Fuzzy Logic Based Hybrid Renewable Energy Source Connected With Smart .
INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 Fuzzy Logic Based Hybrid Renewable Energy Source connected with Smart Grid and Cloud Computing Dr.K.Vimaladevi#1, K.G.srinivasan*2, Dr.S.Chakravarhi 1 Professor, Department of CSE, Velammal Engineering College. 2 Research Scholar, Velammal Engineering College, 1 Professor, Department of CSE, Velammal Engineering College. 1 k.vimaladevi@gmail.com kgsrinivasanped@gmail.com 3 chakra2603@gmail.com 2 Abstract: Nowadays renewable energy sources are blooming up and the major problem is a single type of renewable energy is not sufficient to meet the needs of all types of loads in power system. Hence we combine two or more resources to get constant power all through the day like integrating solar and wind energy generation. While taking about integration of renewable energy sources there arises an issue in integrating these supply to grid there we use smart grid for better integration and power transmission and utilization. The power from solar panel is connected to grid through DC – AC converter and the energy from wind turbine is connected to grid by an AC – AC converter. These converters are controlled by a fuzzy logic controller which gets feedback from the grid and control the converters by controlling firing angle of the circuit. Smart grid is use for easy control of power flow and also for monitoring and scheduling of load and power source. We also use cloud computing for storage of data for load scheduling and weather forecasting of solar panel it also uses to handle smart metering in smart grids. Maximum Power Point Tracking (MPPT) is used for extracting maximum power from the solar panel. Key words --- Hybrid Renewable Energy, Cloud Computing, Smart Grid, MPPT, Fuzzy Logic. 1. INTODUCTION Recent advancements in renewable energy sources show that they are making renewable energy a good supplement to conventional energy sources. Sources such as wind energy and solar energy provide an alternative to the conventional power generation sources and they are friendly to environment too. Solar and wind energies used mostly because other conventional fuels emit gases which are the main cause for global warming. A hybrid renewable energy can reduce the lifecycle cost compared to stand alone system and also provide more reliability and power flow [1].the major disadvantage is wind energy generation is the speed of wind turbine varies with the speed of wind and in solar energy the drawback is it is not available all the day and varies continuously with the duration of day and month of a year. So we can use a battery bank to ensure the system perform in all the conditions [2].an maximum power point tracking algorithm is used to track maximum available power from renewable source [3]. There are number of power point tracking algorithms like peturb and observe, hill climbing, fuzzy logic control, and hybrid based controller are available for extracting maximum power and each method has its advantages and disadvantages [4, 5, 6]. Distributed renewable energy generation make economic benefits like reduction of carbon-di-oxide emission and transmission losses the makes competitive solution for smart grids [7]. Micro grids are the key blocks in building smart grid and have great attention in recent years [8, 9, 10]. Fuzzy logic Controller (FLC) involves three important processes like fuzzification processing and defuzzification and FLC has been used in controlling of converters and can be also used for extracting maximum power from solar panel. Smart grids has moved to next generation with the help of Morden information technologies like cloud computing, the use of smart grid generates larger amount of data which are managed by cloud computing in efficient manner [11,12]. Cloud computing is a large scale data processing platform in parallel distributed environment [13]. Green cloud is referred to optimal usage of power in remote space [14]. The architecture of cloud computing consist of master slave communication and it Volume 5, Issue 11, November 2018 159 http://ijics.com
INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 is divided into two types i) fog computing and ii) centralized computing the centralized computation involves a compact architecture [15]. the hardware technologies in smart grid are smart meters sensor and machinery for power generation and distribution [16]. The software technologies include web applications, big data platform which perform operations over datasets in smart grids [17]. 