Hisham M. Soliman

Guaranteed-cost reliable control with regional pole placement of a power system

This paper deals with the simultaneous coordinated design of power system stabilizer (PSS) and the flexible ac transmission systems (FACTS) controller. The problem of guaranteed cost reliable control with regional pole constraint against actuator failures is investigated. The state feedback controllers are designed to guarantee the closed loop system satisfying the desired pole region, thus achieving satisfactory oscillation damping and settling time, and having the guaranteed cost performance simultaneously. The proposed controllers satisfy desired dynamic characteristics even in faults cases. The controllers parameters are obtained using the linear matrix inequalities (LMI) optimization. Simulation results validate the effectiveness of this approach.

A robust power-system stabiliser design using swarm optimisation

Synchronous generators in power systems are commonly equipped with Power System Stabilisers (PSS) to provide damping signals following disturbances. The design parameters of the PSS are load-dependent. The main objective of this paper is to design a simple robust PSS that can properly function over a wide range of operating conditions. The proposed compensator is designed by stabilising a finite number of characteristic polynomials that are obtained using Kharitonov theorem. The compensators parameters are tuned using a swarm optimisation technique to ensure maximum relative stability. Simulation results illustrate satisfactory performance of the PSS as it is applied to the original non-linear system under wide loading conditions at lagging and leading power factors.

Power System Stabilizer Design for Minimal Overshoot and Control Constraint Using Swarm Optimization

Abstract Power systems are subjected to severe repetitive oscillations that might cause generator shaft fatigue and, consequently, breakdown. In this article, we consider the problem of designing a power system stabilizer that alleviates generator shaft fatigue through the minimization of the maximum overshoot. Moreover, through our design, the levels of control signal, as well as controller parameters, have to be maintained within certain bounds imposed by physical and practical considerations. In this respect, a technique based on the particle swarm approach is proposed to identify the parameters of a fixed structure lead compensator through the solution of a min-max problem while satisfying systems constraints. To enhance the overall performance of the system under wide loading conditions, a set of operating points is considered within our approach. The proposed power system stabilizer is applied to a single-machine infinite-bus system at different loading conditions, and the results showed the effectiveness of the developed approach.

A Neuro-fuzzy Adaptive Power System Stabilizer Using Genetic Algorithms

Abstract This article presents the design technique of an adaptive power system stabilizer using adaptive neuro-fuzzy inference systems trained via data obtained from genetic algorithms. The parameters of a standard power system stabilizer are tuned using adaptive neuro-fuzzy inference systems to achieve a certain damping ratio and settling time at all load points within a wide region of operation. The overall transfer function of the system is derived in terms of the power system stabilizer parameters. A genetic algorithm is used to minimize a multi-objective optimization function that forces the damping ratio and settling time of the system to desired values. The optimization process is separately conducted at selected operating points to yield power system stabilizer parameters that change with load variations. Results of genetic algorithm optimization are used to form a training dataset of an adaptive neuro-fuzzy inference systems agent, which could give the power system stabilizer parameters at any load within the specified region of operation. Results of power system stabilizer testing show that the desired performance indices could be fulfilled from light load to over load under both lagging and leading power factor conditions. System performance shows a remarkable improvement of dynamic stability by obtaining a well-damped time response.

Robust controller design for active suspensions using particle swarm optimisation

The paper presents a design technique for a fixed-structure PD robust controller of car active suspension systems. The design takes into consideration the uncertainty of system parameters, particularly tyre stiffness and body mass. Robustness is achieved by tuning the controller over a set of operating conditions covering the whole range of system parameters, e.g., body mass and tyre stiffness. Particle swarm optimisation (PSO) is used to attain different performance objectives of the system. Settling time of body displacement is minimised, system damping is maximised, and actuator saturation is avoided via control effort reduction. The design of controller parameters is cast in a multi-objective non-linear optimisation problem, and described to ensure the best possible performance. Simulation results show the superiority of the proposed system relative to the classical passive suspension, and signify robustness of the active controller design.

