Neural Networks for Power System Condition Monitoring and Protection B. Cannas, G. Celli, M. Marchesi and F. Pilo Department of Electrical and Electronic Engineering - University of Cagliari Piazza d’Armi 09123 CAGLIARI - ITALY phone +39-70-6755883, +39-70-6755868; fax +39-70-6755900
[email protected],
[email protected],
[email protected],
[email protected], Keywords: Locally recurrent-globally feed-forward neural network, Custom Power, Power Quality, Neural Prediction ABSTRACT To maintain a good voltage quality level to customers in power distribution system, it is essential to minimise transients, line voltage dips and spikes due to a variety of causes, including fault occurrence, power interruption and large load changes. In case of private generating systems it is essential that ultra-rapid switching devices be used which cut off the customer plant from the utility system so quickly that the presence of voltage dips is not perceived by the industrial plant’s sensitive loads. These devices require very fast acquisition and control systems which permit to diagnose and, possibly, predict abnormal events. In this paper a control methodology based on a locally recurrent-globally feedforward neural network and on a neural classifier is proposed. It will be shown that it is possible to predict with good accuracy the value of the control variables based on previously acquired samples and use these values to recognise the kind of abnormal event that is about to occur on the network. INTRODUCTION In the early development stages of electric power distribution systems, the issue of supply quality was not addressed with the due importance. Today this aspect is gaining increasing importance owing to the fact that more sensitive loads are connected to the electrical system, and represents the aim of the modern Custom Power concept [1]. This term describes the value-added power that electric utilities will offer their customers by the application of power electronic controllers to utility distribution systems and/or at the supply end of many industrial and commercial customers. Nevertheless, no electric utility can be expected to provide perfect power supply to customers insofar as disturbances may occur in the power system beyond its control. Of these disturbances, voltage dips are becoming an increasing concern for process industries due to increasing automation. Automated facilities are more difficult to restart, and the electronic controllers used are sometimes more sensitive to voltage dips than other loads. For passive systems measures to reduce the effect of voltage dips on customer loads should be taken based on locality and system connectivity, resorting to installation of Static Var Compensator (SVC) at strategic points in the system or, for very sensitive loads which cannot tolerate voltage dips of any duration, to use of on-line UPS (Uninterruptable Power Supply) or Dynamic Voltage Restores (DVR). On the other hand, for systems equipped with local sources interconnected with an electrical utility system, sensitive loads could be efficiently protected by means of high speed protection equipment. Consequently the industrial plant could be rapidly disconnected from the utility network so that the duration of voltage dips, due to the intervention delay of the interfacing device, is considerably reduced and the industrial process is not affected. In any case, all systems that have to cope with voltage dips and overvoltages are required to intervene as quickly as possible to diagnosis the problem and to implement protective measures. For this purpose it is possible to resort to switching devices based on Solid-State circuit Breakers (SSB), to be used as interface between the electrical utility system and the private generating system with sensitive loads, ‘intelligently’ controlled by artificial neural networks. They are software routines running on fast computers which play nowadays an important role in solving Power System Engineering problems. For the aim of the paper they can be taught to predict different system states by monitoring the most appropriate state variables; then, these predicted variables are processed to recognise the event, using a particular diagnostic strategy. In this way the time required to detect the fault can be reduced to a fraction of a millisecond, compared to the few tens of milliseconds taken by modern protective relays. In fact, this control system does not need to wait for the abnormal event to evolve to perceive its presence but, from the very first samples of the monitored variables, it is already able to forecast how they will behave and decide whether to intervene or not. This result is essential for avoiding that solid-state breakers could be subjected to dangerous stresses and for minimizing voltage dip transients before the protection devices intervene. Furthermore, implementing a suitable control logic procedure, it is possible to realise a controlled interruption of the energy flowing through the power line, enabling voltage spikes to be constrained within acceptable limits [2,3].
