Study of a major oscillations event in northeastern area of the Iranian power network

Iranian power system encountered major oscillations in January 2008 in the northeastern area with an amplitude of about 120 MW. Since not all the events and variables had been recorded, a scenario to simulate the recorded oscillations and results of studies conducted to reproduce the oscillations by simulation are discussed in the first part. Tuning of supplementary controllers, such as PSSs, on the generating units and the use of reactive power compensators in the long transmission line to enhance stability and eliminate severe oscillations between the north and eastern areas are investigated in the second part. Eigen-value analysis and participation factors are used to appreciate the nature of oscillations and the required PSS settings. The results show that, by using the properly tuned PSSs and accurate compensation of the reactive power, transient stability and damping of oscillations are considerably improved.     

An iterative approach to determine the complexity of local models for robust identification of nonlinear systems

In this paper, a new multi-model approach is proposed for identification of nonlinear systems. In similar identification methods, the operating space is partitioned and a local model is suggested for each partition. In such approaches, since the same linear structure is often used for all local models; huge number of local linear models is usually required to reasonably model an operating region with severely nonlinear dynamics. Therefore the size of the global model may exponentially increase; and as a result model robustness may decrease. In the proposed approach the best model structure is selected for the particular nonlinear study system in an iterative approach. At each iteration, a choice is made to increase number of local models and/or increase the local model complexity. Furthermore, it determines the complexity of local models based on increasing the model accuracy and ensuring the model robustness. In order to optimize the model approximation capability and model robustness, a model term selection approach based on a forward orthogonal least squares algorithm and a criterion that minimizes the sum of the variance of the parameter estimates is applied. Simulation results show that the proposed method results in an excellent validation performance with fewer parameters.     

Decentralized robust adaptive-output feedback controller for power system load frequency control

In this paper, a new model reference-decentralized robust adaptive-output feedback controller is proposed for the load frequency control (LFC) of large-scale power systems with unknown parameters. This control strategy requires only local input–output data and can follow random changes in the operating conditions. The controller is designed such that the trajectory errors and the control gains of each area remain uniformly bounded. In the proposed method, firstly an adaptive observer is designed to estimate the state variables and system parameters using local data only. Then a locally linear combination of the estimated states and the model reference states are used to design a robust adaptive-output feedback controller for each area. Simulation results for a three-area power system show that the proposed controller achieves good performance even in the presence of plant parameter changes and system non-linearities. Received: 18 October 2001/Accepted: 24 October 2001     

Synchronous generator third-order model parameter estimation using online experimental data

A method to estimate the dynamic parameters of the commonly used third-order d-q model of a synchronous generator, based on measured electrical power, reactive power, terminal voltage, field current, field voltage and rotor angle following a small perturbation of the field voltage, is described. The parameters are estimated from two newly developed nonlinear functions for electrical power and terminal voltage by using a nonlinear least squares (NLS) algorithm. Results of simulation studies and experimental data collected from an 80 MVA, 10.5 kV generator show the efficacy of the proposed method and also reveal that the proposed method is valid for a wide range of operating conditions. For cases where rotor angle is not available, a new method for rotor angle estimation is also proposed.     

System identification of two-dimensional continuous-time systems using wavelets as modulating functions

In this paper, parameter identification of two-dimensional continuous-time systems via two-dimensional modulating functions is proposed. In the proposed method, trigonometric functions and sine-cosine wavelets are used as modulating functions. By this, a partial differential equation on the finite-time intervals is converted into an algebraic equation linear in parameters. The parameters of the system can then be estimated using the least square algorithms. The underlying computations utilize a two-dimensional fast Fourier transform algorithm, without the need for estimating the unknown initial or boundary conditions, at the beginning of each finite-time interval. Numerical simulations are presented to show the effectiveness of the proposed algorithm.     

