CHAPTER 1

INTRODUCTIONIncreased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control and automation. In liberalized electricity market, the operation and control of power system become complex due to complexity in modeling and uncertainties. Power system models used for intelligent operation and control are highly dependent on the task purpose. In competitive electricity market along with automation, computational intelligent techniques are very useful. As electric utilities are trying to provide smart solutions with economical, technical (secure, stable and good power quality) and environmental goals, there are several challenging issues in the smart grid solutions such as, but not limited to, forecasting of load, price, ancillary services; penetration of new and renewable energy sources; bidding strategies of participants; power system planning & control; operating decisions under missing information; increased distributed generations and demand response in the electric market; tuning of controller parameters in varying operating conditions, etc.

Risk management and financial management in electric sector are concerned with finding an ideal trade-off between maximizing the expected returns and minimizing the risks associated with these investments. Artificial intelligence emerged as a computer science discipline in the mid 1950s. Since then, it has produced a number of powerful tools, many of which are of practical use in engineering to solve difficult problems normally requiring human intelligence. Three of these tools will be reviewed in this paper. They are: fuzzy logic, neural networks and genetic algorithms. All of these tools have been in existence for more than 30 years and have found applications in engineering. This paper lists various potential areas of power systems and provides the roles of Artificial intelligence in the emerging power systems. A brief review of intelligence techniques is also presented. Back-propagation is an iterative, gradient search, supervised algorithm which can be viewed as multiplayer non-linear method that can re-code its input space in the hidden layers and thereby solve hard learning problems. The network is trained

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using ANN technique until a good agreement between predicted gain settings and actual gains is reached. During last three decades, the assessment of potential of the sustainable eco-friendly alternative sources and refinement in technology has taken place to a stage so that economical and reliable power can be produced. Different renewable sources are available at different geographical locations close to loads, therefore, the latest trend is to have distributed or dispersed power system. Examples of such systems are wind-diesel, winddiesel- micro-hydro-system with or without multiplicity of generation to meet the load demand. These systems are known as hybrid power systems. To have automatic reactive load voltage control SVC device have been considered. The multi-layer feed-forward ANN toolbox of MATLAB 6.5 with the error backpropagation training method is employed.

Computer based energy management systems are now widely used in energy control centers. Power system analysis programs and other application programs are employed in Energy Management Systems for the purposes of investigating and predicting the behavior of power systems under steady-state operations. The energy management system (EMS) is the center of a control system organized in a hierarchical structure utilizing remote terminal units, communication links, and various levels of computer processing systems. The function of the EMS is to ensure the secure and economic operation of the power system as well as to facilitate the minute-by-minute tasks carried out by the operations personnel. While these programs are powerful tools, their ability to assist operation engineers to make efficient decisions is very limited when unplanned or unexpected modes of system operation occur. The abnormal modes of system operation may be caused by network faults, active and reactive power imbalances, or frequency deviations. An unplanned Operation may lead to a complete system blackout. Under these emergency situations, power systems are restored back to the normal state according to decisions made by experienced operation engineers. For efficient diagnosis of network faults, determination of operational strategies for network restoration, and balancing active and reactive powers, there is clearly a need to develop new computer techniques and methods to build programs where the precious knowledge of experienced operation engineers can be accounted for in addition to the conventional power system application programs. There is also a need to

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develop fast and efficient methods for the prediction of abnormal system behavior. Artificial intelligence (AI) has provided techniques for encoding and reasoning with declarative knowledge. It provides conventional computing techniques and methods for solving problems of power system planning, operation and control. This paper first reports areas in power systems that artificial intelligence has been applied to. It then summarizes the artificial intelligence techniques which have been employed and makes suggestions for the improvement of existing artificial intelligence tools.

