A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier using Data Reduction and Interval-Valued Representation

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.

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Data reduction and clustering techniques for fault detection and diagnosis in automotives

2010 IEEE International Conference on Automation Science and Engineering ◽

10.1109/coase.2010.5584324 ◽

2010 ◽

Cited By ~ 5

Author(s):

Aurobinda Routray ◽

Aparna Rajaguru ◽

Satnam Singh

Keyword(s):

Fault Detection ◽

Data Reduction ◽

Fault Detection And Diagnosis ◽

Clustering Techniques ◽

Detection And Diagnosis

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Fault detection and diagnosis in air handling using data-driven methods

Journal of Building Engineering ◽

10.1016/j.jobe.2020.101388 ◽

2020 ◽

Vol 31 ◽

pp. 101388 ◽

Cited By ~ 1

Author(s):

Atena Montazeri ◽

Seyed Mohamad Kargar

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Data Driven ◽

Detection And Diagnosis ◽

Using Data

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Fault detection and diagnosis for wind turbines using data-driven approach

2012 7th Colombian Computing Congress (CCC) ◽

10.1109/colombiancc.2012.6398018 ◽

2012 ◽

Cited By ~ 2

Author(s):

Ruben Francisco Manrique ◽

Fabian Andres Giraldo ◽

Jorge Sofrony Esmeral

Keyword(s):

Fault Detection ◽

Wind Turbines ◽

Fault Detection And Diagnosis ◽

Data Driven ◽

Data Driven Approach ◽

Detection And Diagnosis ◽

Using Data

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Review of Artificial Intelligence-Based Failure Detection and Diagnosis Methods for Solar Photovoltaic Systems

Machines ◽

10.3390/machines9120328 ◽

2021 ◽

Vol 9 (12) ◽

pp. 328

Author(s):

Ahmad Abubakar ◽

Carlos Frederico Meschini Almeida ◽

Matheus Gemignani

Keyword(s):

Artificial Intelligence ◽

Fault Detection ◽

Real Life ◽

Failure Detection ◽

Fault Detection And Diagnosis ◽

Solar Pv ◽

Pv Systems ◽

Operation And Maintenance ◽

Detection And Diagnosis

In recent years, the overwhelming growth of solar photovoltaics (PV) energy generation as an alternative to conventional fossil fuel generation has encouraged the search for efficient and more reliable operation and maintenance practices, since PV systems require constant maintenance for consistent generation efficiency. One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems. It explores various fault types that are common in PV systems and various AI-based fault detection and diagnosis techniques proposed in the literature. Of note, there are currently fewer literatures in this area of PV application as compared to the other areas. This is due to the fact that the topic has just recently been explored, as evident in the oldest paper we could obtain, which dates back to only about 15 years. Furthermore, the study outlines the role of AI in PV operation and maintenance, and the main contributions of the reviewed literatures.

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