Wide Area Monitoring Research Articles

WAMS challenges and limitations in load modeling, voltage stability improvement, and controlled island protection—A review

Global energy demand pushes transmission lines to their limits, which causes voltage instability on the power grids. Solar and wind farms are being integrated with increased size and capacity to meet increased load demands, and this increased volumes of renewables, combined with their lower inertia and more sensitive voltage profiles, are complicating system operators’ roles. The execution of phasor measurement unit (PMU) with novel detection and protection schemes has been a focal point for power engineers. The real-time wide-area monitoring system (WAMS) helps grid operators to improve real-time voltage stability, situational awareness, fault detection, and grid planning. WAMS shows the path for developing substation predictions, improving system accuracy, and identifying control strategies to avoid power blackouts. It conducts post-event analysis and reduces the processing time of the power restoration. The novelty of this article is to describe load modeling, voltage stability, and islanding protection in real-time, as well as the technical challenges, limitations, opportunities, and solutions related to the same. Further, the paper explores and presents vital software and hardware resources, including HIL systems, for future use and research with WAMS technology.

Development of cost-effective phasor measurement unit for wide area monitoring system applications

<span>Sustained growth in the demand with unprecedented investments in the transmission infrastructure resulted in narrow operational margins for power system operators across the globe. As a result, power networks are operating near to stability limits. This has demanded the electrical utilities to explore new avenues for control and protection of wide area systems. Present supervisory control and data acquisition/energy management systems (SCADA/EMS) can only facilitate steady state model of the network, whereas synchrophasor measurements with GPS time stamp from wide area can provide dynamic view of power grid that enables supervision, and protection of power network and allow the operator to take necessary control/remedial measures in the new regime of grid operations. Construction of phasor measurement unit (PMU) that provide synchrophasors for the assessment of system state is widely accepted as an essential component for the successful execution of wide area monitoring system (WAMS) applications. Commercial PMUs comes with many constraints such as cost, proprietary hardware designs and software. All these constraints have limited the deployment of PMUs at high voltage transmission systems alone. This paper addresses the issues by developing a cost-effective PMU with open-source hardware, which can be easily modified as per the requirements of the applications. The proposed device is tested with IEEE standards.</span>

Optimal phasor measurement unit placement for power system observability using teaching–learning based optimization

A Phasor measurement unit (PMU) is a device that gives a fast and accurate measurement of both current and voltage with time synchronization. This PMU data facilitates effective monitoring and control of power systems in a smart grid (SG). PMU collects data from various buses linked with it and led to an increase in the data generation of the particular PMU and it encourages cramming in data transmission. The delay in data reception at the control centre affects the performance of the SG protection. In a wide-area monitoring system (WAMS), the data traffic model is introduced to calculate the data traffic index. The net traffic can be reduced by installing the PMUs at suitable buses such that the number of PMUs must be reduced which, minimizes the system cost. This paper presents the optimal PMU placement technique with complete system observability by considering the WAMS data traffic index and installation cost index. The teaching learning-based optimization (TLBO) technique is used to obtain optimal PMU positions. In addition, the zero injection bus (ZIB) case is considered for further reduction in the PMU count which in turn reduces installation cost. The selection of an appropriate PMU location set is identified based on the installation cost and WAMS data traffic for normal and ZIB cases. The proposed optimum PMU placement (OPP) algorithm is tested on various test systems such as IEEE 14-bus, 24-bus, 30-bus, 57-bus, and 118-bus systems for normal and ZIB cases. The MATLAB-based simulation results are compared with the various optimization techniques existing in the state of art. The results show that the effectiveness of the proposed methodology for determining the best PMU locations.

A Robust Algorithm for Real-Time Phasor and Frequency Estimation under Diverse System Conditions

This paper presents a comprehensive approach for performing phasor and frequency estimation from voltage and/or current signals of the modern power system. Undesirable components, such as decaying DC, if present in the input signal, are first attenuated using a complex-gain filter. The initial estimates of phasor and frequency are obtained next using the discrete Fourier transform and an improved estimation of signal parameters via rotational invariance technique, respectively. Finally, the accuracy of phasor and frequency estimates are increased based on the identified system condition. Simulations performed to evaluate the proposed approach confirm that it can do fast and accurate estimation of phasor and frequency under diverse operating conditions, making it ideal for wide-area monitoring, protection, and control applications in power systems.

