Total Harmonics Distortion Prediction at the Point of Common Coupling of industrial load with the grid using Artificial Neural Network  

作　　者：Emenike Ugwuagbo Adeola Balogun Biplob Ray Adnan Anwar Chikodili Ugwuishiwu 

机构地区：[1]Centre for Space Transport and Propulsion,(CSTP),Epe,1 LASU Epe drive,Lagos,106101,Nigeria [2]Department of Electrical&Electronics Engineering,University of Lagos,Akoka,101017,Lagos,Nigeria [3]Centre for Intelligent Systems(CIS),School of Engineering and Technology,CQ University,Norman Gardens,4701,Rockhampton,Australia [4]Strategic Centre for Cyber Security Research Institute(CSRI),School of Information Technology,Deakin University,Geelong,3216,Melbourne,Australia [5]Department of Computer Science,University of Nigeria,Nsukka,410001,Enugu,Nigeria

出　　处：《Energy and AI》2023年第4期294-303,共10页能源与人工智能（英文）

摘　　要：Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the customers.One of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the PCC.The proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic distortion.The input parameter used in the models is easily measured with basic power meters.The model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing plants.Eight(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden layer.The results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.

关 键 词：Total harmonics distortion(THD) Power quality(PQ) Artificial neural network(ANN) 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]