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Ravi Yadav

Bio: Ravi Yadav is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Kernel density estimation & Probability distribution. The author has an hindex of 3, co-authored 5 publications receiving 58 citations.

Papers
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Journal ArticleDOI
TL;DR: A method for accurate detection, temporal localization, and classification of multiple events in real time using synchrophasor data is proposed and a time series classification based method using energy similarity measure (ESM) is proposed.
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.

87 citations

Journal ArticleDOI
TL;DR: A kernel density estimation approach for accurate real-time classification of events in a power system with renewables using synchrophasor data using 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).
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.

35 citations

Journal ArticleDOI
TL;DR: A coherency identification method based on spectrum similarity approach that captures pair-wise similarity between synchrophasor frequency signals is proposed and validated with renewable generation in the IEEE-39 bus system and a real transmission system of India grid.
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.

9 citations

Journal ArticleDOI
TL;DR: This article provides objective-driven models for false setting injection (FSI) and false command injection (FCI) type attacks and proposes a principal component analysis (PCA) assisted sequential deep learning approach for online classification of cyber outages and natural events in a power system.
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.

8 citations

Journal ArticleDOI
TL;DR: In this paper, a multi-channel/multi-sample dynamic injection attack in phasor trends to closely mimic natural disturbances aiming at trends-based applications is proposed, and an unsupervised wavelet probability mapping method is proposed for detecting and correction of dynamic false data sequences.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: 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.
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).

63 citations

Journal ArticleDOI
TL;DR: 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, which alleviates authentic data for accumulation at groups of sensor devices in theICPS.
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 .

60 citations

Journal ArticleDOI
TL;DR: This paper proposes a method based on Hybrid Particle Swarm Optimization in order to design a WADC that ensures robustness to power system operating uncertainties, time delays variations on the WadC channels and the permanent failure of the W ADC communication channels.
Abstract: The presence of low-frequency and low-dampened oscillation modes can compromise the operating stability of power systems. Recent research has shown that the use of phasor measurement units data to compose a wide-area damping controller (WADC) has been shown to be effective in mitigating such oscillation modes but the possibility of loss of communication channels due to cyber-attacks or failures can compromise the proper operation of this controller. Besides, traditional control design methods present difficulties for the WADC control design. This article proposes a method based on hybrid particle swarm optimization in order to design a WADC that ensures robustness to power system-operating uncertainties, time delays variations on the WADC channels, and the permanent failure of the WADC communication channels. Modal analysis and nonlinear time-domain simulations were conducted in the IEEE 68-bus power system considering a set of scenarios.

56 citations

Journal ArticleDOI
TL;DR: A novel scheme is suggested for change detection and fault classification in ac microgrids (μGs) with different operating modes, able to handle both operation modes of the μG and is compared with some other similar methods.
Abstract: A novel scheme is suggested for change detection and fault classification in ac microgrids (μGs) with different operating modes. The proposed method utilizes a Teager–Kaiser energy operator based approach for analyzing the current-based signal to detect changes in the μG. The current-based signal is a summation of squared three-phase currents (SSC), measured at one end of a line in the μG. Since μGs have different operating modes, a proper signal should be defined to have some valuable signatures of the changes in different conditions. The SSC signal is constant during normal conditions of the grid-connected and islanded μG, whereas in any possible change condition experiences considerable variation. After the detection, the faulty phase is determined using three similar indices, derived from the squared currents. These indices are the ratios of squared currents for each phase with half-cycle window length, before and after the change detection. The proposed protection scheme is able to handle both operation modes of the μG. The proposed technique is evaluated in a MATLAB/Simulink simulated network and laboratory small-scaled test bench. The results confirm high accuracy and quickness of the proposed approach. Furthermore, the performance of the proposed methodology is compared with some other similar methods.

39 citations

Journal ArticleDOI
TL;DR: A kernel density estimation approach for accurate real-time classification of events in a power system with renewables using synchrophasor data using 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).
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.

35 citations