Dynamic Stability Improvement Using Genetic Algorithm Tuned Controllers Embeded in Generator Control Loops

This paper presents the comparative study of Model Predictive Controller (MPC) with PID Controller implemented for flow control loop in LABVIEW. Classical PID controller is mostly used in process instrumentation yet tuning of PID controller is the crucial part to be dealt with. Moreover, changes in the parameters of the loop components affects the tuning of the controller, hence over a period of time retuning of PID controller is required. On the other hand MPC controller works on the process model which predicts and optimizes the process performance. Here, Flow control loop performance was studied for MPC and PID controller. MPC worked on 1st order state space system model and PID was tuned using Internal Model Control (IMC) method. The result of implementation shows that, MPC controller performs much better than PID controller. Set point of with less settling time and zero peak overshoot was observed in MPC whereas PID has comparatively more settling time and peak overshoot.

In this paper, the advancement in small signal stability via the optimal placement of Static Synchronous Compensator (STATCOM) and its performance is considered. The Eigen values are employed to analyze the stability of the WSCC system and the analysis has been done by PSAT software. It has been studied through light, medium, heavy, heavy one loading conditions unlike the existing system. At heavy one loading condition, the stability of the system collapses and the system has positive Eigen values. The system with STATCOM has the ability to work with stability though it undergoes heavy one loading conditions compared to the system without STATCOM and the positive Eigen values in the system is nil. The outcome of the proposed approach shows that STATCOM enhances the small signal stability excellently in the power system network.Keywords: Eigen Value, PSAT, Small Signal Stability, STATCOM, WSCC

• Equivalent circuit of GFM-converter system is derived by the analogy-based method, which shows control loop interactions. • Control loop interaction and their impact on small-signal synchronization stability are visualized by equivalent circuits. • It uncovers that AC voltage control brings about negative damping and deteriorates small-signal synchronous stability. • Instructions for improvements of small-signal synchronous stability are given by parameter analyses of AC voltage control. For grid-forming (GFM) converter-based power grids, the impacts of AC voltage control (ACVC) loops on small-signal synchronization stability are rarely discussed in literature. To present the interactions between the synchronization control loop and the ACVC loop in an intuitive and clear approach, this paper establishes the equivalent circuit model of multi-GFM-converter based power systems, and analyzes the impacts of control loop interactions on small-signal synchronization stability. Firstly, the model of multi-GFM converter-based power grids has been established that incorporates the ACVC loop. Then, for the further visualization of control loop interactions, equivalent circuits of the whole systems are derived by the analogy-based method. This analysis allows us to assess the impacts of the ACVC on small-signal synchronization stability. This paper illustrates that ACVC can be equivalently represented as a negative impedance in parallel with the filter inductor. In this case, the ACVC brings about negative damping effects and deteriorates small-signal synchronization stability. Furthermore, the control parameters of the ACVC on small-signal synchronization stability are also quantitatively evaluated. Additionally, interactions between different GFM converters on small-signal synchronization stability are also investigated. Finally, the modified IEEE 10-GFM-converter 39-bus system is built in MATLAB/Simulink platform to validate analyzes and the proposed method.

Single-input interval type-2 fuzzy PID (SI-IT2-FPID) control approach is a simple and effective way of adding nonlinear characteristics to linear PID controllers to increase the control performance and the capability of handling system nonlinearities. In the SI-IT2-FPID approach, the nonlinear characteristic of the PID controller is provided by a single-input–single-output interval type-2 fuzzy controller (IT2-FC) positioned before the PID controller. In the literature, a constant footprint of uncertainty (C-FOU) structure is used in SI-IT2-FPID controller design procedures in almost all studies. However, the C-FOU structure limits the performance of the SI-IT2-FPID controller. In this study, an online footprint of uncertainty (FOU) tuning mechanism is proposed for SI-IT2-FPID controllers in order to increase their control performances. Online tuning of FOUs of interval type-2 membership functions (IT2-MFs) enhances the capability of SI-IT2-FPID controllers to handle system nonlinearities since FOU levels of IT2-MFs have a dominant effect on the control and robustness performance of SI-IT2-FPID controllers. In the proposed method, FOUs of IT2-MFs of an IT2-FC are adjusted by an analytical function based on the system error and the change of the error. In this way, the simple structure of the SI-IT2-FPID controller is preserved. In order to illustrate the control and robustness performance of the proposed controller, simulation studies are carried out on three benchmark systems, namely a second-order nonlinear system, a ball and beam system, and a first-order plus dead time system. Additionally, the performance of the proposed controller is compared to a SI-IT2-FPID controller with C-FOU and a classical PID controller. The simulation results show that the proposed controller exhibits better control and robustness performance than the other controllers.

