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Analysis of Technical Loss Calculation Using Load Curve Approach on 20 kV Distribution Network Maulana, Rizky Rahmat; Salahuddin, S; Ezwarsyah, E; Ismail, Baharuddin; Shrestha, Ashish; Astonkar, Dhiraj Vijayrao
Journal of Renewable Energy, Electrical, and Computer Engineering Vol 1, No 2 (2021): September 2021
Publisher : Institute for Research and Community Service, Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v1i2.5238

Energy loss (losses) is the loss of a certain amount of energy generated when it is distributed to consumers so that it affects the profitability of the company concerned. The size of the losses from an electric power system shows the level of efficiency of the system, the lower the percentage of losses that occur the more efficient the system. Energy losses in the distribution network are generally divided into two, namely technical and non-technical losses. The calculation of technical losses in the 20 KV medium distribution network of PT PLN (Persero) Ulp Matang Glumpang Dua is carried out using the load curve approach method and using the help of the Microsoft Exel program, while the technical losses calculated are technical losses on the Medium Voltage Network and Distribution Transformer. From the results of the analysis of the calculation of technical losses in 2020, the total technical loss value at MG-01 Matang City is in the range of 13.8% to 20.8% which consists of the average technical loss in the Medium Voltage Network feeder of 0.02%. and the loss of Distribution Transformer by 17.6%.

Power losses analysis for reduced switch 9-level cascaded H-bridge multilevel inverter Ismail, Mohammad Haziq; Isa, Zainuddin Mat; Arshad, Mohd Hafiz; Mid, Ernie Che; Ismail, Baharuddin; Talib, Md Hairul Nizam
International Journal of Applied Power Engineering (IJAPE) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v13.i4.pp808-814

This study provides a thorough examination of power losses and total harmonic distortion (THD) in single-phase 9-level cascaded H-bridge multilevel inverters (CHB MLI) at low switching frequencies. The aim is to analyze the efficiency of a single-phase 9-level cascaded CHB MLI using three distinct switch configurations: 16-switch, 11-switch, and proposed 8-switch. The calculated switching angles are optimized using the feed-forward methodology. Two types of load conditions—R load and R-L load—are being examined. The results suggest that the proposed 8-switch design exhibits superior efficiency by limiting power losses compared to other topologies. Regarding THD, the conventional topology yields a somewhat lower value, however, the disparity is less than 1% when compared to both reduced switch topologies.

Analysis of Technical Loss Calculation Using Load Curve Approach on 20 kV Distribution Network Maulana, Rizky Rahmat; Salahuddin, S; Ezwarsyah, E; Ismail, Baharuddin; Shrestha, Ashish; Astonkar, Dhiraj Vijayrao
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 1 No. 2 (2021): September 2021
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v1i2.5238

Energy loss (losses) is the loss of a certain amount of energy generated when it is distributed to consumers so that it affects the profitability of the company concerned. The size of the losses from an electric power system shows the level of efficiency of the system, the lower the percentage of losses that occur the more efficient the system. Energy losses in the distribution network are generally divided into two, namely technical and non-technical losses. The calculation of technical losses in the 20 KV medium distribution network of PT PLN (Persero) Ulp Matang Glumpang Dua is carried out using the load curve approach method and using the help of the Microsoft Exel program, while the technical losses calculated are technical losses on the Medium Voltage Network and Distribution Transformer. From the results of the analysis of the calculation of technical losses in 2020, the total technical loss value at MG-01 Matang City is in the range of 13.8% to 20.8% which consists of the average technical loss in the Medium Voltage Network feeder of 0.02%. and the loss of Distribution Transformer by 17.6%.

Planning to Reconfigure the 150 kV Transmission Network at PT. PLN (Persero) Tualang Cut Network and Substation Hasibuan, Arnawan; Asran, Asran; Adriansyah, Fery; Ismail, Baharuddin; Siregar, Widyana Verawaty; Sayuti, Muhammad
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 3 No. 2 (2023): September 2023
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v3i2.11740

System condition at PT. PLN (Persero) Tualang Cut Substation where the reliability of the 150 kV system depends on the Langsa Substation, which we will reconfigure on the Lgs-P.B and Lgs-T.C channels to P.B-T. C and T.C-Lgs. The planning for the reconfiguration of the 150 kv transmission network of the Tualang Cut Substation was carried out by estimating the distance of the transmission line and the constraints that occurred. The condition of the electric power system that is currently operating at PT. PLN (Persero) Tualang Cut Substation is considered less efficient in receiving power from the 150 kV SUTT interconnection. The SUTT in Tualang Cut currently receives electrical power from the Langsa Substation with a Losses value on lines 1 and 2 of 771,4142474 kW and experiences a Voltage Drop on lines 1 and 2 of 4,38 kV. After we reconfigured the channel, from the results of the reconfiguration there was a decrease in Losses on channels 1 and 2 by 311,298581 kW which before the reconfiguration of power losses on the line amounted to 771,4142474 kW and after being reconfigured to 460,1156668 kW there was a decrease in Voltage Drop on channels 1 and 2 by 1,44 kV, before the reconfiguration of the voltage drop on the line of 4,38 kV and after the reconfiguration to 2,94 kV, the reconfiguration carried out is the best solution to reduce losses and voltage drops.

