Lian Lian Jiang

Chebyshev Functional Link Neural Network-based modeling and experimental verification for photovoltaic arrays

This paper presents a Chebyshev Functional Link Neural Network (CFLNN) based model for photovoltaic modules. There are two basic approaches to build a model - use an analytical modeling technique or use an Artificial Neural Network (ANN) based method. However, both the analytical modeling technique and the traditional Multilayer Perceptron (MLP) model have some disadvantages. For example, in the analytical model, the influence of irradiance and temperature on some parameters of the photovoltaic module, such as the parallel and series resistance and other uncertainty factors, are not taken into consideration. In the case of the multilayer neural network model, there is a large computational complexity in training the network and in its implementation. In order to overcome these advantages, we propose a CFLNN based model for solar modules. The proposed model not only reduces the complexity of the network due to the absence of hidden layers in the network configuration, but also shows better accuracy over the analytical modeling method. In the experimental section, the operating current predicted by CFLNN is compared with the outputs from other two modeling methods - MLP and the two-diode model. Finally, verification is performed using experimental datasets. The results show that the CFLNN modeling method provides better prediction of the output current compared to the analytical model and has a reduced computational complexity than the traditional MLP model.

Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods

Long term exposure of photovoltaic (PV) systems under relatively harsh and changing environmental conditions can result in fault conditions developing during the operational lifetime. The present solution is for system operators to manually perform condition monitoring of the PV system. However, it is time-consuming, inaccurate and dangerous. Thus, automatic fault detection and diagnosis is a critical task to ensure the reliability and safety in PV systems. The current state-of-the-art techniques either cannot provide enough detailed fault information with high accuracy or have too much complexity. This work presents an automatic fault detection and diagnosis method for string based PV systems. It combines an artificial neural network (ANN) with the conventional analytical method to conduct the fault detection and diagnosis tasks. A two-layered ANN is applied to predict the expected power which is then compared with the measured power from the real PV system. Based on the difference between the ANN estimated power and the measured power, the open circuit voltage and short circuit current of the PV string determined using analytical equations are used to identify any of the six defined fault types. The proposed method has a fast detection, compact structure and good accuracy. Simulation results show the effective fault detection and diagnosis capability of the proposed method.

A flann-based controller for maximum power point tracking in PV systems under rapidly changing conditions

In order to increase the efficiency of the Photovoltaic (PV) system, the PV system should be operated at the Maximum Power Point (MPP). The MPP Tracking (MPPT) is an essential part in achieving this improvement. Some of the existing techniques such as Perturb-and-Observe (P&O) and Incremental Conductance (INC) are relatively simpler to implement, but under rapidly changing irradiance and temperature conditions, they fail to track the MPP. Although methods such as Multilayer Perceptron (MLP) and Fuzzy Logic (FL) are efficient in tracking the MPP, their implementation increases the system complexity. In this paper, we propose a novel artificial intelligence based controller for MPPT, which can efficiently track the MPP, while keeping the computational complexity within the limits. Our technique uses Functional Link Artificial Neural Network (FLANN) to predict the PV output voltage at the MPP. Since there is no hidden layer, FLANN is computationally inexpensive. Simulation results verify that the proposed FLANN controller is computationally less intensive and exhibits higher efficiency under rapidly changing weather conditions.

A uniform implementation scheme for evolutionary optimization algorithms and the experimental implementation of an ACO based MPPT for PV systems under partial shading

Partial shading is one of the important issues in maximum power point (MPP) tracking (MPPT) for photovoltaic (PV) systems. Multiple peaks on the power-voltage (P-V) curve under partial shading conditions can result in a conventional MPPT technique failing to track the global MPP, thus causing large power losses. Whereas, evolutionary optimization algorithms exhibit many advantages when applying them to MPPT, such as, the ability to track the global MPP, no requirement for irradiance or temperature sensors, system independence without knowledge of the PV system in advance, reduced current/voltage sensors compared to conventional methods when applied to PV systems with a distributed MPPT structure. This paper presents a uniform scheme for implementing evolutionary algorithms into the MPPT under various PV array structures. The effectiveness of the proposed method is verified both by simulations and experimental setup. The implementation of the ant colony optimization (ACO) based MPPT is conducted using this uniform scheme. In addition, a strategy to accelerate the convergence speed, which is important in systems with partial shading caused by rapid irradiance change, is also discussed.

Estimation of external quantum efficiency for multi-junction solar cells under influence of charged particles using artificial neural networks

External quantum efficiency (EQE) of a solar cell is an important parameter as it determines the design efficiency and overall conversion efficiency. The EQE of solar cells for space applications is adversely affected due to bombardment of charged particles in space. Numerical model based softwares, e.g., PC1D, can be used to estimate the EQE under such situation. By varying the cell parameters to fit the measured EQE one can obtain degradation performance of space solar cells. However, due to complex phenomena and interactions occurring between the junctions of the solar cells and the nonlinear influence of charged particles, the accuracy of these models may be limited. In this paper we propose an artificial neural network (ANN)-based model to estimate the EQE performance of triple junction InGaP/GaAs/Ge solar cells under the influence of wide range of charged particles. With extensive simulation results we have shown that the ANN-based models provide better estimate of the EQE in terms of mean square error and correlation coefficient than the results reported by Sato et al.