Evaluation for Voltage Stability Indices in Power System Using

Available online at www.sciencedirect.com

ScienceDirect Procedia Engineering 118 (2015) 1127 – 1136

International Conference on Sustainable Design, Engineering and Construction

Evaluation for Voltage Stability Indices in Power System Using Artificial Neural Network H.H. Goha,*, Q.S. Chuaa, S.W. Lee a, B.C. Koka, K.C. Goha, K.T.K. Teo b a

Department of Electrical Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia . b Modelling, Simulation and Computing Laboratory, Level 3, Block C, S chool of Engineering and Information Technology, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia

Abstract At present, the evaluation of voltage stability assessment experiences sizeable anxiety in the safe operation of power systems. This is due to the complications of a strain power system. With the snowballing of power demand by the consumers and also the restricted amount of power sources, therefore, the system has to perform at its maximum proficiency. Consequently, the noteworthy to discover the maximum ability boundary prior to voltage collapse should be undertaken. A preliminary warning can be perceived to evade the interruption of power system’s capacity. This paper considered the implementation of real-time system monitoring methods that able to provide a timely warning in the power system before the voltage collapse occurred. Numerous types of line voltage stability indices (LVSI) are differentiated in this paper to resolve their effectuality to determine the weakest lines for the power system. The line voltage stability indices are assessed using the IEEE 9-Bus and IEEE 14-Bus Systems to validate their practicability. Besides that, this paper also introduced the implementation of real-time voltage stability monitoring by using Artificial Neural Network (ANN). Results demonstrated that the calculated indices and the estimated indices by us ing ANN are practically relevant in predicting the manifestation of voltage collapse in the system. Therefore, essential actions can be taken by the operators in order to dodge voltage collapse incident from arising. ©2015 2015The The Authors. Published by Elsevier Ltd. © Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and and Construction Construction 2015 2015. Keywords: Artificial Neural Network (ANN); Line Voltage Stability Indices (LVSI); Maximum Loadability; Voltage Stability Analysis;

* Corresponding author. Tel.: +6016-741-6824. E-mail address: [email protected] (H.H. Goh)

1877-7058 © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of the International Conference on Sustainable Design, Engineering and Construction 2015

doi:10.1016/j.proeng.2015.08.454

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H.H. Goh et al. / Procedia Engineering 118 (2015) 1127 – 1136

