Wide Area Monitoring Of Power Systems Research Articles

심층 신경망을 이용한 재생에너지원의 PMU 빅 데이터기반 새로운 계통 현상 판별

Recently, for real-time monitoring of renewable energy, a wide area power system monitoring and operation technology using PMU has emerged. Through WAMS based on the PMU, time synchronization data for a wide area is acquired and a vast amount of data is accumulated. Therefore, it is a problem to be solved in the future to process this data and deliver highly usable and valuable system status information to system operators. This paper proposes a new systemic phenomenon identification algorithm using big data of PMU installed in RES and a DNN. First, the PMU installed at the RES in the Gangwon region is introduced, and then the data structure collected is explained. Next, by analyzing each system phenomenon from the PMU data, a total of 8 types of system data such as steady state, tap rise, tap fall, feed-in and out, etc. are generated. After conducting supervised learning by constructing learning data for 8 systematic phenomena using a DNN, systematic phenomena discrimination is performed on the DNN model learned through the test data. Finally, the algorithm was designed, implemented, and evaluated to identify robust systematic phenomena for new PMU based big data. The simulation results showed that the proposed new algorithm accurately discriminates all systematic phenomena.

A Secure Hybrid Dynamic-State Estimation Approach for Power Systems Under False Data Injection Attacks

Dynamic-state estimation plays a critical role in achieving real-time wide-area monitoring of power systems. On the other hand, false data injection (FDI) attacks are substantial threats, which can undesirably ruin the state estimation results. To tackle this problem, an effective secure hybrid dynamic-state estimation approach that involves a dynamic model of the attack vector is proposed in this article. In the proposed method, an initial estimation of the system states is first obtained using a designed unknown input observer (UIO). Subsequently, based on the system, UIO models, and the initial estimations of the states, a dynamic model for the attack vector is extracted. Ultimately, the attack model is augmented with the main system model for coestimation of the attack and the system states using a Kalman filter. The onset of the FDI attack is rapidly detected by the accurate estimation of the attack vector. The effectiveness of the proposed approach is demonstrated under different FDI attack scenarios by a thorough theoretical analysis as well as simulations on IEEE 14-bus and 57-bus test systems. In order to show that the proposed method can keep up with typical scan rates of commercial phasor measurement units, a series of software-in-the-loop experiments are also conducted and the real-time feasibility of the proposed approach is guaranteed.

An Independent Component Analysis Approach for Wide-Area Monitoring of Power System Disturbances

Wide-area power system monitoring based on phasor measurement units allows collecting a set of physical variables for evaluating the system security and stability, as well as for detecting power system disturbances. However, trends, noise and non-Gaussian distribution in measurements are important challenges for carrying out the detection, localization and visualization of power system disturbances. In this paper, a methodology that combines independent component analysis with statistical indices for detecting and visualizing anomalous dynamic events from wide-area measurements is proposed. From the statistical indices, two charts are also proposed to provide a better understanding of the system disturbances. Finally, a set of simulated data obtained from a transient stability model of the New England/New York test system is used to demonstrate the effectiveness of the proposed technique.

Situational Awareness Enhancement in Transmission Lines using NI Based PMU

Phasor Measurement Units (PMUs) are becoming prominent in enhancing the situational awareness in wide area power system monitoring, thereby playing a vital role in its protection and control. This paper focuses on enhancing the situational awareness of transmission line using National Instruments (NI) based PMU. The data measured by the virtual PMU is used for fault detection and fault classification. The detection and the classification in the LabVIEW platform are performed using the Fourier Transform and support vector machines (SVMs) respectively. The proposed methodology has been applied on a laboratory set up consisting of transmission line, three phase load and an NI based PMU. The enhanced situational awareness in the detection and classification of transmission line faults helps in restoration of the transmission line as quickly as possible and trigger wide area control actions to maintain power system stability against the disturbances created by a fault.

Wide-Area Monitoring of Power Systems Using Principal Component Analysis and $k$-Nearest Neighbor Analysis

Wide-area monitoring of power systems is important for system security and stability. It involves the detection and localization of power system disturbances. However, the oscillatory trends and noise in electrical measurements often mask disturbances, making wide-area monitoring a challenging task. This paper presents a wide-area monitoring method to detect and locate power system disturbances by combining multivariate analysis known as Principal Component Analysis (PCA) and time series analysis known as $k$ -Nearest Neighbor ( $k{\text{NN}}$ ) analysis. Advantages of this method are that it can not only analyze a large number of wide-area variables in real time but also can reduce the masking effect of the oscillatory trends and noise on disturbances. Case studies conducted on data from a four-variable numerical model and the New England power system model demonstrate the effectiveness of this method.

Distributed Algorithms for Wide-Area Monitoring of Power Systems

Distributed Algorithms for Wide-Area Monitoring of Power Systems

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.