scholarly journals Fuzzy mamdani logic inference model in the loading of distribution substation transformer SCADA system

2021 ◽  
Vol 10 (2) ◽  
pp. 298
Author(s):  
Rahma Farah Ningrum ◽  
Riki Ruli A. Siregar ◽  
Darma Rusjdi
Keyword(s):  
Fuzzy Logic ◽  
Service Life ◽  
Data Acquisition ◽  
Logic Simulation ◽  
Life Duration ◽  
Scada System ◽  
Input Variables ◽  
Logic Inference ◽  
Monitoring Application ◽  
Data Period

<span id="docs-internal-guid-152e332e-7fff-7073-a541-1d420decb47b"><span>The research objective of supervisory control and data acquisition (SCADA), with fuzzy Mamdani logic simulation on the loading section of distribution transformer substations. Data acquisition is available when saving SAIFI SAIDI data and storing the results of monitoring equipment. The method used is Mamdani fuzzy logic, there are two input variables, namely current and voltage devices. The membership function in Mamdani fuzzy logic has been created based on the input current and voltage variables. Currently: parameter {0, 600} low is created {0, 350, 450, 600}, normal {400-650} parameter is created {400, 500, 550, 650}, parameter high {≥600} is created {600, 650, 750, 1000}, when determining the voltage: low {≤10.5} parameters {0 4 7 10.5}, normal {9-14} parameters {9, 10, 13, 14} and high {≥13} - parameters {13, 14, 15, 16}. Based on the results of the Mamdani logic rule test on the output current containing a transformer and a voltage sensor, the results obtained are IF (normal current; (630) AND voltage (high); (13.2) (high load transformer). The components in the simulation tool include miniature substations made with the 1A travel substation model, 3A substation as the main substation, the relay as distribution substation as the monitoring application. Telestatus and Telecontrol use a microcontroller. Initial scenario. After substation is resumed, data is stored after downtime, service life, duration, and data period. Initial scenario After substation is resumed, data is stored after downtime, service life, duration, and data period.</span></span>

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

Prediction of State of Charge (SOC) of Battery Electric Vehicle

2021 ◽  
Vol 104 ◽  
pp. 65-71
Author(s):  
Illa Rizianiza ◽  
Dian Mart Shoodiqin
Keyword(s):  
Fuzzy Logic ◽  
Maximum Energy ◽  
State Of Charge ◽  
Battery Management ◽  
Charge Condition ◽  
Available Energy ◽  
Energy Needs ◽  
Fuzzy Logic Method ◽  
Input Variables ◽  
Charging And Discharging Processes

Batteries have an important thing in development of energy needs. A good performance battery, will support the device it supports. The energy that can save a battery is limited, so the battery will increase its charge and discharge cycles. Incorrect charging and discharging processes can cause battery performance to decrease. Therefore battery management is needed so that the battery can reach the maximum. One aspect of battery management is setting the state which is the ratio of available energy capacitance to maximum energy capacity. One method for estimating load states is the fuzzy logic method, namely by assessing the input and output systems of prediction. Predictor of State of Charge use Mamdani Fuzzy Logic that have temperature and voltage as input variables and State of Charge as output variable. A result of prediction State of Charge battery is represented by the number of Root Mean Square Error. Battery in charge condition has 2.7 for RMSE and level of accuracy 81.5%. Whereas Battery in discharge condition has RMSE 1.5 and level of accuracy 84.7%.

scholarly journals Penerapan metode fuzzy sugeno untuk prediksi persediaan bahan baku

2019 ◽  
Vol 9 (2) ◽  
pp. 12-20
Author(s):  
Julio Warmansyah ◽  
Dida Hilpiah
Keyword(s):  
Fuzzy Logic ◽  
Raw Materials ◽  
Mean Absolute Percentage Error ◽  
Raw Material ◽  
Percentage Error ◽  
Human Power ◽  
Absolute Percentage Error ◽  
Input Variables ◽  
A Company

 PT. Cahaya Boxindo Prasetya is a company engaged in the manufacture of carton boxes or boxes. The company's activities also include cutting and printing services using machinery and human power. The problem faced in this company is the difficulty of predicting the amount of inventory of raw materials that will be  included in the production. The remaining raw materials for production will be used as the final stock to get the minimum, the goal is to reduce excess stock Overcoming this problem, fuzzy logic is used to predict raw material inventories by focusing on the final stock. In this study using Fuzzy Sugeno, with three input variables, namely: initial inventory, purchase, production, while the output is the final stock. Determination of prediction results using defuzzification using the average concept of MAPE (Mean Absolute Percentage Error). The results obtained, using the Fuzzy Sugeno method can predict the inventory of raw materials with a MAPE value of 38%. 

