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High voltage direct current (HVDC) transmission systems are suitable for power transfer to meet the increasing demands of bulk energy and encourage interconnected power systems to incorporate renewable energy sources without any fear of loss of synchronism, reliability, and efficiency. The main challenge associated with DC grid protection is the ti...
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... the scenario of non-linear classification, K(x, x i ) represents the traditional Euclidean inner product of the input vector x with the linear transformation ϕ(x i ) of the support vector x i . The proposed flow chart of support vector machine-based classification is demonstrated in Figure 3. ...
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... scenario-I, the trained SVM is tested with the data of DC voltage and current measured at RS-I to determine the location of PPG fault and no-fault cases, as shown in Figure 30. Piecewise changes for magnitude are observed in positive pole current I p and positive pole voltage V p . ...
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... it provides an insight that this methodology of fault location can be extended to any number of VSCs for an MT-HVDC system, provokes its practical implementation. Moreover, performance evaluation of the proposed algorithm for fault identification, classification, and the location is supported with the confusion matrix results, as shown in Figure 33. The trained algorithm is tested with true and predicted classes of data of different fault locations. ...
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... trained algorithm is tested with true and predicted classes of data of different fault locations. Accuracy is proven because of the synchronism between true classes and predicted classes, as shown in Figure 33a. The fault's location is determined with a 100% true positive rate, and the predicted location is classified accurately, depicting the successful implementation within a period of 0.15 ms, as shown in Figure 33b. ...
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... is proven because of the synchronism between true classes and predicted classes, as shown in Figure 33a. The fault's location is determined with a 100% true positive rate, and the predicted location is classified accurately, depicting the successful implementation within a period of 0.15 ms, as shown in Figure 33b. The probability of predicting the positive value of fault location is 1, as shown in Figure 33c. ...
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... fault's location is determined with a 100% true positive rate, and the predicted location is classified accurately, depicting the successful implementation within a period of 0.15 ms, as shown in Figure 33b. The probability of predicting the positive value of fault location is 1, as shown in Figure 33c. Hence, the probability of discovering the false value of fault is zero, which depicts the excellent performance of the proposed technique for determining fault location in MT-HVDC systems. ...
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... Binary Classification using SVM [49] Fig 2 : Multiclass Classification using SVM[49] ...
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... Numerous studies have attempted to employ machine learning techniques for HVDC system failure detection, as advancements in statistics and machine learning technology continue to progress [8,9]. In reference [10], the characteristics of DC voltage and DC current are extracted using principal component analysis (PCA), and then these features are trained and tested using support vector machine (SVM) for fault identifcation and classifcation. Te authors of [11] proposed a fault identifcation technique based on K-means that utilized voltage and current information from the inverter of a two-terminal HVDC system to identify faults. ...
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... Factors, including but not limited to the structure of the grid, operation mode, equipment efficiency, weather conditions, user behavior, etc. Using PCA, we can convert these complex, multi-dimensional data into a few major influencing factors, which is convenient for analysis and optimization [8][9]. Subsequently, these data are processed by PCA to extract the main components [10]. ...
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... The SVM Method[62]. ...
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... Some researchers consider SVM as a special type of artificial neural network (ANN) (Haykin, 1999;Ren, 2012). The development of SVM involves a rapid mathematical and statistical approach (Zhang and Wang, 2019;Wu et al., 2019;Lala et al., 2019;Wang et al., 2020;Baghaee et al., 2020a,b;Muzzammel, 2019a;Muzzammel and Raza, 2020). SVM is applicable for both binary and multi-class problems. ...
The interconnection of renewable energy sources to conventional power grids is widely recognized and promoted by the deployment of high voltage direct current (HVDC) technology. One of the biggest challenges associated with the HVDC systems is the rapid rate of rising DC fault currents, resulting in the immediate collapse of the converter stations. Conventional schemes of AC protection and low-speed DC protection are not worthy and mature because of the abrupt rising characteristics of DC currents. Therefore, protection techniques are being developed for reliable bulk power transfer and interconnection of unsynchronized grids via DC links. Timely identification, classification, and determination of location are the most fundamental characteristics of any protection scheme. This research paper proposes a machine learning-based protection method that establishes a rapid and reliable solution addressing the drawbacks of existing protection schemes. A multi-terminal high voltage direct current (MT-HVDC) test system is designed in Matlab/Simulink. Pole-to-ground and pole-to-pole faults are applied at different fault locations to assess the validity of the proposed algorithm. For this, DC voltages and currents are measured and analyzed. Power spectral analysis of traveling waves and Wavelet multiscale principal components analysis are employed for the extraction of featured data to classify and locate the DC faults. This reduced dimensional featured data is utilized for training and testing the support vector machine learning algorithm to analyze the accuracy of the proposed protection technique. Simulation results confirm the accuracy of rapid identification, classification, and location of DC faults.
... In addition to NN, a few other ML techniques such as fuzzy logic [145], support vector machine [146][147][148], K nearest neighbor [149,150], etc. are also used for HVDC protection. A fuzzy-logic-based digital distance scheme is proposed in [145]. ...
... Johnson and Yadav [147] proposed an SVM-based method for HVDC fault recognition where the one-end DC voltage and current signals for a half cycle (before and after the occurrence of a fault) are used as input features. Muzzammel and Raza [148] have also suggested the SVM model for fault diagnosis in a VSC-MT-HVDC system, where the features extracted through principal component analysis (PCA) are used for the training and testing of SVM. Johnson and Yadav [149] have tested the applicability of the KNN network for fault-type recognition in a monopolar HVDC system. ...
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... These hyperplanes are selected in a manner that optimizes the distance from the closest data point in every class. The linear SVM is the simplest and fastest procedure, assuming a linear division of classes [19,20]. The specific steps involved in training an SVM model for sentiment analysis are below: ...
... SVM algorithm[20] ...
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... Floods are major weather-related phenomena that continue to generate significant economic and human losses across the world, amounting to tens of billions of dollars in annual damages and thousands of deaths [1,2]. As a result, understanding the causes of floods' temporal change and unpredictability, as well as flood-related variables (such as precipitation, river flow, and flood loss), is both theoretical and practical [1,3]. ...
... After determining the hyperplane, a different dataset may be fed into the SVM classifier. Depending on the direction of the input in relation to the test set, class is given +1 or 1 [2] as shown in Figure 16, in the event of a classification method with many classes. It is often used in flood modeling. ...
... Binary classification of SVM[2]. ...
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... (d) Random forests build multiple decision trees to classify an event and avoid issues such as overfitting data due to outliers [204]. Adapted from CC BY-SA 3.0 [209], CC BY 4.0 [210], CC BY-SA 3.0 [211], and CC BY-SA 4.0 [212]. ...
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