Ravi Yadav

Abstract: Real-time multiple event analysis is important for reliable situational awareness and secure operation of the power system. Multiple sequential events can induce complex superimposed pattern in the data and are challenging to analyze in real time. This paper proposes a method for accurate detection, temporal localization, and classification of multiple events in real time using synchrophasor data. For detection and temporal localization, a Teager–Kaiser energy operator (TKEO) based method is proposed. For event classification, a time series classification based method using energy similarity measure (ESM) is proposed. The proposed method is tested for simulated multiple event cases in the IEEE-118 bus system using DigSilent/PowerFactory and real PMU data for the Indian grid.

Abstract: Real-time classification of events facilitates corrective control strategies, supervisory protection schemes, and on-line transient stability assessment of a power system. The synchrophasor-based event classification techniques face challenges like similar responses for different classes of events, i.e., inter-class similarity (ICS), applicability to limited classes of events, and moderate real-time performance for a large power system. In addition, the enhanced ICS effect of increased renewable penetration on events classification needs to be addressed. This paper proposes a kernel density estimation approach for accurate real-time classification of events in a power system with renewables using synchrophasor data. The proposed method uses a diffusion type kernel density estimator (DKDE) to characterize the shape of 3-D voltage and frequency distribution along time in terms of probability density functions (PDFs). That have distinct scale, shape, and orientation for different classes of events. Thereafter, a set of statistical features is derived from PDFs to train a multi-layered deep neural network for event classification. The proposed method is validated for renewables in IEEE-39 bus system and real transmission system of India grid using DIgSILENT/PowerFactory and also on a real phasor measurement unit data for India grid, where it showed better performance for ICS and renewable integration cases.

Abstract: Increase penetration of intermittent renewable sources causes reduction and time variation in system inertia, forcing dynamic changes in system coherency status. Besides increased penetration levels, other factors like intermittent generation and change in distribution of renewables also influence the system coherency that needs to be addressed. This paper examines the impact of variability of renewables on system coherency and proposes a coherency identification method based on spectrum similarity approach. The proposed method uses discrete cosine Stockwell transform to derive multiple time-frequency (TF) features that captures pair-wise similarity between synchrophasor frequency signals. Thereafter, mean shift spectral clustering is used to cluster the buses with most homogeneous TF spectrum. The proposed method is validated with renewable generation in the IEEE-39 bus system and a real transmission system of India grid.

Abstract: The existing power system utilizes communication infrastructure for fast and reliable transfer of control and protection inputs. This dependency of a power system on communication infrastructure for critical applications makes it vulnerable to cyber attacks. Cyber-induced outages trigger both randomized and intentional switchings in a power system producing relatively similar dynamics as natural events making their classification difficult. This article proposes a principal component analysis (PCA) assisted sequential deep learning approach for online classification of cyber outages and natural events in a power system. This article provides objective-driven models for false setting injection (FSI) and false command injection (FCI) type attacks. The proposed classification method uses PCA to deduce truncated z-score sequences [or principal test sequences (PTSs)] capturing distinct spatio-temporal progression patterns of natural disturbances and cyber outages. The PTSs in the training sets are shuffled and sampled using a stratified random sampling technique and classified using an ensemble long short-term memory network. The proposed method is tested for simulation examples of FSI and FCI attacks in the standard IEEE118-bus test system, where it showed improved accuracy and time performance.

Abstract: The dependency of synchrophasor technology on shared/dedicated communication networks for data transfer and the lack of security specifications in C37.118 standard makes phasor measurements susceptible to data manipulation attacks. More critically, a false data injection attack could influence both accuracy of state estimates and the evaluation of system dynamics. Typically, false data sequences are modeled as multi-constraint non-linear optimization with random, step, or ramp type distribution. However, a carefully designed attack that mimics the dynamic response of a natural disturbance can hugely impact trends-based applications of synchrophasor-based disturbance monitoring and mode metering. This work designs a multi-channel/multi-sample dynamic injection attack in phasor trends to closely mimic natural disturbances aiming at trends-based applications. Also, the work proposes an un-supervised wavelet probability mapping method for the detection and correction of dynamic false data sequences. The proposed method identifies the attack sequences from disturbances using relative wavelet energy measure and replaces the corrupted sub-sequences with coherent un-corrupted trends using a kernel density estimation-based mode mapping method. The efficacy of the proposed method is validated for simulated injection cases in IEEE-118 bus system using DigSILENT/PowerFactory and real phasor data for the Eastern region of the India grid. The results show that the proposed method exhibits high detection accuracy and low correction error for different categories of dynamic false data cases under real system conditions.