Constrained Load-frequency Control

Abstract Load-frequency control (LFC) is one of the major problems when dealing with the design and/or operation of electric power systems. Moreover, due to practical and physical considerations, bounds are often imposed on the level of control signals as well as the maximum frequency deviation. Control strategies that satisfy systems constraints, and, at the same time, guarantee optimal systems performance, are often more difficult to achieve. In this article, a technique is developed for the solution of LFC problems with state and/or control constraints via the solution of constrained linear quadratic problem (LQP). After formulating the problem and deriving the necessary conditions of optimality, an iterative algorithm is proposed that leads to the optimal control strategy while satisfying systems constraints. In order to illustrate the effectiveness of this technique, one- and two-area constrained LFC problems are solved, and the results are demonstrated at the end of the paper.

PSO-based robust PID control for flexible manipulator systems

In this paper, a new control law is designed for flexible manipulator systems. Kharitonov theorem is used as a design tool to derive the robust control in robotic systems. Stability is guaranteed not only at one operating condition but also for a wide range of system uncertainty. Both the non-linear behaviour of the gearbox stiffness and the end load variations are tackled as interval uncertainties in the model. Particle swarm optimisation (PSO) is used to tune controller parameters, such that, the greatest real parts of closed loop eigenvalues among Kharitonov-extreme polynomials is minimised to assure the highest possible relative stability. The flexible manipulator is a realistic industrial benchmark robot manipulator. The proposed controller is tested as well for rejecting disturbances injected at the motor and the tool. The robustness performance of the controller is evaluated in terms of reference tool position tracking in the presence of the mentioned disturbances and uncertainties. The results of ...

An adaptive power system stabiliser based on fuzzy and swarm intelligence

Well-damped transient response of power systems signifies a vital control task. The paper presents a design methodology of an adaptive power system stabiliser (PSS) to achieve such task by targeting certain damping ratio and settling time. A standard simple-structure controller is used with the plant, which includes the synchronous generator and exciter, and the overall transfer function is derived in terms of the PSS parameters. A multi-objective optimisation function is formulated in order to force the damping ratio and settling time of the system to desired values. Particle swarm optimisation (PSO) is applied to independently obtain the PSS parameters which minimise such objective function at selected load points covering a wide range of operation. The data obtained from PSO represent the training data of an adaptive neuro-fuzzy inference system (ANFIS), which could give the PSS parameters at any load within a wide region of operating conditions. Testing of the proposed PSS shows that the desired performance indices could be fulfilled from light load to over load under both lagging and leading power factor conditions.

Power System Reliable Stabilization with Actuator Failure

Abstract This article presents a new approach to design reliable controllers acting on the excitation and governor of a synchronous alternator. The usage of a power system stabilizer is inevitable for the enhancement of dynamic stability of power grids. The suggested reliable power system stabilizer ensures stability either when both controllers are sound or when one of them fails. A redundant feedback controller is designed using particle swarm optimization to achieve a desired degree of stability whether or not the main controller is responding. The design of the redundant controller is based on minimizing an eigenvalue-based objective function using particle swarm optimization. A single-machine infinite-bus system is considered to demonstrate the functionality of the proposed fault-tolerant controller. Results of the eigenvalue analysis reported in the present article show the effectiveness of the proposed power system stabilizer under different loading conditions. The approach is extended to consider reliable stabilization for multi-machine systems where the designed controller could successfully stabilize the system with sound operation as well as under control channel failure.

Robust guaranteed-cost control with regional pole placement of active suspensions

To alleviate mechanical parts fatigue and provide a comfortable ride for passengers, car mechanics require desired dynamic behavior of car suspensions. This paper suggests state feedback controllers that, with system uncertainties, guarantee the closed loop system satisfying the desired pole region, thus achieving satisfactory oscillation damping and settling time, and having the guaranteed cost performance simultaneously. A state feedback controller is applied to the design of an active suspension model for a quarter-car vehicle. The controllers parameters are obtained using the linear matrix inequalities optimization, also allowing considering the model uncertainty represented by a norm-bounded structure. Simulations are carried out taking into account the presence of uncertain parameters. The results are shown to be better than the classical linear quadratic regulator.