Power System Restoration Using Artificial Neural Networks
Chapter One
OBJECTIVE OF THE STUDY
The goal of this thesis is to propose an integrated method to perform each of these tasks using artificial neural networks. A back-propagation based neural network has been used for the purpose of fault detection and another similar one for the purpose of fault classification in transmission lines. To achieve this, we need to design, develop, test and implement a complete strategy for the fault diagnosis in order to restore transmission lines back to service. The first step in the process is fault detection. Once we know that a fault has occurred on the transmission line, the next step is to classify the fault into one of the different categories based on the phases that are faulted. Then, the third step is to pin-point the position of the fault on the transmission line.
CHAPTER TWO
LITERATURE REVIEW
This chapter gives a literature survey that provides an overview of the relevant areas in restoration and artificial neural networks. It is subdivided into thirteen different topics. The first topic provides a general overview of the power system and representation of power system. It is followed by the general overview of restoration process, goals and steps in restoration. Then followed by problems in restoration and conventional restoration techniques. This is then followed by artificial neural networks, the application of artificial neural networks to power system restoration, artificial neural network based restoration scheme using a case study of Island restoration scheme, restoration constraints, power system restoration case studies. Then followed by power system protection stream lining it to transmission lines by learning several techniques used to locate faults and restoring lines back to service.
POWER SYSTEM OVERVIEW
Electric power systems may be of great complexity and spread over large geographical area. An electric power system consists of generators, transformers, transmission lines and consumer equipment (loads). The majority of these systems rely upon three-phase AC power – the standard for large-scale power transmission and distribution across the modern world. Specialized power systems that do not always rely upon three-phase AC power are found in aircraft, electric rail systems, ocean liners and automobiles. The system must be protected against flow of heavy short-circuit currents which can cause permanent damage to major equipment by disconnecting the faulty section of system by means of circuit breakers and protective relaying.
It is necessary to know the maximum short-circuit currents that can occur at the different points of a system in order that circuit breakers may be selected that are adequate to withstand the currents and operate successfully to cut out the faulty section, and also in order that the protective relays may be selected for correct operation. The design of machines, bus-bars, isolators, circuit breakers etc, is based on the consideration of normal and short-circuits currents
It is also necessary to be able to calculate, approximately at least, the size of the protective reactors which must be inserted in the system to limit the short-circuit currents to a value which is not beyond that capable of being withstood by the circuit breakers.
The short-circuit currents in an AC system are determined mainly by the reactance of the alternators, transformers and lines up to the point of the fault in the case of phase to phase faults. When the fault is between phase and earth, the resistance of the earth path plays an important role in limiting the currents.
In the case of circuit breakers, their rupturing capacities are based on the symmetrical short-circuit current which is the most simple calculation among all types of short-circuits. However, for determination of settings of relays it is absolutely necessary to know fault current due to unsymmetrical fault condition too for which knowledge of symmetrical components etc. is required [8].
REPRESENTATION OF POWER SYSTEMS
A complete diagram of power system representing all the three phases becomes too complicated and cumbersome for a system of practical size, so much so that it may no longer convey the information it is intended to convey. It is much more practical to represent a power system by means of simple symbols for each component resulting in what is called a single line diagram.
Single Line Diagram. The single line diagram of a power system network shows the main connections and arrangements of the system components along with their data such as output rating, voltage, resistance and reactance etc. in case of transmission lines sometimes the conductor size and spacing are given. It is not necessary to show all the components of the system on a single line diagram, e.g., circuit breakers need to be shown in a load flow study but are must for a protection study. In a single line diagram, the system components are usually drawn in the form of their symbols. Generators and transformer connections-star, delta and neutral earthing are indicated by symbols drawn by the side of the representation of these elements. Circuit breakers are represented by rectangular blocks. Fig 2.1 represents the single line diagram of a typical power system. The ratings of generator, motor and transformers are given below the diagram [8].
