This paper presents the application of neural networks for analysing voltage stability of load buses in electric

power system. Voltage stability margin (VSM) and load power margin (LPM) are used as the indicators for analysing

voltage stability. The neural networks used in this research are divided into two types. The first type is using the neural

network to predict the values of VSM and LPM. Multilayer perceptron back propagation (MLPBP) neural network and

adaptive neuro-fuzzy inference system (ANFIS) will be used. The second type is to classify the values of VSM and LPM

using the probabilistic neural network (PNN). The IEEE 30-bus system has been chosen as the reference electrical power

system. All of the neural network-based models used in this research is developed using MATLAB

Abdul Kadir, Aida Fazliana

Mohamad Nor, Ahmad Fateh

Omar, Rosli

Sulaiman, Marizan

English

UTHM Institutional Repository

International Journal of Integrated Engineering, Vol 3 N o  2 (2011) p. 55-62 Design and Development of a Multi-sensor Monitoring Device for Arm Rehabilitation R. ~ m b a r " ~ ' * ,  M. S. Ahmad2 and M. M. Abdul ~ a m i l " ~  ' Department of Computer Engineering, Department of Electronics Engineering, ' Modeling and Simulation Research Laboratory, Faculty of Electrical and Electronics Engineering, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, MALAYSIA Received 1 March 201 1; accepted 30 October 201 1, available online 24 December 201 1 1. Introduction Rehabilitation process due to stroke or accident related injuries are based on clinical assessment tools which can be executed by self-report (home-based) and observer-rated (done at rehabilitation centre) [I]. Observer-rated by caregivers can be time consuming and patients require to have repeated observations at rehabilitation centre which can be costly. However, early home-based rehabilitation proved to promote a better physical health because it appeared to permit motor and functional gains that occurred with natural recovery and satisfaction with community integration [2]. Continuous monitoring of physical activity is an important subject in the area of rehabilitation. The results obtained from these observations can be used to determine the progress and effectiveness of a rehabilitation program. How to effectively motivate patients to do the regular physical activity is an important research topic [3-41. Enormous researches have been done focusing on lower limbs rehabilitation. However, there are not too many researchers done on arm rehabilitation. Majority of the arm rehabilitation researches involved the usage of exoskeleton type devices which, although can maintain correct movement form and can produce force inputs for therapy, hut due to its large and complex built, it is not suitable for home-based rehabilitation device 15-91, Assistive devices are another alternative which incorporate many high-tech systems into rehabilitation. These systems involve attaching devices to the affected human limbs in order to monitor patient's physical activity. Miniaturized sensors such as accelerometers, flex sensors and force sensitive resistors are widely used in developing assistive devices [lo-141. Accelerometer is a device which measures acceleration indirectly through inertial force. These can be converted into analogldigital voltage signals. Due to this ability, accelerometers have been used to track motion of human 110-121. Flex sensor can effectively measure movement and flexibility of muscle [13-141. Force sensitive resistor can detect flexion and extension of individual muscles 115-161. These sensors proved effective to measure various physical activities parameters. However, there are no attempt on combining all of these sensors. Therefore, the work described in this paper evolved from the above stated researchers involving the application of several sensors and the development of simple data logging method in the design and development of monitoring device for. arm rehabilitation. Data logging enables clinician to do remote monitoring *Corresponding aufhor:aradzi@uthm.edu.my 2011 UTHM Publ~sher, All right reserved. penerb~t.uthmedu.my/oj slindex.php11jie R. Ambar et al., Int. J. Of Integrated Engineering Val. 3 No. 2 (2011) p. 55-62 5. Conclusion Robot-aided neuro-rehabilitation Journal of -~ - - - - - - -  In this paper we proposed a wearable atm rehabilitation device equipped with monitoring system for post-stroke rehabilitation. The device consists of a main unit and sensory unit that utilize a flex sensor, two force sensitive resistors and an accelerometer. It is equipped with real-time monitoring (LCD monitor) and data logging functions (online real-time data logging via USB). The designed system is low cost, compact and does not restrict movement during usage. The proposed system is easy to be attached onto arm with minimal external assistance. It has data logging systems which can store data into PC for certain period of time that can be used by physical therapist for further analysis. By using this device, doctors and physiotherapists do not have to assists