Low frequency oscillations damping by STATCOM with a fuzzy supplementary controller

This paper presents a theoretical study on a model-reference adaptive fuzzy logic controller for vector-controlled permanent magnet AC servomotor drives. In the proposed system, fuzzy logic is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The results are compared with that provided by a nonadaptive fuzzy controller. The implementation of proposed adaptive fuzzy controller is also discussed.

To achieve a better robustness and get a static response of a certain target missiles in longitudinal control, a mathematical model of target missiles is established. The uncontrolled trajectory, the predetermined trajectory and the requirements of the predetermined trajectory are analyzed next. Then the trajectory instruction angle is designed to ensure the stable flight of the target missiles system. To solve the problem that the PID controller cannot adjust parameters online and the output is easy to be disturbed by external noise, the fuzzy adaptive PID control is designed. It enhanced the robustness of the target missiles by fuzzy control, and also has a good response by using PID control. The simulation experiments show the fuzzy adaptive control system can track the predetermined trajectory rapidly and smoothly, and the static error and overshoot were significantly reduced. When the system are affected by noise, the fuzzy adaptive PID control can achieve a good robustness than traditional PID controller.

Research on A Fuzzy Adaptive PID Control Method for Four Rotor UAV Control System With the continuous improvement of national living standards, people have higher and higher requirements for the reliability of power supply. The emergence of unmanned aerial vehicle inspection technology provides a new idea to solve the contradiction between the increasing equipment scale and the increasing inspection requirements. However, at present, the stability of the traditional proportion integration differentiation (PID) control system of the four rotor unmanned aerial vehicle (UAV) is not high enough. During the inspection, there are accidents of drones hitting power equipment from time to time. In view of the above problems, this paper studies the fuzzy adaptive PID control method to support the stable flight of four rotor UAV. The principle of fuzzy control is analyzed. The fuzzy control method and PID control method are deeply integrated. A fuzzy adaptive PID control system is designed. The universe and membership function of the relevant control parameters of the system are formulated. The tuning rules of fuzzy adaptive PID control system are formulated. Finally, the improved control system is simulated. The simulation results show that the fuzzy adaptive PID control algorithm can effectively improve the stability, reliability and emergency ability of UAV. This method can effectively improve the adaptability of UAV to perform complex missions in complex environments. The research results have guiding significance for expanding the application field of UAV.

Abstract- This paper introduces a linear structure of wind turbine, operator and the pitch angle controller. Then, a new method of adaptive fuzzy on the variable speed wind turbines was provided and it was compared with PID and Fuzzy Logic Control ler and the simulation results were analyzed. So when the results of each simulation using PID and Fuzzy controllers and adaptive fuzzy PID controllers provided, the observed advantages and disadvantages of the rotor speed and wind turbine power can be introduced. It can be seen that in PID method the high overshoot is discussed as a disadvantage while it is overcome by using fuzzy controller and overshoot will decrease. The time of reaching to a sustained speed increases slightly and in adaptive fuzzy PID controller, the less overshoot has provided a good and effective performance for system response. The use of adaptive fuzzy PID controller causes the system does not have any steady-state error and at all moments of time, the response rate is better than PID and fuzzy controller. The importance of the amount of overshoot and rate fluctuations is that by reducing these parameters in addition to reducing the cost of preventive care, maintenance and depreciation, the fluctuations in electricity generated by induction generator also greatly reduced.

In this paper, we study the application of fuzzy set theory, genetic algorithms and rough set theory techniques to the control of the rectifier. A fuzzy adaptive control scheme with the aid of rough set theory via genetic algorithms (GAs) finding the center parameters in place of the classical control is proposed. On the one hand, genetic algorithm gets optimal parameters of an accurate domain model, which tunes the scaling factors of fuzzy adaptive control but is hardly established in a non-liner system. On the other hand, fuzzy adaptive control deals with the dynamics and complexity of responses from rectifier of a HVDC system in the operation points, by adjusting its control parameters with the aid of rough tuner adaptively. Our study includes a brief introduction to fuzzy control and rough set algorithms, both theory and application. We also evaluate the performance of rough fuzzy control by simulation in the paper. The focus of our experiments is on the constant current control in rectifier side of HVDC system. The result shows there are many improvements offered by the fuzzy control scheme based on rough set theory in comparison with the only fuzzy control scheme in the rectifier side of HVDC system.

Motion control is an essential part of industrial machinery and manufacturing systems. In this article, the adaptive fuzzy controller is proposed for precision trajectory tracking control in biaxial X-Y motion stage system. The theoretical analyses of direct fuzzy control which is insensitive to parameter uncertainties and external load disturbances are derived to demonstrate the feasibility to track the reference trajectories. The Lyapunov stability theorem has been used to testify the asymptotic stability of the whole system, and all the signals are bounded in the closed-loop system. The intelligent position controller combines the merits of the adaptive fuzzy control with robust characteristics and learning ability for periodic command tracking of a servo drive mechanism. The simulation and experimental results on square, triangle, star, and circle reference contours are presented to show that the proposed controller indeed accomplishes the better tracking performances with regard to model uncertainties. It is observed that the convergence of parameters and tracking errors can be faster and smaller compared with the conventional adaptive fuzzy control in terms of average tracking error and tracking error standard deviation.

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.

