POWER MANAGEMENT STRATEGY BASED ON ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR AC MICROGRID

2021 ◽  
Vol 1 (10) ◽  
pp. 288-291
Author(s):  
Kartik Katiyar ◽  
Ayush Ayush ◽  
Deep Singh ◽  
Supriya Sharma
Keyword(s):  
Power Management ◽  
Management Strategy ◽  
Fuzzy Inference ◽  
Climatic Conditions ◽  
Electrical Networks ◽  
Renewable Energy Resources ◽  
Inference System ◽  
Power Management Strategy ◽  
Neuro Fuzzy ◽  
Micro Grids

Micro grids increase the efficiency and resiliency of electrical networks. However, the uncertain nature of renewable energy resources integrated into the MGs usually results in different problems. We are trying to achieve a stable system which can power microgrids and can be of commercial use. This would be achieved by simulation of the system under different climatic conditions. Keywords: Microgrid Optimization, ANFIS, Neural Network, Fuzzy Logic

scholarly journals Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for AC Microgrid

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 192087-192100
Author(s):  
Hesham M. Fekry ◽  
Azza Ahmed Eldesouky ◽  
Ahmed M. Kassem ◽  
Almoataz Y. Abdelaziz
Keyword(s):  
Power Management ◽  
Fuzzy Inference System ◽  
Management Strategy ◽  
Fuzzy Inference ◽  
Inference System ◽  
Power Management Strategy ◽  
Neuro Fuzzy ◽  
Ac Microgrid

scholarly journals Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques

2020 ◽  
Vol 10 (6) ◽  
pp. 5749
Author(s):  
Meriem Fikri ◽  
Omar Sabri ◽  
Bouchra Cheddadi
Keyword(s):  
Neural Network ◽  
Power Flow ◽  
Fuzzy Inference ◽  
Flow Analysis ◽  
Electrical Networks ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Voltage Phase ◽  
Intelligent Methods ◽  
Phase Angles

In the field of electrical network, it is necessary, under different conditions, to learn about the behavior of the system. Power Flow Analysis is the tool per excellent that allow as to make a deep study and define all quantities of each bus of the system. To determine power flow analysis there is a lot of methods, we have either numerical or intelligent techniques. Lately, researchers always work on finding intelligent methods that allow them to solve their complex problems. The goal of this article is to compare two intelligent methods that are capable of predicting quantities; Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System using real electrical networks. To do that we used few significant discrepancies. These methods are characterized by giving results in real time. To make this comparison successful, we implemented these two methods, to predict the voltage magnitudes and the voltage phase angles, on two Moroccan electrical networks. The results of the comparison show that the method of Adaptive Neuro-Fuzzy Inference System have more advantages than the method of Artificial Neural Network.

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.

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