It is important to underline the capability of the neural network to generalise its prediction and detection processes to events that it has not learnt before, allowing the control system to be more flexible and efficient than a standard classifier program. SYSTEM MODEL The power system studied is shown in Fig. 1, where the most important electrical devices are described in detail. The loads of the industrial plant have been divided into normal and essential loads. The latter comprise the B6 20 kV machinery that has to be continuously supplied so as not to interrupt the industrial process. The power system of the 20 km industrial plant which includes the private generator and B5 essential loads is said to be an “island network” insofar as M V load it can operate separately from the utility network. 1600 kW U TILITY N ETW ORK cosϕ ϕ = 0.9 1 Normally, the industrial plant [4] is provided with a master 5 km switch immediately after the utility network electric power B4 delivery point, with an interface switch installed at the 630 kVA point where the island network is connected to the I NDUSTRIAL P LANT Vsc=4 remainder of the industrial plant, and with a generator % B3 switch installed after the terminals of each generator. M ain Switch In light of the above, it is clearly required to secure power B2 quality to the essential loads. Thus, in the event of a fault or Normal Loads Interface Switch a voltage dip on the power line, the interface switch should B1 Essential Loads Generator Switch promptly ensure a very fast decoupling so that the essential loads do not notice the presence of voltage dips. G Referring to Fig. 1, the behaviour of this system has been studied for a three-phase short circuit located in position 1. Figure 1 Power system studied The system has been simulated using the EMTP (ElectroMagnetic Transient Program) power system simulator, that permits to simulate electric and electromechanical transients in electrical installations. The interfacing device is usually an electromechanical switch, controlled with suitable relays. The voltage dip duration on the essential loads due to actuation of the device is the sum of the time required by the relay to recognise the fault plus the turn-off time of the switch. Usually it is of about 100÷120 ms, that the most part of sensitive loads cannot tolerate. To reduce this duration, it is possible to use high power solid state circuit breakers based on semiconductor static switches (IGBT), that perform the turn-off phase within few µs. By using an adequate snubber circuit, a smooth disconnection of the two networks is achieved and the overall clearing time is held within half cycle. Moreover, the voltage on busbar B1 is restored to its nominal value immediately after the fault is perceived reducing the dip duration to the detection time [3,5]. Technological advances in the field of static switching, together with the availability on the market of solid state circuit breakers for systems with power in the order of hundreds kVA, has made possible the use of these devices in high power applications. B7
150 kV
CONTROL SYSTEM Prompt action can only be achieved by resorting to a control system that ensure rapid identification of a fault event. Overcurrent relays are not well suited for the proposed application because the protection system has to detect faults in the MV utility network far from the private generation point and therefore fault currents are often too small to allow a rapid action of interface devices. A neural controller based on two different neural networks is able to distinguish among different system states and to command the protection device to intervene in very short time. The first is designed to predict the value of some electrical quantities, the second is used to classify the type of event existing in the power system (normal operating conditions, fault or overload) as a function of the value of predicted quantities. Neural predictor To predict the behaviour of the electrical quantities concerned over time, so as to ensure reliable service to the sensitive loads, it is decided to resort to predictive diagnostic systems based on neural networks having an architecture known as “locally recurrent-globally feedforward” [6-8] that seems to be more suitable than the Multi Layer Perceptron (MLP) neural network. Indeed, the MLP allows the reproduction of a general non linear function but this non linear approximation is static because each input-output couple in the training set is considered as independent from each other. Obviously this procedure is unsuitable when the output depends not only on the current input but also on the previous history of the system.
On the other hand, the application proposed in this paper requires that the solution takes into account the existing link among the current output and the previous inputs and outputs. This goal may be achieved using globally recurrent neural networks, which have feedback connections among all the network layers. However, this kind of networks is very difficult to train and requires a large amount of memory. Thus, a locally recurrent-globally feedforward network architecture has been implemented. These networks are characterised by a feedforward structure whose synapses between adjacent layers have taps and feedback connections. The locally recurrent architecture for a very simple network is shown in Fig. 2 where a synapse having two feedback connections is depicted. x 1 ( 0 )(t)
+
s 1 ( 1 )(t)
sgm
x 1 ( 1 ) (t)
w1 ) (2 1)
1(
z -1 w 1 1 ( 2 )( 2 )
+
y 1 ( 2 ) (t)
w
sgm
x 1 ( 2 ) (t)
z -1
(2 (2 ) )
z -1
11
x 2 ( 0 )(t)
v 1 1 ( 1 )( 2 ) z -1 v 1 1 ( 2 )( 2 )
Hidden Layer ( l=1)
Output Layer (l=2)
