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Arif Afandi
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FRONTIER ENERGY SYSTEM AND POWER ENGINEERING Electrical Engineering, Universitas Negeri Malang Jl. Semarang 5, Malang 65145, Jawa Timur, Indonesia
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Kota malang,
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INDONESIA
Frontier Energy System and Power Engineering
Published by Universitas Negeri Malang
ISSN : -     EISSN : 27209598     DOI : http://dx.doi.org/10.17977/um049v2i1p1-6
Core Subject : Science, Engineering,
Control & Systems Engineering Electrical & Electronics Engineering Energy
Frontier Energy System and Power Engineering, FESPE, is an International Journal registered at e-ISSN: 2720-9598. FESPE is officially published by Electrical Engineering, State University of Malang, Indonesia. This journal is the Peer Review and Open Access International Journal, published twice a year in January and July relating to the broad scope of the Energy System and Power Engineering. FESPE provides a flagship forum for academics, researchers, industry professionals, engineers, consultants, managers, educators, and policymakers who work in engineering to contribute and disseminate new innovative works in energy systems, power engineering, and other related themes.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 39 Documents
 1   2   3   4 
Optimization of Double Exponential Smoothing Using Particle Swarm Optimization Algorithm in Electricity Load Vivi Aida Fitria; Arif Nur Afandi; Aripriharta Aripriharta; Danang Arbian Sulistyo
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p58-64

Abstract

Electricity load forecasting plays a critical role in ensuring the efficient allocation of resources, maintenance optimization, and uninterrupted power supply. The double exponential smoothing (DES) method is widely used in forecasting time series data due to its adaptability and robustness, particularly in handling linear trends without seasonal patterns. However, determining the optimal value of the alpha parameter in DES is crucial for accurate forecasting results. This study proposes the use of the Particle Swarm Optimization (PSO) algorithm to optimize the alpha parameter in DES for electricity load forecasting. PSO is a computational method that iteratively improves candidate solutions by moving particles in the search space based on simple mathematical formulas. By optimizing the alpha parameter using PSO, we aim to enhance the accuracy of short-term electricity load forecasts. Our results demonstrate that the PSO-optimized DES approach achieves a Mean Absolute Percentage Error (MAPE) of 2.89% and an accuracy of 97.11%, indicating significant improvements in forecasting performance. While the PSO algorithm provides promising results, future research may explore the application of other metaheuristic algorithms, such as the whale or orca algorithms, to further enhance the optimization of DES parameters for electricity load forecasting. This study contributes to the advancement of forecasting techniques in the power industry, facilitating more efficient power generation and distribution planning.
Implementation of Backpropagation Artificial Neural Network for Electricity Load Forecasting in Jember District Eko Pambagyo Setyobudi; Ilham Ari Elbaith Zaeni
Frontier Energy System and Power Engineering Vol 5, No 1 (2023): January
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i1p26-31

Abstract

The increase in population and various kinds of human activities in the world has made it possible for changes to increase the need for electrical power with demand that is not the same at any time. Based on this description, this research will propose research on the theme of electricity load forecasting as a preventive measure to determine future electricity load needs. Research was assisted using MATLAB data processing software to process research data. Three forecasting models were carried out, namely day, night and day-night conditions. From these three forecasting models, parameters such as epoch, number of input layers, number of hidden layers, activation function, and etc. The data is divided into two parts, training data and test data with a ratio of 70: 30. Test results using the backpropagation artificial neural network method show the highest MSE values for the three forecasting models, day, night, and day-night, are, 0.0039, 0.0041, and 0.002 while the lowest MSE values were in the three models are, 6.77E-04, 0.001, and 0.0011.
The Enhanced Self-Lift Luo Converter with Qhbm for Maximum Power Extraction on PV Charging Station
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p65-80

