Number Of Phasor Measurement Units Research Articles

Analysis of Bus Vulnerability Conducted Using a Synchronized Phasor Measurement Unit in Order to Achieve the Maximum Observability

Phasor measurement units (PMUs) have gained significant interest in recent decades. These instruments are used to measure synchronized phasor data. PMUs are gradually but definitely taking over power grids because of the significant phasor information that they generate for both regular and irregular conditions for the purpose of maintaining safety and control. PMUs may be used for a variety of purposes, including state estimation, which is a common task. In order to make state estimation more reliable, a variety of approaches have been looked into, and one of them is the positioning of PMUs. This paper provides a plan for the implementation of the PMUs, taking into account the potential for failure and vulnerability posed by PMU-equipped buses. Two separate studies were carried out and evaluated with the goal of solving the optimum PMU placement problem (OPPP), which pertains to the grids. The findings of the first study show that the maximum bus observability may be accomplished with the fewest possible number of PMUs, even while taking into consideration the fact that there is a risk that one or more PMUs would malfunction. This investigation was carried out with common measures such as zero injection bus (ZIB) and branch flow measurements, both with and without them, in order to assess the outcomes. The second research focused on selecting the PMU-equipped bus’s vulnerability analysis as its primary area of investigation. All of the tests were completed by using binary integer linear programming. Specifically, the described method is meant to be used with an existing PMU framework and in the case that new locations for new PMUs are necessary to be furnished with existing PMUs. This results confirm that the recommended strategy can be implemented successfully on the IEEE benchmark test systems.

Adaptive Wide-Area Load Shedding Scheme Based on the Sink and Source Concept to Preserve Power System Stability

In this article, a novel wide-area scheme is proposed based on a new sink and source area (SSA) concept. The SSA concept identifies the sink and source regions using a limited number of system voltage phasors. These voltage phasors can be easily transmitted to the control center using a few number of phasor measurement units (PMUs). The SSA idea is used to determine the priority of load buses or regions in the load shedding procedure. Since the SSAs are directly dependent to the generation and load distributions among the resource and demands, they vary over the time. The load shedding locations identified by SSA will accordingly change, which makes more technical sense versus selection of fixed candidates regardless of the real-time operation status. Furthermore, the amount of load shedding is adaptively determined based on the comprehensive analysis of interaction characteristics between load buses. The performance of the proposed approach is evaluated on two networks under various realistic scenarios. The simulation studies’ results reveal promising operation of the proposed approach comparing to that of other load shedding techniques. Moreover, the required number of PMUs and communication infrastructure are presented and the impact of communication link delay on the proposed algorithm is investigated.

Fault detection in an interconnected power system using optimal number of phasor measurement unit

<p>Fault identification in a power system is crucial. In recent days, there have been multiple microgrids connected to the power system. And if many buses are connected, then there is a need for an increase in the phasor measurement unit. By using an optimization technique, the number of phasor measurement unit PMUs can be reduced by placing them optimally. In this paper, the fault detection algorithm is implemented using a reduced number of PMUs with the help of the particle swarm optimization (PSO) algorithm. The optimal locations of PMUs are identified using the PSO algorithm. Here, the reduction in the count of PMUs and the PMUs are designed in MATLAB as a model. This is done using the Simulink and dashboard block sets. The IEEE 9 and IEEE 30 test systems are used here for the analysis and tests. The IEEE 9 bus system is constructed in simulation and then the PMU is constructed using the data taken from the phasor measurement blocks. This data is used in the dashboard block set to represent the PMU-based fault detection system.</p>

Observability Enhancement of Smart Grid Based on Optimal Placement of PMUs

This paper presents an efficient observation concerning the enhancement of smart grid (SG) based on the optimal placement of phasor measurement units (OPP) using nonlinear programming (NLP). The proposed algorithm tries to achieve two objectives: (i) to ascertain the minimum number of phasor measurement units (PMUs) and (ii) to increase the redundancy of the SG at all the buses. Synchronized current and voltage phasors are obtained to enhance the accuracy of the state estimation results—a minimum number of PMUs results in a lack of communication facilities at the substation. PMU losses will lead to unobservable buses at the SG. Therefore, PMU losses and communication constraints should be considered during the design process. Limited channel capacity, conventional measurement, and zero-injection bus measurements are also included in the proposed PMU formulation. The proposed algorithm is examined on IEEE~14-, 30-, 57-, 118-, and 300-bus test systems in MATLAB to verify its effectiveness. Furthermore, the results are compared with the simplex linear programming and mixed linear programming methods to prove the efficacy of the presented algorithm. The output thus obtained reveals that the NLP algorithm obtains approximately the same PMUs as other methods.

Controlled Islanding under Complete and Partial False Data Injection Attack Uncertainties against Phasor Measurement Units

The widespread application of phasor measurement units has improved grid operational reliability. However, this has increased the risk of cyber threats such as false data injection attack that mislead time-critical measurements, which may lead to incorrect operator actions. While a single incorrect operator action might not result in a cascading failure, a series of actions impacting critical lines and transformers, combined with pre-existing faults or scheduled maintenance, might lead to widespread outages. To prevent cascading failures, controlled islanding strategies are traditionally implemented. However, islanding is effective only when the received data are trustworthy. This paper investigates two multi-objective controlled islanding strategies to accommodate data uncertainties under scenarios of lack of or partial knowledge of false data injection attacks. When attack information is not available, the optimization problem maximizes island observability using a minimum number of phasor measurement units for a more accurate state estimation. When partial attack information is available, vulnerable phasor measurement units are isolated to a smaller island to minimize the impacts of attacks. Additional objectives ensure steady-state and transient-state stability of the islands. Simulations are performed on 200-bus, 500-bus, and 2000-bus systems.