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CHAPTER 2

ARTIFICIAL INTELLIGENCE TECHNIQUES EMPLOYEDThe research in artificial intelligence has developed many techniques and methodologies which are useful for solving complicated power system problems. These include knowledge of representation methods, search strategies, automated reasoning techniques, expert system or knowledge-based system methodologies, general problem solving approach, blackboard architecture and computer languages for symbolic and list processing. The artificial intelligence techniques and the expert system approach are some new tools for power engineers. Increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control and automation. In liberalized electricity market, the operation and control of power system become complex due to complexity in modeling and uncertainties. Power system models used for intelligent operation and control are highly dependent on the task purpose. In competitive electricity market along with automation, computational intelligent techniques are very useful.

As electric utilities are trying to provide smart solutions with economical, technical (secure, stable and good power quality) and environmental goals, there are several challenging issues in the smart grid solutions such as, but not limited to, forecasting of load, price, ancillary services; penetration of new and renewable energy sources; bidding strategies of participants; power system planning & control; operating decisions under missing information; increased distributed generations and demand response in the electric market; tuning of controller parameters in varying operating conditions, etc. Risk management and financial management in electric sector are concerned with finding an ideal trade-off between maximizing the expected returns and minimizing the risks associated with these investments. Computational intelligence (CI) is a new and modern tool for solving complex problems which are difficult to be solved by the conventional techniques. Heuristic optimization techniques are general purpose methods that are very flexible and can be applied to many types of objective functions and constraints. Recently, these new heuristic tools have been combined among themselves and new methods have

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emerged that combine elements of nature-based methods or which have their foundation in stochastic and simulation methods. Developing solutions with these tools offers two major advantages: development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information known as uncertainty. Due to environmental, right-of-way and cost problems, there is an increased interest in better utilization of available power system capacities in both bundled and unbundled power systems.

Natural evolution is a hypothetical population-based optimization process. Simulating this process on a computer results in stochastic optimization techniques that can often perform better than classical methods of optimization for real-world problems. Evolutionary computation (EC) is based on the Darwins principle of survival of the fittest strategy. An evolutionary algorithm begins by initializing a population of solutions to a problem. New solutions are then created by randomly varying those of the initial population. All solutions are measured with respect to how well they address the task. Finally, a selection criterion is applied to weed out those solutions, which are below standard. The process is iterated using the selected set of solutions until a specific criterion is met. The advantages of EC are adaptability to change and ability to generate good enough solutions but it needs to be understood in relation to computing requirements and convergence properties. EC can be subdivided into GA, evolution strategies, evolutionary programming (EP), genetic programming, classified systems, simulated annealing (SA), etc. The first work in the field of evolutionary computation was reported by Fraser in 1957 (Fraser, 1957) to study the aspects of genetic system using a computer. After some time, a number of evolutionary inspired optimization techniques were developed. Computational intelligence (CI) is a new and modern tool for solving complex problems which are difficult to be solved by the conventional techniques. Heuristic optimization techniques are general purpose methods that are very flexible and can be applied to many types of objective functions and constraints. Recently, these new heuristic tools have been combined among themselves and new methods have emerged that combine elements of nature-based methods or which have their foundation in stochastic and simulation methods. Developing solutions with these tools offers two major

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advantages: development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information known as uncertainty. Computational intelligence (CI) methods, which promise a global optimum or nearly so, such as expert system (ES), artificial neural network (ANN), genetic algorithm (GA), evolutionary computation (EC), fuzzy logic, etc. have been emerged in recent years in power systems applications as effective tools. These methods are also known as artificial intelligence (AI) in several works. In a practical power system, it is very important to have the human knowledge and experiences over a period of time due to various uncertainties, load variations, topology changes, etc.