CHAPTER THREE
METHODOLOGY FOR RESEARCH
As discussed in the previous chapter about artificial neural networks been used for the protection of power transmission lines. The excellent pattern recognition and classification abilities of neural networks have been cleverly utilized in this thesis to address the issue of transmission line fault location and restoration of lines after fault.
In this chapter, a complete neural-network based approach has been outlined in detail for the location of faults and restoration of power transmission lines in a power system. To achieve the same, the original problem has been dealt with in three different stages namely fault detection, fault classification and fault location.
FAULTS IN POWER SYSTEM
A fault in an electrical equipment/apparatus is defined as a defect in the electrical circuit due to which current is diverted from the intended path.
The nature of a fault simply implies any abnormal condition which causes a reduction in the basic insulation strength between phase conductors or between phase conductors and earth or any earthed screen surrounding the conductors. Actually the reduction of insulation strength is not considered as a fault until it creates some effect on the system, i.e, until it results either in excessive current or in the reduction of the impedance between conductors or between conductors and earth to a value below that of the lowest load impedance normal to the circuit [79].
In an electrical power system comprising of generators, switchgears, transformers, power receivers and transmission and distribution circuits, it is inevitable that sooner or later some failure will occur somewhere in the system. The probability of the failure or the occurrence of abnormal condition is more on power lines-about one-half of the faults occur on the power lines. This can be explained that the power lines are widely branched, have greater length, operate under variable weather conditions and are subject to the action of atmospheric disturbances of electrical nature.
According to the causes of incidence, causes of failures may be classified, as mentioned below,
- Breakdown may occur at normal voltage due to the deterioration of ageing of insulation and the damages caused by the unpredictable happenings such as blowing of heavy winds, trees falling across lines, vehicles colliding with towers or poles, birds shorting out lines, aircraft colliding with lines, line breaks etc.
- Breakdown may occur due to abnormal voltages caused by switching surges or lightning strokes which may be either direct or induced.
The current practice is of providing a high insulation level of the order of 3 to 5 times the nominal values of the voltage but still the insulation strength is reduced because of pollution on an insulator string, commonly caused by deposited soot or cement dust in industrial areas and by wind borne sea-spray in coastal areas. Initially the insulation resistance is lowered and small leakage currents are diverted and thus the deterioration is hastened. Even in enclosed installations such as sheathed and armored cables and metal-clad switchgear, insulation gets deteriorated because of ageing. Void formation in the insulating compound of underground cables due to unequal expansions and contractions caused by the increase and decrease in temperature is another cause of insulation failure.
The line and apparatus insulation may be subjected transient over-voltages because of the switching operations. The voltage which rises at a rapid rate may achieve a peak value approaching three times phase-to-neutral voltage. This is the reason that a higher insulation level is provided initially. In case the insulation levels have been correctly chosen and they have not been impaired in a way described under (i) above, the system will withstand these routine over-voltages. But if the insulation gets deteriorated due to one or other reason, it is at the time of switching that failure may occur [80].
Faults can be classified broadly into four different categories namely:
CHAPTER FOUR
EXPERIMENTAL RESULTS AND DISCUSSIONS
FAULT DETECTION
For the purpose of fault detection, various topologies of Multi-Layer Perceptron have been studied. The various factors that play a role in deciding the ideal topology are the network size, the learning strategy employed and the training data set size.
After an exhaustive study, the back-propagation algorithm has been decided as the ideal topology. Even though the basic back-propagation algorithm is relatively slow due to the small learning rates employed, few techniques can significantly enhance the performance of the algorithm. One such strategy is to use the Levenberg-Marquardt optimization technique. The selection of the apt network size is very vital because this not only reduces the training time but also greatly enhance the ability of the neural network to represent the problem in hand. Unfortunately there is no thumb rule that can dictate the number of hidden layers and the number of neurons per hidden layer in a given problem.
