Artificial immune system based machine learning for voltage stability prediction in power system

This paper focuses on a special category of machine learning problems arising in cases where the set of available training instances is significantly biased towards a particular class of patterns. Our work addresses the so-called Class Imbalance Problem through the utilization of an Artificial Immune System-(AIS)based classification algorithm which encodes the inherent ability of the Adaptive Immune System to mediate the exceptionally imbalanced “self” / “non-self” discrimination process. From a computational point of view, this process constitutes an extremely imbalanced pattern classification task since the vast majority of molecular patterns pertain to the “non-self” space. Our work focuses on investigating the effect of the class imbalance problem on the AIS-based classification algorithm by assessing its relative ability to deal with extremely skewed datasets when compared against two state-of-the-art machine learning paradigms such as Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs). To this end, we conducted a series of experiments on a music-related dataset where a small fraction of positive samples was to be recognized against the vast volume of negative samples. The results obtained indicate that the utilized bio-inspired classifier outperforms SVMs in detecting patterns from the minority class while its performance on the same task is competently close to the one exhibited by MLPs. Our findings suggest that the AIS-based classifier relies on its intrinsic resampling and class-balancing functionality in order to address the class imbalance problem.

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A Machine Learning Evaluation of an Artificial Immune System

Evolutionary Computation ◽

10.1162/1063656054088503 ◽

2005 ◽

Vol 13 (2) ◽

pp. 179-212 ◽

Cited By ~ 34

Author(s):

Matthew Glickman ◽

Justin Balthrop ◽

Stephanie Forrest

Keyword(s):

Machine Learning ◽

Immune System ◽

Intrusion Detection ◽

Artificial Immune System ◽

Concept Drift ◽

Network Intrusion Detection ◽

Artificial Immune ◽

Data Set ◽

Network Intrusion ◽

Adaptive Mechanisms

ARTIS is an artificial immune system framework which contains several adaptive mechanisms. LISYS is a version of ARTIS specialized for the problem of network intrusion detection. The adaptive mechanisms of LISYS are characterized in terms of their machine-learning counterparts, and a series of experiments is described, each of which isolates a different mechanism of LISYS and studies its contribution to the system's overall performance. The experiments were conducted on a new data set, which is more recent and realistic than earlier data sets. The network intrusion detection problem is challenging because it requires one-class learning in an on-line setting with concept drift. The experiments confirm earlier experimental results with LISYS, and they study in detail how LISYS achieves success on the new data set.

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Optimal Reactive Power Dispatch Using Multistage Artificial Immune System

International Journal of Emerging Technology and Advanced Engineering ◽

10.46338/ijetae1121_20 ◽

2021 ◽

Vol 11 (11) ◽

pp. 176-183

Author(s):

Nur Farzana Nordin ◽

◽

Mohd Helmi Mansor ◽

Karmila Kamil ◽

Nurzanariah Roslan ◽

...

Keyword(s):

Immune System ◽

Power System ◽

Artificial Immune System ◽

Reactive Power ◽

Optimization Method ◽

Artificial Immune ◽

The Past ◽

Optimal Reactive Power Dispatch ◽

Power Dispatch ◽

Reactive Power Dispatch

Most countries over the past few decades have modernized their economies and become more reliant on electricity to run, so the electrical power system has also expanded greatly. Optimal Reactive Power Dispatch (ORPD) has a big influence on the reliability, security, and economic operation of the power system. Another thing to note is that ORPD has a few major targets and objectives which are to reduce the active or real power losses, to improve the voltage profile, to reduce transmission costs, and to increase system stability. Non-convex, non-linear, and multimodal problems make the development of intelligent algorithms to solve the reactive power dispatch problem highly relevant. Some researchers chose to compare and contrast optimization techniques from the past with each other in order to answer some remaining uncertainties such as the effectiveness and complexity of the technique toward the chosen objective function(s). Thus, this paper proposed applying the Multistage Artificial Immune System (MAIS) optimization method for solving the ORPD problem with the objective of reducing the power system losses. This algorithm was made by modifying and upgrading the classical AIS optimization method. Instead of only going through the process one time in the classical AIS algorithm, this MAIS method going through the processes more than one time in multiple stages of the same processes. This process includes cloning and mutation as well as selection. These modifications also aid in the development of new and unique solutions, as opposed to the classical AIS optimization process. Therefore, these enhancements could lead to a rise in the accuracy of the results' because there have been increased comparisons. This study confirms that MAIS optimization can deliver superior results in less time than AIS. Keywords—Optimal reactive power dispatch, computational intelligence, multistage artificial immune system, loss minimization.

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