Smartplug ppt
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This document discusses the implementation of smart home technologies using artificial neural networks (ANN) for energy management and consumption monitoring. It covers the benefits of smart plugs and smart energy meters, highlighting the importance of real-time data and effective load management in modern power systems. The paper also details the hardware specifications, design of ANN, and methodologies used for load identification in household appliances.
Smartplug ppt
- 1. 1 Suman Sahoo Roll No : 97/ELM/124005 By Sumanta Kundu Roll No: 97/ELM/124013 Agniv Mukherjee Roll No : 97/ELM/124016 Raju Ray Roll No : 97/ELM/124017 Under Guidance of Prof. Jitendra Nath Bera Department of Applied Physics, University College of Science & Technology, University of Calcutta 92, A.P.C. Road, Kolkata-700009 West Bengal, India Date: 26-05-2015
- 2. 2 Introduction Solutions Objectives Theoretical Background Experimentation Hardware Implementation Conclusion Future Scope Reference
- 3. 3 Information regarding real time specific data for energy consumption and corresponding tariff Remote control of different Home Appliances Remote notification of usage of energy consumption Managing and storing vast quantities of metering data Ensuring the security of metering data Extra energy (if generated) can be sent back to Grid Today’s Demands Performance degradation analysis of a particular appliance
- 4. 4 Decarbonise electricity Greater visibility to distribution network Participation of the consumers into power system operation through Smart Meter, Smart Plug. Improved ICT (Information & Communication Technology) offers greater monitoring, control, flexibility and low cost operation of power system. Effective management of loads and reduction of losses and wasted energy needs accurate information about the loads. Performance comparison for same appliance of different make Local display of information on smart plug itself Contd.
- 5. Provides some basic information of a particular appliance Includes an embedded ICT unit so that power usage information it collects about the appliance can then be transmitted Access electricity consumption data and determine the best time to use an individual appliance Smart Plug Contd. 5 Smart Plug
- 6. Essential part of the Smart Grid It can provide detailed load flow on real time basis Helps in effective management of the grid operation Helps consumer to realize the energy usage and the corresponding tariff Two way communication - Automatic Meter Reading - Restriction of supply Smart Energy Meter 6 Contd. Smart Energy Meter
- 7. 7 Smart Home •A home equipped with lighting, heating, and electronic devices that can be controlled remotely by smart phone or computer • Smart home relate to the development of some major aspects: (a)Capabilities of home infrastructure and controlled device (b) Usability of mobile and stationary user interfaces Contd.
- 8. 8 Contd. Pictorial Representation of a Typical Smart Home
- 9. 9 Contd. Involvement of Information & Communication Technology Rapid development of Wireless Communication Systems like 3G, 4G, Wi-Fi Introductory involvement of communication interfaces like Bluetooth, ZigBee, Wi-Fi Power Line Communication (PLC)
- 10. 10 Power Line Communication (PLC) as Communication Channel The Home Automation System Using Power Line Communication (PLC) at home is user friendly and cost efficient. It requires only electricity to run the system. Fundamental parts of the smart meter as well as the Smart Grid. Communication through power line by the Utilities to Consumers if possible results to breakthrough in communications. Every household would be connected at any time and services being provided at real-time. Based on electrical signals, carrying information, propagating over the power- line. Contd.
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- 13. 13 Classifiers An algorithm implementing classification, especially concrete implementation A mathematical function , implemented by a classification algorithm, that maps input data to a category Bayesian Support Vector machine Classifier Fuzzy Ruled Based Classifier Artificial Neural Network Types of Classifiers
- 14. 14 In our project we have used the Artificial Neural Network (ANN) as the classifier has some benefits from others which are mentioned below : (a) Adaptive Learning (b) Self-Organization (c) Real Time Operation (d) Fault Tolerance via Redundant Information Coding (e) Implementation Ability Contd.
- 15. 15 Artificial Neural Network (ANN) A computational system inspired by the structure, processing method and learning ability of a biological brain Contd. (a) A large number of very simple processing neuron-like processing elements. (b) A large number of weighted connections between the elements. (c) Distributed representation of knowledge over the connections. (d) Knowledge is acquired by network through a learning process.
- 16. Elements of ANN (a) Processing unit (b) Activation function (c) Learning paradigm 16 Contd.
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- 21. Schematic Overview Ardiuno UNO Board Signal Conditioning Signal Conditioning ADC Atmega 328 µC Communic ation unit (PLC) Local Display Ph N CT 1-ph Elec. Load PT Smart Plug 21 Smart Meter
- 22. Procedural Steps Training And Testing of Neural Network 22 Contd.
- 23. Data Acquisition & Signature Extraction •Voltage & current data of diff. load captured by DSO •The sample data of voltage & current data taken into a spreadsheet using software webstar • Formation of load signature (by mathematical calculation) using captured sampled data of voltage & current of diff. load 23 •The data obtained in the measurement is stored into a computer for further study; the appliances include: i) Fan ii) Bulb ; iii) Tube Light iv) Heater v)1 Ph Induction Motor Contd.
