Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

Abstract

The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT) is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

“…This study aims to remedy the defect of unavailable diagnosis at varying irradiance and temperature in the literature [9], so the BP neural network is adopted. Figure 6 is the schematic diagram of system diagnosis.…”

“…The fault diagnosis module used in this paper is 10-series 2parallel photovoltaic, as shown in Figure 1, and MPPT, the system architecture, is shown in Figure 2; the matlab 2010 is used for simulation. The specifications of SM1611 photovoltaic cell are shown in Table 1 [9]. The photovoltaic cell is set as short circuit to simulate nine fault states to be illustrated by Table 2, and the I-V and P-V characteristic curves at different irradiances and temperatures are observed.…”

“…The sunshine intensity is about 400 W/m 2 ∼ 1000 W/m 2 during 8:00∼16:00 in Taiwan, as shown in Table 3 [15], and the temperature is about 25 ∘ C∼50 ∘ C; the simulation is based on the conditions [9]. Figures 8(a), 8(b), 9(a), and 9(b) show the P-V and I-V characteristic curves at 1000 W/m 2 25 ∘ C and 500 W/m 2 40 ∘ C. First, the irradiance of 400 W/m 2 ∼ 1000 W/m 2 is divided into intervals of 100 irradiance; the temperature is divided into 25 ∘ C, 30 ∘ C, 40 ∘ C, and 50 ∘ C intervals as the input ends of BP neural network.…”

“…However, due to the limitation of extension theory, the diagnostic rate decreased greatly when the temperature and irradiance changed. This study aims to remedy the defects in the literature [9].…”

“…In the literature [9], 10-series 2-parallel solar photovoltaic array was used as the model of fault diagnosis. Chaotic synchronization system was combined with extenics for fault diagnosis.…”