Abstract

This journal presents the development of an innovative algorithm for Maximum Power Point Tracking (MPPT) utilizing the Enhanced Self Lift Luo Converter (ESLLC) developed through Queen Honey Bee Migration (QHBM). The QHBM used for MPPT employs a queen-based decision-making approach to determine the optimal point on solar panels. The queen continuously searches for the Maximum Power Point (MPP), and upon locating it, ceases tracking and starts building a nest. Once the nest is established, the queen resumes the search for MPP. The testing simulation evaluates computing speed, durability, and MPP's margin errors. MATLAB/Simulink is employed for verification. The simulation results demonstrate that the QHBM surpasses other algorithms like PSO, P&O, and FLC in terms of computing speed, durability, and MPP margin errors. The QHBM-based MPPT exhibits superior responsiveness to changes in irradiation and temperature compared to alternative algorithms. This proposed algorithm effectively adapts to varying environmental conditions that influence irradiation and temperature changes. Consequently, the suggested algorithm holds significant promise for practical implementation in dynamic environmental settings.
Implementation of Mamdani Fuzzy Logic on PLN Electricity Sales in East Java Muhammad Hafiizh; Muchamad Wahyu Prasetyo; Aripriharta Aripriharta; Anik Nur Handayani
Frontier Energy System and Power Engineering Vol 5, No 1 (2023): January
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i1p32-40

Abstract

This research aims to provide innovation in the development of a smarter and more adaptive electricity distribution system, able to face the challenges that arise in the digital era. The results of this research can be a guide in improving the efficiency and reliability of electricity sales. The experimental method is applied by implementing mamdani fuzzy logic on PLN electricity sales data in East Java. From the results of the application of mamdani fuzzy by comparing the prediction results with the original data, the Mean Absolute Error (MAE) result is 0.17% and the Root Mean Squared Error (RMSE) value is 0.21%. So it can be concluded that fuzzy logic mamdani method can be used in predicting electricity sold and provide valuable guidance for PLN in improving the efficiency and reliability of electricity sales in the East Java region in a sustainable manner.
Temperature Monitoring Efficiency With Internet of Things-Based Temperature Sensor Variations IGN Sangka; IGS Widharma; IN Sunaya; IM Sajayasa; IGP Arka
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p81-87

Abstract

This research was conducted in the lab. Automation to measure temperature by several temperature sensors monitored through Internet of Things (IoT) technology. The method used is a quantitative method with experiments. The temperature monitoring system with a variety of temperature sensors based on the Internet of Things (IoT) can work well. The difference in temperature measurements taken through the IoT simulation within 1 hour between the sensors used has a small value, an average of 0.07 Celsius. This means that the precession level of each sensor is relatively good. The need for calibration on the sensor in order to obtain the best measurement results. Noting the results of the above analysis, it is recommended that users conduct a more in-depth study to obtain a more appropriate (actual) value. This analysis can also be taken into consideration for users to develop IoT-based monitoring systems in the future.
Analysis of Efforts to Overcome Voltage Drops by Installing Substations in Low Voltage Networks with ETAP Simulation IG Suputra Widharma; IN Sunaya; IGN Sangka; IM Sajayasa; IKG Sri Budarsa
Frontier Energy System and Power Engineering Vol 5, No 1 (2023): January
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i1p01-08

Abstract

Electric power is an energy of secondary that generated, transmitted and distributed to consumers and used to many activities. Electrical power system is electrical power installation system is consists of generation system, transmitting system, and distribution system that integration and function to supply electric power consumption. For this purpose, both of quantity and quality also continuity in PT PLN wished can be provides and distributes electric power to consumers and all of people. One of the main materials of the distribution network to distribute electric power to customers is the distribution substation in which there is a transformer. Transformer as a means of distribution of electrical energy is susceptible to interference. Distribution substation SM 0075 Feeder Bajera is one of the distribution substations that experienced a voltage drop at the end of the JTR in the C direction. So, to fix the problems that occurred, PT. PLN has added a substation. With the addition of substations, it is necessary to pay attention to the power losses that arise, not to cause greater power losses. Based on the results of the calculations at the distribution substation SM0075 Bajera feeder before and after the addition of the insertion substation, it was found that the voltage drop at the end of the JTR direction C at the SM0075 distribution substation was 22.638% in the R phase, 15.745% in the S phase and 14.005% in the T phase. the result of the addition of the substation is a smaller power loss with the difference before and after that is equal to 2739.986472 Watt. then viewed from the quality of the voltage and environmental conditions, the C18D6A1D3 pole is better for insertion substations than the C18D6A1D2 pole based on the Etap 12.6 simulation.
Dynamic System Modeling of Concentrated Electrical Energy Provision with Reducing CO2 Emissions in East Nusa Tenggara Rusman Sinaga; Frans Mangngi; Purnawarman Ginting
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p41-49

