Walid Morsi Ibrahim
PhD, PEng

Impact of Second-Generation Plug-In Battery Electric Vehicles on the Aging of Distribution Transformers Considering TOU Prices

IEEE Power and Energy Society General Meeting

Smart Grid Research

IEEE Research Showcase

Understanding Power Quality in Smart Grids

10th IEEE International Conference for Upcoming Engineers

A New Approach for Fault Classification in Microgrids Using Optimal Wavelet Functions Matching Pursuit

This work introduces a new approach that uses a combination of wavelet functions and Machine Learning (ML) for fault classification in MicroGrids (MGs). Particle Swarm Optimization is applied to identify the optimal wavelet functions combination that serves as a matching pursuit to extract the most prominent features, which are hidden in the current/voltage waveforms when applying the Discrete Wavelet Transform. Four different classification techniques (i.e. Decision Tree, K-Nearest Neighbor, Support Vector Machine, and Naïve Bayes) are used to automate the procedure of fault classification in MGs and their performances are statistically compared. The Consortium for Electric Reliability Technology Solutions (CERTS) MG is used to exemplify the effectiveness of the proposed approach after modeling the MG system in Power Systems Computer Aided Design/Electromagnetic Transient Direct Current (PSCAD/EMTDC) software package.

View more - A New Approach for Fault Classification in Microgrids Using Optimal Wavelet Functions Matching Pursuit

Optimal Secondary Distribution System Design Considering Rooftop Solar Photovoltaics

This study presents a new approach for optimally designing a secondary distribution system, taking into consideration the effect of prosumers owning rooftop solar photovoltaics in residential subdivisions. The methodology is presented and the optimization problem of secondary distribution system (SDS) design in the presence of prosumers is mathematically formulated. Moreover, numerical examples that consider houses of different types/sizes are introduced to exemplify the proposed approach.

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A New Harmony Search Approach for Optimal Wavelets Applied to Fault Classification

This paper presents a novel approach based on the Harmony Search Algorithm (HSA) to optimally determine suitable wavelet functions and wavelet decomposition levels for accurate fault classification in Transmission Lines (TLs) unlike previous works in which only one arbitrary wavelet function is used. Discrete Wavelet Transform (DWT) is used to extract the features in the voltage and/or current signals using the identified wavelet functions. Machine learning classifiers are then used to build a proper classification model to automate the fault classification process. The results of applying the proposed approach are presented and discussed, and conclusions are drawn.

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Nonintrusive Load Monitoring Using Semi-Supervised Machine Learning and Wavelet Design

This paper presents a new approach based on semi-supervised machine learning and wavelet design applied to nonintrusive load monitoring. Co-training of two machine learning classifiers is used to automate the process of learning the load pattern after designing new wavelets. The numerical results demonstrating the effectiveness of the proposed approach are discussed and conclusions are drawn.

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Nonintrusive Load Monitoring Using Wavelet Design and Machine Learning

This paper presents a new concept based on wavelet design and machine learning applied to nonintrusive load monitoring. The wavelet coefficients of length-6 filter are determined using procrustes analysis and are used to construct new wavelets to match the load signals to be detected, unlike previous work which used previously designed wavelet functions that are special cases of Daubechies filters to suit other nonpower system applications such as communications and image processing. The results of applying the new concept to a test system consisting of four loads have shown that the newly designed wavelet can improve the prediction accuracy compared with that obtained using Daubechies filter of order three while keeping the prominent features of the pattern in the detail levels.

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Optimal Secondary Distribution System Design Considering Plug-In Electric Vehicles

In this paper, the problem of secondary distribution system (SDS) design considering plug-in electric vehicles (PEVs) charging demand is addressed using meta-heuristic optimization to minimize the overall secondary system costs. The objective function and the necessary constraints are mathematically formulated and presented. The effectiveness of the proposed SDS design approach is evaluated in terms of the overall SDS costs and transformer loss of life (LOL). The proposed design approach represents an effective tool for electric utilities to accommodate PEVs during the planning stage of SDS and also when retrofitting existing SDS.

