Machine learning techniques for diagnosing and locating faults through the automated monitoring of power electronic components in shipboard power systems

2009 ◽  
Author(s):  
A.J. Mair ◽  
E.M. Davidson ◽  
S.D.J. McArthur ◽  
S.K. Srivastava ◽  
K. Schoder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Machine Learning Techniques ◽  
Power Electronic ◽  
Electronic Components ◽  
Automated Monitoring ◽  
Shipboard Power Systems ◽  
Shipboard Power ◽  
Learning Techniques

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Liu ◽  
Youbo Liu ◽  
Junyong Liu ◽  
Maozhen Li ◽  
Tingjian Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Power Systems ◽  
High Performance ◽  
Iteration Process ◽  
Machine Learning Techniques ◽  
Stability Assessment ◽  
Ensemble Techniques ◽  
The Neural Network ◽  
Learning Techniques

Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

scholarly journals Fast Coordination of Power Electronic Converters for Energy Routing in Shipboard Power Systems

2018 ◽  
Author(s):  
H L Ginn III ◽  
J D Bakos ◽  
Fred Flinstone ◽  
A Benigni
Keyword(s):  
Power Systems ◽  
Power Electronics ◽  
Energy Management ◽  
Energy Flow ◽  
Distribution Systems ◽  
Building Blocks ◽  
Power Electronic ◽  
Power Electronic Converters ◽  
Shipboard Power Systems ◽  
Shipboard Power

A long-term goal of future naval shipboard power systems is the ability to manage energy flow with sufficient flexibility to accommodate future platform requirements such as, better survivability, continuity, and support of pulsed and other demanding loads. To attain this vision of   shipboard energy management, shipboard power and energy management systems must coordinate operation of all major components in real-time. The primary components of a shipboard power system are the generators, energy storage modules, and increasingly power electronics that interface those sources and main load centers to the system. Flexible management of energy flow throughout shipboard distribution systems can be realized by automated coordination of multiple power electronic converters along with storage and generation systems. Use of power converters in power distribution systems has continuously increased due to continued development of the power electronics building blocks (PEBB) concept which reduces cost and increasing reliability of converters. Recent developments in SiC power devices are yielding PEBBs with far greater switching frequencies than Si based devices resulting in an order of magnitude reduction of the time scales as compared to converter systems utilizing conventional IGBT based PEBBs. In addition there have also been advancements in highly modularized converter systems with hundreds of PEBBs such as the Modular Multilevel Converter. Both of those trends have resulted in the continued evolution of the Universal Controller Architecture which attempts to standardize control interfaces for modular power electronic systems.  Further development of interface definitions and increasing communication and computational capabilities of new FPGA based controllers provides opportunities beyond simply supporting SiC PEBBs. Fast control coordination across the system using an appropriate communication architecture provides a degree of energy management not previously realizable in shipboard power systems. The paper will present recent research results in networked control architectures for power electronic converter coordination and control. It will demonstrate that current FPGA and gigabit speed serial communication technologies allow for a very high degree of energy flow control.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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