Mohammad Rasouli

Nonlinear identification of a brushless excitation system via field tests

In this paper, nonlinear identification of the excitation system (EXS) in the gas unit #2 of Rajaee power plant in Iran is presented. Two methods of modeling, i.e., grey-box and black-box modeling are used and compared. In the grey-box (classical) approach, first a block-diagram for the EXS is suggested, then a test procedure for identification of its parameters is outlined. The input-output data corresponding to each block of the system is obtained through field tests. In this approach, the only nonlinearities considered in the block diagram are the limits. The other nonlinearities are reflected in the change of parameters in the linear transfer functions at different operating conditions. In the black-box approach, identification of the system is carried out using discrete wavelet transform. A variable structure wavelet was tried to cope with system changes at different operating conditions, but a wavelet with fixed number of scaling functions or wavelet functions proved to be quite adequate. The simulation results and their comparison, show the good accuracy of both derived models. Although the model obtained through the black-box approach shows a better fit when its output is compared with the measured variables, the model obtained through the grey-box approach reflects the physical properties of the system and may be more useful for power engineers.

Quasiconvexity analysis of the Hammerstein model

In this paper, the Hammerstein identification problem with correlated inputs is studied in a prediction error framework using separable least squares methods. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. A sufficient condition is derived that guarantees that the identification problem is quasiconvex with respect to the parameters that describe the nonlinearity. Simulations using both IID and correlated inputs are used to illustrate the result.

Optimal Planning of a Micro-combined Cooling, Heating and Power System Using Air-Source Heat Pumps for Residential Buildings

This chapter explains a methodology for optimal planning of a micro-combined cooling, heating and power system driven by a solar dish Stirling heat engine. The solar dish concentrator collects the sun radiations and transforms them into thermal energy. The absorber and thermal storage systems are employed to absorb and store the thermal energy collected by a solar dish for continuous energy supplying when the sunlight is insufficient. The solar energy is absorbed and transferred to the working fluid in the hot point of the Stirling engine. The air source heat pump has been proposed to cool and heat the residential buildings in hot and cold weather conditions, respectively. During a hot weather, the air to air heat pump receives heat from the inside air and transfers it into the outside air, and vice versa in a cold climate. The heating energy obtained from air source heat pumps is not generated by a combustion process, rather it is transferred from the inside air to the outside air. Hence, the most promising aspect of the proposed micro-combined cooling, heating and power system is that it can be solar driven and transfer heat from the inside air during summer. Note that the process is reversed in winter times. Due to the increasing rate of carbon dioxide and more attention paid to the greenhouse gas emissions, use of solar energy and air source heat pumps in a micro-trigeneration system, which does not use any fossil fuel such as gasoline or natural gas, not only gives more chances to significant reduction of carbon dioxide, greenhouse gas emissions, and environmental pollution, but also increases the economic saving in fuel consumption. In an air to air heat pump, the electricity energy is only used by indoor/outdoor fans, and a compressor. Hence, the small-scale tri-generation system consumes less electrical energy than the traditional ones. In order to conduct an optimization, the mathematical model and thermodynamic analysis of proposed microsystem have been provided. Several key parameters related to solar dish Stirling heat engine and air to air heat pumps have been selected as the decision variables to minimize the cost of the electricity energy purchased from the main grid.

A contemporary review of the applications of nature-inspired algorithms for optimal design of automatic generation control for multi-area power systems

The modern electric grid is one the most complex man-made control systems. Proportional–integral–derivative (PID) controllers are widely used in a variety of applications including automatic generation control (AGC), automatic voltage regulators, power system stabilizers and flexible AC transmission system devices. Automatic generation control plays an important role in power system operation to maintain the frequency within an acceptable range and to properly respond to load changes under normal conditions. Using the PIDs, AGC keeps the balance between generation and load demand in order to minimize frequency deviations. Furthermore, the AGC regulates the tie-line power exchange and facilitates bilateral contracts spanning over several control areas, thus ensuring reliable operation of the interconnected transmission system. Since the power system load variations occur continually, generation control is set to automatic to restore the frequency after disturbances. The PID controllers have the advantage of simple structure, good stability, and high reliability. However, a robust and efficient tuning of PID parameters are still being investigated using different techniques. One of the recent areas of such studies is nature-inspired algorithms. The main objective of utilizing nature-inspired algorithms is to optimize parameters of several controllers simultaneously. This paper reviews the latest applications of various nature-inspired algorithms for optimal design of AGC control in power systems. Different algorithms, proposed in the recent literature, are classified based on the type of controller, objective function and test systems.

