Grid Signal Research Articles

Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.

Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control

Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control

Fast synchronization with enhanced switching control for grid-tied single-phase square wave inverter using FPGA

Research on grid synchronization has been conducted worldwide by researchers in conjunction with the development of innovative technologies, such as dedicated short-range communication (DSRC) and cellular vehicle-to-everything (C-V2X). However, grid-connected inverters face several challenges, mainly the mismatch in voltage amplitude, frequency, and phase angle, as well as grid voltage disturbance and grid faults. Thus, the control algorithm of this research mainly focused on a half-cycle algorithm to design an enhanced digital switching control for fast synchronization using an FPGA. The control algorithm was developed based on zero-crossing detection (ZCD) and digital phase-locked loop (PLL) modeling techniques using the hardware description language (HDL) and a combination of digital logic blocks in Quartus II software, where the proposed switching was applied using the square-wave switching technique through a 300-watt full-bridge experimental prototype. The performance of the proposed technique was studied, where the total harmonic distortion (THD) for voltage and current resulted in a percentage reduction of 89.29% and 78.05% for voltage and current, respectively, after filter implementation. Also, the resulting signal synchronized in every half cycle and matched the voltage amplitude, frequency, and phase angle of the grid signal in 10 ms.

Application of hilbert transform for power quality indicators monitoring in general purpose grids

During the operation of power grids, abnormal modes occur that lead to a decrease or increase in the grid voltage beyond permissible values, the appearance of high-frequency noise components and grid signal fading’s, which fading’s the power quality and can lead to the failure of electrical equipment. This requires constant monitoring of power quality indicators. The methods of measuring power quality indicators have been examined and analyzed in the article, their advantages and limitations have been considered, and the inefficiency of using the Fourier transform for estimating of power quality indicators in time domain has been justified. The application of Hilbert transform has been suggested for monitoring the indicators of power quality in general-purpose grids, which include measuring the duration and magnitude of voltage dips, the duration of voltage fading, localization and determination of the duration of high-frequency noise interference in the power grid, determination of phase shift during voltage dip. A simulation of the process of determining the indicators of power quality in time domain and the time intervals during which these indicators exceed the established limits has been performed, which confirmed the effectiveness of using the Hilbert transform in the systems of their monitoring. Keywords: general purpose power grid, power quality indicators, Hilbert transform.

Investigating the potential of Machine Learning for fault location on Inverter-Based Resource interconnection lines: Insights and recommendations

Given the increasing penetration of Inverter-Based Resources (IBR) and the impacts of these generators on existing fault location functions, this paper explores the potential of Machine Learning (ML) for designing one-terminal fault locators applied to systems with IBRs. For the studies, a system with a widely used topology for IBR interconnection to a transmission grid is modeled in PSCAD software, considering fault scenarios on the interconnection line with varying fault parameters, in addition to different IBR controls/topologies, grid short-circuit levels and signal noise levels. Besides the investigations on correctly choosing the attributes extracted from the signals for training ML methods, which introduces the concept of learning based on the existing method’s response, the studies cover performance assessments of 35 ML methods for the fault location task, indicating the most suitable ones, and studies about the influence of the training set size on the ML methods performance. Moreover, comparative performance analyses are conducted between the ML-based methodology and state-of-the-art fault locators, highlighting the benefits of the ML-based approach in reducing errors obtained in the fault location task. Finally, the paper recommends stages for applying ML-based methods to real systems, besides providing insights and recommendations on such practical applications.

An estimation and synchronization method based on a new modeling approach of power electrical signals

The authors have recently presented a new modeling approach, for electrical signals in power systems, which is based on the concept, derived from differential geometry, of the contact between a signal and an “osculating sinusoid”. A difficulty in applying the approach lies in the calculation of the “osculating sinusoid” parameters, when the signal contains noise. In order to overcome this problem, the paper proposes a new computation algorithm for determining the “osculating sinusoid” parameters also in presence of noise on the signal. Accodingly, a new method providing a fast and accurate estimation of the frequency, phase and amplitude for noisy grid signals is developed. The good performance of the method is verified by some significant simulation results.

Fast and Accurate Frequency Estimation for Renewable Energy Systems Using Maximum Decay Sidelobes Windows

Fast and accurate signal frequency estimation has great importance i.a. in renewable energy systems and power control systems. The energy produced by these systems shall fulfill quality requirements as defined in the respective directives and standards. More accurate and faster grid signal frequency estimation (which can be used to control the inverter) can improve the energy quality. This article presents an overview of methods for spectrum interpolation and signal frequency estimation and a generalized method for very accurate and fast frequency grid estimation by using the Fast Fourier Transform procedure and maximum decay sidelobes windows. An important feature of this algorithm is the elimination of the impact of the conjugate component on the estimation result. The article shows the statistical properties of the frequency estimator as well as the effect of noise for the estimation. The results of the simulation show that the algorithm can be successfully used for a fast and accurate estimation of the grid signal frequency i.a. in renewable energy systems and power control systems. The accuracy of the frequency estimation is in the order of 5·10-11 Hz for a 5 ms measurement window. Using different windows from the discussed family allows to adjust to the actual requirements of estimation.

