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Artificial Intelligent Application to Power System Protection Article · February 2001 Source: CiteSeer

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Artificial Intelligent Application to Power System Protection M.M. Saha Substation Automation Division ABB Automation Products AB SE-721 59 Västerås, SWEDEN

Abstract: The application of Artificial Intelligence (AI) methods in power system protection has been addressed in this paper. Particular emphasis has been put on Artificial Neural Networks (ANN) and Fuzzy Logic (FL). Several novel concepts have been introduced including ANN application to CT and CVT transients correction, fuzzy criteria signals, fuzzy settings and multi-criteria decision making for digital relays. Attached examples illustrate application of ANN and FL techniques to resolve the selected relaying problems such as the fault classification or CT and CVT dynamic error correction. Differential protection for power transformers is selected as an important example to show efficiency of the proposed concepts of FL and ANN application. I. INTRODUCTION The microprocessor technology brings unquestionable improvements of the protection relays- criteria signals are estimated in a shorter time; input signals are filtered-out more precisely; it is easy to apply sophisticated corrections; the hardware is standardized and may communicate with other protection and control systems; relays are capable of self-monitoring. All this, however, did not make a major breakthrough in power system protection as far as security, dependability and speed of operation are considered. The key reason behind this is that the principles used by digital relays blindly reproduce the criteria known for decades. The relaying task, however, may be approached as a pattern recognition problem - by monitoring its inputs, the relay classifies on-going transients between internal faults and all the other conditions. Or, the protective relaying may be considered as a decision making problem - the relay should decide whether to trip or retrain itself from tripping. This observation directly leads to AI application in power system protection [1-4]. Practically, it includes the artificial neural network approach (pattern recognition), as well as the expert system and fuzzy logic methods (decision making) [5-8]. This paper briefly reviews the general problems and constraints in power system protection and presents the basics of AI methods as applied to protective relaying. After general presentation of the problem (Section II), a brief description of the AI methods is given (Section III). Some examples of the AI approach to power system protection are presented in Section IV and V. The test results are also included in the paper.

E. Rosolowski

J. Izykowski

Department of Electrical Engineering Wroclaw University of Technology 50-370 Wroclaw, POLAND

II. PROBLEMS IN POWER SYSTEM PROTECTION The problems result mainly from the trade-off between the security demand (no false trippings), and the speed of operation and the dependability (no missing operations) requirements. The more secure is the relay (both the algorithm and its particular settings), the more it tends to misoperate or operate slowly. And vice versa, the faster is the relay, the more it tends to operate falsely. The problems listed below reflect the current practice in power system protection. There are basically two ways to mitigate the problem of limited recognition power of the classical relaying principles. One of them is to improve and extend the measurements available to a given relay (for example, optical CTs for improvement and substation integration for extension). The second way is to improve the recognition process itself based on what is already available and either: • search for the new relaying principles, or • apply several of known principles in one relay to improve the recognition, or • apply correction of the CT and CVT transient error, or • improve a type of fault determination by using of the ANNs classifier, or • use self-organizing algorithms such as ANNs to find out automatically a protection principle. It always takes certain time to estimate the criteria signals accurately enough to base the tripping decision on them. Either they are measured fast or accurately. There is no perfect digital measuring algorithm that solves this well known conflict between the speed and the accuracy. Either certain pre-filtering is applied, or the basic algorithm uses longer data window; or certain post-filtering is employed (or even a combination of these three means). There is always a level of uncertainty in the estimate of the criteria signal at the beginning of a disturbance when the relay operation is mostly wanted. In some situations, although unprecise, the value of the criteria signal enables solid decision, but is other cases, such as a fault at the end of the protection zone, the relay must wait for more precise estimate of the criteria signals. III. ARTIFICIAL INTELLIGENCE METHODS AI is a subfield of computer science that investigates how the though and action of human beings can be mimicked by machines [5]. Both the numeric, non-numeric and symbolic computations are included in the area of AI. The

