Diagnostic technology and an expert system for photovoltaic systems using the learning method

Yagi, Yasuhiro; Kishi, Hitoshi; Hagihara, R.; Tanaka, Toshiya; Kozuma, Shinichi; Ishida, T.; Waki, Masahiro; Tanaka, Makoto; Kiyama, Seiichi

doi:10.1016/s0927-0248(02)00149-6

Cited by 69 publications

(28 citation statements)

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“…These studies include the fault finding by using mathematical method diagnosis [4], evaluating performance ratio (PR), capture losses, array and grid power losses analysis [1] and also artificial neural network [5]. Numerous fault detection techniques on DC side of PV system have been applied; such as climatic data independent technique (CDI) [6], electrical current-voltage (I-V) measurement (EM) technique [7], measured and modeled PV system outputs (CMM) technique [8], power loss analysis (PLA) technique [9], Machine learning (ML) techniques [10,11], [12], ground fault detection and interruption (GFDI) fuse [12], residual current monitoring devices (RCDs) [12], insulation monitoring devices (IMDs) [13], frequency spectrum analysis (FSA) of the voltage or current waveforms [13], estimating randomness in the voltage signal (ERV) [13], spread spectrum time-domain reflectometry (SSTDR), infrared (IR)/ thermal imaging [14], visual inspection and lock in thermography (LIT) [13]. Furthermore, fault detection techniques on AC side consists of fault detection technique for converter [15,16] and islanding detection technique [17,18].…”

Section: Introductionmentioning

confidence: 99%

An early fault detection approach in grid-connected photovoltaic (GCPV) system

Muhammad

Zainuddin

Jaaper

et al. 2020

IJEECS

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Faults in any components of PV system shall lead to performance degradation and if prolonged, it can leads to fire hazard. This paper presents an approach of early fault detection via acquired historical data sets of gridconnected PV (GCPV) systems. The approach is a developed algorithm comprises of failure detection on AC power by using Acceptance Ratio (AR) determination. Specifically, the implemented failure detection stage was based on the algorithm that detected differences between the actual and predicted AC power of PV system. Furthermore, the identified alarm of system failure was a decision stage which performed a process based on developed logic and decision trees. The results obtained by comparing two types of GCPV system (polycrystalline and monocrystalline silicon PV system), showed that the developed algorithm could perceive the early faults upon their occurrence. Finally, when applying AR to the PV systems, the faulty PV system demonstrated 93.38% of AR below 0.9, while the fault free PV system showed only 31.4% of AR below 0.9.

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Section: Introductionmentioning

confidence: 99%

An early fault detection approach in grid-connected photovoltaic (GCPV) system

Muhammad

Zainuddin

Jaaper

et al. 2020

IJEECS

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“…The results show that the system can identify more than 90% of fault conditions, even when noisy data are introduced. Learning methods [12], for monitoring system simplifies the operation and maintenance of the PV systems, even if it needs many measurement sensors, which identify shading and inverter failure. A technique [13] that used only few measurement sensors, which can categorize the energy losses in four different types: sustained zero efficiency faults, brief zero efficiency faults, shading, and nonzero efficiency non-shading faults.…”

Section: Introductionmentioning

confidence: 99%

Fault Identification and Islanding in DC Grid Connected PV System

Paul¹,

Mahalakshmi²,

Karuppasamypandiyan³

et al. 2016

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Nowadays, the DC distribution system has been suggested, as a replacement for the AC power distribution system with electric propulsion. This idea signifies a fresh approach of issuing energy for low-voltage installations. It can be used for any electrical application up to 20 MW and works at a nominal voltage of 1000 V DC. The DC distribution system is just an extension of the multiple DC links that previously available in all propulsion and thruster drives, which typically comprise more than 80% of the electrical power consumption on electric propulsion vessels. A fault detection and islanding scheme for DC grid connected PV system is presented in this paper. Unlike traditional ac distribution systems, protection has been challenging for dc systems. The goals of this paper are to classify and detect the fault in the PV system as well as DC grid and to isolate the faulted section so that the system keeps operating without disabling the entire system. The results show the measured values of power at PV panel and DC grid side under different fault condition, which indicates the type of fault that occurs in the system.

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“…PV monitoring system concepts are designed to detect, classify or locate faults when system behavior deviates from the expected [2][3][4][5][6][7][8]10]. To predict the expected PV performance at a given time, various PV system models using meteorological conditions inputs have been created.…”

Section: Introductionmentioning

confidence: 99%

“…Often these models calculate expected power using temperature and irradiance data gathered from sensors [2][3][4] or weather and satellite systems [5][6]. Different PV system models have been employed including PV circuit models [2,7], PV plantspecific fits [6], matter-element models [3], and expert systems with updating warning criteria [8]. The models in conjunction with current, voltage, or power measurements from the physical system are used to detect a number of fault conditions such as shading [2,[5][6][7][8] , inverter failure [5][6]8], snow cover [5][6], module failures or short circuiting [4,[7][8], and string-level malfunctions [2,[5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The models in conjunction with current, voltage, or power measurements from the physical system are used to detect a number of fault conditions such as shading [2,[5][6][7][8] , inverter failure [5][6]8], snow cover [5][6], module failures or short circuiting [4,[7][8], and string-level malfunctions [2,[5][6]. Learning algorithms [4,[8][9], Bayesian networks [10], and fuzzy logic [11][12] have also been used successfully to estimate PV output or perform fault diagnoses. Unfortunately, most of these systems are designed to detect catastrophic failures and do not monitor system degradation over time.…”

Section: Introductionmentioning

confidence: 99%

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Photovoltaic prognostics and heath management using learning algorithms

Riley

Johnson

2012

2012 38th IEEE Photovoltaic Specialists Conference

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A novel model-based prognostics and health management (PHM) system has been designed to monitor the health of a photovoltaic (PV) system, measure degradation, and indicate maintenance schedules. Current state-of-the-art PV monitoring systems require module and array topology details or extensive modeling of the PV system. We present a method using an artificial neural network (ANN) which eliminates the need for a priori information by teaching the algorithm "good" performance behavior based on the initial performance of the array. The PHM algorithm was tested on two PV systems under test at the Outdoor Test Facility (OTF) at the National Renewable Energy Laboratory (NREL). The PHM algorithm was trained using two months of AC power production. The model then predicted the output power of the system using irradiance, wind, and temperature data. Based on the deviation in measured AC power from the AC power predicted by the trained ANN model, system outages and other faults causing a reduction in power were detected. Had these been commercial installations, rather than research installations, an alert for maintenance could have been initiated. Further use of the PHM system may be able to indicate degradation, detect module or inverter failures, or detect excessive soiling.

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Diagnostic technology and an expert system for photovoltaic systems using the learning method

Cited by 69 publications

References 1 publication

An early fault detection approach in grid-connected photovoltaic (GCPV) system

An early fault detection approach in grid-connected photovoltaic (GCPV) system

Fault Identification and Islanding in DC Grid Connected PV System

Photovoltaic prognostics and heath management using learning algorithms

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