Charging load pattern extraction for residential electric vehicles: a training-free nonintrusive method

Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this article presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess method, and the low-frequency approximate component being decomposed by discrete wavelet technology. Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include 1) significant improvement in extraction accuracy; 2) strong noise immunity; 3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones

An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves outperformance for appliance identification in comparison with other recent techniques. Accordingly, high identification accuracies are attained on the GREEND, UK-DALE, and WHITED data sets, where 99.54%, 98.78%, and 100% rates have been achieved, respectively, using the proposed 2D-PEP descriptor. 2020 The Authors. International Journal of Intelligent Systems published by Wiley Periodicals LLCThis paper was made possible by National Priorities Research Program (NPRP) Grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.Scopu

A new method for residential side non-intrusive load monitoring

This thesis proposes a new non-intrusive method for residential load monitoring. The proposed method can detect appliance switching events, separate appliance electric features, and identify appliance types. Compared with other non-intrusive monitoring methods, the proposed method improves the monitoring accuracy and decreases the monitoring response time. Firstly, the monitoring hardware was designed and constructed to sample and acquire the aggregated electric data of one residential area. Secondly, the sampled data were processed and analysed with the proposed method, which achieves the monitoring of individual appliance running conditions and power consumption in this area in a non-intrusive way. The data analysis process includes three steps, 1) the appliance switching event is detected by the Heuristic detection method. 2) the working current of the switched appliance is separated according to the difference method, 3) the type of switched appliance is identified with the K-nearest neighbour method according to the appliance’s current harmonic components, and the identification result is modified and corrected according to appliance operation pattern with the aid of a Back Propagation Neural Network. Thirdly, the proposed NILM method was tested through offline and online applications. The offline application involves three days of pre-recorded data which were processed and analysed. The online application consists of two parts. The first part is a direct application for four domestic homes during one day (24 hours). As for the second part, the proposed monitoring method was applied to one domestic home for ninety days. All the online and offline tests, the running conditions and the power consumption of appliances were monitored and recorded. Due to the test results, the proposed method is reliable and offers a powerful monitoring method for demand side management.This thesis proposes a new non-intrusive method for residential load monitoring. The proposed method can detect appliance switching events, separate appliance electric features, and identify appliance types. Compared with other non-intrusive monitoring methods, the proposed method improves the monitoring accuracy and decreases the monitoring response time. Firstly, the monitoring hardware was designed and constructed to sample and acquire the aggregated electric data of one residential area. Secondly, the sampled data were processed and analysed with the proposed method, which achieves the monitoring of individual appliance running conditions and power consumption in this area in a non-intrusive way. The data analysis process includes three steps, 1) the appliance switching event is detected by the Heuristic detection method. 2) the working current of the switched appliance is separated according to the difference method, 3) the type of switched appliance is identified with the K-nearest neighbour method according to the appliance’s current harmonic components, and the identification result is modified and corrected according to appliance operation pattern with the aid of a Back Propagation Neural Network. Thirdly, the proposed NILM method was tested through offline and online applications. The offline application involves three days of pre-recorded data which were processed and analysed. The online application consists of two parts. The first part is a direct application for four domestic homes during one day (24 hours). As for the second part, the proposed monitoring method was applied to one domestic home for ninety days. All the online and offline tests, the running conditions and the power consumption of appliances were monitored and recorded. Due to the test results, the proposed method is reliable and offers a powerful monitoring method for demand side management

Artificial Intelligence-Based Methods for Power System Security Assessment with Limited Dataset

