Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

Table 4

Observations made on the classification capability of OPNN and APNN for moderate database—with clustering algorithm.

Preprocessing Technique	Misclassifications; in OPNN with LVQ1	Misclassifications in OPNN with LVQ2	Misclassifications in OPNN with LVQ3
Φ-𝑞max-𝑛 (30°)	4 types—3 numbers; (AC2, AC9, EC1); 5 types—6 numbers; (EC1, EC3, EC7SD7, EC8SD8, EC2, EC4)	4 types—2 numbers; (EC2, EC1); 5 types—3 numbers; (EC1, EC6SD6, EC8SD8)	4 types—2 numbers; (EC2, EC1); 5 types—4 numbers; (EC2, EC1, EC7SD7, EC8SD8)
Φ-𝑞min-𝑛 (30°)	4 types—6 numbers; (AC7, AC5, AC2, EC6AC7, EC7AC8, EC8AC9); 5 types—6 numbers; (EC6C7, EC7AC8, EC7SD7, EC8SD8, AC2, EC5AC2)	4 types—3 numbers; (EC1, EC6SD6, EC8SD8); 5 types—8 numbers; (AC2, EC5, SD7, EC6AC7, EC7AC8, EC6SD6, EC8SD8)	4 types—5 numbers; (AC2, EC5, EC6AC7, EC7AC8, EC8AC9); 5 types—8 numbers; (AC2, EC5, SD7, EC6AC7, EC7AC8, EC8AC9, EC7SD7, EC8SD8)
Φ-𝑞max-𝑛 (10°)	4 types—3 numbers; (EC2, EC8SD8, EC1); 5 types—7 numbers; (EC2, EC1AC2, EC5AC2, EC1, EC3, EC7SD7, EC8SD8)	4 types—1 number; (EC1); 5 types—4 numbers; (EC1AC2, EC6AC2, EC6SD6, EC8SD8)	4 types—3 numbers; (EC2, EC5AC2, EC1); 5 types—5 numbers; (EC1AC2, EC6AC2, EC7SD7, EC8SD8)
Φ-𝑞min-𝑛 (10°)	4 types—2 numbers; (AC2, AC7); 5 types—4 numbers; (AC2, AC7, EC7SD7, EC8SD8)	4 types—2 numbers; (EC5SD5, EC6AC2); 5 types—4 numbers; (EC1AC2, EC6AC2, EC6SD6, EC8SD8)	4 types—2 numbers; (AC1, AC2); 5 types—5 numbers; (EC1AC2, EC6AC2, EC7SD7, EC8SD8)