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This paper presents a fault detection and identification system for power systems using an artificial neural network approach while discussing its advantages and disadvantages. The initial data for the proposed technique are a set of simulated post-fault bus voltages and currents obtained at a sampling rate which emulates a complete phasor measurement unit (PMU)-based wide area measurement system (WAMS). Several types of faults are considered, such as short circuits and line and load contingencies. All fault and steady-state simulations have been performed on MATLAB using the Power System Toolbox. The artificial neural network was designed using an architecture proper for pattern recognition with supervised learning. As a result, satisfactory predictions within short time periods are obtained. The test system used in all simulations is the IEEE 39-Bus New England Power System, which has 10 generation units, 21 loads, and three distinct areas alongside transient and sub transient models, with PMUs distributed over up to 14 buses. Future works are also discussed in this paper, showing the possibilities for feature engineering in this type of problem, as well as the application of other machine learning and data analytics techniques for PMU-based WAMS databases. [ABSTRACT FROM AUTHOR]

Summary: Modern power system operates in stressed conditions due to fickle loads, high penetration of renewables, and complex control strategies. The failure of any power system asset may lead to protract power interruption or even catastrophic failures. Synchrophasor‐based extensive research advancements in wide area measurement, protection, and control (WAMPAC) systems are solving critical operation and control problems of the power system. This paper proposes a new combinatorial methodology for monitoring the prime power system components in preference to complete observability of the system. A multicriteria decision support (MCDS) approach, analytical hierarchy process (AHP), has been used to prepare a ranking according to component bus priority for synchrophasor measurement unit installation. The priority list is further processed with intelligent search technique (IST) to compute the optimal solution for synchrophasor meter placement, ensuring complete observability, maximum measurement redundancy, and coverage of prime system components with direct monitoring. Some practical contingencies related to synchrophasor meters placement problem like single meter loss or line outage have also been considered for the case studies. The efficiency of the proposed method has been verified with IEEE 14‐bus, 24‐bus, 30‐bus, 57‐bus, and 118‐bus and New England 39‐bus test systems. To prove the superiority of the method, in essence of synchrophasor meter placement problems, the outcome has been compared with some of the existing methods from the literature. [ABSTRACT FROM AUTHOR]

Active frequency response (AFR) control is needed in current power systems. To solve the over-frequency problems of generators connected to non-disturbed buses during the AFR control period, the generators should be clustered into coherent groups. Thus, the control efficiency can be improved on the premise of ensuring control accuracy. Since the influencing factors (such as the model parameters, operation modes, and disturbance locations, etc.) of power system operation can be comprehensively reflected by the generator frequency, which is easily collected and calculated, the generator frequency can be used as the coherency identification input. In this paper, we propose a coherency identification method of AFR control based on support vector clustering for a bulk power system. By mapping data samples from the initial space to the high-dimensional feature space, the radius of the minimal enclosing sphere that can envelop all the data samples is obtained. Moreover, the coherency identification of generators is determined for AFR control according to the evaluating method of AFR clustering control effects and the evaluating index of cluster compactness and separation. The simulation results for the modified New England IEEE 10-generator 39-bus system and Henan power grid show that the proposed method is feasible and effective. [ABSTRACT FROM AUTHOR]

This letter proposes an online control strategy to enhance the overall damping of low-frequency power system oscillations of large inter-connected electrical networks. To achieve this objective, the proposed approach employs wide area measured signals to estimate an accurate online model of power system dynamics using the dynamic mode decomposition with control (DMDc) method. The DMDc technique applies singular value decomposition (SVD) to the collected data matrices such that an accurate low-order approximation of system dynamics is obtained. The estimated model is thereafter utilized in designing the wide area damping controller (WADC) using discrete linear quadratic regulator (DLQR). Furthermore, kalman filtering is deployed to update the states of the designed controller and to remove the noise that is contained in the measured signals. The effectiveness of the proposed method is tested on the 10 machines, 39-bus New England test system. [ABSTRACT FROM AUTHOR]