Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems

Abstract

The expanding use of photovoltaic (PV) systems as an alternative green source for electricity presents many challenges, one of which is the timely diagnosis of faults to maintain the quality and high productivity of such systems. In recent years, various studies have been conducted on the fault diagnosis of PV systems. However, very few instances of fault diagnostic techniques could be implemented on integrated circuits, and these techniques require costly and complex hardware. This work presents a novel and effective, yet small and implementable, fault diagnosis algorithm based on an artificial intelligent nonlinear autoregressive exogenous (NARX) neural network and Sugeno fuzzy inference. The algorithm uses Sugeno fuzzy inference to isolate and classify faults that may occur in a PV system. The fuzzy inference requires the actual sensed PV system output power, the predicted PV system output power, and the sensed surrounding conditions. An artificial intelligent NARX-based neural network is used to obtain the predicted PV system output power. The actual output power of the PV system and the surrounding conditions are obtained in real-time using sensors. The algorithm is proven to be implementable on a low-cost microcontroller. The obtained results indicate that the fault diagnosis algorithm can detect multiple faults such as open and short circuit degradation, faulty maximum power point tracking (MPPT), and conditions of partial shading (PS) that may affect the PV system. Moreover, radiation and temperature, among other non-linear associations of patterns between predictors, can be captured by the proposed algorithm to determine the accurate point of the maximum power for the PV system.

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

“…The recent works from [39,40] present a similar two-stage architecture when compared with this work. Additionally, they use auto regressive models to estimate the expected power output as a function of current environmental conditions.…”

“…The difference lies in the fault detection methods, which makes the works complimentary. While [39,40] uses fuzzy inference models yielding 98.2% accuracy with 16 combinations of shadowing, short circuit and open circuit, they cannot operate without disturbing the normal operation of the system, disconnecting the whole system to evaluate VxI curves or run the tree search algorithm.…”

“…In a more recent approach from [39,40], another two stage architectures were used, but this time non-linear auto regressive models (NARX) were developed to estimate the generated power under different environmental conditions. Next, fuzzy inference models compared the estimated value to the sensed power in order to classify the system into one of a given set of fault configurations, which includes combinations of shadowing, short circuit and open circuit, yielding 98.2% accuracy, using 16 use cases.…”

A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

“…In the given task to model the expected PV system behavior, the output data are one of the electrical parameters: current (I MPP ), voltage (U MPP ), or power (P MPP ) at the maximum power point. Implementing statistical or intelligent models to predict the expected electrical parameters has also been applied in [18,19] as a step in failure detection. For example, to predict the power, a recursive linear model was used in [19], having the measured irradiance and the previously predicted power as the input, while a neural network was used in [18] to predict the maximum output power given the open-circuit voltage, short-circuit current, and other weather data.…”

Statistical Methods for Degradation Estimation and Anomaly Detection in Photovoltaic Plants

“…Because of the many different types of failures, identifying one type of failure in a PV system is a challenging task. Nowadays increasingly more research is being done on diagnosing a specific set of failures [ 4 , 5 , 6 , 7 ].…”