Azad Ghaffari

Power Optimization and Control in Wind Energy Conversion Systems Using Extremum Seeking

Power optimization and control for grid-coupled wind energy conversion systems (WECS) has been extensively studied for variable speed wind turbines (VSWTs). However, existing methods are model-based, and are highly dependent on the system dynamics. We employ extremum seeking (ES), which is a non-model-based optimization approach, to perform maximum power point tracking (MPPT), i.e., extract maximum power from WECS in their sub-rated power region. Since the performance of the ES design is related to the system dynamics, we also design a nonlinear controller, based on the field oriented control concept and feedback linearization, that guarantees desired closed-loop performance. The outer ES loop tunes the wind turbine speed to maximize power capture for all wind speeds within the sub-rated power operating conditions. The inner-loop nonlinear control maintains closed-loop performance through a matrix converter, by regulating the electrical frequency and voltage amplitude of the stator of the (squirrel-cage) induction generator. Simulation results are presented to show the effectiveness of the proposed design.

Power optimization and control in wind energy conversion systems using extremum seeking

Power optimization and control for grid-coupled wind energy conversion systems (WECS) has been extensively studied for variable speed wind turbines. However, existing methods widely use model-based power optimization algorithms in the outer loop along with linear control techniques in the inner loop. The transient performance of this combination is dependent on the systems operating point, especially under fast varying wind regimes. We employ extremum seeking (ES) in the outer loop, which is a nonmodel-based optimization approach, to perform maximum power point tracking, i.e., extract maximum power from WECS in their subrated power region. Since the convergence rate of the ES design may be limited by the speed of the system dynamics, we also design a nonlinear controller, based on the field-oriented control concept and feedback linearization, that yields improvement in convergence rate by two orders of magnitude. The outer ES loop tunes the turbine speed to maximize power capture for all wind speeds within the subrated power operating conditions. The inner-loop nonlinear control maintains fast transient response through a matrix converter, by regulating the electrical frequency and voltage amplitude of the stator of the (squirrel-cage) induction generator. Simulation results are presented to show the effectiveness of the proposed design.

Power optimization for photovoltaic micro-converters using multivariable gradient-based extremum-seeking

It is well-known that distributed architectures such as micro-converters and micro-inverters for photovoltaic (PV) systems can recover between 10%-30% of annual performance loss or more that is caused by partial shading and/or module mismatch. In this work, we present a novel multivariable gradient-based extremum-seeking (ES) design to extract maximum power from an arbitrary micro-converter configuration of PV modules, that includes cascade and parallel connections. Conventional maximum power point tracking (MPPT) schemes for micro-converters (where each PV module is coupled to its own DC-DC converter) employ a decentralized control, with one peak seeking scheme per each PV module, thereby requiring one control loop and two sensors per module (one each for current and voltage). By contrast, the scheme that we present employs a single control loop with just two sensors, one for the overall array output current and the other one for the DC bus voltage. This centralized design provides more flexibility in tuning the parameters of the controller, and also takes into account interactions between PV modules. The computational effort of our design is not higher than that of the conventional scheme, and simulation results using Simulinks SimPowerSystems toolbox show that our proposed design outperforms the conventional one. Thus, our proposed design offers two benefits: (i) the balance-of-system (BOS) cost reduction as a result of the significantly lower number of sensors, and (ii) improved performance, both contributing towards reduced average cost/watt, and enhancing the economic viability of solar.

Dynamic Contour Error Estimation and Feedback Modification for High-Precision Contouring

Cross-coupling control (CCC), which acts on contour error, is intended to improve contouring precision of multiaxis servosystems. The contour error estimate (CEE) significantly affects contouring precision. Conventional CEE methods rely on static single-point techniques to reconstruct contour error using current position error and an estimate of the reference map at the lead point. The performance of such static CEE methods deteriorates dramatically with increasing contour feedrate and at sharp corners. Hence, a dynamic CEE algorithm based on the Newton update algorithm is proposed to achieve high-precision CEE. Since the convergence rate of the Newton algorithm is user assignable and independent of the reference contour, the proposed CEE stays almost identical to the contour error for vastly different feedrates or sharp corners. Multiaxis cross-coupling adds more design steps for the position control loops. Therefore, in this paper, feedback signals are modified such that a separate cross-coupling controller is no longer needed. It has been shown, analytically and experimentally, that the modified feedback in combination with integral sliding mode control provides simpler design and fewer steps in comparison to conventional CCC designs. Moreover, the proposed CEE and the concept of modified feedback together result in reduced contour error. Various experiments are reported to show the effectiveness of the proposed algorithm at high feedrates and for sharp corners.

Modeling, Control, and Stability Analysis of Heterogeneous Thermostatically Controlled Load Populations Using Partial Differential Equations

Thermostatically controlled loads (TCLs) account for more than one-third of the U.S. electricity consumption. Various techniques have been used to model TCL populations. A high-fidelity analytical model of heterogeneous TCL (HrTCL) populations is of special interest for both utility managers and customers (that facilitates the aggregate synthesis of power control in power networks). We present a deterministic hybrid partial differential equation (PDE) model which accounts for HrTCL populations and facilitates analysis of common scenarios like cold load pick up, cycling, and daily and/or seasonal temperature changes to estimate the aggregate performance of the system. The proposed technique is flexible in terms of parameter selection and ease of changing the set-point temperature and deadband width all over the TCL units. We investigate the stability of the proposed model along with presenting guidelines to maintain the numerical stability of the discretized model during computer simulations. Moreover, the proposed model is a close fit to design feedback algorithms for power control purposes. Hence, we present output- and state-feedback control algorithms, designed using the comparison principle and Lyapunov analysis, respectively. We conduct various simulations to verify the effectiveness of the proposed modeling and control techniques.

