A high performance position sensorless surface permanent magnet synchronous motor drive based on flux angle

A position sensorless Surface Permanent Magnet Synchronous Motor (SPMSM) drive based on flux angle is presented in this paper. The motor equations are written in rotor fixed d-q reference frame. A PID controller is used to process the speed error to generate the reference torque current keeping the magnetizing current fixed. The estimated stator flux using Recurrent Neural Network (RNN) is used to find out the rotor position. The flux angle and the reference current phasor angle are used in vector rotator to generate the reference phase currents. Hysteresis current controller block controls the switching of the 3-phase inverter to apply voltage to the motor stator. Simulation studies on different operating conditions indicate the acceptability of the drive system. The drive system only requires a speed transducer and is free from position sensor requirement. The proposed control scheme is robust under load torque disturbances and motor parameter variations. It is also simple and low cost to implement in a practical environment

Voltage profile improvement for distributed wind generation using D-STATCOM

This paper presents the application of FACTS devices for the enhancement of dynamic voltage stability in distribution networks with distributed wind generation. The analysis is carried over a test distribution system representative of the Kumamoto area in Japan. The detailed mathematical modelling of the system is also presented. Firstly, this paper provides simulation results showing the effects of higher and lower penetration of distributed wind generation on the voltage dynamics in a faulted system. Then, a distribution static synchronous compensator (D-STATCOM) is used to improve the voltage profile of the system. This analysis shows that D-STATCOM has significant performance to improve the voltage dynamics of distribution system compared to shunt capacitor

An integral backstepping controller design for compensated distribution networks with rapid earth fault current limiters in bushfire prone areas

This paper presents a nonlinear integral backstepping controller (I‐BSC) design scheme for a T‐type residual current compensation (RCC) inverter used in compensated distribution networks with rapid earth fault current limiters (REFCLs). The major control task for the proposed scheme is to reduce the fault current to a value suitable for the mitigation of powerline bushfires due to single phase‐to‐ground faults in compensated distribution networks. The key distinct feature of the proposed I‐BSC over the traditional backstepping controller (T‐BSC) is that it introduces an integral action for analyzing the dynamic of the tracking error which minimizes its steady‐state value and ensures better dynamic performance. In order to prove the global asymptotic stability of the RCC inverter with the proposed integral backstepping controller (I‐BSC), the Lyapunov function‐based theory is used. Finally, the performance of the I‐BSC is analyzed on the MATLAB/Simulink environment and compared with a T‐BSC. The performance of both I‐ and T‐BSCs is assessed in terms of transient behaviors of the injected current to the neutral, fault current, and line‐to‐ground voltage of the faulty phase to ensure the standard operational criteria for self‐extinguishing powerline bushfires. Simulation results clearly demonstrate that both controllers fulfill operational standards for REFCL‐compensated networks though the I‐BSC archives better transient behaviors while comparing with the T‐BSC. Results from the processor‐in‐loop (PIL) validations are also included to further justify the applicability of the newly proposed scheme in the real‐time environment

Space vector modulation based fast speed response field-orientation control of induction motor drive with adaptive neural integrator

This paper presents a novel fast speed response control strategy for the poly-phase induction motor drive system based on flux angle. The control scheme is derived in rotor field coordinates and employs the estimation of the rotor flux and its position. An adaptive notch filter is proposed to eliminate the dc component of the integration of signals used for the rotor flux estimation. To improve the performance of the rotor flux estimator, derivative term of the back emf is incorporated in the system. The voltage components in the synchronous reference frame are generated in the controllers which are transformed to stationary reference frame for driving the motor. Space vector modulation technique is used here. Simulation of the drive system was carried out and the results were compared with those obtained for a system that produces the above mentioned voltage components using the conventional PI controller. It is observed that the proposed control methodology provides faster response than the conventional PI controller incorporated system

Three algorithms for learning artificial neural network: A comparison for induction motor flux estimation

This paper presents a comparative study of three algorithms for learning artificial neural network. As neural estimator, back-propagation (BP) algorithm, uncorrelated real time recurrent learning (URTRL) algorithm and correlated real time recurrent learning (CRTRL) algorithm are used in the present work to learn the artificial neural network (ANN). The approach proposed here is based on the flux estimation of high performance induction motor drives. Simulation of the drive system was carried out to study the performance of the motor drive. It is observed that the proposed CRTRL algorithm based methodology provides better performance than the BP and URTRL algorithm based technique. The proposed method can be used for accurate measurement of the rotor flux

