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Power system equipment presents special signatures at the incipient stage of faults. As more renewables are integrated into the systems, these signatures are harder to detect. If faults are detected at an early stage, economical losses and power outages can be avoided in modern power grids. Many researchers and power engineers have proposed a series of signature-specific methods for one type of equipment’s waveform abnormality. However, conventional methods are not designed to identify multiple types of incipient faults (IFs) signatures at the same time. Therefore, we develop a general-purpose IF detection method that detects waveform abnormality stemming from multiple types of devices. To avoid the computational burden of the general-purpose IF detection method, we embed the abnormality signatures into a vector and develop a pre-training model (PTM) for machine understanding. In the PTM, signal “words,” “sentences,” and “dictionaries” are designed and proposed. Through the comparison with a machine learning classifier and a simple probabilistic language model, the results show a superior detection performance and reveal that the training radius is highly related to the size of abnormal waveforms. [ABSTRACT FROM AUTHOR]

The increasing uncertain components of power systems foster the wide applications of Machine Learning (ML) techniques. While traditional ML models demand a large set of data, data-scarce dilemmas exist for new meters, devices, and new grids. Further, for rich historical measurements, valuable data may still be limited, especially for targets like identifying system events that rarely occur in the power system. To enhance the event type differentiation and localization for a data-limited grid, we propose a Transfer Learning (TL) framework to transfer knowledge from a data-rich grid (source grid) to the target grid, using measurements from Phasor Measurement Units (PMUs). The transferring process is challenging because of (1) high-volume data with redundant information, (2) different measurement dimensionalities, (3) dissimilar data distributions, and (4) disjoint event-location-label spaces for two grids. To handle the challenges of (1) to (3), we propose a joint optimization to reduce dimensionality and maximize common knowledge in a shared low-dimensional feature space, where the commonality lies in the same dimensions and close data distributions. Such an optimization-based procedure is verified via rigid mathematical theorems given the same label space, i.e., event-type-label space. However, for event localization, challenge (4) obstructs the optimization. Therefore, we design a label space alignment method to relabel the event location by the event zone location and build an event zone estimation problem. Then, the framework is generalized to both tasks. Finally, comprehensive experiments demonstrate the advantages of the proposed methods over state-of-the-art transfer learning models. [ABSTRACT FROM AUTHOR]

With the developments in underwater wireless optical communication (UWOC) technology, UWOC can be used in conjunction with autonomous underwater vehicles (AUVs) for high-speed data sharing among the vehicle formation during underwater exploration. A beam alignment problem arises during communication due to the transmission range, external disturbances and noise, and uncertainties in the AUV dynamic model. In this article, we propose an acoustic navigation method to guide the alignment process without requiring beam directors, light intensity sensors, and/or scanning algorithms as used in previous research. The AUVs need stably maintain a specific relative position and orientation for establishing an optical link. We model the alignment problem as a partially observable Markov decision process (POMDP) that takes manipulation, navigation, and energy consumption of underwater vehicles into account. However, finding an efficient policy for the POMDP under high partial observability and environmental variability is challenging. Therefore, for successful policy optimization, we utilize the soft actor–critic reinforcement learning algorithm together with AUV-specific belief updates and reward shaping based curriculum learning. Our approach outperformed baseline approaches in a simulation environment and successfully performed the beam alignment process from one AUV to another on the real AUV Tri-TON 2. [ABSTRACT FROM AUTHOR]

Due to limited amplitude and controlled phase of current supplied by inverter-interfaced renewable power plants (IIRPPs), the IIRPP-side distance protection of lines connected to IIRPPs fails to detect the fault location accurately, so it may malfunction. The composite sequence network of a line connected to an IIRPP during asymmetrical faults is analyzed, and an adaptive distance protection based on the analytical model of additional impedance is proposed in this study. Based on open circuit property of negative-sequence network at the IIRPP-side, the equivalent impedance of power grid and current flowing through fault point are calculated in real-time using local measurements, which are substituted into the analytical model of additional impedance to calculate fault location. In the case of negative-sequence reactive current injection from IIRPPs during asymmetrical faults, the error of calculating fault point current from local measurements is analyzed and corrected to ensure reliability of the proposed protection. The proposed protection alleviates the effect of fault resistance in a system with weak sources. In addition, the proposed protection can adapt to different grid codes (GCs), the operation mode change of the power grid, and the capacity change of the IIRPP. PSCAD/EMTDC test results verify the effectiveness of the proposed protection. [ABSTRACT FROM AUTHOR]

