Distributed generation parameter optimization method based on fuzzy C-means clustering under the Internet of Things architecture

Yao, Xin; Xing, Liyun; Xin, Ping

doi:10.1016/j.egyr.2021.10.049

Cited by 4 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are many problems associated with the installation of DG. Yao et al [6] proposed the problem with the objectives of reducing power loss and enhancing the benefits of users and the grid. The optimal installation of DG in Sellami et al [7] is considered to reduce power loss, improve minimum voltage and stabilize voltage.…”

Section: Introductionmentioning

confidence: 99%

Optimal placement of distributed generations on distribution network for reducing power loss and improving feeder balance

Trinh,

Nguyen

2024

TELKOMNIKA

View full text Add to dashboard Cite

The suitable placement and power of distributed generation (DG) can bring technical benefits to the distribution network. This paper applies the Coot optimization algorithm to the DG placement problem with the goal of minimizing power loss and feeder balancing load (LBF). The weight method is used to combine the membership objectives. The evaluation results on the network of 70 nodes for different weight values of the objective function show that the optimal power and installation location of DGs significantly reduces power loss and improves LBF index. In this study, eleven cases were considered. As the weight of power loss part 𝑤 1 increases from 0 to 1, the power loss gradually decreases, the LBF index gradually increases, the maximum current gradually decreases, and the minimum voltage amplitude gradually improves. Comparing the results of Coot with particle swarm optimization (PSO) shows that the indicators are improved. In the three cases where 𝑤 1 is 0, 0.5, and 1, power loss gained by Coot compared to PSO is less than 47.2663 kW, 73.2725 kW, 30.8708 kW, respectively. LBF index of Coot compared to PSO is less than 98.2%, 88.2%, 81.9%, respectively. The maximum, minimum, average, standard deviation, and CPU time of Coot are smaller than those of PSO. So, Coot is one of the promising methods for this problem.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal placement of distributed generations on distribution network for reducing power loss and improving feeder balance

Trinh,

Nguyen

2024

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…Sehingga algoritma dalam Fuzzy C-Means dapat membantu mengklarifikasi mahasiswa yang layak menerima KIP Kuliah, dan tidak layak menerima KIP Kuliah. Penelitian menggunakan metode Fuzzy C-Means pernah dilakukan oleh beberapa peneliti diantaranya Ahmadi & Hartati (2013), Dwitiyanti (2019), Jaroji (2016), Muchsin & Sudarma (2015), Putra (2015), Putra & Hardiyanti (2011), Rahakbauw (2017), Sari & Suranti (2016), Suwardika & Suniantara (2018), Tanjung (2016), serta Yao (2021).…”

unclassified

Penentuan Penerima Kip Kuliah Mahasiswa S1 Unugiri Menggunakan Fuzzy C-Means Clustering

Rahmawati

Fathoni

Ismanto

2022

View full text Add to dashboard Cite

Kartu Indonesia Pintar (KIP) Kuliah adalah salah satu pemberian bantuan pendidikan untuk siswa yang tergolong kurang mampu namun memiliki prestasi untuk melanjukan pendidikan di perguruan tinggi. Banyak perguruan tinggi yang menyediakan program KIP Kuliah sebagai contoh perguruan tinggi swasta yang ada di Bojonegoro yaitu Universitas Nahdlatul Ulama Sunan Giri (UNIGIRI). Proses seleksi penerima KIP Kuliah di UNUGIRI masih dilakukan secara manual, cara tersebut membutuhkan ketelitian dan waktu yang cukup lama dan menyebabkan beberapa hasil penyeleksian kurang tepat. Diperlukan Sistem Pendukung Keputusan (SPK) yang memudahkan dalam meghitung segala ketentuan pengambilan keputusan dengan menggunakan metode Fuzzy C-Means. Metode Fuzzy C-Means dapat membantu dalam proses pengambilan keputusan calon penerima KIP Kuliah sesuai dengan kriteria yang telah ditentukan. Dengan mempertimbangkan 5 variabel yaitu penerima KIP/KKS, status DTKS, besar penghasilan orang tua, jumlah tanggungan keluarga, serta aset yang dimiliki. Dari 346 data calon mahasiswa penerima yang diperoleh, setelah diteliti dengan menggunakan metode Fuzzy C-Means Clustering diperoleh 119 mahasiswa yang layak menerima KIP Kuliah. Selain itu terdapat 227 mahasiswa kurang layak menerima KIP Kuliah karena tergolong mampu yang dibuktikan dengan tidak memiliki kartu tanda kurang mampu serta data aset yang dimiliki keluarga. Adapun hasil perhitungan di lapangan menunjukkan 236 mahasiswa penerima KIP Kuliah dan 110 mahasiswa tidak menerima KIP Kuliah. Setelah dibandingkan dengan data penerima sebelumnya diperoleh 56% kecocokan hasil Fuzzy C-Means dengan hasil penentuan di lapangan, dan pada hasil analisis perbedaan dari kedua metode tersebut metode Fuzzy C-Means lebih akurat dalam penentuan pengambilan keputusan. Jadi metode ini dapat dipertimbangkan untuk dipakai dalam penentuan kelayakan penerima KIP Kuliah.

show abstract

A feature-weighted suppressed possibilistic fuzzy c-means clustering algorithm and its application on color image segmentation

Yu,

Jiang,

Fan

et al. 2024

Expert Systems with Applications

View full text Add to dashboard Cite

Distributed generation parameter optimization method based on fuzzy C-means clustering under the Internet of Things architecture

Cited by 4 publications

References 21 publications

Optimal placement of distributed generations on distribution network for reducing power loss and improving feeder balance

Optimal placement of distributed generations on distribution network for reducing power loss and improving feeder balance

Penentuan Penerima Kip Kuliah Mahasiswa S1 Unugiri Menggunakan Fuzzy C-Means Clustering

A feature-weighted suppressed possibilistic fuzzy c-means clustering algorithm and its application on color image segmentation

Contact Info

Product

Resources

About