scholarly journals Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Prediction in the Powerline Narrowband Frequency Band

2020 ◽  
Author(s):  
Emmanuel Oyekanlu ◽  
Jia Uddin
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Transfer Function ◽  
Smart Grid ◽  
Load Transfer ◽  
Transfer Functions ◽  
Optimization Approach ◽  
Powerline Communication ◽  
Load Transfer Function ◽  
Ensemble Regression

In this chapter, the random forest-based ensemble regression method is used for the prediction of powerline impedance at the powerline communication (PLC) narrowband frequency range. It is discovered that while PLC load transfer function, phase, and frequency are crucial to powerline impedance estimation, the problem of data multicollinearity can adversely impact accurate prediction and lead to excessive mean square error (MSE). High MSE is obtained when multiple transfer functions corresponding to different PLC load transfer functions are used for random forest ensemble regression. Low MSE indicating more accurate impedance prediction is obtained when PLC load transfer function data is selectively used. Using data corresponding to 200, 400, 600, 800, and 1000 W PLC load transfer functions together led to poor impedance prediction, while using lesser amount of carefully selected data led to better impedance prediction. These results show that artificial intelligence (AI) methods such as random forest ensemble regression and deterministic data-optimization approach can be utilized for smart grid (SG) health monitoring applications using PLC-based sensors. Machine learning can also be applied to the design of better powerline communication signal transceivers and equalizers.

scholarly journals Proposed point bearing load transfer function in jointed rock-socketed drilled shafts

2013 ◽  
Vol 53 (4) ◽  
pp. 596-606 ◽  
Author(s):  
Jaehwan Lee ◽  
Kwangho You ◽  
Sangseom Jeong ◽  
Jaeyoung Kim
Keyword(s):  
Transfer Function ◽  
Load Transfer ◽  
Jointed Rock ◽  
Drilled Shafts ◽  
Bearing Load ◽  
Load Transfer Function

scholarly journals Studi Daya Dukung Fondasi Tiang Pada Tanah Lempung Teguh Berdasarkan Pembebanan Kepala Tiang Dan Pergerakan Ujung Tiang Metode T-Z

2020 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Mochamad fikri firmansyah Fikri Firmansyah ◽  
Rakha Fausta ◽  
Helmy Darjanto
Keyword(s):  
Shear Strength ◽  
Transfer Function ◽  
Bearing Capacity ◽  
Surface Properties ◽  
Pile Foundation ◽  
Load Transfer ◽  
Load Transfer Function

Developments in the calculation of foundation planning today have produced many methods and formulas for calculating the bearing capacity of foundations, such as the T-Z method, the Tezaghi method, the Mayerhof method, the Tomlison method, and other methods. So the purpose of this study was to determine the bearing capacity from tip movement of the foundation of each load with the T-Z method. The T-Z method explains rationally the mechanism of load transfer using a load transfer function commonly called TZ. In this method the pile foundation will be divided into several segments and the transfer function on each side segment which is a function of the shear strength of the soil and the surface properties of the side pile. From the analysis results of the TZPILE application, the bearing capacity is due to the settlement. At a settlement of 0,0001m; 0.001m; 0.0015m; 0.0025m; and 0.005m get a bearing capacity of 4.31kN; 31.69 kN; 35.6 kN; 43.44 kN; and 60.10kN. And on the reduction of permits on the foundation that occurs according to SNI 8460 - 2017 is 25mm, so the analysis obtained 12mm which still meets the requirements, 12mm get a bearing capacity of 1200kN at the tip of the pile. At a load of 600 kn the head of the pile can be held at a depth 4 meters. And for the maximum bearing capacity of the 18 meter pole, it can whitstand a bearing capacity of 1200 kn.

scholarly journals Analytical Model and Back-Analysis for Pile-Soil System Behavior under Axial Loading

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hong-Fa Xu ◽  
Ji-Xiang Zhang ◽  
Xin Liu ◽  
Han-Sheng Geng ◽  
Ke-Liang Li ◽  
...  
Keyword(s):  
Transfer Function ◽  
Analytical Model ◽  
Axial Force ◽  
Load Transfer ◽  
Back Analysis ◽  
Axial Loading ◽  
Distribution Functions ◽  
Soil System ◽  
Relationship Model ◽  
Load Transfer Function

The interaction mechanism between piles and soils is very complicated. The load transfer function is generally nonlinear and is affected by factors such as pile side roughness, soil characteristics, section depth, and displacement. Therefore, it is difficult to solve the pile-soil system based on load transfer function. This paper presents a new method to study the soil-pile interaction problem with respect to axial loads. First, the shapes of the axial force-displacement curves at different depths and the displacement distribution curves along pile axis at different pile-top displacements were analyzed. A simple exponential function was taken as relationship model to express the relationship curves between two distribution functions of axial force and displacement along pile shaft obtained by using the geometric drawing method. Second, a new analytical model of the pile-soil system was established based on the basic differential equations for pile-soil load transfer theory and the relationship model and was used to derive the mathematical expressions on the distribution functions of the axial force, the lateral friction, and the displacement along pile shaft and the load transfer function of pile-side. We wrote the MATLAB program for the analytical model to analyze the influence laws of the parameters u and m on the pile-soil system characteristics. Third, the back-analysis method and steps of the pile-soil system characteristics were proposed according to the analytical model. The back-analysis results were in good agreement with the experimental results for the examples. The analysis model provides an effective way for the accurate design of piles under axial loading.

scholarly journals Choice of commutation device and development of the scheme of a universal sectionalizing point with automatic load transfer function for the 0.38 kV electric network

2019 ◽  
Vol 124 ◽  
pp. 02011 ◽  
Author(s):  
A. V. Vinogradov ◽  
A. V. Vinogradova ◽  
M. O. Ward ◽  
A. N. Kharkhardin ◽  
A. I. Psaryov ◽  
...  
Keyword(s):  
Transfer Function ◽  
Power Supply ◽  
Load Transfer ◽  
Power Source ◽  
Power Lines ◽  
Electrical Networks ◽  
Transformer Substation ◽  
Load Transfer Function ◽  
Short Circuits ◽  
Switching Devices

The overestimated length of rural power lines of 0.38 kV leads to the fact that it is often not possible to fulfill both the sensitivity conditions of the protective device installed at the transformer substation and protecting the line from overloads and short circuits, and the requirements of the power supply reliability of consumers. This problem can be solved by installing in-line universal sectionalizing point equipped with an automatic load transfer function. The use of these devices allows dividing a line into sections provides the necessary sensitivity of protection for each section of the line and the possibility of supplying power to undamaged areas from a backup power source. In addition, only with the use of automated switching devices installed in power lines, it is possible to create intelligent electrical networks. When developing these devices, it is necessary to take into account the electromechanical characteristics of switching devices that are planned to be used for switching lines.

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

2022 ◽  
Author(s):  
Danalakshmi D ◽  
Łukasz Wróblewski ◽  
Sheela A ◽  
A. Hariharasudan ◽  
Mariusz Urbański
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Smart Grid ◽  
Power Management ◽  
Power Distribution ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Time Duration ◽  
Renewable Power

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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