TY  - BOOK
AU  - Zaheer Ud Din, Asim 
AU  - Supervisor : Dr. Yasar Ayaz
TI  - DETECTION, ESTIMATION AND FORECAST FOR NONLINEAR SYSTEMS
U1  - 629.8 
PY  - 2022///
CY  - Islamabad
PB  - SMME- NUST; 
KW  - PhD Robotics and Intelligent Machine Engineering
N1  - This thesis presents and implements detection, estimation, and forecast algorithms
in context of smart infrastructure system for smart grid. A novel application of radio
frequency wireless mesh network and general packet radio service technologies in a
telemetry solution has been proposed. The telemetry solution measures power flow in the
energy network of an electricity distribution company. The solution utilizes some selected
circuits of grid stations, and calculates total power consumed, total power imported and
total power exported by the distribution company. The selection of circuits for sensors
installation is the key for reducing solution cost as compare to the case when sensors are
installed on all the power output points. The framework involves installation of specially
developed energy sensors (smart energy meters) and data concentrator units at the selected
grid stations. The approach has been tested on two electricity distribution companies of
Pakistan: Islamabad Electric Supply Company and Peshawar Electric Supply Company.
Also in this work, result of over-load detection based on generalized likelihood ratio test
for an industrial feeder of Islamabad Electric Supply Company is included. Detection
probability of 0.96 with a false alarm probability of 0.04 has been achieved for 30 minutes
data interval.
Further, 4 years power data obtained from above mentioned system is utilized in a
multivariate multi-step-ahead short-term forecasting formulation. The formulation
operates on multiple inputs from multiple variables, and provides multi-step-ahead
forecasts by generating multiple outputs for multiple variables. The presented framework
is effective for large forecasting horizons since it forecasts for temporally dependent sub-
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intervals called runs from large horizon. Thus the framework forecasts are less biased and
suffer low variance, as compared with direct method and iterated method estimators
respectively. Feedward neural network and Long-short-term-memory network models
have been evaluated in presented framework. The proposed framework has demonstrated
forecasts of power import and power export with a horizon value of 48 for Peshawar
Electric Supply Company (PESCO), Pakistan. The averaged mean absolute percentage
error of two forecasted time series is 12.76 %, whereas, 24 hours ahead power consumption
of PESCO total consumers has been forecasted with mean absolute percentage error of
8.6%. Furthermore, exploiting 24 hours ahead power consumption forecasts has resulted
in better power dispatch for PESCO grid stations by reducing mean absolute error by 11.52
times between PESCO power allocated and PESCO power consumed.
Next the thesis presents an Euler approximate discrete-time Sliding Mode observer
(SMO) which simultaneously estimates states and combined effect of unmodeled system
dynamics and disturbances. Emulation Design procedure is employed in designing of
discrete feedback linearization controller. Computer simulations demonstrate performance
of presented output feedback scheme for tracking applications of magnetic levitation and
DC motor systems. Results illustrate that reducing sampling period more adversely affects
Euler approximate discrete observer performance for faster changing system dynamics
than for slower changing dynamics. The proposed scheme also exhibits good performance
in presence of disturbances and parameters perturbation.
Furthermore, it is demonstrated via simulations that robust tracking control is
achived on using estimator (e.g Kalman filter, SMO, SSRLS filter) in sampled-data output
feedback configuration, as compared to performing tracking using sampled-data state
feedback scheme. Simulation results show that SMO based output feedback tracking is
most robust, followed by CKF and EKF based output feedback scheme. UKF based output
feedback scheme is robust against external disturbance; but for case of system parameter
perturbation, UKF tracking error takes longer time to converge. State-Space Recursive
Least Squares (SSRLS) based scheme behaves poorly in presence of external disturbance.
This is because SSRLS estimation is based on constant velocity model and not on actual
nonlinear system model.
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Finally, output feedback control scheme for case of unknown system parameters
has been presented. The scheme employs dual UKF estimation algorithm and Emulation
Design based discrete feedback linearization controller. Implementation results exhibit that
presented output feedback control scheme demonstrates better tracking performance and
parameter estimation error when parameter estimate is initialized with a value (in dual
estimation algorithm) which is closer to actual system parameter value
UR  - http://10.250.8.41:8080/xmlui/handle/123456789/29009
ER  -