Estimation of inherent governor dead-band and regulation using unscented Kalman filter

The inclusion of the governor droop and dead-band in dynamic models helps to reproduce the measured frequency response accurately and is a key aspect of model validation. Often, accurate and detailed turbine-governor information are not available for various units in an area control centre. The uncertainty in the droop also arise from the nonlinearity due to the governor valves. The droop and deadband are required to tune the secondary frequency bias factors, and to determine the primary frequency reserve. Earlier research on droop estimation did not adequately take into account the effect of dead-band and other nonlinearities. In this paper, unscented Kalman filter is used in conjunction with continuously available measurements to estimate the governor droop and the dead-band width. The effectiveness of the approach is demonstrated through simulation

Feasibility study of applicability of recurrence quantification analysis for clustering of power system dynamic responses

A methodology based on Recurrence Quantification Analysis (RQA) for the clustering of generator dynamic behavior is presented. RQA is a nonlinear data analysis method, which is used in this paper to extract features from measured generator rotor angle responses that can be used to cluster generators in groups with similar oscillatory behavior. The possibility of extracting features relevant to damping and frequency of oscillations present in power systems is studied. The k-Means clustering algorithm is further used to cluster the generator responses in groups exhibiting well or poorly damped oscillations, based on the extracted features from RQA. The effectiveness of RQA is investigated using simulated responses from a modified version of the IEEE 68 bus network, including renewable energy resources

Residential end-uses disaggregation and demand response evaluation using integral transforms

