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### E in E & EElectro-Technology In Energy & Environment

### Electrostatic/Electromagnetic systems Bio-Filter-B Device for Airborne Contaminant Disinfection

### Low Cost Stand-alone Renewable systemsPhotovoltaic/Wind Energy Hybrid-Utilization Schemes

Dynamic Error Driven Proportional plus Integral (PI) Controller

and gains (Kp, Ki) are assigned to minimize the time-weighted excursion index J0 (Developed by Dr. Sharaf)

### Electric Utility-Voltage Stabilization And Reactive Compensation Using A Novel FACTS- STATCOM Scheme

Prof. Dr. Adel M. Sharaf, P.Eng,

SM IEEE

UNB, ECE-Department,

Fredericton, NB, Canada

Outline

- Title
- Summary
- Environmental & Environmental Engineering Technologies
- Environmental-Interactions & Requirements
- Electro technology
- Promotional Activities
- Methodology & Approach
- Planned Research Activities
- Dr .Sharaf -Research Activities
- Current Research
- Sample Presentations

Environment-Triangle

Engineering

Economics

Science

Environmental Engineering & Technology is the Science and Engineering of mitigation techniques & tools, Remediation Technology & Standards for :

- Recycle & Reuse and Reduce
- Efficient Utilization of Natural Resources including Alternate/Renewable Energy Sources
- Optimized designs, Management Tools and Standards to prolong life cycle, safety and prevent pollution in water, Air and soil.
- Ensure Personal Safety to personnel and live stock.
- Reduce waste and especially hazardous waste
- Enhance quality of living by reducing Environmental/Safety hazards, Noise and all forms of pollution & Contaminations.
- Sustainable Development and Green/Conservation Products.

Environmental-Interactions & RequirementsAreas:[Mining /Oil & Gas/ Soil Remediation/Transportation /Waste Management/Pollution Abatement]

A

Balanced

Soil

Water

Animals

Humans

B

Harmonious

D

Diverse

Plants

Insects

Energy

Air

C

Clean

Electro-technology

The applications of Electrical Engineering Principles and Phenomena in process industries:

Heating, Cooling, smelting and Environmental pollution abatement Systems& Devices with specific concern to the following

1. Electrical Power Efficiency and Energy Conservation

2. Renewable energy systems (Wind, Photo-voltaic, Fuel Cell, Small Hydro, Hybrid,…..) & utilization and use in Remote/Isolated Communities.

3. Applications of Electromagnetic (EM) and Electrostatic (ES) fields in process stabilization, Disinfection, Odor control, Gaseous absorption, Anomaly / Fault / failure, detection using Electrical Signature FFT Tools, Eddy- Current Mapping/ FFT-Wavelets & Neural Network Mapping & Identification technologies.

4. Application of AI based-Soft Computing Technologies (ANN, Fuzzy, Neuro-Fuzzy and Genetic Algorithms in Fault /Anomaly Detection, Relaying, Control and Safety.

5.Electric Grid Utility Systems :Voltage and Frequency (FACTS-Based) Stabilization, Blackout-Security and Power Quality PQ Enhancement

Promotional Activities (ANN, Fuzzy, Neuro-Fuzzy and Genetic Algorithms in Fault /Anomaly Detection, Relaying, Control and Safety.

Approach (ANN, Fuzzy, Neuro-Fuzzy and Genetic Algorithms in Fault /Anomaly Detection, Relaying, Control and Safety.

- Student-Annual Environmental design Competition
- National International Collaborative Research
- Networks of excellence
- Seminar U/G Environmental Design projects
- Competing in National & International Competitions in Energy & Environmental NRC (Energy Ambassadors) !! Green-Plug-Winner, OTTAWA 2005
- Interfaculty (Science & Engineering) Collaborative Research
- Innovative-Teaching by Using Current Research in Teaching !!

Specialized Courses & Programs (ANN, Fuzzy, Neuro-Fuzzy and Genetic Algorithms in Fault /Anomaly Detection, Relaying, Control and Safety.C. Courses

“ Environmental Engineering & Technology”

Based on

- Case- Studies
- Invited Guest- Seminars & Lectures
- Project Based Learning -PBL

D. M.Eng / M.Sc. & Research Collaboration (ANN, Fuzzy, Neuro-Fuzzy and Genetic Algorithms in Fault /Anomaly Detection, Relaying, Control and Safety.

