scholarly journals An Enhanced K-Means Clustering Algorithm for Pattern Discovery in Big Data Analysis of 3-Phase Electrical Quantities

2018 ◽  
Vol 7 (4.44) ◽  
pp. 8
Author(s):  
Dikpride Despa ◽  
Gigih Forda Nama
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
Power Distribution ◽  
Distribution System ◽  
Clustering Algorithm ◽  
Measurement Data ◽  
Power Distribution System ◽  
Multiple Sensors ◽  
Industry Standard

The Unila Internet of Things Research Group (UIRG) was developed online monitoring of power distribution system based on Internet of Things (IoT) technology on Department of Electrical Engineering University of Lampung (Unila), has been running for several months, this system monitored electrical quantities of 3-phase main distribution panel of H-building. The measurement system involve multiple sensors such current sensors and voltage sensors, the measurement data stored in to database server and shown the information in a real-time through a web-based application.Main objective of this research was to capture, analyze, and identified the knowledge pattern of electrical quantities data measurements, using Cross-Industry Standard Process for Data Mining (CRISP-DM) data mining framework, for helping the stake holders to continuous improvement of the quality of electricity services, the initial research limited to total 770847 electrical quantities recorded data that save on database system, since 1 September - 31 October 2018, the dataset consist of 21 attribute electrical quantities such as; voltage, current, power factor values, energy consumption, frequency, on H building 3-Phase main panel control.Rapidminer as leading application on knowledge discovery application was used to analyze the big data, K-Mean cluster algorithm implemented to identify the data pattern, the result indicated that 3-Phase load was unbalanced, and Phase-0 was the most utilized phase, based on from total 5 cluster analysis result. 

scholarly journals Comparative Study in Determining Features Extraction for Islanding Detection using Data Mining Technique: Correlation and Coefficient Analysis

2017 ◽  
Vol 7 (3) ◽  
pp. 1112 ◽  
Author(s):  
Aziah Khamis ◽  
Yan Xu ◽  
Azah Mohamed
Keyword(s):  
Data Mining ◽  
Power Distribution ◽  
Distribution System ◽  
Harmonic Distortion ◽  
Rate Of Change ◽  
Support Vector ◽  
Islanding Detection ◽  
Data Mining Technique ◽  
Detection Scheme ◽  
Power Distribution System

A comprehensive comparison study on the datamining based approaches for detecting islanding events in a power distribution system with inverter-based distributed generations is presented. The important features for each phase in the island detection scheme are investigated in detail. These features are extracted from the time-varying measurements of voltage, frequency and total harmonic distortion (THD) of current and voltage at the point of common coupling. Numerical studies were conducted on the IEEE 34-bus system considering various scenarios of islanding and non-islanding conditions. The features obtained are then used to train several data mining techniques such as decision tree, support vector machine, neural network, bagging and random forest (RF). The simulation results showed that the important feature parameters can be evaluated based on the correlation between the extracted features. From the results, the four important features that give accurate islanding detection are the fundamental voltage THD, fundamental current THD, rate of change of voltage magnitude and voltage deviation. Comparison studies demonstrated the effectiveness of the RF method in achieving high accuracy for islanding detection.

scholarly journals Energy Flexometer: Transactive Energy-Based Internet of Things Technology

Energies ◽  
2018 ◽  
Vol 11 (3) ◽  
pp. 568 ◽  
Author(s):  
Muhammad Babar ◽  
Jakub Grela ◽  
Andrzej Ożadowicz ◽  
Phuong Nguyen ◽  
Zbigniew Hanzelka ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Energy Management ◽  
Power Distribution ◽  
Distribution System ◽  
Energy System ◽  
Evaluation Study ◽  
New Paradigm ◽  
Power Distribution System ◽  
Transactive Energy ◽  
Internet Of Things Technology

Effective Energy Management with an active Demand Response (DR) is crucial for future smart energy system. Increasing number of Distributed Energy Resources (DER), local microgrids and prosumers have an essential and real influence on present power distribution system and generate new challenges in power, energy and demand management. A relatively new paradigm in this field is transactive energy (TE), with its value and market-based economic and technical mechanisms to control energy flows. Due to a distributed structure of present and future power system, the Internet of Things (IoT) environment is needed to fully explore flexibility potential from the end-users and prosumers, to offer a bid to involved actors of the smart energy system. In this paper, new approach to connect the market-driven (bottom-up) DR program with current demand-driven (top-down) energy management system (EMS) is presented. Authors consider multi-agent system (MAS) to realize the approach and introduce a concept and standardize the design of new Energy Flexometer. It is proposed as a fundamental agent in the method. Three different functional blocks have been designed and presented as an IoT platform logical interface according to the LonWorks technology. An evaluation study has been performed as well. Results presented in the paper prove the proposed concept and design.

