Supervisory Control and Automatic Failure Detection in Grid-Connected Photovoltaic Systems

Polo, Fernando Agustín Olivencia; Alonso, José María Quiroga; García, Gonzalo Cerruela

doi:10.1007/978-3-642-13022-9_46

Cited by 11 publications

(2 citation statements)

References 8 publications

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“…He studied the efficiency of the start-up process in coal power plants while Tinham (2008) evaluated the temperature in chemical reactors for predictive maintenance with a SCADA system. In addition to research in hydropower plants, coal power plants, and nuclear power plants, Olivencia Polo et al (2010) conducted research to predict the failure of a photovoltaic system by comparing real production on SCADA data with theoretical production results. Sánchez & Couso (2012) conducted research about wind power plants about the use of SSA (singular spectral analysis) for predictive maintenance of windmills in Spanish wind turbine fields.…”

Section: Electricity Gas Steam and Air Conditioning Supplymentioning

confidence: 99%

Predictive Maintenance in SCADA-Based Industries: A literature review

Suryadarma

2020

IJIEEM

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The purpose of this paper is to mapping and review what has been done on the topic of research on predictive maintenance in SCADA (Supervisory Control and Data Acquisition) based industries. In the research area of predictive maintenance, various methods for predicting damage or time to failure of a machine have been proposed and applied in various industries. This paper systematically categorizes predictive maintenance in SCADA-based industries research based on industry classifications according to ISIC (International Standard Industrial Classification of All Economic Activities). Furthermore, the research scope is explored its connection to the topics of Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Supervisory Control and Data Acquisition (SCADA). It is found that 81.5% of the research was conducted on the electricity, gas, steam, and air conditioning supply industries, 11.1% of research was conducted on the mining and quarrying industry, and 7.4% of the research conducted in the manufacturing industry. It is also found that 85.2% of studies used AI and ML, 18.5% of the studies used IoT, and 18.5% of research used AI/ML and IoT technology together.

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Section: Electricity Gas Steam and Air Conditioning Supplymentioning

confidence: 99%

Predictive Maintenance in SCADA-Based Industries: A literature review

Suryadarma

2020

IJIEEM

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“…In many cases in order to detect anomalies either some specific expert knowledge is required to analyze the behaviour of the solar plant, or some knowledge is desired to be automatically extracted from its operation, or both. In order to cover theses aspects several Artificial Intelligent techniques have been tested with promising results [8], [9] and [10]. Along this research line, this paper presents a prototype of an intelligent system designed to work in real-time whose main contribution in this field is an architecture that integrates in a unique framework the capability of data acquisition, anomaly detection and diagnosis of an unattended solar power plant.…”

Section: Introductionmentioning

confidence: 99%

Intelligent system for a remote diagnosis of a photovoltaic solar power plant

Sanz-Bobi

Roque

Marcos

et al. 2012

J. Phys.: Conf. Ser.

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Abstract. Usually small and mid-sized photovoltaic solar power plants are located in rural areas and typically they operate unattended. Some technicians are in charge of the supervision of these plants and, if an alarm is automatically issued, they try to investigate the problem and correct it. Sometimes these anomalies are detected some hours or days after they begin. Also the analysis of the causes once the anomaly is detected can take some additional time. All these factors motivated the development of a methodology able to perform continuous and automatic monitoring of the basic parameters of a photovoltaic solar power plant in order to detect anomalies as soon as possible, to diagnose their causes, and to immediately inform the personnel in charge of the plant. The methodology proposed starts from the study of the most significant failure modes of a photovoltaic plant through a FMEA and using this information, its typical performance is characterized by the creation of its normal behaviour models. They are used to detect the presence of a failure in an incipient or current form. Once an anomaly is detected, an automatic and intelligent diagnosis process is started in order to investigate the possible causes. The paper will describe the main features of a software tool able to detect anomalies and to diagnose them in a photovoltaic solar power plant.

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