- Inter/ Multi-DICPLINARY /Inter-Faculty/Inter-Departmental Research
- (M.Eng /M.Sc) Program in Environmental Engineering & Technology

Environmental Engineering/Technology (ANN, Fuzzy, Neuro-Fuzzy and Genetic Algorithms in Fault /Anomaly Detection, Relaying, Control and Safety.Promotion Methodology & Approach

1. Creation of Student-Environmental Innovation Club (SEIC)

2. Annual Green Environmental student Competition

3. Joint Interfaculty Engineering & Science Research using Joint Senior Thesis projects & Co-Supervision of Graduate students

4. Joint Business, Industry and Electric Utility sponsored Value-Added Research.

5. Short-Term Environmental Consulting Services-Office (CESO)

- National and International Collaborative Research , Research links, Bi-lateral International Research Agreements.
- Joint International Educational Programs & Research-Initiatives (AL-AHRAM-Cairo-CUC, Middle East, S.E. Asia)

Planned Research Activities (ANN, Fuzzy, Neuro-Fuzzy and Genetic Algorithms in Fault /Anomaly Detection, Relaying, Control and Safety.

(1) Electricity Power/Energy Conservation & Demand Side Management.

(2) AI-Based Fault Detection and Relaying Protection and Safety Schemes

(3) Harmonic/Noise Mitigation and Power Quality Enhancement

(4) Applications of Ripple orthogonal Pulsating and Rotating Electromagnetic & Electrostatic fields in:-

(a) Germicidal control, sterilization, and Disinfection (Water, Milk, liquids, Hospital, hazardous waste,…)

(b) Zeolite-Enhanced Gaseous-adsorption and Odor control using Air Filters and Muffler systems.

(c) Sick Building mitigation and air filteration systems

(d) Efficient Electro-technology based heating (Resistive, inductive, arc,…) systems

5. Renewable/ alternative dispersed standalone, hybrid and Grid-interconnected electric energy supply systems using wind, small hydro, photovoltaic, fuel cell, Micro Gas turbine, Hybrid Systems.

6. Intelligent Electric Arc/ Fire Detection and Relaying Schemes using harmonic FFT finger printing and Electric Signature Analysis ESA Tools ( for Buildings, commercial Installation, Mining process industries and low- voltage Electric Grid systems..

7. systemsLarge Machinery/Motorized and Electromechanical Vibration/Torsional Monitoring and anomaly/failure/fault diagnostics using ANN-Based nonlinear pattern recognition mapping and vector transformations wavelets, Short -Term FFT, Inverse-Cosine, Temporal, Statistical and Abduction Rules.

8. Active Noise (Traffic & Machinery)- Cancellation in Roads & Buildings

9. Intelligent Fuzzy logic based decision Making Software.

10. Applications of Ground Resistance(-R-ground) Spectra Scans and Electromagnetic field penetration Mapping in personnel Land-Mine detection & other Archeological Site-Detection

Dr. A. M. Sharaf ‘s: Current Research Activities systems

1.The Green Plug & Smart power Filters and Energy Misers/Economics

2. Wind-Utilization schemes

3. Photovoltaic Utilization schemes

4. FACTS Based stabilization Devices For Electric utility Grid Systems

5. Bio-Filter Using (EM/EM/ES/UV)

6. The Electric-Foot-Generator (EFG)

7. Arc Fault-HIF Detection and fire sentry Relaying Systems

- Zeolite-Gaseous Adsorption & Odor control
- NG/PEM-fuel cell Efficiency Enhancement Using (LF/HF)Ripple Electromagnetic Reformer and Cell Polarization filters
- Efficient Hydrogen Based Hybrid Storage/ Reformer Technology using Plasma(Microwave/Laser/Magnetic Field).
- LNG/Hydrogen/PV/Wind/Fuel Cells / Micro-Gas turbines)

Dr. A. M. Sharaf ‘s: Current Research Activities systems

1.The Green Plug & Smart power Filters and Energy Misers/Economics

2. Wind-Utilization schemes

3. Photovoltaic Utilization schemes

4. FACTS Based stabilization Devices For Electric utility Grid Systems

5. Bio-Filter Using (EM/EM/ES/UV)

6. The Electric-Foot-Generator (EFG)

7. Arc Fault-HIF Detection and fire sentry Relaying Systems

- Zeolite-Gaseous Adsorption & Odor control
- NG/PEM-fuel cell Efficiency Enhancement Using (LF/HF)Ripple Electromagnetic Reformer and Cell Polarization filters
- Efficient Hydrogen Based Hybrid Technology (Hydrogen/PV/Wind/Fuel Cells / Micro-Gas turbines)

Prof. Dr. Adel M. Sharaf, SM IEEE

Summary systems

- Background
- Methodology
- Electrostatic Method
- Electromagnetic Method

- Work Completed
- Testing

Background systems

- Reasons for This Project
- growing concern for indoor air quality
(e.g. mold, smoke)

- allergies, asthma, other respiratory problems
- disease control
- threat of biological terrorism, (e.g. Anthrax)

- growing concern for indoor air quality

Background systems

- Goal of the Project
- Design and construct a bio-filter using the combined orthogonal electromagnetic and electrostatic EM/EM/ES Principle developed by Dr. Adel M. Sharaf for his Potable Water/Liquid Germicidal/Sterilization/Disinfectant Filters.

