Nonlinear controller design for a buck converter

This thesis presents five different nonlinear control techniques for voltage regulation of a DC-DC buck converter operating in continuous conduction mode. A state space averaging model is derived from a non-ideal buck converter circuit with the consideration of resistances of each component. Based on this model, different nonlinear control techniques have been developed to control the DC-DC buck converter. These include backstepping control, sliding mode control, backstepping sliding mode control, adaptive backstepping control, and adaptive backstepping sliding mode control. All these proposed controllers have been evaluated by computer simulation and implemented on the DC-DC buck converter which is built for this thesis. Simulation and experimental results show that all the proposed controllers are able to stabilize the closed loop system and to achieve satisfactory voltage regulation performances under source voltage variations and load changes

Nonlinear Control Approaches of Several Power Converters

The exploitation and utilization of renewable energy have become the important measures taken by countries all over the world to solve the contradiction among energy shortage, economic development and ecological environment. As an important bridge and energy conversion channel between renewable energy and electric energy, power converter is a basic form of transformation and control of electric energy, which plays a vital role in the exploitation and utilization of renewable energy. In the past few decades, due to the significance of both theory and practical applications, the study of power converter has become one of the hotspots of research in the field of power electronics and automation. The study of power converter control strategies is an important study research of power converter. The control strategy as the core of the control system directly determines the dynamic and static performance of the power converter. The power converter system is a typical nonlinear system. However, most of power converter control strategies are designed based on linearization control methods, which makes the control system sensitive to system parameter variations, slow dynamic response speed and poor steady-state performance, etc. Thus, in order to further improve the dynamic and static performances of power converters, the investigation of using nonlinear control methods for power converters is a challenging and meaningful work. Based on modern control theory utilizing nonlinear control approaches, this dissertation investigates the nonlinear control strategies design for several typical power converters, and the main contributions are as follows: (1) The important role of power converters in renewable energy power generation systems is introduced. Then the internal and overseas research situations of the control strategies design for the several typical power converters are classified and summarized, where the theoretical significance and the practical application backgrounds are given, and the study structures and contents of this dissertation are presented. (2) Two system models are built for the DC-DC Buck converters, respectively, i.e., the nominal system model and uncertain system model. Based on the nominal system without considering parametric uncertainties, the single-loop adaptive control strategy is built by adaptive and back-stepping control approach, and the double-loop adaptive control strategy is set up by adaptive and sliding mode control approach. Based on the uncertain system model, the single-loop disturbance observer based control strategy is developed using designed disturbance observer and back-stepping control technique, and the double-loop disturbance observer based control strategy is synthesized using designed disturbance observer and sliding mode control method. (3) The control strategy of voltage regulation and current tracking for three phase two-level grid-connected power rectifiers is presented. By using power-invariant Park’s transformation, an averaged mathematical model of power converters is obtained in synchronous reference frame. Then a novel control strategy using adaptive control and technique is proposed to regulate the dc-link output voltage as well as track a desired current reference. More specifically, an efficient adaptive controller is established in the external loop for regulating dc-link output voltage in the presence of external disturbances. A set of controllers are designed in the internal loop to force the input currents track their desired values. (4) A novel robust control strategy is proposed for three-level neutral-point-clamped power rectifiers. The proposed control scheme consists of three control loops, i.e., instantaneous power tracking control loop, voltage regulation loop and voltage balancing loop. First, in the power tracking control loop, a set of adaptive sliding mode controllers are established to drive the active and reactive power tracking their desired values via radial basis function neural network technology. In the voltage regulation loop, an efficient but simple adaptive controller is designed to regulate dc-link output voltage where the load is considered as an external disturbance. Moreover, a composite controller is developed in the voltage balancing loop to ensure imbalance voltages between two dc-link capacitors close to zero, in which a reduced-order observer is used to estimate sinusoidal disturbance improving the converter performance. (5) Based on the second order sliding mode control technique, a novel control strategy is proposed for three-phase power rectifiers under unbalanced grid conditions to achieve cooperative control between power and current. A consolidated control objective which can be flexibly adjust among the degree of oscillation in active and reactive powers and balance of three-phase current is obtained in the stationary frame. Based on the dynamic of the converter and control objective, a control scheme in a cascaded framework is presented, in which an adaptive observer is applied to estimate the positiveand negative-sequence of grid voltage without complex filtering process. In the current tracking loop, the super–twisting algorithm current controller coupled with super-twisting differentiator is implemented to force the currents to their references, featuring a fast dynamic and an improved robustness. Also, in the voltage regulation loop, an effective composite controller is developed for regulation of the output voltage, where a supertwisting observer is used to estimate load disturbance. (6) The problem of regulation output voltage of three-phase two level filtered voltage source inverters is presented using disturbance observer-based integral sliding mode control approach. First, the dynamics of the inverter are reformulated to facilitate the use of the proposed control strategy, which consider the parametric uncertainties of filter. A disturbance observer is designed to estimate the parametric uncertainties and external disturbances. Then, an integral sliding mode surface is established considering the voltage tracking error, its integral and the estimations of the parametric uncertainties and external disturbances. A sliding mode controller is proposed such that the systems are robustness to the admissible uncertainties and disturbances and satisfy the reaching condition. The stability of the closed-loop system is proved based on the Lyapunov theory