This thesis concerns the relationship between the load, load model, and power system stability. It investigates the possibility of developing a dynamic load model to represent the power system load characteristic during system faults when the power system operates at a high percentage of the power generation from wind farms, solar power, and vehicle-to-grid technology. Additionally, with artificial intelligence supporting the seamless integration of an increasingly distributed and multi-directional power system to unlock the vast potential of renewables, new approaches are proposed to improve the training performance for the applications of artificial neural networks in non-intrusive load monitoring and dynamic security assessment. An improved hybrid load model is proposed to represent the load characteristics in the above power system operation. Genetic algorithms and the multi-curve identification method are applied to determine the parameters of the load model, aiming to minimize the error between the estimated and measured values. The results indicate that the proposed hybrid load model has a reasonably low fitting error to represent the load dynamics. In addition, new approaches are proposed to tackle the challenges posed by limited data when training artificial neural networks (ANNs) for their application in power systems. The knowledge transfer approach is utilized to support the ANN training to generate synthetic data for non-intrusive load monitoring. The results indicate that this approach improves the issue of mode collapse and reduces the need for lengthy training iterations, making the ANN effective for generating synthetic data from limited data. Moreover, the knowledge transfer approach also supports ANN training with limited data for dynamic security assessment. Kernel principal component analysis is employed to eliminate the dimensionality reduction step. The results indicate an improvement in the training performance

Artificial Intelligence-Based Methods for Power System Security Assessment with Limited Dataset

This thesis concerns the relationship between the load, load model, and power system stability. It investigates the possibility of developing a dynamic load model to represent the power system load characteristic during system faults when the power system operates at a high percentage of the power generation from wind farms, solar power, and vehicle-to-grid technology. Additionally, with artificial intelligence supporting the seamless integration of an increasingly distributed and multi-directional power system to unlock the vast potential of renewables, new approaches are proposed to improve the training performance for the applications of artificial neural networks in non-intrusive load monitoring and dynamic security assessment. An improved hybrid load model is proposed to represent the load characteristics in the above power system operation. Genetic algorithms and the multi-curve identification method are applied to determine the parameters of the load model, aiming to minimize the error between the estimated and measured values. The results indicate that the proposed hybrid load model has a reasonably low fitting error to represent the load dynamics. In addition, new approaches are proposed to tackle the challenges posed by limited data when training artificial neural networks (ANNs) for their application in power systems. The knowledge transfer approach is utilized to support the ANN training to generate synthetic data for non-intrusive load monitoring. The results indicate that this approach improves the issue of mode collapse and reduces the need for lengthy training iterations, making the ANN effective for generating synthetic data from limited data. Moreover, the knowledge transfer approach also supports ANN training with limited data for dynamic security assessment. Kernel principal component analysis is employed to eliminate the dimensionality reduction step. The results indicate an improvement in the training performance

Smart Metering System: Developing New Designs to Improve Privacy and Functionality

This PhD project aims to develop a novel smart metering system that plays a dual role: Fulfil basic functions (metering, billing, management of demand for energy in grids) and protect households from privacy intrusions whilst enabling them a degree of freedom. The first two chapters of the thesis will introduce the research background and a detailed literature review on state-of-the-art works for protecting smart meter data. Chapter 3 discusses theory foundations for smart meter data analytics, including machine learning, deep learning, and information theory foundations. The rest of the thesis is split into two parts, ‘Privacy’ and ‘Functionality’, respectively. In the ‘Privacy’ part, the overall smart metering system, as well as privacy configurations, are presented. A threat/adversary model is developed at first. Then a multi-channel smart metering system is designed to reduce the privacy risks of the adversary. Each channel of the system is responsible for one functionality by transmitting different granular smart meter data. In addition, the privacy boundary of the smart meter data in the proposed system is also discovered by introducing a data mining algorithm. By employing the algorithm, a three-level privacy boundary is concluded. Furthermore, a differentially private federated learning-based value-added service platform is designed to provide flexible privacy guarantees to consumers and balance the trade-off between privacy loss and service accuracy. In the ‘Functionality’ part, three feeder-level functionalities: load forecasting, solar energy separation, and energy disaggregation are evaluated. These functionalities will increase thepredictability, visibility, and controllability of the distributed network without utilizing household smart meter data. Finally, the thesis will conclude and summarize the overall system and highlight the contributions and novelties of this project