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In an increasingly dynamic power system with growing integration of renewable energy sources, real-time monitoring of primary frequency regulation—or Frequency Containment Reserve (FCR)—is essential for maintaining system stability and operational reliability. This paper presents a comprehensive approach to FCR monitoring based on high-resolution, time-synchronized measurements provided by Phasor Measurement Units (PMUs), with a focus on hydropower plants as primary FCR providers due to their flexibility and rapid response capabilities. The need for accurate and granular FCR monitoring arises from the limitations of traditional SCADA-based systems, which often lack sufficient temporal resolution and synchronization to capture dynamic frequency response behaviour. By contrast, PMUs enable precise tracking of frequency deviations, generator reaction times, and control characteristics such as droop and deadband. This enables a signal-based evaluation of each unit’s contribution to FCR, offering valuable insights into underperformance, delays, or asymmetric responses. However, signal analysis alone is not sufficient to fully evaluate FCR compliance. Therefore, the proposed monitoring framework integrates PMU-based detection performed within the WAMSTER platform with operational data collected from generating units via the PI AVEVA system. While WAMSTER verifies actual frequency response using synchrophasor data, PI AVEVA provides contextual information including production schedules, temporary limitations, and technological constraints. Based on this data, the expected FCR contribution is calculated for each unit, allowing for a comparison between expected and actual response. The monitoring architecture is realized through standardized protocols: IEEE C37.118 for real-time synchrophasor exchange between WAMSTER and PI AVEVA, and IEC 60870-5-104 for communication between local plant systems and PI AVEVA. The result is a scalable and modular system capable of providing system operators and analysts with an integrated, data-driven platform for comprehensive FCR evaluation and reporting. This paper outlines the development of the monitoring solution, including the design of the application, integration of PMU and plant data, and a case study focused on hydropower implementation. The findings highlight how combining PMU technology with contextual operational data provides a more complete understanding of FCR delivery, improves transparency, and supports compliance with regulatory requirements in modern, decarbonizing power systems.

With the advent of Distribution Phasor Measurement Units (D-PMUs) and Micro-Synchrophasors (Micro-PMUs), the situational awareness in power distribution systems is going to the next level using time-synchronization. However, designing, analyzing, and testing of such accurate measurement devices are still challenging. Due to the lack of available knowledge and sufficient history for synchrophasors’ applications at the power distribution level, the realistic simulation, and validation environments are essential for D-PMU development and deployment. This paper presents a vendor agnostic PMU real-time simulation and hardware-in-the-Loop (PMU-RTS-HIL) testbed, which helps in multiple PMUs validation and studies. The network of real and virtual PMUs was built in a full time-synchronized environment for PMU applications’ validation. The proposed testbed also includes an emulated communication network (CNS) layer to replicate bandwidth, packet loss and collisions conditions inherent to the PMUs data streams’ issues. Experimental results demonstrate the flexibility and scalability of the developed PMU-RTS-HIL testbed by producing large amounts of measurements under typical normal and abnormal distribution grid operation conditions.

Smart Grid 3.0 is the latest evolution of the smart grid and incorporates advanced computing and communication technologies. The synchrophasor communication system plays a critical role in wide-area measurement systems (WAMS) for real-time protection and control of power systems, supporting the objectives of Smart Grid 3.0. This system relies on synchrophasor communication technologies, where Phasor Measurement Units (PMUs) transmit synchrophasor data to Phasor Data Concentrators (PDCs) over the synchrophasor communication network. The communication infrastructure of this network is based on the TCP/IP protocol stack, which, unfortunately, is susceptible to cyberattacks, posing security threats such as data tampering and false data injection. These vulnerabilities undermine the intended benefits of synchrophasor applications in terms of situational awareness, observability, grid reliability, resiliency, and synchronized monitoring and control in the smart grid. To address these challenges, it is crucial to enhance the security, integrity, and confidentiality of synchrophasor data within the communication system. This paper proposes a blockchain-based synchrophasor communication system that preserves the security and integrity of synchrophasor data. In this paper, an architecture is proposed for a synchrophasor communication system based on blockchain technology. The proposed architecture aims to enhance the security and integrity of synchrophasor measurements. Furthermore, the architecture is developed as a peer-to-peer distributed blockchain network, leveraging the robustness of a distributed, decentralized, hierarchical PDC architecture. To evaluate the efficacy of the proposed architecture, two case studies, one using the IEEE 9 bus and the other using IEEE 14 bus systems are considered. Moreover, various challenges with potential solutions are also recommended. The proposed work is envisioned to contribute to the advancement of Smart Grid 3.0 by adopting blockchain technology for synchrophasor applications.

Phasor measurement unit (PMU) technology is a need of the power system due to its better resolution than conventional estimation devices used for wide-area monitoring. PMUs can provide synchronized phasor and magnitude of voltage and current measurements for state estimation of the power system to prevent blackouts. The drawbacks of a PMU are the high cost of the device and its installation. The main aspect of using PMUs in electrical networks is the property to observe the adjacent buses, thereby making it possible to observe the system with fewer PMUs than the number of buses through their optimal placement. In the last two decades, exhaustive research has been done on this issue. Considering the importance of this field, a comprehensive review of the progress achieved until now is carried out and the limitations of existing reviews in the literature are highlighted. This paper can be seen as a major attempt to provide an up-to-date review of the research work carried out in this all-important field of PMU placement and indicates that some perspectives of optimal PMU placement still need attention. Eventually, the work will open a new standpoint for the research community to fill the research gap.

