DETECTION, ESTIMATION AND FORECAST FOR NONLINEAR SYSTEMS /
Asim Zaheer Ud Din
- 142p. Soft Copy 30cm.
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- xiii 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. xiv 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.