A functional link network based adaptive power system stabilizer

An on-line identifier using Functional Link Network (FLN) and Pole-shift (PS) controller for power system stabilizer (PSS) application are presented in this thesis. To have the satisfactory performance of the PSS controller, over a wide range of operating conditions, it is desirable to adapt PSS parameters in real time. Artificial Neural Networks (ANNs) transform the inputs in a low-dimensional space to high-dimensional nonlinear hidden unit space and they have the ability to model the nonlinear characteristics of the power system. The ability of ANNs to learn makes them more suitable for use in adaptive control techniques. On-line identification obtains a mathematical model at each sampling period to track the dynamic behavior of the plant. The ANN identifier consisting of a Functional link Network (FLN) is used for identifying the model parameters. A FLN model eliminates the need of hidden layer while retaining the nonlinear mapping capability of the neural network by using enhanced inputs. This network may be conveniently used for function approximation with faster convergence rate and lesser computational load. The most commonly used Pole Assignment (PA) algorithm for adaptive control purposes assign the pole locations to fixed locations within the unit circle in the z-plane. It may not be optimum for different operating conditions. In this thesis, PS type of adaptive control algorithm is used. This algorithm, instead of assigning the closed-loop poles to fixed locations within the unit circle in the z-plane, this algorithm assumes that the pole characteristic polynomial of the closed-loop system has the same form as the pole characteristic of the open-loop system and shifts the open-loop poles radially towards the centre of the unit circle in the z-plane by a shifting factor α according to some rules. In this control algorithm, no coefficients need to be tuned manually, so manual parameter tuning (which is a drawback in conventional power system stabilizer) is minimized. The PS control algorithm uses the on-line updated ARMA parameters to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane. Simulation studies on a single-machine infinite bus and on a multi-machine power system for various operating condition changes, verify the effectiveness of the combined model of FLN identifier and PS control in damping the local and multi-mode oscillations occurring in the system. Simulation studies prove that the APSSs have significant benefits over conventional PSSs: performance improvement and no requirement for parameter tuning

Identification of Induction Machines Stator Currents with Generalized Neurons

A new approach to identify the nonlinear model of an induction machine using two generalized neurons (GNs) is presented in this paper. Compared to the multilayer perceptron feedforward neural network, a GN has simpler structure and lesser requirement in terms of memory storage which is makes it attractive for hardware implementation. This method shows that with less number of weights, GN is able to learn the dynamics of an induction machine. The proposed model is made by two coupled networks. A modified particle swarm optimization algorithm is designed to solve this distinctive GN training problem. A pseudo-random binary sequence signal injected to the induction machine operating at its rated value was chosen as the test input signal. For validation, the trained GN model is applied on the different operating conditions of the system

Dual-Function Neuron-Based External Controller for a Static Var Compensator

The use of wide-area measurements for power system stabilization has recently been given a lot of attention by researchers and the power industry to avoid cascading failures and blackouts, such as the one in North America in August 2003. This paper presents the design of a nonlinear external damping controller based on wide-area measurements as inputs to a single dual-function neuron (DFN)-based controller. This DFN controller is specifically designed to enhance the damping characteristics of a power system over a wide range of operating conditions using an existing static var compensator (SVC) installation. The major advantage of the DFN controller is that it is simple in structure with less development time and hardware requirements for real-time implementation. The DFN controller presented in this paper is realized on a digital signal processor and its performance is evaluated on the 12-bus flexible ac transmission system benchmark test power system implemented on a real-time platform-the real-time digital simulator. Experimental results show that the DFN controller provides better damping than a conventional linear external controller and requires less SVC reactive power. The damping performance of the DFN controller is also illustrated using transient energy calculations