mimicking of intelligence includes not only the ability to make rational decisions, but also to deal with missing data, adapt to existing situations and improve itself in the long time horizon based on the accumulated experience. Three major families of AI techniques are considered to be applied in modern power system protection [1,5]: • Expert System Techniques (XPSs), • Artificial Neural Networks (ANNs), • Fuzzy Logic systems (FL). A. Expert Systems The first expert systems included a few heuristic rules based on the expert's experience. In such systems, the knowledge takes the form of so called production rules written using the If... then... syntax (knowledge base). The system includes also the facts which generally describe the domain and the state of the problem to be solved (data base). A generic inference engine uses the facts and the rules to deduce new facts which allow the firing of other rules. The knowledge base is a collection of domain-specific knowledge and the inference system is the logic component for processing the knowledge base to solve the problem. This process continues until the base of facts is saturated and a conclusion has been reached (Fig.1). To guide the reasoning and to be more efficient, these systems may incorporate some strategies known as metaknowledge. Rule based systems represent still the majority of the existing expert systems. There are few applications of XPS to power system protection reported, but all of them solve the off-line tasks such as settings coordination, post-fault analysis and fault diagnosis [1]. As yet there is no application reported of the XPS technique employed as a decision making tool in an on-line operating protective relay. The basic reason for this is that there is no extensive rule base that describes the reasoning process applicable to protective relaying. Instead, only a few rules or criteria are collected [9]. B. Artificial Neural Networks

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The ANNs are very different from expert systems since they do not need a knowledge base to work. Instead, they have to be trained with numerous actual cases. An ANN is a set of elementary neurons which are connected together in different architectures organized in layers what is biologically inspired (Fig.2) [5]. An elementary neuron can be seen

like a processor which makes a simple non linear operation of its inputs producing its single output. A weight (synapse) is attached to each neuron and the training enables adjusting of different weights according to the training set. The ANN techniques are attractive because they do not require tedious knowledge acquisition, representation and writing stages and, therefore, can be successfully applied for tasks not fully described in advance. The ANN are not programmed or supported by a knowledge base as are Expert Systems. Instead they learn a response based on given inputs and a required output by adjusting the node weights and biases accordingly. The speed of processing, allowing real time applications, is also an advantage. Since ANNs can provide excellent pattern recognition, they are proposed by many researchers to perform different tasks in power system relaying for signal processing and decision making [2-5,7-8,10,12-13]. The common application of the ANN technique assumes: • The ANN is fed either with non-processed samples of the input signals, or by features of those signals extracted using certain measuring algorithms (or by a combination). • The sliding data widow consisting of the recent and a few historical samples of the signals, is fed to the ANN. • The output from the ANN encodes the output decision such as tripping command, type of fault, direction of fault, etc. • The training patterns exposed to the ANN cover the most important operating conditions both internal faults and other disturbances. Typically, only the selected window positions are used for training. • Additional pre- and post-processing may be applied. The most widespread application of ANNs is in pattern classification and associative memory where they can learn to distinguish between classes of inputs and, therefore, they can be successively used for decision making and phenomena classification. A major problem with ANNs is that no exact guide exists for the choice to the number of hidden layers and neurons per hidden layer. On the other hand, the ability to generalize is one of the main advantage of using ANNs.

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Fig.2. Typical three-layer architecture of a feed-forward ANN.