Background systems

HPAC Engineering , January 2002 (http://www.arche.psu.edu/iec/abe/pubs/foam.pdf)

Methodology systems

- Electrostatic Effect------E=V/d
- Process commonly referred to as “Electrostatic Precipitation” or “Electronic Air Cleaning”
- 2-stage process; charging stage and collection stage
- Effective in filtering particles form .01 to 10 microns
- Process consumes relatively low power

Methodology systems

Methodology systems

- Airborne molecules collide with negative ions

Methodology systems

- Airborne molecules acquire a negative net charge and collect on the positively charged plate

Methodology systems

- Electromagnetic Component
- FDA study in 2000 demonstrated that many Bacteria/Cysts/Germs/Micro Living organisms could be destroyed by an Oscillating or Pulsed Magnetic Field.
- Depends on Applied ripple-frequency, magnetic field strength, and duration of Electric-Pulses.
- Pulsed-Non-ionizing Magnetic Low frequency/high frequency magnetic fields PMF-(0-50 kilohertz) can be generated using electromagnetic coils.

Methodology systems

B

- Leading theory states that a PMF can loosen the covalent bonds between ions and proteins in microorganisms.
- Living Micro cells Membrane contains Charged + - ions!!
- Ions move in a circular path when entering a perpendicular magnetic field.
- The motion causes the protein molecules and ions to oscillate and eventually break the covalent bonds that bind them.

Methodology systems

Br

- E = V/d
- E is a function of the Electric Potential divided by the distance between the plates.

E

Orthogonal Electric and Magnetic Fields

Methodology systems

Cross-section of a finite solenoid

Magnetic flux density along the axis of finitely long solenoid.

Inductance of a solenoid

Methodology systems

- Quick-Field FEM-Software- Simulations
- 2-D finite element analysis program (Free Student Edition)
- Electrostatic simulations based on Poisson’s Equation
- Magnetic simulations are based on vector Poisson’s Equation

Work Completed systems

- Researched several methods of airborne filtration and disinfection including electrostatic and electromagnetic methods.
- Simulated magnetic and electric field strengths of the design elements using Quick-Field software.
- Initiated the design of dual variable-frequency triggering circuits for electromagnetic coils.
- Designed a prototype model using EM/ES with a second enhanced model with UV-ULTRA VIOLET and Sonic Wave generators as an ADD-ON Killing Mechanisms

TESTING systems

- Testing (possibly at NB-RPC or NRC Test facilities.)
- Testing for Sterilization/Kill Rate/Germicidal Effect for different:
- Applied frequency
- Flux Density EM- alone
- EM/ES combined effect
- EM/ES with ADD-ON UV and US-Ultra-Sonic waves
- UV-GERMICIDAL Range and US in the range of 20-120 Kilohertz
- Effect of Air Flow rate

References systems

- 1.) Hofmann, G.A. 1985. Deactivation of microorganisms by an oscillating magnetic field. U.S. Patent 4,524,079.
- 2.) Moore, R.L. 1979. Biological effects of magnetic fields. Studies with microorganisms. Can. J. Microbiol., 25:1145-1151.
- 3.) Kinetics of Microbial Inactivation for Alternative Food Processing Technologies . U. S. Food and Drug Administration. Available: URL http://vm.cfsan.fda.gov/~comm/ift-omf.html. Last accessed 10 February 2004
- 4.) Gary Wade and Rifetech. (1998). EXCITING POSSIBILITIES IN PULSED INTENSE MAGNETIC FIELD THERAPY. Rife Healing Energy. Available: URL http://vm.cfsan.fda.gov/~comm/ift-omf.html. Last accessed 10 February 2004.
- 5.) Aerobiological Engineering: Electrostatic Precipitation. The Pennsylvania State University Aerobiological Engineering. Available: URL http://www.arche.psu.edu/iec/abe/electro.html. Last accessed 10 February 2004.
- 6.) What is an Ionizer. What is an Ionizer. Available: URL http://www.ionizer.com.my/What_is_ionizer.htm. Last accessed 10 February 2004.

