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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Prediction of Solar Radiation Intensity using Extreme Learning Machine Hadi Suyono; Hari Santoso; Rini Nur Hasanah; Unggul Wibawa; Ismail Musirin
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp691-698

Abstract

The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m2 and 84.04 W/m2, while using the MLR method they are 141.18 W/m2 and 104.87 W/m2 respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.
Comparison study of fault location on distribution network using PSCAD and DIgSILENT power factory by using matching approaches Lilik J. Awalin; Tasnim Tasnim; Tay Lea Tien; Hadi Suyono
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 1: January 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i1.pp78-85

Abstract

This paper presents the comparative study between PSCAD and DIgSILENT in order to detect fault location on underground distribution network. If a fault occurs in the distribution network, it will generate the voltage dips and over current. It is possible to record the signal output in the primary substation. However, for the research purpose, some of the researcher may use different simulation program. The simulation program may have different performance to generate voltage and current signal when fault simulated. So, it is important to observe the performance of each simulation software. Due to every simulation software may have different advantages, this paper will observe the accuracy of fault distance calculation based on simulation data on the distribution model and when all types of fault are applied to the different simulation program, namely PSCAD and DIgSilent. The matching approach was adopted to calculate the fault distance. To observe the performance of the simulation program, the distance error calculation for every type of fault are compared. By using a matching approach, the PSCAD simulation program produces more accurate fault distance compare with DIgSILENT. However, it may contribute different result if different method and tested network applied.
Implementation of Deep Learning in Spatial Multiplexing MIMO Communication Mahdin Rohmatillah; Hadi Suyono; Rahmadwati Rahmadwati; Sholeh Hadi Pramono
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp699-705

Abstract

Research in Multiple Input Multiple Output (MIMO) communication system has been developed rapidly in order to improve the effectiveness of communication among users. However, trade-off phenomenon between performance and computational complexity always become the hugest dilemma suffered by researchers. As an alternative solution, this paper proposes an optimization in 3x3 spatial multiplexing MIMO communication system using end-to-end based learning, specifically, it adapts autoencoder based model with the knowledge of Channel State Information (CSI) in the receiver side, make it fairly compared with the baseline method. The proposed models were evaluated in one of the most common channel impairment which is fast Rayleigh fading with additional Additive White Gaussian Noise (AWGN). By appropriately determining hyperparameters and the help of PReLU (Parametric Rectified Linear Unit), the results show that this autoencoder based MIMO communication system results in very promising results by exceeding the baseline methods (methods widely used in conventional MIMO communication) by reaching BER lower than at SNR 22.5 dB.
Enhancement of the power system distribution reliability using ant colony optimization and simulated annealing methods Hadi Suyono; Rini Nur Hasanah; Panca Mudjirahardjo; M Fauzan Edy Purnomo; Septi Uliyani; Ismail Musirin; Lilik J. Awalin
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 2: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i2.pp877-885

Abstract

The increasing demand of electricity and number of distributed generations connected to power system greatly influence the level of power service reliability. This paper aims at improving the reliability in an electric power distribution system by optimizing the number and location of sectionalizers using the Ant Colony Optimization (ACO) and Simulated Annealing (SA) methods. Comparison of these two methods has been based on the reliability indices commonly used in distribution system: SAIFI, SAIDI, and CAIDI. A case study has been taken and simulated at a feeder of Pujon, a place in East Java province of Indonesia, to which some distributed generators were connected. Using the existing reliability indices condition as base reference, the addition of two distributed plants, which were micro hydro and wind turbine plants, has proven to lower the indices as much as 0.78% for SAIFI, 0.79% for SAIDI, and 2.32% for CAIDI. The optimal relocation of the existing 16 sectionalizers in the network proved to decrease further the reliability indices as much as 43.96% for SAIFI, 45.52% for SAIDI, and 2.8% for CAIDI, which means bringing to much better reliability condition. The implementation of the SA method on the considered data in general resulted in better reliability indices than using the ACO method.
Intelligent based technique for under voltage load shedding in power transmission systems Saiful Firdaus Abd Shukor; Ismail Musirin; Zulkifli Abd Hamid; Mohamad Khairuzzaman Mohamad Zamani; Mohamed Zellagui; Hadi Suyono
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 1: January 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i1.pp110-117