Efficient centralized traffic grid signal control based on meta-reinforcement learning

Dear Editor, This letter presents a novel method to tackle the two challenges of the centralized traffic control based on reinforcement learning (RL): the curse of dimensionality as the scale of traffic grid increases and the data-inefficiency problem that requires large amounts of samples to learn. First, we use a sequence-to-sequence (seq2seq) model and the attention mechanism to decompose the state-action space into sub-spaces, thus dealing with the first challenge. Second, we propose a new context-based meta-RL model that disentangles task inference and control, which improves the meta-training efficiency and accelerates the learning process in the new environment. We evaluate our approach on real-world datasets and the results demonstrate that our approach outperforms the state-of-the-art deep reinforcement learning (DRL)-based methods and the traditional control methods.

Performance Analysis of Matrix Pencil Method Applied to High-Resolution Measurement of Supraharmonics

Supraharmonics emitted by power electronic equipment will cause electromagnetic interference. However, there is no uniform standard for the measurement of supraharmonics. Unlike non-parametric methods, such as Fourier transform, the matrix pencil method has attracted the attention of researchers in many fields because of its super-resolution characteristics. In this paper, an attempt is made to apply the matrix pencil method to the high-resolution measurement of supraharmonics. Noting that parametric methods offer a promising alternative strategy for high time-resolution analysis of supraharmonics, the matrix pencil method is introduced for high-resolution measurements and estimating the supraharmonic components in grid voltage and current signals. By deriving and analyzing the Cramér–Rao bound of the method, two excellent properties of the matrix pencil method are found: robustness to time-varying signals in supraharmonics measurements and accuracy in frequency localization of components with large amplitudes. Meanwhile, the effectiveness of the matrix pencil method when applied to the high-resolution estimation and measurement of supraharmonics in grid signals is demonstrated by measuring, estimating, and analyzing grid voltage and current signals containing different typical supraharmonics characteristics. This parametric approach opens up a new avenue that allows more signal information to be obtained while maintaining high accuracy, thus enabling the analysis of supraharmonics with higher time resolution.

Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management

Introduction: This paper proposes a deep learning algorithm based on the VMD-SSA-BiLSTM model for time series forecasting in the smart grid financial market. The algorithm aims to extract useful information from power grid signals to improve the timing prediction accuracy and meet the needs of sustainable innovation management.Methods: The proposed algorithm employs the variational mode decomposition (VMD) method to decompose and reduce the dimensionality of historical data, followed by singular spectrum analysis (SSA) to perform singular spectrum analysis on each intrinsic mode function component. The resulting singular value spectrum matrices serve as input to a bidirectional long short-term memory (BiLSTM) neural network, which learns the feature representation and prediction model of the smart grid financial market through forward propagation and backpropagation.Results: The experimental results demonstrate that the proposed algorithm effectively predicts the smart grid financial market's time series, achieving high prediction accuracy and stability. The approach can contribute to sustainable innovation management and the development of the smart grid.Discussion: The VMD-SSA-BiLSTM algorithm's efficiency in extracting useful information from power grid signals and avoiding overfitting can improve the accuracy of timing predictions in the smart grid financial market. The algorithm's broad application prospects can promote sustainable innovation management and contribute to the development of the smart grid.

Investigation of power quality problems and harmonic exclusion in the power system using frequency estimation techniques

This work aims to investigate a problem with power quality and the exclusion of harmonics in the power system using a method called frequency estimation. This study aims to explore the performance of several strategies for estimating phase and frequency under a variety of less-than-ideal situations, such as voltage imbalance, harmonics, dc-offset, and so on. When the grid signals are characterized by dc-offset, it has been shown that most of the approaches are incapable of calculating the frequency of the grid signals. This article introduces a frequency estimation method known as Modified Dual Second Order Generalized Integrator (MDSOGI). This method accurately guesses the frequency under all of the unideal scenarios. Experiments have shown that the findings are accurate. In order to build a control scheme for a shunt active power filter that is able to function under such circumstances, the scheme is further integrated with the theory of instantaneous reactive power. In order to demonstrate enhanced performance, the experimental prototype is being created.