Optimal placement of wide area monitoring system components in active distribution networks

Abstract The optimal placement of micro-Phasor Measurement Units (µPMUs) reduce the cost of wide area monitoring system (WAMS) in active distribution networks (ADNs); therefore, it is becoming a popular research topic. However, µPMUs alone cannot minimize the WAMS cost. An appropriate location of Phasor Data Concentrator (PDC) and a fiber optic communication link (CL) that transfers data from µPMUs to PDCs also need to be optimized. Hence this paper proposes a hybrid algorithm that determines the optimal cost-effective solution of the placement problem of µPMUs, PDC, and CL. The proposed algorithm uses the graph theory and binary integer linear programming (BILP) with the constraints of distribution generation (DG) presence, regular network reconfiguration, and maximizing system redundancy. The proposed algorithm is tested on IEEE 69 bus, IEEE 123 bus, and 345 bus active distribution system. The results obtained show the reduction in cost mainly through the CL and optimal placement of µPMUs in decentralized WAMS.

Wide-area Operation Monitoring of Conveyors Using a Panoramic Vibration Camera

In this study, we propose a novel non-contact vision sensing method for wide-area monitoring of the operation of conveyors in ironworks by using a panoramic vibration camera in real time. This method can capture magnified images, including vibrations from a single camera with mirror-driven viewpoint switching. The rotation of multiple rollers with conveyor belts was detected using a function of full-pixel vibration spectrum imaging, which can calculate peak frequencies from time frequency responses. Through experiments in different situations, such as loading and unloading, we evaluated the efficiency of this method, which can monitor the operation of multiple rollers and conveyors, when the camera is located 15 m away, or more, from the conveyors to be monitored.

INCREASING AN ACCURACY OF CONTROL ACTION VOLUME CALCULATION IN CENTRALIZED EMERGENCY CONTROL SYSTEM FOR STATE ESTIMATION OF POWER SYSTEMS

The paper presents the results of the study on the effectiveness and advisability of voltage’s and current’s angles usage, collected from a wide-area monitoring system, to increase an accuracy of control actions volume calculation in case of power system’s state estimation. Centralized emergency control system architecture of a power pool system is shown to better understand the research core. We emphasize that the state estimation software module is the key module in a high level hardware and software package. Ways of telemetry and synchronized phasor measurements collection are outlined. For research practice, Gauss-Newton mathematical method is modified via measurement vector, vector-function, and scalar matrix of weight coefficients. Experiments are provided by IEEE 14-bus power system and 500–220 kV real backbone network. These power systems have several control areas, connected by interchanges. According to experiment results, we conclude that using not only voltage’s and current’s modules but also angles increases an accuracy of control actions volume calculation and effectiveness of a centralized emergency control system operation in the part of a control action formation. Therewith, the usage of current’s modules and angles raises the execution time of the state estimation software module. It is undesirable for real time systems operation. Therefore, it is reasonable to take into account current’s modules and angles only for those interchanges in emergency mode, when intensity factor, characterizing the limit of static stability, is more than 0.92. We also find out that control action volume calculation is sensitive to mistakes in current’s angles measurements. Thus, for reliable usage of current’s modules and angles as data for a state estimation and control action volume calculation, it is necessary to prevent timing errors of synchronized phasor measurement units and also develop a phase shift correction algorithm.

Operational planning steps in smart electric power delivery system

This paper presents a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids. Distributed energy storage control is classified into automatic voltage regulator and load frequency control according to corresponding functionalities. These control strategies maintain a power balance between generation and demand. Besides, three basic electric vehicle charging technologies can be distinguished, i.e. stationary, quasi-dynamic and dynamic control. For realizing charge-sustaining operation at minimum cost quasi-dynamic and dynamic strategies are adopted for in-route charging, while stationary control can only be utilized when the electric vehicle is in stationary mode. Moreover, power system frequency stability and stabilization techniques in non-synchronous generator systems are reviewed in the paper. Specifically, a synchronverter can damp power system oscillations and ensure stability by providing virtual inertia. Furthermore, it is crucial to manage the massive information and ensure its security in the smart grid. Therefore, several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system. Thus, bidirectional electrical power flows with two-way digital control and communication capabilities have poised the energy producers and utilities to restructure the conventional power system into a robust smart distribution grid. These new functionalities and applications provide a pathway for clean energy technology. Finally, future research trends on smart grids such as IoT-based communication infrastructure, distributed demand-response with artificial intelligence and machine learning solutions, and synchrophasor-based wide-area monitoring protection and control (WAMPC) are examined in the present study.

