Prediction of Hourly Generated Electric Power Using Artificial Neural Network for Combined Cycle Power Plant

Transcription

1 Internatonal Journal of Electrcal Energy, Vol. 4, No., June 016 Predcton of Hourly Generated Electrc Power Usng Artfcal Neural Network for Combned Cycle Power Plant Bayram Akdemr Electrc and Electroncs Dep., Engneerng Faculty, Selcuk Unversty, Konya, Turkey Emal: between nputs varables and output, artfcal ntellgence was used. Artfcal ntellgences are wdely used to solve many knds of nonlnear problems even f dataset ncludes huge data or mult nputs [4], [5]. In ths study, Artfcal Neural Network (ANN) s used to predct CCPP hourly generated power. Artfcal neural network s one of the common and famous ways to solve nonlnear problems n lterature from medcal to constructon [6]-[8]. ANN was reported frst 1956 [9]. An ANN has lots of paralleled nterconnected computatonal unt whch are connected as a herarchcal structure. The elementary of ths unt s called as neuron whch has computatonal ablty. ANN has learnng ablty va neurons and weghts among the neuron connectons. Performance of ANN was evaluated usng two fold cross valdaton and statstcal methods, R, MSE. Twofold cross valdaton s a method to mprove relablty of results va crossng the test and tran dataset [10], [11]. Error between predcted and real output value was evaluated usng Mean Square Error (MSE). MSE s a wdely used method to present error power. In ths study, ANN was arranged as two layers, 0 nodes of frst layer, 1000 epochs, logsg transfer characterstc and Levenberg-Marquart method to ncrease the executon speed. ANN performance was evaluated two fold cross valdaton, R statstcal value and Mean Square Error (MSE). In ths study, R value s a way to show fttng ablty between obtaned test results and real data values. Devaton of the error spreads from 51.3 to On the contrary, the target value vares from 40.6 to Mega watts per hour. Mean Square Error and predctng capacty was calculated as 96%. Mean Square Error s Abstract Energy s one the mportant subjects n the world because of ts cost and achevable. In order to reduce energy costs, some knds of plants may have founded and are managed related to demands and envronmental condtons. Artfcal neural network s one of the famous artfcal ntellgent n lterature to solve nonlnear problems from medcal to constructons. Artfcal neural network uses nodes to weghts to acheve the output. In ths study, obtanable power per hour from combned gas and steam turbne power plant tres to be predcted. Data nclude 9685 features and 4 varables. Artfcal neural network results have been evaluated wth mean square error and two fold cross valdaton. Mean square error and two-fold cross valdaton are statstcal evaluaton methods to evaluate the results. Dataset dvded sectons to test and tran. Two datasets are traned and tested usng two fold cross valdaton and generated R value to evaluate the fttng performance. R s famous comparng method to fgure out the fttng ablty. The obtaned mean square error after two fold cross valdaton and R value are and , respectvely. Index Terms artfcal neural network, power plant, predctng, generated power, two fold cross valdaton I. INTRODUCTION Energy s one of the mportant subjects that must be managed targetng maxmum effcency. There are many ways to generate energy methods such as ol, hydro or renewable sources. For energy generatng method, management s so mportant not to waste tme and energy more over polluton. In ths study, Combned Cycle Power Plant (CCPP) s targeted. CCPP s combned gas and steam turbne and heat recovery steam generator. System performance s related to ambent temperature, exhaust vacuum, relatve humdty and ambent pressure. Envronmental condtons affect the generated power. Weather temperature n wnter season goes to below zero, on the contrary, n summers reach the forty degrees. Changed weather and related condtons make dffcult to manage the power plant [1]-[3]. It s not easy to solve the all varables for undetermned stuatons and more over relatons among the varables are not lnear. In order to fnd out any expectable and vald predcted output II. ANN s famous an mpressve model among the artfcal ntellgences. ANN s based on smulaton of neural actvty. Nodes and connectons among the nodes are form of the structure. Proposed method based on ANN and evaluated usng MSE and two fold cross valdaton Data structure: Data structure has been taken from Unversty of Calforna data base (UCI). UCI has wde repostory for machne learnng to acheve qualfed Manuscrpt receved September 7, 015; revsed February 5, 016. do: /joee PROPOSED METHOD 91

