Sensorless fault detection method for photovoltaic systems through mapping the inherent characteristics of PV plant site: Simple and practical

Yurtseven, Kaan; Karatepe, Engin; Deniz, Esref

doi:10.1016/j.solener.2021.01.011

Cited by 28 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Life cycle management framework of medical device equipment is depicted in Figure 2. The prophase management is mainly composed of the model selection, purchase and install [8]. Effective management of this section can avoid the use of poor quality medical devices.…”

Section: Whole Life Cycle Management Modulementioning

confidence: 99%

See 1 more Smart Citation

Design of Fine Life Cycle Prediction System for Failure of Medical Equipment

Haowei

Chen

Yang

2023

JAIT

View full text Add to dashboard Cite

The inquiry process of traditional medical equipment maintenance management is complicated, which seriously affects the efficiency and accuracy of medical equipment maintenance management, and causes a lot of waste of manpower and materials. In order to accurately predict the failure of medical equipment, an accurate prediction system for failure life cycle of medical equipment was designed. The system is divided into four modules: the whole life cycle management module constructs the life cycle data set of medical devices from the three parts of the management in the early stage, the middle and the later stage; the status detection module monitors the main operation data of the medical device components through the normal value of the relevant sensitive data in the whole life cycle management module; the main function of the fault diagnosis module is based on the medical equipment whole life cycle management module. The operation data of equipment is diagnosed by inference machine; the fault prediction module builds a fine prediction system based on least square support vector machine algorithm, and uses AFS ABC algorithm to optimize the model to obtain the optimal model with the regularized parameters and width parameters, and the optimal model is used to predict the medical equipment failure. In order to verify the effectiveness of the design system, comparative experiments are designed to verify. The results show that the designed system can accurately predict the failure of electrocardiogram diagnostic instrument and incubator, and has high support and reliability. Compared with the comparison system, the prediction error of the design system is the smallest and the program running time is the shortest. Therefore, the design system can accurately predict the different failure types and causes of medical devices.

show abstract

Section: Whole Life Cycle Management Modulementioning

confidence: 99%

“…represents the domain nectar source of the dimension update of and formula (8) represents the behavior of leading and following bees to implement domain nectar source search:…”

Section: Optimization Algorithmmentioning

confidence: 99%

Design of Fine Life Cycle Prediction System for Failure of Medical Equipment

Haowei

Chen

Yang

2023

JAIT

View full text Add to dashboard Cite

show abstract

“…In [15], a fault detection method was developed for large-scale grid-tied PV farms, which requires irradiance, temperature and generated power as the input. Yurtseven et al presented a sensorless method in [16] for fault detection in solar PV farms, using the voltage and current of all inverters in a PV farm.…”

Section: Introductionmentioning

confidence: 99%

“…This is not only an expensive solution for a residential rooftop PV system but also needs access to all PV systems dispersed around the country. The other group of studies, which proposed sensorless fault detection methods, e.g., [9], [12], [14], [16], are also dependent on specific user-defined thresholds as they typically need a "normal" PV generation as the baseline to determine faults by comparison. Furthermore, the calculations required in the algorithms of these studies, e.g., [9], [12], [16], are usually computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

“…The other group of studies, which proposed sensorless fault detection methods, e.g., [9], [12], [14], [16], are also dependent on specific user-defined thresholds as they typically need a "normal" PV generation as the baseline to determine faults by comparison. Furthermore, the calculations required in the algorithms of these studies, e.g., [9], [12], [16], are usually computationally intensive. This means while these methods can be a practical solution for solar PV farms, implementing them for all residential rooftop PV systems demands massive processing power.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy

Karimi-Arpanahi¹,

Pourmousavi²

2023

Preprint

View full text Add to dashboard Cite

The number of rooftop photovoltaic (PV) systems has significantly increased in recent years around the globe, including in Australia. This trend is anticipated to continue in the next few years. Given their high share of generation in power systems, detecting malfunctions and abnormalities in rooftop PV systems is essential for ensuring their high efficiency and safety. In this paper, we present a novel anomaly detection method for a large number of rooftop PV systems installed in a region using big data and a time series complexity measure called weighted permutation entropy (WPE). This efficient method only uses the historical PV generation data in a given region to identify anomalous PV systems and requires no new sensor or smart device. Using a real-world PV generation dataset, we discuss how the hyperparameters of WPE should be tuned for the purpose. The proposed PV anomaly detection method is then tested on rooftop PV generation data from over 100 South Australian households. The results demonstrate that anomalous systems detected by our method have indeed encountered problems and require a close inspection. The detection and resolution of potential faults would result in better rooftop PV systems, longer lifetimes, and higher returns on investment.

show abstract