As residential photovoltaic (PV) system installations continue to increase rapidly, utilities need to identify the locations of these new components to manage the unconventional two-way power flow and maintain sustainable management of distribution grids. But, historical records are unreliable and constant re-assessment of active residential PV locations is resource-intensive. To resolve these issues, we propose to model the solar detection problem in a machine learning setup based on labeled data, e.g., supervised learning. However, the challenge for most utilities is limited labels or labels on only one type of users. Therefore, we design new semi-supervised learning and one-class classification methods based on autoencoders, which greatly improve the nonlinear data representation of human behavior and solar behavior. The proposed methods have been tested and validated not only on synthetic data based on a publicly available data set but also on real-world data from utility partners. The numerical results show robust detection accuracy, laying down the foundation for managing distributed energy resources in distribution grids. [ABSTRACT FROM AUTHOR]

This letter discusses pointing errors in underwater optical communication caused by environmental disturbances and uncertainties that cannot be well measured and controlled in previous optical alignment methods. The bore-sight and jitter effects are identified in the motion model of the mobile platform. We propose to use the sensor suite that includes a pressure sensor, super short baseline (SSBL) acoustic system, Doppler velocity log (DVL), and fiber optic gyro (FOG) to observe and estimate pointing errors during communication. The pointing errors updated by the particle filter can be shared with the pointing, acquisition, and tracking (PAT) system and thruster system. The sea experiments reveal that the proposed method can measure pointing errors and limit error growth by maneuvering the mobile platform. [ABSTRACT FROM AUTHOR]

Increasing renewable penetration in distribution grids calls for improved monitoring and control, where power flow (PF) model is the basis for many advanced functionalities. However, unobservability makes the traditional way infeasible to construct PF analysis via admittance matrix for many distribution grids. While data-driven approaches can approximate PF mapping, direct machine learning (ML) applications may suffer from several drawbacks. First, complex ML models like deep neural networks lack the degradability and explainability to the true system model, leading to overfitting. There are also asynchronization issues among different meters without GPS chips. Last but not least, bad data is quite common in the distribution grids. To resolve these problems all at once, we propose a variational support matrix regression (SMR). It provides structural learning to (1) embed kernels to regularize physical form in observable area while achieving good approximation at unobservable area, (2) integrate temporal information into matrix regression for asynchronized data imputation, and (3) define support matrix for margins to be robust against bad data. We test the performance for mapping rule learning via IEEE test systems and a utility distribution grid. Simulation results show high accuracy, degradability from data-driven model to physical model, and robustness to data quality issues. [ABSTRACT FROM AUTHOR]

The power flow (PF) problem is a fundamental problem in power system engineering. Many popular solvers like PF and optimal PF (OPF) face challenges, such as divergence and network information sharing between multi-areas. One can try to rewrite the PF problem into a fixed point (FP) equation (more stable), which can be solved exponentially fast. But, existing FP methods are not distributed and also have unrealistic assumptions such as requiring a specific network topology. While preserving its stable nature, a novel FP equation that is distributed in nature is proposed to calculate the voltage at each bus. This distributed computation enables the proposed algorithm to compute the voltages for multi-area networks without sharing private topology information. Unlike existing distributed methods, the proposed method does not use any approximate network equivalents to represent the neighboring area. Thus, it is approximation-free, and it also finds use cases in distributed AC OPFs. We compare the performance of our FP algorithm with state-of-the-art methods, showing that the proposed method can correctly find the solutions when other methods cannot, due to high condition number matrices. In addition, we empirically show that the FP algorithm is more robust to bad initialization points than the existing methods. [ABSTRACT FROM AUTHOR]

The increase in the photovoltaic generation on distribution grids may create problems, such as voltage-violations. To gain situational awareness for system operation, e.g., adjusting the tap-settings of the transformers or adjust capacitor banks, utilities need situational awareness about locations and amounts of photovoltaic powers being generated in different feeders. Unfortunately, many utilities not only lack observability of the distribution grid, e.g., no secondary grid schematics but also have no situational awareness on which feeders solar panels locate. To understand where the solar users are roughly, we propose to use the feeder measurements from utilities with solar panel measurements from third-party solar companies. Due to the property of active correlation detection, we propose several sequentially improved methods based on quantitative association rule mining (QARM), where we also provide a lower bound for performance guarantees based on the amount of available data and the size of the bin for clustering. However, the binning of data leads to information loss. So, we design a band to replace bin for creating a new data mining approach for robustness. We validate our result for the IEEE 4-, 8-, 123-, 8500-bus cases with the Pecan-Street dataset, and also for the IEEE 123-bus case under low/high penetration and with radial/weakly meshed configurations. For realistic validation, we also obtain real data from a utility and a solar power company in the same zip codes in a city of California. Numerical results show accurate associations of feeders and solar panels, leading to increased situational awareness of the secondary distribution grids. [ABSTRACT FROM AUTHOR]

