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

AIMS Energy ◽  
2016 ◽  
Vol 4 (3) ◽  
pp. 414-443 ◽  
Author(s):  
Chinthaka Dinesh ◽  
◽  
Pramuditha Perera ◽  
Roshan Indika Godaliyadda ◽  
Mervyn Parakrama B. Ekanayake ◽  
...  
Keyword(s):  
Low Frequency ◽  
Active Power ◽  
Load Monitoring ◽  
Power Measurements

scholarly journals Event Matching Classification Method for Non-Intrusive Load Monitoring

2021 ◽  
Vol 13 (2) ◽  
pp. 693
Author(s):  
Elnaz Azizi ◽  
Mohammad T. H. Beheshti ◽  
Sadegh Bolouki
Keyword(s):  
Energy Management ◽  
Low Frequency ◽  
Training Dataset ◽  
Mode Transition ◽  
Smart Meters ◽  
Available Energy ◽  
Consumption Values ◽  
Load Monitoring ◽  
Transition Times ◽  
Event Based

Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.

scholarly journals Estimating PV Power from Aggregate Power Measurements within the Distribution Grid

2019 ◽  
Author(s):  
Evangelos Vrettos ◽  
Emre Kara ◽  
Emma Stewart ◽  
Ciaran Roberts
Keyword(s):  
Power Generation ◽  
Objective Function ◽  
Reactive Power ◽  
Active Power ◽  
Real Field ◽  
Time Varying ◽  
Distribution Grid ◽  
Pv Systems ◽  
Power Load ◽  
Power Measurements

The increased integration of photovoltaic (PV) systems in distribution grids reduces visibility and situational awareness for utilities, because the PV systems’ power production is usually not monitored by them. To address this problem, a method called Contextually Supervised Source Separation (CSSS) has been recently adapted for real-time estimation of aggregate PV active power generation from aggregate net active and reactive power measurements at a point in a radially configured distribution grid (e.g., substation). In its original version, PV disaggregation is formulated as an optimization problem that fits linear regression models for the aggregate PV active power generation and true substation active power load. This paper extends the previous work by adding regularization terms in the objective function to capture additional contextual information such as smoothness, by adding new constraints, by introducing new regressors such as ambient temperature, and by investigating the use of time-varying regressors. Furthermore, we perform extensive parametric analysis to inform tuning of the objective function weighting factors in a way that maximizes performance and robustness. The proposed PV disaggregation method can be applied to networks with either a single PV system (e.g., MW scale) or many distributed ones (e.g., residential scale) connected downstream of the substation. Simulation studies with real field recorded data show that the enhancements of the proposed method reduce disaggregation error by 58% in winter and 35% in summer compared with previous CSSS-based work. When compared against a commonly used transposition model based approach, the reduction in disaggregation error is more pronounced (78% reduction in winter and 45% in summer). Additional simulations indicate that the proposed algorithm is applicable also for PV systems with time-varying power factors. Overall, our results show that – with appropriate modeling and tuning – it is possible to accurately estimate the aggregated PV active power generation of a distribution feeder with minimal or no additional sensor deployment.

scholarly journals Enhanced Current Reference Calculation to Avoid Harmonic Active Power Oscillations

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4075 ◽  
Author(s):  
Javier Serrano ◽  
Javier Moriano ◽  
Mario Rizo ◽  
Francisco Dongil
Keyword(s):  
Harmonic Distortion ◽  
Low Frequency ◽  
Active Power ◽  
Computational Time ◽  
Time Step ◽  
Peak Current ◽  
Current Harmonic ◽  
Grid Voltage ◽  
Current Reference ◽  
Power Oscillations

Energy storage systems play a key role in the rise of distributed power generation systems, hence there is great interest in extending their lifetimes, which are directly related to DC current ripple. One of the ripple sources is the low-frequency active power fluctuations under unbalanced and distorted grid voltage conditions. Therefore, this paper addresses a multifrequency control strategy where the harmonic reference currents are calculated to reduce harmonic active power oscillations. The stationary reference frame (StRF) approach taken here improves the precision and computational time of the current reference calculation method. Additionally, in order to ensure safe converter operation when a multifrequency reference current is provided, a computational efficient peak current saturator is applied while avoiding signal distortion every time step. If the injected current harmonic distortion is to be minimized, which is a feature included in this work, the peak current saturator is a necessary requirement. Active power ripple is reduced even with frequency variations in the grid voltage using a well-known frequency-adaptive scheme. The simulation and experimental results prove the optimized performance for the control objective: power ripple reduction with minimum current harmonic distortion.

scholarly journals Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3374 ◽  
Author(s):  
Anthony Faustine ◽  
Lucas Pereira
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Representation ◽  
Classification Performance ◽  
Feature Representation ◽  
Recognition Method ◽  
Signal Features ◽  
Load Monitoring ◽  
Power Measurements

Appliance recognition is one of the vital sub-tasks of NILM in which a machine learning classier is used to detect and recognize active appliances from power measurements. The performance of the appliance classifier highly depends on the signal features used to characterize the loads. Recently, different appliance features derived from the voltage–current (V–I) waveforms have been extensively used to describe appliances. However, the performance of V–I-based approaches is still unsatisfactory as it is still not distinctive enough to recognize devices that fall into the same category. Instead, we propose an appliance recognition method utilizing the recurrence graph (RG) technique and convolutional neural networks (CNNs). We introduce the weighted recurrent graph (WRG) generation that, given one-cycle current and voltage, produces an image-like representation with more values than the binary output created by RG. Experimental results on three different sub-metered datasets show that the proposed WRG-based image representation provides superior feature representation and, therefore, improves classification performance compared to V–I-based features.

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