2 Internatonal Journal of Electrcal Energy, Vol. 4, No., June 016 research studes. Combned cycle power plant data structure has been created through the 6 years and uploaded to UCI by Pınar Tüfekc and Heysem Kaya [1]. Combned cycle power plant dataset has 4 attrbutes except output and 9568 number of nstance. The attrbutes are temperature, ambent pressure, relatve humdty, exhaust vacuum and hourly electrcal energy output. Hourly electrcal energy output s output and created from real applcaton from 006 to 011. Table I contans maxmum and mnmum values of the features. occur lower than 40% but after heat recovery processng executed, effcency can be mproved more than 50%. One of the maxmum energy effcency was reported as over 60% by Semens n 011 [16]. After nstallaton and begnnng of the energy producng, mprovng system effcency has much mportance to hold at hgh pont. CCPP has nonlnear characterstcs and CCPP data was tred to solve usng heurstc and numercal methods to acheve acceptable soluton [17]-[0]. III. TABLE I. Attrbutes Temperature Ambent pressure Relatve Humdty Exhaust Vacuum Generated Energy STATISTICAL FEATURES OF THE DATASET Unt C mbar % cmhg MW Max Mn ARTIFICIAL NEURAL NETWORK ANN s the oldest and one the common artfcal ntellgence n lterature. ANN s famous an mpressve model among the artfcal ntellgences. ANN s nspred from neural actvty. ANN emulates the neural neuron characterstcs. ANN has learnng ablty comes from neurons [1], []. Nodes and connectons among the nodes are form of the ANN structure. A generalzed ANN structure ncludes an nput layer, hdden layer and an output layer. Output layer has only one node connected to prevous nodes. Every node has ts own transfer functon. Connectons among the nodes have weghted multpler to affect the followng node nput. A Mult Layer Percepton (MLP) ANN structure s shown n Fg.. Average From the Table I, temperature, ambent pressure and relatve humdty are envronmental condtons. Exhaust vacuum s sgn for stack performance and burned gas. Combned cycle power plant: In electrc power generaton a combned cycle s an assembly of heat engnes that work n tandem from the same source of heat, converts t nto mechancal energy and usually drves electrcal generator [13]. Frst startng s related to gas turbne generator and after frst cycle, workng flud of the frst engne has enough entropy to suck the waste energy. Combnng two or more thermodynamc cycles results n mproved overall effcency, reducng fuel costs. In statonary power plants, a wdely used combnaton s a gas turbne, a steam power plant and steam turbne. In ths study, CCPP based on Gas Turbnes (GT), Steam Turbnes (ST) and heat recovery steam generators. In order to mprove the effcency, gas turbne and steam turbne were merged [14], [15]. Fg. 1 shows the all combned cycle power plant. Fgure. MLP-ANN structure. ANN learns by examples lke human through the learnng process. An ANN structure conssts of an nput layer wth three neurons, a hdden layer wth three neurons, and an output layer. ANN can generate a vald soluton for any knd of nonlnear problems. Ths problem can be related to pattern recognton, predctons or data classfcaton and so on. For an ANN, three nodes from three layers s called Mult Layer Percepton (MLP) [3]. Ths s mnature of an ANN. The back propagaton s wdely used to adjust connecton weghts and bas values usng tranng. Each MLP layer s formed by a number of predefned neurons. The neurons n the nput layer can be explaned as a buffer whch dstrbutes the nput sgnals x to next neurons n the hdden layer wthout humlatng the sgnal. Each neuron j n the hdden layer sums the nput sgnals x after weghtng them wth the strengths of the Fgure 1. Schematc of a combned cycle power plant. Frst step to generate electrcty starts wth gas turbne. Exhaust of the gas turbne leads to bol the water. Boled water s dstrbuted two ways to makes hot the nlet ar and goes to steam boler to go to steam turbne. General concept s to obtan maxmum regeneraton and system force to acheve maxmum heat recovery to mprove the effcency. Effcency at frst cycle may 9

