Kalman Filter based Novel Centralized Dynamic State Estimation in Multi-Machine Power System Incorporating DFIGs
Trivedi, Dishang D.
Kalman Filter based Novel Centralized Dynamic State Estimation in Multi-Machine Power System Incorporating DFIGs by Dishang D. Trivedi - Ahmedabad Nirma Institute of Technology 2017 - 127p Ph. D. Thesis with Synopsis and CD
Guided by: Dr. S. C. Vora With Synopsis and CD 11FTPHDE04
ABSTRACT:
The omnipresent, conventional synchronous generators are playing a pivotal role since
many years to meet ever increasing demand of electrical energy. A steep growth in
energy requirements is now met by power generation mix of fossil fuel based and
renewable energy based generators. The stochastic nature of renewable generations,
expanding power networks, complex interactions among the system components and
loads etc. impose need for superior monitoring and control at power system level
for its stable operation. For efficient operation and control of the power system, it
is essential for the energy management system (EMS) operator to have an accurate
information about every generator's dynamic states and power system behaviour.
Diminishing fossil fuels and environment concerns advocated nations to gradually
adopt renewable energy sources. Wind energy, better on multiple aspects among
other renewable options, is dominating today in the power networks. Among wind
energy generators, doubly fed induction generators (DFIGs) are widely accepted due
to its operational
exibility, small converter size, better power control and low cost.
The parallel operation of DFIGs (or wind farm) with pervasive synchronous generators
brings in enhanced system dynamics. This condition strongly dictates dynamic
state estimation (DSE), not only to infer information about synchronous generators
but concurrently know the states of wind generator(s). The performance of DFIGs
also dependent on the converter control circuitry and its feedback loop. This thesis is
the record of work that appropriately models the generators and the network, suitable
for the adoption by the Kalman filter based algorithms to perform the DSE. With the
help of the availability of centralized measurement data, the states of all the generators
in the multi-machine system can be established simultaneously. Subsequently,
the dynamic states of the DFIG are used for its rotor power control under specific
conditions.The synchronous generators, usually considered as a voltage source in the literature
are presented by relevant state-space model for stability analysis. On the other
hand, widely accepted DFIG based wind generator is presented as current source
state-space model. As the models of both the generators are far apart, it is necessitated
to bring both on the same platform. The thesis contains the work that shows
the possibility of model unification of both kind of generators. Employing traditional
DFIG current-source state model, a current-source state model of synchronous generators
is proposed and validated using standard software platform. Highlighting
feature of the proposed mathematical model is its applicability to power system with
no limits on number of synchronous generators and DFIGs. Considering modelling
intricacies, the use of these models is recommended to achieve concurrent DSE in a
multi-machine power system. Employing current source models of synchronous generators
and with substantial penetration of DFIG in multi-machine system, approach
for concurrent DSE of synchronous generator and DFIG is presented. The mathematical
model is simulated in MATLAB / Simulink platform for the validation. The
power system dynamic conditions realized in the MATLAB / Simulink model are then
treated as the data available from the phasor measurement units (PMUs) (with and
without noise). This is used for the extended Kalman lter (EKF) and unscented
Kalman filter (UKF) based DSE algorithms. Centralized dynamic state estimator
based on EKF and UKF are employed for the faithful state predictions for all the
generators under power system dynamic conditions and results are presented.
Application of dynamic states in real time is equally important to achieve better
control and operation of DFIG. This apparatus, normally operate in hostile condition
whether on-shore or off-shore, can undergo internal sensor erratic operation. Under
such conditions, use of dynamic states obtained using EKF, is proposed to have errorfree,
continuous and smooth operation of DFIG. The results are embodied in the
thesis. As an offshoot of main work, comparative performance of EKF and UKF with
different PMU measurement data update rates under discontinuous measurement is
analysed. Additionally, use of weighted least square estimation (WLSE) algorithm
as an alternate to load
ow under bad measurement condition is deliberated with
results.
