Real-Time Contingency Analysis With Transmission Switching on Real Power System Data

Sahraei-Ardakani, Mostafa; Li, X.; Balasubramanian, Pranavamoorthy; Hedman, Kory W.; Abdi-Khorsand, Mojdeh

doi:10.1109/tpwrs.2015.2465140

Cited by 62 publications

(32 citation statements)

References 8 publications

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“…Many literatures have reported impressive experimental results of various machine learning algorithms with applications in oscillation detection, voltage stability, fault or transient detection and restoration, islanding detection and restoration, postevent analysis, etc. [80][81][82][83]. With the emergence of the Big Data analytics and smart grid technology, the above-mentioned monitoring and detection methods have been greatly improved, and an increasing number of novel approaches are being studied.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

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Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

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“…PJM has posted a list of switching solutions in response to occurrence of contingencies, consisting of mostly transmission line outages and transmission line flow violations [43]. [44] presents a detailed study of corrective switching on PJM, ERCOT, and TVA. This paper takes a similar approach and uses the flexibility of FACTS devices as a corrective mechanism, instead of transmission line switching.…”

Section: Simulation Studiesmentioning

confidence: 99%

Day-Ahead Corrective Adjustment of FACTS Reactance: A Linear Programming Approach

Sahraei-Ardakani

Hedman

2016

IEEE Trans. Power Syst.

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Abstract-Reserve requirements serve as a proxy for N-1 reliability in the security-constrained unit commitment (SCUC) problem. However, there is no guarantee that the reserve is deliverable for all scenarios (post-contingency states). One cheap way to improve reserve deliverability is to harness the flexibility of the transmission network. Flexible AC transmission system (FACTS) devices are able to significantly improve the transfer capability. However, FACTS utilization is limited today due to the complexities these devices introduce to the DC optimal power flow problem (DCOPF). With a linear objective, the traditional DCOPF is a linear program (LP); when variable impedance based FACTS devices are taken into consideration, the problem becomes a nonlinear program (NLP). A reformulation of the NLP to a mixed integer linear program, for day-ahead corrective operation of FACTS devices, is presented in this paper. Engineering insight is then introduced to further reduce the complexity to an LP. Although optimality is not guaranteed, the simulation studies on the IEEE 118-bus system show that the method finds the globally optimal solution in 98.8% of the cases. Even when the method did not find the optimal solution, it was able to converge to a nearoptimal solution, which substantially improved the reliability, very quickly.Index Terms-FACTS devices, linear programming, power system reliability, power transmission reliability, transfer capability, transmission topology optimization.

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“…Although lots of effort has been made to predict renewable generations, the power grid could still be at risk due to the emergencies caused by quick variations of these resources, especially during extreme weather conditions when the variations are hard to accurately predict and the time left for decision-making could be short. Thus, a fast and acceptable engineering scheme is more significant than an accurate global optimal solution [11].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization Approach for Corrective Switching of Transmission Systems in Emergency Scenarios

Cao

Zhang

et al. 2017

Energies

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Abstract:The large capacity transmission of power over long distance and the rapid development of renewable energy increase the probability of unexpected emergencies such as overload and under-voltage. To tackle these emergencies and defend future disturbances, the corrective switching is implemented as an online control and a multi-objective scheme-making approach is proposed. A multi-objective 0-1 integer optimization model is established to cover a set of contradictory objectives from the aspects of economics, security and reliability. A two-phase optimization approach is proposed to ensure computation efficiency and coordinate the trade-off between these objectives: in the first phase, a feasible set silting method is utilized to quickly search for a set of candidate corrective switching schemes; in the second phase, the technique for order preference by similarity to an ideal solution (TOPSIS) method is applied to the candidate set to coordinate the contradictory objectives and determine the ultimate engineering scheme. Two case studies are conducted to verify the proposed approach in overload and under-voltage scenarios. The results are discussed to show the strengths when the performance indices of economics, security and reliability are considered.

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Real-Time Contingency Analysis With Transmission Switching on Real Power System Data

Cited by 62 publications

References 8 publications

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Day-Ahead Corrective Adjustment of FACTS Reactance: A Linear Programming Approach

A Multi-Objective Optimization Approach for Corrective Switching of Transmission Systems in Emergency Scenarios

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