(637e) Simulation and Analysis of Power Systems Using Non-Intrusive Appliance Load Monitoring | AIChE

(637e) Simulation and Analysis of Power Systems Using Non-Intrusive Appliance Load Monitoring


Patel, N. - Presenter, Indian Institute of Technology Gandhinagar
Srinivasan, B., Indian Institute of Technology Gandhinagar
Srinivasan, R., Indian Institute of Technology Gandhinagar
With the increase in world population and constant improvement in peopleâ??s living standard, world electricity demand is increasing every year. In 2013, 67.4 % of electricity was generated through fossil fuels. Electricity generation through fossil fuel is generated in bulk and is transmitted to long distances by stepping up the voltage. The conventional gridâ??s infrastructure is made considering bulk generation from fossil fuels. But fossil fuel are finite in quantity and lead to environmental pollution. This has motivated us to shift to a grid which will have rapid monitoring and transmit great share of electricity generated from renewable energy sources (RES). Also the renewable energy sources (RES) have non uniform spatial distribution and the generation is either DC or at variable frequency. So the future gridâ??s source will have intermittent and distributed characteristics of the load.

The future grid with high penetration of RES, electric vehicle and storage will need of an optimal approach to match generation and demand. A significant amount of total generated electricity is consumed by residential sector. The residential sector has the potential to reduce the electricity waste and shift the demand on time scale. But to know when and what to shift we need proper monitoring and modelling of residential grid. An approach to monitor the residential load using information available from smart meter connected at utility entry point in a house is called Non-intrusive Load Monitoring (NILM) [2,3] The monitoring done through smart meter requires measurement of the aggregate electricity consumed in a house at a frequency of 1 Hz or higher. Non-intrusive load monitoring (NILM) technique identifies the appliance level electricity consumption pattern from the aggregate monitored data of a house. At industrial level technique was used for real time power factor correction [1]. NILM can also identify power quality offending loads.

This paper will discuss a Non-Intrusive Load Monitoring (NILM) technique which will identify the appliances operating at any time from a house hold aggregate energy meter data. Depending on the basic electrical components, the front end circuit and the task performed by an appliance, each appliance will have distinct power consumption level, power consumption trend and transient behaviour. The proposed approach uses multiple algorithms, each one specific to a class of appliances, with active & reactive power, states of operation in an appliance, power consumption levels and other features to detect the on/off state of various appliances [3,4,5]. The proposed approach is to be tested on various household aggregate energy datasets for identification of operating state of various appliances. Results from application of this approach to various Indian households will help identify the device utilization profile along with its power consumption, a key ingredient for modelling and simulation of residential electric systems. The information extracted form NILM will help in development of bottom up models to study the demand profile with vary with time and the region specific capability of AC/DC grid.


[1] G. W. Hart, â??Nonintrusive Appliance Load Monitoring,â? 1992.

[2] J. Liang, S. K. K. Ng, G. Kendall, and J. W. M. Cheng, â??Load Signature Study â?? Part Iâ?¯: Basic Concept , Structure , and Methodology,â? IEEE Trans. POWER Deliv., vol. 25, no. 2, pp. 551â??560, 2010.

[3] P. Ducange, F. Marcelloni, and M. Antonelli, â??A novel approach based on finite-state machines with fuzzy transitions for nonintrusive home appliance monitoring,â? IEEE Trans. Ind. Informatics, vol. 10, no. 2, pp. 1185â??1197, 2014

[4] D. Srinivasan, S. Member, W. S. Ng, A. C. Liew, and S. Member, â??Neural-Network-Based Signature Recognition for Harmonic Source Identification,â? IEEE Trans. Power Deliv., vol. 21, no. 1, pp. 398â??405, 2006.

[5] D. He, S. Member, L. Du, S. Member, Y. Yang, R. Harley, and T. Habetler, â??Front-End Electronic Circuit Topology Analysis for Model-Driven Classi fi cation and Monitoring of Appliance Loads in Smart Buildings,â? vol. 3, no. 4, pp. 2286â??2293, 2012.