Development of a Solar-Powered Wildfire Detector System for Remote Locations with XBee and GSM Capabilities

In the United States wildfires are rampant every year, taking lives, damaging properties and causing huge economic losses. This project designs a wildfire detector system using a Many-to-One communication method with XBee/Zigbee and GSM technologies. Testing of the prototypes has shown the system advantageous features, namely low-power, long-lasting, compact, scalable and communication-effective. The maximum power consumption of a Xbee fire detector and a GSM detector is 14W and 27W, respectively. In detection mode, the XBee detector consumes only 1.29W. The fire detectors are powered by solar panels and Ni-MH battery packs. A fully charged battery pack can sustain a detector up to around 19.3 hours in detection mode, and up to about 4.5 hours in alarm mode. The system has a high potential to be used for wide-area outdoor fire monitoring and detection. The covered area can be flexibly adjusted by varying the number of detectors. Early fire detection and alert provided by the system will enable timely responses that save human lives, as well as minimize property damages and other economic losses.

IGS-CMAES: A Two-Stage Optimization for Ground Deformation and DEM Error Estimation in Time Series InSAR Data

Interferometric synthetic aperture radar (InSAR) has become an increasingly recognized remote sensing technology for earth surface monitoring. Slow and subtle terrain displacements can be estimated using time-series InSAR (TSInSAR) data. However, a substantial increase in the availability of exclusive time series data necessitates the development of more efficient and effective algorithms. Research in these areas is usually carried out by solving complicated optimization problems, which is very computationally expensive and time-consuming. This work proposes a two-stage black-box optimization framework to jointly estimate the average ground deformation rate and terrain digital elevation model (DEM) error. The method performs an iterative grid search (IGS) to acquire coarse candidate solutions, and then a covariance matrix adaptive evolution strategy (CMAES) is adopted to obtain the final local results. The performance of our method is evaluated using both simulated and real datasets. Both quantitative and qualitative comparisons using different optimizers support the reliability and effectiveness of our work. The proposed IGS-CMAES achieves higher accuracy with a significantly fewer number of objective function evaluations than other established algorithms. It offers the possibility for wide-area monitoring, where high precision and real-time processing is essential.

Composite load modeling by spatial-temporal deep attention network based on wide-area monitoring systems

With the development of the wide-area monitoring system (WAMS), power system operators are capable of providing an accurate and fast estimation of time-varying load parameters. This study proposes a spatial-temporal deep network-based new attention concept to capture the dynamic and static patterns of electrical load consumption through modeling complicated and non-stationary interdependencies between time sequences. The designed deep attention-based network benefits from long short-term memory (LSTM) based component to learning temporal features in time and frequency-domains as encoder-decoder based recurrent neural network. Furthermore, to inherently learn spatial features, a convolutional neural network (CNN) based attention mechanism is developed. Besides, this paper develops a loss function based on a pseudo-Huber concept to enhance the robustness of the proposed network in noisy conditions as well as improve the training performance. The simulation results on IEEE 68-bus demonstrates the effectiveness and superiority of the proposed network through comparison with several previously presented and state-of-the-art methods.

An approach for economic design of wide area monitoring system by co‐optimizing phasor measurement unit placement and associated communication infrastructure

An approach for economic design of wide area monitoring system by co‐optimizing phasor measurement unit placement and associated communication infrastructure

Component Contribution to the Total Reliability of the WAMS Network

Nowadays, the control of power systems relies on wide-area monitoring and control system (WAMS), which continuously measures and registers state vector values and is synchronized by signals from the uniform time system. A significant part of this system is the local information network, whose reliability largely determines the proper functioning of WAMS. One can assess the said reliability by dividing it into components. These are hardware or technical reliability associated with failure (destruction) of transmission channel elements or the integrity of communication lines, traffic reliability determined by time loss or data distortion without failure of a transmission channel element, software reliability related to errors in the development of exchange execution programs, and resilience against an external deliberate impact on the transmitted information. This paper addresses the assessment of the first three reliability components of the information network, shows its total value, and estimates the contribution of each component. The last component (resistance to an external deliberate action) is described in a huge number of works, which is why it is not considered in this paper.