Figure 2 A simple locally recurrent network with two inputs and one output The synapse outputs depend on the previous inputs and, if feedback connections exist, they depend also on the output past values. A rigorous description of the mode of operation of these networks is published in [8]. The learning algorithm used is based on the minimisation of the global instantaneous square error. The weights can be adjusted using a simple gradient method derived by the application of the chain rule in locally recurrent networks [6,7]. By applying this learning algorithm, the network is trained to simulate the behaviour of the electric system both in the event of a fault and of a sudden overloading. At the end of the training procedure the network is in fact able to solve on line the non-linear and time variant integral differential equations governing the system in abnormal operating conditions or when abrupt load variations occur. Moreover, if it is trained properly, the network is also able to predict the value of the control variables at time t+1 as the values at t, t-1, t-2, ..., t-N are known, where N is the network memory amplitude. System state plane Once the values of the control variables in the instant immediately after the one considered have been predicted, it is possible to establish whether the system is at risk or not. In order to promptly re-establish voltage on the essential loads and limit the duration of voltage dips, the interface switch should be turned off using currents no higher than the nominal value. On the other hand, if only the current i(t) were monitored as control variable for the state of the switch, the problem would arise of inappropriate actions during high load insertion transients. This problem may be solved by using the instantaneous values of both the current i(t) and its time rate, as well as of voltage, to diagnose the system state. Fig. 3 shows an example of graphic representation of a system’ state-plane taking as coordinates the instantaneous current i(t) and its time rate di(t)/dt. For convenience the derivative values are shown in the scale (1/ω), where ω is the angular frequency of system voltage. In steady conditions the system state is represented by a point that follows a clockwise path on circular curves, with centre in the origin O and radius equal to the current amplitude, with angular frequency of ω. In Fig. 3 three concentric circles are shown corresponding to the rated current Ir, the maximum overload Iov, tolerated only for limited duration, and the short circuit fault current I f. Studying the transient following abrupt load variations that determine the transition between two different steady currents, it is possible to find the most severe situation for maximum transient current i(t) that does not need to be immediately interrupted by the interface device. The turnoff procedure should be started immediately (fault or excessive overload condition) if the system state moves out of the area enclosed by the curves ABCD and A'B'C'D', Figure 3 System state-plane. while it should be delayed when the system state first enters
into the areas EBCD and E'B'C'D' (tolerated overload condition). A rigorous approach to define the regions and the logic of the interventions of the interface device is developed in [3]. The Neural Classifier Once the control variables have been properly predicted, the second control phase is the identification of the operative state in the electrical power system. The need to employ a neural network to perform this kind of classification is due to the fact that states to be recognised are often near, for example a short circuit on the utility line far from the industrial plan or a small increase in the power demand of loads lead to variations in the control variables which is very difficult to recognise properly with thresholds controllers. Moreover, the capability of neural networks to generalise and to identify states not employed in the learning phase permits to obtain very good control system performances when the fault position varies, the power demand changes and electrical network in the industrial plant is subjected to limited entity variations. As depicted in Fig. 4, the input variables for the neural classifier are voltage v(t), current i(t) and the discrete predicted current rate calculated by using the neural predictor outputs. Each input is constituted by its instant value and by the three most recent past values stored in a suitable memory register; this choice is due to the need of monitoring the behaviour of physical quantities during the changes between different operating states. Due to this choice, the neural network has 12 input neurons and 3 output neurons, each of them represents a particular operating system state, while the number of the hidden neurons is one of the variables to choose in order to optimise the control system functioning. The classical backpropagation algorithm has been employed to train the neural network using a suitable set of training parameters achieved with a large number of power system simulations. To improve generalisation capability a cross validation test has also been performed using a sequence of states not employed in the learning phase as input pattern. Control system logic The control system logic is depicted in Fig. 4. v(t)
i(t)
v(t + 1)
IIR Neural Network
Short Circuit i(t +1)
i(t +1)
di(t + 1) dt
1 ∆t
+ -
MLP Neural Network
Overloading Normal operating
i(t)
Figure 4 Control system model The samples of the electrical quantities in question (voltage v(t) and current i(t) in the busbar B1 - Fig. 1), measured by means of a rapid sampling (i.e. 20 kHz) data acquisition system, are then processed by the locally-recurrent neural network to predict their trend. The predicted current value i(t+1), is inputted to a simple comparator along with its last predicted value i(t), to calculate its discrete derivative over a time interval equal to the sampling interval (i.e. 50 µs). At this point, the predicted current values and their derivatives, as well as the predicted voltage values, are used as input for the classifier in order to determine, on the basis of the model implemented therein, the state of the electrical system being monitored. The classifier has three binary output neurons and only one of them is fired depending on the type of event recognised (short circuit, overloading or normal operation). Using this reply, the control system will open or shut the solid state switches of the interface device. RESULTS State prediction The described strategy has been applied referring to the power system depicted in Fig. 1. By simulating different operating conditions with the EMTP program it is possible to know the control variables that should be employed to train the neural network. After the learning procedure the neural network, whose size is achieved by means of heuristic techniques, is able to predict with high accuracy the control variables both for the training set and the validation one.
