Clustering methods for the efficient voltage regulation in smart grids

In this paper, clustering methods are presented to enhance the stability of automatic voltage regulators using the efficient adjustment of their respective gains. The results show that implementations of some of the clustering algorithms provide better reliability and stability for the feedback-based voltage regulators as compared to the other methods, namely, a model predictive controller (MPC), a gaussian mixture model (GMM), a self-organizing mapping (SOM) and hierarchical clustering (HC) methods. Specifically, the K-Means clustering approach (KM) provided superior stability but a slower rise time of the output voltage of the voltage regulators as compared to the other methods. Furthermore, coordination of the clustering methods is tested for a 10 machine, 39 bus power grid system. The results show that the clustering approach could be applied to improve the efficiency of voltage regulation methods in smart grids and related cyber-physical systems

Smart Control of Automatic Voltage Regulators using K-means Clustering

The future cyber physical systems consist of voltage regulators distributed across wide geographical areas. In this paper, a smart control approach of voltage regulators is presented for cyber physical system applications. The approach is implemented using K-means clustering algorithms that use data from voltage and current sensors, compute the correlation of changes across the regulators and generate a proportional feedback. Advanced estimation methods are used in cases where the data from the sensors was not available. The results show that the approach could be used to improve the performance of networked, power dependent systems by 94.5% in terms of overshoot and 9.52% in terms of response time as compared to other methods of controlling voltage regulators

Affine projection algorithm based adaptive control scheme for operation of variable-speed wind generator

This study presents a novel adaptive control scheme for variable-speed wind turbine (VSWT) driven permanent magnet synchronous generator (PMSG) to ensure its operation under different operating conditions. The adaptive control scheme is based on the affine projection algorithm (APA) which provides a faster convergence and less computational complexity than the least-mean-square algorithm. The proposed adaptive controller is used to control both the generator-side converter and the grid-side inverter without giving additional tuning efforts. Each vector control scheme for the converter/inverter has four APA-based adaptive proportional-integral (PI) controllers. Detailed modelling and the control strategies of the system under study are demonstrated. Real wind speed data extracted from Hokkaido island, Japan is used in this study. The dynamic characteristics of a grid-connected VSWT-PMSG are investigated in details to ensure the proposed controller operation under different operating conditions. The effectiveness of the proposed adaptive controller is compared with that obtained using optimised PI controllers by Taguchi method. The validity of the adaptive vector control scheme is verified by the simulation results which are performed using PSCAD/EMTDC environment

Two degree of freedom fractional PI scheme for automatic voltage regulation

The effectiveness of the inferential control scheme based on robust fractional-order proportional integral (FOPI) controller is presented for automatic voltage regulation (AVR) applications. The method uses two degree of freedom (2DOF) in FOPI scheme, which is tuned with the whale optimization algorithm (WOA). Actually, any AVR needs to keep the reactive power of synchronous generator at demand level, stable voltage and frequency of the electrical power supplies. In this study, the 2DOF FOPI controller is proposed to deviate away from the standard integer order, to show the superiority of extra degree of freedom in both structure and controller. To improve the AVR performance, a new performance measure is proposed for the parameter tuning. The method acquires the significant robustness in parameter perturbation and disturbance interruptions. It is observed in the step response quality that the overshoot and settling time can be reduced to approximately by half than the recently published scheme. The various analyses are shown to accept the dominance of the proposed controller in terms of robustness

Design of Intelligent PID Controller for AVR System Using an Adaptive Neuro Fuzzy Inference System

This paper presents a hybrid approach involving signal to noise ratio (SNR) and particle swarm optimization (PSO) for design the optimal and intelligent proportional-integral-derivative (PID) controller of an automatic voltage regulator (AVR) system with uses an adaptive neuro fuzzy inference system (ANFIS). In this paper determined optimal parameters of PID controller with SNR-PSO approach for some events and use these optimal parameters of PID controller for design the intelligent PID controller for AVR system with ANFIS.  Trial and error method can be used to find a suitable design of anfis based an intelligent controller. However, there are many options including fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performance. An optimization algorithm facilitates this process and finds an optimal design to provide a desired performance. This paper presents a novel application of the SNRPSO approach to design an intelligent controller for AVR. SNR-PSO is a method that combines the features of PSO and SNR in order to improve the optimize operation. In order to emphasize the advantages of the proposed SNR-PSO PID controller, we also compared with the CRPSO PID controller. The proposed method was indeed more efficient and robust in improving the step response of an AVR system and numerical simulations are provided to verify the effectiveness and feasibility of PID controller of AVR based on SNRPSO algorithm.DOI:http://dx.doi.org/10.11591/ijece.v4i5.652

A Taguchi approach for optimum design of proportional-integral controllers in cascaded control scheme

This study aims at resolving a specific problem in the area of renewable energy, energy storage systems, variable-speed drives, and flexible AC transmission system (FACTS) devices used in the electric power systems. The use of power conversion system (PCS) is very common in the aforementioned areas. A cascaded control scheme based on four proportional-integral (PI) controllers is widely used in the control of the PCS unit. Setting the parameters of the four cascaded PI controllers simultaneously is cumbersome, especially when the application is in the area of electrical power system which is difficult to express using a mathematical model or transfer function due to its nonlinear properties. This paper presents an optimum design procedure for the cascaded controller of the PCS unit using Taguchi method. To apply the design parameters obtained from Taguchi method, a renewable energy application is chosen where a variable-speed wind turbine generator system is connected to the power grid through two back-to-back PCS units. The effectiveness of the designed parameters using Taguchi method is then compared with that obtained using response surface methodology and genetic algorithm (RSM-GA) method under the grid fault condition

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder-Mead algorithm

[EN] In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder¿Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.This work was supported by the Vanier Canada Graduate Scholarship, the Michael Smith Foreign Study Supplements Program from the Natural Sciences and Engineering Research Council of Canada and by the Ministerio de Economia y Competitividad (Spain), project DPI2015-71443-R. It was also supported by the Bourse Mobilite Etudiante from Ministere de l'Education du Quebec, the CEMF Claudette MacKay-Lassonde Graduate Engineering Ambassador Award and the SWAAC Bourseau merite pour etudiantes de cycles superieurs.Blondin, MJ.; Sanchís Saez, J.; Sicard, P.; Herrero Durá, JM. (2018). New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder-Mead algorithm. Applied Soft Computing. 62:216-229. https://doi.org/10.1016/j.asoc.2017.10.007S2162296

Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES

This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) system to enhance the transient stability of wind farms connected to a multi-machine power system during network disturbances. The control strategy of SMES depends mainly on a sinusoidal pulse width modulation (PWM) voltage source converter (VSC) and an adaptive ANN-controlled DC-DC converter using insulated gate bipolar transistors (IGBTs). The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of proportional-integral (PI)-controlled SMES optimized by response surface methodology and genetic algorithm (RSM-GA) considering both of symmetrical and unsymmetrical faults. For realistic responses, real wind speed data and two-mass drive train model of wind turbine generator system is considered in the analyses. The validity of the proposed system is verified by the simulation results which are performed using the laboratory standard dynamic power system simulator PSCAD/EMTDC. Notably, the proposed adaptive ANN-controlled SMES enhances the transient stability of wind farms connected to a multi-machine power system