Distribution-level Phasor Measurement Units Research Articles

Event-Triggered Distribution System State Estimation: Sparse Kalman Filtering With Reinforced Coupling

A novel distribution system state estimation (DSSE) method is proposed for power distribution networks with low-observablity, where the measurements come from only a few distribution-level phasor measurement units (D-PMUs). The proposed DSSE method is event-triggered, which means the state variables are updated based on the information that is extracted from the events in the power distribution system. In this regard, the estimations of the state variables during the previous events are used as priori information to predict the state variables at the current event. Accordingly, a novel data-driven method based on elastic net regression analysis is proposed to learn the event-triggered state transition matrix. The DSSE problem is formulated as a generalized group Lasso problem, which is augmented based on the knowledge on the sparsity patterns of the state variables that are extracted from the analysis of the events. Here, in the absence of direct power measurements, we enhance our ability in sparse recovery by developing a new reinforced physics-based coupling method among the state variables, in which we add a novel set of linear differential power flow equations to the DSSE problem formulation in forms of virtual measurements. Finally, two different approaches are proposed to solve the formulated sparse event-triggered DSSE problem. The first approach is exact but computationally expensive, as it requires conducting a batch alternating direction method of multipliers (ADMM) analysis. The second approach is approximate, but it is much faster as it works based on a novel modified Kalman filter/smoother in the presence of ℓ1-norm.

Membership-Function-Based Secondary Frequency Regulation for Distributed Energy Resources in Islanded Microgrids With Communication Delay Compensation

Secondary frequency control is one of the most effective measures to ensure the stable operation of islanded microgrids (MGs). Most research on secondary frequency regulation has only focused on realizing steady-state operation objectives, that is, frequency restoration and power sharing. However, improving the dynamic performance of secondary frequency control is of great importance, especially in synchronous distributed energy resources. These synchronous units can introduce undesired oscillation modes, which may cause the instability conditions of MGs. To improve the dynamic performance of islanded MGs, a membership-function (MF) -based control strategy is proposed. The proposed strategy can trade-off between transient frequency regulation and frequency error elimination using the MF values calculated by the time-stamped synchronized measurements of distribution-level phasor measurement units. Besides, considering the time-varying communication delays in secondary frequency control loops, an adaptive delay compensator is proposed. The weights of the proposed compensator are updated by real-time delay measurements to compensate for the phase lag of control signals. Therefore, the adverse effect of communication delays on secondary frequency control is weakened effectively. Numerical simulations on an IEEE 34-bus system and a typical 40-bus islanded MG system demonstrate the advantages of the proposed method in the secondary frequency regulation of islanded MGs.

Node Vulnerability-Aware co-deployment of D-PMUs and FTUs for active distribution networks

This study proposes a co-deployment strategy of distribution-level phasor measurement units (D-PMUs) and feeder terminal units (FTUs) for active distribution networks (ADNs) considering node vulnerability to efficiently monitor and perceive the operational state of ADNs. The proposed measurement deployment strategy preferentially configures measurement devices with high precision and superior monitoring functions on vulnerable nodes while ensuring state estimation (SE) accuracy and cost economy. Based on complex network theory and risk assessment, a vulnerability index reflecting the “bridge” characteristics of nodes was defined. Considering the system monitorability, SE accuracy, and measurement cost, a measurement co-deployment strategy based on a combination weighting method was developed. An improved discrete particle swarm optimization algorithm was proposed to solve the co-deployment model by establishing a new particle coding format, improving the dynamic inertia weight and learning factors, and introducing chaos theory. Simulation results based on the IEEE 33-bus and 119-bus distribution systems verify the effectiveness and show that the proposed strategy not only improves system monitorability but also enhances SE robustness.

Artificial Neural Network-Based Voltage Stability Online Monitoring Approach for Distributed Generation Integrated Distribution System