2. HYBRID RENEWABLE ENERGY Wind energy is generated by a wind turbine which converts the kinetic energy of wind into electrical energy. The system mainly depends on speed of the wind to enhance the performance the turbine in mounted on a tall tower. Wind energy conversion system has a wind turbine, permanent magnet synchronous generator and AC-AC converter. As wind strikes the turbine blades start to rotate and permanent magnet synchronous generator connected to it also rotates and produces an ac supply and the supply is given to ac to ac converter to get a constant power and frequency output. The output from ac to ac converter is given to the grid directly. Solar energy is unlimited energy emitted from sun; this energy can be trapped by a solar photo voltaic cell. PV cells are elements which convert solar energy into electrical energy. The electrical energy is in the form of DC source we can use this directly for dc loads but when connecting to a AC grid we need to convert the DC supply into AC by using a converter. To extract maximum power from a solar photovoltaic cell Maximum Power Point Tracking is equipped. Fig. 1 Hybrid Renewable energy Hybrid renewable energy system is a combination of wind and solar power generation technique which has many advantages over standalone system. In Hybrid system we can regularly provide power to the grid with the absence of any one of the source. Three are two conversion processes in this circuit one is AC - AC conversion for wind energy system and DC – AC conversion for solar energy generation system. The converted AC supply is given to the grid and then it is given to the load 3. FUZZY LOGIC CONTROLER AND MPPT The converters are controlled by a fuzzy logic controller. The input to controller is the actual and reference speed of the wind turbine and details from the solar photovoltaic cell. The input values are given to the controller and the output is in the form of control pulse given the converters When the pulse width of the control pulse varies it directly control the convertor switch on and off time so the supply voltage can be varies and we can get constant frequency output despite of changes in the rotor speed. When solar PV is used in the input side of grid the operation point is determined by the load the wind speed and solar radiation is not constant all the times and it varies with respect to time. Hence MPPT technique is used to transfer maximum power to load side. The output power of solar panel is input to maximum power point tracking which is used to increase the efficiency of both solar and wind energy generation. Volume 5, Issue 11, November 2018 160 http://ijics.com
INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 Fig. 2 Flow Chart for MPPT 4. SMART GRID In Future power transmission technologies sensors and transducers have a important role in monitoring energy and also load demand. The electrical system is complex and interconnected and known as grid. The smart grid is a part of green cloud environment in which decentralized distributed units do a functional operation into intelligent self driven environment. Fig. 3 Operation of smart grid The smart grid is an advanced grid designed to handle the unforecasted load change and distributed resources management using information communication and intelligent technology employing smart meters and control system. In smart premises, there is a smart controller to interconnect renewable energy sources, energy storage device, load and grid. Smart meters are also installed in the smart premises which are two port meters that can record energy consumption from grid, to grid and RES separately. The smart grid can feed power into and from the smart premises Smart grid is a two port grid having real time market participation and is a future power supply concept the smart grid consists of many perspectives simultaneously. The idea being smart grid subjects to enhancement of regular electric grid with intelligent technologies. The intelligent technologies referred to digitalization of grid with two way communications and usage of smart meters at distribution end. Information from customers are stored and processed for further enhancement and performance analysis features smart grid also has a self healing technology which helps in the time of blackouts and failures. The smart grids also act as an information transfer and used in integrating grids in different parts of a country. Volume 5, Issue 11, November 2018 161 http://ijics.com
INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 There are also challenges in smart grid they are API Dependency Data Location Fault Recovery Policy Making Compatibility Fig. 4 Smart Grid Challenges 5. CLOUD COMPUTING Cloud computing is a platform that supports large scale data processing, and consist of client server architecture which includes services protocols and infrastructure to perform a remote task in efficient manner. Fig. 5 Cloud Computing The major advantage of cloud computing is it is more secured than traditional techniques and also cloud provide large scale data processing and data storage. The response to a problem in cloud computing is marginally higher compared to other computational system and hence cloud computing is most reliable. The smart grid is to be capable to accommodate inter operability standards. Information and communication network of smart grid is made secure from cyber attack. The policy and frame work for the implementation of smart grid is required for each country considering their available electrical infrastructure and growth rate of energy consumption. The worldly accepted standards for communication and security of network are also to be developed to make the smart grid as a global grid Volume 5, Issue 11, November 2018 162 http://ijics.com
INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 6. CLOUD COMPUTING AND SMART GRID In the proposed system we integrate the hybrid renewable energy source with smart grid and cloud computing technologies this is done by integrating cloud computing in smart grid application as we know in smart grid we use and information and control line to connect with every member in the grid from generation, transmission and utilization end from this we get enormous amount of data these data are stored and processed in cloud computing arena. Fig. 6 Proposed System The cloud gets data from distribution side through smart meters and it also contact with bus bar and the controller in hybrid renewable energy system. We get data about air flow and the weather data about solar radiation through internet and it can be shared to the controller for optimising the overall efficiency of the system. The cloud computing also manages the difficulties in interconnection of smart grid and make the system more simple although the smart grid integration in complex function both in technology and in infrastructure wise cloud computing reduces the complex in technological issues. As most of the grid now a days becoming central and nationalized it is easy to maintain through cloud by the help of internet. 7. CLOUD SUPPORTED IoT WITH SMARTGRID Internet of Things (IoT) is a system of physical things embedded with sensors, software, and electronic devices connected to each other to perform better exchange in information with other connected device. With the support from cloud commuting data from different IoT terminals of smart grid are collected by local servers and given to the cloud server. Volume 5, Issue 11, November 2018 163 http://ijics.com
INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 Fig.7 Cloud Supported IoT with Smart Grid Data in the cloud can be accessible by various types of data uses but all the data are continuously monitored by staffs in power grid to know the status of power system. Other persons are using the data for data analysis research or for policy making 8. CONCLUSION AND FUTURE WORK Cloud and Iot techniques are mostly used in smart grid large amount of data are collected by IoT local servers and transmitted to cloud network In this paper we discus about integrating renewable energy sources and also interconnecting smart grid with cloud computing and IoT based system. The proposed system can provide secure and reliable communication along the various components in smart grid. In future we focus to work on green cloud and also in minimizing complexity in cloud computation and in smart grid integration. With the easy generation and usage of renewable energy we can turn the world with full of green energy by the help of smart sustainable power generation methods. REFERENCES [1] N. Adhikari, B. Singh, A.L. Vyas, A. Chandra, "Analysis design and control of a standalone hybrid renewable energy conversion system", 4th IEEE International Power Electronics for Distributed Generation Systems (PEDG), pp. 1-8, 8-11 July 2013. [2] Diego Arcos-Aviles ; Julio Pascual ; Luis Marroyo ; Pablo Sanchis ; Francesc Guinjoan “Fuzzy Logicbased Energy Management System Design for Residential Grid-Connected Microgrids” IEEE Transactions on Smart Grid Year: 2018, Volume: 9, Issue: 2 Pages: 530 - 543 [3] K. Rabhar, J Xu and R. Zhang “Real time energy storage management for renewable integration in micro grid: An offline optimization approach” UEEE Trans. Smart Grid, Vol 6, no.1, pp. 124 -134, jan 2015. [4] S. Bacha, D. Picault, B. Burger, I. Etxeberria-Otadui, J. Martins, "Photovoltaics in microgrids: An overview of grid integration and energy management aspects", IEEE Ind. Electron. Mag., vol. 9, no. 1, pp. 33-46, Mar. 2015. [5] N. W. A. Lidula, A. D. Rajapakse, "Microgrids research: A review of experimental microgrids and test systems", Renew. Sustain. Energy Rev., vol. 15, no. 1, pp. 186-202, Jan. 2016. [6] P. Siano, C. Cecati, H. Yu, J. Kolbusz, "Real time operation of smart grids via FCN networks and optimal power flow", IEEE Trans. Ind. Inf., vol. 8, no. 4, pp. 944-952, Nov. 2012. [7] J. Singh, T. Pasquier, J. Bacon, H. Ko, D. Eyers, "Twenty security considerations for cloud-supported Internet of Things", IEEE Internet Things J., vol. 3, no. 3, pp. 269-284, Jun. 2016. [8] L. Jiang et al., "An IoT-oriented data storage framework in cloud computing platform", IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1443-1451, May 2014. [9] . A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, S. U. Khan, "The rise of „big data‟ on cloud computing: Review and open research issues", Inf. Syst., vol. 47, pp. 98-115, Jan. 2015. Volume 5, Issue 11, November 2018 164 http://ijics.com
INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTING SCIENCE ISSN NO: 0972-1347 [10] A. L. Di Salvo, F. Agostinho, C. M. Almeida, B. F. Giannetti, "Can cloud computing be labeled as „green‟? Insights under an environmental accounting perspective", Renew. Sustain. Energy Rev., vol. 69, pp. 514-526, Mar. 2017. [11] O. Osanaiye, S. Chen, Z. Yan, R. Lu, K. Choo, M. Dlodlo, "From cloud to fog computing: A review and a conceptual live VM migration framework", IEEE Access, vol. 5, pp. 8284-8300, 2017. [12] M. H. Cintuglu, O. A. Mohammed, K. Akkaya, A. S. Uluagac, "A survey on smart grid cyber-physical system testbeds", IEEE Commun. Surveys Tuts., vol. 19, no. 1, pp. 446-464, 1st Quart. 2017. [13] H. Cai, B. Xu, L. Jiang, A. V. Vasilakos, "Iot-based big data storage systems in cloud computing: Perspectives and challenges", IEEE Internet Things J., vol. 4, no. 1, pp. 75-87, Feb. 2017. [14] M. Kuzlu, M. Pipattanasompom, S. Rahman, "A comprehensive review of smart grid related standards and protocols", Proc. 5th Int. Istanbul Smart Grid Cities Congr. Fair (ICSG), pp. 12-16, Apr. 2017. [15] G. Bruni, S. Cordiner, V. Mulone, V. Rocco, F. Spagnolo, "A study on the energy management in domestic micro-grids based on model predictive control strategies", Energy Convers. Manage., vol. 102, pp. 50-58, Sep. 2015. [16] A. El Khetab, N.A. Rahim, J. Selvaraj et al., "Fuzzy-logic-controller-based SEPIC converter for maximum power point tracking", IEEE Trans. Ind. Electron., vol. 50, no. 4, pp. 2349-2358, 2014 [17] Ligade Gitanjali Vasant ; V. R. Pawar “Optimization of solar-wind energy system power for battery charging using MPPT” 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) Year: 2017 Pages: 1308 – 1310. Volume 5, Issue 11, November 2018 165 http://ijics.com
Wind energy is generated by a wind turbine which converts the kinetic energy of wind into electrical energy. The system mainly depends on speed of the wind to enhance the performance the turbine in mounted on a tall tower. Wind energy conversion system has a wind turbine, permanent magnet synchronous generator and AC-AC converter. As wind .
2.2 Fuzzy Logic Fuzzy Logic is a form of multi-valued logic derived from fuzzy set theory to deal with reasoning that is approximate rather than precise. Fuzzy logic is not a vague logic system, but a system of logic for dealing with vague concepts. As in fuzzy set theory the set membership values can range (inclusively) between 0 and 1, in
ing fuzzy sets, fuzzy logic, and fuzzy inference. Fuzzy rules play a key role in representing expert control/modeling knowledge and experience and in linking the input variables of fuzzy controllers/models to output variable (or variables). Two major types of fuzzy rules exist, namely, Mamdani fuzzy rules and Takagi-Sugeno (TS, for short) fuzzy .
Fuzzy Logic IJCAI2018 Tutorial 1. Crisp set vs. Fuzzy set A traditional crisp set A fuzzy set 2. . A possible fuzzy set short 10. Example II : Fuzzy set 0 1 5ft 11ins 7 ft height . Fuzzy logic begins by borrowing notions from crisp logic, just as
fuzzy controller that uses an adaptive neuro-fuzzy inference system. Fuzzy Inference system (FIS) is a popular computing framework and is based on the concept of fuzzy set theories, fuzzy if and then rules, and fuzzy reasoning. 1.2 LITERATURE REVIEW: Implementation of fuzzy logic technology for the development of sophisticated
A Short Fuzzy Logic Tutorial April 8, 2010 The purpose of this tutorial is to give a brief information about fuzzy logic systems. The tutorial is prepared based on the studies [2] and [1]. For further information on fuzzy logic, the reader is directed to these studies. A fuzzy logic system (FLS) can be de ned as the nonlinear mapping of an
Tutorial On Fuzzy Logic Jan Jantzen 1 Abstract Fuzzy logic is based on the theory of fuzzy sets, where an object’s membership of a set is gradual rather than just member or not a member. Fuzzy logic uses the whole interval of real numbers between zero (False) and one (True) to develop a logic as a basis for rules of inference.
Fuzzy logic versus neural networks The idea of fuzzy logic is to approxi-mate human decision-making using nat-ural-language terms instead of quantita-tive terms. Fuzzy logic is similar to neur-al networks, and one can create behav-ioral systems with both methodologies. A good example is the use of fuzzy logic for automatic control: a set of .
Fuzzy logic has two different meanings. In a narrow sense, fuzzy logic is a logical system, which is an extension of multivalued logic. However, in a wider sense fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects with unsharp boundaries in which membership is a matter of degree. In .