QUESTIONS ?? systems

Prof. Dr. A. M. Sharaf, SM IEEE

Presentation Outline systems

- Introduction
- Research Objectives
- Low Cost Stand-alone Photovoltaic/Wind Schemes and Error Driven Controllers
- Preliminary Results

Introduction systems

- PV cells
- PV modules
- PV arrays
- PV systems
- Stand-alone photovoltaic systems
- Hybrid renewable energy systems

The advantages of PV solar energy: systems

- Clean and green energy source that can reduce green house gases
- Highly reliable and needs minimal maintenance
- Costs little to build and operate
- Almost has no environmental polluting impact
- Modular and flexible in terms of size, ratings and applications

Economical uses of stand-alone PV systems: systems

- Small village electricity supply
- Water pumping and irrigation systems
- Cathodic- protection
- Communications
- Lighting and small appliances
- Emergency power systems and lighting systems

I-V characteristics of the single solar cell systems

The circuit diagram of the single solar cell

Maximum Power Point Tracking (MPPT) systems

- The photovoltaic system displays an inherently nonlinear current-voltage (I-V) relationship, requiring an online search and identification of the optimal maximum power operating point.
- MPPT controller is a power electronic DC/DC converter or DC/AC inverter system inserted between the PV array and its electric load to achieve the optimum characteristic matching
- PV array is able to deliver maximum available solar power that is also necessary to maximize the photovoltaic energy utilization

I-V and P-V characteristics of a typical PV array at a fixed ambient temperature

and solar irradiation condition

The performance of any stand-alone PV system depends on: conditions

- Electric load operating conditions/Excursions/ Switching
- Ambient/junction temperature (Tx)
- Solar Insolation/irradiation variations (Sx)

Research Objectives conditions

- Develop/test/validate mathematical models for stand-alone photovoltaic PV and PV/wind energy conversion schemes in MATLAB/Simulink/Sim-Power Systems software environment.
- Design/test/validate novel maximum photovoltaic power tracking controllers for photovoltaic PV and PV/wind energy conversion schemes namely:
(1) Photovoltaic Four-Quadrant PWM converter PMDC

motor drive: PV-DC scheme I.

(2) Photovoltaic DC/DC dual converter: PV-DC scheme II.

(3) Photovoltaic DC/AC inverter: PV-AC scheme.

(4) Hybrid photovoltaic/wind energy utilization scheme.

Low Cost Stand-alone Photovoltaic/Wind Schemes and Error Driven Controllers

- Photovoltaic Four-Quadrant PWM Converter PMDC Motor Drive: PV-DC Scheme I
- Photovoltaic DC/DC Dual Converter:PV-DC Scheme II
- Photovoltaic DC/AC Inverter: PV-AC Scheme
- Hybrid Photovoltaic/Wind Energy Utilization Scheme

Photovoltaic Four-Quadrant PWM Converter PMDC Motor Drive: PV-DC Scheme I

Photovoltaic powered Four-Quadrant PWM converter PMDC motor drive system

Dynamic Error Driven Proportional plus Integral (PI) Controller

Dynamic tri-loop error driven Proportional plus Integral control system (Developed by Dr. Sharaf)

The loop weighting factors ( Controllerγw, γi and γp)

and gains (Kp, Ki) are assigned to minimize the time-weighted excursion index J0 (Developed by Dr. Sharaf)

where

- is the total excursion error
- N= T0/Tsample
- T0: Largest mechanical time constant (10s)
- Tsample: Sampling time (0.2ms)

Dynamic Error Driven Self Adjusting Controller (SAC) Controller

Dynamic tri-loop self adjusting control (SAC) system (Developed by Dr. Sharaf)

The loop weighting factors ( Controllerγw, γI and γp) and the parameters k0 and β are assigned to minimize the time-weighted excursion index J0

(Developed by Dr. Sharaf)

where

- N= T0/Tsample
- T0: Largest mechanical time constant (10s)
- Tsample: Sampling time (0.2ms)
- t(k)=k·Tsample: Time at step k in seconds

Photovoltaic DC/DC Dual Converter: PV-DC Scheme II Controller

Stand-alone photovoltaic DC/DC dual converter scheme for village electricity use

Dynamic Error Driven Proportional plus Integral (PI) Controller

Dynamic tri-loop error driven Proportional plus Integral control system (Developed by Dr. Sharaf)

The loop weighting factors ( Controllerγw, γi and γv)

and gains (Kp, Ki) are assigned to minimize the time-weighted excursion index J0 (Developed by Dr. Sharaf)

where

- is the total excursion error
- N= T0/Tsample
- T0: Largest mechanical time constant (10s)
- Tsample: Sampling time (0.2ms)

Dynamic Sliding Mode Controller (SMC) Controller

Dynamic error driven sliding mode control system (Developed by Dr. Sharaf)