TT000057 / TRI
Kalman Filter based Novel Centralized Dynamic State Estimation in Multi-Machine Power System Incorporating DFIGs by Dishang D. Trivedi - Ahmedabad Nirma Institute of Technology 2017 - 127p Ph. D. Thesis with Synopsis and CD
Guided by: Dr. S. C. Vora With Synopsis and CD 11FTPHDE04
ABSTRACT:
The omnipresent, conventional synchronous generators are playing a pivotal role since
many years to meet ever increasing demand of electrical energy. A steep growth in
energy requirements is now met by power generation mix of fossil fuel based and
renewable energy based generators. The stochastic nature of renewable generations,
expanding power networks, complex interactions among the system components and
loads etc. impose need for superior monitoring and control at power system level
for its stable operation. For efficient operation and control of the power system, it
is essential for the energy management system (EMS) operator to have an accurate
information about every generator's dynamic states and power system behaviour.
Diminishing fossil fuels and environment concerns advocated nations to gradually
adopt renewable energy sources. Wind energy, better on multiple aspects among
other renewable options, is dominating today in the power networks. Among wind
energy generators, doubly fed induction generators (DFIGs) are widely accepted due
to its operational
exibility, small converter size, better power control and low cost.
The parallel operation of DFIGs (or wind farm) with pervasive synchronous generators
brings in enhanced system dynamics. This condition strongly dictates dynamic
state estimation (DSE), not only to infer information about synchronous generators
but concurrently know the states of wind generator(s). The performance of DFIGs
also dependent on the converter control circuitry and its feedback loop. This thesis is
the record of work that appropriately models the generators and the network, suitable
for the adoption by the Kalman filter based algorithms to perform the DSE. With the
help of the availability of centralized measurement data, the states of all the generators
in the multi-machine system can be established simultaneously. Subsequently,
the dynamic states of the DFIG are used for its rotor power control under specific
conditions.The synchronous generators, usually considered as a voltage source in the literature
are presented by relevant state-space model for stability analysis. On the other
hand, widely accepted DFIG based wind generator is presented as current source
state-space model. As the models of both the generators are far apart, it is necessitated
to bring both on the same platform. The thesis contains the work that shows
the possibility of model unification of both kind of generators. Employing traditional
DFIG current-source state model, a current-source state model of synchronous generators
is proposed and validated using standard software platform. Highlighting
feature of the proposed mathematical model is its applicability to power system with
no limits on number of synchronous generators and DFIGs. Considering modelling
intricacies, the use of these models is recommended to achieve concurrent DSE in a
multi-machine power system. Employing current source models of synchronous generators
and with substantial penetration of DFIG in multi-machine system, approach
for concurrent DSE of synchronous generator and DFIG is presented. The mathematical
model is simulated in MATLAB / Simulink platform for the validation. The
power system dynamic conditions realized in the MATLAB / Simulink model are then
treated as the data available from the phasor measurement units (PMUs) (with and
without noise). This is used for the extended Kalman lter (EKF) and unscented
Kalman filter (UKF) based DSE algorithms. Centralized dynamic state estimator
based on EKF and UKF are employed for the faithful state predictions for all the
generators under power system dynamic conditions and results are presented.
Application of dynamic states in real time is equally important to achieve better
control and operation of DFIG. This apparatus, normally operate in hostile condition
whether on-shore or off-shore, can undergo internal sensor erratic operation. Under
such conditions, use of dynamic states obtained using EKF, is proposed to have errorfree,
continuous and smooth operation of DFIG. The results are embodied in the
thesis. As an offshoot of main work, comparative performance of EKF and UKF with
different PMU measurement data update rates under discontinuous measurement is
analysed. Additionally, use of weighted least square estimation (WLSE) algorithm
as an alternate to load
ow under bad measurement condition is deliberated with
results.
TT000057 / TRI