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    • INTERNATIONALIssue 3, October – December (2012), © IAEME 0976 – 6545(Print), ISSNInternational Journal of Electrical Engineering and Technology (IJEET), ISSN0976 – 6553(Online) Volume 3, JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET)ISSN 0976 – 6545(Print)ISSN 0976 – 6553(Online)Volume 3, Issue 3, October - December (2012), pp. 200-210 IJEET© IAEME: www.iaeme.com/ijeet.aspJournal Impact Factor (2012): 3.2031 (Calculated by GISI) ©IAEMEwww.jifactor.com TRANSIENT STABILITY ENHANCEMENT BY ANN BASED ADAPTIVE LOAD SHEDDING Mukesh kumar Kirar1, Ganga Agnihotri2 1 (Electrical Engineering Department, MANIT, Bhopal, India, mukeshkirar@rediffmail.com) 2 (Electrical Engineering Department, MANIT, Bhopal, India, ganga1949@gmail.com) ABSTRACT This paper presents frequency stability enhancement using Artificial Neural Network based adaptive load shedding during the various fault contingencies. At any given moment, the electrical power produced by the generators must be equal to the electrical power consumed by the network to continue stable operation. Every change in this balance that disturbs the steady-state operation of the power system is referred to as a power imbalance. This power imbalance in the network leads frequency instability. To ensure system stability and availability during disturbances, generation trip, transmission and distribution equipment failure, generally utilize some type of load shedding scheme. Conventional non-adaptive load-shedding algorithm is not the most efficient scheme for all power system disturbances. Artificial Neural Network based load shedding method is employed to calculate the minimum amount of load to be shed due to the effect of the contingencies, to improve the transient stability of the system. The IEEE-9 bus test system is simulated on ETAP software and Transient stability is analyzed by considering various contingencies on test system such as generator outage, faults on transmission lines & buses, transformers etc. Artificial Neural Network (ANN) has been implemented on MATLAB. Keywords: Transient stability, Artificial Neural Network, ETAP, MATLAB, load shedding. 1. INTRODUCTION To prevent the complete blackout of the power system during power imbalance condition, it is necessary to curtail partial loads in the plant to maintain the balance between available generation and load as well as to restore the system frequency [1-4]. The load shedding technique primarily can be classified as convention load shedding technique and Adaptive or Intelligent load shedding technique. Conventional load shedding schemes, Breaker Interlock Load Shedding [4], Under-Frequency Relay (81) Load Shedding [5-8], Programmable Logic Controller-Based LS are most common and easiest way to isolate the excess amount of load during generation deficit in the power system. These methods of load shedding are totally independent of the system dynamics, Pre-disturbance operating 200
    • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN0976 – 6553(Online) Volume 3, Issue 3, October – December (2012), © IAEMEconditions, Post-disturbance operating conditions, Nature and duration of the disturbance,System transient response to a disturbance, and total loss of the system is an assumedpossibility. Conventional methods of system load shedding are too slow and do not effectivelycalculate the correct amount of load to be shed because the amount of load shedding iscalculated for the worst-case scenario. [11]. Several schemes are reported in the literature [9-18] to overcome the shortcomings of the conventional LS scheme, by making it adaptivethrough complete understanding of power system dynamics and process constraints,combined with knowledge of system disturbances[12]. The best load shedding scheme in power systems is one that is able to separate theleast possible loads of