A SAX-based method for extracting features of electricity price in power markets

Mori, H.; Umezawa, Yasushi

doi:10.1109/td-asia.2009.5356833

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…[4], that features data dimensionality reduction and lower bounding capability [5] which are essential for data mining tasks. Only a few applications of this method in power systems [6] have been observed so far. Authors in this paper have reduced a number of electricity price time series data into SAX form prior to clustering resulting in improved computational performance.…”

Section: Introductionmentioning

confidence: 99%

A SAX-Based Advanced Computational Tool for Assessment of Clustered Rooftop Solar PV Impacts on LV and MV Networks in Smart Grid

Alam

Muttaqi

Sutanto

2013

IEEE Trans. Smart Grid

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Future distribution networks with increasing level of solar PV penetration will be managed using smart grid technologies capable of producing appropriate and timely response during normal and abnormal operational events. Distribution feeder loads vary throughout the day according to the trend of consumption of the customers. Solar PV outputs fluctuate in proportion to irradiance level of sun. Simultaneous occurrence of both of these variations would result in various operating conditions that may lead to unexpected events, and would require a large amount of network data to be processed and analyzed for decision making. It is envisaged that such data will be available in the future grids with the availability of smart technologies and advanced communication in residential dwellings, commercial buildings and industrial complexes. In this paper, an advanced intelligent computational tool is developed to characterize and analyze the large amount of data associated with wide variations in network behavior using SAX (Symbolic Aggregate Approximation) and pattern recognition. The proposed tool is capable of dealing with network asymmetry, load unbalance, single-phase solar PV integration and their impacts on upstream networks and will assist in making right and timely decision to mitigate adverse impacts of solar PV. The proposed tool has been tested with a practical three-phase distribution system in Australia and can provide an extensive assessment with less computational efforts and time. © 2010-2012 IEEE.

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Section: Introductionmentioning

confidence: 99%

A SAX-Based Advanced Computational Tool for Assessment of Clustered Rooftop Solar PV Impacts on LV and MV Networks in Smart Grid

Alam

Muttaqi

Sutanto

2013

IEEE Trans. Smart Grid

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“…Further, the tool also employs a load flow algorithm demanding less computation effort and performs in-depth analysis of the identified data of interest. Reference [57] uses SAX to extract important features of electricity prices from a real time market data. Electricity price curves are initially discretized using SAX and then a kmeans algorithm is employed to extract different features.…”

Section: This Thesis Employs a Discretization Technique Called Symbolmentioning

confidence: 99%

Data Mining Framework for Monitoring Attacks In Power Systems

Alluri¹

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Vast deployment of Wide Area Measurement Systems (WAMS) has facilitated in increased understanding and intelligent management of the current complex power systems. Phasor Measurement Units (PMU's), being the integral part of WAMS transmit high quality system information to the control centers every second. With the North American Synchro Phasor Initiative (NAPSI), the number of PMUs deployed across the system has been growing rapidly. With this increase in the number of PMU units, the amount of data accumulated is also growing in a tremendous manner. This increase in the data necessitates the use of sophisticated data processing, data reduction, data analysis and data mining techniques. WAMS is also closely associated with the information and communication technologies that are capable of implementing intelligent protection and control actions in order to improve the reliability and efficiency of the existing power systems. Along with the myriad of advantages that these measurements systems, informational and communication technologies bring, they also lead to a close synergy between heterogeneous physical and cyber components which unlocked access points for easy cyber intrusions. This easy access has resulted in various cyber attacks on control equipment consequently increasing the vulnerability of the power systems. This research proposes a data mining based methodology that is capable of identifying attacks in the system using the real time data. The proposed methodology employs an online clustering technique to monitor only limited number of measuring units (PMU's) deployed across the system. Two different classification algorithms are implemented to detect the occurrence of attacks along with its location. This research also proposes a methodology to differentiate physical attacks with malicious data attacks and declare attack severity and criticality. The proposed methodology is implemented on IEEE 24 Bus reliability Test System using data generated for attacks at different locations, under different system topologies and operating conditions. Different cross validation studies are performed to determine all the user defined variables involved in data mining studies. The performance of the proposed methodology is completely analyzed and results are demonstrated. Finally the strengths and limitations of the proposed approach are discussed. First, I would like to sincerely thank my advisor, Dr. Sarika Khushalani Solanki, for her invaluable guidance and encouragement. Next, I would like to thank my second committee member Dr. Jignesh Solanki for his constant motivation and support. I would also like to thank my other committee member Dr. Muhammed A. Choudhry, whose feedback and reviews helped me improve the quality of this thesis. I would like to specially thank the 'Three Ladies in my life', my two sisters and my mother. Their love and affection have helped me overcome the toughest of challenges during these two and half hard years. Also I would also like to thank my dad for his support throughout my studies...

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Power system state recognition using data mining algorithms

Alluri

Solanki

2013

2013 North American Power Symposium (NAPS)

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A SAX-based method for extracting features of electricity price in power markets

Cited by 4 publications

References 13 publications

A SAX-Based Advanced Computational Tool for Assessment of Clustered Rooftop Solar PV Impacts on LV and MV Networks in Smart Grid

A SAX-Based Advanced Computational Tool for Assessment of Clustered Rooftop Solar PV Impacts on LV and MV Networks in Smart Grid

Data Mining Framework for Monitoring Attacks In Power Systems

Power system state recognition using data mining algorithms

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