Analytic Modeling and Integral Control of Heterogeneous Thermostatically Controlled Load Populations

Thermostatically controlled loads (TCLs) account for approximately 50% of U.S. electricity consumption. Various techniques have been developed to model TCL populations. A Highfidelity analytical model of heterogeneous TCL populations facilitates the aggregate synthesis of power control in power networks. Such a model assists the utility manager to increase the stability margin of the network. The model, also, assists the customer to schedule his/her tasks in order to reduce his/her energy cost. We present a deterministic hybrid partial differential equation (PDE) model which accounts for heterogeneous populations of TCLs, and facilitates analysis of common scenarios like cold load pick up, cycling, and daily and/or seasonal temperature changes to estimate the aggregate performance of the system. The proposed technique is flexible in terms of parameter selection and ease of changing the set-point temperature and deadband width all over the TCL units. We provide guidelines to maintain the numerical stability of the discretized model during computer simulations. Moreover, the proposed model is a close fit to design output feedback algorithms for power control purposes. Our integral output feedback control, designed using the comparisonprinciple, guaranteesfast andefficientpower tracking for various real-world scenarios. We present simulation results to verify the effectiveness of the proposed modeling and control technique.

Power optimization for photovoltaic micro-converters using multivariable Newton-based extremum-seeking

Extremum seeking (ES) is a real-time optimization technique that has been applied to maximum power point tracking (MPPT) design for photovoltaic (PV) microconverter systems, where each PV module is coupled with its own dc/dc converter. Most of the existing MPPT designs are scalar, i.e., employ one MPPT loop around each converter, and all designs, whether scalar or mutivariable, are gradient based. The convergence rate of gradient-based designs depends on the Hessian, which in turn is dependent on environmental conditions, such as irradiance and temperature. Therefore, when applied to large PV arrays, the variability in environmental conditions and/or PV module degradation results in nonuniform transients in the convergence to the maximum power point (MPP). Using a multivariable gradient-based ES algorithm for the entire system instead of a scalar one for each PV module, while decreasing the sensitivity to the Hessian, does not eliminate this dependence. We present a recently developed Newton-based ES algorithm that simultaneously employs estimates of the gradient and Hessian in the peak power tracking. The convergence rate of such a design to the MPP is independent of the Hessian, with tunable transient performance that is independent of environmental conditions. We present simulation as well as the experimental results that show the effectiveness of the proposed algorithm in comparison with the existing scalar designs, and also to multivariable gradient-based ES.

Design of distributed controllers for component swapping modularity using linear matrix inequalities

The problem of component swapping modularity (CSM) refers to distributed control design in networks of smart components such that specific design constraints are satisfied. The CSM intends to reduce control design effort and complexity in platform-based systems. Existing CSM methods achieve promising results for low order multi-input-multi-output (MIMO) systems. However, lack of generalization, heavy computational burden, and, to a lower extent, the level of designer involvement limit the applications of the existing CSM methods. Thus, this paper presents a generalized CSM algorithm using linear matrix inequalities (LMIs) such that almost full automatic control distribution is achieved for an arbitrary linear system. The LMI-based CSM is designed to maintain both disturbance attenuation and quadratic stability. Also, it is desired to satisfy specific time response criteria. Thus, the proposed algorithm combines H2 optimization and robust H∞ optimization to satisfy given design constraints. The designer involvement is dramatically reduced to iterative tuning of two scalar parameters in the robust H∞ problem. The proposed algorithm incorporates reference tracking. Also, stability measures and design criteria are checked numerically at each step. The LMI-based CSM algorithm has been numerically verified using an engine idle speed control (ISC) example.

Extremum seeking for wind and solar energy applications

Invented in 1922, extremum seeking (ES) is one of the oldest feedback methods. However, its purpose is not regulation but optimization. For this reason, applications of ES have often come from energy systems. The first noted publication on ES in the West is Draper and Lis application to spark timing optimization in internal combustion engines. In the ensuing decades, ES has been applied to gas turbines and even nuclear fusion reactors. Renewable energy applications have brought a new focus on the capabilities of ES algorithms. In this article we present applications of ES in two types of energy conversion systems for renewable energy sources: wind and solar energy. In both areas the goal is maximum power point tracking (MPPT), i.e., the extraction of the maximum feasible energy from the system under uncertainty and in the absence of a priori modeling knowledge about the systems. For the wind energy conversion system (WECS) we perform MPPT by tuning the set point for the turbine speed using scalar ES. For the photovoltaic (PV) array system, we perform MPPT by tuning the duty cycles of the DC/DC converters employed in the system using multivariable ES. For the photovoltaic system we provide experimental results.

Experimental verification of component swapping modularity for precision contouring

Design for component swapping modularity (CSM) is experimentally verified for a precision contouring algorithm. Prior work focuses on developing CSM algorithms for various systems and verifying the results using numerical simulations [1], [3], [8]. This work uses empirical and analytical methods to support the effectiveness of CSM in precision distributed manufacturing systems. The concept of a modified reference contour is introduced, which provides a fully modular cross-coupling control (CCC), to facilitate CSM design. First, the unified linear CCC algorithm with proven stability is presented for multi-axis servo-systems. Then, an empirical calibration and sensitivity analysis is conducted such that the optimal control configuration, which achieves the lowest feasible contour error, is obtained for all possible configurations of the servo-system. Despite dramatic differences between the servo-system variants, it is shown experimentally that full CSM is achieved with the same controller for all the variants of the servo-system.