Operational planning of power systems, especially in terms of overall reliability and security, is a key issue in the smart grid development. Hence, it is necessary to develop new strategies to cope with increasing uncertainties arising from the fast changing ways power systems are being operated. This paper presents a comprehensive approach to determine an optimal transmission network expansion plan considering the enhancement of small-signal stability through wide-scale deployment of the existing and planned transmission system assets. The dynamic model of the transmission network operational planning (TNOP) is solved based on a combination of the Mean-Variance Mapping Optimization (MVMO), and the classic dynamic programming method embedded with a heuristic procedure. Besides, a probabilistic eigenanalysis-based recursive method is proposed to determine the optimal control strategies that are highly relevant to the enhancement of the system small-signal stability performance throughout the planning horizon. Numerical results demonstrate the viewpoint and the effectiveness of the proposed approach in providing optimal strategies of minimum cost while avoiding the instability risk associated to poorly damped low-frequency electromechanical oscillations.

We had a contract from Navy in 1997 to work on the control of piezoelectric actuators. The controller consists of two loops: feedforward and feedback. The feedforward loop consists of a Fuzzy CMAC controller, which is used to compensate hysteresis nonlinearity. Fuzzy CMAC is a new type of neural net developed by Intelligent Automation Incorporated. It has a learning speed that is an order of magnitude faster than conventional multilayer perceptron neural nets. The advantage of feedforward control is that it can increase the system response speed without interfering with the system stability. We used a PID controller in the feedback loop because the feedforward compensation may have some residual errors and having a PID controller in the loop will help to reduce the error even further. In our experiment, we operate an actuator manufactured by Burleigh Instruments in the region of 100 Hz whereas the actuator resonance peak is about 1 kHz. Experimental results showed our approach can achieve excellent linearity.

There are a lot of methods for the stabilization of quadcopters and the newest are based on AI. A neural network is a simplified model that imitates the human brain's processes. In the research paper, we present a neural network control model for quadcopter stabilization. A single hidden layer network model was estimated to investigate the dynamics of the UAV. A control system with a classical PID controller was used to train the neural network model. This method is used for examining how the neural network imitates the stabilization of the quadcopter in real flight mode. The novelty of the work was to design of small size 3 layers NN model that runs in real-time in a quadcopter. The PID and machine learning controllers' operation results were compared to each other andshown in the experiment.

In this paper, the fractional order PID (FOPID) control scheme of a 3-axis motion platform is proposed. Firstly, a mathematical model of the 3-axis motion platform is derived, then a control law is developed to track the angular position. Tuned FOPID controller is compared with classic PID controller (IOPID). The simulation results are provided to verify the efficiency and correctness of the proposed control algorithm.

The wheeled mobile robots have been widely applied in daily applications for its simplicity, robustness, stability and low-cost of manufacturing. Typically, PID controller is implemented to drive a mobile robot. However, the performance of the robot is greatly depended on the PID control parameters are tuned and the robustness of the mechanical system. Since the analog controllers were replaced by digital controller, PID control parameters could be automatically tuned to adapt the variation of the system parameters and operating condition. In this manuscript, a mathematical model is developed for a line following mobile robotic system which is used as a restaurant serving robot. The serving robot is controlled by an adaptive PID controller using Q-learning algorithm. The simulation and experimental results are compared to verify the advantage of the adaptive PID controller over classical PID controller.

In this paper, a high-performance optimal fractional emotional intelligent controller for an Automatic Voltage Regulator (AVR) in power system using Cuckoo optimization algorithm (COA) is proposed. AVR is the main controller within the excitation system that preserves the terminal voltage of a synchronous generator at a specified level. The proposed control strategy is based on brain emotional learning, which is a self-tuning controller so-called brain emotional learning based intelligent controller (BELBIC) and is based on sensory inputs and emotional cues. The major contribution of the paper is that to use the merits of fractional order PID (FOPID) controllers, a FOPID controller is employed to formulate stimulant input (SI) signal. This is a distinct advantage over published papers in the literature that a PID controller used to generate SI. Furthermore, another remarkable feature of the proposed approach is that it is a model-free controller. The proposed control strategy can be a promising controller in terms of simplicity of design, ease of implementation and less time-consuming. In addition, in order to enhance the performance of the proposed controller, its parameters are tuned by COA. In order to design BELBIC controller for AVR system a multi-objective optimization problem including overshoot, settling time, rise time and steady-state error is formulated. Simulation studies confirm that the proposed controller compared to classical PID and FOPID controllers introduced in the literature shows superior performance regarding model uncertainties. Having applied the proposed controller, the rise time and settling time are improved 47% and 57%, respectively.