Parameter tuning of PIDG controller on maximum photovoltaic power point for battery charging system Irwanto, Muhammad; Timoteus Gultom, Togar; Satria, Habib; Ismail, Baharuddin; Erniati Panjaitan, Christin; Syukri, Mahdi
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.11044

Maximum photovoltaic power point (MPVPP) based on DC-DC buck converter is supplied by photovoltaic module. A controller method is needed to control the signal that it drives the switching component of DC-DC buck converter. The previous researcher conducts proportional integral derivative (PID) controller applying the DC-DC buck converter, but only its parameters (proportional, KP, integral, KI, and derivative, KD) are studied. This paper presents MPVPP based on PID with gain (PIDG) controller on the DC-DC buck converter by tuning the parameters of KP. KI and KD and adding a gain, G connected to PIDG controller for charging 12 V, 7 Ah battery. The DC-DC buck converter is designed for the output voltage of 14.7 V and output power of 150 W and modelled using Simulink MATLAB. The simulation results show that the parameters of KP=0.0032, KI=1, and KD=4×10-7 are suitable to control the switching component. The gain, G gives significant effect on the settling time and the time to reach their steady state value of output voltage of 14.7 V. The battery SOC can increase 1.36% per second, if the initial SOC is 25%, thus it needs arround 55 seconds to reach the fully charging condition.

Democratizing Climate Intelligence Through Localizez Large Language Models for Education and Governance Kusworo, Zulfikar Aji; Siregar, Widyana Verawaty; Ismail, Baharuddin; Hamdhana, Defry
VOCATECH: Vocational Education and Technology Journal Vol 8, No 1 (2026): April
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v8i1.325

AbstractClimate change presents complex challenges in Indonesia, where local governments and communities frequently experience information asymmetry and limited access to expert knowledge, particularly in low-resource and low-connectivity regions. This study aims to develop and evaluate a localized, domain-adapted Large Language Model (LLM) that functions as offline-capable climate knowledge infrastructure for education and local governance in Indonesia. The research method employs a design science research methodology comprising four stages: (1) selection of Qwen3-4B as the base model, (2) curation of an Indonesian climate and energy transition corpus containing approximately 12,400 instruction-response pairs (~38 MB) drawn from national climate policy documents, NDC/RPJMN frameworks, renewable energy guidelines, and educational climate science texts, (3) parameter-efficient fine-tuning using QLoRA with LoRA rank r=16, alpha=32, learning rate 2e-4, 3 epochs, per-device batch size 2 with gradient accumulation 4, and 4-bit NF4 quantization, and (4) offline deployment on consumer-grade hardware with task-oriented evaluation against three baseline models (Qwen3-4B-Thinking, Gemma-3-4B, LLaMa-3.1-8B). The results show that the fine-tuned model (Qwen3-4B-REnewbie v1) achieved a 15.4% perplexity reduction on domain-specific test data and an average qualitative score of 9.3/10 across factual accuracy, reasoning structure, and Bahasa Indonesia language compliance, outperforming all baselines (score range 7.0–8.2). The system operates fully offline on consumer-grade hardware with acceptable inference latency. The conclusion drawn from this study is that localized, resource-efficient LLMs can function as practical climate knowledge infrastructure for vocational education and local governance in Indonesia, aligning with Green AI principles and supporting the democratization of climate intelligence in low-connectivity settings. AbstrakPerubahan iklim menghadirkan tantangan kompleks di Indonesia, khususnya bagi pemerintah daerah dan komunitas lokal yang sering mengalami asimetri informasi dan keterbatasan akses terhadap pengetahuan pakar di wilayah dengan sumber daya dan konektivitas terbatas. Penelitian ini bertujuan mengembangkan dan mengevaluasi Large Language Model (LLM) yang dilokalkan dan diadaptasi ke domain iklim sebagai infrastruktur pengetahuan iklim berbasis offline untuk pendidikan dan tata kelola lokal di Indonesia. Metode penelitian ini menggunakan pendekatan design science research yang meliputi (1) pemilihan Qwen3-4B sebagai base model, (2) kurasi korpus iklim dan transisi energi Indonesia berisi sekitar 12.400 pasangan instruksi-respons (~38 MB) dari dokumen kebijakan iklim nasional, kerangka NDC/RPJMN, panduan energi terbarukan, serta teks ilmiah iklim, (3) parameter-efficient fine-tuning berbasis QLoRA (LoRA rank r=16, alpha=32, learning rate 2e-4, 3 epoch, batch size 2 per perangkat dengan gradient accumulation 4, dan kuantisasi 4-bit NF4), dan (4) deployment offline pada perangkat keras kelas konsumen dengan evaluasi berorientasi tugas terhadap tiga baseline (Qwen3-4B-Thinking, Gemma-3-4B, LLaMa-3.1-8B). Hasil penelitian ini menunjukkan model hasil fine-tuning (Qwen3-4B-REnewbie v1) menghasilkan penurunan perplexity sebesar 15,4% pada data uji domain dan skor kualitatif rata-rata 9,3/10 pada dimensi akurasi faktual, struktur penalaran, dan kepatuhan Bahasa Indonesia, mengungguli seluruh baseline (kisaran 7,0–8,2), serta beroperasi sepenuhnya secara offline pada perangkat konsumen dengan latensi inferensi yang dapat diterima. Kesimpulan yang diperoleh dari penelitian ini adalah LLM yang dilokalkan dan hemat sumber daya dapat berfungsi sebagai infrastruktur pengetahuan iklim yang praktis bagi pendidikan vokasi dan tata kelola lokal di Indonesia, selaras dengan prinsip Green AI dan mendukung demokratisasi kecerdasan iklim di wilayah berkonektivitas terbatas.

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