A Diode-Based Fault Detection, Classification, and Localization Method for Photovoltaic Array

Mehmood, Ahsan; Sher, Hadeed Ahmed; Murtaza, Ali Faisal; Al‐Haddad, Kamal

doi:10.1109/tim.2021.3077675

Cited by 26 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is unlike a situation where it is found that a PV module has faulty bypass diodes in open-and short-circuit conditions at the exact instant. Because when a bypass diode fails open, it can develop a hotspot in the sub-string, and subsequently, as time progresses, the diodes will be affected by excessive forward current and cause these to fail in open mode [6] as well. In contrast, when a bypass diode fails short-circuit, the voltage of the PV module will drop, so there will be little likelihood of diodes failing in the open mode at the same time.…”

Section: B Proposed Ann Modelmentioning

confidence: 99%

“…The second metric is the sensitivity (ideal case = 100%) which is the ratio of true positives samples divided by the total number of true positives and false negatives. This metric is calculated using (6).…”

Section: Tim-22-05067mentioning

confidence: 99%

“…The fundamental PV modules are central units of a PV system, and frequently, they are subjected to hardware faults. Such faults can be temporary or permanent, depending on the source [3], and there are various fault types such as mismatch conditions, module ageing, potential induced degradation (PID) [4], shading, short circuit faults [5], and bypass diodes faults [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

Dhimish

Tyrrell

2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Due to the importance of determining faulty bypass diodes in photovoltaic systems, faulty bypass diodes have been of widespread interest in recent years due to their importance to improving PV system durability, operation, and overall safety. This paper presents new work in developing an artificial intelligence (AI) based model using the principles of artificial neural networks (ANN) to detect short and open PV bypass diodes fault conditions. With only three inputs from the PV system, namely the output power, short-circuit current, and open-circuit voltage, the developed ANN model can determine whether the PV bypass diodes are defective. In the experimentally validated case of short and open bypass diodes, 93.6% and 93.3% of faulty bypass diodes can be detected. Furthermore, the developed ANN model has an average precision and sensitivity of 96.4% and 92.6%, respectively.

show abstract

Section: B Proposed Ann Modelmentioning

confidence: 99%

Section: Tim-22-05067mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

Dhimish

Tyrrell

2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

show abstract

“…Accordingly, most of the previous research has been done based on theorical assumptions [2], on data generated by simulation [3,4], or on limited recorded data from laboratory tests [5]. Moreover, in most of these studies, only electrical faults such as line to line (LL), line to ground (LG), and open circuit (OC) were considered for detection [5][6][7][8][9][10]. Non-electrical faults such as glass breakage were not considered and only a limited number of studies were undertaken to detect some of the physical faults such as connector faults [3,11] or potential induced degradation (PID) faults [4,7,12].…”

Section: Introductionmentioning

confidence: 99%

IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods

Hojabri

Kellerhals

Upadhyay³

et al. 2022

Energies

View full text Add to dashboard Cite

Faults on individual modules within a photovoltaic (PV) array can have a significant detrimental effect on the power efficiency and reliability of the entire PV system. In addition, PV module faults can create risks to personnel safety and fire hazards if they are not detected quickly. As IoT hardware capabilities increase and machine learning frameworks mature, better fault detection performance may be possible using low-cost sensors running machine learning (ML) models that monitor electrical and thermal parameters at an individual module level. In this paper, to evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation. The faults are emulated and applied to a real PV system, allowing the collection and labelling of panel-level measurement data. Then, different ML methods are used to classify these faults in comparison to the normal condition. Results confirm that NN obtain 93% classification accuracy for seven selected classes.

show abstract

“…The two standards, that is, national electric code (NEC) and the international electro-technical commission (IETC) are adopted to ensure the safe installation of the PV and DCMG [5,6]. The traditionally used devices, that is, overcurrent protection device (OCPD) and ground fault protection device (GFPD) presented in the literature have practical defects as these devices fail to detect the L-L/ L-G faults in PVs for longer durations [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a fault detection method for a PV‐fed DC‐microgrid with power control mechanism

Z̲ākir

Arshad

Sher

et al. 2022

IET Electric Power Appl

Self Cite

View full text Add to dashboard Cite

This paper presents practical implementation of a fault detection, localisation, and categorisation (FDLC) method in PV‐fed DC‐microgrid (DCMG). The DCMG is implemented by utilising a group of two DC nanogrids (DCNG) that have power control mechanism (PCM). The FDLC uses a voltage calculating circuit comprising a single voltage sensor and diode network. Moreover, the architecture is based on six statements extracted from the investigation of line to line (L–L) and line to ground (L–G) faults at a DCNG of the cluster. The PCM in the proposed system utilises a power triggering circuit for effective power flow among the different units of the DCNG considering the load demands and the resource availability. Experimentation is carried out by creating L–L/L–G faults at different points in the DCMG. Detection, localisation, and classification of faults is performed by utilising the sensor's voltage and power of the individual DCNG. FDLC offers less computational burden, perform fast detection of fault, and is capable of distinguishing between the L–L/L–G faults and uniform irradiance and partial shading conditions in the PV‐array. The proposed FDLC technique and its six statements are verified through experimental results.

show abstract

A Diode-Based Fault Detection, Classification, and Localization Method for Photovoltaic Array

Cited by 26 publications

References 34 publications

Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods

Design and implementation of a fault detection method for a PV‐fed DC‐microgrid with power control mechanism

Contact Info

Product

Resources

About