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Impact of Second-Generation Plug-In Battery Electric Vehicles on the Aging of Distribution Transformers Considering TOU Prices

This paper investigates the impact of second-generation (SG) plug-in battery electric vehicles (PBEVs) on distribution transformers insulation life considering the time-of-use (TOU) prices. The effect of dual Level 2 charging, 3.7kW, and 6.6kW on the loss of life (LOL) of a distribution transformer is also studied. The LOL results demonstrating the effectiveness of SG PBEVs charging as two clusters versus one cluster are discussed and conclusions are drawn.

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Impact of Wind-Based Distributed Generation on Electric Energy in Distribution Systems Embedded With Electric Vehicles

In this paper, the synergy between wind-based distributed generation (DG) and plug-in electric vehicles (PEVs) is studied. MonteCarlo is used to address the uncertainties associated with wind speed variations and charging of PEVs hence simulating their impact at the distribution system (DS) level considering different DG penetration (up to 35%) and different PEV penetration (up to 50%). The excess in active/reactive power, energy exceeding normal (EEN), unserved energy (UE), and energy losses are investigated in this study. Forty-eight penetration scenarios involving DGs and PEVs are studied in this work and simulated in the IEEE 123-bus radial power distribution test system after modeling its secondary circuit in OpenDSS. The results of the simulation show that 30% wind-based DG penetration may be adequate to supply the active energy needed to charge PEVs. However, this might result in a reverse reactive power flow back to the substation.

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Probabilistic Impact of Transportation Electrification on the Loss-of-Life of Distribution Transformers in the Presence of Rooftop Solar Photovoltaic

In this paper, the impact of plug-in electric vehicle (PEV) charging on distribution transformer overload and loss-of-life (LOL) in the presence of rooftop solar photovoltaic (PV) is probabilistically quantified. The Monte Carlo (MC) method is used to address the uncertainties resulting from solar irradiance and temperature in case of solar PV and also to emulate the probabilistic aspect of PEV charging. Twenty scenarios of different penetration levels of solar PVs and PEVs are considered in this work. The results have shown significant reduction in percentage LOL due to solar PV contribution in the case of all-electric (AE) residential dwellings and hence the transformer replacement may be deferred by nearly 4 years, while it has a minor effect in the case of residential dwellings with gas heat and electric water heaters (WWH).

View more - Probabilistic Impact of Transportation Electrification on the Loss-of-Life of Distribution Transformers in the Presence of Rooftop Solar Photovoltaic

Power Quality Assessment in Distribution Systems Embedded with Plug-in Hybrid and Battery Electric Vehicles

The impact of electric vehicles on power quality in electric distribution system is evaluated. Voltage deviations such as under/over voltage and voltage imbalance are probabilistically quantified using Monte Carlo. Moreover, distribution transformers overload and unbalance are assessed for different vehicle types (i.e., plug-in hybrid and battery electric), different vehicle penetration (up to 50%) while considering level 1 and level 2 charging. The results of Monte Carlo reveal that battery electric vehicles can cause more overload to distribution transformers compared to plug-in hybrid electric vehicles. Also level 2 and level 1 can be problematic resulting in secondary bus undervoltage and transformer unbalance, respectively.

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Outstanding Master's Thesis Award

Under the supervision of Dr. Morsi, Jessie Michael Gillis, Master of Applied Science in Electrical and Computer Engineering student received the 2017 Outstanding Master’s Thesis Award for his thesis entitled: Time-Frequency Analysis Techniques for Non-Intrusive Load Monitoring.

Senior Member

Dr. Morsi was appointed to this role for his contributions to advancing the field.

Poster Award

Dr. Morsi and his students received this award for their poster entitled: Intelligent Energy Monitoring System Design for Smart Homes.

Adjunct Professor

In this role, Dr. Morsi collaborates on research projects and supervises student research.

Poster Award

Dr. Morsi and his students received this award for their poster entitled: Solar Generation Tracking System.

Centre of Learning and Teaching Award

Dr. Morsi received this award for Improvement in Teaching Scores.

Best Paper Award

Dr. Morsi received this award for his paper entitled: A New Islanding Detection Approach Using Wavelet Packet Transform for Wind-based Distributed Generation.

Institute of Electrical and Electronic Engineers (IEEE)

IEEE Power Engineering Society

Professional Engineers Ontario

Ontario Society of Professional Engineers