scholarly journals Measurement-based harmonic current modeling of mobile storage for power quality study in the distribution system

2017 ◽  
Vol 66 (4) ◽  
pp. 801-814 ◽  
Author(s):  
Christoph Wenge ◽  
Hui Guo ◽  
Christian Roehrig
Keyword(s):  
Power System ◽  
Power Quality ◽  
Power Distribution ◽  
Distribution System ◽  
Measurement Data ◽  
Load Flow ◽  
Harmonic Current ◽  
Current Harmonics ◽  
Power Distribution System ◽  
The Impact

Abstract Electric vehicles (EVs) can be utilized as mobile storages in a power system. The use of battery chargers can cause current harmonics in the supplied AC system. In order to analyze the impact of different EVs with regardto their number and their emission of current harmonics, a generic harmonic current model of EV types was built and implemented in the power system simulation tool PSS®NETOMAC. Based on the measurement data for different types of EVs three standardized harmonic EV models were developed and parametrized. Further, the identified harmonic models are used by the computation of load flow in a modeled, German power distribution system. As a benchmark, a case scenario was studied regarding a high market penetration of EVs in the year 2030 for Germany. The impact of the EV charging on the power distribution system was analyzed and evaluated with valid power quality standards.

A Correlation Analysis Model of Fault Location of Distribution System Based on RS-IA Data Mining

2017 ◽  
Vol 863 ◽  
pp. 345-354
Author(s):  
Zhang You Xu ◽  
Yi Fang Su ◽  
Pan Zhang ◽  
Rong Gang Ge ◽  
Fu Jian Chi ◽  
...  
Keyword(s):  
Data Mining ◽  
Correlation Analysis ◽  
Power Distribution ◽  
Distribution System ◽  
Fault Location ◽  
Distribution Network ◽  
Decision Table ◽  
Analysis Model ◽  
Power Distribution System ◽  
Current Limit

Fault location is an effective way to ensure quickly power recovery after a failure in power distribution system. Bad operation environment of FTU, damaged elements or lost information usually cause variations in fault information for correlation analysis in fault location of power distribution system. A data mining (DM) correlation analysis model based on rough sets (RS) theory and immune algorithm (IA) is proposed in this paper. Firstly, using RS theory to extract domain knowledge, a set of the given variant fault pattern is converted into a decision table in RS. Secondly, an attribute reduction of the decision table is made by using the IA theory, and the intrinsic correlation rules between input vector (condition attribute) and output vector (decision attribute) are mined. Then, the data mining method is used to deal with the distortion of FTU real-time input information. According to the current limit information sequence of section switches, line fault states in each section are judged to realize the fault location in power distribution network. Finally, the feasibility and effectiveness of the fault location model of distribution network based on RS-IA data mining model is verified by simulation.

scholarly journals An Early Warning Method of Distribution System Fault Risk Based on Data Mining

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yeying Mao ◽  
Zhengyu Huang ◽  
Changsen Feng ◽  
Hui Chen ◽  
Qiming Yang ◽  
...  
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Clustering Algorithm ◽  
Feature Subset ◽  
Power Distribution System ◽  
Softmax Classifier ◽  
Feature Extraction Method ◽  
Data Investigation ◽  
Optimal Feature Subset ◽  
Optimal Feature

Accurate warning information of potential fault risk in the distribution network is essential to the economic operation as well as the rational allocation of maintenance resources. In this paper, we propose a fault risk warning method for a distribution system based on an improved RelieF-Softmax algorithm. Firstly, four categories including 24 fault features of the distribution system are determined through data investigation and preprocessing. Considering the frequency of distribution system faults, and then their consequences, the risk classification method of the distribution system is presented. Secondly, the K-maxmin clustering algorithm is introduced to improve the random sampling process, and then an improved RelieF feature extraction method is proposed to determine the optimal feature subset with the strongest correlation and minimum redundancy. Finally, the loss function of Softmax is improved to cope with the influence of sample imbalance on the prediction accuracy. The optimal feature subset and Softmax classifier are applied to forewarn the fault risk in the distribution system. The 191-feeder power distribution system in south China is employed to demonstrate the effectiveness of the proposed method.

SFCL for Energy Storage Protection in a Power Distribution System

2016 ◽  
Vol 4 (1) ◽  
pp. 15-24
Author(s):  
V. Mohanbabu ◽  
◽  
Sk. Moulali ◽  
Ju Chan Na ◽  
Peng Cheng ◽  
...  
Keyword(s):  
Energy Storage ◽  
Power Distribution ◽  
Distribution System ◽  
Power Distribution System
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