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CHAPTER 3

EXPERT SYSTEMSAn expert system is a software paradigm where knowledge concerning a complex problem. It is encoded into a computer program. The framework of expert systems is designed to enable easy encoding of knowledge and easy checkout of the expert systems performance. A general architecture for expert systems is shown in Fig. 1. Four major software elements comprise an expert system: the knowledge base, an inference engine, building and checkout utilities, and the user interface. Expert systems also provide the ability to explain the reasoning used (e.g., to trace the rules used in a rule-based system) which is important in checking it out. Depending on the representation scheme, an AI program becomes either rule based, frame-based, or logic-based. AI programs that achieve expert-level competence in solving the problems by bringing knowledge about specific tasks are called knowledge-based or expert systems (ES) which was first proposed by Feigenbaum et al. in the early 1970s (Feigenbaum et al, 1971). ES is a knowledge-based or rule based system, which uses the knowledge and interface procedure to solve problems that are difficult enough to be solved by human expertise. Main advantages of ES are: a) It is permanent and consistent b) It can be easily transferred or reproduced c) It can be easily documented. Main disadvantage of ES is that it suffers from a knowledge bottleneck by having inability to learn or adapt to new situations. The knowledge engineering techniques started with simple rule based technique and extended to more advanced techniques such as object-oriented design, qualitative reasoning, verification and validation methods, natural languages, and multi-agent systems. For the past several years, a great deal of ES applications has been developed to prepare plan, analyze, manage, control and operate various aspects of power generation, transmission and distributions systems. Expert system has also been applied in recent years for load, bid and price forecasting.

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Figure 1: Architecture of Expert Systems

3.1 Rule-Based System: The rule-based system has two kinds of memory: short-term (or working memory) and longterm. The short-term memory (STM) contains factual knowledge, to be modified as the Computation proceeds. The long-term memory (LTM) contains the production rules themselves. The inference engines of the rule-based system test the premise-part by matching it against the factual knowledge in the STM (matching cycle). If it succeeds, the action-part of the rule is executed resulting in some changes to the STM (firing cycle). The engine then goes back to the matching cycle. There may be more than one rule which succeeds in matching and the inference engine then invokes a conflict resolution mechanism to decide which rule shall be used. The rule-based method was applied to the areas of fault diagnosis and control of nuclear power plants. 3.2 Frame-Based System: In the rule-based system, factual knowledge is stored in the STM without regard to relationships between different objects. However, a relation between the objects of many problems and a frame-based knowledge representation allows the user to set up and make use of these relationships. For example, consider the objects of a substation such as breakers, switches, buses, transformers, and transmission lines. Several objects comprise a substation, and a set of substations becomes an area. Depending on the status of individual breakers and switches, buses may be split or deenergized. Transformers and lines may be connected, open-ended, or deenergized depending on the status of the terminating bus sections, etc.

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3.3 Logic-Based System: The frameworks we have dealt with so far are appropriate to represent procedural knowledge such as: what to do when certain conditions are met. A different way to represent knowledge requires one to specify what instead of how. A logic-based system provides us with such means. Prolog is a programming language to represent a what-type knowledge. Logic-based systems have an advantage when specifying system requirements, but they have a disadvantage in specifying procedure-oriented knowledge. Systems developed based on logic and logic programming has demonstrated that these techniques are suitable for building expert systems and artificial intelligence systems for solving power system combinatorial problems.

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CHAPTER 4

AI METHODS USED IN POWER SYSTEMSome of the method or algorithms by the help of which we can implement Artificial Intelligence in Power System are as follows:

a) FUZZY LOGIC b) NEURAL NETWORKS c) GENETIC ALGORITHM 4.1 Fuzzy Logic: Fuzzy logic (FL) was developed by Zadeh (Zadeh, 1965) in 1964 to address uncertainty and imprecision, which widely exist in the engineering problems. FL was first introduced in 1979 for solving power system problems. Fuzzy set theory can be considered as a generalization of the classical set theory. In classical set theory, an element of the universe either belongs to or does not belong to the set. Thus, the degree of association of an element is crisp. In a fuzzy set theory, the association of an element can be continuously varying. Mathematically, a fuzzy set is a mapping (known as membership function) from the universe of discourse to the closed interval [0, 1]. Membership function is the measure of degree of similarity of any element in the universe of discourse to a fuzzy subset. Triangular, trapezoidal, piecewise-linear and Gaussian functions are most commonly used membership functions. The membership function is usually designed by taking into consideration the requirement and constraints of the problem. Fuzzy logic implements human experiences and preferences via membership functions and fuzzy rules. Due to the use of fuzzy variables, the system can be made understandable to a non-expert operator. In this way, fuzzy logic can be used as a general methodology to incorporate knowledge, heuristics or theory into controllers and decision makers. The advantages of fuzzy theory are as follows: i. ii. It more accurately represents the operational constraints of power systems and Fuzzified constraints are softer than traditional constraints.