C. Fuzzy Logic

TABLE I. COMPARISON OF AI METHODS IN POWER SYSTEM PROTECTION

With reference to Fig.3 the fuzzy logic approach to protective relaying assumes [6]: • The criteria signals are fuzzified in order to account for dynamic errors of the measuring algorithms. Thus, instead of real numbers, the signals are represented by fuzzy numbers. Since the fuzzification process provides a special kind of flexible filtering, faster measuring algorithms that speed up the relays may be used. • The thresholds for the criteria signals are also represented by fuzzy numbers to account for the lack of precision in dividing the space of the criteria signals between the tripping and blocking regions. • The fuzzy signals are compared with the fuzzy settings. The comparison result is a fuzzy logic variable between the Boolean absolute levels of truth and false. • Several relaying criteria are used in parallel. The criteria are aggregated by means of formal multi-criteria decision-making algorithms that allow the criteria to be weighted according to their reasoning ability. • The tripping decision depends on multi-criteria evaluation of the status of a protected element. Additional decision factors may include the amount of available information, or the expected costs of relay maloperation. The XPS, ANN and FL approaches have their own advantages and limitations. Table I compares the basic features of these AI methods. IV. APPLICATION TO POWER TRANSFORMER PROTECTION As an examples authors have considered fuzzy logic and ANN application to differential transformer protection. The differential relaying principle in the case of a power transformer shows certain limitations - detection of a differential current does not provide a clear distinction between internal faults and other conditions. Inrush magnetizing currents, stationary overexcitation of a core, external faults combined with saturation of the CTs and/or CTs and protected transformer ratio mismatch are the most relevant phenomena which may upset the current balance causing the relay to maloperate. A. Fuzzy Logic application To enhance the performance of transformer protection a multi-criteria fuzzy logic based relaying frame may be used

Fuzzyfication

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Fig.3. Simplified block diagram of the fuzzy logic approach.

Feature Knowledge used Troubleshooting and improving a relay Self-learning Handling unclear cases Robustness Setting a relay

XPS Expert knowledge in the form of rules, objects, frames, etc. Change of rules required.

Possible. Possible. Not-critical and easy to ensure. Convenient.

Computations Extensive.

Approach ANNs Information extracted from the training set of cases. Difficult - the internal signals are almost impossible to interpret. Natural. Natural. Difficult to ensure. Large number of simulation required. Dedicated hardware.

FL Expert knowledge in the form of protection criteria. Convenient - the internal signals are understandable and analyzable Possible. Natural. Not-critical and easy to ensure. Convenient. Both knowledge and simulation are used. Moderate.

[1,6]. The relay presented in [11] uses 12 protection criteria which indicate the following transformer states (Fig.4): • Magnetizing inrush • Stationary overexcitation of a core • External faults combined with saturation of the CTs • External faults without saturation of the CTs. The relay uses fuzzy settings and multi-criteria decision making methods and is self-set using the EMTP simulations. The selected example concerning the 140/10.52kV, 5.86 MVA three-phase two-winding transformer demonstrates both the relay stability and sensitivity. Fig.5 displays the differential and through currents as well as the tripping signal for the turn-to-turn internal fault occurring after 50ms of transformer energizing and involving 16% turns of the Yside winding on the column S. The relay activates at the beginning of energizing but remains blocked during inrush conditions. The tripping command is sent 16ms after the fault inception. B. ANN application The paper [10] presents a wide optimization process involving the type of an ANN as well as both pre- and postprocessing algorithms for the power transformer protection. M E A S U R I N G U N I T

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Fig.4. Simplified block diagram of the considered fuzzy logic relay for power transformers.

Fig.5. Fuzzy Logic based relay operation under sample turn-to-turn fault occurring during energizing of the transformer.

With reference to Fig.6, a neural network relay (NNR) consists of three basic units: a pre-processor, an ANN itself and a post-processor. In the case of a power transformer, a differential relay measures the currents on all the sides through the Current Transformers (CTs) - and sometimes also the voltages through the Voltage Transformers (VTs). These signals are pre-filtered using the analog anti-aliasing filters and next sampled (in this paper 2nd order RC circuit with cut-off at 350Hz; sampling at 1kHz). The differential and restraining currents are next formed according to the art of differential relaying for power transformers. Three different levels of pre-processing have been developed and tested: - NNR without essential pre-processing; - with separation of 12 criteria signals; and - with using of settings obtained from comparison of the criteria signals with their appropriate thresholds. In the first case an ANN is fed just by samples of differential and restraining currents and operates as a protective relay for power transformer. The length of the sliding data window and the set of employed signals are the only parameters to be optimized in such an approach. In the second approach a NNR may combine the advantages of the ANN technology with the expert knowledge having the form of relaying principles. The relaying criteria (such as 2nd harmonic restraint) enable to convert the natural relaying signals into the space of features to be fed into an ANN itself. In the paper, the feature universe consists of 8 criteria signals (per phase), achieved by applying 12 pro&7V

