Shikhar Pandey

Bio: Shikhar Pandey is an academic researcher from Washington State University. The author has contributed to research in topics: Phasor measurement unit & Computer science. The author has an hindex of 5, co-authored 13 publications receiving 86 citations. Previous affiliations of Shikhar Pandey include Commonwealth Edison & Pacific Northwest National Laboratory.

A Real Time Event Detection, Classification and Localization Using Synchrophasor Data

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).

Online Estimation of Steady-State Load Models Considering Data Anomalies

Abstract: Several techniques have been developed to estimate the load parameters in power systems. Most of the existing algorithms mainly focus on estimating the parameters for offline studies. With on-going smart grid development, high-resolution data at faster rates are available to allow estimation of load parameters in real time. This paper addresses the challenges in online estimation of the load parameters using phasor measurement unit data. A novel adaptive search-based algorithm to estimate load model parameters is presented here. In this paper, a static load model is used with the Z (constant impedance), I (constant current), and P (constant power) components of the load. Developed estimation algorithms for the ZIP parameter estimation are validated using the IEEE 14-bus system and data provided by the industry collaborators. Simulation results demonstrate the accurate estimation of the ZIP load model using the developed method. Also, various techniques to eliminate anomalies in the input data for accurate estimation of the load parameters have been presented in this paper.

Resiliency-Driven Proactive Distribution System Reconfiguration With Synchrophasor Data

Abstract: Measuring and enabling resiliency of electric distribution systems with increasing weather and cyber events are important. Some of the extreme events (e.g. Earthquakes, Hurricanes) and associated paths are predicted and monitored closely in advance and allow to take pre-event proactive control actions. The Distribution Phasor Measurement Units (D-PMUs) provide new opportunities and supporting such proactive actions. A synchrophasor based resiliency driven pre-event reconfiguration can ensure minimizing impact of the expected event on the power distribution system and associated performance. However, the D-PMUs will also face challenges in terms of data quality similar to the transmission PMUs. The focus of this paper is to provide data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data. Results are validated for real industrial feeders and test cases with satisfactory response.

Real-Time Synchrophasor Data Anomaly Detection and Classification Using Isolation Forest, KMeans, and LoOP

Abstract: Power grid operators assess situational awareness using time-tagged measurements from phasor measurement units (PMUs) placed at multiple locations in a network. However, synchrophasor measurements are prone to anomalies which may impact the performance of phasor based applications. Anomalies include any deviation from expected measurements resulting from power system events or bad data. Bad data include data errors or loss of information due to failures in supporting synchrophasor cyber infrastructure. It is necessary to flag bad data before utilizing for an application. This work proposes a tool for the detection and classification of anomalous data using an unsupervised stacked ensemble learning algorithm. The proposed synchrophasor anomaly detection and classification (SyADC) tool analyzes a selected window of data points using a combination of three unsupervised methods, namely: isolation forest, KMeans and LoOP. The method classifies the data as anomalies or normal data with more than 99% recall. The method also provides a probability of the data to be an event or bad data with more than 99% recall. Results for the IEEE 14 and 68 bus systems with synchrophasor data obtained using Real-Time Digital Simulator and data of industrial PMUs highlight the superiority of the algorithm to detect and classify anomalies.

Data-driven failure diagnosis in transmission protection system with multiple events and data anomalies

Abstract: To guarantee the reliable power supply, the expected operation of all the components in the power system is critical. Distance protection system is primarily responsible of isolating the faulty section from the healthy part of the grid. Failure in protection devices can result in multiple conflicting alarms at the power grid operation center and complex events analysis to manually find the root cause of the observed system state. If not handled in time, it may lead to the propagation of the faults/failures to the adjacent transmission lines and components. With availability of the synchronized measurements from phasor measurement units (PMUs), real-time system monitoring and automated failure diagnosis is feasible. With multiple adverse events and possible data anomalies, the complexity of the problem will be escalated. In this paper, a PMU based algorithm is presented and discussed to detect the root cause of the failure in transmission protection system based on the observed state, e.g. multiple line tripping, breaker failures. The failure diagnosis algorithm is further enhanced to come up with the fully functional version of the failure diagnosis tool, which is tailored for the cases in which the PMU anomalies are present. In the developed algorithm the validity of the PMU data is critical; however, such causes as communication errors or cyber-attacks might lead to the PMU data anomalies. This issue is well-addressed in this paper and some major types of anomaly detection methods suitable for PMU data are discussed. Results show that the ensemble approach has some distinct advantages in data anomaly detection compared to the previously used standalone algorithms. Additionally, the enhanced failure diagnosis method is developed to clean the inaccurate data in case of the anomaly in measured voltage magnitudes. Finally, both original and enhanced versions of the tool are tested on 96-bus test system using the real-time OPAL-RT simulator. The results show the accuracy of the enhanced tool and its advantages over the primary version of the tool.

Simple Linear Regression

Abstract: One of the most widely used statistical techniques is simple linear regression. This technique is used to relate a measured response variable, Y, to a single measured predictor (explanatory) variable, X, by means of a straight line. It uses the principle of least squares to come up with values of the “best” slope and intercept for a straight line that approximates the relationship. By means of the example below we motivate the technique, indicate the rationale underlying the calculations and come up with the formulas for the “best” slope and intercept based on the data.

Detecting the Location of Short-Circuit Faults in Active Distribution Network Using PMU-Based State Estimation

Abstract: With the rapid advancement of phasor measurement units (PMUs) technology, system operators in different level of power systems have access to new and abundant measurements. Taking into account these measurements in active distribution systems (ADNs), a new algorithm for short-circuit fault detection and identification based on state estimation (SE) is introduced in this paper. In this regard, as the first step, traditional SE process is revised to be compatible with fault conditions. Then, a fault location algorithm (FLA) based on the revised SE (RDSSE) is presented which attends to detect the location of fault after diagnosing faulted zone. For this purpose, current and voltage synchrophasors captured by PMUs as well as pre-fault SE results are used and according to calculated measurement residual indexes, the correct location of fault is diagnosed. The performance of RDSSE and SE based fault location method are tested by applying on an ADN, considering different fault scenarios in the network. The results proved that the proposed method is more accurate and reliable than traditional SE based methods in fault conditions and can precisely determine the real location of fault at lower SE execution times.

A Real Time Event Detection, Classification and Localization Using Synchrophasor Data

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).

Implementation of Real-Time Impedance-Based Stability Assessment of Grid-Connected Systems Using MIMO-Identification Techniques

Abstract: Grid impedance has a major effect on the operation of inverter-connected systems, such as renewable energy sources. Stability of such system depends on the ratio of the inverter output impedance and the grid impedance at the point of common coupling. Because the grid impedance varies over time with many parameters, online grid-impedance measurement acquired in real time is most preferred method for observing the stability. Recent studies have presented methods based on multiple-input-multiple-output (MIMO) identification techniques, where the stability of grid-connected system is rapidly assessed in the dq domain. In the methods, orthogonal injections are used with Fourier techniques, and the grid impedance d and q components are measured. The Nyquist stability criterion is then applied to assess the stability. This paper extends previous studies, and presents a real-time implementation for the online stability analysis using MIMO-identification methods. The practical implementation is discussed in detail and experimental results based on a grid-connected three-phase inverter are provided to demonstrate the effectiveness of the methods.