Evolutionary neurocontrol: A novel method for low-thrust gravity-assist trajectory optimization

This article discusses evolutionary neurocontrol, a novel method for low-thrust gravity-assist trajectory optimization

Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter Under Disturbance, Dynamic and Power Converter Switching Conditions

Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using backpropagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system

Nested-loop neural network vector control of permanent magnet synchronous motors

With the improvement of battery technology over the past two decades and automotive technology advances, more and more vehicle manufacturers have joined in the race to produce new generation of affordable, high-performance electric drive vehicles (EDVs). Permanent magnet synchronous motors (PMSMs) are at the top of AC motors in high performance drive systems for EDVs. Traditionally, a PMSM is controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations. This paper investigates how to mitigate such problems using a nested - loop recurrent neural network architecture to control a PMSM. The neural networks are trained using backpropagation through time to implement a dynamic programming (DP) algorithm. The performance of the neural controller is studied for typical vector control conditions and compared with conventional vector control methods, which demonstrates the neural vector control strategy proposed in this paper is effective. Even in a highly dynamic switching environment, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for complex EDV drive needs

HVDC Systems Fault Analysis Using Various Signal Processing Techniques

The detection and fast clearance of faults are important for the safe and optimal operation of HVDC systems. In HVDC systems, various types of AC faults (rectifier & inverter side) and DC faults can occur. It is therefore necessary to detect the faults and classify them for better protection and diagnostics purposes. Various techniques for fault detection and classification in HVDC systems using signal processing techniques are presented and investigated in this research work. In this research work, it is shown that the wavelet transformation can effectively detect abrupt changes in system signals which are indicative of a fault. This research has focused on DC faults at various distances along the lines and AC faults on the converter side. The DC line current is chosen as the input to the wavelet transform. The 5th level coefficients have been used to identify the various faults in the LCC-HVDC system. Moreover, the value of these coefficients has been used for the classification of the different faults. For more accurate classification of faults, the wavelet entropy principle is proposed. In LCC-HVDC systems, a different approach for fault identification and classification is proposed. In this investigation an algorithm is developed that provides the trade-off between large input data size and minimal number of neurons in the hidden layer, without compromising the accuracy. The claim is confirmed by the results provided from the investigation for various fault conditions and its corresponding ANN output which confirms the specific fault detection and its classification. A fault identification and classification strategy based on fuzzy logic for VSC–HVDC systems is proposed. Initially, the developed Fuzzy Inference Engine (FIE) detects AC faults occurring in the rectifier side and DC faults on the cable successfully. However, it could not identify the line on which the fault has occurred. Hence, to classify the faults occurring in either AC section or DC section of the HVDC system, the FIE has to be restructured with appropriate data input. Therefore, a FIE which identifies different types of fault and the corresponding line where the fault occurs anywhere in the HVDC system was developed. Initially the developed FIE with three input and seven output parameters results in an accuracy level of 99.47% being achieved. After a modified FIE was developed with five inputs and seven output parameters, 21 types of faults in the VSC HVDC system were successfully classified with 100% accuracy. The FIE was further developed to successfully classify with 100% accuracy faults in Multi-Terminal HVDC systems

Power System Dynamics Enhancement Through Phase Unbalanced and Adaptive Control Schemes in Series FACTS devices

This thesis presents novel series compensation schemes and adaptive control techniques to enhance power system dynamics through damping Subsynchronous Resonance (SSR) and low-frequency power oscillations: local and inter-area oscillations. Series capacitive compensation of transmission lines is used to improve power transfer capability of the transmission line and is economical compared to the addition of new lines. However, one of the impeding factors for the increased utilization of series capacitive compensation is the potential risk of SSR, where electrical energy is exchanged with turbine-generator shaft systems in a growing manner which can result in shaft damage. Furthermore, the fixed capacitor does not provide controllable reactance and does not aid in the low-frequency oscillations damping. The Flexible AC Transmission System (FACTS) controllers have the flexibility of controlling both real and reactive power which could provide an excellent capability for improving power system dynamics. Several studies have investigated the potential of using this capability in mitigating the low-frequency (electromechanical) as well as the subsynchronous resonance (SSR) oscillations. However, the practical implementations of FACTS devices are very limited due to their high cost. To address this issue, this thesis proposes a new series capacitive compensation concept capable of enhancing power system dynamics. The idea behind the concept is a series capacitive compensation which provides balanced compensation at the power frequency while it provides phase unbalance at other frequencies of oscillations. The compensation scheme is a combination of a single-phase Thyristor Controlled Series Capacitor (TCSC) or Static Synchronous Series Compensator (SSSC) and a fixed series capacitors in series in one phase of the compensated transmission line and fixed capacitors on the other two phases. The proposed scheme is economical compared to a full three-phase FACTS counterpart and improves reliability of the device by reducing number of switching components. The phase unbalance during transients reduces the coupling strength between the mechanical and the electrical system at asynchronous oscillations, thus suppressing the build-up of torsional stresses on the generator shaft systems. The SSR oscillations damping capability of the schemes is validated through detailed time-domain electromagnetic transient simulation studies on the IEEE first and second benchmark models. Furthermore, as the proposed schemes provide controllable reactance through TCSC or SSSC, the supplementary controllers can be implemented to damp low-frequency power oscillations as well. The low-frequency damping capability of the schemes is validated through detail time-domain electromagnetic transient simulation studies on two machines systems connected to a very large system and a three-area, six-machine power system. The simulation studies are carried out using commercially available electromagnetic transient simulation tools (EMTP-RV and PSCAD/EMTDC). An adaptive controller consisting of a robust on-line identifier, namely a robust Recursive Least Square (RLS), and a Pole-Shift (PS) controller is also proposed to provide optimal damping over a wide range of power system operations. The proposed identifier penalizes large estimated errors and smooth-out the change in parameters during large power system disturbances. The PS control is ideal for its robustness and stability conditions. The combination results in a computationally efficient estimator and a controller suitable for optimal control over wider range of operations of a non-linear system such as power system. The most important aspect of the controller is that it can be designed with an approximate linearized model of the complete power system, and does not need to be re-tuned after it is commissioned. The damping capability of such controller is demonstrated through detail studies on a three-area test system and on an IEEE 12-bus test system. Finally, the adaptive control algorithm is developed on a Digital Signal Processing Board, and the performance is experimentally tested using hardware-in-the-loop studies. For this purpose, a Real Time Digital Simulator (RTDS) is used, which is capable of simulating power system in real-time at 50 µs simulation time step. The RTDS facilitates the performance evaluation of a controller just like testing on a real power system. The experimental results match closely with the simulation results; which demonstrated the practical applicability of the adaptive controller in power systems. The proposed controller is computationally efficient and simple to implement in DSP hardware