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[Vol. 05, No. 07] [July 2019]

Paper Title :: Electricity Load Forecasting Based on EMD and GRU
Author Name :: Haiqing Liu || Weijian Lin
Country :: China
Page Number :: 01-05
This paper presents a load forecasting method based on empirical mode decomposition algorithm and GRU neural network. In view of the randomness and fluctuation of historical data of electric load, EMD algorithm is used to decompose historical data of electric load, and a series of smooth IMF components are obtained. The components are regarded as GRU neural network. Input is used to forecast the electricity load for the next day. Compared with the prediction model constructed by GRU neural network and SVM, the experimental results prove the validity of the prediction method proposed in this paper.
Keywords:Load forecasting, EMD, GRU, randomicity, volatilit
[1]. B. Zhang, C. Shao, R. Zhao, Short-term load forecasting based on deviation correction, Electric Power Automation Equipment,35(11), 2015, 152-157
[2]. J. Murata, S. Sagara, One-day-ahead load forecasting via self-organization of model, Electrical Engineering in Japan, 110(5), 1990, 31-43
[3]. Y. Dou, H. Zhang, A. Zhan, An Overview of Short-term Load Forecasting Based on Characteristic Enterprises, 2018 Chinese Automation Congress, 2018, 3176-3180
[4]. H. Mori, A. Yuihara, Deterministic annealing clustering for ANN-based short-term load forecasting, IEEE Transactions on Power Systems, 16(3), 2001, 545-551
[5]. M. Huo, D. Luo, J. He, Chaos Optimization Method of SVM Parameters Selection for Short-term Load Forecasting, Proceedings of the CSU-EPSA, 21(5), 2009, 124-128

Paper Title :: Research on Non-technical Loss Detection Method Based on Smart Grid
Author Name :: Yingying Zhao || Xiangrong Zu
Country :: China
Page Number :: 06-10
With the popularity of smart meters in the power grid, the use of distribution network and user-side measurement data to achieve efficient and accurate detection of NTL has received extensive attention from the academic community and the industry.In this paper, different machine learning algorithms are used to study the anomaly detection methods.
Keywords: electricity usage; non-technical loss; anomaly detection; data driven; smart grid
[1]. JIANG, LU Rongxing, WANGYe, et al. Energy-theft detection issues for advanced metering infrastructure in smart grid[J].Tsinghua Since and Technology,2014,19(2):105-120.
[2]. FRAGKIOUDAKI A,CRUZ-ROMERO P,GOMEZEXPOSITO A, et al. Detection of non-technical losses in smart distribution networks: a review[M].New York, USA: Springer International Publishing,2016:43-54.
[3]. P. Jokar, N. Arianpoo and V. C. M. Leung, "Electricity Theft Detection in AMI Using Customers’ Consumption Patterns," in IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 216-226, Jan. 2016.
[4]. Q. Zhang, M. Zhang, T. Chen, J. Fan, Z. Yang and G. Li, "Electricity Theft Detection Using Generative Models," 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, 2018, pp. 270-274.
[5]. Zheng, Zibin,;Yang, Yatao; Niu, Xiangdong; Dai, Hong-Ning; Zhou, Yuren;” Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids” in IEEE Transactions on Industrial Informatics, v 14, n 4, p 1606-1615, April 2018

Paper Title :: Reconstruction Strategy of Distribution Network with DG based on improved binary Particle Swarm Optimization algorithm
Author Name :: Shunyan Li || Chuyang Li || Yupeng Ruan
Country :: China
Page Number :: 11-21
Distribution network reconfiguration is a complex nonlinear combinatorial optimization problem. In order to overcome the problem that the original particle swarm optimization (pso) algorithm is easy to fall into the local optimal and difficult to jump out, an improved pso algorithm is proposed to solve the distribution network reconfiguration model with distributed power supply (DG).By introducing nonlinear dynamic adjustment of inertia coefficient and direction matrix to guide the update of particle velocity, the particle is moved towards the lowest voltage node, and a large number of infeasible solutions are processed to improve the global search efficiency and convergence speed of binary particle swarm optimization algorithm. Finally, the effectiveness of the algorithm is verified by simulation of IEEE33 node distribution system.
Keywords: Distribution network restructuring,BPSO,Nonlinear inertial weight,Direction matrix
[1]. Civanlar S, Grainger J J, Yin H, et al. Distribution feeder reconfiguration for loss reduction[J]. IEEE Trans on Power Delivery, 1988, 3(3): 1217-1223.
[2]. Bi Pengxiang, Liu Jian, Zhang Wenyuan. A refined branch-exchange algorithm for distribution networks reconfiguration [J]. Proceedings of the CSEE, 2001, 21(8): 98-103.
[3]. Baran M E, Wu F F. Network reconfiguration in distribution systems for loss reduction and load balancing [J]. IEEE Trans on Power Delivery, 1989; 4(2): 1401-1407.
[4]. TIANHao, LÜ Lin, GAO Hongjun, et al. Dynamic reconfiguration of distribution network considering power gridoperation characteristic [J]. Power System Protection and Control, 2015, 43(1): 9-14
[5]. Li Zhenkun, Chen Xingying,Yu Kun, et al. Hybrid particleswarm optimization of distribution network reconfiguration[J]. Proceedings of CSEE, 2008, 28(31): 35-41.