Due to the growth of electric power demand and the intricacy of modern distribution system structure, the voltage stability issue is evolving as a critical problem in distribution grids. Therefore, it is imperative to investigate the corrective measures. In this paper, artificial neural network (ANN) based voltage stability online monitoring approach for distribution systems with distribution generators (DGs) is proposed. The proposed technique employs a local voltage stability index known as the stability index (SI) to identify the weak bus information, which is more effective compared to the conventional load margin techniques. Furthermore, the nonlinear relationship of the distribution grid control status and the resultant SI is mapped using ANN. From the installed distribution-level phasor measurement units (PMUs), the state parameters of buses can be obtained, and the resultant values of SI can be estimated. This approach can significantly enhance the computational speed of SI and evaluate the voltage stability measurement of distribution network in real-time, which assist the operator of the network in order to determine the operational condition and execute actions quickly. The proposed approach is applied on the modified IEEE 33 and IEEE 69-bus system with DGs. It is found that the computation time needed for assessment of voltage stability by CPF method is 16.2500 s and 21.8872 s whilst the computation time needed for the proposed method for the same assessment is 0.0677 s and 0.0749 s respectively for modified IEEE 33 and IEEE 69-bus system. This demonstrates that the proposed method has high accuracy and efficacy.

GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements

This paper is concerned with the complex task of identifying the type and cause of the events that are captured by distribution-level phasor measurement units (D-PMUs) in order to enhance situational awareness in power distribution systems. Our goal is to address two fundamental challenges in this field: a) scarcity in measurement locations due to the high cost of purchasing, installing, and streaming data from D-PMUs; b) limited prior knowledge about the event signatures due to the fact that the events are diverse and infrequent, and have unknown characteristics. To tackle these challenges, we propose an unsupervised graph-representation learning method, called GraphPMU, to significantly improve the performance in event clustering under locationally-scarce data availability by proposing the following two new directions: 1) using the topological information about the relative location of the few available phasor measurement units on the graph of the power distribution network; 2) utilizing not only the commonly used fundamental phasor measurements, bus also the less explored harmonic phasor measurements in the process of analyzing the signatures of various events. Through a detailed analysis of several case studies, we show that GraphPMU can highly outperform the prevalent methods in the literature.

Optimal placement of distribution-level synchrophasor sensors for distribution system

This paper proposes a new method for the placement of the important synchrophasor sensors, the distribution-level phasor measurement units (DPMUs). A new objective function is developed to achieve minimization of distribution system state estimation errors where the limited number of DPMUs, the zero injection buses, the limited DPMU channels and the supervisory control and data acquisition measurements are taken into account. The A-optimal experimental design criterion is used to formulate the proposed objective function and constraints. The state estimates are obtained by using the maximum likelihood estimator where both Gaussian and non-Gaussian measurement noise scenarios are studied. The performance of the proposed method is demonstrated by simulations on IEEE 34-bus and 123-bus distribution systems.

Denoising and Detection of Bad Data in Distribution Phasor Measurements Using Filtering, Clustering, and Koopman Mode Analysis

Distribution-level phasor measurement units (D-PMU) data are prone to different types of anomalies given complex data flow and processing infrastructure in an active power distribution system with enhanced digital automation. It is essential to preprocess the data before being used by critical applications for situational awareness and control. In this work, two approaches for detection of data anomalies are introduced for offline (larger data processing window) and online (shorter data processing window) applications. A smoothing wavelet denoising method is used to remove high-frequency noises. An ensemble approach built upon the margin-based maximum likelihood estimator (MB-MLE) method is developed to detect anomalies in denoised data by integration of the results from different base detectors including Hampel filter, Quartile detector, and DBSCAN. The processed data with offline analysis is used to fit a model to the underlying dynamics of synchrophasor data using Koopman mode analysis, which is subsequently employed for online denoising and bad data detection using Kalman filter (KF). The parameters of the KF are adjusted adaptively based on similarity to the training dataset for model fitting purposes. Developed techniques have been validated for the modified IEEE test system with multiple D-PMUs, modeled and simulated in real-time for different case scenarios using the OPAL-RT hardware-in-the-loop simulator.

Sparse Tracking State Estimation for Low-Observable Power Distribution Systems Using D-PMUs

A new state estimation method is proposed for power distribution networks that suffer from low-observability. The proposed distribution system state estimation (DSSE) method leverages the high reporting rate of only a small number of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, to unmask and characterize sparsity among the state variables. The DSSE problem is formulated over differential synchrophasors as an adaptive group sparse recovery problem to track the changes that are made in the states of the system due to the events that are captured in D-PMU measurements. The formulated DSSE is further augmented to use adequate side information on the support of the vector of unknowns that is obtained from the outcome of an event-zone identification analysis prior to solving the DSSE problem. The sufficient conditions for the uniqueness of the obtained sparse recovery solution are derived with respect to the available side information. Moreover, a calibration mechanism is developed to address drifting in the tracking state estimation to enhance robustness.

