Non-intrusive load monitoring based on low frequency active power measurements

A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on ac- tive power signal is presented. This method works e ectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the Karhunen Lo ́ eve (KL) expan- sion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible ap- pliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Pos- teriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: tracebase and reference energy disaggregation data set (REDD). The pre- sented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real household

Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

IMU-based Modularized Wearable Device for Human Motion Classification

Human motion analysis is used in many different fields and applications. Currently, existing systems either focus on one single limb or one single class of movements. Many proposed systems are designed to be used in an indoor controlled environment and must possess good technical know-how to operate. To improve mobility, a less restrictive, modularized, and simple Inertial Measurement units based system is proposed that can be worn separately and combined. This allows the user to measure singular limb movements separately and also monitor whole body movements over a prolonged period at any given time while not restricted to a controlled environment. For proper analysis, data is conditioned and pre-processed through possible five stages namely power-based, clustering index-based, Kalman filtering, distance-measure-based, and PCA-based dimension reduction. Different combinations of the above stages are analyzed using machine learning algorithms for selected case studies namely hand gesture recognition and environment and shoe parameter-based walking pattern analysis to validate the performance capability of the proposed wearable device and multi-stage algorithms. The results of the case studies show that distance-measure-based and PCA-based dimension reduction will significantly improve human motion identification accuracy. This is further improved with the introduction of the Kalman filter. An LSTM neural network is proposed as an alternate classifier and the results indicate that it is a robust classifier for human motion recognition. As the results indicate, the proposed wearable device architecture and multi-stage algorithms are cable of distinguishing between subtle human limb movements making it a viable tool for human motion analysis.Comment: 10 pages, 12 figures, 28 reference

Robust non-intrusive load monitoring (NILM) with unknown loads

A Non-Intrusive Load Monitoring (NILM) method, robust even in the presence of unlearned or unknown appliances (UUAs) is presented in this paper. In the absence of such UUAs, this NILM algorithm is capable of accurately identifying each of the turned-ON appliances as well as their energy levels. However, when there is an UUA or set of UUAs are turned-ON during a particular time window, proposed NILM method detects their presence. This enables the operator to detect presence of anomalies or unlearned appliances in a household. This quality increases the reliability of the NILM strategy and makes it more robust compared to existing NILM methods. The proposed Robust NILM strategy (RNILM) works accurately with a single active power measurement taken at a low sampling rate as low as one sample per second. Here first, a unique set of features for each appliance was extracted through decomposing their active power signal traces into uncorrelated subspace components (SCs) via a high-resolution implementation of the Karhunen-Loeve (KLE). Next, in the appliance identification stage, through considering power levels of the SCs, the number of possible appliance combinations were rapidly reduced. Finally, through a Maximum a Posteriori (MAP) estimation, the turned-ON appliance combination and/or the presence of UUA was determined. The proposed RNILM method was validated using real data from two public databases: Reference Energy Disaggregation Dataset (REDD) and Tracebase. The presented results demonstrate the capability of the proposed RNILM method to identify, the turned-ON appliance combinations, their energy level disaggregation as well as the presence of UUAs accurately in real households

A real-time non-intrusive load monitoring system

A complete real-time (RT) implementation of a NonIntrusive Load Monitoring (NILM) system based on uncorrelated spectral components of the active power consumption signal is presented. Unlike existing NILM techniques that rely on multiple measurements taken at high sampling rates and, yet only proven in simulated environments, this proposed RT-NILM solution yield accurate results even with a single active power measurement taken at a low sampling rate from real-time hardware. An Active Power Meter (APM) was developed and constructed, then, used with the designed MATLAB ™ Graphical User Interface (GUI) to break down the acquired active power signal of an appliance into subspace components (SCs) so as to construct a unique information rich appliance signature via the Karhunen Love expansion (KLE). Using the same GUI, signatures for all possible device combinations were constructed to form the appliance signature database. Then, a separate GUI was designed to identify the turned-on appliance combination in the current time window after reading the total power consumption of a device combination via the constructed APM. There in the identification process, SC level power conditions were used to reduce the number of possible appliance combinations rapidly before applying the maximum a posteriori estimation. The proposed RT-NILM implementation was validated by feeding the data in real-time from a laboratory arrangement consisting of ten household appliances

