John E. Seem

Sequential ESC-Based Global MPPT Control for Photovoltaic Array With Variable Shading

The photovoltaic (PV) systems are often subject to shading in practical operation, which often results in multipeak power-voltage (P-V) characteristics. Most existing maximum power point tracking (MPPT) control strategies are local-optimum oriented, based on the gradient search or its variations, e.g., perturb-and-observation (P&O) or extremum seeking control (ESC). In order to deal with the multimodal P-V characteristics for PV array with variable shading, this study proposes a sequential ESC-based global MPPT control strategy, based on approximate modeling and analysis for the P-V characteristics under variable-shading circumstances. For the multipeak P-V characteristic curve, the bound of variation for the turning-point voltage is found, and based on which the initial voltage for the segmental search can be set. The local minimum of power for the previous segment is used as the start of the next segment, and thus the initial voltage can be set with consistent bound. Such a sequential scheme can thus significantly reduce the searching interval, i.e., the searching efficiency. Another parallel analysis was conducted for the staircase current-voltage (I-V) characteristic for variable-shading situation. It reveals that the current step size is proportional to the change of shading levels. The current profiles obtained by the sequential ESC-based global MPPT search can thus be used to indicate the shading distribution. Simulation study of partially shaded PV panels validates the modeling analysis and the proposed global MPPT.

A New Pattern Recognition Adaptive Controller with Application to HVAC Systems

This paper describes a novel method for automatically adjusting the gain and integral time of proportional-integral controllers based upon patterns that characterize the closed-loop response. The method was developed for self-regulating systems that can be modeled as first-order plus dead-time systems. This new pattern recognition adaptive controller (PRAC) is easy to use and provides near-optimal performance for a range of systems and noise levels. Also, the algorithm is computationally efficient and does not have large memory requirements. PRAC has successfully tuned control systems for heating, ventilating, and air-conditioning (HVAC) equipment in office buildings, high schools, universities, national laboratories, department stores, hospitals, clinics, and large sports stadiums. Simulation and field test results are included in this paper.

Maximizing Wind Turbine Energy Capture using Multivariable Extremum Seeking Control

Maximizing energy capture has become an important issue as more turbines are installed in low wind areas. This paper investigates the application of extremum seeking control (ESC) to maximizing the energy capture of variable speed wind turbines. The optimal control torque and pitch angle are searched via ESC based on the measurement of the rotor power. The advantage of this method is the independency from accurate turbine modelling and wind measurement. Simulation was conducted on FAST for a wind turbine dynamic model, under smooth, turbulent and field recorded wind profiles. The simulation results demonstrated significant improvement in energy capture compared to the standard control with fixed reference. An anti-windup ESC was applied to overcome the integral windup due to actuator saturation which would otherwise disable the ESC process. Finally, the integrator and high-pass filter resetting schemes were applied to improve the transient under the abrupt changes of wind.

Maximum Power Point Tracking for Photovoltaic System Using Adaptive Extremum Seeking Control

In order for photovoltaic (PV) systems to maximize their efficiency of power generation, it is crucial to locate the maximum power point (MPP) in real time under realistic illumination conditions. The current-voltage (I-V) characteristics of PV devices are nonlinear, and the MPP may vary with intrinsic and environmental conditions. Maximum power point tracking (MPPT) control is expected to seek the MPP regardless of the device and ambient changes. This brief presents the application of the adaptive extremum seeking control (AESC) scheme to the PV MPPT problem. A state-space model is derived via averaging method, with the control input being the duty ratio of the pulse-width modulator of the dc-dc buck converter. To address the nonlinear PV characteristics, the radial basis function neural network is used to approximate the unknown nonlinear (I-V) curve. The convergence of the system to an adjustable neighborhood of the optimum is guaranteed by utilizing a Lyapunov-based adaptive control method. The performance of the AESC is verified with simulation.

A New Sequencing Control Strategy for Air-Handling Units

Air-handling unit (AHU) controllers commonly use sequencing logic to determine the most economic way to use the components of the AHU to maintain the supply air temperature at a setpoint value. The objective of this paper is to introduce the finite state machine (FSM) sequencing control strategy for AHUs and to compare this strategy with a traditional split-range sequencing control strategy. The FSM control strategy represents a paradigm shift in the way sequencing logic is developed, illustrated, and implemented for the control of HVAC systems. Simulation results obtained from representative weather data for each month of the year are presented to demonstrate the performance of the two sequencing strategies with respect to the controller gains (proportional gain and integral time) used for the heating coil, cooling coil, and mixing box dampers. Simulation results demonstrate that in comparison to the split-range control strategy, the FSM control strategy leads to more stable operation of the AHU when the...