scholarly journals Fuzzy logic simulation of oat yield after using hydrogel and nitrogen biopolymer management

2020 ◽  
pp. 1319-1327
Author(s):  
Osmar Bruneslau Scremin ◽  
José Antonio Gonzalez da Silva ◽  
Ivan Ricardo Carvalho ◽  
Ângela Teresinha Woschinski De Mamann ◽  
Odenis Alessi ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Air Temperature ◽  
Biomass Yield ◽  
New Technologies ◽  
Block Design ◽  
Fuzzy Model ◽  
Logic Simulation ◽  
Efficient Tool ◽  
Randomized Block Design ◽  
Maximum Air Temperature

The fuzzy logic is an efficient tool for simulation and validation of new technologies in agriculture. The objective of the study is to adapt the fuzzy logic model for simulation of biomass and oat grain yield by nitrogen involving the nonlinearity of the maximum air temperature in the conditions of use of the biopolymer hydrogel, considering high succession systems and low release of residual N. The study was conducted in 2014 and 2015, in a randomized block design with four replicates in a 5 x 5 factorial. Five hydrogel doses (0, 30, 60, 90 and 120 kg ha-1) were added in the groove next to the seed; and 5 doses of N-fertilizer (0, 30, 60, 90 and 120 kg ha-1) applied at the fourth expanded leaf stage, respectively. The cultivar was URS Corona. The pertinence functions and the linguistic values established in the input and output variables to simulate the biomass yield and oat grains in the succession systems are adequate observed productivity. The fuzzy model makes it possible to estimate the biomass and oat grains productivity efficiently under the conditions of use of the hydrogel as a function of the nitrogen doses and maximum air temperature, adding to the existing models of simulation.

scholarly journals Pemetaan Tingkat Kerawanan Longsor Berdasarkan Curah Hujan dan Geologi Menggunakan Metode Fuzzy Logic Di Kecamatan Leupung Kabupaten Aceh Besar

2018 ◽  
Vol 3 (2) ◽  
pp. 434-441
Author(s):  
Rasyid Alkhoir Lubis ◽  
Muhammad Rusdi ◽  
Hairul Basri
Keyword(s):  
Fuzzy Logic ◽  
Data Analysis ◽  
Analysis Data ◽  
Fuzzy Logic Method ◽  
Input Variables ◽  
Logic Method

Abstrak. Penelitian ini bertujuan untuk mengetahui tingkat kerawanan longsor di Kecamatan Leupung Kabupaten Aceh Besar. Penelitian ini dilakukan menggunakan SIG dengan Metode Fuzzy Logic. Curah Hujan dan Geologi sebagai variabel input dan tingkat kerawanan longsor sebagai variabel output metode fuzzy logic. Beberapa tahapan yang dilakukan dalam metode ini antara lain : fuzzyfication, inferensi dan defuzzyfication. Secara umum, tahapan penelitian persiapan, pra analisis data, analisis data dan output.. Penelitian ini dilakukan karena Kecamatan Leupung berbukit, berlereng, tersusun dari material sedimen termasuk batuan pegunungan dan memiliki curah hujan yang lebih tinggi dibandingkan dengan kecamatan lainnya di lingkup Kabupaten Aceh Besar.Hasil penelitian memperoleh hasil bahwa Kecamatan Leupung didominasi dengan tingkat kerawanan longsor kategori rendah dan sedang. Tingkat kerawanan longsor rendah seluas 16.486,01 ha (97,97 %) dan tingkat kerawanan longsor sedang seluas 342,37 ha (2,03 %). Kedua faktor yaitu curah hujan dan geologi saling mempengaruhi sehingga membedakan nilai defuzzyfication serta kelas kerawanan longsor. Abstract. This study aims to determine the level of landslide vulnerability in Leupung District, Aceh Besar District. This research was conducted using GIS with Fuzzy Logic Method. Rainfall and Geology as input variables and landslide vulnerability as output variables fuzzy logic method. Some of the steps performed in this method include: fuzzyfication, inference and defuzzyfication. In general, the stages of preparatory research, pre-data analysis, data analysis and output. This research was conducted because the hilly Leupung District, the slopes, composed of sedimentary materials including mountainous rocks and had higher rainfall compared to other sub-districts in Aceh Besar .The result of this research is that Leupung District is dominated by low and medium category avalanche vulnerability. Low landslide vulnerability of 16,486.01 ha (97.97%) and moderate landslide vulnerability of 342.37 ha (2.03%). Both factors are rainfall and geology influence each other so as to distinguish the defuzzyfication value and the class of landslide vulnerability.

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