کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
399338 1438723 2016 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Critical Clearing Time prediction within various loads for transient stability assessment by means of the Extreme Learning Machine method
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Critical Clearing Time prediction within various loads for transient stability assessment by means of the Extreme Learning Machine method
چکیده انگلیسی


• Predict CCT for online assessment in transient stability.
• Transient stability analysis using artificial intelligent to predict CCT.
• Online assessment in transient stability using critical trajectory.

The Critical Clearing Time (CCT) is a key issue for Transient Stability Assessment (TSA) in electrical power system operation, security, and maintenance. However, there are some difficulties in obtaining the CCT, which include the accuracy, fast computation, and robustness for TSA online. Therefore, obtaining the CCT is still an interesting topic for investigation. This paper proposes a new technique for obtaining CCT based on numerical calculations and artificial intelligence techniques. First, the CCT is calculated by the critical trajectory method based on critical generation. Second, the CCT is learned by Extreme Learning Machine (ELM). This proposed method has the ability to obtain the CCT with load changes, different fault occurrences, accuracy, and fast computation, and considering the controller. This proposed method is tested by the IEEE 3-machine 9-bus system and Java-Bali 500 kV 54-machine 25-bus system. The proposed method can provide accurate CCTs with an average error of 0.33% for the Neural Network (NN) method and an average error of 0.06% for the ELM method. The simulation result also shows that this method is a robust algorithm that can address several load changes and different locations of faults occurring. There are 29 load changes used to obtain the CCT, with 20 load changes included for the training process and 9 load changes not included.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Electrical Power & Energy Systems - Volume 77, May 2016, Pages 345–352
نویسندگان
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