A smart power system stabilizer for dynamic reduction of a power system model

This thesis focuses on studying the dynamic stability of power systems and improving them by the addition of smart power system stabilizers (PSSs). A conventional design technique of a power system stabilizer that uses a single machine connected to an infinite bus through a transmission line (SMIB) has been widely used for study of elecromechanical perturbations. This approach requires estimating the external equivalent impedance and the voltage at an external bus for each machine in a multi-machine system. This study will use the conventional mathematical method, which represents a power system with some modifications. The dynamic model is linearized by taking the high voltage side on the generation unit as a reference instead of the infinite bus voltage. By using this modification, several improvements are accomplished, the main ones of which are: the estimation of states is eliminated, the time consumed in estimating calculations is reduced, the parameters of the model are independent of the external system, and the PSS design for each machine is independent in a multi-machine environment system. This strategy enables a PSS to be designed for a single machine and then implemented in a multi-machine system. Power systems have advanced to the point that they now cover vast geographical areas. Consequently, they are not only quite complicated, but the system orders are also high. As the complexity of these systems increases, so does the difficulty of examining their dynamic stability and adjusting their controllers. In this research, to address these issues, the reduced model technique has been employed to mathematically define smaller system models from existing models, such that the properties of both systems are comparable properties. The parameters of the PSS are determined based on a modified Heffron- Phillips model of the power system at certain operating conditions where it can provide reliable performance. Since the power systems are highly nonlinear with configurations and parameters that change with time, a typical PSS design, which is based on a linearized model of the power system, cannot guarantee its performance in practical operating environments. The present study attempts to overcome this limitation by implementing smart power system stabilizers. In the context of this thesis the word smart means novel technique. An artificial neural network power system stabilizer (ANN-PSS), a novel multi input fuzzy logic power system stabilizer (FLPSS), and a modified multi-resolution proportional-integral-derivative power system stabilizer (MMR-PID-PSS), based on the dynamic reduction of a power system model. These PSSs have been developed to refine the power system dynamic performance by adjusting the regulator’s parameters in real-time simulation under various operating conditions. In the first part of this research, the digital simulations results using the proposed ANN-PSS and FLPSS are carried out on a single machine connected to a network and are then compared with conventional Lead-Lag PSS. The results show that the power system with FLPSS has a better dynamic response over a wide range of operating conditions and parameter changes. Next, the digital simulations results using the proposed MMR-PID-PSS is carried out on a single machine connected to the network, a 4-machine 10-bus power system, and a 10-machine 39-bus power system and then compared with FLPSS. The results validate the effectiveness of the proposed MMR-PID-PSS regarding reduced overshoot, undershoot, and settling time under a different type of disturbances

Nonlinear self-tuning control for power oscillation damping

Power systems exhibit nonlinear behavior especially during disturbances, necessitating the application of appropriate nonlinear control techniques. Lack of availability of accurate and updated models for the whole power system adds to the challenge. Conventional damping control design approaches consider a single operating condition of the system, which are obviously simple but tend to lack performance robustness. Objective of this research work is to design a measurement based self-tuning controller, which does not rely on accurate models and deals with nonlinearities in system response. Designed controller is required to ensure settling of inter-area oscillations within 10−12s, following disturbance such as a line outage. The neural network (NN) model is illustrated for the representation of nonlinear power systems. An optimization based algorithm, Levenberg-Marquardt (LM), for online estimation of power system dynamic behavior is proposed in batch mode to improve the model estimation. Careful study shows that the LM algorithm yields better closed loop performance, compared to conventional recursive least square (RLS) approach with the pole-shifting controller (PSC) in linear framework. Exploiting the capability of LM, a special form of neural network compatible with feedback linearization technique, is applied. Validation of the performance of proposed algorithm is done through the modeling and simulating heavy loading of transmission lines, when the nonlinearities are pronounced. Nonlinear NN model in the Feedback Linearization (FLNN) form gives better estimation than the autoregressive with an external input (ARX) form. The proposed identifier (FLNN with LM algorithm) is then tested on a 4−machine, 2−area power system in conjunction with the feedback linearization controller (FBLC) under varying operating conditions. This case study indicates that the developed closed loop strategy performs better than the linear NN with PSC. Extension of FLNN with FBLC structure in a multi-variable setup is also done. LM algorithm is successfully employed with the multi-input multi-output FLNN structure in a sliding window batch mode, and FBLC controller generates multiple control signals for FACTS. Case studies on a large scale 16−machine, 5−area power system are reported for different power flow scenarios, to prove the superiority of proposed schemes: both MIMO and MISO against a conventional model based controller. A coefficient vector for FBLC is derived, and utilized online at each time instant, to enhance the damping performance of controller, transforming into a time varying controller

Implementation of a PSO Based Online Design of an Optimal Excitation Controller

The Navypsilas future electric ships will contain a number of pulsed power loads for high-energy applications such as radar, railguns, and advanced weapons. This pulse energy demand has to be provided by the ship energy sources, while not impacting the operation of the rest of the system. It is clear from studies carried out earlier that disturbances are created at the generator ac bus. This paper describes an online design and laboratory hardware implementation of an optimal excitation controller using particle swarm optimization (PSO) to minimize the effects of pulsed loads. The PSO algorithm has been implemented on a digital signal processor. Laboratory results show that the PSO designed excitation controller provides an effective control of a generatorpsilas terminal voltage during pulsed loads, restoring and stabilizing it quickly

Hardware Implementation of an AIS-Based Optimal Excitation Controller for an Electric Ship

The operation of high energy loads on Navy\u27s future electric ships, such as directed energy weapons, will cause disturbances in the main bus voltage and impact the operation of the rest of the power system when the pulsed loads are directly powered from the main dc bus. This paper describes an online design and laboratory hardware implementation of an optimal excitation controller using an artificial immune system (AIS) based algorithm. The AIS algorithm, a clonal selection algorithm (CSA), is used to minimize the effects of pulsed loads by improved excitation control and thus, reduce the requirement on energy storage device capacity. The CSA is implemented on the MSK2812 DSP hardware platform. A comparison of CSA and the particle swarm optimization (PSO) algorithm is presented. Hardware measurement results show that the CSA optimized excitation controller provides effective control of a generator\u27s terminal voltage during pulsed loads, restoring and stabilizing it quickly

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 274)

This bibliography lists 128 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1985