A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Hybrid Signal Processing and Machine Learning Algorithm for Adaptive Fault Classification of Wind Farm Integrated Transmission Line Protection

The technological advancement in integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photovoltaic (PV) system into conventional power system as a future solution to meet the increase in global energy demands in order to reduce the cost of power generation, and improve on the climate change impact. This innovation also introduces challenges in the power system protection by it being compromised due to injected fault current infeeds on existing facilities. These infeed lead to the undesired trip of a healthy section of the line, and protection system failure. This paper presents a soft computational approach to adaptive fault classification model on High Voltage Transmission Line (HVTL) with and without RGES-WFG integration topologies, using extracted one-cycle fault signature of voltage and current signals with wavelet statistical approach in Matlab. The results are unique signatures across all fault types and fault distances with distinct entropy energy values on proposed network architecture. The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. Best suitable for adaptive unit protection scheme integration

The Impact of Transmission Protection System Reliability on Power System Resilience

Power transmission operation regimes are being changed for various technical and economic reasons seeking an improved power system resilience as a goal. However, some of these changes introduce new challenges in maintaining conventional transmission protection system dependability and security when meeting the operating complexities affecting power system resilience. Frequently evolving network topology, as a result of multiple switching actions for corrective, predictive and post event purposes, as well as high penetration of distributed generation into the system are considered as major contradictory changes from the legacy transmission protection standpoint. This research investigates the above-mentioned challenges and proposes effective solutions to improve the transmission protection reliability facing the above-mentioned risks and power system resilience consequently. A fundamental protection scheme based on the Hierarchically Coordinated Protection (HCP) concept is proposed to illustrate various approaches to predictive, adaptive and corrective protection actions aimed at improving power system resilience. Novel computation techniques as well as intelligent machine-learning algorithms are employed in proposing predictive, adaptive, and corrective solutions which fit various layers of the HCP concept and incorporate a fundamental HCP-based approach to supervise the legacy transmission protection function for a dynamic balance between dependability and security. The proposed predictive, adaptive, and corrective protection approaches are tested and verified on various systems, including real-life and IEEE test systems, and their performance effectiveness is compared with the state of the art

WAMS-based Hierarchical Active Power Differential Signal Algorithm for Backup Protection of a FACTS Compensated Transmission Network

This paper proposes a hierarchical active power differential signal-based generalized backup protection algorithm using Wide Area Mea- surement System (WAMS) data for Flexible AC Transmission System (FACTS)-compensated trans- mission networks. The proposed algorithm can be used for backup protection of transmission systems with any shunt and series-type FACTS devices. The increased number of FACT compensators affects the reliable operation of primary and backup protection of the transmission lines. Both shunt and series com- pensated lines cause malfunctioning of existing backup protection schemes. The proposed algorithm utilizes the sequence components of bus voltages and active power differential signals of lines to identify the faulty line. The algorithm is validated on a modified 9-bus system under MATLAB/SIMULINK platform. It is observed that the algorithm is suitable for identifying a faulty line in transmission systems containing both uncompensated and compensated lines with series or shunt-type FACTS controllers. This algorithm has the advantage that it uses a generalized backup protection logic and can be used for the accurate identification of a faulty line irrespective of the type of compensation devices

Transmission Line Protection Using Dynamic State Estimation and Advanced Sensors: Experimental Validation

This paper presents the experimental validation of a protection scheme for a transmission line based on dynamic state estimation along with the practical application of advanced sensors in this protection scheme. The scheme performs dynamic state estimation with high-frequency measurements provided by the sensors, assesses the operating condition (i.e., health) of the transmission line in real-time, and thereby determines the tripping signal whenever a fault is detected. The validation was carried out in two steps, first with simulation studies for a three-phase fault and then with the experimental implementation using a physical scaled-down model of a power system consisting of transmission lines, transformers, and loads. The simulation and validation results have shown that the scheme performs adequately in both normal and fault conditions. In the fault case with the experimental setup, the scheme could correctly detect the fault and send the trip signal to the line’s circuit breakers with a total fault clearing time of approximately 65 milliseconds which is comparable to conventional protection methods. The average processing time for a measurement sample block is 12.5 milliseconds. The results demonstrate that this scheme and the sensors would work for transmission line protection which can avoid relay coordination and settings issues

Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

The first-ever Ukraine cyberattack on power grid has proven its devastation by hacking into their critical cyber assets. With administrative privileges accessing substation networks/local control centers, one intelligent way of coordinated cyberattacks is to execute a series of disruptive switching executions on multiple substations using compromised supervisory control and data acquisition (SCADA) systems. These actions can cause significant impacts to an interconnected power grid. Unlike the previous power blackouts, such high-impact initiating events can aggravate operating conditions, initiating instability that may lead to system-wide cascading failure. A systemic evaluation of "nightmare" scenarios is highly desirable for asset owners to manage and prioritize the maintenance and investment in protecting their cyberinfrastructure. This survey paper is a conceptual expansion of real-time monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework that emphasizes on the resulting impacts, both on steady-state and dynamic aspects of power system stability. Hypothetically, we associate the combinatorial analyses of steady state on substations/components outages and dynamics of the sequential switching orders as part of the permutation. The expanded framework includes (1) critical/noncritical combination verification, (2) cascade confirmation, and (3) combination re-evaluation. This paper ends with a discussion of the open issues for metrics and future design pertaining the impact quantification of cyber-related contingencies

Reputation-Based Trust for a Cooperative, Agent-Based Backup Protection Scheme for Power Networks

This thesis research explores integrating a reputation-based trust mechanism with an agent-based backup protection system to improve the performance of traditional backup relay methods that are currently in use in power transmission systems. Integrating agent technology into relay protection schemes has been previously proposed to clear faults more rapidly and to add precision by enabling the use of adaptive protection methods. A distributed, cooperative trust system such as that used in peer-to-peer file sharing networks has the potential to add an additional layer of defense in a protection system designed to operate with greater autonomy. This trust component enables agents in the system to make assessments using additional, behavioral-based analysis of cooperating protection agents. Simulation results illustrate the improved decision-making capability achieved by incorporating this cooperative trust method when experiencing abnormal or malicious communications. The integration of this additional trust component provides an added push for implementing the proposed agent-based protection schemes to help mitigate the impact from wide-area disturbances and the cascading blackouts that often follow. As the push for electric grid modernization continues, an agent-based trust system including this type of behavioral-based analysis will also benefit other smart components connecting critical grid control and monitoring information systems