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Learn in synthetic intelligence has built many suggestions and methodologies that may be tailored or used on to clear up complicated energy method difficulties
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Additional resources for Artificial intelligence techniques in power systems
Rules are chained based on the data and/or goals. Since rule-based systems are data- or goal-driven, it is difficult to predict the worst case rule chains over all possible domain scenarios. A method reported in Reference 6 provides a systematic way to obtain an upper bound of the worst case processing time for rule-based systems. This method is based on the RETE rule-matching algorithm; the knowledge representation and match techniques of the RETE algorithm will be discussed later in this chapter.
Execute the rule Rn. This would transform the PWM to a new state. Let E' be this state. Match the rule base to the new state of the PWM, E'. Let S' be the conflict set. The rules in set ( £ ' - S) have a cause-effect relationship with rule R» . 32 Artificial intelligence techniques in power systems Mutual-exclusion 7. 8. Analyse each rule of the set (R-S) as follows. Choose a rule R, from the set (R-S). Form a minimum set of PWMEs for rule Ri as shown in step 1. Match the PWMEs to the rule Rn. If the rule Rn is not instantiated by the PWMEs then the two rules Rt and Rn are mutually exclusive.
Let R be the set of rules present in the rule base. 1. 2. 3. Form a minimum set of PWMEs to satisfy Rn. Let E be this set. Form an action set of the rule Rn. Let A be this set. An action set records the actions on the RHS of the rule. Thus it represents the changes to the PWM as a result of firing of this rule. Match the rule base R to the state of the PWM formed in step 1. Let S be the set of satisfied rules (conflict set). If the set S is empty then stop. The new rule Rn does not exhibit any relation with the rules in the existing rule base.