- 24. Captured Data for Signature Extraction current Time Time voltage Current nature obtained for 200 Watt bulb switching phenomenon . Voltage nature obtained for 200 Watt bulb switching phenomenon 24 Contd.
- 25. Amplitude Power spectrum Time Frequency Simulated MATLAB output of 200W bulb current switching phenomenon FFT Analysis using MatLab Simulated MATLAB FFT analysis of 200W bulb current data 25 Contd.
- 26. ANN Start up Window 26 NN toolbox can be open by entering the command on command window >>nnstart Contd.
- 27. >> nprtool or Pattern Recognition Application from Neural Network Start Window 27 Neural Pattern Recognition Application Contd.
- 28. 28 Neural Network Training and Target Data Input Contd.
- 29. Training and Target Data are Browse from Data Bank 29 Contd.
- 31. Neural Network Training • Training Algorithm • Data Partition 31 Contd.
- 32. The Figure shows the changes between the validation, Training and testing where the Mean Square Error is minimum. NN Training Performance Contd. 32
- 34. In evaluation of the network , if the learning process fails then retraining is required to achieve the goal 34 Contd.
- 35. Generation of NN Program Code 35 Contd.
- 36. 36 Performance Analysis of ANN Contd.
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- 38. Overview of Hardware 38 Technical Specifications of Arduino UNO Microcontroller : ATmega328 Operating Voltage : 5V Supply Voltage (recommended) : 7-12V Maximum supply voltage : 20V Digital I/O Pins : 14 (of which 6 provide PWM output) Analog Input Pins : 6 DC Current per I/O Pin :40 mA DC Current for 3.3V Pin :50 mA Flash Memory : 32 KB (ATmega328) of which 0.5 KB used by boot loader SRAM : 2 KB (ATmega328) EEPROM : 1 KB (ATmega328) Clock Speed :16 MHz
- 39. Matlab Hardware Support Package for Ardiuno UNO Board Matlab to communicate with Arduino UNO Board over a USB cable 39 Start MATLAB Start Support Package Installer Select Arduino UNO from a list of support packages Math Works Account Continue and Complete the Installation Contd.
- 40. Matlab Simulink Model of Load Identification 40 Contd.
- 41. Procedure to run the Model on Arduino UNO Hardware Load the voltage and current samples into constants v1 & i1in the command window of Matlab 41 Contd.
- 42. Configuration parameter setting to run the model on Arduino UNO hardware 42 Procedure to run the Model on Arduino UNO Hardware Contd.
- 43. Target hardware :Arduino UNO Host COM port : Automatically 43 Contd.Procedure to run the Model on Arduino UNO Hardware
- 44. Deploy the Model to run on Hardware 44 Contd.
- 45. 45 Load Identification Indication Contd. Simulink Model run on Arduino UNO Hardware
- 46. Read analog signals using Arduino UNO ADC 46 Contd. Arduino IDE 1.0.6
- 47. Run Matlab code to read serial data Run Simulink Model for Load Identification automatically 47 Contd. Simulink model of Load Identification
- 48. 48 Contd. Test set up to read analog signal through Arduino UNO ADC
- 49. 49 Contd. Arduino UNO ADC data plotted on Matlab
- 50. 50 • Convenient and efficient use of electric appliances • Remote access of the home electrical appliances • Energy management strategies of Utility • Utility to control the energy supply to a particular appliance • Brief cost estimation of development of a Smart Plug
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- 52. 52 1. Smart Grid Technology and Applications by Janaka Ekanayake(Cardiff University, UK), Kithsiri Liyanage(University of Peradeniya, Sri Lanka), Jianzhong Wu (Cardiff University, UK), Akihiko Yokoyama (University of Tokyo, Japan), Nick Jenkins (Cardiff University, UK). 2. Simulator for Smart Load Management in Home Appliances by Michael Rathmair and Jan Haase (Vienna University of Technology, Institute of Computer Technology). 3. Smart Power Grids 2011 by Professor Ali Keyhani Department of Electrical and Computer Engineering. 4. Experimental Study and Design of Smart Energy Meter for the Smart Grid by Anmar Arif, Muhannad AI-Hussain, Nawaf AI-Mutairi, Essam AI-Ammar Yasin Khan and Nazar Malik Saudi Aramco Chair in Electrical Power, Department of Electrical Engineering, College of Engineering (King Saud University). 5. A model for generating household electricity load profiles by Jukka V. Paatero and Peter D. Lund Advanced Energy Systems, (Helsinki University of Technology Finland). 6. Analysis and Application of Artificial Neural Network by L.P.J Veelenturf. 7. ANN Based Load Identification And Forecasting System For The Built Environment by Hosen Hasna (University of Nebraska-Lincoln, hhasna@unomaha.ed). 8. Principles Of Artificial Neural Networks 2nd Edition by Wai-Kai Chen (Univ. Illinois, Chicago, USA) 9. Neural Networks by M. Hajek
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