Utilities continuously observe cable failures on aged cables that have an unknown degraded basic insulation level (BIL). One of the root causes is the transient overvoltage (TOV) associated with circuit breaker reclosing. To solve this problem, researchers propose a series of controlled switching methods, most of which belong to deterministic control. However, in power systems, especially in distribution networks, the switching transient is buffeted by stochasticity. Since it is hard to model transient overvoltage due to its complexity, we propose a model-free stochastic control method for reclosers under the existence of uncertainty and noise. Concretely, to capture high-dimensional dynamics patterns, we formulate the recloser control problem by incorporating the temporal sequence reward mechanism into a deep Q-network (DQN). Meanwhile, we embed our physical understanding of the problem into the action probability allocation and develop an infeasible-action-space-elimination algorithm. Through PSCAD simulation, we first reveal the impact of load types on cables’ TOVs. Then, to reduce the training burden for the proposed reinforcement learning (RL) control method in different applications, we establish a post-learning knowledge transfer method. After the validation with our industrial partner, we exhibit several learning curves to show the enhanced performance. The learning efficiency is proved to be outstanding due to the proposed time sequence reward mechanism and infeasible action elimination method. Moreover, the results on knowledge transfer demonstrate the capability of method generalization. Finally, a comparison with conventional methods is conducted. It illustrates the proposed method is most effective in mitigating the TOV phenomenon among three methods. [ABSTRACT FROM AUTHOR]

Ensuring the stability of power systems is gaining more attention today than ever before due to the rapid growth of uncertainties in load and increased renewable energy penetration. Lately, wide-area measurement system (WAMS)-based centralized controlling techniques are offering flexibility and more robust control to keep the system stable. WAMS-based controlling techniques, however, face pressing challenges of irregular delays in long-distance communication channels and subsequent responses of equipment to control actions. This paper presents an innovative control strategy for damping down low-frequency oscillations in transmission systems. The method uses a reinforcement learning technique to overcome the challenges of communication delays and other non-linearity in wide-area damping control. It models the traditional problem of oscillation damping control as a novel faster exploration-based deep deterministic policy gradient (DDPG-S). An effective reward function is designed to capture necessary features of oscillations enabling timely damping of such oscillations, even under various kinds of uncertainties. A detailed analysis and a systematically designed numerical validation are presented to prove feasibility, scalability, interpretability, and comparative performance of the modelled low-frequency oscillation damping controller. The benefit of the technique is that stability is ensured even when uncertainties of load and generation are on the rise. [ABSTRACT FROM AUTHOR]

The Phasor measurement unit (PMU) measurements are mandatory to monitor the power system’s voltage stability margin in an online manner. Monitoring is key to the secure operation of the grid. Traditionally, online monitoring of voltage stability using synchrophasors required a centralized communication architecture, which leads to the high investment cost and cyber-security concerns. The increasing importance of cyber-security and low investment costs have recently led to the development of distributed algorithms for online monitoring of the grid that are inherently less prone to malicious attacks. In this work, we proposed a novel distributed non-iterative voltage stability index (VSI) by recasting the power flow equations as circles. The processors embedded at each bus in the smart grid with the help of PMUs and communication of voltage phasors between neighboring buses perform simultaneous online computations of VSI. The distributed nature of the index enables the real-time identification of the critical bus of the system with minimal communication infrastructure. The effectiveness of the proposed distributed index is demonstrated on IEEE test systems and contrasted with existing methods to show the benefits of the proposed method in speed, interpretability, identification of outage location, and low sensitivity to noisy measurements. [ABSTRACT FROM AUTHOR]

