Your SlideShare is downloading. ×
Ak methodology nnloadmodeling
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Ak methodology nnloadmodeling

14
views

Published on


0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
14
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Composite Neural Network Load Models for Power System Stability Analysis Ali Keyhani, Fellow, IEEE, Wenzhe Lu, Student Member, IEEE, Gerald T. Heydt, Fellow, IEEE Abstract — Proper load models are essential to power system stability analysis. This paper proposes a methodology for the development of neural network (NN) based composite load mod- els for power system stability analysis. A two-step modeling pro- cedure is proposed. First knowledge is acquired from a test bed of power systems based on detail load models of a bus to the dis- tribution level. Then, the test bed data is used to develop a com- posite NN model. The developed NN model is updated based on measurements. A case study on a power inverter controling an induction motor load is presented. Index Terms — Artificial neural networks, composite load model- ing, power systems, stability analysis. I. INTRODUCTION N power system stability analysis, all power system compo- nents are represented by their models. Generally, detailed data about components such as generators, transformers, and transmission lines are available, and accurate models can be obtained for them. However, corresponding data for individual loads are not always available, which makes the modeling of loads an important area of research. Increasingly nonlinear dynamic loads have been connected into power systems; such as variable speed drives, robotic factories and power electron- ics loads. This adds to the complexity of load modeling. In distribution systems, there are often multiple loads connected to a single bus, as shown in Figure 1. Normally the power of individual load is not measured or not available, but the total power transmitted through the bus is measured. In these cases, the loads can be considered as one composite load, which con- sists of static loads and dynamic or nonlinear loads. In recent years, many different techniques have been proposed to model such loads [1-9]. However, most of them are based on an as- sumed load equation and the parameters of the equation are estimated through curve fitting. Because of the complexity of modern loads (for example, power electronics loads), the as- sumed models may not capture power, frequency, and voltage phenomena simultaneously and accurately. It is necessary to investigate new load modeling techniques and establish accu- rate load models for power system stability analysis. Ali Keyhani and Wenzhe Lu are with the Department of Electrical Engineer- ing, The Ohio State University, Columbus, OH 43210 (e-mail: key- hani.1@osu.edu). Gerald Thomas Heydt is with the Arizona State University, Tempe, AZ 85287-5706, USA. M Static P/Q Loads Motor Loads Power Electronics Loads Other Nonlinear Loads Dynamic Loads Composite Load Figure 1 Composite load in a distribution system In this paper, a neural network based composite load model is proposed. The methodology for the development of the model is given in details. First, a simulation testbed is setup based on the nominal parameters of the loads. The simulation data will be collected to develop a two-layer recurrent neural network (NN), which estimates the load power from terminal voltage and system frequency. The developed composite neural net- work will be retrained using the measured data. II. KNOWLEDGE ACQUISITION FROM A TEST-BED OF TRANSIENT STABILITY CASE STUDY Because of the nonlinear nature of the load power with respect to voltage and frequency, a neural network is proposed to map their relationship. To obtain an adequate representation of complex loads, the neural network needs to be trained with large set of data in expected operating conditions. In power system normal operation, the voltage and frequency only vary in a very narrow range. If the neural network is trained with only with these data, then the NN will not characterize the load dynamics during operation when the voltage or frequency varies outside nominal operating conditions. To address this problem, a two-step procedure is used in the composite NN load model development. In the first step, a simulation test bed of transient stability is constructed on a system bus with detail load models using the nominal parame- ters in the distribution systems. In the modeling of distribution systems, the motor loads of large industrial systems are mod- eled in detail with associated converters. P-Q load models are used for all buses except the bus for which a composite load model would be developed. At each time step of the transient stability studies, voltages are computed from a load flow based on constant P and Q. Detailed load data of the bus under I
