Real-time inertia monitoring is essential for maintaining stable power system operations, particularly in grids with high levels of renewable energy penetration. Existing inertia estimation methods often overlook the contributions of loads or inverter-based resources synthetic inertia. They also do not verify individual inertia values through response measurements. To address this challenge, a real-time, low-cost, and accurate inertia monitoring and estimation technology is developed to quantify the effective inertia for the actual grid response. First, the mechanism of the synchrophasor response during pumped storage hydro (PSH) is analyzed. Then, a comprehensive synchrophasor traces-based trigger is designed to detect the operation from PSH plants, utilizing its instantaneous step change in active power at the plant’s connection point during the pump turn-offoperation. Next, an improved adaptive-window prior-event rate of change of frequency tolerant inertia calculation method is proposed, with the advantage of real-time, noise immunity, and high accuracy. Furthermore, an inertia monitoring system based on PSH operation traces is established, integrating measurements from frequency disturbance recorders. By partnering with NERC, Dominion Energy, and Tennessee Valley Authority, this initiative addresses one of the primary challenges of operating a high-penetration renewable, thereby paving the way for a carbon-free power sector with significantly improved operational performance in the U.S.

Power system resilience describes the system’s ability to withstand and recover quickly from unexpected power outages due to extreme events. As society’s electrification continues at a rapid rate, it is imperative electric infrastructure is designed and operated with extra security. This work studies the impact of severe hurricane storms on the power system located along the United States’ Eastern Seaboard. Specific focus is designated to steady state system operation and loss of large generation step up power transformers. A series of phasor domain power system simulations are conducted to determine the critical point at which the system loses steady state operability. Additionally, active power load shedding and generator dispatching are studied as mitigation and recovery strategies. Results indicate generator dispatching as an effective proactive mitigation strategy and load shedding as an effective recovery strategy.

This article presents a novel approach to physical-displacement-based power grid measuring via an intensity-modulated fiber-optic sensor (IMFOS). An IMFOS utilizes one fiber to transmit the intensity modulated light from its electro-optic controller to a fiber-optic probe. The power grid voltage and current can induce physical displacements in transducers via the piezoelectric effect and the Lorentz law, respectively, which then result in a distance change between the optical probe and the reflective surface of the transducers. In parallel, multiple fibers are used to collect the reflective light for electro-optic conversion. A National-Instruments-based characterization platform is set up for performance evaluation. The testing result demonstrates that the IMFOS is immune to the inherent dc and low-frequency saturation issues prevalent in conventional potential and current transformers. Finally, the IMFOS is implemented in a universal grid analyzer to illustrate its applicability for phasor estimation in actual power grids.

The high voltage direct current (HVdc) intertie has been applied to provide ancillary-services for ac grids, utilizing the real-time feedback from phasor measurement units (PMUs). However, PMU data communication is vulnerable to false data injection attacks (FDIA) due to protocol defects, thus the HVdc ancillary control and system stability will be threatened. To address this issue, this article proposes a novel HVdc control strategy based on a hybrid data-driven (HDD) methodology. The HDD methodology is first proposed to detect the types and duration time of multiple frequency attacks. Specifically, the Hilbert Huang transform (HHT) is used to decompose the frequency data, using variational mode decomposition instead of the traditional empirical mode decomposition, to extract data features. Second, a multikernel support vector machine is proposed to classify the attacked data based on the designed distinctive features from HHT. Meanwhile, the attacking duration time is decided using an unsupervised technique. Third, an HDD-based HVdc ancillary control strategy is established to eliminate the effect of FDIAs on the HVdc frequency response. Comprehensive experiments of HDD-based HVdc ancillary controls under different FDIAs suggest that the proposed HDD could fast and accurately classify the FDIAs, and the HDD-based HVdc ancillary control strategy could significantly suppress the impact of the FDIAs.

The increasing deployment of phasor measurement units and the advances of their reporting rates are challenging the present data centers in terms of storing and analyzing large-volume data. Under power system disturbance conditions, it is difficult to retain critical information while compressing the synchrophasor data effectively. This article combines the cross entropy and the singular value decomposition, proposing a novel model to compress the synchrophasor data to an extremely small size yet keep superior accuracy. The proposed model is extensively tested and compared with the state-of-the-art algorithms using the simulated and the FNET/GridEye field-collected data. The result indicates that the proposed algorithm has superior performance in compressing the data while retaining critical information under disturbance conditions.