The loop weighting factors ( Controllerγw and γp) and

the parameters C0 and C1 are assigned to minimize the time-weighted excursion index J0

(Developed by Dr. Sharaf)

where

- N= T0/Tsample
- T0: Largest mechanical time constant (10s)
- Tsample: Sampling time (0.2ms)

Photovoltaic DC/AC Inverter: PV-AC Scheme Controller

Stand-alone photovoltaic DC/AC inverter scheme for village electricity use

Dynamic Error Driven Proportional plus Integral (PI) Controller

Dynamic tri-loop error driven Proportional plus Integral control system (Developed by Dr. Sharaf)

The loop weighting factors ( Controllerγv, γi and γp)

and gains (Kp, Ki) are assigned to minimize the time-weighted excursion index J0 (Developed by Dr. Sharaf)

where

- is the total excursion error
- N= T0/Tsample
- T0: Largest mechanical time constant (10s)
- Tsample: Sampling time (0.2ms)

Hybrid Photovoltaic/Wind Energy Utilization Scheme Controller

Stand-alone hybrid photovoltaic/wind energy utilization scheme

Dynamic tri-loop error driven Proportional plus Integral control system (Developed by Dr. Sharaf)

The loop weighting factors ( Controllerγv, γi and γp)

where

- is the total excursion error
- N= T0/Tsample
- T0: Largest mechanical time constant (10s)
- Tsample: Sampling time (0.2ms)

Preliminary Results Controller

Photovoltaic powered Four-Quadrant PWM converter PMDC motor drive system model using the MATLAB/Simulink/SimPowerSystems software

Variation of Controller

ambient temperature (Tx)

Variation of

solar irradiation (Sx)

Test Variations of ambient temperature and solar irradiationP Controllerg vs. Ig & Vg

ωref & ωm vs. time

Iam vs. time

ωm vs. Te

For trapezoidal reference speed trajectory(Continue)P Controllerg vs. Ig & Vg

ωref & ωm vs. time

Iam vs. time

ωm vs. Te

For sinusoidal reference speed trajectory(Continue)The digital simulation results validate the tri-loop dynamic error driven PI controller and ensures:

- Good reference speed trajectory tracking with
a small overshoot/undershoot and minimum

steady state error

- The motor inrush current Iam is kept to a specified
limited value

- Maximum PV solar power/energy tracking near
knee point operation can be also achieved under varying Insolation /Irradiation Conditions and Load Excursions

Time Line error driven PI controller and ensures:

HIF-ARC FAULT DETECTION error driven PI controller and ensures:

Problem to be solve error driven PI controller and ensures:

High Impedance Arc-Type Fault-Detection!!!!

(HIF) on Meshed Electrical Distribution/Utilization networks are characterized by an intermittent Arc-type nature and low-level of the fault currents.

In the multi-grounded distribution line, there exists a imbalance in three phase loads, therefore, overcurrent ground relays are usually set somewhat high to allow some large neutral currents due to this imbalance.

The detection of low-level ground-currents-GC using any conventional over-current or ground fault type relays is both difficult and sometimes inaccurate.

Each detection method may increase the possibility of the detection for high impedance faults to some extent, but it also has some drawbacks as well. Until now, no technique has offered a complete solution for this HIF Relaying-problem.

Aim of the paper detection for high impedance faults to some extent, but it also has some drawbacks as well. Until now, no technique has offered a complete solution for this HIF Relaying-problem.

The paper presents the application of the cross correlation-Statistical Pattern identification technique as a pattern recognition of high impedance faults (HIFs). The third and fifth harmonics current components are extracted from the fault current using Fast Fourier Transform (FFT).

CROSS CORRELATION AS AN HIF ARC FAULT-PATTERN CLASSIFICATION detection for high impedance faults to some extent, but it also has some drawbacks as well. Until now, no technique has offered a complete solution for this HIF Relaying-problem.

Correlation is a measure of the relation between two or more inter-Lated variables. The measurement scales used should be at least interval scales, but other correlation coefficients are available to handle other types of data.

Following the definition of the cross correlation function between x[n] and y[n] Variables given by (1).

(1)

where k is a delay units.

The cross correlation functions of power signals are redefined such that the summations and integrations are replaced by averages. For two discrete power signals x[n] and y[n] the cross correlation function is defined as (2):

(2)

This Summation-Feature of pattern classification is used for HIF ARC FAULT detection on Electric Grids

OVERALL PROCEDURE OF THE TECHNIQUE redefined such that the summations and integrations are replaced by averages. For two discrete power signals x[n] and y[n] the cross correlation function is defined as (2):

The HIF Fault-Detection Technique is based on a novel low frequency (the third and fifth harmonic feature diagnostic vector).