1. Introduction Present-day, the power systems need to adapt to the new situation since the actual scene no longer exists as it used to be. Due to the climate change throughout the world, it is expected to lead the electricity consuming demand to operate closely to the numbers of generated electricity [1, 2]. Besides that, aggressive business conditions have enforced electric utilit ies to fully make use of accessible resources. Moreover, the current power systems are extremely loaded as compared to the past because of the arising demand, maximu m economic advantages and the effectiveness of utilizing the available transmission capacity [3-5]. After examining the sequence of incidents that caused the major blackout in the year 2003, the reasons of the blackouts were due to a shortage of reliable real-t ime data [6, 7]. The established decentralized way of operating systems by Transmission System Operators (TSOs) where each TSO take cares of its own control area and limited informat ion to exchange, resulted in insufficient and delay response towards contingencies. Therefore, a real-t ime security assessment and control are needed to maintain the system security [7]. The significance of real-t ime data is to allow the operators to carry out important and practically p reventive action to avoid cascading or else will lead the system to incorrect or delayed correction actions and thus will give a chance of instability occurrence. Vo ltage stability assessment and control are not considered as any new issue [8], but they have now attained special attentions to maintain the stability of the transmission networks in order to avoid recurrence of major blackouts as experienced by the particular countries. The power system can be classified in the voltage stability region if it can maintain steady acceptable voltages at all buses in the system under normal operating conditions and after being subjected to a disturbance [9, 10]. In order to be reliable, the power system must be stable at most of the time. The research works on voltage stability can be break down into various approaches, but the estimat ion on the power system’s distance towards voltage collapse can be very handy to the operators before they take any remedial actions [11, 12]. The details on the distance towards voltage instability can be obtained by using Voltage Stability Indices (VSI) [13]. Vo ltage stability analysis is still widely being imp lemented in the industries by calculating the P -V and Q-V curves at selected load buses [14]. Co mmon ly these curves are created by a large nu mber of load flows using conventional methods and models. However, these methods are time consuming and do not provide sufficient practical information towards the stability problems [15]. Various causes are recited in [16] as the commencement of the power systems outages. Most of all, power systems outages can be categorized into two types; unpreventable event and preventable event. Some of the p ower systems outages can be classified as the unpreventable events from the system operator. During the unpreventable event took place; the system operator cannot control the damaged level happening in the power system. In the mean while, in several cases, the power system outages can be prevented with the utilizat ion of a sufficient system protection and situational awareness. If the power system is not equipped with the suitable p rotection for the system, then the power system is prone to critical operatio nal situations and leads to instabilit ies. Hence, voltage instability is one of the significant problems in causing the power outages. Basically, voltage stability indices can be classified into Jacobian matrix – based voltage stability ind ices and system variable – based voltage stability indices. In this paper, the motive for focusing on the system variable – based voltage stability indices is because it requires fewer amounts of co mputing time. Besides that, it can precisely verify the weak bus or lines in the power systems. Therefore, the scope has been narrowed into focusing on system variable based voltage stability indices. In addition, two test-power system cases are utilized in this paper, wh ich is IEEE 9-Bus test case and IEEE 14Bus test case. IEEE 9-Bus test case represents a portion of the Western System Coordinating Council (WSCC) 3 Machines 9-Bus system. Fundamentally, this IEEE 9-Bus test case consists of three generators, nine buses and three loads. The IEEE 14-Bus test case actually represents a part of the A merican Electric Power System wh ich is situated in the Midwestern US. Fundamentally, this system consists of two generators, three units of synchronous condensers, 14 buses and 11 loads. The rest of this paper was organized as follows. A background study on the overview of definit ions of voltage stability and voltage stability indices will be discussed in section 2. The implementation of the voltage stability indices and the details of the simulat ion cases will be talked about in section 3. Results and discussion will be comprised in section 4. Finally, this paper is concluded in section 5.