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. It reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.

As one of the most popular cloud services, data storage has attracted great attention in recent research efforts. Key-value (k-v) stores have emerged as a popular option for storing and querying billions of key-value pairs. So far, existing methods have been deterministic. Providing such accuracy, however, comes at the cost of memory and CPU time. In contrast, we present an approximate k-v storage for cloud-based systems that is more compact than existing methods. The tradeoff is that it may, theoretically, return errors. Its design is based on the probabilistic data structure called “bloom filter”, where we extend the classical bloom filter to support key-value operations. We call the resulting design as the kBF (key-value bloom filter). We further develop a distributed version of the kBF (d-kBF) for the unique requirements of cloud computing platforms, where multiple servers cooperate to handle a large volume of queries in a load-balancing manner. Finally, we apply the kBF to a practical problem of implementing a state machine to demonstrate how the kBF can be used as a building block for more complicated software infrastructures.

This paper introduces a Discrete Fourier Transform (DFT)-based algorithm to extract the Electric Network Frequency (ENF) information from an audio recording for use in audio authentication. The basic idea of the proposed algorithm is to calculate the specific spectral lines by DFT in the frequency domain at the desired frequency point instead of throughout the entire frequency band. Then a binary search technique is employed to search the next desired frequency bin to repeat the spectral line calculation until the hidden ENF information is extracted. The purpose is to improve the accuracy and precision of conventional ENF extraction methods and also to enhance the calculation efficiency. Both simulated audio signals with different signal-to-noise ratios (SNRs) and actual audio recordings are studied to verify the performance of the proposed algorithm. Two error-evaluation criteria, frequency offset and frequency bias, are defined to evaluate the algorithm performance on accuracy and precision. The test results and the error evaluation prove the validation and demonstrate the improvement of the proposed algorithm.

A novel forensic tool used for assessing the authenticity of digital audio recordings is known as the electric network frequency (ENF) criterion. It involves extracting the embedded power line (utility) frequency from said recordings and matching it to a known database to verify the time the recording was made, and its authenticity. In this paper, a nonparametric, adaptive, and high resolution technique, known as the time-recursive iterative adaptive approach, is presented as a tool for the extraction of the ENF from digital audio recordings. A comparison is made between this data dependent (adaptive) filter and the conventional short-time Fourier transform (STFT). Results show that the adaptive algorithm improves the ENF estimation accuracy in the presence of interference from other signals. To further enhance the ENF estimation accuracy, a frequency tracking method based on dynamic programming will be proposed. The algorithm uses the knowledge that the ENF is varying slowly with time to estimate with high accuracy the frequency present in the recording.

Catastrophic failures of power systems are phenomena which occur with some regularity throughout the world. It is recognized that these cannot be prevented, although with the use of newer developments in power engineering, in communication systems, and in computer engineering it would be possible to reduce their frequency and their impact on society. Analyses of many blackouts point to some salient features which are common to most such events: power systems under stress because of high load levels or outages of important facilities, some initiating event-usually a fault, often followed by cascading effects due to unwanted operation of some protection systems. In particular, the role of hidden failures (HFs) in protection systems in propagating power system disturbances has become clearer with some of the recent research reported in the literature. This paper explores further the issue of HFs of protection systems and possible countermeasures. Regarding the countermeasures, adapting the protection systems so that they would change their operational logic from OR to a VOTING protocol has been discussed in the literature, and is well within the capability of present technology. Other hardware solutions, such as "Hidden Failure Monitoring and Protection Systems," have also been discussed in the literature. Most of these countermeasures will require intensive use of communication networks. Communication infrastructure will be utilized for real-time data transfer, as well as for slower speed data gathering tasks related to the condition of the power system. In this paper, we concentrate on the communication facilities and their applications for providing countermeasures against catastrophic failures of power systems.

While energy storage technologies do not represent energy sources, they provide valuable added benefits to improve stability power quality, and reliability of supply. Battery technologies have improved significantly in order to meet the challenges of practical electric vehicles and utility applications. Flywheel technologies are now used in advanced nonpolluting uninterruptible power supplies. Advanced capacitors are being considered as energy storage for power quality applications. Superconducting energy storage systems are still in their prototype stages but receiving attention for utility applications. The latest technology developments, some performance analysis, and cost considerations are addressed. This paper concentrates on the performance benefits of adding energy storage to power electronic compensators for utility applications.