Tomo Popovic

Substation Data Integration for Automated Data Analysis Systems

The paper discusses a concept of substation data integration for implementation of new automated analysis functions. Proper data integration is the main requirement to enable automation of the analysis and its future extensions. The integrated data concept is making the whole solution transparent to adding new types or product versions of IEDs, as well as new analysis functions. There are several types of modern IEDs used in todays substations. Different file formats and data collection strategies are implemented by different vendors. The paper addresses the issue of integrating IED data recorded by different IED types and then focuses on how to facilitate the use the integrated data. Several new analysis functions can be performed on the collected IED data and the results of the analysis can serve different groups of utility users. The paper introduces examples of automated analysis functions configured using the proposed concept for integration of IED data. The solution is open for adding new analysis functions that can utilize both the integrated IED data and the processing (analysis) results from other automated analysis functions. The analysis functions, system configuration, and the whole concept of data integration are evaluated using digital power system simulator.

Reliable Implementation of Robust Adaptive Topology Control

The topology (transmission line) switching to achieve economic and reliability gains in the power grid has been proposed some time ago. This approach did not gain much attention until recently when large penetration of renewable generation created incentives to use transmission line switching to control sudden changes in power flows and mitigate contingencies caused by the generation variability. This paper explores implementation issues related to circuit breaker (CB) monitoring, relay setting coordination and detection of relay misoperations in the context of the topology switching sequence implementation. The paper covers risk-based assessment of CB status needed for determination of reliable switching sequences; it indicates how relay settings may be changed due to switching actions; it also provides an on-line algorithm for detection of relay misoperations, which identifies the lines that may be switched back to service after being trip erroneously by a relay.

Smart grid data analytics for digital protective relay event recordings

Information systems and intelligent smart grid data analytics will have a critical role in managing the massive amount of data becoming available in power system substations. Digital protective relays are multi-functional intelligent electronic devices based on microprocessors, which are being installed in substations throughout the power grid. New digital relays are replacing old-fashioned electro-mechanical or solid-state relays, and besides their protective function, they are coming equipped with monitoring capabilities. These monitoring capabilities are creating potential for better observability of power systems, redundancy in measurements, and improved decision-making process when operating the system. This article discusses the implementation requirements for a fully automated data analytics solution that provides data integration, fault analysis, and visualization based on the event data recorded by digital protective relays.

Circuit breaker operational health assessment via condition monitoring data

This paper presents a practical approach to evaluation of the operational health and reliability of circuit breakers in substations. Motivated by the recent failure surveys conducted by CIGRE working groups, circuit breaker condition data, obtained by monitoring critical parameters such as operational timings, tank gas pressure and temperature, mechanism traveling time and speed, etc., is used to evaluate the proposed health index. The proposed measure would help identify the transmission lines available for switching actions from the circuit breaker reliability view point. The proposed health index also provides a valuable input for substation maintenance personnel. The applicability of the proposed methodology is explored and validated using the actual circuit breaker condition data collected in the field.

Measures of Value: Data Analytics for Automated Fault Analysis

The power industry is experiencing an enormous expansion of computer and communication devices in substations. As a result, a massive amount of measurement data is being continuously collected, communicated, and processed. This is partly due to the need for much better monitoring capability as power system loading and complexity of operation have increased. The installation of a large number of intelligent electronic devices (IEDs) to accomplish the monitoring task has created new challenges, such as cyber and physical security, time-synchronized data storage, configuration management, and efficient visualization. Automated data analytics solutions are the key to efficient use of IED recordings. This automated process includes conversion of measurements to data, processing data to obtain information, and extraction of cause-and-effect knowledge. This article provides real-life implementation examples of data analytics developed to handle measurements from digital fault recorders (DFRs) and digital protective relays (DPRs). The discussion addresses the implementation challenges and business benefits of such solutions.

Voltage Stability Prediction Using Active Machine Learning

An active machine learning technique for monitoring the voltage stability in transmission systems is presented. It has been shown that machine learning algorithms may be used to supplement the traditional simulation approach, but they suffer from the difficulties of online machine learning model update and offline training data preparation. We propose an active learning solution to enhance existing machine learning applications by actively interacting with the online prediction and offline training process. The technique identifies operating points where machine learning predictions based on power system measurements contradict with actual system conditions. By creating the training set around the identified operating points, it is possible to improve the capability of machine learning tools to predict future power system states. The technique also accelerates the offline training process by reducing the amount of simulations on a detailed power system model around operating points where correct predictions are made. Experiments show a significant advantage in relation to the training time, prediction time, and number of measurements that need to be queried to achieve high prediction accuracy.

Integration of Substation Data

The paper introduces a concept for integration of substation IED data, primarily coming from digital protective relays (DPRs) and digital fault recorders (DFRs). Modern substations are equipped with different types of IEDs. DFRs are traditionally used to capture data from various events and disturbances in the power system that may need to be analyzed. Most of the modern IEDs such as digital relays, power quality meters, and sequence of event recorders can offer data recording function that is quite similar to that of DFRs. In some recent substation designs there are cases where DFR function is replaced by an equivalent function provided by DPRs. There are also cases when both DFRs and DPRs are used and configured to record the same events. In this case records from DPRs are used as a redundant source for event analysis. The paper addresses main requirements for integration of substation data: communication and automated IED data retrieval, unifying event data file formats, verification of event data content, and proper handling of the system configuration parameters. Examples of substation data integration using the proposed concept are discussed through expansion of an existing DFR data integration and analysis system and through introduction of data coming from different substation IEDs. Digital simulator was used to evaluate the concept in the example with multiple IED types

Substation data integration and utilization

Substation Intelligent Electronic Devices (IEDs) generate huge amount of data that may be utilized and integrated automatically. Data recorded by IEDs may be used by analysis applications in attempt to describe disturbance and device action in detail. After collection data may be stored in the substation or another database at a different enterprise location. Those data may be merged in real time with data from another data sources, such as Remote Terminal Units (RTU), to active full substation data utilization. Besides the issue of designing an analysis application there are issues of handling different data formats, database, and interfaces. This paper discusses the research solutions for those issues followed by an implementation example.

Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study

Abstract This paper discusses a case study of designing a private Internet of Things (IoT) enabled platform for the research in precision agriculture and ecological monitoring domains. The system architecture is gradually derived using an approach of multiple, concurrent views. Each view represents an architectural perspective describing the solution from the viewpoint of different stakeholders, such as end-users, researchers, developers, and project managers. The end-user requirements have been identified using a set of high-level scenarios, which capture the context and illustrate the motivation for building the platform. The requirements and architecture of the proposed platform have been derived so that the users of the platform, researchers, and developers on the project, can utilize it for prototyping solutions for these high level use cases. The paper further describes the implementation of the platform and its evaluation using various sensor nodes deployed at the research and end-user facilities. The solution is open to further development with respect to supporting additional IoT protocols, data types, and interfacing to various analytics tools. The proposed architecture can also be implemented using different server platforms and cloud technologies.

Improved Frequency Estimation in Unbalanced Three-Phase Power System Using Coupled Orthogonal Constant Modulus Algorithm

A new iterative method for frequency estimation in the unbalanced three-phase power system is proposed. The proposed solution is based on the transformation of unbalanced three-phase voltage signal into a complex exponential by using the orthogonal constant modulus algorithm. In addition, based on the transformed signal, a simple adaptive algorithm for frequency estimation is proposed. Beside the proposed approach, any frequency estimator designed for a single-tone complex exponential can be used here, which is also the advantage of the proposed method. Simulation results confirm that the proposed algorithm exhibits better performance compared to the considered algorithms.