Setting and Testing of the Out-of-Step Protection at Mongolian Transmission System

Modern distance relays have integrated numerous protection functions, including power-swing blocking and out-of-step or pole-slip tripping functions. The main purpose of the power-swing blocking function is to differentiate faults from power swings and block distance or other relay elements from operating during stable or unstable power swings. Most power-swing blocking elements are based on traditional methods that monitor the positive sequence impedance rate. The required settings for the power-swing blocking elements could be difficult to calculate in many applications, particularly those where fast swings can be expected. For these cases, extensive stability studies are necessary to determine the fastest rate of possible power swings. This paper presents a detailed step-by-step method for settings calculation of out-of-step (OOS) protection, both blocking and tripping functions considering a generic two-source system. Then the method is applied to define the protection relay settings installed at the interconnection between the Russian and Mongolian power systems, as it is crucial to feed the demand-rich Mongolian power system. In this paper, a specific impedance method is used for defining the OOS protection settings. This paper innovates by testing the settings using the recordings of the major events of 15 September 2018 in two approaches: hybrid co-simulation and cyber-physical. Both tests have demonstrated the appropriate performance of the proposed settings and proving the proposed methodology works appropriately.

A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model

Out-of-step protection of one or a group of synchronous generators is unreliable in a power system which has significant renewable power penetration. In this work, an innovative out-of-step protection algorithm using wavelet transform and deep learning is presented to protect synchronous generators and transmission lines. The specific patterns are generated from both stable and unstable power swing, and three-phase fault using the wavelet transform technique. Data containing 27,008 continuous samples of 48 different features is used to train a two-layer feed-forward network. The proposed algorithm gives an automatic, setting free and highly accurate classification for the three-phase fault, stable power swing, and unstable power swing through pattern recognition within a half cycle. The proposed algorithm uses the Kundur 2-area system and a 29-bus electric network for testing under different swing center locations and levels of renewable power penetration. Hardware-in-the-loop (HIL) tests show the hardware compatibility of the developed out-of-step algorithm. The proposed algorithm is also compared with recently reported algorithms. The comparison and test results on different large-scale systems show that the proposed algorithm is simple, fast, accurate, and HIL tested, and not affected by changes in power system parameters.