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This paper investigates the use of Discrete Linear Quadratic Gaussian (DLQG) Compensator to damp sub synchronous oscillations in a Thyrisor Controlled Series Capacitor (TCSC) compensated power system. The study is conducted on IEEE First Benchmark Model (FBM) in which, TCSC is modelled as a discrete linear time-invariant modular unit in the synchronously rotating DQ reference frame. This modular TCSC is then integrated with the Linear Time Invariant (LTI) model of the rest of the system. The design of DLQG includes the design of a Kalman filter for full state estimation and a full state feedback for control. Since the order of the controller is as large as the order of the system considered here(27 states), the practical implementation of the controller is difficult. Hence by using Hankels norm approximation technique, the order of the controller is reduced from 27 to 15 without losing the significant system dynamics. The eigen analysis of the system shows that the use of DLQG can damp torsional oscillations as well as the swing mode oscillations simultaneously, which is practically difficult for a conventional sub-synchronous damping controller. The performance of the system with DLQG is appreciable for all operating conditions and it shows the robustness of the controller.

Background/Objectives: To analyze the SSR phenomenon in a Squirrel Cage Induction Generator based Wind farm connected to series compensated transmission line at varying operating conditions. Methods/Statistical Analysis: There is a remarkable rise in the renewable energy sector in the current energy demand scenario, but their grid integration leads to various power system stability issues. In the case of wind power, wind farms being located far away from the grid, the integration of large scale wind farms to the utility grid requires high transmission line capacity to transport huge amount of wind power. In order to provide the power transfer capability and to improve the system stability, series compensation is the effective solution. But the series compensation may cause SSR oscillations in the system. Eigen value analysis is the method chosen to perform the Small Signal Stability analysis which examines the potential of SSR in the study system. Findings: The study investigates on the application of series capacitor for series compensation which is required during the grid integration. Series compensation of transmission line causes electro-mechanical oscillations at a sub-synchronous frequency which may finally result in serious damages in the system such as shaft failure. In order to perform the Eigen value analysis, detailed mathematical modeling of the system is developed. The pitch angle control is not taken into account while developing the system, whereas the strength of the transmission line is considered. The interconnection of three sub systems provides the complete model of the study system. The identification of system modes and damping of torsional and network modes are analyzed for various wind speed levels, various series compensation levels and various grid impedance levels. It has been observed that different grid impedance levels have a significant effect on the torsional mode oscillations of the system. Application/Improvements: As the overall system model is linearised, optimal control techniques can be developed as an application to mitigate the SSR oscillations and thereby improve the system stability.

Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions.

The paper presents a new design of adaptive and dynamic neural network-based controller architecture with feedback connection for non-linear multivariable systems. The network is trained online at each sampling interval using the desired output trajectory and the training method used is the Real Time Recurrent Learning Algorithm (RTRL). The recurrent network is a fully connected one, with feedback from output layer to the input layer through a delay element. Since the synaptic weights to the neurons are adjusted on-line, this controller has potential applications in real time control also. Moreover, it can be used for both continuous and discrete systems. The simulation results obtained by applying the algorithm to a non-linear multivariable system demonstrate the effectiveness of the proposed method.

Electrical energy generation from solar and wind is increasing day by day and the extraction of good quality electrical energy effectively as well as efficiently is really challenging. This paper focuses on the extraction of electrical energy from solar cells and interconnecting it to the single phase grid through a Quadratic Buck-Boost Converter (QBBC) and Multi Level Inverter (MLI). MLI uses three DC sources, one of which is derived from the solar array through QBBC with Maximum Power Point Tracking (MPPT) controller. In this paper, a new converter topology which accepts wide variations in input voltage has been designed to produce minimum distortion in MLI output with reduced number of switches. The proposed system is designed and implemented in the laboratory and the output is analyzed in different operating conditions

Unified Power Flow Controller can control the power system parameters like voltage, line reactance and phase angle to control power flow in the transmission line. The control of power flow in the transmission line done by injecting a suitable voltage with a phase angle. In this paper design a decoupled control scheme for shunt and series converter to track the reference power inputs by using conventional PI controller. Modeling is done by using MATLAB/SIMULINK software. The analysis is done on the system with different power factors and load conditions. The results show that UPFC is able to track the power changes and inject a suitable voltage into the power system so that power flow can be maintained.

Electrical energy generation from renewable energy sources such as sun, wind etc., are widely adopted due to the increase in electricity consumption. The integration of renewable energy sources with the grid plays an important role in energy utilization. It is difficult to utilize electricity from renewable energy sources directly for the injection of power into the grid. Hence the system needs power electronic converters as an interface between renewable energy sources and grid/load. This paper discusses about the integration of three phase six level voltage source inverter into the grid. Three phase 2000 VA inverter is designed by using three , single phase eight switch six level inverters (in quarter cycle) and each single phase inverter uses three DC voltage sources which can be derived from renewable energy sources such as solar, wind and fuel cell. The pure sinusoidal 415 V three phase voltage is obtained from inverter to inject the power into grid and to the three phase resistive load. The proposed three phase Multi Level Inverter (MLI) is compared with the conventional three phase inverter and the observed THD of the conventional MLI is 31%. THD of the proposed MLI output voltage is analyzed which is 0.13% and found to be very less compared to six and 12 pulse conventional converter topologies. The simulation on the proposed inverter topology is done in MATLAB/Simulink and the results are verified.