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2. Background of study 2.1. Definitions of voltage stability Vo ltage stability can be interpreted as the potential of the power system to sustain steady voltages at all buses in the system after being vulnerable to a disturbance from a given initial operating condition [9]. Besides that, voltage stability is the consequence on the ability of the power system to maintain o r restore the equilibriu m between the load demand and load supply [9, 17]. A system is treated as voltage instable if at least one bus in the system experiencing voltage magnitude decreases once the reactive power injection is increasing [18]. In addition, voltage stability can also be considered as load stability. If the power system lacks of the capability to transfer an infinite amount of electrical power to the loads, hence voltage instability will be present. The main reason for contributing to voltage instability is the inability of the power system to meet the requirements for reactive power in the extremely stressed system keeping the desired voltages [19]. In order to restore the increasing demand of loads in the systems will cause further voltage decrement [17]. Vo ltage collapse is the event when acco mpanied by voltage instability leads to a blackout or abnormally low voltages in a significant part of the power system [9] Vo ltage instability and collapse effectively extent a t ime range fro m seconds to one hour. The time length of a distraction in a power sys tem, originating a possibility of a voltage instability problem, can be categorized into short term and long term. The examp le of short-term or transient voltage instability can be found main ly caused by rotor angle variance or loss of synchronism. In the meanwh ile, long term voltage instability problems main ly occurred in heavily loaded systems where the electrical distance is huge between generator and the load. Mainly, in order to analyze the voltage stability, it is often convenient to categorize the problem into smalldisturbance and large-disturbance voltage stability. Small-disturbance voltage stability refers to the system’s capability to continue steady voltages when disposed to small perturbations such as incremental changes in system load [9]. Generally, the analysis for small-disturbances is done in steady-state stability analysis. Steady-state stability analysis is useful in obtaining the qualitative overview of the system such as how stress the system is and the system’s stability to the point of instability. Aside fro m, the large-disturbance voltage stability refers to the system’s ability to maintain stable voltages followed by large d isturbances such as system fau lts, disappearance of generation and loss of line. Large-d isturbance voltage stability can be analyzed by using non -linear time-do main simulation in short term time frame and load flow analysis in the long term time frame [19]. 2.2. Voltage Stability Indices Vo ltage stability indices are very applicab le in retrieving the voltage stability of the power system. Vo ltage stability indices are the scalar magnitudes that are being imp lemented to observe the changes of the parameters in the system. Besides that, the indices are also used to quantify the distance of the particular operating point with the point of voltage collapse [20]. These indices will be very handy to the operators before they started to imp lement the prevention actions [12]. According to [21, 22], the authors mentioned that voltage stability indices particularly could be subdivided into two parts, which are Jacobian matrix based voltage stability indices and system va riables based voltage stability indices. Jacobian matrix based voltage stability ind ices are able to calculate the voltage collapse point or maximu m load ability of the system and discover the voltage stability marg in. However, these indices required h igh computational time and for this part icular reason, the Jacobian matrix based voltage stability indices are not appropriate for online assessment. In the meanwhile, system variables based voltage stability indices required less computational time. The reasons are due to the system variab le based voltage stability indices that used the elements of the admittance matrix and some system variables such as bus voltages or power flow through the lines. With the benefit of less computational time, system variab les based voltage stability indices are suitable to be imp lemented on the online assessment and monitoring purposes. At the same time, system variables based voltage stability indices cannot efficiently estimate the margin because their responsibilit ies are mo re to determine the critical lines and buses.

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The differences between Jacobian matrix based voltage stability indices and system variables based voltage stability indices is provided in Table 1. The d ifferentiation is more likely based on the two aspects which were being defined in [9]. The two aspects are proximity towards voltage collapse – (How close is the system to voltage instability?) and mechanism of voltage instability – (How and why does instability occur?). T able 1. Differentiation between Jacobian matrix based voltage stability indices and system variables based voltage stability indices. Jacobian matrix based voltage stability indice s

Syste m variable s base d voltage stability indice s

Require more amount of computing time

Require less amount of computing time

Suitable for offline monitoring purpose

Suitable for online monitoring purpose

Discover voltage stability margin

Discover weak buses and lines

(Proximity towards voltage collapse)

(Mechanism of voltage instability)

3. Methodologies 3.1. Power System Test Cases Two different types of test cases are being utilized in this paper. They are IEEE 9-Bus test system and IEEE 14Bus test system. The main intention of increasing the number of buses for each test case is to determine the feasibility of line voltage stability indices. At first, IEEE 9-Bus test system network was used for the simulat ion. This IEEE 9-Bus system represents a portion of the Western System Coordinating Council (WSCC) 3-Machines 9-Bus System. Basically, this 9-Bus system contains of three generators, nine buses and three loads. The wiring d iagram for IEEE 9-Bus system is illustrated in Fig. 1(a). The IEEE 14-Bus test case represents a portion of the American Electric Power System which is located in the Midwestern US as of February, 1962. Basically, this system consists of five generators, 14 buses and 11 loads. The wiring diagram for IEEE 14-Bus system is illustrated in Fig. 1(b). a

b

Fig. 1. (a) IEEE 9-Bus test network; (b) IEEE 14-Bus test network.

3.2. Voltage stability indices formulation Basically in this research, six different types of line voltage stability indices are being implemented. They are Lmn index, LQP index, FVSI index, VCPI(Po wer), VCPI(Loss) and LCPI index. Main ly, the line stability indices are formulated based on the power transmission concept in a single line as shown in Fig. 2.


