A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

Moriano, Javier; Rodríguez, Francisco; Martín, Pedro Sánchez; Jiménez, Javier; Vuksanović, Branislav

doi:10.3390/s16010085

Cited by 6 publications

(3 citation statements)

References 28 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding gain and offset error detection, an approach to error analysis, similar to that introduced in [ 25 ], has been implemented. The equipment involved in the data acquisition, mainly current transformers and measurement equipment, introduces an inherent measurement error, the value of which has been quantified.…”

Section: Experimental Results: Error Detectionmentioning

confidence: 99%

A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

Martín

Moreno

Rodríguez

et al. 2018

Sensors

Self Cite

View full text Add to dashboard Cite

Bad data as a result of measurement errors in secondary substation (SS) monitoring equipment is difficult to detect and negatively affects power system state estimation performance by both increasing the computational burden and jeopardizing the state estimation accuracy. In this paper a short-term load forecasting (STLF) hybrid strategy based on singular spectrum analysis (SSA) in combination with artificial neural networks (ANN), is presented. This STLF approach is aimed at detecting, identifying and eliminating and/or correcting such bad data before it is provided to the state estimator. This approach is developed to improve the accuracy of the load forecasts and it is tested against real power load data provided by electricity suppliers. Depending on the week considered, mean absolute percentage error (MAPE) values which range from 1.6% to 3.4% are achieved for STLF. Different systematic errors, such as gain and offset error levels and outliers, are successfully detected with a hit rate of 98%, and the corresponding measurements are corrected before they are sent to the control center for state estimation purposes.

show abstract

Section: Experimental Results: Error Detectionmentioning

confidence: 99%

A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

Martín

Moreno

Rodríguez

et al. 2018

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Complex forecasting techniques, such as non-linear interpolating polynomials and time series, have been widely used in the literature in applications requiring sensor measurements [79][80] [81]. Although very accurate, these highly nonlinear models require a lot of computational time, which is not feasible in the case of SMV.…”

Section: Online Lost Packet Forecasting For Iec 61850 Sampled Measurementioning

confidence: 99%

“…features, of the system. There is extensive work in the literature that relies on artificial intelligence techniques in intrusion detection [81][122] [123]. However, such techniques do not incorporate the physical characteristics of the system in intrusion detection.…”

Section: Performance Of the Neural Networkmentioning

confidence: 99%

Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

Hariri¹

View full text Add to dashboard Cite

vii Secondly, the feasibility of using neural network forecasters to detect spoofed sampled values was investigated. We showed that although such forecasters have high spoofeddata-detection accuracy, they are prone to the accumulation of forecasting error. In this research, we proposed an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed algorithms was experimentally verified on the Smart Grid testbed at FIU. The test results showed that the proposed techniques have a minimal detection latency, in the range of microseconds. Also, in this research we developed a network-in-the-loop co-simulation platform that seamlessly integrates the components of the smart grid together, especially since they are governed by different regulations and owned by different entities. Power system simulation software, microcontrollers, and a real communication infrastructure were combined together to provide a cohesive smart grid platform. A data-centric communication scheme was selected to provide an interoperability layer between multi-vendor devices, software packages, and to bridge different protocols together. viii

show abstract

Environmental Monitoring and Evaluation of 10 KV Distribution Station Room Based on Internet of Things

Sun

Niu

et al. 2021

The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

View full text Add to dashboard Cite

A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

Cited by 6 publications

References 28 publications

A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

Environmental Monitoring and Evaluation of 10 KV Distribution Station Room Based on Internet of Things

Contact Info

Product

Resources

About