- The instantaneous current values at feeder substation buses shown in Fig. 1 are captured and transformed into frequency domain using one cycle Fast Fourier Transform- FFT.

The FFT harmonic current vectors extraction [i3] and [i5] are processed to obtain a feature vector.

- The current pattern is classified using the cross correlation function given in (2).

X km are processed to obtain a feature vector.

S

R

F2

F1

FFT

Fault Vector Feature

Pattern Classification

R1

HIF

with Linear or Non-linear ARC

Threshold are processed to obtain a feature vector.

The slop of the Cross Correlation Function “XCF“ can be calculated to discriminate between a linear and non-linear arc-type fault conditions

- If the slop of “XCF“ goes lower than some THR_SLOP value, the technique will identify that the fault is a linear HIF
- If the slop of “XCF“ goes higher than a THR_SLOP value, HIF is identified as a non-linear arc fault

SYSTEM are processed to obtain a feature vector.

The Test Electric-Utility Grid system includes a 138 kV. X is taken in per unit length. Data for verifying the proposed technique was generated by modeling the selected system using the Matlab/Simulink model

The model are processed to obtain a feature vector.

TEST RESULTS are processed to obtain a feature vector.

The Performance of the Proposed Arc detection Scheme is tested for different Arc conditions,distances,Arc Types,..

Effect of Internal Linear Fault are processed to obtain a feature vector.

Performance of the HIF-SAFTEY Relay for a phase-a-to ground fault on the transmission line is shown in the following figure i3-vs-i5

The fault is located at 30% of transmission line length from R1

The corresponding computed “XCF“ for R1 has positive value. For the selected threshold boundary THR_SLOP, the “XCF“ slop is lower than this boundary. This indicates that the fault is a linear fault.

Effect of Internal Non-Linear Fault value. For the selected threshold boundary THR_SLOP, the “XCF“ slop is lower than this boundary. This indicates that the fault is a linear fault.

The computed [i3] and [i5] pattern is shown in Figure. The corresponding computed “XCF“ for R1 has very rise value 1.6E-02. For the selected threshold boundary, the “XCF“ does cross the selected threshold boundary

CONCLUSIONS value. For the selected threshold boundary THR_SLOP, the “XCF“ slop is lower than this boundary. This indicates that the fault is a linear fault.

The paper introduced a novel low order harmonic current pattern for HIGH IMPEDANCE FAULT ARC detection and discrimination.

- The technique is based on analyzing the Harmonic i3-vs-i5 current pattern shape.

- The suggested technique was tested under different HIF-ARC fault conditions.

The paper introduced a novel low order harmonic current pattern for HIGH IMPEDANCE FAULT ARC detection and discrimination.

- The great selectivity and reliability are the main features in discrimination between linear and non-linear arc faults.

Professor Dr. A.M. Sharaf

Department of Electrical/Computer Engineering, University of New Brunswick

PO Box 4400-UNB, Fredericton, N.B., Canada, E3B 5A3

Email : [email protected]

Static Synchronous Compensator (STATCOM) Compensation Using A Novel FACTS- STATCOM Scheme

- STATCOM Definition
The Static Synchronous Compensator is a shunt-connected reactive power compensation device that is capable of generating and/or absorbing reactive power at a given bus location and in which the output can be varied.

- Structure
It consists of a step-down transformer with leakage reactance , a three phase GTO voltage source converter (VSC), and a DC side-capacitor. The AC voltage difference across the interface transformer leakage reactance XT produces reactive power exchange between the STATCOM local bus and the power system bus at the point of shunt interface.

OBJECTIVES Compensation Using A Novel FACTS- STATCOM Scheme

- STATCOM-FACTS DEVICE!!!!
- FLEXILE AC TRANSMISSION DEVICE = ELECTRONIC CONVERTERS+FLEXIBLE CONTROLLER for Electric UTILITY SECURITY/STABILITY and RELIABILITY Enhancement!!!
- Dynamic voltage control in transmission and distribution systems;
- Power electromechanical-oscillation damping in power transmission system;
- Transient stability Enhancement;
- Voltage flicker control; and
- Possible control of not only the reactive power Q but also the active power in the connected line, this requires a sustainable dc side energy source (Battery or DC source).
- THE STATCOM DEVICE is A VOLTAGE STABILIZATION THAT ENHANCES GRID SYSTEM SECURITY/STABILITY AND RELIABILITY and REDUCES ROLLING- BLACKOUTS!!!!!!!!!!!

- The exchange of reactive Power Can be controlled by varying the amplitude of Es.
- If Es>Et, the reactive power flow from the VSC STATCOM to AC system (Capacitive Operation).

