Heng Deng

PWM methods to handle time delay in digital control of a UPS inverter

With the popularity of micro-processors, digital controllers are widely used in uninterruptible power supply (UPS) inverters. These digital control systems of UPS inverters require a time interval for sampling and computation, which sometimes affects the performance of inverters. In this paper, the problem of time delay in digital control of a UPS inverter is discussed. Then two novel pulsewidth modulation (PWM) methods, the two-polarity PWM method and the asymmetric PWM method, are proposed to handle the time-delay problem. Both of these PWM methods can achieve a wide range of duty ratio between 0.05-0.95, which is independent of inverter model. Furthermore, they are easy to implement using a digital micro-controller. Experimental results are presented in the paper to verify feasibility of the proposed PWM methods.

A Simple Control Method for High-Performance UPS Inverters Through Output-Impedance Reduction

The purpose of a voltage controller for uninterruptible-power-supply inverters is to produce stable output voltage with low distortion under all loading conditions, particularly under nonlinear loads and load transients. Since the output impedance of the inverter is the source for tracking error caused by load current, this paper proposes a simple digital feedback controller that focuses on reducing the output impedance of inverters by a feedback of the load current. The proposed control strategy ensures high-quality steady-state and dynamic responses from the inverter system. The design of the proposed digital controller is simple and requires only a reasonably accurate knowledge of the output L-C filter parameters. In addition, only the output voltage and the load current are sensed if the dc link voltage is constant. The results of simulations and experiments show that the proposed controller can achieve very low total harmonic distortion and fast dynamic response under varied loads including nonlinear loads with low switching frequency.

Modeling and Control of Single-Phase UPS Inverters: A Survey

High performance UPS inverters have stringent control requirements both under steady-state and under transient conditions. Many different control techniques have been applied to UPS inverters. However, there are few publications reviewing the literature on these control techniques and classifying the same, particularly with regard to the more recent developments in this area. In this paper, the control problem of a single-phase UPS inverter is presented and discussed. Both the transfer function and the state-space models of the inverter are presented. Then a review of the most popular solutions for control of UPS inverters is carried out. These control techniques are classified as model-based instantaneous feedback control, feedforward learning control and nonlinear control. The major advantages and disadvantages of these methods are highlighted and compared.

Neural Controller for UPS Inverters Based on B-Spline Network

This paper proposes a controller for uninterruptible power supply inverters based on a particular type of online-trained neural network, which is called the B-spline network (BSN). Due to its linear nature and local weight updating, the BSN controller is more suitable for real-time implementation than conventional multilayer feedforward neural controllers. Based on a frequency-domain stability analysis, a design methodology for determining the two main parameters of the BSN are presented. The model is found to be similar to that of an iterative learning control (ILC) scheme. However, unlike ILC, which requires a complex digital filter design that involves both causal and noncausal parts, the design procedure of the proposed BSN controller is straightforward and simple. Experimental results under various conditions show that the proposed controller can achieve excellent performance, comparable to that of a high-performance ILC scheme developed earlier. The proposed controller is an attractive alternative to both the multilayer feedforward neural controller and iterative learning controller in this and similar applications.

A neural network-based adaptive controller of single-phase inverters for critical applications

Single-phase inverters are widely used in critical applications such as dynamic voltage restorer (DVR) and uninterrupted power supply (UPS) system. In such applications, the inverter must be capable of operating with low total harmonic distortion (THD) and quick transient response. This paper and its companion paper present two promising control methods based on neural networks. This paper presents a novel neural network-based adaptive controller for high performance single-phase inverters capable of maintaining a high quality output voltage with very low THD of less than 2% in the presence of unknown loads, either linear or nonlinear. In the proposed scheme, a voltage source inverter with its corresponding LC load-filter is controlled by an adaptive linear neuron (ADALINE) controller with only one sensor. The on-line adaptive learning algorithm developed in this paper guarantees steady-state controller stability. The principle, stability, robustness, and the special features of the control scheme are discussed. The scheme is simple to implement and has superior performance compared to other popular control strategies, such as the dead-beat controller. An important merit of the proposed control scheme is that it can be designed and implemented without knowing the exact parameters of the PWM inverter system. Simulation and experimental results are presented to verify the feasibility and performance of proposed approach.

Adaptive digital control for UPS inverter applications with compensation of time delay

This paper presents a digital adaptive controller based on the generalized minimum variance (GMV) control approach for high performance single-phase inverters capable of maintaining very low harmonic distortion in the presence of unknown loads which could be linear or nonlinear. The controller gains are determined by minimizing a cost function that reduces both the tracking error and the control signals. The on-line adaptive learning algorithm for the controller guarantees the high robustness of the proposed controller. The principle of operation, stability, robustness, and the special features of the control scheme are discussed. The major advantage of the proposed scheme is its simplicity of implementation. Also, only the output voltage and the load current are required to be sensed. Another important merit of the proposed control scheme is that it can be designed and implemented without knowing the exact parameters of the PWM inverter system. Simulation and experimental results are presented to verify the feasibility of the proposed approach and also its performance.

High-performance control of a UPS inverter through iterative learning based on zero-phase filtering

The purpose of a voltage controller for UPS inverters is to produce stable and regulated output voltage with low distortion under all loading conditions, especially under nonlinear loads. Since the loads which cause the tracking errors in the output voltage are typically periodic, iterative learning control is an obvious and excellent intuitive choice for achieving this close voltage tracking. In this paper, an iterative learning controller based on zero-phase filtering is directly combined with the open-loop UPS inverter. Due to the feedforward involved in the open-loop inverter operation, the overall dynamic response can be expected to be reasonably satisfactory. A zero-phase filter designed in frequency domain is applied to compensate the open-loop inverter so as to ensure error convergence. This acts to compensate the poor dynamics of the inverter owing to the presence of the L-C circuit. Furthermore, a forgetting factor is introduced in this control algorithm to increase the robustness of the scheme against measurement noise, initialization error and/or variation of system dynamics due to parameter drift. The experimental results show that the proposed controller can achieve very low total harmonic distortion and fast error convergence under different loads even with only one sensor. The proposed method can be an effective solution for UPS products where high-quality output voltage is more important than fast dynamic response.

A B-spline network based neural controller for power electronic applications

Conventional multi-layer feedforward ANN controllers with back-propagation training are too complex to be implemented in fast-dynamic power electric systems. This paper proposes a controller for power electric systems based on a type of on-line trained neural network called the B-spline network (BSN). Due to its linear nature and local weight updating, the BSN controller is more suitable for real-time implementation than conventional multi-layer feedforward neural controllers. Based on a frequency domain stability analysis, a design methodology for determining the two main parameters of the BSN is presented. The design procedure of the proposed BSN controller is straightforward and simple. Experimental results of UPS inverters with the proposed controller under various conditions show that the proposed controller can achieve excellent performance.