Stand-Alone Wind Power Generation using Adaline Based Integrated Electronic Load Controller

Wind power is clean, economical and environmentally friendly. It is promising alternative electric power generation source at the time of unavailability of fossil fuel and reduces concern over the harmful effects of climate change due to excessive pollution caused by use of fossil fuels. This paper proposes the adaptive linear element (adaline) algorithm based integrated electronic load controller for an isolated windturbine-driven power generation. The adaline extracts the fundamental component of load current to control the voltage and frequency of generator with balancing loads in an integrated manner. The IELC is realized using zigzag/three single-phase transformers and a six-leg insulated-gate bipolar-transistor-based current controlled voltage-source converter, a chopper switch, and an auxiliary load on its dc bus. The generating system is modeled and simulated in MATLAB environment using Simulink and Simpower System toolboxes. Keywords: Integrated Electronic Load Controller (IELC), Voltage and Frequency Control, Adaptive linear element (ADALINE), Wind Turbine, Wind Power, Wind Farm (WF)

Implementation of Neural Network Based Least Mean Square Algorithm with PID-VPI Controller and Integrated Electronic Load Controller for Isolated Asynchronous Small Hydro Generation

The Hydro power is recognized as the promising and widely used renewable source of energy for power generation in large scale, it is gaining popularity due to rising rate of depletion as well as increasing cost of fossil fuels. The Hydro power is very economical in case of run-of-the-river scheme, and environmental friendly keeping in mind the harmful effect of fossil fuels on the climate change. This paper deals with neural network (NN) based least mean square (LMS) algorithm known as adaptive linear element (ADALINE) algorithm, with PID-VPI controller for isolated asynchronous generator (IAG) with integrated electronic load controller (IELC) in small hydro generation feeding three-phase four-wire nonlinear load with neutral-current compensation. The integrated electronic load controller (IELC) is based on zigzag/three single-phase transformers and a six-leg insulated-gate bipolar-transistor-based current-controlled voltage-source converter, a chopper switch, and an auxiliary load on its dc bus. The integrated electronic load controller (IELC) utilizes Adaptive linear element (Adaline) to extract the positive-sequence fundamental-frequency component of load current to obtain load balancing in integrated manner and to control the voltage and frequency of the isolated asynchronous generator (IAG). Non-linear loads are considered for critical evaluation of system, as they have the capability to introduce harmonics that are deleterious for any system. The propound system is modeled and simulated in MATLAB environment to demonstrate the effectiveness of the proposed integrated electronic load controller for the control of isolated asynchronous generator. Keywords:Neural Network (NN), Least Mean Square (LMS), Adaptive Linear Element (ADALINE) Proportional integral derivative controller (PID), Vector proportional integral controller (VPI), integrated electronic load controller (IELC), isolated asynchronous generator (IAG), small hydro generation, voltage-source converter (VSC), voltage and frequency control

A new discrete-time Robust Adaptive Predictive Control-based RMRAC applied to grid connected converters with LCL Filter / Um novo controlador preditivo robusto adaptativo baseado em RMRAC em tempo discreto aplicado a conversores conectados à rede com filtro LCL

In this work, is developed a new discrete-time robust adaptive predictive control based on combining the adaptive structure of a Model Reference Adaptive Controller  (MRAC),  to adjust gains online, with the control law One Sample Ahead Preview (OSAP), a particular case of deadbeat controller, for the current loop of a grid-connected voltage-source converter, also,  this control not need resonant controllers to reject exogenous disturbances. A case application is presented, the grid-side currents control of a three-phase full-bridge static converter connected to the electrical grid by LCL filter. Simulation results are presented to show the performance of the proposed controller in a grid connected system

Critical clearing time determination and enhancement of grid-forming converters embedding virtual impedance as current limitation algorithm