[EN] Demand response is a basic tool used to develop modern power systems and electricity markets. Residential and commercial segments account for 40%-50% of the overall electricity demand. These segments need to overcome major obstacles before they can be included in a demand response portfolio. The objective of this paper is to tackle some of the technical barriers and explain how the potential of enabling technology (smart meters) can be harnessed, to evaluate the potential of customers for demand response (end-uses and their behaviors) and, moreover, to validate customers' effective response to market prices or system events by means of non-intrusive methods. A tool based on the Hilbert transform is improved herein to identify and characterize the most suitable loads for the aforesaid purpose, whereby important characteristics such as cycling frequency, power level and pulse width are identified. The proposed methodology allows the filtering of aggregated load according to the amplitudes of elemental loads, independently of the frequency of their behaviors that could be altered by internal or external inputs such as weather or demand response. In this way, the assessment and verification of customer response can be improved by solving the problem of load aggregation with the help of integral transforms.This work has been supported by Spanish Government (Ministerio de Economia, Industria y Competitividad) and EU FEDER fund (No. ENE2013-48574-C2-2-P&1-P, No. ENE2015-70032-REDT).Gabaldón Marín, A.; Molina, R.; Marin-Parra, A.; Valero, S.; Álvarez, C. (2017). Residential end-uses disaggregation and demand response evaluation using integral transforms. Journal of Modern Power Systems and Clean Energy. 5(1):91-104. https://doi.org/10.1007/s40565-016-0258-8S9110451Chardon A, Almén O, Lewis PE (2009) Demand response: a decisive breakthrough for Europe. Capgemini, Enerdata. https://www.capgemini.com/resources/demand_response_a_decisive_breakthrough_for_europeFaruqui A, Harris D, Hledick R (2010) Unlocking the €53 billion savings from smart meters in the EU: how increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy 38(10):6222–6231Federal Energy Regulatory Commission (FERC) (2015) Assessment of demand response and advanced metering, staff report. http://www.ferc.gov/legal/staff-reports/2015/demand-response.pdfFERC Order 745 (December 2011) & 719 (December 2009). http://www.ferc.gov/legal/maj-ord-reg.aspEuropean Commission (2011) Energy efficiency plan 2011, COM (2011) 109 final. http://eur-lex.europa.eu/LexUriServJoitn Research Center (JRC), European Commission (2010) Energy Service Companies market in Europe, status report 2010. http://publications.jrc.ec.europa.eu/repository/handle/111111111/15108European Commission (2011) Impact assessment accompanying document “Energy Efficiency Plan 2011”, SEC (2011) 277 final. http://www.eurosfaire.prd.fr/7pc/bibliotheque/consulter.php?id=2357European Environment Agency (EEA) (2012) Final energy consumption by sector and fuel. http://www.eea.europa.eu/data-and-maps/indicators/final-energy-consumption-by-sector-8/assessment-2Piette MA, Watson D, Motegi N et al (2007) Automated critical peak pricing field tests: 2006 pilot program description and results. California Energy Commission, PIER Energy Systems Integration Research Program: CEC-500-03-026Gomatom K, Holmes C, Kresta D (2013) Non-intrusive load monitoring. https://www.e3tnw.orgDimetrosky S, Parkinson K, Lieb N (2013) Residential lighting evaluation protocol, Report NREl/SR-7A30-53827. National Renewable Energy LaboratoryZeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook. IEEE Trans Consum Electron 57(1):76–84Liang J, Simon K, Kendall G et al (2010) Load signature study part I: basic concept, structure and methodology. IEEE Trans Power Deliv 25(2):551–560Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891Powers J, Margossian B, Smith B (1991) Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data. IEEE Comput Appl Power 4(2):42–47Farinaccio L, Zmeureanu R (1999) Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy Build 30(3):245–259Marceau ML, Zmeureanu R (2000) Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings. Energy Convers Manag 41(13):1389–1403Baransju M, Voss J (2004) Genetic algorithm for pattern detection in NIALM systems. In: IEEE international conference on systems, man and cybernetics, 10–13 Oct, pp 3462–3468Baranski H, Voss J (2004) Detecting patterns of appliances from total load data using a dynamic programming approach. In: Fourth IEEE international conference on data mining (ICDM’04), 1–4 Nov, Brighton, UK, pp 327–330Sankara A (2015) Energy disaggregation in NIALM using hidden Markov models. Masters Theses, Missouri University of Science and Technology, Paper 7414Kolter J, Jaakkola T (2012) Approximate inference in additive factorial HMMs with application to energy disaggregation. J Mach Learn 22:1472–1482Leeb SB, Shaw SR, Kirtley SL (1995) Transient event detection in spectral envelope estimates for nonintrusive load monitoring. IEEE Trans Power Deliv 10(3):1200–1210Patel SN, Robertson T, Kientz JA et al (2007) At the flick of a switch: detecting and classifying unique electrical events on the residential power line. In: Conference on ubiquitous computing, pp 271–288Bonglifi R, Squartini S, Fagiani M et al (2015) Unsupervised algorithms for non-intrusive load monitoring: an up-to-date overview. In: EEEIC conference. doi: 10.1109/EEEIC.2015.7165334Browne TJ, Vittal V, Heydt GT et al (2008) A comparative assessment of two techniques for modal identification from power system measurements. IEEE Trans Power Syst 23(3):1408–1415Senroy N, Suryanarayanan S, Ribeiro PF (2007) An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality. IEEE Trans Power Syst 22(4):1843–1850Gabaldon A, Ortiz M, Molina R et al (2014) Disaggregation of electric loads of small customers through the application of the Hilbert transform. Energ Effic 7(4):711–728. doi: 10.1007/s12053-013-9250-6Kolter JZ, Johnson MJ (2011) REDD: a public data set for energy disaggregation research. http://101.96.10.59/people.csail.mit.edu/mattjj/papers/kddsust2011.pdfFibaro wall switches. http://www.fibaro.com/en/the-fibaro-system/IP-Symcon: innovative centre for the entire building automation. https://www.symcon.de/Energy Information Administration (2001) End-use consumption electricity 2001. http://www.eia.gov/emeu/recs/recs2001/enduse2001/enduse2001.htmlPoularikas AD (1999) The Hilbert transform, handbook of formulas and tables for signal processing. CRC Press LLC, Boca RatonHuang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proced R Soc Lond 454(1971):903–995Deering R, Kaiser JF (2005) The use of a masking signal to improve empirical mode decomposition. In: Proceedings IEEE international conference acoustic, speech, and signal processing, pp 485–488Liang J, Simon K, Kendall G, Cheng J et al (2010) Load signature study part II: disaggregation framework, simulation, and applications. IEEE Trans Power Deliv 25(2):561–569Load participation in ancillary services. http://www1.eere.energy.gov/analysis/pdfsGabaldón A, Guillamón A, Ruiz MC et al (2010) Development of a methodology for clustering electricity-price series to improve customer response initiatives. IET Gener Transm Distrib 4(6):706–71

An improved Hilbert–Huang method for analysis of time-varying waveforms in power quality

The Hilbert-Huang method is presented with modifications, for time-frequency analysis of distorted power quality signals. The empirical mode decomposition (EMD) is enhanced with masking signals based on fast Fourier transform (FFT), for separating frequencies that lie within an octave. Further, the instantaneous frequency and amplitude of the constituent modes obtained by Hilbert spectral analysis are improved by demodulation. The method shows promising time-frequency-magnitude localization capabilities for distorted power quality signals. The performance of the new technique is compared with that of another multiresolution analysis tool, the S-transform-a phase corrected wavelet transform. Analysis on actual measurements of transformer inrush current from an existing laboratory setup is used to demonstrate this technique

Small signal analysis of integrated power systems

SMALL SIGNAL ANALYSIS OF INTEGRATED POWER SYSTEMS is an essential tool to discover possible low frequency oscillations which if undamped may lead to major power failures. This book covers various aspects of this phenomenon from modeling to techniques to control them. The book covers low frequency in the 1-3 Hz range as well as sub synchronous oscillations in the 10-50Hz range. Damping techniques for both types of oscillations are discussed as well as design of Power System stabilizers. Modeling and design of FACTS devices in included. Selective computation of Eigenvalue(s) in a large system is discussed. Wind power systems and its integration into the existing grid is discussed along with small signal analysis

Fast online identification of power system dynamic behaviour

This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors