STATCOM-Based Voltage Regulator for Self-Excited Induction Generator Feeding Nonlinear Loads

This paper deals with the performance analysis of a static compensator (STATCOM)-based voltage regulator for self-excited induction generators (SEIGs) supplying nonlinear loads. In practice, a number of loads are nonlinear in nature, and therefore, they inject harmonics in the generating systems. The SEIG's performance, being a weak isolated system, is very much affected by these harmonics. The additional drawbacks of the SEIG are poor voltage regulation and that it requires an adjustable reactive power source with varying loads to maintain a constant terminal voltage. A three-phase insulated-gate-bipolar-transistor-based current-controlled voltage source inverter working as STATCOM is used for harmonic elimination, and it provides the required reactive power for the SEIG, with varying loads to maintain a constant terminal voltage. A dynamic model of the SEIG-STATCOM feeding nonlinear loads using stationary d-q axes reference frame is developed for predicting the behavior of the system under transient conditions. The simulated results show that SEIG terminal voltage is maintained constant, even with nonlinear balanced and unbalanced loads, and free from harmonics using STATCOM-based voltage regulator     

Boiler plant control using a minimum order dynamic pole placementcompensator

A quadratic performance index with a prescribed degree of stability is used to select the desired closed loop poles of a boiler plant in a 150 MW power station. Using only the available output variables for feedback. Fortran computer programs are written to design a dynamic compensator of minimum order p to achieve placement of the desired closed loop poles. The plant and the dynamic output feedback controller are simulated on a CDC Cyber 170-835 digital computer. The performance of the boiler plant controlled by the dynamic compensator is compared to performances under observer-based and state feedback controllers     

Secondary control for a series reactive compensator based on a voltage-source PWM inverter

This work describes a secondary control scheme of a series reactive compensator for a power system based on a single voltage-source pulse-width-modulated (PWM) inverter. The controllable capacitive reactance can be used as a supplementary control variable for the secondary (external) controller (SC) of a series capacitive reactance compensator to improve the dynamic transient and damping performances of the power system. From the viewpoint of agent-based global dynamic optimization of a system, the selection and use of suitable input signals for the SC are investigated. Detailed simulation results show that the SC with local feedback loop (LFL) has a powerful control performance; however, it requires the controllable compensation for a reference change due to different operating conditions. On the other hand, the SC with global feedback loop (GFL) avoids the need of reference compensation; moreover, its dynamic control performance is improved when the dual inputs (frequency and active power signal) are used, compared to when only the frequency is used as an input signal.     

Modeling faulted switched reluctance motors using evolutionaryneural networks

This paper examines the feasibility of using artificial neural networks (ANNs) and evolutionary algorithms (EAs) to develop discrete time dynamic models for fault-free and faulted switched reluctance motor (SRM) drive systems. SRMs are capable of functioning despite the presence of faults. Faults impart transient changes to machine inductances that are difficult to model analytically. After this transient period, SRMs are capable of functioning at a reduced level of performance. ANNs are applied for their well-known interpolation capabilities for highly nonlinear systems. A dynamical model for an SRM is constructed by feeding values for state variables back to ANN inputs. EAs are employed for their ability to search complex structural and parameter spaces to find good ANN solutions. Furthermore, the ANN structure and training regimen parameters are searched for using EAs. Finally, an analysis of the search is performed, and the resulting model is presented. The results of using the ANN-EA-based model to predict the performance characteristics of prototype SRM drive motion under normal and abnormal operating conditions are presented and verified by comparison to test data     

A microprocessor-based adaptive power factor corrector fornonlinear loads

A microprocessor-based adaptive power factor corrector for poor power factor (linear or nonlinear) loads is introduced. The system power factor is measured by the microprocessor and compared with a predetermined reference value. Accordingly, the microprocessor adjusts the power factor to get the predetermined value. This is achieved by controlling the firing angle of a thyristorized static VAR (volt-ampere reactive) compensator through microcomputer software. The system power factor is measured by the microprocessor at every supply cycle, and the above sequence is repeated. The proposed scheme achieves both accurate measurement and adjustment of the system power factor     

Model-Based Real-Time Dynamic Power Factor Measurement in AC Resistance Spot Welding With an Embedded ANN

Today, real-time measurement of dynamic power factor in resistance spot welding (RSW) is of increasing importance. On the basis of the welding transformer circuit model, a new method is proposed to measure the peak angle of the welding current and then calculate the dynamic power factor in each half-wave. The tailored sensing and computing system ensures that the measuring method possesses a real-time computational capacity with satisfying accuracy. Since the power factor cannot be represented via an explicit function with respect to measurable parameters, the traditional method(s) has to approximate the power factor angle with a constant phase lag angle and fails to detect its dynamic characteristics. An offline-trained embedded artificial neural network (ANN) successfully realizes the real-time implicit function calculation or estimation. A digital-signal-processor-based RSW monitoring system is developed to perform ANN computation. Experimental results indicate that the proposed method is applicable for measuring the dynamic power factor in single-phase half-wave controlled rectifier circuits     

