IEEE Power Electronics Society

Membership co-Chair

Membership development, recruitment and retention.

IEEE Power Electronics Society

Mentorship co-Chair

As the co-chair of the PELS Mentorship initiative and steering committee, I organize and oversee the cadre of volunteers who shape and run the various mentoring activities for the society. As the co-Chair, my style is to provide the structure and organization to empower volunteers to contribute their ideas, time, energy, and enthusiasm. Since 2017 we have held successful mentoring roundtable events at each of the ECCE North America and APEC conferences. In 2019 my goal is to syndicate this concent to our conferences in the rest of the world, to benefit PELS members outside of the USA.

I report to President of IEEE PELS and provide periodic updates to the ADCOM.

IEEE Power Electronics Society

Chair, Graduates of the Last Decade (GOLD) Committee

I Initiated the IEEE Power Electronics Society's (PELS) Graduates of the Last Decade (GOLD) program. Since September 2010, we have held an event at every ECCE / APEC conference. Past events include social mixers, career guidance speakers, a logo design contest, and opportunities for young professionals and students to interact one-on-one and in small groups with senior members of the power electronics society for career mentoring.

Background:

The first ten years after graduation from university can be challenging for young professionals. Employment searches, new jobs, professional growth, career development, and life status changes are common experiences for many recent graduates. Going through these challenges can be daunting but IEEE members never have to go through these experiences alone or uninformed. IEEE Graduates of the Last Decade (GOLD) was created in 1996 as a membership program to help students transition to young professionals within the larger IEEE community. IEEE young professionals are automatically added to the GOLD member community as they graduate.

IEEE Power Electronics Society

Distinguished Lecturer

Robert worked at IEEE Power Electronics Society as a Distinguished Lecturer

IEEE Power Electronics Society

Member-at-Large, Administrative Committee (ADCOM)

Voting member of the Power Electronics Society (PELS) Administrative Committee (ADCOM), elected by the members of the society for a three-year term: 1 Jan 2015 - 31 Dec 2017.

Auto-tuning the cost function weight factors in a model predictive controller for a matrix converter VAR compensator

IEEE Energy Conversion Congress and Exposition (ECCE)

This paper presents an auto-tuning technique for online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute error of the optimization objective and the corresponding constraints. The application considered is a reactive power compensation technique using MPC of a direct matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). The result demonstrates that the proposed auto-tuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties such as load variation. The proposed capacitor-less reactive power compensator based on auto tuned MPC cost function weight factor is implemented experimentally using dSpace DS1007.

Auto-tuning the cost function weight factors in a model predictive controller for a matrix converter VAR compensator

IEEE Energy Conversion Congress and Exposition (ECCE)

This paper presents an auto-tuning technique for online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute error of the optimization objective and the corresponding constraints. The application considered is a reactive power compensation technique using MPC of a direct matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). The result demonstrates that the proposed auto-tuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties such as load variation. The proposed capacitor-less reactive power compensator based on auto tuned MPC cost function weight factor is implemented experimentally using dSpace DS1007.

Auto-tuning the cost function weight factors in a model predictive controller for a matrix converter VAR compensator

IEEE Energy Conversion Congress and Exposition (ECCE)

This paper presents an auto-tuning technique for online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute error of the optimization objective and the corresponding constraints. The application considered is a reactive power compensation technique using MPC of a direct matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). The result demonstrates that the proposed auto-tuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties such as load variation. The proposed capacitor-less reactive power compensator based on auto tuned MPC cost function weight factor is implemented experimentally using dSpace DS1007.

Arc Fault Signal Detection - Fourier Transformation vs. Wavelet Decomposition Techniques using Synthesized Data

40th IEEE Photovoltaic Specialists Conference (PVSC)

Auto-tuning the cost function weight factors in a model predictive controller for a matrix converter VAR compensator

IEEE Energy Conversion Congress and Exposition (ECCE)

This paper presents an auto-tuning technique for online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute error of the optimization objective and the corresponding constraints. The application considered is a reactive power compensation technique using MPC of a direct matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). The result demonstrates that the proposed auto-tuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties such as load variation. The proposed capacitor-less reactive power compensator based on auto tuned MPC cost function weight factor is implemented experimentally using dSpace DS1007.

Arc Fault Signal Detection - Fourier Transformation vs. Wavelet Decomposition Techniques using Synthesized Data

40th IEEE Photovoltaic Specialists Conference (PVSC)

Arc Generator for Photovoltaic Arc Fault Detector Testing

43rd Photovoltaic Specialists Conference

Auto-tuning the cost function weight factors in a model predictive controller for a matrix converter VAR compensator

IEEE Energy Conversion Congress and Exposition (ECCE)

This paper presents an auto-tuning technique for online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute error of the optimization objective and the corresponding constraints. The application considered is a reactive power compensation technique using MPC of a direct matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). The result demonstrates that the proposed auto-tuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties such as load variation. The proposed capacitor-less reactive power compensator based on auto tuned MPC cost function weight factor is implemented experimentally using dSpace DS1007.