3 Internatonal Journal of Electrcal Energy, Vol. 4, No., June 016 respectve connectons w,j from the nput layer, and computes ts output yj as a functon f of the sum (Eq. (1)): y f ( w, j may be appled [8], [9]. For huge structures, two fold cross valdaton could be adequate. N-fold cross valdaton name comes from data dvded n. For 10-fold cross valdaton, data s dvded by ten and 9 peces of dvded data wll be used for tran and the rest pece of data s for test. Ths executon wll be processed for ten tmes and every route one peces of data s changed to one of the tran peces. Fg. 4 shows a ten-fold cross valdaton to understand. In ths study, because of the number of data two-fold cross valdaton arranged. (1) y ) where, f s the actvaton functon whch s needed to transform the weghted sum of all sgnals nfluence a neuron. Although there are many actvaton functon for ANN applcaton due to dfferent dataset groups, logsg s very common actvaton functon for ANN applcatons. All actvaton functons have dfferent transfer curve that may be threshold, lnear tangent etc. In the end, the output neuron n the output layer can be calculated smlarly. Dataset after dvded as two parts; the tranng parameters and structure of the MLP was set as 40% tranng, 30% valdaton and 30% testng. In ths study, the expermental studes were performed on MATLAB( ) 6.5 envronment. IV. EVALUATION METHOD Performance measurement methods Performance measurement s a way to compare and present for any mplementaton. Every measurement method has ts own characterstc and mplementaton method. Mean square error and R are famous methods of success measurement for any mplementaton. N-fold cross valdaton s a method to check system relablty. In ths study, n-fold was arranged as two fold cross valdaton. System performance s characterzed usng MSE and R. Tranng and testng are 50% of whole data. Next step s same executon of the rest data to obtan two-fold cross valdaton. Fg. 3 shows one cycle of the executon of two fold cross valdaton. Fgure 4. Executon of 10-fold cross valdaton. N-fold cross valdaton s a way to present relablty of the applcaton and has common usng among the artfcal ntellgence applcatons [30], [31]. In ths study, CCPP data structure was accepted as huge thus two fold cross valdaton was used. R value: R, statstcal value, s a way to present smlarty of two successve pctures that how they cover each other. Fg. 5 shows the geometrcal meanng of R. R=0 0< R<1 R=1 Fgure 5. Meanng of R. If two attempts or nput and output patterns are same R equals to 1. Meanng of 0 s maxmum dssmlarty; on the contrary 1 s parameter of maxmum smlarty. Equaton (3) shows R value and R s common statstcal method to measure of the success [3], [33]. Fgure 3. Evaluaton method for ANN results. Mean square error: Mean square error s one of the smplest ways to evaluate any success. Mean and depends on mean of dfference among observatons and real values [4], [5]. Equaton () shows MSE calculaton. MSE measures the average of the squares of the errors, between target and observed value. R 1 ( y yˆ ) (y y) (3) MSE 1 n (y n t yˆ t ) In ths study, R used to measure valdaton of ANN for CCPP dataset. () t 1 MSE s wdely used to show success and relablty n lterature [6], [7]. Two-Fold cross valdaton: Two fold cross valdaton s way to mprove relablty of the results. Accordng to number of the samples, n-fold cross valdaton vary from to 10. For low data structure 10 fold cross valdaton V. RESULT AND DISCUSSION In ths study, CCPP dataset evaluated wth ANN to acheve hourly maxmum power generatng. ANN outputs were evaluated usng R and MSE. In order to create a cross check and mprove the ANN relablty, 93