Synchrophasor devices guarantee situation awareness for real-time monitoring and operational visibility of smart grid. With their widespread implementation, significant challenges have emerged, especially in communication, data quality and cybersecurity. The existing literature treats these challenges as separate problems, when in reality, they have a complex interplay. This paper conducts a comprehensive review of quality and cybersecurity challenges for synchrophasors, and identifies the interdependencies between them. It also summarizes different methods used to evaluate the dependency and surveys how quality checking methods can be used to detect potential cyberattacks. This paper serves as a starting point for researchers entering the fields of synchrophasor data analytics and security.

Ensuring the transient stability of power systems is critical for preventing widespread outages and maintaining the reliability and security of modern grids. Phasor measurement units (PMUs) have transformed power system monitoring by providing high-precision time-synchronized measurements across the grid. Integrating machine learning techniques with PMU data significantly advances the Transient stability assessment (TSA). This approach improves the precision and efficiency of stability evaluations and enables more accurate predictions of the system responses to disturbances. This review critically examines various TSA methodologies, focusing on studies that examine machine-learning techniques for TSA by detailing the characteristics and performance of supervised, unsupervised, reinforcement, and ensemble learning models. The articles were assessed based on their relevance to TSA, the robustness of their models, the accuracy of their results, scalability across power system sizes, and their consideration of real-world challenges such as data loss, noise, and computational efficiency. This review highlights recent TSA advancements that integrate synchrophasors data and machine learning techniques, offering researchers a new direction for developing accurate and efficient transient stability assessment methods.

Synchrophasor technology opens a new window for power system observability. Phasor measurement units (PMUs) are able to provide synchronized and accurate data such as frequency, voltage and current phasors, vibration, and temperature for power systems. Thus, the utilization of PMUs has become quite important in the fast monitoring, protection, and even the control of new and complicated distribution systems. However, data quality and communication are the main concerns for synchrophasor applications. This study presents a comprehensive survey on wide-area monitoring systems (WAMSs), PMUs, data quality, and communication requirements for the main applications of PMUs in a modern and smart distribution system with a variety of energy resources and loads. In addition, the main challenges for PMU applications as well as opportunities for the future use of this intelligent device in distribution systems will be presented in this paper.

The development of digital techniques in control engineering leads to the creation of innovative algorithms for measuring specific parameters. In the field of electric power engineering these parameters may be amplitude, phase and frequency of voltage or current occurring in the analyzed electric grid. Thus, the algorithms mentioned, applied in relation to the quoted parameters, may provide precise and reliable measurement results in the electric grid as well as ensure better grid monitoring and security. Signal analysis regarding its identification due to the type of interference is very difficult because the multitude of information obtained is very large. In order to indicate the best method for determining errors in measuring synchronous parameters of the measured current or voltage waveforms, the authors propose in this paper a new form of one error for all testing functions, which is called an equivalent error. This error is determined for each error’s value defined in the applicable standards for each of selected 15 methods. The use of the equivalent error algorithm is very helpful in identifying a group of methods whose operation is satisfactory in terms of measurement accuracy for various types of disturbances (both in the steady state and in the dynamic state) that may occur in the power grid. The results are analyzed for phasor measurement unit (PMU) devices of class P (protection) and M (measurement).

Synchrophasor systems, providing low-latency, high-precision, and time-synchronized measurements to enhance power grid performances, are deployed globally. However, the synchrophasor system as a physical network, involves communication constraints and data quality issues, which will impact or even disable certain synchrophasor applications. This work investigates the data quality issue for synchrophasor applications. In Part I, the standards of synchrophasor systems and the classifications and data quality requirements of synchrophasor applications are reviewed. Also, the actual events of synchronization signal accuracy, synchrophasor data loss, and latency are counted and analyzed. The review and statistics are expected to provide an overall picture of data accuracy, loss, and latency issues for synchrophasor applications.

Recent advances in distribution networks, driven by the integration of renewable energy sources, have spurred the emergence of microgrids, elevating concerns regarded reliability and stability. In this context, precise monitoring of events, particularly those elusive to detection like high-impedance faults (HIFs), becomes imperative. The development of phasor measurement units (PMUs) with their harmonic synchronized measurements has enhanced the monitoring task and fostering the application of synchrophasors even on microgrids. This work introduces a novel method for event classification in microgrids, utilizing combined low-rate PMU data and harmonic synchrophasor time series. Central to our approach is the usage of a state-of-the-art transformer neural network, based on the attention mechanism, to effectively discern HIFs from other faulty and non-fault events. Notably, this methodology accounts for prevalent PMU data quality issues, including noise, missing data, and synchronism errors. Results from real-world HIF data demonstrate a robust performance, with an accuracy rate of approximately 98% in event classification. This harmonic synchrophasor-based strategy showcases promise as an original approach for handling commercial PMU data, offering sufficient robustness for deployment in real-world applications.