The energy management system becomes increasingly indispensable with the extensive penetration of new players in the distribution networks, such as renewable energy, storage, and controllable load. Also, the operation optimization of the active distribution system requires information on operation state monitoring. Smart measuring equipment enables the topology identification and branch line parameters estimation from a data-driven perspective. Nevertheless, many current methods require the nodal voltage angles measured by phasor measurement units (PMUs), which might be unrealistic for conventional distribution networks. This paper proposes a numerical method to identify the topology and estimate line parameters without the information of voltage angles. We propose a two-step framework: the first step applies a data-driven regression method to provide a preliminary estimation on the topology and line parameter; the second step utilizes a joint data-and-model-driven method, i.e., a specialized Newton-Raphson iteration and power flow equations, to calculate the line parameter, recover voltage angle and further correct the topology. We test the method on IEEE 33 and 123-bus looped networks with load data from 1000 users in Ireland. The results demonstrate that the proposed method can provide an accurate estimation of the topology and line parameters based on limited samples of measurement without voltage angles. [ABSTRACT FROM AUTHOR]

Understanding the feasible power flow region is of central importance to power system analysis. In this paper, we propose a geometric view of the power system loadability problem. By using rectangular coordinates for complex voltages, we provide an integrated geometric understanding of active and reactive power flow equations on loadability boundaries. Based on such an understanding, we develop a linear programming framework to 1) verify if an operating point is on the loadability boundary, 2) compute the margin of an operating point to the loadability boundary, and 3) calculate a loadability boundary point of any direction. The proposed method is computationally more efficient than existing methods since it does not require solving nonlinear optimization problems or calculating the eigenvalues of the power flow Jacobian. Standard IEEE test cases demonstrate the capability of the new method compared to the current state-of-the-art methods. Understanding the feasible power flow region is of central importance to power system analysis. This paper proposes a geometric view of the power system loadability problem. By using rectangular coordinates for complex voltages, this paper provides an integrated geometric understanding of active and reactive power flow equations on loadability boundaries. Based on such an understanding, this paper develops a linear programming framework to 1) verify if an operating point is on the loadability boundary, 2) compute the margin of an operating point to the loadability boundary, and 3) calculate a loadability boundary point of any direction. The proposed method is computationally more efficient than existing methods since it does not require solving nonlinear optimization problems or calculating the eigenvalues of the power flow Jacobian. Standard IEEE test cases demonstrate the capability of the new method compared to the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Underwater wireless optical communication (UWOC) has attracted increasing interest in various underwater activities because of its order-of-magnitude higher bandwidth compared to acoustic and radio-frequency technologies. Testbeds and pre-aligned UWOC links were constructed for physical layer evaluation, which verified that UWOC systems can operate at tens of gigabits per second or close to a hundred meters of distance. This holds promise for realizing a globally connected Internet of Underwater Things (IoUT). However, due to the fundamental complexity of the ocean water environment, there are considerable practical challenges in establishing reliable UWOC links. Thus, in addition to providing an exhaustive overview of recent advances in UWOC, this article addresses various underwater challenges and offers insights into the solutions. In particular, oceanic turbulence, which induces scintillation and misalignment in underwater links, is one of the key factors in degrading UWOC performance. Novel solutions are proposed to ease the requirements on pointing, acquisition, and tracking (PAT) for establishing robustness in UWOC links. The solutions include light-scattering-based non-line-of-sight (NLOS) communication modality as well as PAT-relieving scintillating-fiber-based photoreceiver and large-photovoltaic cells as the optical signal detectors. Naturally, the dual-function photovoltaic–photodetector device readily offers a means of energy harvesting for powering up the future IoUT sensors. [ABSTRACT FROM AUTHOR]

The high impedance fault (HIF) has random, irregular, and unsymmetrical characteristics, making such a fault difficult to detect in distribution grids via conventional relay measurements with relatively low resolution and accuracy. This paper proposes a stochastic HIF monitoring and location scheme using high-resolution time-synchronized data in $\mu $ -PMUs for distribution network protection. Specifically, we systematically design a process based on feature selections, semi-supervised learning (SSL), and probabilistic learning for fault detection and location. For example, a wrapper method is proposed to leverage output data in feature selection to avoid overfitting and reduce communication demand. To utilize unlabeled data and quantify uncertainties, an SSL-based method is proposed using the information theory for fault detection. For location, a probabilistic analysis is proposed via moving window total least square based on the probability distribution of the fault impedance. For numerical validation, we set up an experiment platform based on the real-time simulator, so that the real-time property of $\mu $ -PMU can be examined. Such experiment shows enhanced HIF detection and location, when compared to the traditional methods. [ABSTRACT FROM AUTHOR]