Applications of ANFIS in Loss of Excitation Faults Detection in Hydro-Generators

2016 ◽  
Vol 5 (2) ◽  
pp. 63-79 ◽  
Author(s):  
Mohamed Salah El-Din Ahmed Abdel Aziz ◽  
Mohamed El Samahy ◽  
Mohamed A. Moustafa Hassan ◽  
Fahmy El Bendary
Keyword(s):  
Design Process ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Impedance Measurements ◽  
Line Voltage ◽  
Artificial Intelligent ◽  
Phase Current ◽  
Neuro Fuzzy ◽  
Generator Terminal

This article presents a new methodology for Loss of Excitation (LOE) faults detection in Hydro-generators using Adaptive Neuro Fuzzy Inference System. The proposed structure was trained by data from simulation of a 345kV system under different faults conditions and tested for various loading conditions. Details of the design process and the results of performance using the proposed technique are discussed in the article. Two different techniques are discussed in this article according to the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements (R and X) and the generator RMS Line to Line voltage and Phase current (Vtrms and Ia). The two proposed techniques results are compared with each other and are compared with the traditional distance relay response in addition to other techniques. The results show that the proposed Artificial Intelligent based technique is efficient in the Loss of Excitation faults (LOE) detection process. The obtained results are very promising.

Loss of Excitation Faults Detection in Hydro-Generators Using an Adaptive Neuro Fuzzy Inference System

2016 ◽  
Vol 1 (2) ◽  
pp. 300 ◽  
Author(s):  
Mohamed Salah El-Din Abdel Aziz ◽  
Mohamed Elsamahy ◽  
Mohamed Moustafa ◽  
Fahmy Bendary
Keyword(s):  
Design Process ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Impedance Measurements ◽  
New Approach ◽  
Line Voltage ◽  
Artificial Intelligent ◽  
Neuro Fuzzy ◽  
Generator Terminal

<em>This paper presents a new approach for Loss of Excitation (LOE) faults detection in Hydro-generators using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a 345kV system under various faults conditions and tested for different loading conditions. Details of the design process and the results of performance using the proposed technique are discussed in the paper. Two different techniques are discussed in this article according to the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements (R &amp; X) and the generator RMS Line to Line voltage and Phase current (Vtrms &amp; Ia). The two proposed techniques results are compared with each other and are compared with the traditional distance relay response in addition to other technique. The results show that the proposed Artificial Intelligent based technique is efficient in the Loss of Excitation faults (LOE) detection process and the obtained results are very promising</em>.

Loss of Excitation Protection of Medium Voltage Hydro Generators Using Adaptive Neuro Fuzzy Inference System

2018 ◽  
pp. 253-285
Author(s):  
Mohamed Salah El-Din Ahmed Abdel Aziz ◽  
Mohamed Ahmed Moustafa Hassan ◽  
Fahmy M. A. Bendary
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Positive Sequence ◽  
Inference System ◽  
Phase Current ◽  
Neuro Fuzzy ◽  
Sequence Components ◽  
Terminal Voltage ◽  
Generator Terminal ◽  
Excitation Conditions

This chapter presents a new method for loss of excitation (LOE) faults detection in hydro-generators using adaptive neuro fuzzy inference system (ANFIS). The investigations were done under a complete loss of excitation conditions, and a partial loss of excitation conditions in different generator loading conditions. In this chapter, four different techniques are discussed according to the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements (R and X) and the generator terminal voltage and phase current (Vtrms and Ia), the positive sequence components of the generator terminal voltage magnitude, phase current magnitude and angle (│V+ve│, │I+ve│ and ∟I+ve) in addition to the stator current 3rd harmonics components (magnitudes and angles). The proposed techniques' results are compared with each other and are compared with the conventional distance relay response in addition to other techniques. The promising obtained results show that the proposed technique is efficient.

Enhancement of Turbo-Generators Phase Backup Protection Using Adaptive Neuro Fuzzy Inference System

2017 ◽  
Vol 6 (1) ◽  
pp. 58-76 ◽  
Author(s):  
Mohamed Salah El-Din Ahmed Abdel Aziz ◽  
Mohamed Elsamahy ◽  
Mohamed A. Moustafa Hassan ◽  
Fahmy M. A. Bendary
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Research Work ◽  
Dynamic Simulations ◽  
Inference System ◽  
Backup Protection ◽  
Common System ◽  
Promising Tool ◽  
Neuro Fuzzy ◽  
Generator Terminal

This research work presents an advanced solution for the problem due to the current setting of Relay (21). This problem arises when it is set to provide thermal backup protection for the generator during two common system disturbances, namely a system fault and a sudden application of a large system load. These investigations are carried out using Adaptive Neuro Fuzzy Inference System (ANFIS). The results of the investigations have shown that the ANFIS has a promising tool when applied for turbo-generators phase backup protection. The effect of this tool varies according to the type of input data used for ANFIS testing and validation. The proposed method in this paper proposes the use of two different sets of inputs to the ANFIS, these inputs are the generator terminal impedance measurements (R and X) and the generator three phase terminal voltages and currents (V and I). The dynamic simulations of a test benchmark have been conducted using the PSCAD/EMTDC software. The results obtained from the ANFIS scheme are encouraging.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

PENGEMBANGAN SISTEM MANAJEMEN PENDUKUNG KEPUTUSAN PENILAIAN MUTU KEPEGAWAIAN DINAS PENDIDIKAN REMBANG MENGGUNAKAN ALGORITMA ANFIS (ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM)

2020 ◽  
Vol 12 (1) ◽  
pp. 6-17
Author(s):  
Angga debby frayudha ◽  
Aris Yulianto ◽  
Fatmawatul Qomariyah
Keyword(s):  
Fuzzy Inference System ◽  
Industry 4.0 ◽  
Fuzzy Inference ◽  
System Model ◽  
Inference System ◽  
Neuro Fuzzy

Di era revolusi industry 4.0 terdapat banyak sekali kemudahan yang diberikan teknologi kepada manusia. Tentu ini akan menjadi baik apabila manusia mampu memanfaatkan hal tersebut dengan baik pula. Namun disisi lain juga bisa mengakibatkan dampak negative terhadap manusia, misalnya dengan adanya internet bisa mengakibatkan manusia melakukan penipuan di media social. Selain itu dengan canggihnya teknologi dapat menjadikan manusia menjadi malas yang bisa berimbas menurunnya kualitas sumber daya manusia. Maka dari itu untuk menghadapi hal ini perlu menyiapkan pendidikan yang baik.Pendidikan akan berjalan baik apabila lembaga yang mengurusnya berkompeten dalam melakukan tugasnya .Penulis coba memberikan ide untuk memprediksi kinerja pegawai Dinas Pendidikan Kabupaten Rembang menggunakan mentode ANFIS (Adaptive Neuro Fuzzy Inference System) guna untuk membantu lembaga tersebut menyeleksi maupun menilai kinerja karyawan demi meningkatkan kualitas dari segi sumber daya manusia. ANFIS merupakan jaringan adaptif yang berbasis pada sistem kesimpulan fuzzy (fuzzy inference system). Model penilaian kinerja pegawai di Dinas Pendidikan Kabupaten Rembang dengan menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) menghasilkan penilaian  yang lebih baik dan akurat.  Hasil pengujian metode tersebut memiliki nilai akurasi 65%. Dengan metode ANFIS (Adaptive Neuro Fuzzy Inference System) dapat memprediksi kinerja karyawan sebagai salah satu pengambilan keputusan terhadap kinerja pegawai. Selain itu nantinya system penlaian kinerja pegawai akan lebih tertata dan efisien.

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