DPMU-based multiple event detection in a microgrid considering measurement anomalies

A microgrid may be subjected to various unexpected events, such as sudden tripping of a generator/load, line outages due to faults, sudden switching of large capacitor banks, etc. Detection and classification of events play an essential role in the reliable operation, control, and restoration of microgrids. Due to low observability in microgrids, anomalies in measurements pose a challenge for reliable detection of events using conventional methods. This work proposes a data-driven approach for event detection in microgrids using phasor measurements collected from distribution-level phasor measurement units (DPMUs). The proposed method can determine whether the change in phasor measurement is caused by an event or due to any measurement anomalies. The model explores the interdependency among all phasor measurements collected from an unobservable microgrid. This method can identify and replace anomalies in DPMU measurements using a Rauch–Tung–Striebel (RTS) smoother. The proposed algorithm performance is validated on a test system developed in an OPAL-RT/HYPERSIM real-time simulator and phasor measurements from DPMUs.

Adaptive Congestion Control for Electric Vehicle Charging in the Smart Grid

This article proposes an adaptive control algorithm for plug-in electric vehicle charging without straining the power system. This control algorithm is decentralized and merely relies on congestion signals generated by sensors deployed across the network, e.g., distribution-level phasor measurement units. To dynamically adjust the parameter of this congestion control algorithm, we cast the problem as multi-agent reinforcement learning where each charging point is an independent agent which learns this parameter using an off-policy actor-critic deep reinforcement learning algorithm. Simulation results on a test distribution network with 33 primary distribution nodes, 1760 low voltage end nodes, and 500 electric vehicles corroborate that the proposed algorithm tracks the available capacity of the network in real-time, prevents transformer overloading and voltage limit violation problems for an extended period of time, and outperforms other decentralized feedback control algorithms proposed in the literature. These results also verify that our control method can adapt to changes in the distribution network such as transformer tap changes and feeder reconfiguration.

MILP-Based Fault Diagnosis Model in Active Power Distribution Networks

With a rapid growth in the deployment of renewable and distributed energy resources (DERs) in active power distribution networks, especially the inverter-interfaced distributed generators (IIDGs), existing fault section estimation methods are facing additional challenges. This paper proposes an optimization model for fault diagnoses in active power distribution systems, which aims at simultaneous identifications of suspected fault line sections and potential false alarms sent by remote fault indicators (RFIs). First, mathematical expressions of expected and false alarms of oriented RFIs (ORFIs) and non-oriented RFIs (NRFIs) are formulated to construct a basic fault diagnosis model. Minimizing the number of false alarms is chosen as the objective function to find the most credible one from all possible fault hypotheses which represent different combinations of elements in the outage area. Next, considering IIDGs, a modified fault diagnosis model is established by eliminating misleading components from the objective function which are produced by incompetent RFIs and have failed to detect weak currents. The monitoring data provided by distribution level phasor measurement units (D-PMUs) for weak IIDG fault currents are leveraged to construct the equivalent RFI current, which is compared with the trigger threshold to find incompetent sensors. The proposed problem is formulated as a mixed-integer linear programming (MILP) model, which is solved by off-the-shelf commercial solvers. The effectiveness of the proposed fault diagnosis model is validated by test cases representing several DERs and false alarms applied to a utility feeder.

Coordinated Optimal Volt/Var Control for Distribution Networks via D-PMUs and EV Chargers by Exploiting the Eigensystem Realization

This article proposes a novel optimal hybrid control framework to improve the voltage profile of highly unbalanced Distribution Grids (DGs) by coordinating the injection of reactive power from multiple off-board Electrical Vehicles (EVs) chargers. It exploits the time-synchronized measurements advantages, centralized control, and local controllers embedded into a hierarchical and scalable scheme, coordinated by the distribution system operator (DSO). The proposed framework is aimed to increase the modern electrical networks' flexibility, reliability, and stability. It is powered by the Eigensystem Realization (ER)-based system identification technique, a Linear Quadratic Gaussian (LQG) controller, the Distribution-level Phasor Measurement Units (D-PMUs), and a new aggregator agent that handles the EV chargers power injection to precisely and timely regulate the dynamic voltage response in a sub-area or the entire DG. The robustness and feasibility of the proposal are demonstrated by employing simulated scenarios with unbalanced faults, latency in communications, and highly unbalanced loads conditions on the IEEE 13 and 123 nodes test feeders, compensating the voltage variations accurately in less than 205 ms. The promising outcome of this study suggests a novel application for an emerging measurement-based control system in the operation of modern active DGs.