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Fig. 2. 2-Bus system.

Where: VS and VR are the sending end and receiving end voltages respectively G S and G R are the phase angle at the sending and receiving buses Z is the line impedance R is the line resistance X is the line reactance T is the line impedance angle PR is the active power at the receiving end QR is the reactive power at the receiving end 3.2.1. Line stability index (Lmn) This index was derived by [23] by using an overall system stability index based on the concept of power flow through a single line as shown in Fig. 3. This index imp lemented a technique to reduce the system into a single line equivalent network. The Lmn index is provided in Equation 1.

Lmn

4 XQR

>VS sin(T G )@

2

(1)

3.2.2. Line stability factor (LQP) This line stability factor (LQP) is derived in [24] by the authors using the same notion in section 3.2.1. Hence, LQP index is calculated as provided in Equation 2.

ª§ X ·§ X ·º LQP = 4 «¨ 2 ¸¨ 2 PS 2 + QR ¸ » «¬© VS ¹© VS ¹ »¼

(2)

3.2.3. Fast voltage stability index (FVSI) A novel fast voltage stability index (FVSI) was proposed by the authors in [25]. Th is index is being simplified fro m a pre-developed voltage stability index referred to a line fro m the voltage quadratic equation at the sending end of a representation of a two bus system. The formu lated index was tes ted on the IEEE 30-Bus reliability test system in order to verify the performance of the proposed indicator. Hence, the equation for FVSI index can be defined in Equation 3.

FVSI =

4Z 2 QR VS 2 X

(3)

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3.2.4. Voltage collapse proximity indicators (VCPI) The voltage collapse proximity indicators (VCPI) is proposed in [26] are based on the maximu m power transferred through a line in the power network. The VCPI(Power) in Equation 4 and VCPI(Loss) in Equation 5 are used to find the stability point for each line connection between two bus bars in an interconnected network. As long as the indices remain less than one, then the system is considered to be stable.

VCPI ( Power ) =

VCPI ( Loss) =

PR PR (max) Ploss

Ploss (max)

or

QR QR (max)

(4)

or

Qloss Qloss (max)

(5)

3.2.5. Line collapse proximity indicator (VCPI) The authors in [27] have proposed LCPI index based on the derivation of A BCD parameters in transmission line. The forte of this index is by taking into consideration the line charg ing reactance during the derivation of equations. The significant of line charg ing reactance may support voltage stability. The relat ionship between the parameters can be explained in Equation 6.

LCPI =

4 A cos D PR B cos E QR B sin E

VS cos G

2

(6)

Where:

A = 1 Z *Y / 2 { AD

B = Z { BE

C = Y * 1 Z *Y / 4 D=A

3.3. Feed forward back propagation neural network (FFBPNN) A two-layer feed forward neural network with error back-propagation learning is one of the most commonly implemented neural networks [28]. Neural networks are used to model co mplex relat ionships between inputs and outputs or to find patterns in data. In this paper, ANN is used to find the patterns and equation fro m the data. It is a structure (network) co mposed of a nu mber of interconn ected units. In this paper, the input data was as many as 100 data in which 30 data were used as testing data. The algorithm for this study is illustrated in Fig. 3.

Fig. 3. Simulation process of FFBPNN method.


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4. Results and discussion In this paper, there are six line voltage stability indices and each of the indices consists of different number of inputs but with one general output. The details of the inputs for the indices are summarised in the Table 2. T able 2. T he details of the number of inputs for different line voltage stability indices. Inde x Lmn FVSI LQP VCPI (Power) VCPI (Loss) LCPI

Numbe r of inputs 6 4 4 5 5 9

4.1. Prediction for the most critical line in IEEE 9-Bus test system The results indicated that line 9 – 6 is the most crit ical line at 465 seconds during the real-t ime simu lation. Therefore, the prediction is mainly focused on the most critical line by using the artificial neural network model.