Due to power system oscillations induced by complex interconnections, power system stability has been a major concern and will continue to be a major concern in power system operations. For improving power system stability, efforts have been directed toward damping power system oscillations. The small signal stability is the instability that has been influenced by complex interconnections. Small signal stability can be dampened with power system stabilisers (PSS). PSS, on the other hand, have certain disadvantages, such as significant variations in voltage profiles and the potential for leading power factor operations, which may reduce system stability in the face of severe disturbances. In wide multi machine power systems, using only traditional PSS can not be enough to dampen inter-area oscillations and the effect of voltage profile variation. Power electronic devices known as Flexible Alternating Current Transmission System (FACTS) devices, on the other hand, are good at damping inter-area oscillations and can handle voltage variations. As a result, based on computational intelligence techniques, this paper provides a thorough analysis and evaluation of PSS and FACTS power oscillation damping (POD) controllers in reducing low frequency oscillations. The importance of technical paper publications on PSS and FACTS-based controller design, optimum location, and parameter tuning is highlighted. Finally, the potential future research directions for novel PSS and FACTS devices is proposed as a reference for interested researchers.

Scope of this paper is to present a design process of a supervisory control scheme that optimally coordinates a parallel operation of FACTS devices and an HVDC link to synchronously counteract inter-area oscillations in a power system. The control system is based on Linear Quadratic Gaussian (LQG) optimal control theory. The scheme provides coordinating control signals to the power electronic devices to coherently damp electromechanical oscillations inherent in the system. The scenarios considered involve transient disturbances, signal time delays and reduced system inertia. The feasibility of the proposed scheme is validated on a two-area network incorporating HVDC link, TCSC and SVC devices. The modelling was performed in MATLAB and DIgSILENT simulation environments. Main outcome from this research is the demonstration of improved performance and robustness measures achieved by the novel control scheme, utilizing flexible transmission assets to effectively damp power oscillations in inter-connected power systems.

Robust design and best control channel selection of FACTs-based WADC for improving power system stability using Grey Wolf Optimizer

Electromechanical oscillations are the major stability limits which constrain the power transfer in the interconnected system and influence the transmission line capacity. Power System Stabilizer (PSS) and Flexible AC Transmission Systems (FACTS) devices can be deploy to overcome these oscillations. In this paper the focus has been made on the damping of the inter-area oscillations and Unified Power Flow Controller (UPFC) which has been chosen to be installed at the midpoint of the tie-line between Pakistan and Iran. The region has been experiencing extensive blackouts due to oscillation. A comprehensive analysis for the control of power transmission and damping of inter-area oscillations has been performed with UPFC and PSS based on a dynamic control scheme. The complete system has been simulated in the Matlab/Simulink Environment for different fault conditions. The results verify the potency of the suggested control strategy.

In electromechanical oscillation damping within power system, power system stabilizers (PSSs) are often deployed. However, a PSS is less effective in damping interarea oscillation and is limited by changes in network configuration due to weak tie-lines and load variations. Consequently, this paper presents a wide-area coordination approach that damps interarea oscillations using FACTS devices and phasor measurement units. We selected a static synchronous compensator (STATCOM) and static series synchronous compensator (SSSC) for realistic power system interarea oscillation damping. The performance of the coordinated FACTS installed in a power system depends on their suitable locations, sizes, tuned parameters, and remote input signal selection. Hence, we formulated a multiobjective problem to provide the STATCOM and SSSC's optimal solutions using multiobjective particle swarm optimization. In addition, we employed a participation factor to select suitable generator speed deviations as the wide area stabilizing signal. The proposed approach was tested on different configurations of the Kundur $2$-area $4$-machine test system within MATLAB and Psat environments. The outcome of the nonlinear simulation proved that the multimachine power system stability was enhanced.

In order to solve the poor dynamic characteristics and adjustment effect of traditional voltage filter control system of welding machine. In this paper, the mathematical model of the voltage filter control system of welding machine is established, and the fuzzy control rule is applied to the Voltage Control System, and two improved fuzzy control schemes including fuzzy-PID compound control and fuzzy adaptive control are proposed. Finally, the MATLAB software is used to compare the simulation of the three controllers. Simulation results show that fuzzy-PID compound control and Fuzzy adaptive control have better dynamic performance than traditional PID control; Compared with fuzzy-PID compound control, Fuzzy adaptive control has better steady-state performance and stronger anti-interference ability.

Fuzzy logic systems (FLSs) can be designed using training data (i.e. given M numerical input/output pairs) and supervised learning algorithms. Orthogonal least-squares (OLS) learning decomposes a FLS into a linear combination of M/sub s/<M nonlinear fuzzy basis functions (FBFs), which are optimized during OLS to match the training data. The drawback to OLS is that the resulting system still contains information from all M initial rules, derived from the training points, even though only the most important M/sub s/ rules have been established by OLS. This is due to a normalization of the FBFs, and leads to excessive computation times during further processing. Our solution is to construct new FBFs out of the reduced rule-base and to run OLS a second time. The resulting system not only is of reduced computational complexity, but is of very similar behaviour to the unreduced system. The second run of OLS can be applied to a larger set of training data which greatly improves the precision. We illustrate our two-pass OLS algorithm for prediction of the Mackey-Glass chaotic time series.<<ETX>>

Harmony search algorithm and Lyapunov theory based hybrid adaptive fuzzy controller for temperature control of air heater system with transport-delay