Delays are a common physic effect that is present in a huge quantity of industrial systems with feedback control. Sometimes the impact of the delay presence in a system is neglected without any difference in the performance of the controller. But in some cases, the Delay quantity reaches levels that increase the overshoot significantly or destabilizes the system. For this system the CTCR method can be used to design a “delayed scheduled” controller able to reject the delays effect. To be able to apply this method it is necessary for both the system and the controller to be Linear or linearized. In this article the study case is an articulated inverted pendulum or also called “Pendubot”. This configuration of pendulum was selected because, in spite of being a simple pendulum, it has four equilibrium points, where two equilibrium points were selected to be controlled, the most unstable which has the maximum potential energy for each link and the most stable with the lowest potential energy. The purpose of work with those equilibrium points is compare the difference between the delay rejection in each point with similar setting times and overshoots from controllers. In order to get an accurate model, the state of the current is added along with the viscous friction terms for each joint in the pendulum. To tune the linear and non-linear model an experimental validation was carry on the physical prototype from the Universidad Industrial de Santander (VIE-5373 UIS). The control laws applied were a classical PID and LQR control. Due the controller sample frequency is extremely high in comparison to the states response, all the controllers and linear models were implemented in continuous space. In the first tuning process it was observed that the PID control gets a significantly lowest performance than the LQR control in the unstable equilibrium point, for that reason the comparison between points only was carried on with the LQR equation of feedback system, this can be done in different ways, but for the PID control the Mason theorems was applied and for LQR control only with matrix operation the equation can be obtained. After applying the “Delay scheduling” it was observed that the tuned LQR get a highest delay rejection that PID. An observer fact was that although the controllers for each point have a similar performance the delay pockets have completely different values due the final poles locations for each point. Also was observed that the system only gets one stability pocket, this could because only one delay in the actuator was induced.

The purpose of the study is to develop an adaptive electropneumatic automatic system based on the classical PID controller using modern technology for controlling like fuzzy controlling. The need of an adaptive setting occurred because of the fact that the classic controller has high sensitivity towards interferences with the main signal and the need for the parameters to be regulated often and be done by hand. Another reason is the inability to provide optimal parameters for the controller for nonlinear processes and changes of the parameters in time. In this article the work of electropneumatic system with linear actuator, is examined. A mathematical model describing the dynamics of the processes, is developed. An adaptive fuzzy PID controller with variable coefficients is synthesized. The system is studied in the environment of Matlab Simulink. The transient processes in the system are compared to processes where the classical PID controller. The results are presented in graphical form.

It is important to be able to assess the quality of control provided by PID controllers or indeed by any classical or advanced control method. Since many PID controllers are set up using intuition or empirical tuning rules, it is particularly important that PID designs can be benchmarked and the quality of control assessed. Furthermore, benchmarking methods can provide guidance for tuning controllers.The most common performance measures used for PID control include step-response tracking performance, disturbance-rejection performance properties and the stochastic performance in regulating or tracking processes.With respect to the regulating performance, assessment and benchmarking methods can mostly be considered part of the minimum variance or generalised minimum variance family of techniques. There are commercial tools which use such methods, but these are often based on rather simplistic strategies. One of the advances made in the last decade has been the development of so-called restricted structure benchmarking which provides a figure of merit which is much more representative of what might be achievable if the controller is tuned optimally.With respect to step-input and disturbance rejection performance, the benchmarking should ideally determine how well the output of the system follows a step change in the system inputs (either a set-point or a disturbance change). This suggests a comparison with optimum tracking controllers, of which the most representative would be a model-based predictive controller. Thus, in the second part of this chapter the performance of a PID controller will be compared with the performance of a predictive controller.

The paper presents a comparison of tuning procedures for a multi-region fuzzy-logic controller used for nonlinear process control. This controller is composed of local PID controllers and fuzzy-logic mechanism that aggregates local control signals. Three off-line tuning procedures are presented. The first one focuses on separate tuning of local PID controllers gains in the case when the parameters of membership functions of fuzzy-logic mechanism are know a priori. The second one consists of two major steps. In the first step, the local PID controllers gains are calibrated and in the next step the parameters of membership functions of fuzzy-logic mechanism are tuned. The third one focuses on tuning of PID controllers gains and membership functions parameters jointly. These procedures exploit evolutionary algorithm to optimize the performance of the multi-region fuzzy-logic controller with respect to integral quality criterion such as Integral Absolute Error (IAE). Effectiveness of these procedures is verified based on a well known from literature continuous nonlinear pH neutralization reactor model. For comparison a single PID controller tuned for specific work point is also taken into account.

The PID controller is one of the control strategies having simple structure was understood by plant operator, which is comparatively easy to tune. Irrespective of the attractiveness, the tuning characteristic of PID controllers is a task for engineers and scientists. Numerous procedures for tuning PID-controllers are recommended such as: Ziegler Nichols tuning method auto-tuning, artificial intelligence and optimization techniques. Evolutionary optimization techniques (EOTs) as emergent techniques, an efficient GA-based tuning approach aiming at enhancing PID control for complex processes is proposed. The test and simulation results demonstrate that the GA-based proposed scheme is capable and can be used to overcome the flaws of traditional fixed gain controller. The main role of the article is to appraise the algorithm for tuning of PID-controller gains along with minimization of cost function and argue about the capability, computational efficiency of GA to solve the problems in control systems.