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iii.

Momoh et al. (2000) have presented the overview and literature survey of fuzzy set theory application in power systems.

A recent survey shows that fuzzy set theory has been applied mainly in voltage and reactive power control, load and price forecasting, fault diagnosis, power system protection/relaying, stability and power system control, etc. Fuzzy logic has rapidly become one of the most successful of today's technologies for developing sophisticated control systems. The reason for which is very simple. Fuzzy logic addresses such applications perfectly as it resembles human decision making with an ability to generate precise solutions from certain or approximate information. It fills an important gap in engineering design methods left vacant by purely mathematical approaches (e.g. linear control design), and purely logic-based approaches (e.g. expert systems) in system design. While other approaches require accurate equations to model real-world behaviors, fuzzy design can accommodate the ambiguities of real-world human language and logic. It provides both an intuitive method for describing systems in human terms and automates the conversion of those system specifications into effective models. As the complexity of a system increases, it becomes more difficult and eventually impossible to make a precise statement about its behavior, eventually arriving at a point of complexity where the fuzzy logic method born in humans is the only way to get at the problem. Fuzzy logic is used in system control and analysis design, because it shortens the time for engineering development and sometimes, in the case of highly complex systems, is the only way to solve the problem. The fuzzy logic analysis and control method is, therefore: First, Receiving of one, or a large number, of measurement or other assessment of conditions existing in some system we wish to analyze or control. Second, processing all these inputs according to human based, fuzzy "If-Then" rules, which can be expressed in plain language words, in combination with traditional non-fuzzy processing. Third, Averaging and weighting the resulting outputs from all the individual rules into one single output decision or signal which decides what to do or tells a controlled system what to do. The output signal eventually arrived at is a precise appearing, defuzzified, "crisp" value. Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth- truth-values between "completely true" and

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"completely false". As its name suggests, it is the logic underlying modes of reasoning which are approximate rather than exact. The importance of fuzzy logic derives from the fact that most modes of human reasoning and especially common sense reasoning are approximate in nature. The essential characteristics of fuzzy logic as founded by Zadeh Lotfi are as follows. In fuzzy logic, exact reasoning is viewed as a limiting case of approximate reasoning. In fuzzy logic everything is a matter of degree. Any logical system can be fuzzified. In fuzzy logic, knowledge is interpreted as a collection of elastic or, equivalently, fuzzy constraint on a collection of variables Inference is viewed as a process of propagation of elastic constraints. The third statement hence, defines Boolean logic as a subset of Fuzzy logic.

A paradigm is a set of rules and regulations, which defines boundaries and tells us what to do to be successful in solving problems within these boundaries. For example the use of transistors instead of vacuum tubes is a paradigm shift - likewise the development of Fuzzy Set Theory from conventional bivalent set theory is a paradigm shift. Bivalent Set Theory can be somewhat limiting if we wish to describe a 'humanistic' problem mathematically. The whole concept can be illustrated with this example. Let's talk about people and "youthness". In this case the set S (the universe of discourse) is the set of people. A fuzzy subset YOUNG is also defined, which answers the question "to what degree is person x young?" To each person in the universe of discourse, we have to assign a degree of 5 membership in the fuzzy subset YOUNG. The easiest way to do this is with a membership function based on the person's age. Young (x) = {1, if age (x)