Abstract

Electric Power Generation (EPG) sourced from Fossil Energy (FE) has triggered an increase in CO_2 emissions in the atmosphere resulting in global warming which has a systemic impact on climate change. Whatever form of mechanism to reduce the increase in earth's temperature must be implemented. One way is to use a Solar Water Pump (SWP). This research aims to determine the use of EPG in East Nusa Tenggara (ENT) and find the amount of CO_2 emissions that can be reduced using SWP. This research uses dynamic modeling methods. The research results show that the production of electrical energy sourced from FE until 2030 is 2.270.656 MWh (87.77%), while the production of electrical energy sourced from RE is 316.441 MWh (12.23% ). EPG sourced from FE in ENT results in CO_2 emissions at 22.603.641 tons. According to survey results, conversion of FEPWP to SWP can reduce CO_2 emissions at 424.942 tons.
Comparative Analysis of External Lightning Protection Systems Using Protective Angle and Rolling Sphere Method in Areas That Have High Lightning Intensity Gilang Indrianto Pramono; Mohamad Rodhi Faiz; Langlang Gumilar; Sujito Sujito
Frontier Energy System and Power Engineering Vol 5, No 1 (2023): January
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i1p09-15

Abstract

Indonesia is a tropical country with a geographical condition where 71% of its area is water. This is what coueses Indonesia is considered a country with a high density of lightning strikes. This results in buildings in Indonesia being at a high risk of damage caused by lightning strikes. According to data from BMKG, the city of Malang has a high lightning intensity, with 108 thunderstorm days per year, posing a serious threat to the Electrical Engineering Building of the Faculty of Engineering at the University of Malang. Considering the building's age and expansion on the building's side, it is necessary to evaluate the lightning protection system to determine whether the existing system is still working effectively and efficiently. Improving the system's performance against lightning disturbances can be done by analyzing the previous external lightning protection system and then evaluating it using the angle of protection and rolling sphere methods. The analysis and evaluation of the external protection system are based on PUIPP, PUIL, SNI, and IEEE standards.
Forecasting Hourly Energy Fluctuations Using Recurrent Neural Network (RNN) Aji Prasetya Wibawa; Ade Kurnia Ganesh Akbari; Akhmad Fanny Fadhilla; Alfiansyah Putra Pertama Triono; Andien Khansa’a Iffat Paramarta; Faradini Usha Setyaputri; Agung Bella Putra Utama; Jehad A.H. Hammad
Frontier Energy System and Power Engineering Vol 5, No 2 (2023): July
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um049v5i2p50-57

Abstract

Energy forecasting is currently essential due to its various benefits. Energy data analysis for forecasting requires a functional method due to the complexity of the observed data. This forecasting study used the Recurrent Neural Networks (RNN) method. Parameters used include batch size, epoch, hidden layers, loss function, and optimizer obtained from hyperparameter tuning grid search. A comparison of different normalization methods, namely min-max, and z-score, was conducted. Using min-max normalization yielded the best performance with MAPE of 3.9398%, RMSE of 0.0630, and R2 of 0.8996. In testing with z-score normalization, it showed a performance of MAPE of 10.6120%, RMSE of 0.7648, and R2 of 0.4142.

Page 4 of 4 | Total Record : 39

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