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

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Energy and AI 2023年第4期14卷 294-303页

作者： Emenike Ugwuagbo Adeola Balogun Biplob Ray Adnan Anwar chikodili ugwuishiwu Centre for Space Transport and Propulsion (CSTP)Epe1 LASU Epe driveLagos106101Nigeria Department of Electrical&Electronics Engineering University of LagosAkoka101017LagosNigeria Centre for Intelligent Systems(CIS) School of Engineering and TechnologyCQ UniversityNorman Gardens4701RockhamptonAustralia Strategic Centre for Cyber Security Research Institute(CSRI) School of Information TechnologyDeakin UniversityGeelong3216MelbourneAustralia Department of Computer Science University of NigeriaNsukka410001EnuguNigeria

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 *** 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 *** proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic *** input parameter used in the models is easily measured with basic power *** 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 ***(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 *** 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)

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Influence of automobile industrial wastewater on soil quality

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International Journal of Agricultural and Biological Engineering 2014年第6期7卷 123-128页

作者： Ugwu Samson Nnaemeka Anyadike Chinenye Chukwuemeka Echiegu Emmanuel Nwoke Oji Achuka ugwuishiwu Boniface Department of Agricultural and Bioresources Engineering University of NigeriaNsukkaNigeria

Reuse of industrial wastewater(effluent)for irrigation purpose is a common ***,lack of adequate treatment of the effluent can cause soil deterioration,vegetation destruction and contamination of the aquatic *** adverse effects necessitated the study of wastewater irrigation in Emene Industrial Layout,Enugu State,*** and soil samples were collected,analyzed and subjected to Analysis of Variance(ANOVA)for Completely Randomized Design(CRD)at 5% probability level using GENSTAT *** results obtained from the study were compared with FAO soil and water *** wastewater analysis suggested that contamination at the untreated stage was very high and results at the treatment level were within the FAO reuse *** study also found wide variation in chemical status of industrial wastewater treated *** all the values for the wastewater treated soil were not within the FAO irrigated soil chemical properties *** suggests high re-contamination along the open channel(from non-point sources)before *** calls for proper monitoring and treatment of the industrial effluent prior to irrigation application.

关键词： irrigation soil quality contamination wastewater automobile industry

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