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tection criteria (as in fuzzy logic approach). In the criteria-based approach, the pre-processor converts the natural relaying signals into 8 features. These signals, are always real positive and either their low or high values indicate on an internal fault. These criteria signals are next fed into an ANN. Only the most recent samples are forwarded (no sliding data window). To analyze logic signals produced by comparison of an appropriate settings, instead of using an output logic circuit, an ANN based classifier is employed. Each criterion is aptimized individually prior to usage with the objective to minimize the percentage of missing and false indications as well as the provided average identification time [11]. No sliding data window is applied - only the most recent samples are fed. Two methods was analyzed for determination of the final decision (post-processing) of the relay: natural postprocessing and filter application. Since only two classes of disturbances are relevant in power system protection (internal faults and other conditions), ANNs operating as protective relays are usually trained to respond with two distinctive values, respectively. Thus, to analyze the output from an ANN only the thresholding is needed. Such kind of postprocessing was denoted as natural post-processing. Instead of this also filters was used: mean-value filter (averaging of the result) and median filter. For all considered cases the feed-forward three-layer fully interconnected sigmoidal ANNs was used. Two basic configurations of a NNR of a three-phase power transformer was tested: with single ANN for each transformer phase and with ANN observing the three phases. The actual number of training patterns presented to ANNs count in tens of thousands - due to the three-phase structure of a protected transformer and the sliding data window of a NNR. Wide comparative analysis with different NNR structure has been provided. Majority of the developed NNRs handle well the special testing cases [10]. V. APPLICATION TO CT AND CVT CORRECTION Certain construction limitations of the instrument transformers may in some cases cause maloperation or substantial delay in tripping of the protective relays. One of the method for correction of CTs saturation con-

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Very generally, the dynamics of a CVT is determined by two factors: • nonlinear oscillations under saturation of magnetic core of the CVT step-down transformer, • discharging of the CVT internal energy during short circuits on an associated transmission line. The greater influence has the second source of transient error. Especially, faults at zero crossing of the primary voltages result in substantial transient errors that, in turn, affect the operation of supplied relays. The idea of CVT transients error compensation presented in [12] is based on finding of an inverse transfer function of the CVT model and reproducing it with an ANN - what is similar to the approach used for CT correction. Therefore, the proposed ANN corrector has general structure as in Fig. 7. Different ANN sizes and input/feedback connections have been tested and analyzed. As an example, Fig. 9 presents result of CVT correction during phase-to-ground fault at the 400 kV substation. The fault is applied when the primary voltage of the faulted phase crossing zero.

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Fast fault detection and classification of the fault type is one of the most important task of the protection relays and fault location function. The basic idea of the fault type estimation consists in analysing of the phase and zero-sequence voltages and currents. The ANNs have good pattern recognition and classification feature and that is what is here expected. The proposed neural fault type estimator (NFTE) consists of 4 neural networks: three recurrent nets for particular phase fault detection and the fourth feedforward one for fault to ground recognition [13]. The NFTE uses feature vectors formed by V (voltage) and I (current) trials. The architecture