the network in the shortest time by considering power systemconstraints. Artificial neural Network (ANN) have attracted a great deal of attention in thepast two decades in the area such as power system stabilizer [13-16], power system security[17-21], harmonic detection [22-24], power system protection [25-29] and load Forecasting[30-33]. In this paper adaptive load shedding strategy by executing the Artificial NeuralNetworks is used to improve transient stability analysis for IEEE-9 bus system. The fastcalculation time is an important advantage of artificial neural-network based adaptive loadshedding technique compared to other methods. The inputs to Adaptive load sheddingmethods is provided through Conventional Supervisory Control Data Acquisition/ EnergyManagement System, which runs at periodicity of few minutes and data scan rate of 2-10 sec.2. SYSTEM SIMULATION AND STUDIES IEEE 9 bus system is used as the test system in this paper. The test system issimulated on ETAP 7.5.1. The single line diagram (SLD) of the simulated test system onETAP is shown in Fig 1. For this test system generator and load parameters are given inappendix. The total generation is 352.5MW and total load is 336.6MW. The test systemcontains 6 lines connecting the bus bars in the system. The generator is connected to networkthrough step-up transformer at 230kV transmission voltage. Power-system studies principally incorporate the techniques used to predict orimprove the performance of an existing or proposed power system under specified conditions[34-36]. Power system studies include load flow, short circuit, protective device coordination,transient stability, motor starting, grounding, transient overvoltage, power-factorimprovement. The results of the load flow and short circuit studies are inputs to theequipment sizing and selection, reactive power compensation, stability analysis, protectioncoordination etc. The results of load flow analysis and short circuit (SC) analysis when allgenerators and loads are operating at rated power is given in Table.1 and Table.2 "respectively. Table.2 represents short circuit analysis results, ‫ܫ‬௄ (subtransient symmetrical SCcurrents), IP (peak SC currents) for 3-phase faults on buses. The dynamic performance of the system with respect to change in total generation andload can be represented by swing equation [38]. The relationship that define variation offrequency with total generation and load mismatch can be obtain from swing equation, ீு ௗ మ ఋ೘ గ௙బ ௗ௧ మ ൌ ܲ௔ (1)Where,G: nominal MVA of generatorH: inertia constantδ: generator rotor anglef0: nominal frequencyPa: net accelerated or decelerated power (mismatches between generation and load) 201
    • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN0976 – 6553(Online) Volume 3, Issue 3, October – December (2012), © IAEME Fig. 1 Single Line Diagram of IEEE-9 Bus Test System TABLE.1 LOAD FLOW REPORT Bus No. Bus KV Voltage Voltage Gen. Gen. Load Load Mag. (%) Angle (MW) (Mvar) (MW) (Mvar) Bus 1 16.5 100.0 1.0 65.67 44.46 0 0 Bus 2 18.0 100.0 4.3 163.00 36.81 0 0 Bus 3 13.8 100.0 1.2 85.00 51.68 0 0 Bus 4 230 98.20 -0.5 0 0 0 0 Bus 5 230 98.10 -0.4 0 0 124.04 49.62 Bus 6 230 98.22 -0.5 0 0 89.32 29.77 Bus 7 230 98.30 -0.4 0 0 0 0 Bus 8 230 98.10 -0.4 0 0 99.25 34.74 Bus 9 230 98.30 -0.5 0 0 0 0 TABLE.2 SHORT CIRCUIT CURRENTS FOR 3-PHASE FAULT " Bus No. KV I K (KA) IP (KA) Bus 1 16.5 78.37 205.73 Bus 2 18 104.66 280.53 Bus 3 13.8 110.29 288.72 Bus 4 230 10.37 25.25 Bus 5 230 10.44 25.69 Bus 6 230 10.36 25.19 Bus 7 230 10.47 25.99 Bus 8 230 10.42 25.47 Bus 9 230 10.38 25.37 202
    • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN0976 – 6553(Online) Volume 3, Issue 3, October – December (2012), © IAEMEConsider the generator speed variation due to a disturbance which is given by, ݀ߜ ߱ ൌ ߱଴ ൅ ൌ 2ߨ݂ ݀‫ݐ‬ Where ߱଴ is the synchronous speed in rad/secDifferentiating above equation with respect to time, ௗఠ ௗమ ௗ௙ ௗ௧ ൌ ௗ௧ మ ൌ 2ߨ ௗ௧ (2)Substituting equation (2) in equation (1), we get ೌ బ ௗ௙ ௉ ௙ ൌ ଶீு ௗ௧ (3) Equation (3) defines the rate of change of frequency in Hz with, total power mismatch Pa,system nominal frequency f0, and inertia constant H. This equation can be used for an individualgenerator or for an equivalent which represents the total generation in a system. For equivalent case,the inertia constant (H) can be derived from the following, ‫ܪ‬ଵ ‫ܣܸܯ‬ଵ ൅ ‫ܪ‬ଶ ‫ܣܸܯ‬ଶ ൅ ‫ ڮ‬൅ ‫ܪ‬௡ ‫ܣܸܯ‬௡ ‫ܪ‬ൌ ‫ܣܸܯ‬ଵ ൅ ‫ܣܸܯ‬ଶ ൅ ‫ ڮ‬൅ ‫ܣܸܯ‬௡Where n is the number of generators in a power system. System frequency response for different generation-load scenarios and contingencies as givenin table 3 is shown in fig. 2. The contingencies considered for study includes loss of generators G1, G2and G3 for different load and generation scenarios. Bus frequency which starts falling after thecontingency continuously along as the generator load deficit exists. As the difference between availablegeneration and load increases decay rate increases. TABLE.3 GENERATION AND LOADS SCENARIOS Scenarios Total Generation Total Load Mismatch Scenario-1 248.9 293.9 45.0 Scenario-2 238.9 288.9 50.0 Scenario-3 248 313.7 65.7 Scenario-4 228.7 313.7 85.0 Scenario-5 130 288.9 158.9 Scenario-6 150.7 313.7 163.0 100 90 Frequency (in %age) 80 70 scenario1 scenario2 60 scenario3 scenario4 scenario5 50 scenario6 40 0 2 4 6 8 10 Time (sec) Fig. 2 System Frequency Response for Different Load Generation Scenario. Underfrequency or the rate of frequency decline is used to determine overload or generationand load mismatch in system. In a large system, there can be an almost infinite number of possibilitiesthat can result in load–generator imbalance. Load-Generation imbalance problem can be solved byeither generation control or load control. Load control or Load shedding (LS) is the last option tomaintain balance between load and generation. 203
    • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN0976 – 6553(Online) Volume 3, Issue 3, October – December (2012), © IAEME3. ANN BASED ADAPTIVE LOAD SHEDDING An artificial neural network (ANN) is a flexible mathematical structure which is capable ofidentifying complex nonlinear relationships between input and output data sets. A neural network is aparallel-distributed processor made up of simple processing units, is known as neurons, which has atendency for storing, and making easily available, experimental information. A multi-layers network can have several layers, each layer has a weight matrix W, a biasvector b, and an output vector a. The three-layer network is shown below in figure and in theequations at the bottom of the figure. 1 1 2,1 2 2 3,2 3 3 1,1 n1 1 a1 lw1,1 n1 2 a1 lw1,1 n1 f 3 a1 iw1,1 f f p1 b1 1 2 b1 3 b1 3 p2 n 1 2 1 a 1 2 n 2 2 2 a 2 2 n2 3 3 a2 f f f p3 1 2 3 b2 b2 b2 p R1 n S1 1 1 nS2 2 2 3 n S3 3 a S3 1 a S1 2 a 3 1,1 f f S2 f iw S , R 2,1 lw S 2 ,S1 3,2 1 2 lw S 3 ,S 2 3 b S1 b S2 bS3 3 a = f ( LW a + b ) 3 3,2 2 3 2 a 1 = f ( LW 2,1a1 + b2 ) 1 ( IW 1,1 p + b1 ) a 2 = f a = f ( LW f ( LW f ( IW p + b ) + b ) + b ) 3 1,1 3 1 2 3,2 3 2 2.1 1 Fig.3 Multi-Layer Feed Forward Neural Network Where the input signal p with R variables is expressed as [p1, p2, p3, ……pR]T . Input-outputdata sets which are used to training, testing and validation of ANN are {(p1, q1), (p2, q2), (p3, q3),……(pR, qR)}. Where q is desired output with R variables is calculated by system analysis. The Levenberg–Marquardt Back-Propagation (LMBP) algorithm is used for training of theANN model because of the low error and least epochs. To prepare the training data sets for ANN, thetransient stability analysis has been performed to solve the minimum load shedding for variousoperating scenarios with the help of ETAP software. The data is propagated from the input layer, multiplied by their respective weight, to thehidden layers