CHAPTER FIVE
CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
This thesis has studied the usage of neural networks as an alternative method for the detection, classification and location of faults and restoration of power system transmission lines. The methods employed make use of the phase voltages and phase currents (scaled with respect to their pre-fault values) as inputs to the neural networks. Various possible kinds of faults namely single line-ground, line-line, double line-ground and three phase faults have been taken into consideration into this work and separate ANNs have been proposed for each of these faults.
All the neural networks investigated in this thesis belong to the back-propagation neural network architecture. A fault location scheme for the transmission line system, right from the detection of faults on the line to the fault location stage has been devised successfully by using artificial neural networks.
The simulation results obtained prove that satisfactory performance has been achieved by all of the proposed neural networks in general. As further illustrated, depending on the application of the neural network and the size of the training data set, the size of the ANN (the number of hidden layers and number of neurons per hidden layer) keeps varying. The importance of choosing the most appropriate
ANN configuration, in order to get the best performance from the network, has been stressed upon in this work. The sampling frequency adopted for sampling the voltage and current waveforms in this thesis is just 720 Hz which is very low compared to higher frequencies used in other works. This is of significant importance because, the lower the sampling frequency, the lesser the computational burden on the industrial PC that uses the neural networks. This means a lot of energy savings because a continuous online detection scheme of this kind consumes a large amount of energy, a major portion of which is due to the continuous sampling of waveforms. The above mentioned are some significant improvements that this thesis offers over existing neural network based techniques for transmission line fault location.
To simulate the entire power transmission line model and to obtain the training data set, MATLAB R2010a has been used along with the SimPowerSystems toolbox in Simulink. In order to train and analyze the performance of the neural networks, the Artificial Neural Networks Toolbox has been used extensively.
Some important conclusions that can be drawn from this thesis are:
- Neural Networks are indeed a reliable and attractive scheme for an ideal transmission line fault location scheme especially in view of the increasing complexity of the modern power transmission systems.
- It is very essential to investigate and analyze the advantages of a particular neural network structure and learning algorithm before choosing it for an application because there should be a trade-off between the training characteristics and the performance factors of any neural network.
- Back Propagation neural networks are very efficient when a sufficiently large training data set is available and hence Back Propagation networks have been chosen for all the three steps in the fault location process namely fault detection, classification, fault location and restoration of transmission lines.
RECOMMENDATIONS
- As a possible extension to this work, it would be quite useful to analyze all the possible neural network architectures and to provide a comparative analysis on each of the architectures and their performance characteristics. The possible neural network architectures that can be analyzed apart from back propagation neural networks are radial basis neural network (RBF) and support vector machines (SVM) networks.
- For the implementation of this thesis to have effect in Nigerian power system, a total overhauling of the system is required by changing all analogue system to automatic and modern computer compliant systems.
- Personals should be trained on and about artificial intelligence so as to make the system work effectively.
REFERENCES
- R. Das, D. Novosel, “Review of fault location techniques for transmission and sub – transmission lines”. Proceedings of 54th Annual Georgia Tech Protective Relaying Conference, 2000.
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- M. M. Saha, R. Das, P. Verho, D. Novosel, “Review of fault location techniques for distribution systems”, Proceedings of Power Systems and Communications Infrastructure for the Future Conference, Beijing, 2002, 6p.
- L. Eriksson, M. M. Saha, G. D. Rockefeller, “An accurate fault locator with compensation for apparent reactance in the fault resistance resulting from remote-end feed”, IEEE Trans on PAS 104(2), 1985, pp. 424-436.
- M. M. Saha, J. Izykowski, E. Rosolowski, “Fault Location on Power Networks”, Springer publications, 2010.
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- J. B. Gupta, A Course in Electrical Power, s.k. kataria & sons January 1, 2005
- R.J Kafka, D.R Penders, S.H Bomchey, M.M Adibi., “System restoration plan development for a metropolitan electrical system”, IEEE Trans Power Apparatus Syst, PAS-100 (8) (1981), pp. 3703–3713.
