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Optimum placement of measurement devices on distribution networks using Integer Linear K-means Clustering method

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Warianty tytułu
PL
Optymalizacja rozmieszczenia urządzeń do pomiaru fazy PMU na przykładzie sieci rozdzielczej w Indonezji
Języki publikacji
EN
Abstrakty
EN
In distribution networks, PMU (Phasor Measurement Unit) is required for each node or bus, but the cost of installing PMU is quite expensive, so optimization of PMU placement is required. This study uses the Integer Linear K-means Clustering method and uses the parameters of voltage, current and impedance. This method is a combination of Linear Integration and K-Means methods used for optimizing PMU placement. The object used for research is the Bendul-Merisi distribution network which has 11 buses. The results showed that the Integer Linear K-means Clustering method can be used for PMU placement optimization. With a network of 11 buses, only 3 PMU is needed, resulting in a reduction in the number of PMU by 73%.
PL
W artykule zaproponowano metodę optymalizacji rozmieszczenia w sieci układów do pomiaru fazy. Zastosowano kombinację metod: Linear Integration I K-means. Na przykładzie sieci w Bendul Merisi (Indonezja) zredukowano liczbę mierników o 73%.
Rocznik
Strony
124--128
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya- Indonesia, 2)Department of Electrical Engineering, Institut Teknologi Adhi Tama Surabaya, Indonesia
Bibliografia
  • [1]. A.G. Phadke, Precise Synchronization of Phasor Measurements in Electric Power Systems, http://tycho.usno.navy.mil/ptti/(1990)vol 2022-32.
  • [2]. Z. Kun, Impact of input uncertainties on power system state estimation robustness. IncorporatingPMUmeasurements,www.ee.kth.se/php/modules/ publications/reports/2008/XR-EE-ICS_2008_022.
  • [3]. X. Bei, Y. J. Yoon, A. Abur, Optimal Placement and Utilization of Phasor Measurements for State Estimation, www.pserc.waisc.edu/ecow/get/ publication / reports/2005 report (2007) Modern Grid Initiative. U.S. Department of Energy, National Energy Technology Laboratory.
  • [4]. F R Yu, P Zhang, W Xiao and P Choudhury (2011) Communication Systems for Grid Integration of Renewable Energy Resources. IEEE Network 25: 22-29.
  • [5]. Singh S P and Singh S (2014) Optimal PMU placement in power system considering the measurement redundancy. Advance in Electronic and Electric Engineering 4: 87 – 94.
  • [6]. M Pau, P A Poegoraro and S Sulis (2012) Branch current state estimator for distribution system based on synchronized measurements. Proc. IEEE Int. Workshop AMPS: 53-58.
  • [7]. M Pau, P A Poegoraro and S Sulis (2013) Efficient Branch- Current-Based Distribution System State Estimation Including Synchronized Measurements. IEEE Transactions on Intrumentation and Measurement 62.
  • [8]. M Pau, P A Poegoraro and S Sulis (2013) WLS Distribution System State Estimator Based on Voltage or Branch-currents: Accuracy and Perfomance Comparison. IEEE Conference Publications.
  • [9]. Roy B S, Sinha A and Pradhan A (2012) An optimal PMU placement technique for power system observability. International Journal of Electrical Power & Energy Systems 42: 71–77.
  • [10]. Fei Z, Hao X, Daonong Z, Xiaoyi Z and Yubo Y (2014) A new strategy for optimal PMU placement based on limited exhaustive approach. International Conference on Power System Technology (POWERCON): 67–74.
  • [11]. Hajian M, Ranjbar A M, Amraee T and Mozafari B (2011) Optimal placement of PMU to maintain network observability using a modified BPSO algorithm. International Journal of Electrical Power & Energy Systems 33: 28–34
  • [12]. Golberg D E (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Reading.
  • [13]. Muhammad Nizam (2010) Kohonen Neural Network Clustering For Voltage Control In Power Systems. Telkomnika, ISSN: 1693-6930.
  • [14]. Riny Sulistyowati, Dedet Riawan, Mochamad Ashari (2017) Clustering based Optimal sizing and placementof PV- DG Using Neural Network. American Journal 23: 2373-2375.
  • [15]. Gheorghe Grigoras, Gheorghe Cartina, Florina Rotaru (2015) Using K-Means Clustering Method in Determination of the Energy Losses Levels from Electric Distribution Systems. Mathematical Methods and Computational Techniques.
  • [16]. Han J, Kamber M, Pei J (2012) Data Mining Concept and Techniques 3rd ed. Morgan Kaufmann-Elsevier, Amsterdam.
  • [17]. Kirti Aggarwal, Neha Aggarwal, Priyanka Makkar (2012) Analysis of K-Means Clustering Algorithm for Data Mining. National Conference on Emerging trends in Electronics and Information Technology.
  • [18]. Richa loohach, Dr Karnwal Garg (2012) An insight overview of issues and challenges associated with clustering algorithms. IJRIM 2.
  • [19]. Ren Jingbiao and Yin Shaohong (2010) Research and Improvement of Clustering Algorithm in Data Mining. 2nd International Conference on Signal Processing Systems (ICSPS).
  • [20]. K A Abdul Nazeer, M P Sebastian (2009) Improving the Accuracy and Efficiency of the K- Means Clustering Algorithm. World congress on Engineering 1.
  • [21]. Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth Silverman, and AngelaY Wu (2010) An efficient kmeans clustering algorithm: Analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24:881– 892.
  • [22]. M Srinivas and C Krishna Mohan (2010) Efficient Clustering Approach using Incremental and Hierarchical Clustering Methods. 978-1-4244-8126- 2/10 IEEE.
  • [23]. Ahamed Shafeeq B M and Hareesha K S (2012) Dynamic clustering of data with modified K-means algorithm. International conference on Information and Computer Networks ICICN.
  • [24]. Asmita Yadav et al, Dr S P Singh Assistant Professor BITs, Mesra (2013) Study of K-Means and Enhanced K-Means Clustering. International Journal Of Advanced Research In Computer Science 4.
  • [25]. Hillier F S, Lieberman G J (2003) Introduction to Mathematical Programming. McGraw-Hill.
  • [26]. Nuqui R F, Phadke A G (2005) Phasor measurement unit placement techniques for complete and incomplete observability. IEEE Trans. Power Del 20: 2381-2388.
  • [27]. Gou B (2008) Optimal placement of PMUs by integer linear programming. IEEE Trans. Power Syst 23: 1525-1526.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-33b687ac-1918-49f5-994e-48c50c699230
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