before reaching the final output layer. The error signals between the desire output andactual output at the output layer are then propagated back to the hidden and input layers. The sum ofsquare error is then minimized by adjusting the synaptic weights and bias in any layers during thetraining process of ANN model. For a multi-layer network, the net input nk+1(i) and output ak+1(i) ofneuron i in the k+1 layer can be expressed as: skn k +1 (i ) = ∑ wk +1 (i, j ) y k ( j ) + bk +1 (i ) (4) j =1a k +1 (i ) = f k +1 (nk +1 (i )) (5) By representing the sum of the output square error as the performance index for the ANN,the error function is given by 1 R k k 1 R E= ∑ (q r − a r )T (q r − a r ) = 2 ∑ (er )T er 2 r =1 r =1 (6) Where er = qr − ark is the output error and ark is the final output of the rth input. TheLevenberg–Marquardt algorithm is used to minimize the mean square error function in Eq. (6) The input-output data sets are generated by performing transient stability analysis fordifferent contingencies. In this paper the input variables of the ANN model are the total powergeneration of the system (Pg), total load demand (PL) and the frequency decay rate (df/dt) and theoutput is the minimum amount of load shedding (PLS). 204
    • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN0976 – 6553(Online) Volume 3, Issue 3, October – December (2012), © IAEME4. IMPROVEMENT OF TRANSIENT STABILITY USING LOAD SHEDDING The objective of the ANN based adaptive load shedding is to trip minimum requiredload during generation deficit. An ANN-based system should be implemented to execute loadshedding in an automatic manner. When a fault occurs and results in generation shortage ofthe system, the values of input neurons for ANN controller can be captured by the SCADAsystem in real-time basis. The results of the ANN load shedding analyses with 21 different scenarios ofgeneration and load conditions are shown in Table.4. For these study cases, the values of PG,PL and df/dt are varied between 150–270 MW, 270-315 MW and 0.24-4.81Hz/s respectively. TABLE.4 ANN-BASED LOAD SHEDDING RESULTS Pg PL df/dt PLS PANN ERROR (MW) (MW) (Hz/s) (MW) (MW) 270 315 -0.731 39.07 40.5 1.42 270 300 -0.486 27.40 27 -0.40 270 285 -0.240 13.17 13.5 -0.32 248 315 -0.930 60.58 60 -0.58 240 315 -1.366 66.76 67.5 0.73 240 300 -1.090 54.21 54 -0.21 240 285 -0.817 35.07 35 -0.07 240 270 -0.540 21.30 22 0.69 228 315 -1.664 87 87.25 0.25 210 315 -2.190 96.33 94.5 -1.83 210 300 -1.879 82.66 81 -1.66 210 285 -1.560 61.48 60.5 -0.98 210 270 -1.248 64.50 63 -1.51 180 315 -3.200 119.69 121.5 -1.00 180 300 -2.910 108.61 108 -0.61 180 285 -2.540 89.88 90 0.11 180 270 -2.190 81.11 81 -0.11 150 315 -4.810 180.21 180 -0.21 150 300 -4.380 149.63 148.5 -1.13 150 285 -3.942 115.64 115 -0.63 150 270 -3.500 107.32 108 -0.67 For a case, 228MW total generation, 36MW spinning reserve and 315MW total loaddemand, conventional and ANN based Adaptive load shedding results are shown in fig. 4-5.The conventional load shedding is performed by underfrequency relay. 205
    • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN0976 – 6553(Online) Volume 3, Issue 3, October – December (2012), © IAEME Fig.4 Frequency plot for conventional load shedding The underfrequency relay is set to operate at 58.5Hz and 58Hz for first and secondstep load shedding respectively. For this condition total load shedding required by theconventional load shedding and ANN method is 95 MW and 80 MW respectively. Fig.5 Frequency plot for adaptive load shedding5. CONCLUSION In this paper an approach for improvement of frequency stability using adaptiveminimum load-shedding scheme by the ANN model is developed for IEEE 9 bus system. Byexecuting the transient stability analysis for various operation scenarios of the test system, thetraining data set of ANN model, which includes of total system power generation, totaldemand, frequency decay