  • 2. study for a composite NN load model would be collected when the system is subjected to line outages for up to five seconds of the disturbance. In addition, the system frequency will also be collected for NN modeling. The data collected from the test bed would be utilized to develop an NN load model as described in the next section. III. RECURRENT NEURAL NETWORK LOAD MODELS Neural networks are composed of simple elements operating in parallel. These elements are inspired by biological systems. As in nature, the network function is determined largely by the connections between elements. It is possible to train a neural network to perform a particular function by adjusting the val- ues of the connections (weights) between elements. Com- monly neural networks are adjusted, or trained, so that a par- ticular input leads to a specific target output. The simplest type of neural network is the feedforward NN, which has no feedback in the structure. Therefore, it can only perform a “memoryless” mapping: at any time instant, the output of the neural network is determined by the current in- put. Another type of neural network is the recurrent NN, which has feedback from the output of later layers to the input of previ- ous layers in the structure, with appropriate delays. The delay in the recurrent connection stores values from previous time steps, which can be used in the current time step. The recur- rent connection allows the neural network to both detect and generate time-varying patterns. The structure of the proposed NN is shown in Figure 2. The input quantities to the neural network are the voltage ( ), and frequency f (instantaneous values and/or values with ap- propriate delays), and the output is the load power P or Q. (used also as the training objective). 2 ,VV The output of the first layer is fed back to the input with some delays to form a recurrent neural network. Because active power P and reactive power Q have different characteristics, it is suggested to utilize two distinct neural networks to map these quantities. A hyperbolic tangent sigmoid transfer function – “tansig(…)” is chosen to be the activation function of the input layer, which gives the following relationship between its inputs and outputs, ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ×+⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ×+×= −×−× − ×× 2 1 2 2101210 1,1 210110 ,1 }2,1{}1,1{}2,1{ k k k k kk V V IW V V IWnLWn [ ] 110110 }1{}3,1{ ×× +×+ bfIW k )( 110 1 110 1 ×× = ntansiga . (1) A pure linear function is chosen to be the activation of the output layer, which gives, 11110 1 10111 2 }2{}1,2{ ×××× +×= baLWn 222 )()( nnpurelinaQorP === . (2) The combination of these transfer functions can approximate any function (with a finite number of discontinuities) with arbitrary accuracy [15]. Recurrent n1 a1 n2 a2 Input First Layer Second Layer Output Figure 2 Structure of a recurrent neural network for composite load modeling
  • 3. IV. OFFLINE KNOWLEDGE ACQUISITION FROM MEASURED DATA At time step t, k=0 )()( ttVV k ∆−=Since the simulation testbed is based on nominal parameters of the loads, it is not accurate enough to fully represent the char- acteristics of the real loads. The neural network based model trained with the simulation data cannot be directly used in power system stability analysis. It must be improved by meas- ured data. However, the substantial a priori information based on the nominal load data can be used to establish an adequate first pass estimation of the complex loads in a compact NN model Load modelfVV ,, 2 P, Q Stability Program )1( +k VP, Q During normal operation and occasional systems disturbances such as fault and line switching, the bus data such as power, voltage, and frequency are measured and recorded. These data will be used to retrain the neural network developed in Section II. After retraining, the load model will represent the loads at normal operating condition very accurately. Since the NN model is also trained using the disturbance data, the NN model gives an adequate representation of load dynamic perform- ance. V. UTILIZATION IN POWER SYSTEM STABILITY ANALYSIS When the neural