The behaviour of the control variables is depicted in Fig. 5-6; as can be easily noticed the electric variables are predicted with really high accuracy even in the event of an overloading. In Fig 7-8 the behaviour of current, voltage and current rate in the short circuit case is shown; the prediction is very good and it must be observed that the neural network has not been trained on this case. 2000
Voltage and current [p.u.]
1
di(t)/dt [A/s]
1000
0
0
1 0.048
0.05
0.052
0.054
0.056
0.058
0.06
0.062 0.062
time [s]
0.0624
0.0626
0.0628
0.063
Estimate current rate Real current rate
Figure 5 Control variables in the event of a sudden overloading
Figure 6 Control variables rates in the event of a sudden overloading
2000
di(t)/dt [A/s]
1 voltage and current [p.u.]
0.0622
time [s]
Predicted current Predicted voltage Real current
0
1000
0 1 0.086
0.088
0.09
0.092
0.094
0.096
0.098
time [s]
Predicted current Predicted voltage Real current
Figure 7 Control variables in the event of a short circuit
0.0975
0.0977
0.0979
0.0981
0.0983
0.0985
time [s]
Estimate current rate Real current rate
Figure 8 Control variables rates in the event of a short circuit
In Fig 5-7 the real current curve has been lightly down shifted in order to make them more clear but the real and the predicted curves are almost overlapped. The high accuracy for the predicted electric variables allows the neural network to recognise the presence of a fault and to distinguish between short circuits and overloads, after only few samples from the beginning of the abnormal event. Thus, the time required by the control system to intervene is constrained into 150÷200 µs, permitting to disconnect the industrial plant from the utility network without the essential loads can perceive any voltage dip. State Recognition The neural network classifier described above has been trained using a training set and a validation one achieved with different simulations of electrical power system in different operating states. The proper choice of the training set has a great impact on the neural network effectiveness, for this reason it is important that the examples presented to the neural network are representative of the most important situations that may happen in the power system. In this application a training set and a validation one of 135 different examples has been developed with the aid of the EMTP program. Both the training set and the validation one have the same number of examples coming from short circuits, overloads and normal functioning for three different power system configurations that involve different current values in the busbar B1. In order to consider the impact of large transients phenomena, strong overloads due to the insertion of capacitor banks have been simulated and employed in both the training and the validation set. Short circuits and overloads happen in a randomly chosen time instant within the period of electrical quantities. To ensure good generalisation, during the neural network training, a separate set of test date (validation set) is supplied as input to the net and its performance is evaluated, making the forward pass and calculating the error. When the error on the validation sample goes up, training is stopped and the weights that produce the lowest error on this sample are saved. In order to choose the optimal neural network architecture the training phase with the cross validation test has been performed using neural nets differing for the number of neurons in the hidden layer. The optimal configuration has 5 neurons in the hidden layer and after 990 epochs is able to classify with high accuracy the state of the electrical system being monitored, both for the training set and for the validation one. The percentage of success is 100% for the training set and 99% for the validation set. However, even when the classification is less than perfect, the neural network is able to correctly decide whether to intervene or not under any condition, due to the fact
that the short circuit conditions are always recognised, and the errors only concern ambiguous situations of small variations in 1 power demand that are regarded as normal conditions. 0.5 Moreover, a random noise equal to ±10% of its value is subsequently added to the input pattern; it can includes 0 inaccuracies in the data that may be introduced by measuring instruments or by the predictor. The classifier is able to recognise -0.5 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 with very good accuracy the kind of event that is about to occur on the network also in this noisy situation. 400 Figure 9 depicts voltage in the busbar B1 with the presence of a very high harmonic pollution. As it may be easily noticed the 200 control system is able to detect the presence of a fault and to 0 command the interface switch to disconnect sensitive loads (open command equal to 1). Considering that only 150 µs are necessary -200 to identify the abnormal event, sensitive loads are disconnected -400 so quickly that voltage dip is not perceived. These features have 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 been found for all the situations examined [5]. Therefore, the Time [s] achieved results clearly show that the control system composed of Fig. 9 - Intervention of the control system in an the predictor and the classifier is able to make correct decisions harmonic pollution case. under short circuit conditions and to command to open the solid state switches of the interfacing device in a very short time even in critical situations with harmonic pollution. Voltage in B1 [V]