patient in home-based rehabilitation. Our fume work will focus on further enhancing the capability of sensory unit by designing a portable data logging unit using secure digital (SD) card. We also hope to use a Z-axis gyro sensor. By using Z-axis gyro sensor, we predict to be able to improve the rehabilitation process which can be determined by measuring arm joints rotational motion which can give more accurate analysis such as sub je~ ' s  performance and limitation towards any specific rehabilitation workout. References [l] R. Chapman et al, "A Motorless Artificial Limb and its Control Architecture", in Proc IEEE Canadian Conference on Electrical and Computer Engineering S h m  Conference Center, Edmonton,Canada; May 9-12, 1999. [2] Roy S et al, "A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke"; IEEE Transactions On Neural Systems And Rehabilitation Engineering, Vo1.17, No. 6, 2009. [3] Nancy E. Mayo et al, "No Place Like Home : An Evaluation of Early Supported Discharge for Stroke", Stroke, Journal of The American Heart Association, 2000. [4] Jon Eriksson, Maja 1. Mataric and Carolee 1. Weinstein, "Hands-off Assistive Robotics for Post- Stroke Ann Rehabilitation", in Proc. IEEE gh International Conference on Rehabilitation Robotics, 2005. [5] Tobias Nef, Robert Riener (2005). ARMin-Design of a Novel Arm Rehabilitation Robot Proceedings of the 2005 IEEE gh International Conference on Rehabilitation Robotics, USA. 57-60. [6] H. I. Krebs, B. T. Volpe, M. L. Aisen, N. Hogan (2000). Increasing productivity and quality of care: Rehabilitation Research and Development. (37) 6:639-652. [7] P. S. Lum, C. G. Burgar, S. P.C., M. Majmundar, and M. Van der Loos (2002). Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper limb motor function following stroke.Arch. Phys. Med. Rehabil., 83, 952-959, 2002. [8] J.  L. Panon, G. Dawe, C. Scharver, F. A. Muss- Ivaldi, and R. Kenyon (2006). Robotics and virtual reality: A perfect marriage for motor control research and rehabilitation.Assistive Technology, 18: 181-195, 2006. [9] S. Masiero, A. Celia, G. Rosati, and M. Armani (2007). Robotic-assisted rehabilitation of the upper limb after acute stroke. Arch Phys Med Rehabil, 88, 142-149, 2007. [lo] Allison L. Hall, "Method For The Acquisition Of Arm Movement Data Using Accelerometersn.Degree Thesis, MIT, June 2005. [ I l l  Takashi Sakaguchi et al, "Human Motion Capture by Integrating Gyroscopes and Accelerometers". International Conference on Mullisensor Fusion and Integration for Intelligent Systems, 1996. [I21 Sam Naghshineh, Golafsoun Ameri, Mazdak Zereshki, "Human Motion Capture using Tri-Axial accelerometers", 2009. [I31 L.K. Simone et al.,"A Low Cost Method to Measure Finger Flexion in Individuals with Reduced Hand and Finger Range of Motion",ln Proc. 26th Annual International Conference of the IEEE EMBS San Francisco USA; September 1-5 2004. [I41 Jae-Myung Yoo and Yong-Myung Ahn., "A Study on A Sensing System For Artificial Arm's Control", SICE-ICASE International Joint Conference 2006. Oct. 18-2 1, Bexco, Busan, Korea. [IS] Oliver Aml? et al, "Sensing Muscle Activities with Body-Worn Sensors", International Worhhop on Wearable and Implantable Body Sensor Networks,LEEE Computer society. 2006. [16] R. Amhar, M. S. Ahmad, and M. M. Abdul Jamil, "Design and Development of A m  Rehabilitation Monitoring Device", IFMBE proceedings: vol. 35, pp. 781-784, 5th Kuala 1,umpur International Conference on Biomedical Engineering (Biomed), Kuala Lumpur, Malaysia, in conjuction with the 8" asian Pacific Conference on Medical and Biological Engineering (APCMBE 2011) 20-23 June 2011, Springer-Verlag Berlin . [17] http:l/ww.ladvada.net/learnisensors/fsr.html (accessed on 161312011). [I 81 htto:!www.Arduino.cc (accessed on 13131201 I). 

Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn)

This paper presents the application of neural networks for analysing voltage stability of load buses in electric power system. Voltage stability margin (VSM) and load power margin (LPM) are used as the indicators for analyzing voltage stability. The neural networks used in this research are divided into two types. The first type is using the neural network to predict the values of VSM and LPM. Multilayer perceptron back propagation (MLPBP) neural network and adaptive neuro-fuzzy inference system (ANFIS) will be used. The second type is to classify the values of VSM and LPM using the probabilistic neural network (PNN). The IEEE 30-bus system has been chosen as the reference electrical power system. All of the neural network-based models used in this research is developed using MATLAB

Sulaiman , Marizan

Universiti Teknikal Malaysia Melaka (UTeM) Repository

Voltage Stability Analysis Of Load Buses In Electric Power System Using Adaptive Neuro-Fuzzy Inference System (Anfis) And Probabilistic Neural Network (Pnn)

http://eprints.utem.edu.my/id/eprint/18656/2/marizan_57.pdf

Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn)

Abstract

Similar works

Full text

Available Versions

UTHM Institutional Repository

Universiti Teknikal Malaysia Melaka (UTeM) Repository