A fast event detection algorithm for residential loads within normal and disturbed operating conditions

A fast event detection algorithm for residential loads within normal and disturbed operating conditions

Faten Mouelhi Houda Ben Attia Sethom Ilhem Slama-Belkhodja Laurence Miègeville Patrick Guerin

Université de Tunis El Manar, Ecole Nationale d’Ingénieurs de Tunis, LR 11ES 15, Laboratoire des Systèmes Electriques

Université de Carthage, Ecole Nationale d’Ingénieurs de Carthage 2035, Tunis, Tunisia

Université de Nantes, Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA), l'IREENA, EA 4642, Saint-Nazaire

Corresponding Author Email: 
3 May 2015
2 February 2016
30 April 2016
| Citation



This paper deals with the classification and identification methods applied for the residential sector power management issue. A fast event detection algorithm is then proposed and applied to the identification of the load status changes occurred during a home facilities operation. Because the residential load current or the supply voltage can have harmonic components, the second idea developed by this paper is the consideration of the electrical grid harmonic disturbances that may affect the detection signals notifying the load status changes. Then the proposed event detection algorithm was applied to properly identify the status changes of a large variety of grid connected residential loads.


demand side management, event detection, steady and transient state, households, power quality, harmonic disturbances.

1. Introduction
2. Survey and analysis of residential loads
3. Algorithm implementation for load event detection
4. Conclusion

Basu K., Debusschere V., Douzal-Chouakria A., Bacha S. (2015). Time series distance-based methods for non-intrusive load monitoring in residential buildings. Energy Build., vol. 96, p. 109-117.

Berges M., Goldman E., Matthews H.S., Soibelman L., Anderson K. (2011). User-centered non-intrusive electricity load monitoring for residential buildings. J. Comput. Civil Eng, vol. 25, n° 6, p. 47-480.

Berriri H., Naouar M.W., Belkhodja I.S. (2012). Easy and Fast Sensor Fault Detection and Isolation Algorithfor Electrical Drives. IEEE Trans. on Power Electronics, vol. 27, n° 2, p. 49-499.

Chaouch M. (2014). Clustering-Based Improvement of Nonparametric Functional Time Series Forecasting: Application to Intra-Day Household-Level Load Curves. IEEE Trans. on Smart Grid, vol. 5, n° 1, p. 411-419.

Chen Z., Wu L. (2013). Residential Appliance DR Energy Management With Electric Privacy Protection by Online Stochastic Optimization. IEEE Trans. on Smart Grid, vol. 4, n° 4, p. 1861-1869.

Duarte C., Delmar P., Barner K. and Goossen K. (2015). A signal acquisition system for non-intrusive load monitoring of residential electrical loads based on switching transient voltages. Power Systems Conference (PSC). Clemson University, USA.

EN 50160 (2010). Voltage Characteristics in Public Distribution Systems, European standard.

Gerbec D., Gasperic S., Smon I., Gubina F. (2003). Consumers’ Load Profile Determination Based on Different Classification Methods. Power Engineering Society General Meeting Conf. vol. 2, p. 99-995.

Ghorbani M.J., Shafiee Rad M., Mokhtari H. (2011). Residential Loads Modeling by Norton Equivalent Model of Household Loads. Asia Pacific Power EEC, Conf. 2011, Wuhan, Chin, p. 1-4.

Grandjean A., Adnot J., Binet G. (2012). A review and an analysis of the residential electric load curve models. Renewable Sustainable Energy Review vol. 16, n° 9, p. 6539-6565.

He D. (2012). Front-End Electronic Circuit Topology Analysis for Model-Driven Classification and Monitoring of Appliance Loads in Smart Buildings. IEEE Trans. on Smart Grid, vol. 3, n° 4, p. 2286-2293.

IEEE Std 519 (1992). IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems.

Kim J. S., Kang H., Lee B. (2007). Analysis of Low Frequency Current Ripples of Fuel Cell Systems based on a Residential Loads Modeling. Proc of International Conf. EMS, 2007,Seoul, Korea, p. 282-287.

Kim Y., Kong S., Ko R., Joo S. (2014). Electrical Event Identification Technique for Monitoring Home Appliance Load Using Load Signatures. IEEE Inter. Conf. Consumer Electronics (ICCE), Seoul, Korea.

Kong S., Kim Y., Joo S. (2014). Cepstrum Smoothing-based Feature Extraction Method for Electric Loads Disaggregation. IEEE Inter. Conf. Consumer Electronics (ICCE), Las Vegas, vol. 3, n° 4, p. 290-291.

Kuzlu M. Pipattanasomporn M. Rahman S. (2012). Hardware Demonstration of a Home Energy Management System for Demand Response Applications. IEEE Trans. on Smart Grid, vol. 3, n° 4, p. 1704-1711.