Arc Fault Signal Detection - Fourier Transformation vs. Wavelet Decomposition Techniques using Synthesized Data

40th IEEE Photovoltaic Specialists Conference (PVSC)

Arc Generator for Photovoltaic Arc Fault Detector Testing

43rd Photovoltaic Specialists Conference

A Thermal Model to Predict Lifetime Operating Temperature of a Photovoltaic Module, Suitable for Reliability Prediction of a PV Module-Integrated Inverter

IEEE - ECCE

Auto-tuning the cost function weight factors in a model predictive controller for a matrix converter VAR compensator

IEEE Energy Conversion Congress and Exposition (ECCE)

This paper presents an auto-tuning technique for online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute error of the optimization objective and the corresponding constraints. The application considered is a reactive power compensation technique using MPC of a direct matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). The result demonstrates that the proposed auto-tuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties such as load variation. The proposed capacitor-less reactive power compensator based on auto tuned MPC cost function weight factor is implemented experimentally using dSpace DS1007.

Arc Fault Signal Detection - Fourier Transformation vs. Wavelet Decomposition Techniques using Synthesized Data

40th IEEE Photovoltaic Specialists Conference (PVSC)

Arc Generator for Photovoltaic Arc Fault Detector Testing

43rd Photovoltaic Specialists Conference

A Thermal Model to Predict Lifetime Operating Temperature of a Photovoltaic Module, Suitable for Reliability Prediction of a PV Module-Integrated Inverter

IEEE - ECCE

Sensitivity Analysis to Model Parameter Errors of MPPT by Model Predictive Control for Photovoltaic Applications

IEEE First Workshop on Smart Grid & Renewable Energy (SGRE)

Due to variability of solar energy resources, maximum power point tracking (MPPT) of photovoltaic (PV) is required to ensure continuous operation at the maximum power point (MPP) and maximize the energy harvest. This paper presents a digital model predictive control technique to employ the MPPT for flyback converter for photovoltaic applications. The MPP operating point is determined by using perturb and observe (P&O) technique. The proposed two-steps predictive model based MPPT presents significant advantages in dynamic response and power ripple at steady state. A characteristic of MPC is the use of system models for selecting optimal actuations, thus evaluating the effect of model parameter mismatch on control effectiveness is of interest. In this paper the load model is eliminated from the proposed MPC formulation by using an observer based technique. The sensitivity analysis results indicate a more robust controller to uncertainty and disturbances in the resistive load.

Auto-tuning the cost function weight factors in a model predictive controller for a matrix converter VAR compensator

IEEE Energy Conversion Congress and Exposition (ECCE)

This paper presents an auto-tuning technique for online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute error of the optimization objective and the corresponding constraints. The application considered is a reactive power compensation technique using MPC of a direct matrix converter. This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). The result demonstrates that the proposed auto-tuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties such as load variation. The proposed capacitor-less reactive power compensator based on auto tuned MPC cost function weight factor is implemented experimentally using dSpace DS1007.

Arc Fault Signal Detection - Fourier Transformation vs. Wavelet Decomposition Techniques using Synthesized Data

40th IEEE Photovoltaic Specialists Conference (PVSC)

Arc Generator for Photovoltaic Arc Fault Detector Testing

43rd Photovoltaic Specialists Conference

A Thermal Model to Predict Lifetime Operating Temperature of a Photovoltaic Module, Suitable for Reliability Prediction of a PV Module-Integrated Inverter

IEEE - ECCE

Sensitivity Analysis to Model Parameter Errors of MPPT by Model Predictive Control for Photovoltaic Applications

IEEE First Workshop on Smart Grid & Renewable Energy (SGRE)

Due to variability of solar energy resources, maximum power point tracking (MPPT) of photovoltaic (PV) is required to ensure continuous operation at the maximum power point (MPP) and maximize the energy harvest. This paper presents a digital model predictive control technique to employ the MPPT for flyback converter for photovoltaic applications. The MPP operating point is determined by using perturb and observe (P&O) technique. The proposed two-steps predictive model based MPPT presents significant advantages in dynamic response and power ripple at steady state. A characteristic of MPC is the use of system models for selecting optimal actuations, thus evaluating the effect of model parameter mismatch on control effectiveness is of interest. In this paper the load model is eliminated from the proposed MPC formulation by using an observer based technique. The sensitivity analysis results indicate a more robust controller to uncertainty and disturbances in the resistive load.

Information Theoretically Secure, Enhanced Johnson Noise Based Key Distribution over the Smart Grid with Switched Filters

PLOS One

AbstractWe introduce a protocol with a reconfigurable filter system to create non-overlapping single loops in the smart power grid for the realization of the Kirchhoff-Law-Johnson-(like)-Noise secure key distribution system. The protocol is valid for one-dimensional radial networks (chain-like power line) which are typical of the electricity distribution network between the utility and the customer. The speed of the protocol (the number of steps needed) versus grid size is analyzed. When properly generalized, such a system has the potential to achieve unconditionally secure key distribution over the smart power grid of arbitrary geometrical dimensions.