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 3

Classification capability of PNN and APNN for moderate database—without clustering algorithm.

Pre-processing Technique	Misclassifications; in OPNN	Misclassifications; in APNN
Φ-𝑞max-𝑛 (30°)	4 types—7 numbers; (AC2, AC7, EC1AC2, EC2AC7, EC5AC2, EC6AC7); 5 types—15 numbers; (AC2, AC7, EC1AC2, EC2AC7, EC3AC8, EC4AC9,; EC2SD2, EC3SD3, EC4SD4, EC1, EC6AC7, EC6SD6, EC7SD7, EC8SD8)	4 types—5 numbers; (EC1, EC5, SD6, SD9); 5 types—6 numbers; (EC1, EC6AC7, EC6SD6, EC7AC8, EC7SD7, EC8SD8)
Φ-𝑞min-𝑛 (30°)	4 types—8 numbers; (EC5AC2, EC3AC8, EC2AC7, EC1AC2, EC6AC7, EC7AC8, EC8AC9); 5 types—15 numbers; (EC1AC2, EC2AC7, EC3AC8, EC5AC2, EC1SD1, EC2SD2, EC3SD3, EC4SD4, EC5SD5, EC1, EC6AC7, EC6SD6, EC7SD7, EC8SD8)	4 types—5 numbers; (EC1, EC6AC7, EC7AC8,; EC8AC9); 5 types—7 numbers; (EC1, EC6AC7, EC6SD6,; EC7AC8, EC7SD7, EC8SD8)
Φ-𝑞max-𝑛 (10°)	4 types—7 numbers; (AC2, AC7, EC1AC2, EC5AC2, EC6AC7, EC8AC9); 5 types—8 numbers; (EC5SD5, EC4SD4, EC1, EC6AC7, EC7AC8, EC8AC9, EC7SD7)	4 types—4 numbers; (EC2, EC3, EC5, SD6); 5 types—4 numbers; (EC1, EC5, SD6, SD9)
Φ-𝑞min-𝑛 (10°)	4 types—8 numbers; (AC2, AC7, EC3AC7, EC6AC2, EC8AC9, EC3AC8, EC2, EC4, EC6); 5 types—12 numbers; (AC2, AC7, EC3AC7, EC1AC2, EC6SD6, EC7SD7, EC8SD8, EC6AC2, EC2, EC4)	4 types—6 numbers; (S6, S7, S10, V6C2, V8C9); 5 types—5 numbers; (EC7SD7, EC6AC2, SD10, SD7, SD6)