A Large-scale Replication of Smart Grids Power Consumption Anomaly Detection

2020 ◽  
Author(s):  
Bruno Rossi
Keyword(s):  
Anomaly Detection ◽  
Power Consumption ◽  
Smart Grids ◽  
Large Scale

scholarly journals An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data

2019 ◽  
Vol 11 (1) ◽  
pp. 251 ◽  
Author(s):  
Huijuan Wang ◽  
Wenrong Yang ◽  
Tingyu Chen ◽  
Qingxin Yang
Keyword(s):  
Power Consumption ◽  
Optimization Model ◽  
Smart Grids ◽  
Large Scale ◽  
Reactive Power ◽  
Low Frequency ◽  
Performance Comparison ◽  
Consumption Pattern ◽  
Frequency Data ◽  
Load Disaggregation

In recent years, Smart Grids have been developing globally. Since smart meters only acquire low-frequency data, non-intrusive load monitoring technology using the signature extracted from high-frequency data needs an additional measurement device to be installed, so it is not suitable for promotion to the smart grid environment. However, methods using low-frequency features are poorly-suited when several appliances are switched on at the same time, or devices with similar power values are used. In response to these problems, this paper proposes a load disaggregation method based on the power consumption patterns of appliances, combining an improved mathematical optimization model and optimized bird swarm algorithm (OBSA) for load disaggregation. Experiments show that the method can effectively identify the operating states of appliances, and deal with situations in which multiple instruments have similar power characteristics or are simultaneously switching. The performance comparison proves that the improved model is more efficient than the traditional active and reactive power (PQ) optimization model in load disaggregation performance and computation time, and also verifies the robustness of the proposed method and the convergence of OBSA. As an inexpensive method without extra measurement hardware installed, the process is suitable for large-scale applications in smart grids.

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

scholarly journals Large-scale smart grids as system of systems

2013 ◽  
Author(s):  
Jennifer Pérez ◽  
Jessica Díaz ◽  
Juan Garbajosa ◽  
Agustín Yagüe ◽  
Eloy Gonzalez ◽  
...  
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
System Of Systems

scholarly journals An integral control formulation of mean field game based large scale coordination of loads in smart grids

Automatica ◽  
2019 ◽  
Vol 100 ◽  
pp. 312-322 ◽  
Author(s):  
Arman C. Kizilkale ◽  
Rabih Salhab ◽  
Roland P. Malhamé
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
Mean Field ◽  
Integral Control ◽  
Mean Field Game

scholarly journals Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

2021 ◽  
Vol 294 ◽  
pp. 01002
Author(s):  
Xiaoyan Xiang ◽  
Yao Sun ◽  
Xiaofei Deng
Keyword(s):  
Solar Energy ◽  
Extreme Learning Machine ◽  
Power Output ◽  
Smart Grids ◽  
Large Scale ◽  
Solar Power ◽  
Learning Algorithm ◽  
Power Performance ◽  
Energy Prediction ◽  
Learning Machine

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.

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