Monitoring, Modeling and Management of Water Quality

In this special issue, we are able to present a selection of high-level contributions showing the manifold aspects of the monitoring, modeling, and management of water quality. Monitoring aspects range from cyanobacteria in water using spectrophotometry via wide-area water quality monitoring and exploiting unmanned surface vehicles, to using sentinel-2 satellites for the near-real-time evaluation of catastrophic floods. Modeling ranges from small scale approaches by deriving a Bayesian network for assessing the retention efficacy of riparian buffer zones, to national scales with a modification of the MONERIS (Modeling Nutrient Emissions in River Systems) nutrient emission model for a lowland country. Management is specifically addressed by lessons learned from the long-term management of a large (re)constructed wetland and the support of river basin management planning in the Danube River Basin.

Power System Transient Stability Assessment Using Stacked Autoencoder and Voting Ensemble

Increased integration of renewable energy sources brings new challenges to the secure and stable power system operation. Operational challenges emanating from the reduced system inertia, in particular, will have important repercussions on the power system transient stability assessment (TSA). At the same time, a rise of the “big data” in the power system, from the development of wide area monitoring systems, introduces new paradigms for dealing with these challenges. Transient stability concerns are drawing attention of various stakeholders as they can be the leading causes of major outages. The aim of this paper is to address the power system TSA problem from the perspective of data mining and machine learning (ML). A novel 3.8 GB open dataset of time-domain phasor measurements signals is built from dynamic simulations of the IEEE New England 39-bus test case power system. A data processing pipeline is developed for features engineering and statistical post-processing. A complete ML model is proposed for the TSA analysis, built from a denoising stacked autoencoder and a voting ensemble classifier. Ensemble consist of pooling predictions from a support vector machine and a random forest. Results from the classifier application on the test case power system are reported and discussed. The ML application to the TSA problem is promising, since it is able to ingest huge amounts of data while retaining the ability to generalize and support real-time decisions.

WAMS-Supported Power Mismatch Optimization for Secure Intentional Islanding

It is expected that a coordinated operation of several system integrity protection schemes will become a necessity in the future. This research represents an innovative strategy for coordinating under-frequency load shedding and intentional controlled islanding schemes for improving electric power system stability and resilience. In the great majority of real-world cases, both approaches follow conventional tactics, i.e., disconnecting a fixed number of feeders at predefined frequency thresholds and isolating a predefined area of a power system regardless of the actual conditions. Under the newly arisen network conditions in which weather-dependent distributed energy sources introduce a significant level of intermittency, conventional approaches need to be upgraded in order to retain a high level of power system operation security. In this paper, a mixed-integer linear programming approach is used to adjust the island size, including/excluding additional substations according to the available amount of generation in the region. The fine-tuning of the power rebalancing is achieved by potentially blocking selected load shedding stages. This minimizes the power imbalance of the newly formed islands, which helps to reduce the number of partial or even total blackouts and also accelerates the power system’s restoration process. The optimization approach was tested on a generic IEEE 39-bus network and shows promising results along with the capability of coping with real-world applications using wide-area monitoring systems as a source of real-time measurements. The results also indicated the importance of appropriate load modelling since both voltage and frequency dependence are recognized to have a significant effect on intentional controlled islanding.

LDCA: Lightweight Dynamic Clustering Algorithm for IoT-Connected Wide-Area WSN and Mobile Data Sink Using LoRa

Wide-area monitoring applications of the Internet-of-Things (IoT) connected wireless sensor network (WSN) consists of sensor nodes (SNs) with limited hardware and energy sources. The distributed nature of such a network and the difficulty of remote access make it more demanding to design an energy-efficient WSN. Moreover, long-range and low-power wireless connectivity is a challenge in IoT-connected applications. Present WSN topologies deal mainly with fixed SNs, SN distribution, and fixed data sink (DS). The majority of the control layer is implemented in the lower hierarchical layer or in a virtual middle layer, which reduces the network lifetime due to excessive processing and data transmission activities. This article proposes a real-time lightweight dynamic clustering algorithm (LDCA) for a WSN that supports the following two scenarios with limited processing resources: 1) with mobile DSs and static SNs (such as DS and SNs mounted on unmanned aerial vehicles, autonomous vehicles) and 2) with mobile SNs and static DSs (such as livestock monitoring or autonomous robots in smart farming, and urban monitoring). The proposed algorithm is based on the received signal strength indicator and signal-to-noise ratio of a long-range (LoRa) interface and its residual energy. Mathematical models were derived for real-time clustering using LoRa. Memory requirement and clustering efficiency of the constrained SN and DS for various mobility scenario were evaluated. The proposed LDCA reduces the energy requirement to 33% compared to static clustering, by reducing the number of concurrent clusters and hops. In addition, a hardware-based approach was used to validate the LDCA algorithm, and evaluate its performance in terms of energy efficiency, packet delivery rate, and network lifetime.

