30-06-2014, 02:09 PM
ARTIFICIAL INTELLIGENCE IN POWER SYSTEMS
ARTIFICIAL INTELLIGENCE IN POWER SYSTEMS.docx (Size: 324.64 KB / Downloads: 26)
Abstract
The application of Artificial Intelligence (AI) methods in power system protection has been addressed in this paper. Particular emphasis has been put on ArtificialNeural Networks (ANN) and Fuzzy Logic (FL). Several novel concepts have been introduced including ANN applicationto CT and CVT transients’ correction, fuzzy criteria signals, fuzzy settings and multi-criteria decision makingfor 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 ANNapplication.
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
PROBLEMS IN POWER SYSTEM PROTECTION
off between the security demand, and the speed of operation and the dependability requirements. The more secure is the relay, 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
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. Both the numeric, non-numeric and symbolic computations are included in the area of AI. Themimicking of intelligence includes not only the ability tomake rational decisions, but also to deal with missing data,adapt to existing situations and improve itself in the longtime horizon based on the accumulated experience. Three major families of AI techniques are considered tobe applied in modern power system protection.
•Expert System Techniques (XPSs),
•Artificial Neural Networks (ANNs),
•Fuzzy Logic systems (FL).
APPLICATION TO CT AND CVT CORRECTION
Certain construction limitations of the instrument transformersmay in some cases cause maloperation or substantial delayin tripping of the protective relays.One of the method for correction of CTs saturation consists in application of CT’s inverse transfer function in the
form of ANN . The correction function and the transferfunction of CT set up in series should assure identity of CTprimary and compensated secondary currents. Since the CT’s transfer function is nonlinear, usage of the nonlinearartificial multilayer neural network structure with some formof feed-back (recurrent network), as presented in Fig.7, isrequired.. The sigmoidal tangent activation function has beenassigned to neurons in hidden layers and the linear one to theoutput neuron of the selected ANN architecture.The sliding data widow consisting of the recent and afew historical
APPLICATION TO FAULT TYPECLASSIFICATION
Fast fault detection and classification of the fault type is one of the most important tasks of the protection relays and fault location function. The basic idea of the fault type estimation consists in analyzing 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.
CONCLUSIONS
The paper reviews the AI approaches to power systemprotection and focuses on the application of ANN and fuzzylogic techniques.A number of novel application and concepts have beenpresented including fuzzy logic approach to differentialtransformer protection and ANN application to the transformerprotection, CT and CVT transients’ correction, and fault-type classification. Included examples demonstrate applicationof the AI methods and their features