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of the NFTE for a faulty phase selection is sketched in Fig.10. The ANNs are free layer networks with activation functions of both hidden layers of hyperbolic tangent type and linear functions in output neurons. The nets work in parallel indicating faulted phases (the nets ANN-Ph - Fig.10) and eventually fault to ground events (the net ANN-G - not presented in Fig.10). Changes of outputs of particular ANN-Ph classifiers from -1 to 1 indicate fault detection in scanned phase and changes of output of fault to ground detector inform about faults as follows: R-G, S-G, T-G, R-S-G, R-T-G or S-T-G. The decision threshold in both detectors equal to 0 has been chosen. Introducing only absolute values of voltage and current samples at the input (Fig.10) reduces number of different patterns to be analysed. Usage of ANNs with the feedback connection makes the output signal from ANN-Ph more stable and the decision taken more reliable [13].

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VII. CONCLUSIONS The paper reviews the AI approaches to power system protection and focuses on the application of ANN and fuzzy logic techniques. A number of novel application and concepts have been presented including fuzzy logic approach to differential transformer protection and ANN application to the transformer protection, CT and CVT transients correction, and. fault-type classification. Included examples demonstrate application of the AI methods and their features. REFERENCES [1]

International Journal of Engineering Intelligent Systems, The special issue on AI applications to power system protection, edited by M.M. Saha and B. Kasztenny, Vol.5, No.4, December 1997, pp.185-93.

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Dalstein T., Kulicke B., Neural network approach to fault classification for high speed protective relaying. IEEE Transactions on Power Delivery, Vol.10, No.2, April 1995, pp.1002-1009. Sidhu T.S., Singh H., Sachdev M.S., Design, implementation and testing of an artificial neural network based fault direction discriminator for protecting transmission lines. IEEE Transactions on Power Delivery, Vol.11, No.2, April 1995, pp.697-703. Sanaye-Pasand M., Malik O.P., Performance of a recurrent neural network-based power transmission line fault directional module. Eng Int Syst (1997) 4, pp. 221-228. Warwick K., Ekwue A. And Aggarwal R. (ed). Artificial intelligence techniques in power systems. The Institution of Electrical Engineers, London, 1997. Saha M.M, Kasztenny B., Application of fuzzy logic in power system protection, International Conference 'Modern Trends in the Protection Schemes of Electric Power Apparatus and Systems', 28-30 October 1998, New Delhi, paper IX-1. Bachmann B., Novosel D., Hart D., Hu Y., Saha M. M., Application of artificial neural networks for series compensated line protection, Proc. of the Int. Conf. on Intelligent System Application to Power Systems, Orlando, January 28 - February 2, 1996, pp.68-73. Lukowicz M., Rosolowski E., Artificial neural network based dynamic compensation of current transformer errors. Proceedings of the 8th International Symposium on Short-Circuit Currents in Power Systems, Brussels, 8-10 October 1998, pp. 19-24. Wiszniewski A. and Kasztenny B., Primary protective relays with elements of expert systems, Proceedings of the 1992 CIGRE Session, Paris, France, August 1-5, 1992, Paper 34,2,CN. Kasztenny B., Lukowicz M., Rosolowski E., Selecting type of a neural network, pre- and post-processing algorithms for power transformer relaying. Proceedings of the 32nd Universities Power Engineering Conference, Manchester, UK, Sept. 10-12, 1997, vol. 2, pp. 708-712. Kasztenny B., Rosolowski E., Saha M. and Hillstrom B., A self-organizing fuzzy logic based protective relay - an application to power transformer protection, IEEE Transactions on Power Delivery, Vol.12, No.3, July 1997, pp.1119-27. Lukowicz M., Rosolowski E., Izykowski J., ANN application to CVT transient errors compensation. 3U]HJOG Elektrotechniczny, R.LXXV 9/1999, s. 224229 (in polish). Lukowicz M., Rosolowski E., Fault type classification in high voltage power systems using artificial neural networks. Proceedings of the 11th International Conference on Power System Protection PSP'98, Bled, Slovenia, 30. Sept. - 2. Oct. 1998, pp. 141-146.