Optimization Techniques to tune the PID and PIDA Controllers for AVR Performance Enhancement

Volume 5 Issue 1 November - January 2017

Research Paper

Optimization Techniques to tune the PID and PIDA Controllers for AVR Performance Enhancement

Ahmed M. Mosaad*, Mahmoud A. Attia**, 0***

* PG Scholar, Department of Electrical Power and Machines, Ain Shams University (ASU), Cairo, Egypt.

**, *** Professor, Department of Electrical Engineering, Ain Shams University (ASU), Cairo, Egypt.

Mosaad, A.M., Attia, M.A., and Abdelaziz, A.Y. (2017). Optimization Techniques to tune the PID and PIDA Controllers for AVR Performance Enhancement. i-manager’s Journal on Instrumentation and Control Engineering, 5(1), 1-10. https://doi.org/10.26634/jic.5.1.10336

Abstract

This paper presents three types of optimization techniques; Teaching Learned Based Optimization (TLBO), Harmony Search Algorithm (HSA), and Local Unimodal Sampling (LUS) which are used to tune the controller parameters in Automatic Voltage Regulator (AVR) system. AVR system is used to adjust the terminal voltage of synchronous generator. This paper presents two types of controllers: Proportional-Integral-Derivative (PID) and Proportional-Integral-Derivative- Acceleration (PIDA). Each controller is used with TLBO, HSA, and LUS. For PID controller, these techniques show better performance than Many Optimizing Liaisons (MOL), Gravitational Search Algorithm (GSA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Differential Evolution (DE) in previous works. For PIDA controller, these techniques show better performance than Bat Search (BAT), Current Search (CS), Tabu Search (TS), and Genetic Algorithm (GA) in previous works. By comparing both controllers, PIDA shows better response in overshoot and steady state error than PID.

Detection and Control of Saturation Level in the Magnetic Core of a Welding Transformer by ANN Detector and PI Controller

Volume 4 Issue 4 August - October 2016

Research Paper

Detection and Control of Saturation Level in the Magnetic Core of a Welding Transformer by ANN Detector and PI Controller

S. Rama Subbanna*, Suryakalavarthi, M**

* Research Scholar, Jawaharlal Nehru Technological University, Hyderabad, India.

** Professor, Department of Electrical and Electronics Engineering, Jawaharlal Nehru Technological University, Hyderabad, India.

Subbanna, S. R., and Suryakalavathi, M. (2016). Detection And Control Of Saturation Level In The Magnetic Core of a Welding Transformer by ANN Detector and PI Controller. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 37-44. https://doi.org/10.26634/jic.4.4.8232

Abstract

In the paper, an Artificial Intelligence is proposed for detecting and controlling the magnetization level in magnetic core of a Welding Transformer, which is a part of a Middle-Frequency Direct Current (MFDC) Resistance Spot Welding System (RSWS). It consists of an input rectifier, which produces a DC bus voltage, an inverter, a welding transformer, and a fullwave rectifier that is mounted on the output of a transformer. During normal RSWS operation, welding transformer's magnetic core can become saturated due to the unbalanced resistances of both transformer secondary windings and different characteristics of output rectifier diodes, which causes current spikes and over-current protection switch-off of the entire system. In order to prevent saturation of the transformer magnetic core, the RSWS control must detect that the magnetic core is approaching the saturated region and maintains the saturation within optimal limits. The aim of this paper is to present a reliable method for detection and control of magnetic core saturation. Here, an Artificial Neural Network (ANN) is proposed for detecting the current spikes in the primary current and PI controller is used to maintain the saturation within optimal limits. This is achieved by a combined closed-loop control of the welding current and iron core saturation level.

Designing an optimal controller for Cart Triple Link Inverted Pendulum

Volume 4 Issue 4 August - October 2016

Research Paper

Designing an optimal controller for Cart Triple Link Inverted Pendulum

Kalyani Sharma*, Vikas Sahu**

* M.E. Scholar, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, C.G, India.

** Assistant Professor, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, C.G,

Sharma, K., and Sahu, V. (2016). Designing an optimal controller for Cart Triple Link Inverted Pendulum. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 1-36. https://doi.org/10.26634/jic.4.4.8231

Abstract

In this paper the main aim is to stabilize the Triple Link Inverted Pendulum (TLIP) system on a cart. The mathematical model and state space dynamics of the TLIP system is discussed. The TLIP system is inherently unstable, so a Linear Quadratic l Regulator Design (with a degree of stability) is presented. The stability, controllability, and observability are investigated. The choice of weighting matrices in LQR is also discussed. Then an Observer-based controller is designed for the TLIP system. The simulation is done with MATLAB environment and a comparative study of the time domain characteristics are shown.

Dynamic Analysis of Vector Controlled Induction Motor Using Artitifcial Intelligent Controller

Volume 4 Issue 4 August - October 2016

Research Paper

Dynamic Analysis of Vector Controlled Induction Motor Using Artitifcial Intelligent Controller

0*, Brajpal Singh**

* Associate Professor, Cadets Training Wing (CTW), Military College of Electronics and Mechanical Engineering, Secunderabad, India.

** Assistant Professor, Cadets Training Wing (CTW), Military College of Electronics and Mechanical Engineering, Secunderabad, India.

