Control techniques for power system stabilisation

The conventional PSS was first proposed earlier based on a linear model of the power system to damp the low frequency oscillations in the system. But they are designed to be operated under fixed parameters derived from the system linearized model. Due to large interconnection of power system to meet the load demand brings in deviations of steady-state and non-linearity to power system. The main problem is that PSS includes the locally measured quantities only neglecting the effect of nearby generators. This is the reason for the advent of Wide area monitoring for strong coupling between the local modes and the inter-area modes which would make the tuning of local PSSs for damping all modes nearly impossible when there is no supervisory level controller. Wide area control addresses these problems by proposing smart topology changes and control actions. Dynamic islanding and fast load shedding are schemes available to maintain as much as possible healthy transmission system. It is found that if remote signals from one or more distant locations of the power system can be applied to local controller design, system dynamic performance can be enhanced. In order to attain these goals, it is desirable to systematically build a robust wide area controller model within an autonomous system framework

Implementation of Adaptive Critic-Based Neurocontrollers for Turbogenerators in a Multimachine Power System

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller\u27s training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in system operating conditions and configurations. This paper also shows that it is possible to design and implement optimal neurocontrollers for multiple turbogenerators in real time, without having to do continually online training of the neural networks, thus avoiding risks of instability

A Heuristic Dynamic Programming Based Power System Stabilizer for a Turbogenerator in a Single Machine Power System

Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design of power system stabilizer (PSS) based on heuristic dynamic programming (HDP) is proposed in this paper. HDP combining the concepts of dynamic programming and reinforcement learning is used in the design of a nonlinear optimal power system stabilizer. The proposed HDP based PSS is evaluated against the conventional power system stabilizer and indirect adaptive neurocontrol based PSS under small and large disturbances in a single machine infinite bus power system setup. Results are presented to show the effectiveness of this new technique

Design and real-time implementation of robust FACTS controller for damping inter-area oscillation

Published versio

A Heuristic-Dynamic-programming-Based Power System Stabilizer for a Turbogenerator in a Single-Machine Power System

Power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low-frequency power system oscillations. To overcome the drawbacks of a conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design based on heuristic dynamic programming (HDP) is presented in this paper. HDP, combining the concepts of dynamic programming and reinforcement learning, is used in the design of a nonlinear optimal power system stabilizer. Results show the effectiveness of this new technique. The performance of the HDP-based PSS is compared with the CPSS and the indirect-adaptive-neurocontrol-based PSS under small and large disturbances. In addition, the impact of different discount factors in the HDP PSS\u27s performance is presented

Power system damping controllers design using a backstepping control technique

The objective of this dissertation is to design and coordinate controllers that will enhance transient stability of power systems subject to large disturbances. Two specific classes of controllers have been investigated, the first one is a type of supplementary signals added to the excitation systems of the generating units, and the second is a type of damping signal added to a device called a Static Var Compensator that can be placed at any node in the system. To address a wide range of operating conditions, a nonlinear control design technique, called backstepping control, is used. While these two types of controllers improve the dynamic performance significantly, a coordination of these controllers is even more promising. Control coordination is presented in two parts. First part concerns simultaneous optimization of selected control gains of exciter and SVC in coping with the complex nature of power systems. Second part proposes a combination of reinforcement learning and a backstepping control technique for excitation control system. The reinforcement learning progressively learns and adapts the backstepping control gains to handle a wide range of operating conditions. Results show that the proposed control technique provides better damping than conventional power system stabilizers and backstepping fixed gain controllers

A novel method for power system stabilizer design

Power system stability is defined as the condition of a power system that enables it to remain in a state of operating equilibrium under normal operating conditions and to regain an acceptable state of equilibrium after being subjected to a finite disturbance. In the evaluation of stability, the focus is on the behavior of the power system when subjected to both large and small disturbances. Large disturbances are caused by severe changes in the power system, e.g. a short-circuit on a transmission line, loss of a large generator or load, loss of a tie-line between two systems. Small disturbances in the form of load changes take place continuously requiring the system to adjust to the changing conditions. The system should be capable of operating satisfactorily under these conditions and successfully supplying the maximum amount ofload. Power system stability is defined as the condition of a power system that enables it to remain in a state of operating equilibrium under normal operating conditions and to regain an acceptable state of equilibrium after being subjected to a finite disturbance. In the evaluation of stability, the focus is on the behavior of the power system when subjected to both large and small disturbances. Large disturbances are caused by severe changes in the power system, e.g. a short-circuit on a transmission line, loss of a large generator or load, loss of a tie-line between two systems. Small disturbances in the form of load changes take place continuously requiring the system to adjust to the changing conditions. The system should be capable of operating satisfactorily under these conditions and successfully supplying the maximum amount ofload. This dissertation deals with the use of Power System Stabilizers (PSS) to damp electromechanical oscillations arising from small disturbances. In particular, it focuses on three issues associated with the damping of these oscillations. These include ensuring robustness of PSS under changing operating conditions, maintaining or selecting the structure of the PSS and coordinating multiple PSS to ensure global power system robustness. To address the issues outlined above, a new PSS design/tuning method has been developed. The method, called sub-optimal Hoo PSS design/tuning, is based on Hoo control theory. For the implementation of the sub-optimal Hoo PSS design/tuning method, various standard optimization methods, such as Sequential Quadratic Programming (SQP), were investigated. However, power systems typically have multiple "modes" that result in the optimization problem being non-convex in nature. To overcome the issue of non-convexity, the optimization algorithm, embedded in the 111 University of Cape Town sub-optimal Hoo PSS design/tuning method, is based on Population Based Incremental Learning (PBIL). This new sub-optimal Heo design/tuning method has a number of important features. The method allows for the selection of the PSS structure i.e. the designer can select the order and structure of the PSS. The method can be applied to the full model of the power system i.e. there is no need for using a reduced-order model. The method is based on Heo control theory i.e. it uses robustness as a key objective. The method ensures adequate damping of the electromechanical oscillations of the power system. The method is suitable for optimizing existing PSS in a power system. This method improves the overall damping of the system and does not affect the observability of the system poles. To demonstrate the effectiveness of the sUb-optimal Hoo PSS design/tuning method, a number of case studies are presented in the thesis. The sub-optimal Hoo design/tuning method is extended to allow for the coordinated tuning of multiple controllers. The ability to tune multiple controllers in a coordinated manner allows the designer to focus on the overall stability and robustness of the power system, rather than focusing just on, the local stability of the system as viewed from the generator where the controllers are connected

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