State-of-the-Art in Synchrophasor Measurement Technology Applications in Distribution Networks and Microgrids

Although synchrophasor measurement technology (SMT) has become a somewhat mature and operational tool used in transmission-level power networks, its use in active distribution networks (ADNs) is still in very early stages. Due to distinct features (such as unbalanced operation, very low phase angle differences, greater harmonics pollution, and lower signal-to-noise ratios in distribution networks), transmission-level devices and applications may not be fully effective at the distribution level. Extensive research and development efforts are in progress to bring distribution-level phasor measurement units (DPMUs) to an operational stage in ADNs. This essential work is made even more urgent by changing ADNs that are widely integrating distributed energy resources and enabling demand-side entities as active network players, in some cases considering authorizing them for peer-to-peer energy trading. This paper focuses on SMT/DPMU applications in ADNs and microgrids. We specifically elaborate on how SMT/DPMUs are enabling 1) effective situational awareness, 2) enhanced protection and fault location functions, 3) novel control algorithms and augmented energy management applications, and 4) long-term baselining and analysis. Various barriers and challenges that come with rapid and broad deployment of SMT/DPMUs and their associated applications are also discussed.

Resilience-Oriented D-PMU Based Frequency Controller for Islanded Microgrids With Flexible Resources Support

This paper proposes a novel frequency regulation outlook focused on the improvement of islanded microgrids resilience. The proposed method takes advantage of power systems enhanced situation awareness enabled by distribution-level phasor measurement units (D-PMUs) monitoring to overcome current design boundaries that limit an effective harnessing of flexible resources potential to support microgrids resilience. For this, capitalizing on D-PMUs high resolution, low latency and time synchronized measurements, a beforehand assessment of the power mismatch leading the microgrid to frequency nadir condition is achievable during the early stages of the system dynamics. Based on this information, new measurement-based control modules are designed to address individual stages of the frequency regulation process in a dedicated manner, respectively arrest, rebound and recovery stages. These controllers provide a significant improvement of microgrid's frequency arrest and rebound ability, while ensuring a smooth recovery process toward the system steady-state realization. The proposed approach stability is mathematically demonstrated and comparative case-studies developed considering monitoring and communication systems non-idealities. Obtained results indicate the proposed controller superior ability to address single and cascade large-scale disturbances, while simultaneously providing an improved frequency regulation performance and greater economic benefit for flexible resource support.

Cyberattacks Against Event-Based Analysis in Micro-PMUs: Attack Models and Counter Measures

The recent advent of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, has introduced a wide range of new applications in power distribution systems. A sub-class of such emerging applications are called event-based methods. These methods focus on the analysis of events in the stream of micro-PMU measurements to achieve situational awareness, enhance load modeling, integrate distributed energy resources, etc. In this article, we explore a scenario, where a cyberattack compromises the micro-PMU measurements during an event. Such a targeted attack could be limited in scope but result in a major impact on the operation of the power grid by highly deviating the outcome of the event-based methods. First, we investigate and model two types of such attacks, event-unsynchronized (basic) attacks and event-synchronized (advanced) attacks. We then conduct a geometric analysis to understand each attack type, in a setting where the events are represented in the phasor domain in a differential mode . Next, we introduce a novel method to detect the presence of the attack and then identify which micro-PMUs are compromised so as to discard the compromised measurements as a defense mechanism. The proposed approach makes critical use of magnitude as well as phase angle measurements from micro-PMUs. The method is tested on the IEEE 33-bus power distribution test system.

Field experiment using transient energy method to locate a single-phase to ground fault

Distribution networks in China and several other countries are predominantly neutral inefficiently grounding systems (NIGSs), and more than 80% of the faults in distribution networks are single-phase-to-ground (SPG) faults. Because of the weak fault current and imperfect monitoring equipment configurations, methods used to determine the faulty line sections with SPG faults in NIGSs are ineffective. The development and application of distribution-level phasor measurement units (PMUs) provide further comprehensive fault information for fault diagnosis in a distribution network. When an SPG fault occurs, the transient energy of the faulted line section tends to be higher than the sum of the transient energies of other line sections. In this regard, transient energy-based fault location algorithms appear to be a promising resolution. In this study, a field test plan was designed and implemented for a 10 kV distribution network. The test results demonstrate the effectiveness of the transient energy-based SPG location method in practical distribution networks.

Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements

Distribution-level phasor measurement units, a.k.a, micro-PMUs, report a large volume of high resolution phasor measurements which constitute a variety of event signatures of different phenomena that occur all across power distribution feeders. In order to implement an event-based analysis that has useful applications for the utility operator, one needs to extract these events from a large volume of micro-PMU data. However, due to the infrequent, unscheduled, and unknown nature of the events, it is often a challenge to even figure out what kind of events are out there to capture and scrutinize. In this paper, we seek to address this open problem by developing an unsupervised approach, which requires minimal prior human knowledge. First, we develop an unsupervised event detection method based on the concept of Generative Adversarial Networks (GAN). It works by training deep neural networks that learn the characteristics of the normal trends in micro-PMU measurements; and accordingly detect an event when there is any abnormality. We also propose a two-step unsupervised clustering method, based on a novel linear mixed integer programming formulation. It helps us categorize events based on their origin in the first step and their similarity in the second step. The active nature of the proposed clustering method makes it capable of identifying new clusters of events on an ongoing basis. The proposed unsupervised event detection and clustering methods are applied to real-world micro-PMU data. Results show that they can outperform the prevalent methods in the literature. These methods also facilitate our further analysis to identify important clusters of events that lead to unmasking several use cases that could be of value to the utility operator.

D-PMU Based Distributed Voltage and Frequency Control for DERs in Islanded Microgrids

Seeking the improvement of frequency and voltage regulation of distributed energy resources (DERs) in islanded microgrids, this paper presents a novel distributed control paradigm based on distribution-level phasor measurement units (D-PMUs) monitoring. First, a novel strategy denoted synchrophasor aggregation is proposed. This strategy takes advantage of the high resolution, low latency and time-stamped synchronized measurements provided by D-PMUs to obtain a local foreknowledge of DERs steady-state operating condition during the system dynamics. Based on this information, novel measurement-based control parcels are developed to provide stabilizing and corrective adjustments of generators contribution. These controllers are able to effectively improve DERs dynamic performance, i.e. damping oscillations, overshoot and frequency nadir, while significantly speeding-up the realization of steady-state goals. Stability proof and steady-state analysis are developed. Comparative case-studies with state-of-art controllers, as well as with traditional SCADA systems, are conducted to evaluate the performance and robustness of the proposed controller under different scenarios, including: loading variation, communication failure, loss of generation, and plug-and-play capacity. Results showcase the proposed controller ability to significantly improve islanded microgrid regulation.

A novel complex linear state estimator for smart power distribution systems: methodology and implementation

The electric power distribution systems become increasingly complex due to the high penetration of renewable energy sources, which gradually lead to more challenges or even risks to the grid operation. The need to enhance monitoring and control of the power distribution systems is in great demand. Phasor measurement units (PMUs) have been widely deployed in transmission networks to provide better situational awareness of the system. The same philosophy can be applied to power distribution systems by using the distribution level PMUs (D-PMUs). This paper proposes a novel complex linear state estimator considering parameter noise (CLSE-PN) using D-PMU data for power distribution systems. An implementation of the system architecture is also proposed to accommodate the CLSE-PN algorithm. Case studies demonstrate the effectiveness of the algorithm and prove that CLSE-PN provides more accurate results than the CLSE which does not consider parameter noise.

Measurement-Based Optimal DER Dispatch With a Recursively Estimated Sensitivity Model

This paper presents a measurement-based method to determine distributed energy resource (DER) active- and reactive-power setpoints that minimize bus voltage deviations from prescribed reference values, bus active- and reactive-power deviations from desired setpoints, as well as cost of DER outputs. Central to the proposed method is the estimation of a linear sensitivity model from synchronized voltage and power-injection data collected from distribution-level phasor measurement units installed at only a subset of buses in the distribution system. As new measurements become available, the linear sensitivity model is updated via the recursive weighted partial least-squares estimation method. The estimated sensitivity model is then embedded as an equality constraint in a convex quadratic optimization problem, which can be solved via, e.g., the alternating direction method of multipliers. Numerical simulations involving the IEEE 33-bus distribution test system illustrate key benefits of the proposed method, including (i) eliminating the need for an accurate offline system model, (ii) adapting to online network-topology and operating-point changes, and (iii) being robust against delays potentially attributed to communication, computation, and actuation. Additional numerical simulations involving larger test systems demonstrate computational scalability.