An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work.
The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.
PHOTOVOLTAIC ENERGY SYSTEM
The schematic diagram of the DC-DC boost converter is shown in Figure 1. Basically, this converter is required to track the PV MPP by adjusting its duty cycle D between [0,1]. A Pulse Width Modulation (PWM) generator is used to generate the appropriate pulse signal to MOSFET Q according to the given duty cycle. The DC-DC boost converter is characterized by its non-linearity. The input equivalent resistance of DC-DC boost converter.
PROPOSED ADAPTIVE NEURAL FUZZY CONTROL SYSTEM
The indirect adaptive neural fuzzy control system is used to control PV output power. Initially, the Hermite Wavelet-based Adaptive Neural Fuzzy Controller (HWANFC) is adopted as the MPP tracker for the PV system. In the proposed system, the HWNF-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The proposed intelligent control system is shown in Figure 2.
RESULTS AND DISCUSSION
The irradiance level varies over a day depending on the appearance of sun. From Figure 4, the Sun rises at 6:04 h (424 min) and sets at 19:26 h (1166 min). During the day time, the average solar irradiance level reaches 1000 W/m 2. Similarly, the average temperature during the night time is 20◦C, while in the day time, it reaches up to 42◦C. The HWANFC for the PV system tracks the MPP by keeping the slope close to zero. In order to analyze the performance of HWANFC, a PID controller-based IC and P&O and FLC are also used to track the MPP of the PV system.
In this work, an intelligent wavelet-based neuro-fuzzy indirect method with high adaptive capability is designed for the MPPT of a PV system. A five-layer NFC is adopted as the process feedback controller. The proposed control is initialized from the traditional fuzzy control by means of expert knowledge, which decreases the weight of the lengthy pre-learning. With a derived learning scheme, the parameters are updated in the proposed structure adaptively by observing and adjusting the tracking error.
A neural network is developed to provide the HWANFC with the gradient information. The Hermite wavelets are integrated to improve the performance of the proposed controller. Various simulation results and comparison indexes have shown that the HWANFC can track the MPP quickly with high robustness to the parameter variations and external load disturbances and out-performs compared with the traditional MPPT techniques.
Source: Chongqing University
Authors: Syed Zulqadar Hassan | Hui Li | Tariq Kamal | Ugur Arifoglu | Sidra Mumtaz | Laiq Khan