Kwac J., Flora J., Rajagopal R. (2014). Household Energy Consumption Segmentation Using Hourly Data. IEEE Trans. on Smart Grid, vol. 5, n° 1, p. 420-430.

Laughman C. Lee K. Cox R. Shaw S. Leeb S. Norford L. and Armstrong P. (2003). Power signature Analysis. IEEE Power Energy Mag. vol. 1, n° 2, p. 56-63.

Li Y., Wolfs P. J. (2013). A Hybrid Model for Residential Loads in a Distribution System With High PV Penetration. IEEE Trans. on Power Systems, vol. 28, n° 3, p. 3372-3379.

Liang J., Ng S. K., Kendall G., Cheng J. W. (2010). Load signature study. Part II: Disaggregation framework, simulation, and applications. IEEE Trans. on Power Delivery, vol. 25, n° 2, p. 561-569.

Manera M., Marzullo A. (2005). Modelling the load curve of aggregate electricity consumption using principal components. Environ. Model. Softw. vol. 20, n° 11, p. 1389-1400.

Mathieu J., Dyson M., Callaway D. (2012). Using Residential Electric Loads for Fast Demand Response: The Potential Resource and Revenues, the Costs, and Policy Recommendations. American Council Energy EE Conf. 2012, Berkeley, California, p. 189-203.

Munir M., Li Y. (2013). Residential Distribution System Harmonic Compensation Using PV Interfacing Inverter. IEEE Trans. on Smart Grid, vol. 4, n° 2, p. 816-827.

Najmeddine H. (2009). Méthode d’identification et de classification de la consommation d’énergie par usages en vue de l’intégration dans un compteur d’énergie électrique. Thèse en Electromagnétisme, Université Blaise Pascal, Clermont II et EDF.

Norford L. K., Leeb S. B.(1996). Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy Build., vol. 24, n° 1, p. 51-64.

Nourbakhsh G., Eden G., McVeigh D., Ghosh A. (2012). Chronological Categorization and Decomposition of Customer Loads. IEEE Trans. on Smart Grid, vol. 27, n° 4, p. 2270-2277.

Ozturk Y., Senthilkumar D., Kumar S., Lee G. (2013). An Intelligent Home Energy Management System to Improve Demand Response. IEEE Trans. on Smart Grid, vol. 4, n° 2, p. 694-701.

Patel S. N. Robertson T. Kientz J. A. Reynolds M. S. and Abowd G. D. (2007). At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award). UbiComp, Springer Berlin Heidelberg, p. 271-288.

Pipattanasomporn M., Kuzlu M., Rahman S., Teklu Y. (2014). Load Profiles of Selected Major Household Appliances and Their Demand Response Opportunities. IEEE Trans. on Smart Grid, vol. 5, n° 2, p. 742-750, March.

Pipattanasomporn M., Kuzlu M., Rahman S. (2012). An Algorithm for Intelligent Home Energy Management and Demand Response Analysis. IEEE Trans. on Smart Grid, vol. 3, n° 4, p. 2166-2173.

Richardson I., Thomson M. Infield D., Clifford C. (2010). Domestic electricity use: A high-resolution energy demand model. Energy Build., vol. 42, n° 10, p. 1878-1887.

Singh R., Singh A. (2010). Energy loss due to harmonics in residential campus. A case study. Universities Power Engineering Conference (UPEC) 2010 45th International, Cardiff, Wales. p. 1-6.

Tasdighi M., Ghasemi H., Rahimi-Kian A. (2014). Residential Microgrid Scheduling Based on Smart Meters Data and Temperature Dependent Thermal Load Modeling. IEEE Trans. on Smart Grid, vol. 5, n° 1, p. 349-357.

Tsagarakis G., Collin A.J., Kiprakis A. E. (2012). Modeling the Electrical Loads of UK Residential Energy Users. 47th International Universities Power Engineering Conference (UPEC) 2012, London, United Kingdom, p. 1-6.

Walker CF., Pokoski JL. (1985). Residential load shape modeling based on customer behavior. IEEE Power Eng. Rev. vol. PER-5, n ° 7, p. 34-34.

Wang Z., Zheng G. (2012). Residential Appliances Identification and Monitoring by a Nonintrusive Method. IEEE Trans. on Smart Grid, vol. 3, n° 1, p. 80-92.

Yi P., Dong X., Iwayemi A., Zhou C., Li S. (2013). Real-Time Opportunistic Scheduling for Residential Demand Response. IEEE Trans. on Smart Grid, vol. 4, n° 1, p. 227-234.

Zeifman M., Roth K. (2011). Nonintrusive appliance load monitoring: Review and outlook. IEEE Trans. Consum. Electron., vol. 57, n° 1, p. 76-84.

Zoha A., Gluhak A., Imran M., Rajasegarar S. (2012). Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey. Sensors, vol. 12, p. 6838-16866.