Figure1: The STATCOM principle diagram

- If Es<Et, the reactive power flow from the AC System Bus to the Converter (Inductive Operation).
- If Es = Et, STATCOM is (floating non-active state) only P small flow.
- The net instantaneous power at the ac output terminals must always be equal to the net instantaneous power at dc-input terminals by neglecting switching losses.
- The converter simply interconnects the three phase terminals so that the reactive output phase currents can flow freely among them.
- Although the reactive current is generated by the action of the solid state switches. The capacitor is still needed to provide a circulating current path as well as act as voltage source storage.

Digital Simulation Model the Converter (Inductive Operation).

Electrical & Computer Engineering DepartmentUniversity of New BrunswickFigure 2: Sample three-bus study Grid Utility system with the STATCOM located at Bus B2.

Table 1: Table of selected power system parameters the Converter (Inductive Operation).

48 Pulse Voltage Source Converter the Converter (Inductive Operation).

Figure 3: 48-pulse Voltage Source Converter STATCOM-Building Block.

6 pulse the Converter (Inductive Operation).

12 pulse

48 pulse

Figure 4: output phase voltage for the 6, 12 and 48-pulse VSC.

Decoupled (d-q) current controller the Converter (Inductive Operation).

PI Controller

Figure 5: Proposed STATCOM Decoupled Current Control System.

Electrical & Computer Engineering Department the Converter (Inductive Operation).University of New Brunswick

- The new control system is based on a decoupled current control strategy using decoupled direct and quadrature current components of the STATCOM AC current.
- The supplementary additional damping regulator is to correct the phase angle of the STATCOM device voltage, , with respect to the positive/ negative sign of this variations.
- The operation of the STATCOM-FACTS scheme is Validated for both capacitive and inductive modes of operations.

Dynamic Performance of the STATCOM the Converter (Inductive Operation).

STATCOM ENSURES SYSTEM SECURITY!!!!

The STATCOM Validated for both Capacitive & Inductive modes of operations under the following System load disturbance.

Load Switching

1- At t = 0.5 Sec, Load 2 (PL2 = 0.7 pu & QL2=0.5 pu) is added to load 1 (PL1 = 1 pu & QL1=0.8 pu) that connected from beginning.

2- At t = 1 Sec, Capacitive Load 3 (PL3 = 0.6 pu & QL3=0.4 pu) is added to load 1& 2

3- At t = 1.5 Sec., both loads 1 & 2 are removed

Figure 5: Comparison of Bus Voltage VB2, PL and QL for uncompensated

and compensated system.

Digital Simulation Results uncompensated

1,2

1,2

1,2

1,2

Figure 6 : The digital simulation results of the STATCOM operation under electric load excursion.

Digital Simulation Results uncompensated

1,2

Figure 7 : The digital simulation results of The STATCOM operation under electric load excursion.

NB: uncompensated (THD) is at minimum value due to the use of 48 pulse (VSC) Converter

Figure 9: Reference & Measured current as

the input of current regulator.

Figure 8: Reference & Measured Voltage as

the input of Voltage regulator.

Figure 10: The Total Harmonic Distortion of

the Converter output voltage.

Conclusion uncompensated

1) This paper presented a novel full STATCOM 48 pulse model of cascade converter and its use for reactive power compensation and voltage regulation. A detailed model of the ±100 MVAR STATCOM has been developed and connected to the 230 kV AC grid network in order to provide the required reactive compensation. The full 48 pulse model of STATCOM is controlled by a novel dual loop current decoupled controller and the STATCOM facts device is validated as an effective reactive power compensator and Voltage stabilization scheme. The control process has been developed based on a decoupled current strategy using (D and Q decoupled) STATCOM current.

2) The operation of the STATCOM is validated in both capacitive and inductive operational modes in the sample power transmission system. The dynamic simulation results have demonstrated the high quality of the 48 pulse STATCOM for reactive power compensation and voltage regulation while the system subjected to disturbances such as switching different types of loads. The full 48 pulse model can be utilized in other Facts Based Devices such as :

Active Power Filters APF and new hybrid stabilization topologies using new DPF/SCC!!!

THE proposed device can ensure Dynamic- Voltage Stability, reduce BLACKOUTS and Enhance Grid system Security and Stability!!!

A Novel On-line Intelligent Shaft-Torsional Oscillation Monitor for Large Induction Motors and Synchronous Generators

Prof. Dr. A. M. Sharaf, SM IEEE

University of New Brunswick

May 1-4, 2005

PRESENTATION OUTLINE Monitor for Large Induction Motors and Synchronous Generators

- Introduction-SSR FAILURE MODES
- Modeling details for
-Synchronous generators

-Induction motors

- Sample dynamic simulation results
- Conclusions

Introduction Monitor for Large Induction Motors and Synchronous Generators

What is Subsynchronous Resonance (SSR)?