Open Command
1.5
CONCLUSION Power and voltage quality level for various categories of sensitive loads can be significantly improved by means of the application of ultra rapid switching devices and intelligent control system. The protection effectiveness achieved is due to the high speed of both solid state relays employed as breaking switches and the control system which, by resorting to modern neural strategies, is able to single out different system states with negligible time delays. In this paper a neural control system, employed to predict abnormal events and to control disconnection procedures, is described. It is shown that locally-recurrent globally feed-forward nets are able to diagnose faults within 150-200 µs, and to improve the reliability of the protection system. A state classifier based on a simple MLP neural network is able to recognise with very good accuracy the kind of event that is about to occur on the network also in noisy situations. The achieved results clearly show that the control system composed of the predictor and the classifier is able to make correct decisions under short circuit conditions and to command to open the solid state switches of the interfacing device in a very short time. Further researches will deal with the development of a scale model of the system. REFERENCES [1] N. G. Hingorani, Introducing Custom Power, IEEE Spectrum, (June 1995) 41- 48. [2] G. Celli, M. Marchesi, F. Mocci and F. Pilo, Application of neural networks in power distribution systems diagnosis and control, Proc. UPEC'97, Manchester, 1997, 523-526. [3] G. Celli, F. Mocci, R. Sannais and M. Tosi, Voltage dips and short interruption mitigation by modern techniques, Proc. POWER QUALITY - Assessment of Impact Conference - CIGRE 97, New Delhi, India, 1997, III.1-III.11. [4] Italian Standard CEI 11-20, number 1444, January 1991. [5] G. Celli, F. Pilo, R. Sannais and M. Tosi, Voltage quality improvment by custom power devices: applications of solid state breakers and neural controllers”, Proc. SPEEDAM’98, Sorrento, 1998, C4.25-C4.30 [6] A. D. Back and A. C. Tsoi, A simplified gradient algorithm for IIR synapse multilayer perceptrons, Neural Computation 5 (1993) 456-462. [7] P. Campolucci, F. Piazza and A. Uncini, Causal backpropagation through time for locally recurrent neural networks, Proc. ISCAS’96, IEEE Int. Symposium on Circuit and Systems, Atlanta, 1996. [8] S. Cincotti, A. Fanni, M. Marchesi, F. Pilo and M. Usai, Performance analysis of locally recurrent neural networks”, Proc. ISTET ′97, Palermo, 1997, 423-426.
BIOGRAPHY
Barbara Cannas was born in Cagliari, Italy, in 1971. She received the Dr. Eng. degree in Electrical Engineering from the University of Cagliari in 1996; she is a Ph. D student from 1996. Her major field of interest includes neural networks, cellular neural networks and optimisation problems. Gianni Celli was born in Cagliari, Italy, in 1969. He is Assistant Professor in the Power System group at the Dept. of Electrical and Electronic Engineering of the University of Cagliari, Italy. He holds this position from January 1997. He graduated in Electrical Engineering at the University of Cagliari in 1994 and since 1996 he has been research collaborator of Power System group at the same University. Current research interests are in the field of MV distribution network planning optimisation, Power Quality and use of Neural Networks in the field of Power System. He is author of several papers presented in various international conferences. Michele Marchesi is professor of Electrical Engineering, and head of the Circuits, Fields and Systems Group at the Dept. of Electrical and Electronic Engineering of the University of Cagliari, Italy. He graduated in electronic engineering in 1975 from the University of Genoa, and in applied mathematics in 1980 from the same university. From 1976 to 1987 he was researcher at the Institute for Electronics Circuits of National Research Council (CNR) in Genoa. He was associate professor of Electric Network Theory from 1987 to 1990 at the Department of Electronics and Automatics, University of Ancona, and from 1990 to 1994 at the Department of Biophysics and Electronic Engineering University of Genoa. Since November 1994 he is professor of Electrical Engineering in Cagliari. He is author or co-author of more than one hundred international papers in the fields of analog and digital circuits, electrical modelling, optimisation for fields and circuits, neural networks and object-oriented programming. Fabrizio Pilo was born in Sassari, Italy, 1966. He received the Dr. Eng. degree in Electrical Engineering from the University of Cagliari in 1992. He became Assistant Professor of Electrical Engineering in 1995 at the Department of Electrical and Electronics Engineering of the University of Cagliari. His major field of interest includes electrical power systems, network planning and optimisation and neural networks. He is author of several papers published on international journals or presented in various international conferences. EMBED
