Single-diode model is commonly used for modeling the electrical properties of photovoltaic (PV) modules, in which the current–voltage (I–V) equation is implicit and not convenience in parameter identification and power prediction. In this article, a novel method based on the power-law model (PLM) is proposed to model and characterize the electrical properties of PV modules under various operating conditions. Due to inherent simplicity and explicit trait of PLM, I–V properties of PV module under different operating conditions are predicted without using any nonelementary functions or iterative process, which reduces the computational complexity and costs. The dependence of the parameters in PLM on solar irradiance and module temperature is derived and investigated in detail. Moreover, the dependence of shape parameters in PLM on operating conditions is discussed thoroughly for the first time. Using our method, the parameters in PLM under various operating condition are calculated and then the I–V curve is predicted accurately and effectively. The accuracy and reliability of the model are validated by massive I–V curves measured outdoor in large-scale conditions. The capability of the model is demonstrated for different types of PV modules. The proposed processes are simple and especially useful to calculate the actual performances of PV modules under operating conditions, making it scalable for direct online application.

The single-diode model (SDM) is widely used in photovoltaic (PV) system modeling and output power estimation because of its simplicity and accuracy. This article proposed an improved and comprehensive method for parameter identification and performance estimation by using manufacturer datasheet information for PV module. The temperature dependence of all physical parameters in SDM of PV module is comprehensively considered and investigated for varying operating conditions. In the proposed method, all physical parameters under reference condition and their temperature coefficients are calculated from information in manufacturer datasheet, which can be used for parameter calculation and output power estimation under varying operating conditions. To improve the accuracy and efficiency, the analytical and optimization methods are combined in calculation procedure of parameter extraction. By using analytical derivation from I–V equations at key operating points and their temperature coefficients, it reduces the dimensions of the search space and save computational cost for parameter optimization. The accuracy and effectiveness of the proposed method are validated by massive experimental data under varying operating conditions for different type PV modules. The proposed processes are simple and especially useful to calculate the actual performances of PV modules under operating conditions, making it scalable for direct online application.

The current–voltage (I–V) equation in the equivalent circuit model of the photovoltaic (PV) module is implicit, and the dependence of model parameters on environmental conditions is uncertain, which causes inconvenience in output performance prediction. In this paper, a novel method based on the power-law model (PLM) is proposed to predict the I–V characteristics and output power of PV modules under varying operating conditions. The relationship between parameters in the PLM and manufacturer datasheet information is established. The irradiance and temperature dependences of shape parameters in PLM are obtained and investigated thoroughly. Due to inherent simplicity and explicit expression of PLM, the proposed method predicts the I–V characteristics and output power without using any iterative process, which reduces the computational complexity. The proposed method is validated by different types PV modules and under a wide range of environmental conditions. Comparing with traditional methods based on a single-diode model, the proposed method has better agreements with experimental results in all irradiance and temperature intervals. The accuracy and effectiveness are verified both in short-term and long-term output power prediction. The proposed method is simple and suitable to predict the actual output properties of PV modules under varying operating conditions.

In coreless axial flux permanent magnet synchronous generators (AFPMSGs), magnetic leakage and fringing effect is significant due to the large air gap and increases the difficulty in magnetic field calculation. This paper presents a 3‐D magnetic equivalent circuit (MEC) model for the magnetic field calculation in coreless AFPMSGs by considering the magnetic leakage and fringing flux. Field distribution is calculated based on the magnetic circuit analysis. The magnetic flux in the air gap between the permanent magnets is analysed using the magnetic field division method. Compared with the traditional magnetic circuit model, the magnetic leakage from PM to the rotor iron plates is considered in both radial and tangential directions in the proposed model. The proposed MEC method is applied to a specific AFPMSG, and the magnetic field distribution, no‐load back electromotive force (EMF), and torque are calculated and compared with finite elements (FEM) results. AFPMSG machines with different pole‐arc ratios are prototyped to validate the effectiveness and accuracy of the proposed 3‐D MEC model. The results obtained by the proposed MEC model are in good agreement with those of the FEM and experimental results.

Many methods are proposed for parameter identification and performance estimation of photovoltaic (PV) modules, including analytical methods, numerical methods, and optimization methods. In this paper, a hybrid method based on analysis expressions and optimization algorithms is proposed to determine the parameters of the single-diode model and the double-diode model for PV modules. The method is based on the reduced forms and combines the analytical method and the optimization method. By using three key points in I–V curves and making some approximations or simplifying assumptions without sacrificing accuracy, the dimensions of the search space are reduced to only one independent parameter, which reduces the computational complexity and cost. After identifying the parameters under certain conditions, the parameters under any operating conditions are estimated based on the dependence of parameters on environmental conditions. The proposed method is validated by massive experimental data of I–V curves under varying operating conditions. Comparing with other methods with high accuracy, the proposed method shows similar accuracy in parameter identification under certain conditions. Using the proposed method, the output performance estimation is found to be better than the results of other methods. Moreover, the influence of condition selection on the accuracy of output power performance estimation is investigated thoroughly.

An error criterion is essential in the process of parameter extraction of photovoltaic (PV) modules by fitting I–V curves, which exerts a huge influence on the accuracy of the extracted parameters. This paper proposes a new integrated current–voltage error criterion, named EC-I&V(x), which takes into account the intrinsic I–V properties of the PV module. The deviation in both current and voltage is considered by combining the mean squared error of the current and voltage in different data regions. Four optimization methods are used to validate the proposed error criterion, including guaranteed convergence particle swarm optimization, differential evolution, shuffled complex evolution, and an artificial bee colony algorithm. Different methods with the proposed error criterion are applied to synthetic I–V curves with variable error levels and measured I–V data under different operating conditions. Comparing with the traditional current based error criterion, more accurate results are obtained by using the proposed EC-I&V(x) at different error levels for different optimization methods. The proposed EC-I&V(x) not only improves the accuracy of each extracted parameter but also improves the accuracy of the estimated I–V property near maximum power points.