Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management

Abstract

Currently, research work is primarily dependent on the collection of large sets of data from systems and making predictions based on the knowledge obtained from the data, which is generally termed as 'data mining'. These data mining algorithms are of great importance in improving the performance of different applications. In this regard, Machine Learning (ML) algorithms have been demonstrated to be excellent tools to cope with difficult problems. In this paper, a classification learner based supervised ML algorithm is proposed for intentional islanding of DERs based on the live data collected from supervisory control and data acquisition (SCADA) system in post disaster situations. Literature presents various islanding detection techniques and also intentional islanding algorithms to address different problems in AC networks. These algorithms majorly work based on the control of current source or voltage source inverters. On the other hand, a low voltage DC distribution system allowing the removal of inverter is proposed, which is supposed to be more advantageous by reducing losses and is also economical when working with DERs. In this paper, ML based intentional islanding algorithm for DERs based low voltage DC distribution system is proposed by considering the effects of natural disasters. The learner models trained are fine tree, linear SVM, quadratic SVM and Gaussian SVM. The training of fine tree model is achieved with higher accuracy of 99.8%. The main objective of this work is to achieve a faster and accurate decision making. The performance of the ML based intentional islanding algorithm is compared with the earlier proposed artificial intelligence (AI) based intentional islanding algorithms. The AI algorithms proposed earlier are fuzzy inference systems (FIS), artificial neural networks (ANN) and adaptive network based fuzzy inference system (ANFIS). The comparison shows that, the decision making with ML based intentional islanding algorithm is faster and accurate than all other algorithms.

Research on Data Mining Algorithm of Associated User Network Based on Multi-Information Fusion

“…Today, recommendation system is online shopping, video websites, and search engines, and many other fields have been rapidly developed. Relevant researches in China emerge endlessly and become a hot topic in academia and industry in recent years [2].…”

Predicting Students Performance Using Supervised Machine Learning Based on Imbalanced Dataset and Wrapper Feature Selection

“…The algorithms fall in approaches such as (decision tree (DT) (Random Forest, Random Tree, LMT, and J48), naïve Bayes (Naïve Bayes, and Bayes Net), Logistic (Logistic and Simple Logistic), and Support Vector Machine (SMO)). DT is one of the supervised ML approaches that aim to build a training model to be used in predicting the final class attribute [29]. DT classifiers are widely used in different sectors and have proved their accuracies in the fields of education [11], [30][31], healthcare [32], wireless sensor networks [33], image processing [34][35], and disaster management [36][37].…”

“…The more FP, the more predicted incorrect cases. The precision represents the relevant cases among the predicted cases [29]- [31]. One of the performance criteria that determines the optimal classifiers is the Receiver Operating Characteristic (ROC) curve, where ROC is considered one of the standard techniques that summarize classifier performance over a range of tradeoffs between TP and FP error rates [32][28].…”

Pattern classification of bearing faults in PMSM based on time domain feature ensembles

“…The identification of bearing faults using traditional machine-learning techniques is highly dependent on features and classification techniques. To diagnose bearing faults using pattern classification, researchers have investigated various time domain [7], frequency domain [8], and time-frequency domain [9] features using a variety of classifiers [10], and [11]. Features for bearing fault diagnostics must be able to recognize bearing defects regardless of their signals under all operating circumstances.…”