Abstract: In this article, an industrial cyber-physical system (ICPS) is utilized for monitoring critical events such as structural equipment conditions in industrial environments. Such a system can easily be a point of attraction for the cyberattackers, in addition to system faults, severe resource constraints (e.g., bandwidth and energy), and environmental problems. This makes data collection in the ICPS untrustworthy, even the data are altered after the data forwarding. Without validating this before data aggregation, detection of an event through the aggregation in the ICPS can be difficult. This article introduces TrustData , a scheme for high-quality data collection for event detection in the ICPS, referred to as “ Trust worthy and secured Data collection” scheme. It alleviates authentic data for accumulation at groups of sensor devices in the ICPS. Based on the application requirements, a reduced quantity of data is delivered to an upstream node, say, a cluster head. We consider that these data might have sensitive information, which is vulnerable to being altered before/after transmission. The contribution of this article is threefold. First, we provide the concept of TrustData to verify whether or not the acquired data are trustworthy (unaltered) before transmission, and whether or not the transmitted data are secured (data privacy is preserved) before aggregation. Second, we utilize a general measurement model that helps to verify acquired signal untrustworthy before transmitting toward upstream nodes. Finally, we provide an extensive performance analysis through a real-world dataset, and our results prove the effectiveness of TrustData .

Abstract: The integration of renewable energy sources into power systems has gathered significant momentum globally because of its unlimited supply and environmental benefits. Within the portfolio of renewable energy, wind power is expected to have a soaring growth rate in the coming years. Despite its well known benefits, wind power poses several challenges in grid integration. The inherent intermittent and non-dispatchable features of wind power not only inject additional fluctuations to the already variable nature of frequency deviation, they also decrease frequency stability by reducing the inertia and the regulation capability. This paper closely examines these effects as well as the effect on tie-line flows and area control error, which causes a larger and longer frequency deviation in the integrated system. Further, the effect of wind power on frequency regulation capability at different penetration levels is also examined. The analytical and simulation results presented here provide some guidance on determining maximum wind power penetration level given a frequency deviation limit.

Abstract: With an increasing number of extreme events, grid components and complexity, more alarms are being observed in the power grid control centers. Operators in the control center need to monitor and analyze these alarms to take suitable control actions, if needed, to ensure the system's reliability, stability, security, and resiliency. Although existing alarm and event processing tools help in monitoring and decision making, synchrophasor data along with the topology and component location information can be used in detecting, classifying and locating the event, which is the focus of this work. Phasor Measurement Unit's (PMU's) data quality issue is also addressed before using data for event analysis. The developed algorithms include statistic, clustering, and Maximum Likelihood Criterion (MLE) based anomaly detection, Density-based spatial clustering of applications with noise (DBSCAN) for event detection and physics-based rule/ decision tree for event classification. Further, topology information, statistical techniques, and graph search algorithms are used for event localization. Developed algorithms have been validated with satisfactory results for IEEE 14 bus and 39 Bus as well as with real PMU data from the western US interconnection (WECC).

Abstract: Power grid operation continuously undergoes state transitions caused by internal and external uncertainties, e.g., equipment failures and weather-driven faults, among others. This prompts an observation of different types of waveforms at the measurement points (substations) in power systems. Modern power systems utilize phasor measurement units (PMUs) and intelligent electronic devices embedded with PMU functionality to capture the corresponding peculiarities through synchrophasor measurements. However, existing PMU devices are equipped with only one synchrophasor estimation algorithm (SEA) and are, thus, not always robust to handle different types of signals across the network. This article proposes a PMU-embedded framework that ensures real-time grid surveillance and potentially enables adaptive selection of preinstalled SEAs in the PMU. Therefore, it ensures high-fidelity measurements at all times and irrespective of the input signals. Our proposed framework consists of: 1) a pseudocontinuous quadrature wavelet transform which generates the featured scalograms and 2) a convolutional neural network for event classification based on the extracted features in the scalograms. Our experiments demonstrate that the proposed framework achieves high classification accuracy on multiple types of prevailing events in power grids, through which an enhanced grid-scale situational awareness in real time can be realized.

Abstract: Power grid operation continuously experiences state transitions caused by the internal and external uncertainties, e.g., equipment failures and weather-driven faults. This prompts an observation of different types of waveforms at the measurement points (substations) in power systems captured by the phasor measurement units (PMUs) and intelligent electronic devices (IEDs) embedded with PMU functionality, e.g., digital relays and fault recorders. The PMU should be, hence, equipped with either one synchrophasor estimation algorithm (SEA) that is accurate and robust to many different types of signals any time across the network, or should adaptively select the promising SEA, among an embedded suite of algorithms. This paper proposes a PMU-embedded framework that can ensure real-time grid surveillance and potentially enables adaptive selection of SEA for more accurate synchrophasor estimation. Our proposed framework is consisted of two components: (i) a pseudo continuous quadrature wavelet transform (PCQ-WT) algorithm using a modified Gabor wavelet transform, which generates the featured-scalograms; and (ii) a convolutional neural network (CNN), that classifies the events based on the extracted features in the scalograms. Our experiments demonstrate that the proposed framework achieves state-of-the-art classification accuracy on multiple types of prevailing events in power grids, through which an enhanced grid-scale situational awareness in real-time can be realized.