rate and the minimum amount of load shedding required has beenprepared. With the smallest training time and the least epochs, the LMBP algorithm is used toderive the ANN model of minimum load shedding for the IEEE-9 bus test system. To verifythe effectiveness of the proposed ANN model for load shedding with system faultcontingency, traditional and adaptive load-shedding methods are applied in the simulation toinvestigate the dynamic response of system frequency. It is found that the ANN based loadshedding method requires less load shedding amount as compare to conventional method forthe same fault contingency. 206
    • International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN0976 – 6553(Online) Volume 3, Issue 3, October – December (2012), © IAEMEREFERENCES[1] M.J Basler, R.C. Schaefer, Understanding Power-System Stability, Industry Applications, IEEE Transactions on , vol.44, no.2, pp.463-474, March-april 2008[2] P. Kundur. J. Paserba, Ajjarapu, V Andersson, A Bose, C Canizares, N Hatziargyriou, D. Hill, D. A. Stankovic, C. Taylor, T Van Cutsem, V. Vittal, Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions, Power Systems, IEEE Transactions on , vol.19, no.3, pp. 1387- 1401, Aug. 2004.[3] P. Kundur, J. Paserba, S. Vitet, Overview on definition and classification of power system stability, Quality and Security of Electric Power Delivery Systems, 2003. CIGRE/PES 2003. CIGRE/IEEE PES International Symposium , vol., no., pp. 1- 4, 8-10 Oct. 2003[4] J. C. Das, Transient stability of small plant generators connected to a weak utility system-a case study, Industry Applications, IEEE Transactions on , vol.41, no.1, pp. 155- 162, Jan.-Feb. 2005[5] F. Ceja-Gomez, S. S. Qadri, F. D. Galiana, Under-Frequency Load Shedding Via Integer Programming, Power Systems, IEEE Transactions on , vol.27, no.3, pp.1387-1394, Aug. 2012[6] M. Q. Ahsan, A. H. Chowdhury, S. S. Ahmed, I. H. Bhuyan, M. A. Haque, H. Rahman, Technique to Develop Auto Load Shedding and Islanding Scheme to Prevent Power System Blackout, Power Systems, IEEE Transactions on , vol.27, no.1, pp.198-205, Feb. 2012[7] Charles F. Dalziel, Edward W. Steinback, Underfrequency Protection of Power Systems for System Relief Load Shedding-System Splitting, Power Apparatus and Systems, Part III. Transactions of the American Institute of Electrical Engineers , vol.78, no.4, pp.1227-1237, Dec. 1959[8] Chen Chao-Shun, Hsu Cheng-Ting, Lee Yih-Der, Protective Relay Settings of Tie Line Tripping and Load Shedding for an Integrated Steelmaking Cogeneration System, Industry Applications, IEEE Transactions on , vol.46, no.1, pp.38-45, Jan.-feb. 2010[9] L.J. Shih, W.J Lee, J.C. Gu, Y.H. Moon, Application of df/dt in power system protection and its implementation in microcontroller based intelligent load shedding relay, Industrial and Commercial Power Systems Technical Conference, 1991. Conference Record. Papers presented at the 1991 Annual Meeting , vol., no., pp.11-17, 6-9 May 1991[10] W.-J Lee, J.-C. Gu, A microcomputer-based intelligent load shedding relay, Power Delivery, IEEE Transactions on , vol.4, no.4, pp.2018-2024, Oct 1989[11] A. Maiorano, R. Sbrizzai, F. Torelli, M. Trovato, Intelligent load shedding schemes for industrial customers with cogeneration facilities, Power Engineering Society 1999 Winter Meeting, IEEE , vol.2, no., pp. 925- 930 vol.2, 31 Jan-4 Feb 1999[12] H. Bevrani, A. G. Tikdari, T. Hiyama, An intelligent based power system load shedding design using voltage and frequency information, Modelling, Identification and Control (ICMIC), The 2010 International Conference on , vol., no., pp.545-549, 17-19 July 2010[13] Y. Zhang, O. P. Malik, G. S. Hope, G. P. Chen, Application of an inverse input/output mapped ANN as a power system stabilizer, Energy Conversion, IEEE Transactions on , vol.9, no.3, pp.433-441, Sep 1994 207
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