network based load model is well trained, it can be applied directly to power system stability analysis. Here it is assumed that the general stability analysis program, which takes the load power (P, Q) as input will produce out- puts as the voltage V (and frequency f). At each time step t, the digital model first uses the voltage and frequency obtained from previous time step to estimate load power P and Q. Subsequently the power levels are used to compute the voltage and frequency. Since this estimated load power might not be accurate, the calculated V and f may propagate the error. Iterations will generally be needed to at- tain convergent model parameters: first update P and Q with the calculated V and f; and then calculate new values of V and f. This process is repeated until convergence is attained (with ε≤−=∆ −1kk VVV , where k is the iteration step). A flowchart of the process is shown in Figure 3. The developed NN model is a recurrent NN model in which the time step represents the simulation time step in transient stability studies. This class of NN reflects the changing dy- namics of the loads due to the changes in frequency and volt- age magnitude when the system is subjected to disturbances in the two cycles time frame of stability studies. It should be pointed out that the weights of the NN model remain the same, however the expected loads change as the input to the NN changes during the disturbance. Figure 3 Flowchart: utilizing composite NN load model in power system stability analysis VI. CASE STUDY To test the proposed load model, a case study has been per- formed on a power inverter controlled induction motor load in simulation. The depiction in Figure 4 represents a composite load – an induction motor that connects to power system through a rectifier and an IGBT power inverter. The load torque connected to the motor is a function of the rotor speed. In addition, a speed controller is implemented to control the induction motor. In simulation, the motor is operated in the steady state, and the supply voltage is decreased / increased over the range of 0.0 to 1.2 p.u., with different rates. The active power P and reactive power Q at the load terminals are computed and recorded. Similar simulations are performed at different system frequen- cies (55 Hz through 65 Hz). All data are collected for neural network training and load model validation. The data collected from simulation are separated into two parts. One part is used to train the neural network; the other is used to test the validity of the results. After the neural net- works are trained by one part of the collected data, the NNs are validated with the other part of data. The procedure is to send the recorded sequence of voltage and frequency to the neural networks, and compare the estimated power sequence with recorded power sequences. Figure 5 shows one of the validation results. In Fig. 5, the voltage is shown as a dashed curve; the simulated power is shown as a solid curve; and the )()1( ε<−+ kk VV ttt ∆+= 1+= kk No Yes
  • 4. Figure 4 Simulation testbed: a composite load – induction motor controlled by power inverters (a) Validation of active power P (b) Validation of reactive power Q Figure 5 Model validation results from noise-free data estimated power (from neural network) is shown as small cir- cles. Note that in this simulation, the motor is initially running at rated voltage, and then the voltage is decreased to zero. Af- ter reaching steady state, the voltage is increased from 0.0 to 1.2 p.u. Finally, the voltage is returned to rated value. Due to the performance of the speed controller, the load power changes nonlinearly. In view of this behavior, simple load models will not capture the phenomenon accurately. But from the results here, one can see that the proposed neural network based load model captures the load dynamics very well. The mean of estimation error for P is 3.07x10-4 p.u., and that for Q is 4.04x10-5 p.u. thus suggesting a satisfactory estimation. The validation of other data shows similar results. VII. CONCLUSIONS This paper presents a methodology for development of neural network based load models which can be used in power sys- tem stability analysis. A two-step procedure is proposed to first develop a recurrent neural network with simulation data and then update it with measured data. A case study on an induction motor load proves the availability of the suggested NN load model. VIII. ACKNOWLEDGEMENT This work is supported in part by NSF Grant NSF ECS0105320. IX. REFERENCES [1] Les M. Hajagos, Behnam Danai, “Laboratory Measurements and Models of Modern Loads and Their Effect on Voltage Stability Studies,” IEEE Transactions on Power Systems, Vol. 13, No. 2, May 1998, pp. 584-592. [2] IEEE Task Force on Load Representation for Dynamic Per- formance, “Standard Load Models for Power Flow and Dy- Composite Load Parameters of Testbed: Induction Motor: 50HP, 460V, 60Hz Ω= 087.0sR mHLls 8.0= Ω= 228.0' rR mHLlr 8.0' = mHLm 7.34= 2 662.1 mkgJ ⋅= IGBT Switching: Vector- controlled PWM
