Implementation of a Fault Diagnosis System Using Neural Networks for Solar Panel

Hwang, Hye-Rin; Kim, Berm-Soo; Cho, Tae-Hyun; Lee, In-Soo

doi:10.1007/s12555-018-0153-3

Cited by 18 publications

(12 citation statements)

References 19 publications

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“…In [29], [44], [47], [48] [53] to diagnose earth fault, diode SC, and PS. Besides, the combination of neural networks with a heuristic approach is presented in [54], for the diagnose of SC, ageing, and shading.…”

Section: Fdd Electrical-based Techniquesmentioning

confidence: 99%

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A comprehensive study of diagnosis faults techniques occurring in photovoltaic generators

et al. 2021

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Recently, many focuses have been done in the field of renewable energies, especially in solar photovoltaic energy. Photovoltaic generator, considered as the heart of any photovoltaic installation, exhibits sometimes malfunctions which involve degradations on the overall photovoltaic plant. Therefore, diagnosis techniques are required to ensure failures detection. They avoid dangerous risks, prevent damages, allow protection, and extend their healthy life. For these purposes, many recent studies have given focuses on this field. This paper summarizes a large number of such interesting works. It presents a survey of photovoltaic generator degradations kinds, several types of faults, and their major diagnosis techniques. Comparative studies and some critical analyses are given. Other trending diagnosis solutions are also discussed. A proposed neural networks-based technique is developed to clarify the main process of diagnosis techniques, using artificial intelligence. This method shows good results for modelling and diagnosing the healthy and faulty (shaded) photovoltaic array.

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Section: Fdd Electrical-based Techniquesmentioning

confidence: 99%

“…The work in [2], identifies and localizes failures at Open-Circuits in PV plant. The ANN-based technique has been developed in [48] with success to diagnose OC fault, using two NNs with a multilayer NN. Furthermore, in [74] a backpropagation neural-network have provided effective detection of OC and other faults.…”

Section: C) Oc-based Fddmentioning

confidence: 99%

A comprehensive study of diagnosis faults techniques occurring in photovoltaic generators

et al. 2021

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“…Examples that can be highlighted are that more than one fault occurs at the same time in the system. In this case, if only one Neural Network is used, the FDD system should indicate each fault combination in the output [15], [18].…”

Section: S Si Im Mu Ul La At Ti Io On N O Of F F Fa Au Ul Lt Ts Smentioning

confidence: 99%

Fault Diagnosis in Dynamic Systems Using Recurrent Neural Networks

Fkirin

Elhag

2020

Menoufia Journal of Electronic Engineering Research

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The aim of this paper is to discuss the methodology of Fault Detection and Diagnosis (FDD) in dynamic systems. Fault diagnosis is characterized as a control system that controls the ability to adapt system component faults automatically. Fault detection is an implementation of using the error signals, where when error signal is zero or approximately zero. A coupled water tank system was used as a study case model for implementing and testing the proposed methodology. The developed system should generate a set of signals to notify the process operator about the faults that are occurring, enabling changes in control strategy or control parameters. Due to the damage risks involved with sensors, actuators and structural faults of the real plant, the data set of the faults are computationally generated and the results will be collected from numerical simulations of the process model. A Recurrent Neural Networks (RNN) is employed in this paper for modeling the used system. This paper shows how to determine the structure and how to estimate the results using the gradient-based algorithm which it is allowed to obtain a Neural Network with relatively small modeling uncertainly. It describes how to increase the stability of dynamic systems using FDD based on RNN. Faults in the study case are faults in the sensors, faults in the actuators and the structural faults. The faults which are presented in the controlled system are reduced to 98% from the default value. K Ke ey yw wo or rd d: :Fault Detection and Diagnosis (FDD), Recurrent Neural Networks (ANN), Level control system for two coupled tanks.

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“…It suggests situating a backup inverter and PV array, which is disconnected from the system during normal operation, and connect it upon the detection of a fault on the system to continue its operation throughout the fault condition. Another work [14] also uses artificial intelligence for fault diagnosis in PV systems. However, it does not specify any restorative action or control solution upon detection of a fault.…”

Section: Introductionmentioning

confidence: 99%

Fault‐tolerant control of inverter for the integration of solar PV under abnormal conditions

Moradmand¹,

Dorostian²,

Ramezani³

et al. 2020

J. eng.

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Solar photovoltaic (PV) is a prominent technology for the generation of electricity and its utility is on the rise. The PVbased generation facilities are susceptible to faults which if mismanaged, can result in an interruption in the supply of load demand and damage to the system. Faults in PV systems are caused by a broad range of reasons and hence, it is crucial to situate a fault-tolerant system for reliable operation. This study proposes a fault-tolerant control strategy for power electronics inverters for the integration of PV systems into power systems. This is a supervisory mechanism designed to aid PV systems to continue their operation during faults. A computer simulation verifies the performance of the proposed control strategy under a series of common fault conditions assuming a wide range of configurations for the PV system and load variations.

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Implementation of a Fault Diagnosis System Using Neural Networks for Solar Panel

Cited by 18 publications

References 19 publications

A comprehensive study of diagnosis faults techniques occurring in photovoltaic generators

A comprehensive study of diagnosis faults techniques occurring in photovoltaic generators

Fault Diagnosis in Dynamic Systems Using Recurrent Neural Networks

Fault‐tolerant control of inverter for the integration of solar PV under abnormal conditions

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