A control method for distributed interconnected power generation units is developed. The power system comprises permanent-magnet synchronous generators (PMSGs), which are connected to each other through transformers and tie-lines. A derivative-free nonlinear Kalman filtering approach is introduced aiming at implementing sensorless control of the distributed power generators. In the proposed derivative-free Kalman filtering method, the generator's model is first subjected to a linearization transformation that is based on differential flatness theory and next state estimation is performed by applying the standard Kalman filter recursion to the linearized model. Unlike Lie algebra-based estimator design methods, the proposed approach provides estimates of the state vector of the PMSG without the need for derivatives and Jacobian calculation. Moreover, by redesigning the proposed derivative-free nonlinear Kalman filter as a disturbance observer, it is possible to estimate at the same time the nonmeasurable elements of each generator's state vector, the unknown input power (torque), and the disturbance terms induced by interarea oscillations. The efficient real-time estimation of the aggregate disturbance that affects each local generator makes possible to introduce a counterdisturbance control term, thus maintaining the power system on its nominal operating conditions. Index Terms-Derivative-free nonlinear Kalman filtering, differential flatness theory, distributed power generation, electric power grid, nonlinear control, permanent-magnet synchronous generator (PMSG), sensorless control.
This paper analyzes distributed state estimation methods for condition monitoring of electric power transmission and distribution systems. When a fault occurs in such large‐scale systems, it is usually difficult to detect it and to determine its exact position. Moreover, due to the cost of installation and maintenance of measurement devices and due to the excessive size of the electric power grid, the complete monitoring of the associated infrastructure is impractical. Therefore, to monitor the condition of the power grid, some form of estimation is required. As suitable approaches for distributed state estimation this paper proposes the extended information filter (EIF) and the unscented information filter (UIF). The Extended Information Filter is actually an implementation of distributed extended Kalman filtering while the unscented information filter is an implementation of distributed unscented Kalman filtering. With the use of the aforementioned filtering algorithms on processing units located at different parts of the power grid, one can produce local estimates of the system's state vector which in turn can be fused into an aggregate state estimation. The produced global state estimate enables continuous monitoring of the condition of the electric power system and early fault diagnosis if used by a suitable fault detection and isolation algorithm.
The paper studies differential flatness properties and\ud
an input–output linearization procedure for doubly fed induction\ud
generators (DFIGs). By defining flat outputs which are associated\ud
with the rotor’s turn angle and the magnetic flux of the stator, an\ud
equivalent DFIG description in the Brunovksy (canonical) form is\ud
obtained. For the linearized canonical model of the generator, a\ud
feedback controller is designed. Moreover, a comparison of the differential\ud
flatness theory-based controlmethod against Lie algebrabased\ud
control is provided. At the second stage, a novel Kalman\ud
Filtering method (Derivative-free nonlinear Kalman Filtering) is\ud
introduced. The proposed Kalman Filter is redesigned as disturbance\ud
observer for estimating additive input disturbances to the\ud
DFIG model. These estimated disturbance terms are finally used\ud
by a feedback controller that enables the generator’s state variables\ud
to track desirable setpoints. The efficiency of the proposed\ud
state estimation-based control scheme is tested through simulation\ud
experiments
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