  • 5. namic Performance Simulation,” IEEE Transactions on Power Systems, Vol. 10, No. 3, August 1995, pp. 1302-1313. [3] Les Pereira, Dmitry Kosterev, Peter Mackin, Donald Davies, John Undrill, and Wenchun Zhu, “An Interim Dynamic In- duction Motor Model for Stability Studies in the WSCC,” IEEE Transactions on Power Systems, Vol. 17, No. 4, No- vember 2002, pp. 1108-1115. [4] M. K. Pai, “Voltage Stability: Analysis Needs, Modeling Requirements, and Modeling Adequacy,” IEE Proceedings-C, Vol. 140, No. 4, July 1993, pp. 279-286. [5] A. Borghetti, R. Caldon, A. Mari, C. A. Nucci, “On Dynamic Load Models for Voltage Stability Studies,” IEEE Transac- tions on Power Systems, Vol. 12, No. 1, February 1997, pp. 293-303. [6] S. Z. Zhu, Z. Y. Dong, K. P. Wang, and Z. H. Wang, “Power System Dynamic Load Identification and Stability,” Proceed- ing of International Conference on Power System Technology 2000, Vol. 1, Dec. 4-7, 2000, pp. 13-18. [7] Y. G. Zeng, A. Berizzi, and P. Marannino, “Voltage Stability Analysis Considering Dynamic Load Model,” Proceeding of the 4th International Conference on Advances in Power Sys- tem Control, Operation and Management, APSCOM-97, Hong Kong, November 1997, pp. 396-401. [8] David J. Hill, “Nonlinear Dynamic Load Models with Recov- ery for Voltage Stability Studies,” IEEE Transactions on Power Systems, Vol. 8, No. 1, , February 1993, pp. 166-176. [9] Y. Baghzouz, Craig Quist, “Composite Load Model Deriva- tion from Recorded Field Data,” IEEE Power Engineering Society 1999 Winter Meeting, Vol. 1, Jan. 31 - Feb. 4 1999, pp. 713 –718. [10] William W. Price, Kim A. Wirgau, Alexander Murdoch, James V. Mitsche, Ebrahim Vaahedi, and Moe A. El-Kady, “Load Modeling for Power Flow and Transient Stability Computer Studies,” IEEE Transactions on Power Systems, Vol. 3, No. 1, February 1988, pp. 180-187. [11] Y. Kataoka, “A Probabilistic Nodal Loading Model and Worst Case Solutions for Electric Power System Voltage Sta- bility Assessment,” IEEE Transactions on Power Sys- tems, Vol. 18, Issue 4, Nov. 2003, pp. 1507-1514. [12] J. R. Shin, B. S. Kim, M. S. Chae, S. A. Sebo, “Improvement of Precise P/V Curve Considering Effects of Voltage- Dependent Load Models and Transmission Losses for Volt- age Stability Analysis,” IEE Proceedings on Generation, Transmission and Distribution, Vol. 149, No. 4, July 2002, pp. 384-388. [13] Qisheng Liu, Yunping Chen, Dunfeng Duan, “The Load Modeling and Parameters Identification for Voltage Stability Analysis,” Proceedings of 2002 International Conference on Power System Technology. Vol. 4, Oct. 2002, pp. 2030-2033. [14] Dingguo Chen, R. R. Mohler, “Neural Network Based Load Modeling and its Use in Voltage Stability Analysis,” IEEE Transactions on Control System Technology, Vol. 11, No. 4, July 2003, pp. 460-470. [15] Elman, J. L., "Finding Structure in Time," Cognitive Science, vol. 14, 1990, pp. 179-211. X. BIOGRAPHIES Wenzhe Lu (S’00) received his BS from Xi’an Jiaotong University, Xi’an, China in 1993, and MS from Tsinghua University, Beijing, China in 1996, respectively. Now he is a PhD student in Electrical Engineering Department of The Ohio State University, Columbus, Ohio. Mr. Lu's research interests include power system modeling and analysis, and modeling and control of switched reluctance motors for electric vehicle applications. Ali Keyhani (S’72-M’76-SM’89-F’98) received his Ph.D degree from Pur- due University, West Lafayette, Indiana in 1975. From 1976 to 1980, he was a professor of Electrical Engineering at Tehran Polytechnic, Tehran, Iran. Cur- rently, Dr. Keyhani is a Professor of Electrical Engineering at the Ohio State University, Columbus, Ohio. Gerald Thomas Heydt (S’62–M’71–SM’79–F’91) received the B.E.E.E. degree from the Cooper Union, New York, and the M.S.E.E. and Ph.D. de- grees from Purdue University, West Lafayette, IN. Currently, he is a Regents’ Professor of Electrical Engineering at Arizona State University, Tempe, AZ. Dr. Heydt is a member of that National Academy of Engineering.