Genetic algorithm based PI controller tuning for induction motor drive with ANN flux estimator

This paper presents a Genetic Algorithm (GA) based fast speed response controller for poly-phase induction motor drive. Here the proportional and integral gains of PI controller are optimized by GA to achieve quick speed response. An adaptive Recurrent Neural Network (RNN) with Real Time Recurrent Learning (RTRL) algorithm is proposed to estimate rotor flux. An online tuning scheme to update the weight of RNN is presented to overcome stator resistance variation problem. This tuning scheme requires torque estimator to calculate the torque error. Space vector modulation (SVM) technique is used to produce the motor input voltage. Simulation tests have been performed to study the dynamic performances of the drive system for both the classical PI and the genetic algorithm based PI controllers

Voltage Stability Enhancement of Distribution Systems with Renewable Energy

Due to the increasing use of distributed generation (DG) in existing distribution networks, it is essential to have prior knowledge regarding the relevant power system stability in different generation scenarios. To mitigate the constraints related to voltage and power, the development of a proper reactive power planning methodology and the design and implementation of high-performance controllers are crucial.The first contribution of this thesis is an investigation into the dynamic behavior of active distribution systems with different types of load models to identify critical issues. The analyses show that a high penetration of DG affects the voltage stability of distribution systems and dynamic characteristics of induction motors influence the transient voltage recovery phenomenon. The scope of such analyses demonstrates that it is important to consider dynamic characteristics of loads in order to specify any planning task. To ensure fast voltage recovery after a sudden disturbance, a reactive power planning algorithm is developed based on a new index which reduces the costs of compensation by reducing the required sizes of compensating devices. The effectiveness of the proposed algorithm is verified on widely used distribution test systems with different types of DG units.The second contribution of this research is to analyze the impact of various load compositions on the dynamic voltage stability of distribution networks with renewable energy and present a new control methodology for distribution static compensators (D-STATCOMs) to ensure grid code-compatible performances of DG-integrated systems. The results show that the stability of a system can be drastically affected by a higher proportion of induction motors in load compositions. As loads in a practical system constantly change due to variations in consumer demands, the controller design process takes into account different load compositions in the composite load model. The performance of the proposed control scheme is demonstrated through detailed simulations using nonlinear models of the devices. The other significant contribution of this dissertation is the design of a novel controller for damping oscillatory voltage modes of distribution systems. These modes have been identified as a result of the work in this thesis and they arise due to the proximity of a DG unit to an induction motor load. The critical parameters which affect these modes are carefully identified and, during the controller design process, a bound is provided for their variations. Thus, uncertainties are targeted very carefully which keeps the bound on the modeling uncertainty to a minimum level and it is found that the proposed voltage controller is robust against all types of domestic motor loads considered in this thesis. The final contribution of this research is an examination of controller interactions in distribution networks with multiple DG units and the design of decentralized output-feedback robust controllers which consider intermittent natures of renewable energy as modeling uncertainties. As the results show that nonlinear interactions among different DG controllers cause oscillatory behavior in the system, the interconnection effects of other subsystems are considered in the design process to avoid negative interactions among DG-subsystems. The performances of the designed controllers are compared with those of conventional controllers. Simulation results indicate that the designed controllers are robust and ensure stability under varying operating conditions

Vector control of a position sensorless SPMSM drive with RNN based stator flux estimator

A position sensorless Surface Permanent Magnet Synchronous Motor (SPMSM) drive based on single layer Recurrent Neural Network (RNN) is presented in this paper. The motor equations are written in rotor fixed d-q reference frame. A PID controller is used to process the speed error to generate the reference torque current keeping the magnetizing current fixed. The RNN estimator is used to estimate flux components along the stator fixed stationary axes. The flux angle and the reference current phasor angle are used in vector rotator to generate the reference phase currents. Hysteresis current controller block controls the switching of the three phase inverter to apply voltage to the motor stator. Simulation studies on different operating conditions indicate the acceptability of the drive system. The proposed estimator can be used to accurately measure the motor fluxes and rotor angle over a wide speed range. The proposed control scheme is robust under load torque disturbances and motor parameter variations. It is also simple and low cost to implememnt in a practical environmen