Decentralized robust adaptive load frequency control using interactions estimation

In this paper, a new model reference decentralized adaptive output feedback controller is proposed for load-frequency control (LFC) of large-scale power systems with unknown parameters. The main problem with a decentralized robust LFC is that the interactions are treated as disturbances. This results in a conservative control action to maintain stability in the worst-case scenario. Furthermore, to improve the performance of the decentralized LFC, the proposed method estimates the interactions from other subsystems to modify the adaptive controller so that the interactions are effectively neutralized. The other important features of the proposed controller are: (1) no prior information about the system parameters is required, (2) random changes in the operating conditions are traced, (3) only the local input–output data are needed, (4) the robustness of the overall system against the system parameter uncertainties is guaranteed. To show the effectiveness of the proposed controller, a three-area power system is studied. The simulation results are promising and highlight the remarkable performance of the controller even in the presence of both plant parameter changes and high interactions.     

Comprehensive control strategy for a variable speed cage machine wind generation unit

A comprehensive control strategy, that addresses all three control objectives in a wind generation system, i.e. control of the local bus voltage to avoid voltage rise, capture of the maximum power in the wind and minimization of the power loss in the induction generator is proposed. The control signals are the desired current wave shapes (instantaneous three-phase currents) of the rectifier and the inverter in a double-sided PWM converter system connected between the wind generating unit and the grid. Studies performed on a complete model for a variable speed cage machine wind generation unit, including wind profile, wind turbine, induction generator, PWM converter, local load and transmission line, show that even as the wind speed changes randomly, the proposed control strategy leads the system to the optimum operating conditions.     

HIERARCHAL CONTROL SYSTEM FOR A VARIABLE SPEED CAGE MACHINE WIND GENERATION UNIT USING NEURAL NETWORKS

A hierarchal control strategy, that addresses three control objectives for a wind generation system, is proposed in this paper. It controls the local bus voltage (to avoid voltage rise), captures the maximum power in the wind and also minimizes the power loss in the induction generator. In the first level, given the instantaneous wind speed, electrical torque and output power, the designed neural networks calculate the desired rotor speed, air‐gap flux and the grid side reactive power. In the second level, the desired current wave shapes (instantaneous three‐phase currents) of the rectifier and the inverter in a double‐sided PWM converter system are calculated. In the third level, the PWM controller guides the system towards the optimum operating conditions. Simulation results show that even as the wind speed changes randomly, the proposed control strategy leads the system to the optimum operating conditions.     

Minimum cost load balanced multipath routing protocol for low power and lossy networks

Conventional shortest path routing mechanisms in low power and lossy networks (LLNs) impose excessive traffic load on some nodes and cause their early battery depletion. Load balancing via multipath routing is a promising solution to increase lifetime. This idea is practised by some algorithms, mostly through limited number of disjoint paths, to reduce inter-path interference. In this paper a proactive multipath routing algorithm called MRPL is proposed, based on the recent standard routing protocol for LLNs. The algorithm tries to distribute the traffic load through a set of braided paths, with the objective of maximizing the network lifetime and minimizing total transmission cost. The traffic distribution mechanism is formulated by a linear program and a heuristic method is proposed to implement it in a distributed manner. Simulation results provide enough evidence for energy and cost efficiency of the proposed routing mechanism.     

Identification of physical parameters of a synchronous Generator from online measurements

A new method to determine physical parameters of a synchronous generator based on an online measurement of the electrical power, terminal voltage, and field voltage, following a small perturbation of the field voltage, and the rotor angle at the same steady-state operating condition is described in this paper. A multivariable linear transfer function, identified using the sampled input-output data, is converted to the parameters of the Heffron-Phillips model. Using the relations of the Heffron-Phillips parameters with the physical parameters, the physical parameters are estimated. These estimated parameters are then used in a nonlinear structure to model the synchronous generator. Experimental results with the proposed method applied to a micromachine show good accuracy of the model and also show that the identified nonlinear model is valid at other operating conditions. At dramatically different operating conditions, however, to include the effects of unstructured nonlinearities such as magnetic saturation, the parameters of the nonlinear structure can be slightly adjusted for better performance.