Small Signal Monitoring of Power System using Subspace System Identification

In this paper, small signal analysis of power systems is investigated using Subspace System Identification (SSI) methods. Classical small signal analysis methods for power systems are based on mathematical modeling and linearized model of power system in an especial operating point. There are some difficulties when such a classical method is applied, specially, in the case of large power systems. In this paper, such difficulties and their bases are investigated and in order to avoid them, it is suggested to use SSI algorithms for small signal analysis of power systems. The paper discusses extracting of small signal properties of power systems and presents some new suggestions for application of subspace system identification methods. Different types of subspace system identification algorithms were applied to different power system case studies using the presented propositions. The benefits and drawbacks of subspace system identification methods and the presented suggestions are studied for small signal analysis of power systems and power system monitoring. Several comparisons were investigated using computer simulations. The results express the usefulness and easiness of proposed methods

Adaptive sliding neural network-based vibration control of a nonlinear quarter car active suspension system with unknown dynamics

This study investigates adaptive sliding neural network (NN) control for quarter active suspension system with dynamic uncertainties and road disturbances. A Multilayer Perceptron (MLP) neural network is adopted to estimate the unknown dynamics of the system. In addition, sliding mode controller is introduced to compensate the function of estimation error for improving the performance of the system. Furthermore, the uniformly and bounded of closed-loop signals is guaranteed by Lyapunov analysis; the adaptation laws for training of MLP are derived from stability analysis. The simulation results demonstrate that the proposed controller can effectively provide a good ride and makes great trade-off between passenger comfort and handling despite the dynamic uncertainties

Nonlinear Modeling and Forecasting Tax of Legal Entities

This paper deals with forecasting the tax revenues of legal entities in Iran. For this purpose, the structural natures of time series of tax revenues for Iranian legal entities are detected. Based on the separation among the resources (government and NGOs), the linearity, nonlinearity, chaotic, and random behaviors are diagnosed via the Lyapunov exponential analysis. Using Box- Jenkins and Neural Networks models with different numbers of input, output, hidden layers, learning algorithm, learning rate and etc., the performance of each model are evaluated during the years of 1381- 1387. Finally, the optimal forecasting model is proposed as a multi input- multi output neural network structure with a novel algorithm. The performance of the proposed structure is evaluated during  the years of 1388- 1393 in the forecasting process