Paper Title :: Decreasing Energy Consumption in Mining by Combined Plasma-Mechanical Rock Fracturing
Author Name :: J. Fresner || O. Terentiev || A. Kleshchov
Country :: Ukraine
Page Number :: 22-34
This research focuses on a novel approach to increasing the productivity of rock fracturing by combining plasma and mechanical fracturing. The combination of plasma and mechanical stress increased the productivity of fracturing hematite with a SBSh-250 drilling unit by more than 50%. By applying a current of 10 A the advance rate increased from 14.32 m/day to 22.39 m/day. A new mathematical model for electro-thermal rock fracturing was designed integrating the effect of the plasma stream with given inductance and current into a drilling model.
Keywords: plasma-mechanical stress; crystalline structures; rock fracturing;productivity
[1]. Griffith, A. (1921). The phenomena of rupture and flows in solids. Philos. Trans. R. Soc, (221), pp.163-198.
[2]. Irwin, G. (1958). Fracture I. In: S. Flugge, ed., Handbuch der Physik, 6th ed. NewYork: Springer, pp.558-590.
[3]. Erdogan, F. (2000). Fracturemechanics. InternationalJournalofSolidsandStructures, 37, pp.171-183.
[4]. Sedov, L. (1972). Mechanicsinthe USSR for 50 years, Volume 3, Mechanicsof a deformablesolids (pp. 457-467). Moscow: Nauka.
[5]. Cherepanov, G. (1983). Fracturemechanicsofcompositematerials. Moscow: Nauka.

Paper Title :: Approximation of Porosity in Clay Formations
Author Name :: M.Sc. Zenteno Jiménez José Roberto
Country :: Mexico
Page Number :: 35-53
Two proposed expressions are presented for the calculation of porosity for deposits with present clay, using sonic tools, mentioning the equations developed over the years and in practice their use, 3 cases will be tested where the equations obtained for to calculate the porosity part of the methodology is detailed by Karter H. Makar and Mostafa H Kamel (2011), statistical estimators of error and approximation are considered to verify their results.
Keywords: Wyllie, Raymer, Raiga – Clemenceau Equations, Porosity, Clay
[1]. An approach for minimizing errors in computing effective porosity in reservoirof shaly nature in view of Wyllie–Raymer–Raiga relationshipKarter H. Makar ⁎, Mostafa H. KamelGeophysics Department, Faculty of Science, Cairo University, Giza, Egypt
[2]. Raiga-Clemenceau, J., Martine, J.P., Nicoletis, S., 1988. The conceptof acoustic formation factor for more accurate porositydetermination from sonic transit time data. Log Anal.,
[3]. Raymer, L.L., Hunt, E.R., Gardner, J.S., 1980. An improved Sonic transit time-to-porosity transform. SPWLA Trans., 21st Ann.Log. Symp., Paper P. The Society of Professional Well LogAnalyst (SPWLA), Tulsa, OK.
[4]. Bassiouni, Z., 1994. Theory, Measurements, and Interpretation ofWell Logs. The Society of Petroleum Engineering, USA, p. 372.ISBN 1-55563-956-1.
[5]. Porosity estimation using a combination of Wyllie–Clemenceau equations in clean sand formation from acoustic logs Mostafa H. Kamel*, Walid M. Mabrouk, Abdelrahim I. Bayoumi Geophysics Department, Faculty of Science, Cairo University, Giza, Egypt.

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