Menghal, P.M., and Singh, B. (2016). Dynamic Analysis of Vector Controlled Induction Motor Using Artitifcial Intelligent Controller. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 23-30. https://doi.org/10.26634/jic.4.4.8230

Abstract

In the industrial sector, especially in the field of electric drives & control, induction motors play a vital role. Without proper controlling of the speed, it is virtually impossible to achieve the desired task for a specific application. Vector control has been preferred over scalar controlled induction motor due to its superior dynamic performance. As the PI controller has bounded operating limits and poor transient response, a search for an alternative controller arises. Due to advancement in Artificial Intelligence Technique such Fuzzy Logic (FL), Artificial Neural Network, ANFIS has gained momentum as a controller for nonlinear systems. In this paper, artificial intelligent controller such as FLC and ANN have been designed in a vector controlled induction motor drive. The dynamic modeling and simulation of induction motor drive has been done in MATLAB/SIMULINK. The performance evaluation of vector controlled drive has been presented for no load, positive and negative load as well zero speed conditions. The simulation results are presented with the PI controller and Artificial Intelligent controller such as Fuzzy logic and Neural Network. It proves that ANN based vector controlled induction motor drive have better performance as compared to PI and FLC controllers.

Frequency Control of a Hybrid Power System with Wind Energy in Presence of Classical Particle Swarm Optimization tuned PI-Controller

Volume 4 Issue 4 August - October 2016

Research Paper

Frequency Control of a Hybrid Power System with Wind Energy in Presence of Classical Particle Swarm Optimization tuned PI-Controller

Ch. Durga Prasad*, G.Pavan Kumar**

*-** Assistant Professor, Department of Electrical and Electronics Engineering, SRKREC, Bhimavaram, Andhra Pradesh, India.

Prasad, Ch,D., and Kumar, G.P. (2016). Frequency Control of a Hybrid Power System with Wind Energy in Presence of Classical Particle Swarm Optimization tuned PI-Controller. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 16-22. https://doi.org/10.26634/jic.4.4.8229

Abstract

In this paper, an Inter Connected Hybrid Power System with Wind Energy in presence of Classical Controllers Tuned by Particle Swarm Optimization is studied. Integration of renewable resources into existing plants will affect the system frequency and hence design of a suitable controller is needed to maintain frequency within limits. In this paper, PSO technique is used to design conventional controllers such as I-controller, and PI- controller. For classical thermal and diesel units, the PSO method will tune the controller parameters in the secondary loop, whereas in case of wind turbine model; the pitch angle is controlled by the same Particle Swarm Optimization algorithm. Results are carried out in Matlab/Simulink software.

Approximation Signal: An Efficient Approach to Diagnose Induction Motor Faults in Inverter Fed Induction Motor Drives

Volume 4 Issue 4 August - October 2016

Research Paper

Approximation Signal: An Efficient Approach to Diagnose Induction Motor Faults in Inverter Fed Induction Motor Drives

Khadim Moin Siddiqui*, Kuldeep Sahay**, V. K. Giri***

* Teaching-cum Research Scholar, Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, India.

** Professor, Department of Electrical Engineering, Institute of Engineering & Technology, Lucknow, India.

*** Director, Government Rajkiya Engineering College, Sonbhadra, Uttar Pradesh, India.

Siddiqui, K.M., Sahay, K., and Giri, V.K. (2016). Approximation Signal: An Efficient Approach to Diagnose Induction Motor Faults in Inverter Fed Induction Motor Drives. i-manager’s Journal on Instrumentation and Control Engineering, 4(4), 1-15. https://doi.org/10.26634/jic.4.4.8228

Abstract

Nowadays, the inverter driven induction motor drives are being popular in the industries for variable speed applications and replacing existing techniques such as DC motors and thyristor bridges. In the present paper, a proposed dynamic simulation model is used to diagnose squirrel cage induction motor faults by wavelet transform's low frequency band approximation signal on the transient condition. The obtained results clearly demonstrate that the developed diagnostic technique may reliably separate healthy and faulty conditions of the motor in the transient conditions. Therefore, averted the motor faults before reaching in the disastrous condition and may save large revenues and unexpected shutdown for industries.

Diagnosis of Rotor Broken Bar Fault in Inverter Driven Induction Motor Drive by Signal Processing Techniques

Vol. 4  Issue 3
Year:2016
Issue:May-Jul 
Title:Diagnosis of Rotor Broken Bar Fault in Inverter Driven Induction Motor Drive by Signal Processing Techniques
Author Name:Khadim Siddiqui, Kuldeep Sahay and V.K. Giri
Synopsis:
In the present time, the inverter-driven induction motor drives are being widely employed in the industries for variable speed applications. These drives are replacing DC motors and thyrister bridges day-by-day in the industries. Therefore, health monitoring of these variable speed drives are receiving large attention from the researchers. In this research paper, the rotor broken bar fault has been diagnosed in the steady state as well as transient conditions by advanced signal processing techniques. For fault detection in the steady state condition, the Fast Fourier Transform (FFT) algorithm is used and for transient fault detection, time domain as well as wavelet transform techniques have been used. The time domain and frequency domain techniques show some kind of lacks for diagnosis of this fault. Therefore, a wavelet transform based new technique for early broken rotor bar, fault diagnosis purpose has been proposed and the rotor fault is efficiently diagnosed in the transient condition. These approximation signals based new technique has diagnosed the fault in the transient condition and consequently, fault may be averted before they become more catastrophic. As a result, industries may save large revenues and unexpected failure conditions.