International audienceThe present paper deals with the post-fault synchronization of a voltage source converter based on the droop control. In case of large disturbances on the grid, the current is limited via current limitation algorithms such as the virtual impedance. During the fault, the power converter internal frequency deviates resulting in a converter angle divergence. Thereby, the system may lose the synchronism after fault clearing and which may lead to instability. Hence, this paper proposes a theoretical approach to explain the dynamic behavior of the grid forming converter subject to a three phase bolted fault. A literal expression of the critical clearing time is defined. Due to the precise analysis of the phenomenon, a simple algorithm can be derived to enhance the transient stability. It is based on adaptive gain included in the droop control. These objectives have been achieved with no external information and without switching from one control to the other. To prove the effectiveness of the developed control, experimental test cases have been performed in different faulted conditions

Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage

This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) to enhance the transient stability of a grid-connected wind generator system. The control strategy of the SMES unit is developed based on cascaded control scheme of a voltage source converter and a two-quadrant DC-DC chopper using insulated gate bipolar transistors (IGBTs). The proposed controller is used to control the duty cycle of the DC-DC chopper. Detailed modeling and control strategies of the system are presented. The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of a conventional proportional-integral (PI)-controlled SMES. The validity of the proposed system is verified with the simulation results which are performed using the standard dynamic power system simulator PSCAD/EMTDC

Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES

This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) system to enhance the transient stability of wind farms connected to a multi-machine power system during network disturbances. The control strategy of SMES depends mainly on a sinusoidal pulse width modulation (PWM) voltage source converter (VSC) and an adaptive ANN-controlled DC-DC converter using insulated gate bipolar transistors (IGBTs). The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of proportional-integral (PI)-controlled SMES optimized by response surface methodology and genetic algorithm (RSM-GA) considering both of symmetrical and unsymmetrical faults. For realistic responses, real wind speed data and two-mass drive train model of wind turbine generator system is considered in the analyses. The validity of the proposed system is verified by the simulation results which are performed using the laboratory standard dynamic power system simulator PSCAD/EMTDC. Notably, the proposed adaptive ANN-controlled SMES enhances the transient stability of wind farms connected to a multi-machine power system

Voltage stability enhancement using an adaptive hysteresis controlled variable speed wind turbine driven EESG with MPPT

This paper investigates the enhancement in voltage stability achieved while connecting a variable speed wind turbine (VSWT) driven electrically excited synchronous generator (EESG) into power systems. The wind energy conversion system (WECS) uses an AC-DC-AC converter system with an uncontrolled rectifier, maximum power point tracking (MPPT) controlled dc-dc boost converter and adaptive hysteresis controlled voltage source converter (VSC). The MPPT controller senses the rectified voltage (VDC) and traces the maximum power point to effectively maximize the output power. With MPPT and adaptive hysteresis band current control in VSC, the DC link voltage is maintained constant under variable wind speeds and transient grid currents.The effectiveness of the proposed WECS in enhancing voltage stability is analysed on a standard IEEE 5 bus system, which includes examining the voltage magnitude, voltage collapse and reactive power injected by the systems. Simulation results show that the proposed WECS has the potential to improve the long-term voltage stability of the grid by injecting reactive power. The performance of this scheme is compared with a fixed speed squirrel cage induction generator (SCIG), a variable speed doubly-fed induction generator (DFIG) and a variable speed permanent magnet synchronous generator (PMSG)

Voltage stability enhancement using an adaptive hysteresis controlled variable speed wind turbine driven EESG with MPPT

This paper investigates the enhancement in voltage stability achieved while connecting a variable speed wind turbine (VSWT) driven electrically excited synchronous generator (EESG) into power systems. The wind energy conversion system (WECS) uses an AC-DC-AC converter system with an uncontrolled rectifier, maximum power point tracking (MPPT) controlled dc-dc boost converter and adaptive hysteresis controlled voltage source converter (VSC). The MPPT controller senses the rectified voltage (VDC) and traces the maximum power point to effectively maximize the output power. With MPPT and adaptive hysteresis band current control in VSC, the DC link voltage is maintained constant under variable wind speeds and transient grid currents.The effectiveness of the proposed WECS in enhancing voltage stability is analysed on a standard IEEE 5 bus system, which includes examining the voltage magnitude, voltage collapse and reactive power injected by the systems. Simulation results show that the proposed WECS has the potential to improve the long-term voltage stability of the grid by injecting reactive power. The performance of this scheme is compared with a fixed speed squirrel cage induction generator (SCIG), a variable speed doubly-fed induction generator (DFIG) and a variable speed permanent magnet synchronous generator (PMSG)