Steam turbine lube oil system protections using SCADA &amp; PLC

Nagar, Astha; Mehta, Sandip

doi:10.1109/iccons.2017.8250695

Cited by 5 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Jika pembacaan variabel suhu dan getaran dilakukan secara manual maka operator tidak bisa melihat secara realtime terus menerus pada tiap turbin. Selain itu, operator tidak bisa secara langsung melakukan pengamanan seperti mematikan turbin ketika suhu dan getaran melebihi standar sehingga membuat monitoring secara manual tidak efektif (human error) [10]. Ketika turbin overload dan operator sedang tidak standby untuk mengecek turbin maka semakin lama getaran pada turbin akan semakin membesar dan membuat turbin mengalami keretakan di beberapa bagian [11].…”

Section: Pendahuluanunclassified

Kinerja Sistem Monitoring Suhu dan Getaran Pada Turbin Uap Berbasis HMI-PLC

Situmorang,

Handoyo,

Dwiyaniti

et al. 2023

Electrices

View full text Add to dashboard Cite

Pengukuran suhu dan getaran pada turbin uap sangat penting karena variabel tersebut menentukan lifetime turbin uap. Jika suhu dan getaran diluar batas aman, turbin uap akan mengalami kerusakan seperti terbakar dan komponen rusak/retak. Hal ini akan membahayakan mesin, manusia, dan lingkungan sekitar. Untuk itu, diperlukan sistem kontrol dan monitoring suhu dan getaran pada turbin uap berbasis PLC dan HMI dengan integrasi sensor. Data pengukuran suhu dan getaran dapat dilihat pada layar HMI dan di update secara realtime setiap detik. Dengan adanya sistem ini diharapkan kinerja dan keselamatan turbin meningkat dan efisien dalam kontrol dan monitoring suhu dan getaran pada turbin uap. Hasil dari sistem kontrol dan monitoring suhu dan getaran dapat berfungsi dengan baik. Hal ini ditandai dengan nilai suhu turbin normal sekitar 100 dan nilai getaran normal sekitar 1,1 mm/s.

show abstract

Section: Pendahuluanunclassified

Kinerja Sistem Monitoring Suhu dan Getaran Pada Turbin Uap Berbasis HMI-PLC

Situmorang,

Handoyo,

Dwiyaniti

et al. 2023

Electrices

View full text Add to dashboard Cite

show abstract

“…An essential requirement is a reliable oil supply over the whole operating range. The oil pumps used for that purpose provide the lube oil [40]. The oil pumps are directly driven by an electric motor (AC supply).…”

Section: Turbine Motor Failure Analysismentioning

confidence: 99%

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

2021

View full text Add to dashboard Cite

Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.

show abstract

“…Sedangkan dengan menggunakan Persamaan (5) untuk menghitung sudut beban (Belly et al, 2010). Power Meter PM820 digunakan untuk membaca keseluruhan daya listrik dan besaran lainnya saat sedang digunakan dan data tersebut dikirim ke PLC, untuk ditampilkan pada SCADA (Nagar & Mehta, 2018). PLC berfungsi untuk mengaktifkan atau mematikan sistem, mengumpulkan data dari PM820, memprogram keseluruhan sistem, dan juga memberikan perintah tindakan saat terjadi load shedding.…”

Section: Perancangan Metode Sistem Load Sheddingunclassified

Simulasi Sistem Otomasi Load Shedding menggunakan Prediksi Beban

2019

View full text Add to dashboard Cite

ABSTRAKSimulator penggunaan energi listrik sangat membantu dalam perencanaan pasokan listrik secara terus menerus atau perlu pemadaman bila terjadi gangguan. Agar kinerja pembangkit dapat kembali normal, bertahap, dan terencana akibat mengalami gangguan, maka digunakan metoda Load Shedding. Sistem otomasi dibutuhkan untuk merealisasikan metoda Load Shedding, dan melalui simulator dapat mengurangi resiko terhadap kerusakan bila langsung diimplementasikan. PLC digunakan untuk memprediksi beban saat load shedding, sedangkan SCADA digunakan untuk menampilkan prioritas dan status beban. Load shedding 1 adalah tindakan pelepasan beban bila pada salah satu genset mengalami gangguan, sedangkan Load shedding 2 bila pada kedua genset mengalami gangguan. Simulasi sistem load shedding 1 dan 2 berhasil dilakukan pelepasan beban secara otomatis. Setelah pelepasan beban, kapasitas beban yang ditanggung genset sesuai dengan prediksi dari PLC. Terdapat perbedaan hasil antara daya yang diprediksi secara perhitungan dan daya terukur, kemungkinan disebabkan beban masih belum steady saat data diambil.Kata kunci: Otomasi, Simulator, Sistem Load shedding, SCADA, PLC ABSTRACTSimulator of the use of electrical energy is very helpful in planning electricity supply continuously or needs to be suppressed if a disturbance occurs. In order for the generator performance to return to normal, gradual, and planned due to interference, the Load Shedding method is used. Automation systems are needed to realize the Load Shedding method, and through simulators can reduce the risk of damage if implemented immediately. PLC was used to predict load during load shedding, while SCADA was used to display priority and load status. Load shedding 1 was a load release action if one of the generator sets was disrupted, while Load shedding 2 if in both gensets were disrupted. Load system simulation of shedding 1 and 2 was successfully released by load automatically. After the load was released, the load capacity borne by the generator was in accordance with the predictions of the PLC., the load capacity borne by the generator was in accordance with the predictions of the PLC. There was a difference in results between the predicted power and measured power, possibly because the load was stillnot steady when the data was taken. Keywords: Automation, Simulator, Load Shedding System, SCADA, PLC

show abstract

Steam turbine lube oil system protections using SCADA & PLC

Cited by 5 publications

References 1 publication

Kinerja Sistem Monitoring Suhu dan Getaran Pada Turbin Uap Berbasis HMI-PLC

Kinerja Sistem Monitoring Suhu dan Getaran Pada Turbin Uap Berbasis HMI-PLC

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Simulasi Sistem Otomasi Load Shedding menggunakan Prediksi Beban

Contact Info

Product

Resources

About