the method is applicable for a wide category of PV cells to PV modules.