Fault Detection, Classification and Localization Algorithm for Photovoltaic Array

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

doi:10.1109/tec.2021.3062049

Cited by 17 publications

(4 citation statements)

References 27 publications

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“…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

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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.

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Section: Introductionmentioning

confidence: 99%

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

Hojabri

Kellerhals

Upadhyay³

et al. 2022

Energies

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“…[15]. However, these methods demand a large number of auxiliary equipment and require a complex platform to detect the faults, resulting in a cost‐inefficient system [16]. A sensor‐less approach for the detection of L–L/L–G faults is done by Ref.…”

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

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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.

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“…To name a few, Satpathy and Sharma [6] studied the sensitivity of PVA topology to local coloring and electrical faults using various electrical parameters based on Matlab/Simulink environment and verified by experimental analysis. Mehmood et al [7] used switches to reconfigure electrical wiring under different shadow profiles, focusing on improving the performance and efficiency of traditional static photovoltaic systems (PVSs). They adopted a metaheuristic algorithm (MHA) and firefly algorithm (FFA) to control the switching mode under nonuniform shadow profile and tracked the highest global peak of multiple switching mode-generated power coefficient (PC).…”

Section: Introductionmentioning

confidence: 99%

Application of Wireless Sensor Network Technology Using Intelligent Algorithm in Mismatch Detection of Photovoltaic Power Generation

Xie

Chen

2022

Wireless Communications and Mobile Computing

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The paper was aimed at ensuring the stable operation of the photovoltaic power generation system (PVPGS) and improving the accuracy of automatic mismatch detection. Consequently, this paper presents a PVPGS-oriented mismatch detection system based on wireless sensing technology (WSN). Firstly, the photovoltaic array (PVA) is constructed using a microcontroller, power management chip, nRF24L01, temperature sensor, voltage, and current sensor. Then, a fault detection and localization (FDL) scheme based on the Hampel algorithm is optimized, and Matlab/Simulink implements the PVA simulation model. Finally, several typical mismatch faults are simulated to verify the feasibility of the proposed FDL scheme using the measured voltage and current data. The empirical findings corroborate that the proposed FDL scheme can automatically and regularly collect photovoltaic (PV) electrical characteristic data and quickly and accurately identify and position a mismatch. In the case of a PVA open-circuit fault, the output current loss of the PVA is equal to the sum of the current of the open-circuit fault string in the array during normal operation. When the PVA is short-circuited, the PVA output voltage loss equals the sum of the output voltages of the faulty components in the most serious fault string under normal operation. Overall, the classification accuracy of the proposed FDL scheme is 97.556%. Lastly, the experiment reveals that the classification accuracy of the proposed FDL scheme is 100% for array aging, shadow, and the open circuit. Therefore, the research proposal has a good application prospect.

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Fault Detection, Classification and Localization Algorithm for Photovoltaic Array

Cited by 17 publications

References 27 publications

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

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

Application of Wireless Sensor Network Technology Using Intelligent Algorithm in Mismatch Detection of Photovoltaic Power Generation

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