A complete state estimation algorithm for a three-phase four-wire low voltage distribution system with high penetration of solar PV

Low Voltage Distribution Grids (LVDGs) become highly unbalanced due to the advent of single-phase solar PV plants. As a result, the voltage and current levels of the neutral conductor show a significant increase. Therefore, monitoring of the entire state of the network is essential. However, the existing state estimation algorithms estimate voltage states of the phase conductors while ignoring the state of the neutral conductor. This paper presents a novel approach to estimate the complete state of the LVDGs. A novel state reduction method was introduced to model the three-phase four-wire feeder line using a admittance matrix, which incorporates the neutral coupling effect on phase conductors. Next, the reduced admittance matrix together with the linear approximations of active and reactive power functions were combined to formulate the Low Voltage-Linear State Estimation (LV-LSE) algorithm. Finally, the performance of LV-LSE algorithm was analyzed for different measurement uncertainties, scales of line lengths of the network, and data-loss conditions. Results show that, for all the cases, LV-LSE algorithm together with the proposed reduction method can estimate voltage states with an average maximum voltage magnitude error of less than pu and current states with an average maximum current magnitude error of less than pu

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

Real-time non-intrusive appliance load monitoring under supply voltage fluctuations

This paper presents a complete real-time implementation of a Non-Intrusive Appliance Load Monitoring (NIALM) system that, is robust under residential voltage level fluctuations. Existing NIALM techniques rely on multiple measurements taken at high sampling rates, but, only have been proven in simulated environments without even considering the effect of residential voltage level fluctuations - which is a severe problem in power systems of most developing countries like Sri Lanka. In contrast, through the NIALM method proposed in this paper, accurate load monitoring results were obtained in realtime using only smart meter measurements taken at a low sampling rate from a real appliance setup under residential voltage level fluctuations. In the proposed NIALM method, initially in the learning phase, a properly constructed MATLABTM Graphical User Interface (GUI) was used to acquire signals of each appliance active power consumption and voltage levels. Then, obtained active power measurements were separated into subspace components (SCs) via the Karhunen Loeve' Expansion (KLE) while also taking the voltage variations into account. Using those SCs, a unique information rich appliance level signature database was constructed and it was then used to obtain the signatures for all possible device combinations. Next, a separate GUI was designed to identify the turned ON appliance combination in the current time window using the pre-constructed signature databases, after reading the total residential active power consumption and the supply voltage. To validate the proposed real-time NIALM implementation, data from a laboratory arrangement consisting of ten household appliances was used. From the results, it was found that the proposed method is capable of accurately identifying the turned on appliances even under severe residential supply voltage level fluctuations

Generalized approach to assess and characterise the impact of solar PV on LV networks

Many of the studies which analyse the impact of solar photovoltaic (PV) on low voltage distribution networks (LVDNs) are based on sample networks or synthetic networks such as IEEE test cases. Therefore, the conclusions drawn in these studies are often specific to the study cases, limiting their applicability for generalization. This paper proposes a methodology that can generate a multitude of network topologies that have statistically similar characteristics to a selected cohort of existing networks. Furthermore, a stochastic evaluation based on the Monte Carlo technique is utilized to analyse the impacts of solar PV on generated LVDN models. A case study was conducted on ten networks that were generated statistically similar to existing urban networks to allow for a more generalized study. A total of 1000 Monte Carlo simulations for each network was carried out to accurately identify the most representative parameters that reflect the voltage rise and voltage unbalance in the considered LVDN cohort. Two parameters, namely: the momentum of the PV capacity and the mean absolute deviation, were identified from the case studies as the most representative parameters to analyse the impact of voltage rise and voltage unbalance factor. Thereafter a generalized framework based on these two parameters was derived to determine the impact of PV connections to the selected cohort of networks. This framework facilitates an efficient process for the utility supplier to determine the impact of incorporating new PV connections without the need for extensive studies