Subsynchronous Frequency:

- Sub-Synchronous Resonance is an electric power system condition where the electric network exchanges energy with a turbine generator at one or more of the natural frequencies of the combined electrical and mechanical system below the synchronous frequency of the system.
- Example of SSR oscillations:
- SSR was first discussed in 1937
- Two shaft failures at Mohave Generating Station (Southern Nevada, 1970’s)

Where:

- Synchronous Frequency

= 60 Hz

- Electrical Frequency

- Inductive Line Reactance

- Capacitive Bank Reactance

Introduction Monitor for Large Induction Motors and Synchronous Generators

- Categories of SSR Interactions:
- Torsional Modes: Electrical-Mechanical –Resonance-Interactions:
- Induction generator effect
- Shaft torque amplification
- Combined effect of torsional interaction and induction generator
- Self-excitation

- Other sources for excitation of SSR oscillations
- Power System Stabilizer (PSS)
- HVDC Converter
- Static Var Compensator (SVC)
- Variable Speed Drive Converter

Modeling for Synchronous Generator Monitor for Large Induction Motors and Synchronous Generators

Sample Study System

Figure 1. Sample Series Compensated Turbine-Generator and Infinite Bus System

Figure 2. Turbine-Generator Shaft Model

Table 1. Mechanical Data

Modeling for Synchronous Generator Monitor for Large Induction Motors and Synchronous Generators

Figure 3. Matlab/Simulink Unified System Model for the Sample Turbine- Synchronous Generator and Infinite Bus System

Modeling for Induction Motor Monitor for Large Induction Motors and Synchronous Generators

Figure 4. Induction Motor Unified Study- System Model

The Intelligent Shaft Monitor (ISM) Scheme Monitor for Large Induction Motors and Synchronous Generators

Figure 5. Proposed Intelligent Shaft Monitoring (ISM) Scheme

Detect SSR Oscillations and Possible Damage to Mechanical System!!!

The Intelligent Shaft Monitor (ISM) Scheme Monitor for Large Induction Motors and Synchronous Generators

- The result signal of (LPF, HPF, BPF)

= 377 –Radians/Second

T0 = 0.15 s, T1 = 0.1 s,

T 2 = 0.1s, T3 = 0.02 s

Figure 6. Matlab Proposed Intelligent Shaft Monitoring (ISM) Scheme

with Synthesized Special Indicator Signals ( )

Control System Design Monitor for Large Induction Motors and Synchronous Generators

Figure 7. DFC Device Using Synthesized Damping Signals ( ) Magnitudes

Simulation Results for Synchronous Generator Monitor for Large Induction Motors and Synchronous Generators

without DFC Compensation

without DFC Compensation

Figure 8. Monitoring -Synthesized Signals ( ) Under Short Circuit Fault Conditions

Simulation Results for Synchronous Generator Monitor for Large Induction Motors and Synchronous Generators

with DFC Compensation

with DFC Compensation

Figure 9. Monitoring Synthesized Signals ( ) Under Short Circuit Fault Conditions

Simulation Results for Synchronous Generator Monitor for Large Induction Motors and Synchronous Generators

without DFC Compensation

without DFC Compensation

Figure 10. SSR Oscillatory Dynamic Response Under Short Circuit Fault Condition

Simulation Results for Induction Motor Monitor for Large Induction Motors and Synchronous Generators

Without Damping DPF Device

With Damping DPF Device

Figure 11. Monitoring Signals P & Q

Figure 12.Monitoring Signals P & Q

Simulation Results for Induction Motor Monitor for Large Induction Motors and Synchronous Generators

Without Damping DPF Device

With Damping DPF Device

Figure 13. Shaft Torque Oscillatory Dynamic Response

Figure 14. Load Power versus Current, Voltage Phase Portrait

Conclusions Monitor for Large Induction Motors and Synchronous Generators

- For both synchronous generators and induction motor drives, the SSR shaft Unstable-Torsional oscillations can be monitored using the online Intelligent Shaft Monitor (ISM) scheme.
- The ISM monitor is based on the shape of these 2-d and 3-dimensional phase portraits recognition and polarity of synthesized signals
- The proposed Dynamic Power Filter (DPF) scheme is validated for SSR torsional modes damping
- Transformed / Synthesized Signals atre used in Detection of Faults/Safety/Anomaly detection using Pattern Recognition Tools.

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