Power quality: Overview and monitoring

"Power Quality" (PQ) is a generic term often used in relation with unwanted disturbances of the electricity supply. In recent years, there has been an increased number of PQ related problems. This is mainly due to the rapid growth in the use of equipment that generate PQ disturbances and also increase of equipment that are sensitive to these disturbances. This increased concern about PQ issues from both suppliers and consumers of electricity has increased the demand for advanced PQ monitoring systems. Each PQ disturbance has a unique wave shape resembling its characteristics. Therefore PQ disturbances could be identified by monitoring the voltage/current signal waveform and analysing its features. PQ monitoring instruments can vary from a simple true r.m.s. meter to advanced techniques that are capable of automatically capturing and classifying PQ events. This paper presents an overview of the characteristics, effects and causes of PQ events and addresses recent trends in PQ monitoring

Residential Appliance Identification Based on Spectral Information of Low Frequency Smart Meter Measurements

A nonintrusive load monitoring (NILM) method for residential appliances based on uncorrelated spectral components of an active power consumption signal is presented. This method utilizes the Karhunen Loéve expansion to breakdown the active power signal into subspace components (SCs) so as to construct a unique information rich appliance signature. Unlike existing NILM techniques that rely on multiple measurements at high sampling rates, this method works effectively with a single active power measurement taken at a low sampling rate. After constructing the signature data base, SC level power conditions were introduced to reduce the number of possible appliance combinations prior to applying the maximum a posteriori estimation. Then, an appliances matching algorithm was presented to identify the turned-on appliance combination in a given time window. After identifying the turned-on appliance combination, an energy estimation algorithm was introduced to disaggregate the energy contribution of each individual appliance in that combination. The proposed NILM method was validated by using two public databases: 1) tracebase; and 2) reference energy disaggregation data set. The presented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned-on appliance combinations in real households

Residential appliance identification based on spectral information of low frequency smart meter measurements

A nonintrusive load monitoring (NILM) method for residential appliances based on uncorrelated spectral components of an active power consumption signal is presented. This method utilizes the Karhunen Loéve expansion to breakdown the active power signal into subspace components (SCs) so as to construct a unique information rich appliance signature. Unlike existing NILM techniques that rely on multiple measurements at high sampling rates, this method works effectively with a single active power measurement taken at a low sampling rate. After constructing the signature data base, SC level power conditions were introduced to reduce the number of possible appliance combinations prior to applying the maximum a posteriori estimation. Then, an appliances matching algorithm was presented to identify the turned-on appliance combination in a given time window. After identifying the turned-on appliance combination, an energy estimation algorithm was introduced to disaggregate the energy contribution of each individual appliance in that combination. The proposed NILM method was validated by using two public databases: 1) tracebase; and 2) reference energy disaggregation data set. The presented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned-on appliance combinations in real households