Using an Artificial Neural Network as a Rotor Resistance Estimator in the Indirect Vector Control of an Induction Motor

This paper presents a rotor resistance estimator based on an artificial neural network (ANN) used in the indirect vector control (IVC) of an induction motor (IM). Attention is focused on the dynamic performance of ANN rotor estimator, which gives superior performance over the fuzzy logic based rotor estimator reported in technical literature. The simulation was done using a 1.5 HP induction motor. The same ANN rotor estimator was proved with other IM having different rated power. The use of the same ANN was possible because the scaling and descaling (normalization) of the input and output of ANN was property done for each motor. The ANN training was done offline using the Levenberg-Marquardt algorithm. The neuronal network is a three-layer network; the first layer has fourteen neurons (or nodes), the hidden layer has five neurons and the output layer has only one neuron because the unique output signal is the rotor resistance value.     

A three-phase parallel active power filter operating with PCC voltage compensation with consideration for an unbalanced load

The performance and dynamic characteristics of a three-phase parallel active power filter (APF) with point of the common coupling (PCC) voltage compensation with consideration for an unbalanced load is presented and analyzed in this paper. The proposed scheme employs a pulse-width modulation (PWM) voltage-source inverter and has two operation modes. First, it operates as a conventional active filter with reactive power compensation when PCC voltage is within the 15% voltage drop range. Second, it operates as a voltage compensator when PCC voltage is not within the 15% voltage drop range. Both the APF and the voltage compensator compensate asymmetries caused by nonlinear loads. Finally, the validity of this scheme is investigated through the analysis of simulation and experimental results for a prototype APF system rated at 10 kVA.     

Nonlinear joint angle control for artificially stimulated muscle

Designs of both open- and closed-loop controllers of electrically stimulated muscle that explicitly depend on a nonlinear mathematical model of muscle input-output properties are presented and evaluated. The muscle model consists of three factors: a muscle activation dynamics factor, an angle-torque relationship factor, and an angular velocity torque relationship factor. These factors are multiplied to relate output torque to input stimulation and joint angle. An experimental method for the determination of the parameters of this model was designed, implemented, and evaluated. An open-loop nonlinear compensator, based upon this model, was tested in an animal model. Its performance in the control of joint angle in the presence of a known load was compared with a PID controller, and with a combination of the PID controller and the nonlinear compensator. The performance of the nonlinear compensator appeared to be strongly dependent on modeling errors. Its performance was roughly equivalent to that of the PID controller alone: somewhat better when the model was accurate, and somewhat worse when it was inaccurate. Combining the nonlinear open loop compensator with the PID feedback controller improved performance when the model was accurate.     

An approach for sensorless position estimation for switchedreluctance motors using artifical neural networks

This paper presents a new approach to the sensorless control of the switched-reluctance motor (SRM). The basic premise of the method is that an artificial neural network (ANN) forms a very efficient mapping structure for the nonlinear SRM. Through measurement of the phase flux linkages and phase currents the neural network is able to estimate the rotor position, thereby facilitating elimination of the rotor position sensor. The ANN training data set is comprised of magnetization data for the SRM with flux linkage (λ) and current (i) as inputs and the corresponding position (&thetas;) as output in this set. Given a sufficiently large training data set, the ANN can build up a correlation among λ, i and &thetas; for an appropriate network architecture. This paper presents the development, implementation, and operation of an ANN-based position estimator for a three-phase SRM     

Design and performance analysis of mismatched Doherty amplifiers using an accurate load-pull-based model

An active load-pull-based large-signal modeling approach, suitable for designing and optimizing load modulated amplifiers such as Doherty or linear amplification using nonlinear components based amplifiers, is proposed. A Doherty amplifier was designed by optimizing the dynamic loads seen by the amplifier's transistor using a large-signal load-pull-based behavior model built into computer-aided-design software. Simulation and measurement results showed good agreement, while results obtained using an empirical model of this transistor demonstrated discrepancies. The load-pull-based model was then used to study performance degradation of the Doherty amplifier when load impedance was moved out from the perfect 50 /spl Omega/. It has been shown that the load mismatch can greatly affect the linearity and efficiency performance of the amplifier unless its phase is controlled and kept within a specific range. A load mismatch system level compensator scheme, capable of restituting the linearity loss and maintaining the power-added efficiency close to its maximum range, is proposed.     

Nonlinear identification and optimal feedforward friction compensation for a motion platform

In this study, we present a method of nonlinear identification and optimal feedforward friction compensation for an industrial single degree of freedom motion platform. The platform has precise reference tracking requirements while suffering from nonlinear dynamic effects, such as friction and backlash in the driveline. To eliminate nonlinear dynamic effects and achieve precise reference tracking, we first identified the nonlinear dynamics of the platform using Higher Order Sinusoidal Input Describing Function (HOSIDF) based system identification. Next, we present optimal feedforward compensation design to improve reference tracking performance. We modeled the friction using the Stribeck model and identified its parameters through a procedure including a special reference signal and the Nelder–Mead algorithm. Our results show that the RMS trajectory tracking error decreased from 0.0431 deg/s to 0.0117 deg/s when the proposed nonlinear identification and friction compensation method is utilized.     

Neural net-based robust controller design for brushless DC motordrives

A nonlinear neuro-controller is developed for controlling the speed of brushless dc motors operating in a high performance drives environment. The control inputs and the identification parameters of the system are adjusted simultaneously in real time using a system composed of three hidden-layer dynamic neural networks while the system is in operation. The control architecture adapts and generalizes its learning to a wide range of operating conditions and provides the necessary abstraction when measurements are contaminated with noise. The problem of persistently spanning excitation faced with the use of an online neuro-controller is addressed. In particular, the ability of the neuro-controller to “remember” previously trained reference tracks when confronted with an input excitation that is markedly different from what it was trained with is investigated. The intent is to capture the nonlinear dynamics of a brushless dc motor over any arbitrary time interval in its range of operation. The sensitivity of real time neuro-controllers to random changes in the load torque also is investigated and very promising results are observed     

Asynchronous digital linear?nonlinear control

At present, digital control solutions are becoming more attractive than analogue implementations in some power converter applications owing to easy design of complex control strategies and control reconfigurability. The digital implementation of an asynchronous linear-nonlinear (LnL) compensator to improve the dynamic response of interleaved buck converters is proposed and validated. It is important to highlight that the same digital LnL compensator is able to keep the dynamic response even with very important variations of the control and the power stage parameters.     

半导体激光器混沌相位共轭反馈控制方法

提出了半导体激光器混沌相位共轭反馈(PCF)控制方法,建立了相位共轭反馈控制条件下激光器电流激发混沌的物理模型,发现其混沌控制物理机制是相位共轭反馈影响改变了激光器非线性增益和线宽增强因子特性,控制了系统的动力学行为及频率特性,其影响程度与反馈系数、延迟时间和光线宽增强因子等有关。数值模拟结果表明,在不同强度的光反馈下,通过调节相位共轭反馈光的延迟时间可以控制混沌激光到周期态、双周期态、多周期态等;发现相位共轭反馈控制特点是反馈光场和输出激光处于相干增强状态去实现混沌控制,且能控制实现激光器功率增强的周期脉冲输出。     

A comprehensive review for industrial applicability of artificial neural networks

This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.     

Adaptive neural network based controller for robots

A new Adaptive Neural Network (ANN) controller for robot trajectory trackingproblem is developed. A novel and efficient training algorithm for the proposed controller ispresented in this paper. The proposed training algorithm is based on updating the weights of thenetwork each step by minimizing the quadrant tracking errors and their derivatives.A simulation study is carried out on a polar robot manipulator to assure the effectivenessof the proposed trajectory tracking robot control system. The effects of the new controllerparameters and noisy external load disturbances on the control performance are studied. Thesimulation results of the proposed adaptive ANN controller are compared with those of aconventional ANN controller. The obtained results assured the robustness of the proposed ANNcontroller for: (i) uncertainties of the robot arm dynamic model and/or parameters, (ii) variousnoisy external load disturbances. Also, the simulation results assure the effectiveness of theproposed adaptive ANN controller against the conventional ANN one.     

Linear and Nonlinear Control Techniques for a Three-Phase Three-Level NPC Boost Rectifier

This paper deals with three control techniques for a three-phase three-level neutral-point-clamped (NPC) boost rectifier to study their relative performance. Linear, nonlinear, and nonlinear model reference adaptive control (MRAC) methods are developed to control power factor (PF) and regulate output and neutral point voltages. These controllers are designed in Simulink and implemented in real time using the DS1104 DSP of dSPACE for validation on a 1.2-kW prototype of an NPC boost rectifier operating at 1.92 kHz. The performance of boost converter with three control methods has been investigated respectively in steady state in terms of line-current harmonic distortion, efficiency, and PF and during transients such as load steps, utility disturbances, reactive power control, and dc-bus voltage tracking behavior. The linear PI controllers are characterized by reduced complexity but poor performance, whereas the nonlinear control technique has improved the converter performance significantly, while nonlinear MRAC exhibits much better performance in a wide operating range     

Performance analysis of a PWM inverter VAr compensator

The performance of a three-phase solid-state reactive power compensator with fast dynamic response is analyzed. The compensator consists of a three-phase pulse-width modulated voltage-source inverter connected to a self-controlled DC bus. The principal advantage of this scheme is that it can maintain a near-unity source power factor without sensing and computation of the associated reactive power component. A mathematical model for the compensator connected across a variable power factor load is derived. The frequency response is obtained for open-loop operation. This allows the design of the controller. Predicted results are verified experimentally for both open and closed-loop responses