A security-based observability method for optimal PMU-sensor placement in WAMS

Bashian, Amir; Assili, Mohsen; Anvari‐Moghaddam, Amjad

doi:10.1016/j.ijepes.2020.106157

Cited by 29 publications

(18 citation statements)

References 50 publications

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“…System (DAMS) [18]. Transmission Standardization [19] Flexible AC Transmission Systems (FACTS), PMU, Synchro-phasors, connected with communication devices [18,19].…”

Section: Distributed Automation and Managementmentioning

confidence: 99%

“…The growth of the energy sector could be curtailed in an instance when the transmission system remains stagnant. Some important building blocks of transmission system s are, PMU, phasor data collector (PDC), FACTS, advanced storage apart from the ever-increasing transmission in case of HVDC and HVAC, wide-area monitoring, and WAMPAC assures reliable transmission system [17][18][19]33]. Although the capabilities of the transmission system can sustain the current load, SG implementation on a larger power system scale would have to upgrade the capacity of the transmission system prompting a considerable influx of capital [44].…”

Section: Sg For Power Systems Scope Towards Transmission Sidementioning

confidence: 99%

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Power systems automation, communication, and information technologies for smart grid: A technical aspects review

et al. 2021

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Smart grid (SG) introduced proven power system, based on modernized power delivery system with introduction of advanced data-information and communication technologies (ICT). SGs include improved quality of power transmission/distribution from power generation to end-users with optimized power flow and efficiency. In addition to above modern automation, two-way communications, advanced monitoring, and control to optimize power quality issues are the classic features of SGs. This ensures the efficiency and reliability of all its interconnected power system elements against potential threats and life time cycle. By integrating ICT into the power system SGs improved the working capabilities of the utility companies. Resultant of ICT with SG leads to better management of assets and ensure energy management for end users. This review article presents the different areas of communication and information technology areas involved in SG automation.

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“…System (DAMS) [18]. Transmission Standardization [19] Flexible AC Transmission Systems (FACTS), PMU, Synchro-phasors, connected with communication devices [18,19].…”

Section: Distributed Automation and Managementmentioning

confidence: 99%

Section: Sg For Power Systems Scope Towards Transmission Sidementioning

confidence: 99%

Power systems automation, communication, and information technologies for smart grid: A technical aspects review

et al. 2021

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“…Overall, considering the security reliability of the PME in power system [32], a rotation strategy for practical PME reliability regulation and management can be developed so as to ensure the normal operation of PME. The PME can be replaced to prevent lack of equipment and reduce the cost of manufacturing, purchasing, and shipping.…”

Section: Model Interpretation and Prediction Of Reliabilitymentioning

confidence: 99%

Probability-Based Failure Evaluation for Power Measuring Equipment

et al. 2021

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Accurate reliability and residual life analysis is paramount during the designing of reliability requirements and rotation of power measuring equipment (PME). However, the sample dataset of failure is usually sparse and contains inevitable pollution data, which has an adverse effect on the reliability analysis. To tackle this issue, this paper first applies nonlinear regression to fuse the failure rate and environmental features of PME collected from various locations. Then, a novel binary hierarchical Bayesian probability method is proposed to model the failure trend and identify outliers, in which the outlier identification structure is embedded into hierarchical Bayesian. Integrating binary hierarchical Bayesian and the bagging method, a binary hierarchical Bayesian with bagging (BHBB) framework is further introduced to improve predictive performance in a small sample dataset by resampling. Last, the influence of typical environmental features, failure rate, and reliability are obtained by the BHBB under the real sample dataset from multiple typical locations. Experiments show that our framework has superior performance and interpretability comparing with other typical data-based approaches.

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“…These measurements are critical because if the grid supply/demand balance is not perfectly matched, frequency imbalances and voltage amplitude fluctuations can cause stress on the grid, which is a potential cause of power outages [32]. It is worth emphasizing that optimal PMU placement in WAMS is one of the most investigated issues by researchers and engineers in order to improve the observability and security of the power system [33,34].…”

Section: Introductionmentioning

confidence: 99%

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

et al. 2021

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Nowadays, the main grid is facing several challenges related to the integration of renewable energy resources, deployment of grid-level energy storage devices, deployment of new usages such as the electric vehicle, massive usage of power electronic devices at different electric grid stages and the inter-connection with microgrids and prosumers. To deal with these challenges, the concept of a smart, fault-tolerant, and self-healing power grid has emerged in the last few decades to move towards a more resilient and efficient global electrical network. The smart grid concept implies a bi-directional flow of power and information between all key energy players and requires smart information technologies, smart sensors, and low-latency communication devices. Moreover, with the increasing constraints, the power grid is subjected to several disturbances, which can evolve to a fault and, in some rare circumstances, to catastrophic failure. These disturbances include wiring issues, grounding, switching transients, load variations, and harmonics generation. These aspects justify the need for real-time condition monitoring of the power grid and its subsystems and the implementation of predictive maintenance tools. Hence, researchers in industry and academia are developing and implementing power systems monitoring approaches allowing pervasive and effective communication, fault diagnosis, disturbance classification and root cause identification. Specifically, a focus is placed on power quality monitoring using advanced signal processing and machine learning approaches for disturbances characterization. Even though this review paper is not exhaustive, it can be considered as a valuable guide for researchers and engineers who are interested in signal processing approaches and machine learning techniques for power system monitoring and grid-disturbance classification purposes.

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A security-based observability method for optimal PMU-sensor placement in WAMS

Cited by 29 publications

References 50 publications

Power systems automation, communication, and information technologies for smart grid: A technical aspects review

Power systems automation, communication, and information technologies for smart grid: A technical aspects review

Probability-Based Failure Evaluation for Power Measuring Equipment

Overview of Signal Processing and Machine Learning for Smart Grid Condition Monitoring

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