An artificial intelligence system for power system contingency screening

Šobajić, D.J.; Pao, Yoh‐Han

doi:10.1109/59.192918

Cited by 66 publications

(14 citation statements)

References 16 publications

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“…One of the possible methods to speed up CA is to build a computationally efficient model which replicates the function of conventional approach. To achieve this different methods has been used propose as Fuzzy Logic [2], expert systems [3] and Artificial Neural Network (ANN) [4,5]. Recently machine learning techniques like ANN and Support Vector Machine (SVM) are gaining popularity for different power system studies.…”

Section: Introductionmentioning

confidence: 99%

“…A rule based method was developed by [3] for N-1 contingency screening in which rules were independent of size of power system but rules were derived partly from human operator expertise. An Artificial Neural Networks (ANNs) based method [4], is proposed for "N-1" contingency screening in which preprocessing of inputs is done using Fast Fourier Transform (FFT) to improve the performance and speed up the ANN.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid GA-SVR technique for contingency screening in power system

Kumar¹,

Tripathi

2012

2012 IEEE Fifth Power India Conference

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Contingency analysis is essential to operate power system in secure state. Normally Contingency analysis is performed offline since it is a time consuming task. Due to continuously changing nature of power system it is imperative to have a fast contingency analysis method. This work proposed a very efficient, fast and accurate method for online contingency screening using the hybrid Genetic Algorithm (GA) and Support Vector Regression (SVR), hence named GA-SVR. SVR is used to build a model which takes a network topology and load profile as input and maps them into contingency performance indices. It is desirable to find the optimal value of SVR parameters to make it fast and accurate. Therefore GA is applied for searching global optimal parameters for SVR model. Standard IEEE-30 bus system is tested to illustrate the effectiveness of presented GA-SVR technique.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid GA-SVR technique for contingency screening in power system

Kumar¹,

Tripathi

2012

2012 IEEE Fifth Power India Conference

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“…Recently, majority of research concentrates in finding an optimal location to install UPFC and various FACTS devices using evolutionary optimization techniques [34][35][36][37]. Optimal location of UPFC can be identified through contingency selections to enhance the steady state security level [38][39][40][41][42]. Differential Evolution algorithm has been successfully implemented to minimize generation fuel cost in the presence of FACTS devices such as TCSC and TCPS [43].…”

Section: Introductionmentioning

confidence: 99%

Analysis and effect of multi-fuel and practical constraints on economic load dispatch in the presence of Unified Power Flow Controller using UDTPSO

Suresh

Sivanagaraju

2015

Ain Shams Engineering Journal

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This paper presents an attempt to analyze the effect of multi-fuel and practical constraints on economic load dispatch problem using a novel uniform distributed two-stage particle swarm optimization (UDTPSO) algorithm without and with unified power flow controller (UPFC) while satisfying equality, inequality, practical constraints such as ramp-rate and prohibited operating zone (POZ) limits and device operating limits. A Novel severity function is formulated based on the transmission line overloads and bus voltage violations to identify an optimal location to install UPFC. A multi-objective optimization problem is solved for multi-fuel non-convex cost and transmission power loss objectives. Obtained results for considered standard test functions and electrical systems indicate the effectiveness of the proposed algorithm and can obtain efficient solution when compared to existing methods. Hence, the proposed method is a promising method and can be easily applied to optimize the power system objectives. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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“…This operation is conducted under the single line contingency analysis. In the contingency analysis area, a significant development has been achieved by implementing different contingency methods by different researchers [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

New Approach for Optimal Location and Parameters Setting of UPFC for Enhancing Power Systems Stability under Contingency Analysis

Zahid

Chen

et al. 2017

Energies

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Operation of power system within specified limits of voltage and frequency are the major concerns in power system stability studies. As power system is always prone to disturbances, which consequently affect the voltage instability and optimal power flow, and therefore risks the power systems stability and security. In this paper, a novel technique based on the "Artificial Algae Algorithm" (AAA) is introduced, to identify the optimal location and the parameters setting of Unified Power Flow Controller (UPFC) under N-1 contingency criterion. In the first part, we have carried out a contingency operation and ranking process for the most parlous lines outage contingencies while taking the transmission lines overloading (NOLL) and voltage violation of buses (NVVB) as a performance parameter (PP = NOLL + NVVB). As UPFC possesses too much prohibitive cost and larger size, its optimal location and size must be identified before the actual deployment. In the second part, we have applied a novel AAA technique to identify the optimal location and parameters setting of UPFC under the discovered contingencies. The simulations have been executed on IEEE 14 bus and 30 bus networks. The results reveals that the location of UPFC is significantly optimized using AAA technique, which has improved the stability and security of the power system by curtailing the overloaded transmission lines and limiting the voltage violations of buses.

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An artificial intelligence system for power system contingency screening

Cited by 66 publications

References 16 publications

Hybrid GA-SVR technique for contingency screening in power system

Hybrid GA-SVR technique for contingency screening in power system

Analysis and effect of multi-fuel and practical constraints on economic load dispatch in the presence of Unified Power Flow Controller using UDTPSO

New Approach for Optimal Location and Parameters Setting of UPFC for Enhancing Power Systems Stability under Contingency Analysis

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