Fig. 4. T he comparison between the actual and the predicted line voltage stability indices based on line 9 – 6 at 465 seconds in IEEE 9-Bus test system.

The blue bar in Fig. 4 rep resents the actual output for the line voltage stability indices while the red bar indicates the predicted line voltage stability indices by using artificial neural network. It can be observed from the figure, the trend of the pred icted output values is almost identical with the original calculated values. As it can be seen, the errors found were not significant and there is only a minor difference between both data values in the figure which is acceptable. Overall, the calcu lated and the predicted values are similar and this showed that artificial neural network is also sufficient to be used for voltage stability monitoring purpose. The details fo r the line stability indices based on line 9 – 6 at 465 seconds are presented in Tab le 3. The first column of the table shows the type of the index. Besides that, column two showed the actual output values for the indices based on the calculation. Consequently, column three showed the prediction output values from the art ificial neural network model. Moreover, colu mn four and five showed the network error and error percentage subsequently. In general, the predict ion outputs shown in Table 3 are relevant with the actual output and this once again indicates artificial neural network is sufficient to be used for voltage stability asse ssment purpose.

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T able 3. Details for line voltage stability indices based on line 9 – 6 at 465 seconds in IEEE 9-Bus test system. Inde x

Actual O utput

Pre diction O utput

Ne twork Error

Lmn FVSI LQ P VCPI (Powe r) VCPI (Loss) LCPI

0.994159 0.922800 0.980285 1.010020 0.674455 1.011878

0.994160 0.922799 0.980284 1.010000 0.674453 1.011878

-0.000001 0.000002 0.000001 0.000020 0.000002 0.000000

Error Pe rce ntage (%) 0.000028 0.000166 0.000056 0.001970 0.000319 0.000017

4.2. Prediction for the most critical line in IEEE 14-Bus test system The line voltage stability results indicated that line 5 – 6 is the most critical line at 55 seconds. Therefore, the prediction is based on the most critical line by applying the artificial neural network model.

Fig. 5. T he comparison between the actual and the predicted line voltage stability indices based on line 5 – 6 at 55 seconds in IEEE 14-Bus test system.

The blue bar illustrated in Fig . 5 represents the actual output for the line voltage stability ind ices while the red bar indicates the predicted line voltage stability indices by using artificial neural network. It can be observed from Fig. 5, the trend of the predicted output values is almost similar with the original calculated values. As it can be seen, the errors found were not significant and there is only a minor difference between both data values in the figure which is acceptable. Overall, the calcu lated and the predicted values are similar and this showed that artificial neural network is also sufficient to be used for voltage stability monitoring purpose. T able 4. Details for line voltage stability indices based on line 5 – 6 at 55 seconds in IEEE 14-Bus test system. Inde x

