Abstract: In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is the intelligence exhibited by machines or software. It is the subfield of computer science. Artificial Intelligence is becoming a popular field in computer science as it has enhanced human life in many areas. Artificial intelligence in the last two decades has greatly improved the performance of the manufacturing and service systems. Study in the area of artificial intelligence has given rise to the rapidly growing technology known as an expert system. Application areas of Artificial Intelligence are having a huge impact on various fields of life as the expert system is widely used these days to solve complex problems in various areas as science, engineering, business, medicine, weather forecasting. The areas employing the technology of Artificial Intelligence have seen an increase in quality and efficiency. This paper gives an overview of this technology and the application areas of this technology. This paper will also explore the current use of Artificial Intelligence technologies in the PSS design to damp the power system oscillations caused by interruptions, in Network Intrusion for protecting computer and communication networks from intruders, in the medical area medicine, to improve hospital inpatient care, for medical image classification, in the accounting databases to mitigate the problems of it and in the computer games. Keywords: Artificial Intelligence, Intrusion Detection Systems, Neural Networks (computer), Power System Stabilizer.
Become a master of Artificial Intelligence by going through this online Artificial Intelligence Course:http://bit.ly/2NODBVj
II. AREAS OF ARTIFICIAL INTELLIGENCE
A. Language understanding: The ability to "understand" and respond to the natural language. To translate from spoken language to a written form and to translate from one natural language to another natural language. 1.1 Speech Understanding 1.2 Semantic Information Processing (Computational Linguistics) 1.3 Question Answering 1.4 Information Retrieval 1.5 Language Translation.
B. Learning and adaptive systems: The ability to adapt behavior bagged on previous experience, and to develop general rules concerning the world based on such experience. 2.1 Cybernetics 2.2 Concept Formation
C. Problem-solving: Ability to formulate a problem in a suitable representation, to plan for its solution and to know when new information is needed and how to obtain it. 3.1 Inference (Resolution-Based Theorem Proving, Plausible Inference and Inductive Inference) 3.2 Interactive Problem Solving 3.3 Automatic Program Writing 3.4 Heuristic Search
D. Perception (visual): The ability to analyze a sensed scene by relating it to an internal model that represents the perceiving organism's "knowledge of the world." The result of this analysis is a structured set of relationships between entities in the scene. 4.1 Pattern Recognition 4.2 Scene Analysis
E. Modeling: The ability to develop an internal representation and set of transformation rules which can be used to predict the behavior and relationship between some set of real-world objects or entities. 5.1 The Representation Problem for Problem Solving Systems 5.2 Modeling Natural Systems (Economic, Sociological, Ecological, Biological, etc.) 5.3 Hobot World Modeling (Perceptual and Functional Representations)
F. Robots: A combination of most or all of the above abilities with the ability to move over terrain and manipulate objects. 6.1 Exploration 6.2 Transportation/Navigation 6.3 Industrial Automation (e.g., Process Control, Assembly Tasks, Executive Tasks) 6.4 Security 6.5 Other (Agriculture, Fishing, Mining, Sanitation, Construction, etc.) 6.6 Military 6.7 Household G. Games: The ability to accept a formal set of rules for games such as Chess, Go, Kalah, Checkers, etc., and to translate these rules into a representation or structure which allows problem-
solving and learning abilities to be used in reaching an adequate level of performance. 7.1 Particular Games (Chess, Go, Bridge, etc.)
III. APPLICATIONS OF ARTIFICIAL INTELLIGENCE
A. Application of Artificial Intelligent Techniques in Power system stabilizers (PSSs) Design Since the 1960s, PSSs have been used to add damping to electromechanical oscillations. The PSS is an additional control system, which is often applied as a part of an excitation control system. The basic function of the PSS is to apply a signal to the excitation system, producing electrical torques to the rotor in phase with speed differences that damp out power oscillations.
They perform within the generator‟s excitation system to create a part of electrical torque, called damping torque, proportional to speed change. A CPSS can be modeled by a two-stage (identical), a lead-lag network which is represented by a gain K and two-time constants T1 and T2. This network is connected with a washout circuit of a time constant Tw. The signal washout block acts as a high-pass filter with the time constant Tw that allows the signal associated with the oscillations in rotor speed to pass unchanged. Furthermore, it does not allow the steady-state changes to modify the terminal voltages. The phase compensation blocks with time constants T1i – T4i supply the suitable phase-lead characteristics to compensate for the phase lag between the input and the output signals.
Artificial Neural Network (ANN) in PSS: In the power systems the most applications of the artificial neural network use a multilayer feed-forward network. In the neural adaptive PSS, a feed-forward neural network with a single hidden layer is proposed which includes two sub-networks: adaptive neuro-identifier, in which the dynamic characteristics of the plant are tracked and adaptive neuro controller to damp the low-frequency oscillations. Radial basis function network (RBFN) has three layers: input layers, hidden layers, and output layers. The hidden layer finds centers and widths of the radial basis functions for individual pattern units and the output layer finds the weights between the pattern units and the output units using an unsupervised learning algorithm. A recurrent neural network (RNN) stabilization controller is proposed to improve the transient stability of power systems in which both the governor and AVR is used. The weight of the proposed controller is adjusted on-line. The signal output of the first RNN is added to the PSS signal output for excitation control. The signal output of the second RNN is used as a stabilizing signal for the governor system. ANNs are intelligent controllers to control nonlinear, dynamic systems through learning, which can easily accommodate the nonlinearities and time dependencies.
Fuzzy Logic (FL) in PSS: In 1964, Lotfi Zadeh developed FL to address inaccuracy and uncertainty which usually exist in engineering problems . A design process for a fuzzy logic-based PSS (FLPSS) was proposed for a multi-machine power system. The input signal to FLPSS is the speed deviation of the synchronous generator and its derivative. For the robustness of the FLPSS, five generator power systems were used and designing a normalized sum-squared deviation index was used. A novel input signal based FLPSS was applied in the multi-machine environment.