Time-frequency based cyber security defense of wide-area control system for fast frequency reserve

Global power systems are transiting from conventional fossil fuel energy to renewable energies due to their environmental benefits. The increasing penetration of renewable energies presents challenges for power system operation. The efficiency and sufficiency of responsive reserves have become increasingly important for power systems with a high proportion of renewable energies. The Fast Frequency Reserve (FFR), especially the Wide-area Monitoring System (WAMS)-based FFR, is a promising and effective solution to secure and enhance the stability of power systems. However, cyber security has become a new challenge for the WAMS-based FFR system. Cyber attacks on the FFR control system may threaten the safety of power system operation due to the rapid power controllability requirement of FFR. To address this problem, a time-frequency based cyber security defense framework is proposed to detect the cyber spoofing of synchrophasor data in WAMS-based FFR control systems. This paper first introduces the Continuous Wavelet Transforms (CWTs) to decompose spoofing signals. Then, the Dual-frequency Scale Convolutional Neural Networks (DSCNN) is proposed to identify the time-frequency domains matrix from two frequency scales. Integrating CWTs and DSCNN, an identification framework called CWTs-DSCNN is further proposed to detect the spoofing attacks in the WAMS-based FFR system. Multiple experiments using the actual data from FNET/GridEye are performed to verify the effectiveness of the framework in securing WAMS-based FFR systems.

Trend Analysis of Modal Identification based Real-time Power System Oscillations using L1 Trend Filtering

This paper is looking to show to use of system data collected from wide-area monitoring systems (WAMS). They allow monitoring of the dynamics of power systems. Among the WAMS applications, there is the modal identification algorithm, which identifies critical oscillatory modes from PMU measurements. This application permits using data processors for estimating of frequency, damping, and amplitude of dominant mode oscillations observable in a specific electric signal (e.g., active power, frequency) recorded for the analyzed period. However, since modal identification of real-time measurements is based on an online optimization, the results usually have considerable fluctuations. Thus, it is essential to consider the complementary implementation of trend analysis for acquiring convenient early-warning indicators of oscillatory problems. This consideration allows avoiding erroneous information of the systems oscillatory behavior of the system real-time that modal identification of crude results could deliver. In this paper, the application of a l1 filter for determining the trend analysis of high-dimensional data set resulted from a commercial modal identification is explored. The algorithm is applied to an oscillatory event registered by the WAMS of the Ecuadorian National Interconnected System with promising results.

Identification of Optimal Sensor Location Based on Trade-Off Approach to Improve Resiliency of Electricity Market in Smart Grid

The integrity of control operations in the smart grid (SG) heavily depend on its monitoring system, which relies on the sensors mounted across the wide-area network (WAN). Utilizing the monitoring capability of SG for a conducive electricity market operation demands high accuracy in real-time sensor measurement. While synchronized measurements from phasor measurement units (PMUs) have improved the reliability compared to the classical sensors (CSs), the high installation cost often hinders the installation of PMUs throughout the WAN. The selection of sensors for wide-area monitoring dependent operations like the electricity market involves maintaining a trade-off between the conflicting objectives of sensor installation cost and accuracy in measurement. Although the objective of the necessary requirement of network observability using sensor data can be fulfilled through various configuration, the impact of sensor information on the electricity market differs as per their location. In this regard, the present work proposes a binary optimization-based approach for identifying strategic sensor placement locations in SG to minimize the disruption in the electricity market. The strategic sensor location aims at improving the resiliency of electricity market operation by minimizing the impact of sensor accuracy on nodal electricity price. The resiliency is achieved while simultaneously meeting the objectives of reduced sensor installation cost and fulfilment of network observability criterion. The effectiveness of the trade-off solution has been validated for standard IEEE 14, 30, and 57 test systems.