Actual O utput

Pre diction O utput

Ne twork Error

Lmn FVSI LQ P VCPI (Powe r) VCPI (Loss) LCPI

1.036085 0.991151 1.081265 1.074979 0.681614 1.081265

1.036085 0.992118 1.080954 1.074977 0.681430 1.080954

0.000000 -0.000967 0.000311 0.000002 0.000184 0.000311

Error Pe rce ntage (%) 0.000000 0.097611 0.028734 0.000195 0.027010 0.028734


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The summaries for the line stability indices based on line 5 – 6 at 55 seconds are presented in Table 4. The first column of the table shows the type of the index. Besides that, column two showed the actual output values for the indices based on the calculation. Consequently, column three showed the prediction output values from the art ificial neural network model. Moreover, co lu mn four and five showed the network error and error percentage respectively. In general, the prediction outputs shown in Table 4 are similar with the actual output and this once again indicates artificial neural network is sufficient to be used for voltage stability assessment purpose. 5. Conclusion In the nut shell, the application of art ificial neural network h ad been executed in this paper as well. The main purpose for conducting this was to predict the most critical line in the power system based on the line voltage stability indices assessment. In the meantime, this artificial intelligent technique is sufficient to be imp lemented in the voltage stability monitoring purpose. Feed forward back propagation artificial neural network was used to predict the line voltage stability indices for the most critical line in three different types of power system test networks. The results showed that the implemented ANN model is indicative in forecasting the occurrence of system collapse and hence suitable prevention action can be taken beforehand to avoid voltage collapse. Acknowledgements The authors would like to thank the Ministry of Science, Technology and In novation, Malaysia (MOSTI), and the Office for Research, Innovation, Commercialization, Consultancy Management (ORICC), Universiti Tun Hussein Onn Malaysia (UTHM) for financially supporting this research under the Science Fund grant No.S023. References [1] Parkpoom, S., G. Harrison, and J. Bialek. Climate change impacts on electricity demand. in Universities Power Engineering Conference, 2004. UPEC 2004. 39th International. 2004. IEEE. [2] Belzer, D.B., M.J. Scott, and R.D. Sands, Climate change impacts on US commercial building energy consumption: an analysis using sample survey data. Energy Sources, 1996. 18(2): p. 177-201. [3] Wu, Y.-K., A novel algorithm for AT C calculations and applications in deregulated electricity markets. International Journal of Electrical Power & Energy Systems, 2007. 29(10): p. 810-821. [4] Rahi, O., et al., Power System Voltage Stability Assessment through Artificial Neural Network. Procedia Engineering, 2012. 30: p. 53-60. [5] Savulescu, S.C., Real-time stability assessment in modern power system control centers. Vol. 42. 2009: John Wiley & Sons. [6] Pourbeik, P., P.S. Kundur, and C.W. Taylor, The anatomy of a power grid blackout - Root causes and dynamics of recent major blackouts. Power and Energy Magazine, IEEE, 2006. 4(5): p. 22-29. [7] Bialek, J.W. Why has it happened again? Comparison between the UCTE blackout in 2006 and the blackouts of 2003. in Power T ech, 2007 IEEE Lausanne. 2007. [8] Ajjarapu, V., Computational techniques for voltage stability assessment and control. 2007: Springer. [9] Kundur, P., N.J. Balu, and M.G. Lauby, Power system stability and control. Vol. 7. 1994: McGraw-hill New York. [10] Azmy, A.M. and I. Erlich. Impact of distributed generation on the stability of electrical power system. in Power Engineering Society General Meeting, 2005. IEEE. 2005. IEEE. [11] Goh, H., et al., Power stability monitoring based on voltage instability prediction approach through wide area system. Americ an Journal of Applied Sciences, 2014. 11(5): p. 717-731. [12] Goh, H., et al., Comparative study of different Kalman filter implementations in power system stability. American Journal of Applied Sciences, 2014. 11(8): p. 1379-1390. [13]Wen, J., et al., Optimal coordinated voltage control for power system voltage stability. Power Systems, IEEE Transactions on, 2004. 19(2): p. 1115-1122. [14] Ajjarapu, V. and C. Christy, The continuation power flow: a tool for steady state voltage stability analysis. Power Systems, IEEE T ransactions on, 1992. 7(1): p. 416-423. [15] Balamourougan, V., T. Sidhu, and M. Sachdev. Technique for online prediction of voltage collapse. in Generation, Transmission and Distribution, IEE Proceedings-. 2004. IET. [16] McLinn, J., Major Power Outages in the US, and around the World. IEEE Reliability Society, 2009. [17] Kundur, P., et al., Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms an d definitions. Power Systems, IEEE Transactions on, 2004. 19(3): p. 1387-1401. [18] Kundur, P., J. Paserba, and S. Vitet. Overview on definition and classification of power system stability. in Quality and Security of Electric Power Delivery Systems, 2003. CIGRE/PES 2003. CIGRE/IEEE PES International Symposium. 2003. IEEE.

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