CenLight: Centralized traffic grid signal optimization via action and state decomposition

AbstractThe centralized traffic grid signal control by the reinforcement learning method is challenging due to the difficulties of searching policy in the large state and action space. In order to solve these problems, a deep reinforcement learning (DRL) method via the action and state decomposition mechanism is proposed. We apply long short‐term memory to construct the agent which decomposes the high‐dimensional state and action space into sub‐spaces and makes decisions incrementally. This is a significant difference between our method and other methods. Through the specifically designed structure of the agent, the difficulty of searching policies can be mitigated, and our method can effectively control the traffic lights in a grid with hundreds of intersections. Experiments on synthetic data and real‐world data show that our method has better performance than traditional control methods and state‐of‐the‐art DRL‐based methods with an improvement of 21% on the queue length on synthetic data and the best travel time with an improvement of 9.35% on real‐world data.

Exploring the Impacts of Power Grid Signals on Data Center Operations Using a Receding-Horizon Scheduling Model

Data centers (DCs) can help decarbonize the power grid by helping absorb renewable power (e.g., wind and solar) due to their ability to shift power loads across space and time. However, to harness such load-shifting flexibility, it is necessary to understand how grid signals (carbon signals and market price/load allocations) affect DC operations. An obstacle that arises here is the lack of computationally-tractable DC operation models that can capture objectives, constraints, and information flows that arise at the interface of DCs and the power grid. To address this gap, we present a receding-horizon resource management model (a mixed-integer programming model) that captures the resource management layer between the DC scheduler and the grid while accounting for logical constraints, different types of objectives, and forecasts of incoming job profiles and of available computing capacity. We use our model to conduct extensive case studies based on public data from Microsoft Azure and MISO. Our studies show that DCs can provide significant temporal load-shifting flexibility that results in reduced carbon emissions and peak demand charges. Models and case studies are shared as easy-to-use Julia code.

Analyzing Harmony and Discord Among Optimal Building Controllers Responding to Energy, Cost, and Carbon Reduction Objectives

AbstractOptimization and control of building thermal energy storage holds great potential for unlocking demand-side flexibility, an asset that is being given much attention in current grid reforms responding to the climate crisis. As greater information regarding grid operations is becoming available, grid-interactive building controls inherently have become a multi-objective problem. Typical multi-objective optimization frameworks often introduce greater complexity and computational burden and are less favorable for achieving widespread adoption. With the overall goal of easing deployment of advanced building controls and aiding the building-to-grid integration, this work aims to evaluate the trade-offs and degrees of sub-optimality introduced by implementing single-objective controllers only. We formulate and apply a detailed single-objective, model predictive control (MPC) framework to individually optimize building thermal storage assets of two types of commercial buildings, informed by future grid scenarios, around energy, economic, environmental, and peak demand objectives. For each day, we compare the building’s performance in every category as if it had been controlled by four separate single-objective model predictive controllers. By comparing the individual controllers for each day, we reveal the level of harmony or discord that exists between these simple single-objective problems. In essence, we quantify the potential loss that would occur in three of the objectives if the optimal control problem were to optimally respond to only one of the grid signals. Results show that on most days, the carbon and energy controllers retained most of the savings in energy, cost, and carbon. Trade-offs were observed between the peak demand controller and the other objectives, and during extreme energy pricing events. These observations are further discussed in terms of their implications for the design of grid-interactive building incentive signals and utility tariffs.

Quantum computing for future real-time building HVAC controls

Buildings contribute to more than 70% of overall U.S. electricity usage and greenhouse gas (GHG) emissions. HVAC systems in buildings often consume more than 40% of the total building energy usage. To reduce such high energy use, numerous control strategies including optimal and predictive controls have been developed and demonstrated. To achieve a near real-time solution, most previous research has simplified the non-linearity of building thermodynamics and provided an approximate optimal solution. The future HVAC control optimizes more connected devices in buildings, which requires a rapid and accurate response, not only to the building itself but also to the grid signals. It also poses the challenge of solving non-linear problems with discrete variables. With the recent development of quantum computers, this has become feasible. In this paper, we developed a new optimization solution based on quantum annealing for model predictive control (MPC) of a rooftop unit (RTU). Compared to traditional optimization methods, we obtained similar solutions with less than 2% differences and improved computational speed from hours to seconds. We also demonstrated an 80% reduction in total electricity consumption and a 21% reduction in electricity bills by considering day-ahead price time-of-use demand response signals. Quantum computing has proven capable of solving large-scale non-linear discrete optimization problems for building energy systems.