Abstract

The increasing demand of electric power energy and the presence of disturbances can be identified as the factors of voltage instability condition in a power system. A secure and reliable power system should be considered to ensure smooth delivery of electricity to the consumers. A power system may experience undesired event such as voltage instability condition leading to voltage collapse or cascading collapse if the system experiences lack of reactive power support. Thus, to avoid blackout and cascaded tripping, load shedding is the last resort to prevent a total damage. Under Voltage Load Shedding (UVLS) scheme is one of the possible methods which can be conducted by thepower system operators to avoid the occurrence of voltage instability condition. This paper presents the intelligent based technique for under voltage load shedding in power transmission systems. In this study, a computational based technique is developed in solving problem related to UVLS. The integration between a known computational intelligence-based technique termed as Evolutionary Programming (EP) with the under-voltage load shedding algorithm has been able to maintain the system operated within the acceptable voltage limit. Loss and minimum voltage control as the objective function implemented on the IEEE 30-Bus Reliability Test System (RTS) managed to optimally identify the optimal location and sizing for the load shedding scheme. Results from the studies, clearly indicate the feasibility of EP for load shedding scheme in loss and minimum voltage control in power system.
IMLANNs for Congestion Management in Power System Nur Zahirah Mohd Ali; Ismail Musirin; Hasmaini Mohamad; Saiful Izwan Suliman; Hadi Suyono
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i2.pp630-636