Term selection for an induction motor via nonlinear Lasso

In this paper, a commonly used third-order model of an induction motor with eight parameters is analyzed in order to classify the model parameters based on their degree of significance in the model behavior. Using the results of this classification, only the significant parameters of an induction motor need to be estimated from the measurements. The remainder of the parameters can be replaced by their typical values, which results in an optimization problem with a reduced dimension. The reduced parameter model needs less computation time and thus is better suited for real-time applications. The significance of this approach is greater when many induction motors or dynamic inductive loads in the system need to be identified. A nonlinear Least absolute shrinkage and selection operator (Lasso) term selection method is employed for this study. The Lasso method minimizes the sum of squared errors, with a constraint on the L1 norm of the parameter vector, which is used to push some parameters to zero. The main idea, when using this method for nonlinear models, involves incorporating the Lasso constraint in an iterative solution approach such as Gauss-Newton algorithm. This method reduces the variance of the parameter estimates, and simplifies the interpretation of the model. To evaluate the performance of the proposed algorithm, the parameters of an induction motor are estimated. Estimation is performed both for simulated and experimental data. The results of the proposed approach are compared to those of a method based on sensitivity analysis.

A Sufficient Condition to Guarantee the Quasiconvexity of the Hammerstein Identification Problem

Abstract The Hammerstein identification problem is studied using a prediction error method in a separable least squares framework. Thus, the identification is recast as an optimization over the parameters used to describe the nonlinearity. Under certain conditions the identification problem is quasiconvex. First, the identification problem is shown to be quasiconvex under certain assumptions, including the use of an IID input. Next, the IID requirement is relaxed, and a sufficient condition for quasiconvexity is derived. The results are illustrated using a series of simulations.

Incorporating term selection into nonlinear block structured system identification

Subset selection and shrinkage methods locate and remove insignificant terms from identified models. The least absolute shrinkage and selection operator (Lasso) is a term selection method that shrinks some coefficients and sets others to zero. In this paper, the incorporation of constraints (such as Lasso) into the linear and/or nonlinear parts of a Separable Nonlinear Least Squares algorithm is addressed and its application to the identification of block-structured models is considered. As an example, this method is applied to a Hammerstein model consisting of a nonlinear static block, represented by a Tchebyshev polynomial, in series with a linear dynamic system, modeled by a bank of Laguerre filters. Simulations showed that the Lasso based method was able to identify the model structure correctly, or with mild over-modeling, even in the presence of significant output noise.

Incorporating Term Selection Into Separable Nonlinear Least Squares Identification Methods

In this paper, a method for the integration of the Least absolute shrinkage and selection operator (Lasso) into Separable Nonlinear Least Squares (SNLS) algorithms is presented. Lasso is reformulated as an equality constrained linear regression. The original SNLS problem is then solved subject to the resulting equality constraints. Simulations using the proposed algorithm to fit a Laguerre model to the output of a linear system are used to demonstrate its performance.

A Real Time Power Update Scheme for the Smart Grid Using TVWS

The U.S. electrical power grid is going through a major transformation from the traditional centralized, failure prone system to an intelligent, resilient, and less-centralized grid that facilitates two-way flow of energy and information. The smart grid is conceived to have the ability to adopt technologies covering the areas of sensing, wireless connectivity, pervasive computing and adaptive control to significantly improve the efficiency. Tremendous amount of data is being used by different utilities today in the U.S. This led us to explore new spectrum bands and better spectrum management beyond what is available today. In this paper, we analyze various schemes that use the IEEE 802.11af TV white space (TVWS) spectrum to employ power saving channel access mechanism. We also propose a smart real-time power update algorithm with minimal power overhead using the TVWS. Next, we implement a MAC layer scheduling algorithm for wireless Community Area Network (CAN) system to reduce collision rate and transmission delay of smart meters to a utility base station in a neighborhood. Through detailed simulations, we show that the proposed scheme offers intelligent real-time power updates to power facilities with minimal power overhead.

Load balancing in a microgrid with uncertain renewable resources and loads

The growing penetration of renewable energy resources poses a high degree of uncertainty in the electric grids behavior due to the intermittent nature of such resources. Handling the uncertainty becomes even more challenging when it is extended to the loads as well. The modern grid intends to address such issues using a two-way communication method between the utilities and consumers. As an attempt for demand response and dynamic pricing, Advanced Metering Infrastructure (AMI) is being deployed in distribution systems to further support consumer participation. In this structure, household appliances can be scheduled to respond to the demand and price signals for multiple purposes such as cost minimization and peak shaving. In this paper, we propose a novel scheduling strategy for smooth load balancing in a grid framework with uncertain power generation and loads considering the comfort of consumers with respect to all household activities. The study focuses on the following aspects: i) efficient use of available energy ii) dynamic load balancing, and iii) energy cost minimization for consumers. This approach uses the concept of load queuing and scheduling in order to provide cost efficient energy to time varying loads. Hence, the design is more consumer friendly while keeping the environment and stability of the grid into consideration. Also, to maintain consumer satisfaction, we introduce a decision-making process (for cost and priority) in the queue so that high priority loads are not queued for a considerable amount of time. Simulation studies are conducted to evaluate the performance of the proposed algorithm.