A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Damala, Rajesh Babu; Patnaik, Rajesh Kumar; Dash, Ashish Ranjan

doi:10.1186/s41601-022-00247-w

Cited by 11 publications

(6 citation statements)

References 29 publications

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“…However, it easily deviates from the minimization of capital cost because of the cost difference between different cables. To solve this problem, the DMST is proposed by further taking the cost weights of different cables into account [10]. Both MST and DMST can rapidly generate a feasible design solution for CST, but easily lead to high capital cost for a large-scale OWF without combination with other global optimization methods.…”

Section: ) Graph Theory Based Methodsmentioning

confidence: 99%

“…The main difference between the two testing systems is the testing model, including the location distribution of WTs, number of WTs, and types and cost coefficients of alternative submarine cables. In this paper, 4 algorithms combined with the improved DMST respectively, including GA, IA, SA, and ISA, are proposed to compare to the other two algorithms which are the modified genetic algorithm (MGA) and DMST [15], and DMST [10]. The main parameters needed in the optimization algorithms are given in Table Ⅲ, with the evaluation times of Pattern 1 at 5000.…”

Section: ⅳ Case Studymentioning

confidence: 99%

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Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing

Chen,

Zhang,

et al. 2024

Prot. Control Mod. Power Syst.

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During the construction of an offshore wind farm (OWF), the capital cost of the collector cable system accounts for a large proportion of the total cost. Consequently, the optimal design of the collector system topology (CST) is one of the most crucial tasks in OWF planning. However, for a large-scale OWF, the optimal design of CST is a complex integer programming problem with high-dimension variables and various constraints. Therefore, it is difficult to acquire a high-quality optimal design scheme. To address this issue, this paper proposes a new grouping-based optimal design of CST for a large-scale OWF. First, all the wind turbines are divided into multiple groups according to their geographical locations and the maximum allowed connected wind turbines by each cable. This not only reduces the optimization dimension and difficulty, but also effectively satisfies the 'no cross' constraint by putting the geographically closed wind turbines into the same group. Secondly, the electrical topology among different wind turbines in each group is initially generated by an improved dynamic minimum spanning tree (DMST). The division groups of the OWF are then adjusted to further reduce the capital cost by improved simulated annealing. To verify the proposed technique, comparison case studies are carried out with five algorithms on two different OWF. Index Terms-Offshore wind farm, collector system topology, grouping-based optimal design, meta-heuristic algorithm, graph theory. NOMENCLATURE A. Abbreviations OWFoffshore wind farm CST collector system topology DMST dynamic minimum spanning tree WT wind turbine _____________________________________

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Section: ) Graph Theory Based Methodsmentioning

confidence: 99%

Section: ⅳ Case Studymentioning

confidence: 99%

Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing

Chen,

Zhang,

et al. 2024

Prot. Control Mod. Power Syst.

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“…The accuracy rate in their method was approximately 82% and two classes were considered for diagnosis. Damala et al (2022) proposed the Teager-Kaiser Energy Operator (TKEO) algorithm associated with a decision tree-based fault classifier to identify and classify different DC faults. Shakiba et al (2022) presented a comprehensive review of different machine learning methods, including naive Bayesian classifier, decision tree, random forest, k-nearest neighbor and SVM, as well as artificial neural networks such as feed-forward neural network, convolutional neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of predicted fault tolerance based on C5.0 decision tree algorithm in irrigation system of paddy fields

Rahi,

Ebrahimnejad,

Motameni

2023

IJICC

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PurposeTaking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.Design/methodology/approachThe proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.FindingsThe proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.Research limitations/implicationsBy expanding the dimensions of the problem, the model verification space grows exponentially using automata.Originality/valueUnlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

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“…The classification and regression tree (CART) method can be utilized for the fault location of a single-phase grounding in real-time power converters [15]. The DC faults can be analyzed using the teager-kaiser energy operator algorithm which has low computation time and complexity [16].…”

Section: Introductionmentioning

confidence: 99%

FPGA-based fault analysis for 7-level switched ladder multi-level inverter using decision tree algorithm

Ramalingam

Thiagarajan

2023

IJRES

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The proposed method involves the fault analysis of the inverter switches present in the multi-level inverter (MLI) circuitry. The decision tree machine learning algorithm is incorporated for the fault analysis of the inverter switches. The multi-level inverter utilized in this work is a 7-level switched ladder multi-level inverter. There is 4 number of switches in the design of a 7-level inverter driven by the non-carrier digital pulse width modulation signals. The non-carried-based digital pulse-width modulator (DPWM) generation is generated using the event angle for the 7-level of the switched ladder inverter. The proposed method investigates the stuck-at-fault occurrences of the 4 switches in the inverter by manipulating the decision tree parameters such as entropy, information gain, and decision tree. Based on the decision tree, the very high-speed integrated circuit hardware description language (VHDL) code is developed by making use of the behavioral modeling and validated for the power, area in the Xilinx Vivado tool. The real-time feasibility is verified for the proposed method by synthesizing the developed VHDL code in the field programmable gate array (FPGA) device.

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A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Cited by 11 publications

References 29 publications

Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing

Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing

Evaluation of predicted fault tolerance based on C5.0 decision tree algorithm in irrigation system of paddy fields

FPGA-based fault analysis for 7-level switched ladder multi-level inverter using decision tree algorithm

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