Abstract

In this paper, Integrated Multi-Layer Artificial Neural Networks (IMLANNs) model has been developed for congested line prediction in a power system. The master characteristic of an ANN is the superiority to achieve complicated input-output mappings through a learning procedure, without exhaustive programming efforts. The IMLANNs model was developed to predict the congested lines in a power system. Before the IMLANNs model is developed, a case study was selected to receive an early result in power system load current during normal condition and contingency based on heavily loaded term. In order to optimize the architecture of the neural network and minimize the computational effort, but those state variables with major impact on the power system are selected as inputs. A pre-developed index, namely Fast Voltage Stability Index (FVSI) is employed as a benchmark to identify the locations declared as congested lines. This indicator was produced which aims for an analytic thinking, sustainable power system when an excessive load was imposed on the power system network. In addition, voltage collapse can be identified when the index is approaching 1.000 or unity. The value of FVSI is chosen as the targeted output in the IMLANNs model. The strength of the proposed IMLANNs model has been validated on the IEEE 30- Bus RTS. Results obtained from the study demonstrated that the proposed IMLANNs is feasible for congested line prediction, which in turns beneficial to power system operators in the planning unit of a utility.
Co-Authors Abd. Rasyid Syamsuri Abraham Latumahina Abraham Lomi Achmad Yahya Chasanuddin Adharul Muttaqin Adhi Purbo Putranto Agam Rido Priawan Ahmad Fadly Irawan Ahmad Faisol Ahmad Fajar Robiyanto Ahmad Hadid Muntaha Ahmad Reza Yahya Aji Rizky Hakim Alan Stevrie Balantimuhe Alfian Nur Ferdianzah Alfian Sakti Pamungkas Amrizal Karim Amrulloh Anargya Widyatma Anthony Wijoyo Anthony Wijoyo Ardiyallah Akbar Arnold J. Kastanja Aswin, Muhammad Aulia, Fitrah Bambang Siswojo Boby Satriya Nugraha Budi Agung Raharjo Chandra Wiharya Cita Rahiim Tama Dheo Kristianto Dhofir, Mochammad Dicky Indratama Dikma Hartanjung Dimas Mudya Permadi Dimas Nofiantoro Dinda Oki Prabawanti Dwi Cahya Ramadhan Dwi Indra Kusumah Eko Kuncoro Engga Kusumayoga Erlangga Dinda Permana Erwin Hery Setiyawan Fairuz Milkiy Kuswa Faiz Yusky Ahlian Fajariyah Mulyani Fauzan, Vito Faza Azmi Hidayat Fitriana Suhartati Gahara, Ahda Galuh Prawestri Citra Handani Gatut Yulisusianto Hafidh Fadhlir Rahman Hakim, Luqman Halomoan Marthin Muskita Hari Santoso Harry Soekotjo Dachlan HARRY SOEKOTJO DACHLAN Hasanah, Rini Nur Hasmaini Mohamad Hatorangan, Orlando Hazlie Mokhlis Helmy Mukti Himawan Hendra Sumitro Sinurat Hery Purnomo I Kadek Adi Satya Putra Ibadi Mulyatama Iksan , Santoso Ilham Ramadhan Maulana Imam Suwandi Indah Permata Sari Safti Indratama, Dicky Indri Kusuma Dewi Ismail Musirin Ismail Musirin Ismail Musirin Jasri Kariadi Ginting Johanis Tupalessy Kevin Rachman Firdaus Khusnul Hidayat Kosa Shantia Laksono, Mico Norman Lilik J. Awalin Liza Putri Dafroni Lukman Hakim Lunde Ardhenta Luthfan Akbar Azizan Firdaos M Fauzan Edy Purnomo M. Aziz Muslim M. Fauzan Edy Purnomo M. Rif’an Mahdin Rohmatillah Mahfudz Shidiq Mahfudz Shidiq Malinda Dinna Auliya Mamdouh Abdel-Akher Marcelino Dendy Ramadhani Markus D. Letik Mas Ahmad Baihaqi Maulana, Eka Moch Fahrulrozi Moch. Dhofir Moch. Dofir Moechammad Sarosa Mohamad Khairuzzaman Mohamad Zamani Mohamad Najib Priyo Prakoso Mohamed Zellagui Mohammad Salman Abdurrohim Mu'ammar Faris L. Muammar Zainuddin Muchammad Ali Mudjirahardjo, Panca Muhamad Hazim Lokman Muhammad Arsyad Muhammad Fadillah Kurniawan Muhammad Fauzan Edy Purnomo Muhammad Ghufron Auliya Rahman Muhammad Rahmatullah Al-Qaedi Muhammad Rizky Wira Utomo Muhammad Sholikhin Muhammad Syaiful Arifin Muhammad Syarifuddin Anshor Mujib Ridwan Muslimin Muslimin n/a Soemarwanto n/a Soeprapto n/a Wijono Nadila Adza Savira Yaniar Nico Gautama Ginting Nur Vidya Ramadhani Nur Zahirah Mohd Ali Nurwati, Tri Olivia Ferlita Onny Setyawati Pristian, Candra Adha Putera, R. P. Ravie O. Mucheyz R. A. Setyawan Raden Arief Setyawan Radian Hepta Martha Hardaka Rahmadwati, n/a Rendy Hari Widodo., Hari Widodo Riko Nur Akbar Rini Nur Hasanah Rini Nur Hasanah Rini Nur Hassanah Rizki Chandra Maulana Rizki Tirta Nugraha Rizky Adhiputra Wallad Rudy Yuwono S. Irawan, Yudy Saiful Firdaus Abd Shukor Saiful Izwan Suliman Saiful Izwan Suliman Salim, Mohammad Agus Septi Uliyani Septian Kevin Aditama Sharifah Azma Syed Mustafa Sholeh Hadi Pramono Sigi Syah Wibowo Soleman Sesa Subekti, Elditya Suci Imani Putri Surya Adi Purwanto Syaiful Amri Syamsu Dhuha Tasnim Tasnim Tay Lea Tien Teguh Utomo Thoriq Kamal Septianhasri Triyudha Yusticea Sulaksono Tumpak Samosir Uliyani, Septi Unggul Wibawa Usman Nurhasan Wardana, ING Widyananda, Eka Putra Widyananda., Putra Widyatama, Anargya Wijono Wijono Wijono Wijono Wilda Faradina Wildan Alfi Syahri Wira R. A., Imantaka Wisnu Adi Suryo Yakin Gabrielsa Yamadika Okto Ahiro Yanuar Alfa Tri Susanto Yoga Candra Setyawan Yoga Prasetya Yuniar Adi Setiawan Zainuri, Akhmad Zulkifli Abd Hamid