Extremum seeking control based integration of MPPT and degradation detection for photovoltaic arrays

Regarding the reduction of the cost of energy (COE) for photovoltaic (PV) systems, two important issues are maximizing the efficiency of power generation and fault diagnosis. This study presents an integrated framework that achieves both maximum power point tracking (MPPT) control and diagnosis of change in internal resistance simultaneously. An extremum seeking control (ESC) strategy is developed to maximize the power output of the PV array by regulating the voltage input to the DC-DC converter. Simulation results show that the ESC can achieve MPPT with periodic dither signals such as sinusoidal or square-wave signals. The degraded PV cells often demonstrate certain change of internal resistance, i.e. increase in the series resistance and decrease in the shunt resistance. The change of these internal resistances can induce the change of transient behavior of the dithered output remarkably for square wave dithering, which is validated with simulation study. The ESC with square-wave dithering can thus provides the dual benefits of MPPT and degradation detection simultaneously. Furthermore, simulation study on the PV modules under partial shading, though the P-V characteristic has changed, the proposed option is to pick the optimal regions to narrow down the ESC searching intervals and thus can locate the global maximum power point faster.

Consistent initialization of system of differential-algebraic equations for dynamic simulation of centrifugal chillers

For simulation of centrifugal chiller system with differential algebraic equations, consistent initial conditions are difficult to obtain due to the two-phase refrigerant cycle and the connections between several components. Existing rigorous initialization methods are not suitable for the particular problems in centrifugal chiller simulation, such as the surge instability. In this paper, the dynamic model of a water-cooled centrifugal chiller is developed in Modelica with Dymola and the TLK/IfT Library (TIL). A preprocessing scheme and a direct initialization method are proposed for the consistent initialization of chiller DAE system. Simulation results have shown the effectiveness of the proposed modelling framework and the initialization method.

Adaptive Demand Limiting Control Using Load Shedding

This paper presents a new algorithm for controlling the peak electrical demand in buildings below a target level through load shedding. The algorithm uses statistical methods to determine the load shedding requirements, without unnecessarily shedding loads. Specifically, a random walk model is used to forecast the uncontrolled electrical demand, and a statistical parameter is used to characterize the forecast errors. The statistical parameter is adjusted in an adaptive manner. Thus, the algorithm is easy to use because it automatically adjusts to a specific buildings characteristics. Electrical demand data from a fast food restaurant were used to develop and test the algorithm. The algorithm has successfully been used to limit peak electric demand in a number of buildings.

Recent advances in dynamic modeling of HVAC equipment. Part 1: Equipment modeling

Dynamic modeling of HVAC equipment is a subject of particular importance for control system design and fault detection and diagnosis, while the transient behaviors of the associated processes are, in principle, very complicated and feature strong interactions among multiple physical domains. Part I of this article reviews the research advancement in dynamic modeling of HVAC equipment, focusing on advancement of the past several years, including vapor compression cycles, air-handling units, major types of chillers, cooling tower, heating systems, and renewable-energy driven systems. In Part II, we will present a detailed review of advances in dynamic modeling of HVAC equipment using Modelica, an equation-based multi-physical dynamic simulation platform.

Dynamic Modeling of Mechanical Draft Counter-Flow Wet Cooling Tower With Modelica

Cooling towers are important equipments for the heating, ventilation and air conditioning systems in commercial buildings, rejecting the process heat generation to the atmosphere. Dynamic modeling of cooling tower is beneficial for control design and fault detection and diagnostics of the chilled-water systems. This paper proposes a simple and yet effective dynamic model for a typical mechanical draft counter-flow cooling tower. The finite volume method is applied to the one-dimensional heat and mass transfer analysis. With control volumes defined separately for the water and air sides, the dynamic equations are constructed with the mass and energy balances. The steady-state performance of the proposed model is evaluated with the experimental data from literature. The transient behavior is simulated under the changes of tower inlet conditions, with the performance to be evaluated in the future with field test data.Copyright