4 Internatonal Journal of Electrcal Energy, Vol. 4, No., June 016 two-fold cross valdaton was appled. Frst, dataset was dvded parts as 50% and one part desgned as tran and the rest part s arranged for test. After ANN evaluaton, tran and test parts are changed each other s and appled to ANN agan. Table II shows the results after two fold cross valdaton and was executed ANN. In ths study, artfcal ntellgence, ANN, was used to manage CCPP plant and try to obtan predctable energy level accordng to envronmental condtons. Real CCPP data has mportance to test the ANN performance. Result of ANN sgns the plant capablty durng the managng. ANN performance tested usng MSE and R and relablty was mproved wth two-fold cross valdaton. The success was obtaned 96.7% after two fold cross valdatons and MSE. R shows that how real and obtaned results cover each other and close to 1 accordng to success. ANN fnds the vald results accordng to real nputs. ANN covers untested values related to learned abltes and generates an output to manage the system. Ths study encourages us to use ANN for predctng CCPP energy capablty and shows that artfcal ntellgence could be a trustable method to manage CCPP plant. TABLE II. STATISTICAL FEATURES OF THE DATASET Tran Test Valdaton All Step Step Two-Fold From the Table II, obtaned R values are close to 1 that sgn of the success. It s mportant that not easy to understand f ANN learned or memorzed. Good tranng and low test and low valdaton value pont out memorzng. On the contrary balanced and regular acceptable results for test and valdaton sgn the learnng capablty. Fg. 6 shows R evaluaton of ANN results. From the Fg. 6, x and y axs ncludes same values. In case of same values for predcted and observed data, R turns a lnear lne as y=x functon. ACKNOWLEDGMENT Selçuk Unversty supports ths study and project number s gven by BAP. REFERENCES [1] [] [3] [4] [5] [6] [7] Fgure 6. Evaluaton of ANN results after two fold cross valdaton usng R. [8] Clusterng on the y=x lne pont out the success rate. Fg. 6 shows test and valdaton of ANN results. The obtaned values for the R show the success as much close to 1. R testng method was both two-fold cross valdaton segments. VI. [9] [10] CONCLUSION ANN s famous an mpressve model among the artfcal ntellgences. CCPP s a method to generate energy n real lfe and based on energy recovery dea to mprove effcency durng the producng. Envronmental condtons effect power staton work and change the cost and amount of energy. [11] [1] [13] 94 M. Amer, P. Ahmad, and S. Khanmohammad, Exergy analyss of a 40MW combned cycle power plant, Internatonal Journal of Energy Research, vol. 3, no., pp , 008. F. E. Carapca and G. Gacchetta, Managng the condton-based mantenance of a combned-cycle power plant: An approach usng soft computng technques, Journal of Loss Preventon n the Process Industres, vol. 19, no. 4, pp , 006. A. L. Polyzaks, C. Koroneos, and G. Xyds. Optmum gas turbne cycle for combned cycle power plant, Energy Converson and Management, vol. 49, no. 4, pp , 008. B. Akdemr and N. Çetnkaya, Long-Term load forecastng based on adaptve neural fuzzy nference system usng real energy data, Energy Proceda, vol. 14, pp , 01. S. Paul and J. Thomas, Comparson of MPPT usng GA optmzed ANN employng PI controller for solar PV system wth MPPT usng ncremental conductance, n Proc. Internatonal Conference on Power Sgnals Control and Computatons, 014, pp B. Akdemr and L. Yu, Ellot waves predctng for stock marketng usng Eucldean based normalzaton method merged wth artfcal neural network, n Proc. Fourth Internatonal Conference on Computer Scences and Convergence Informaton Technology, 009, pp S. C. Lee, Predcton of concrete strength usng artfcal neural networks, Engneerng Structures, vol. 5, no. 7, pp , 003. P. J. G. Lsboa, A revew of evdence of health beneft from artfcal neural networks n medcal nterventon, Neural Networks, vol. 15, no. 1, pp , 00. N. Rochester, J. H. Holland, L. H. Habt, and W. L. Duda, Tests on a cell assembly theory of the acton of the bran, usng a large dgtal computer, IRE Transactons on Informaton Theory, vol., no. 3, pp , A. Bayram, O. Bülent, G. Salh, and S. Karaaslan, Predcton of aortc dameter values n healthy Turksh nfants, chldren, and adolescents by usng artfcal neural network, Journal of Medcal Systems, vol. 33, no. 5, pp , 009. P. Refaelzadeh, L. Tang, and H. Lu, Cross-Valdaton, n Encyclopeda of Database Systems, Sprnger US, 009, pp UCI Machne Learnng Repostory. 348 data sets. [Onlne]. Avalable: M. P. Boyce, Handbook for Cogeneraton and Combned Cycle Power Plants, second ed., ASME, 010.

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