Protection systems are one of the most critical components in transmission system and becoming more digital with ongoing automation. These prone to vulnerabilities/attacks, exploitation these vulnerabilities may cause major impacts on electric grid performance. Multiple alarms reported control center could be a result faults (expected operations) or failures protection (anomalies/ unexpected operation). Situational awareness gained through sensors such as phasor measurement unit (PMU) data...

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units (PMUs) have been deployed for monitoring control. PMU data can multiple anomalies, many researchers in past concentrated on training machine/deep learning algorithms offline anomaly detection over (i.e., not real-time). These algorithms, when trained a sample rather than population dataset, fail to consider dynamic behavior grid real-time, resulting low accuracy. Considering (e.g., change...

Protection devices are considered to be the most critical components responsible for protecting electrical grid. Due recent technological advancements in grid, digitalization has played an influential role integration of digital protection systems. Incorporation systems made Transmission System more prone vulnerabilities and cyber-attacks. A cyber attack exploiting aims disrupt normal operations by raising multiple false alarms on a large scale creating conflicting confusing observation...

This work proposes a deep graph learning framework to identify, locate, and classify power, cyber, cyber power events at the distribution system level. The proposed algorithm jointly exploits spatial, temporal, node-level physical data features. developed neural network, together with autoencoder, utilizes measurements from level phasor measurement units communication network logs. spatial structure of synchrophasor is incorporated through weighted adjacency matrix. temporal by defining...

Phasor Measurement Units (PMUs) are located at different geographic positions in the transmission system, generating measurement data that can be analyzed to monitor and control power grid. One utilization for such is monitoring based on machine learning <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">event analysis</i> which includes detection, localization, classification</i> . The approach developed this work exploits latent spatial...

Buried pipelines, used by petrochemical industries in North America for transporting oil and derivatives, are often subjected to large deformations resulting from geo-environmental factors operating conditions, such as geotechnical movements, thermal strains, internal fluid pressure. Exceeding the critical deformation limit of these pipes initiates wrinkles, further increase may result fracture, thus jeopardizing safe operation a field pipeline. A recent fracture failed laboratory specimens...

Steel pipelines, buried in cold regions, often respond to thermal strains and/or geotechnical movement caused by factors such as thaw settlement, frost heave, and slope instability. These complex field conditions can impose large displacements on these resulting localized wrinkles well into the plastic range of pipe material. Eventually, there is a possibility fracture occurring at wrinkled location under continuous deformation. A recent failed laboratory specimen have been observed within...

Onshore steel pipelines, particularly buried in cold region, often subjected to extreme geo-environmental conditions, where significant inelastic deformation may occur resulting localized wrinkles. Under continued deformation, there is a possibility of excessive cross-sectional at wrinkle locations, eventually leading fracture or damage the pipe wall. A recent field and failed laboratory specimens under monotonic load history address necessity conducting comprehensive research program better...

We demonstrate Kronos, a framework and system that automatically extracts highly dynamic knowledge for complex event analysis in Cyber-Physical systems. Kronos captures events with anomaly-based model, integrates various by correlating their temporal associations realtime, from heterogeneous, continuous cyber-physical measurement data streams. It maintains lightweight base, enabled online, window-based ensemble learning incremental association detection linkage, respectively. These...

Data-driven anomaly detection systems are increasingly becoming essential for protecting critical cyber-physical system (CPS) infrastructure, such as the power grid, against growing number of sophisticated cyber-attacks. The development tools is reliant on availability high-fidelity datasets that cover a diverse variety potential cyber events. In this work, co-simulation smart grid platform utilized to develop realistic dataset, which used train and test machine learning-based (ADS)....

Buried pipelines, used by petrochemical industries in North America for transporting oil and derivatives, are often subjected to large deformations resulting from geoenvironmental factors such as: geotechnical movements, thermal strains, internal fluid pressure. Exceeding the critical deformation limit of these pipes initiates wrinkles further increase may result fracture; thus jeopardizing safe operation a field pipeline. A recent fracture failed laboratory specimens under monotonic load...

Small product of roto molding process is not an economically competitive way to produce hollow plastic materials such as blow process, hence the study parameters small products very important for design techniques, in this percentage powder conversion into final product, thickness and rotational speeds Using a cylindrical mold (diameter = 61, height 111 mm) experimental model process.The obtained results showed that 58.4 77.9% converted from weights samples were used at operating conditions...