Improvement of power factor and voltage for renewable energy systems using PLC's new fuzzy module

Wang, Li; Liu, Kuo-Hua

doi:10.1109/apccas.2004.1413039

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Then, we can generate predictions to get a visual indication of the ability of this model through processing samples. Once the training finish, we can accept the generated data as a reference model output compared to the reference model input, as seen in Figure (6). Here, we examine the effect of the number of sampled data on the performance of our method.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, PLCs have shown their ability to achieve significant improvements in real time processes [13] [14], ranging from basic process control to complicated maintenance and data management applications. In [6], a new control module based on a PLC and fuzzy approaches has been proposed for hybrid wind power generation system to enhance its fullload power factor. Moreover, a production line controlled by a solenoid valve based on PLC is shown in [15] [16].…”

Section: E3mentioning

confidence: 99%

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A model-based machine learning to develop a PLC control system for Rumaila degassing stations

Al-Radhi¹,

Al-Kamil

Szakács

2020

Journal of Petroleum Research and Studies

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Degassing station breakdowns can be dangerous to the operator health and the environment. Programmable logic controllers (PLCs) are key modules of manufacturing control systems that are applied in the complex oil and gas units to reduce manpower and unnecessary faults. However, feeding a PLC with data is a difficult part due to the need of system log files which records all events that occur in the oil fields and provide visibility to a given environment. Moreover, most critical chemical processing plants and oil distributions are visualized and inspected by Supervisory Control and Data Acquisition Systems (SCADA). These systems have been focused on safety, and there are issues that they could be the target of worldwide terrorists. Along with the frequently rising internet-related attacks, there is indication that our degassing stations may similarly be susceptible; for that reason, it is essential to secure PLC and SCADA from undesired incidents. Recently, machine learning (ML) has been increasing interest in industrial systems to detect, identify, and store information. Therefore, we propose to apply an advance ML based on deep neural networks to the PLC system with the purpose of: 1) detecting anomalous or irregular PLC actions; 2) Optimizing the operation of systems and its facilities; 3) allowing the equipment to respond to changing and novel scenarios; 4) Making predictive maintenance possible. The SIMATIC S7-1214 CPU universal TIA platform was used as the main decision-making module. Experimental results demonstrate the effectiveness and utility of the proposed approach to process large amounts of data analytics and sensor measurements, allows it to spot potential problems and provide possible solutions.

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Section: Resultsmentioning

confidence: 99%

Section: E3mentioning

confidence: 99%

A model-based machine learning to develop a PLC control system for Rumaila degassing stations

Al-Radhi¹,

Al-Kamil

Szakács

2020

Journal of Petroleum Research and Studies

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show abstract

“…The use of PLC with power electronics in electric machines applications has been introduced in the manufacturing automation [1], [2]. It offers advantages such as lower voltage drop when turned on and the ability to control motors [3].…”

Section: Introductionmentioning

confidence: 99%

Intelligent and innovative monitoring of water treatment plant in large Gas Refinery

Firoozshahi

Kim

2010

2010 IEEE International Conference on Control Applications

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A PLC can communicate through its inputs and output signals with the plant. The capabilities of PLCs have developed over the years, with reliability, performance, and operational. Developing monitoring in Automation Control System is a major industrial concern since those systems are more and more complex and involved in many safety critical application fields. The Automation system is being widely used in Power, Oil and Gas, and Petrochemicals for monitoring, advance process control and various other functions. Automation system is operated either in single mode and or it is interfaced with other systems such as PLC, SCADA, and Etc for enhanced functionality. Water treatment plants have to provide good water quality and at the same time low operational costs. Owing to various physical, chemical and biological interactions water treatment processes are often difficult to handle and reliable predictions for the course of processes are difficult to obtain. This paper focuses on an innovative and intelligent monitoring system for Water Treatment Plant that has been successfully designed, implemented and commissioned for large Gas Refinery after case study in international standards.

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Innovative and intelligent industrial automation for water treatment plant in large Gas Refinery

Firoozshahi

2010

2010 2nd International Conference on Advanced Computer Control

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Improvement of power factor and voltage for renewable energy systems using PLC's new fuzzy module

Cited by 4 publications

References 6 publications

A model-based machine learning to develop a PLC control system for Rumaila degassing stations

A model-based machine learning to develop a PLC control system for Rumaila degassing stations

Intelligent and innovative monitoring of water treatment plant in large Gas Refinery

Innovative and intelligent industrial automation for water treatment plant in large Gas Refinery

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