Variational Bayesian Inference Time Delay Estimation for Passive Sonars

In passive sonars, distance and depth estimation of underwater targets is often limited by the accuracy of time delay estimations. The estimation accuracy of the existing methods of time delay estimation is limited by the uniform discrete grid (signal sampling rate). When a true time delay is out of the grid, the estimation accuracy deteriorates due to the mismatch between the real-time delay and the discrete grid. This paper proposes a new method for time delay estimation, which realizes the time delay estimation under the framework of variational Bayesian inference. The proposed method is grid-less, that is, continuous in the time domain. Unlike the popular grid-less compressive time delay estimation method, this method does not require parameter adjustment, and can automatically estimate the number of time delays, noise variance, and amplitude variance. The simulation results showed that the performance of the proposed method was superior to the reference state-of-the-art time delay estimation methods.

PQ Event Detection After Noise Removal Using Fuzzy Transform and Hilbert Spectral Analysis on EMD

In this paper, a noise removal technique from Power Quality (PQ) signals is proposed which is based on a soft thresholding method. Thresholding is accomplished by using Fuzzy Membership Function (MF) and Empirical Mode Decomposition (EMD). To assess the denoising performance, the proposed technique is applied to detect PQ disturbances from the denoised power system signals. The EMD is applied to decompose the signal into different components. Next, the Intrinsic Mode Functions (IMFs) obtained from the EMD are utilized to determine the IMF dependent thresholding parameters, which are used into the modified fuzzy MF for the noise removal. For improved performance, several random versions of the first IMF are created, independently soft thresholded, and averaged to yield the final denoised version of the first IMF. A separate measurement is used to decide how many IMFs are required to be thresholded. For the evaluation of the noise removal performance, 1) the proposed method is compared with a wavelet-transform-based state of art technique, and 2) an application is presented on the denoised signal for the detection of PQ events from real and simulated power system disturbance data. The denoised and processed IMFs are subjected to Hilbert spectral analysis for the identification of the localized features, which are then utilized for the detection of PQ events in smart grid signal.

Benchmarking Power Delivery Network Designs at the 5-nm Technology Node

We evaluate a total of 96 different power delivery designs for a 5-nm node FinFET CMOS technology using eight levels of interconnect wiring. Our methodology considers the impact of a given design on the gate delay of an inverter as a figure of merit, while highlighting the tradeoff between power grid density and signal track density. Our design space includes designs with variable power line pitch at multiple levels of interconnect wiring as well as designs with continuous power rails replaced with power staples, covering both low-power and high-performance design points. We also evaluate the impact of the proposed technology features, such as skip-level vias and wafer front-side power rail removal. This work demonstrates the importance of power delivery design in advanced technology nodes and proposes a useful methodology for benchmarking designs as well as technology elements early on in a technology development cycle, ahead of more involved analysis such as plane-and-route.

A Finite-Time Observer-Based Identification of Sinusoidal Signal With Unknown Frequency

In this article, we studied the parameters identification problem for the single sinusoidal signal based on the finite-time observer technique. After representing the sinusoidal signal with known frequency as a second-order time-invariant system, a finite-time observer-based estimation strategy is designed based on the homogeneous system theory to obtain unknown amplitude and phase angle. When the signal frequency is unknown, the original signal will be constructed into a third-order nonlinear system, which is challenging to design the corresponding state estimator. Based on the Lyapunov analysis method and adding a power integrator technique, a finite-time estimation scheme is developed and analyzed strictly. Finally, for a single-phase grid signal, some experimental results are provided to illustrate the efficacy of the proposed method.

Discrete Multiple Second-Order Generalized Integrator With Low-Pass Filters and Frequency-Locked Loop for DC Rejection

The multiple second-order generalized integrator (MSOGI), which is composed of parallel second-order generalized integrators (SOGI), is widely used in grid synchronization and signal extraction, and it usually suffers from dc offset issue. In continuous domain, low-pass filter (LPF) can be added to SOGI to eliminate the dc offset in its quadrature channel. However, in digital implementation, directly discretized LPF-based methods may not be able to eliminate dc offset and ac discretization error simultaneously, where the steady-state error will also be introduced by the conventional frequency-locked loop (FLL). In this article, a modified impulse invariant method is adopted to discretize SOGIs, which presents zero ac errors and good dynamics, but shows dc offset in both output channels. Then, an LPF is introduced to eliminate the dc offset of the discrete SOGI, forming a discrete SOGI-LPF. And, a discrete MSOGI-LPF (DMSOGI-LPF) is formed by paralleling multiple DSOGI-LPFs, which can extract different frequency components. Furthermore, a discrete FLL is proposed to estimate the frequency of the input signal, which suppresses the influence of the dc offset and achieves good dynamics. Finally, the stability analysis of the proposed method based on discrete linear-time periodic (LTP) model is provided. The effectiveness of the proposed method and its discrete LTP model are validated through experimental results.