Application of solar cells and wireless system for detecting faults in phasor measurement units using non-linear optimization

This research study examines the integration of wireless system and solar cells for detection and location of faults in power grids by implementing minimum number of Phasor Measurement Units (PMUs) after considering zero injection buses. A fault in the grid networks indicates a failure in the entire network that interfaces the normal flow of current which can be measured using wireless systems. The fault location in power grid is problematic because of high existence in several lateral tap-offs. In addition, the identification of exact location with presence of faults in the power system and severity of the faults are challenging tasks due to bulk and complex nature of the electrical power system state estimation. The major requirement of PMU placement approach is to find the minimum number of PMUs and their location with maximum wireless coverage under fault conditions with a prerequisite for the projected algorithm. The integrated algorithm is tested and validated for different fault conditions where the results are compared with different conventional methods using different optimization models such as heuristics, decision rules and mathematical programming where the outcomes proves to be much effective in terms of number of PMU installation for proper placements and detection, location in number of fault systems thus achieving 82% reliability using Ant Lion Optimizer (ALO).

A-star algorithm based on admissible searching for strategically placing PMU considering redundancy and cost/benefit analysis

This research examines an admissible search algorithm-dependent A-star strategy and takes into account redundancy and cost/benefit analysis under normal operating conditions. The goal is to allocate a phasor measurement unit (PMU) for maximal observability of the interconnected power network. To determine the fewest number of PMU required to make the connected power network totally observable using redundancy analysis, the A-star approach is utilized. The redundancy analysis of the power network is carried out in order to determine the appropriate PMU placement, which results in the acquisition of total power network observability and reliability. To put the suggested technique through its paces, it has been tested on IEEE-standard test systems such as IEEE-14 bus, IEEE-30 bus, New England-39 bus, IEEE-57 bus, and IEEE-118 bus. The results obtained using the suggested methodology are compared to those obtained through standard literature research. The experimental findings of the suggested method revealed the resilience and accuracy of the A-star algorithm as well as its effectiveness in achieving maximum observability of the connected power network.

Tie-line modelling in interconnected synchrophasor network for monitoring grid observability, cyber intrusion and reliability

The incorporation of a tie-line between two areas may be beneficial in two ways. First, the reserve capacity of the assisting area support to the assisted area, and second, the number of Phasor Measurement Unit (PMU) requirements will become smaller for complete observability of the interconnected grid. The objective function is formulated to integrate the observability and reliability analysis for the two interconnected synchrophasor networks. The effect of Zero Injection Bus (ZIB) is included in the observability constraints to reduce the number of PMUs deployed in the system. The number of optimal PMU deployments will be greater for two interconnected systems in comparison with a single area. Therefore, interconnected systems become more vulnerable to cyber risk. The paper discusses the cumulative analysis of system observability and reliability during an anomaly situation that occurs with a PMU device due to a cyber-attack. The reliability indices Interconnected System Load Interruption Probability (ISLIP) and Interconnected System Demand Not Supplied (ISDNS) are evaluated when an anomaly occurs with optimally deployed PMU in the network by including and excluding the effect of ZIB. By doing so, the most reliable location for PMU deployment can be obtained for both the area. Reliability Test System (RTS)-24 bus is used for each area to modify the test system by incorporating tie-lines between them.

Optimization of Phasor Measurement Unit (PMU) Placement: A Review

In today’s world, a Phasor Measurement Unit (PMU) is a crucial component of our power network for managing, controlling, and monitoring. PMU can provide synchronized voltage current, and frequency measurements in real time. We can't put a PMU in every bus in the electrical grid because it's not viable from a productivity and economic standpoint, and it's also not practical for handling huge data. As a result, it's critical to reduce the amount of PMU in the power network while also increasing the power network's observability. The optimal PMU placement problem is solved using a variety of methodologies. The paper's main goal is to provide a brief overview of synchrophasor technology, phasor measurement units (PMU), and optimal PMU placement in order to reduce the number of PMUs in the system while maintaining complete observability and application in today's power systems.

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 New Backup Protection Scheme for Transmission Lines with Limited Phasor Measurement Units

— This paper proposes a new scheme for backup protection of transmission lines with a limited number of phasor measurement units (PMUs). In the presented methodology, the PMUs are located (or installed) in accordance with a scheme allowing all faults to be observable. The network is divided into a few zones with 1, 2 or 3 transmission lines. Following this, the nearest bus to the fault location, named as the faulted bus, is selected through the voltage data measured by PMUs. A faulted area including the suspected zones, the zones connected to the faulted bus, is then identified. The faulted zone is subsequently recognized by using the phase deviation between the positive- (negative-) sequence voltage of the faulted bus and the positive- (negative-) sequence of the input current to the suspected zones. The faulted line is finally detected based on the type of the faulted zone and corresponding algorithm. The effectiveness of the presented scheme is assessed under several fault situations in MATLAB/Simulink platform. Simulation results demonstrate that the proposed method detects the faulted line accurately for all fault conditions. Moreover, the proposed scheme protects the shunt-compensated transmission lines as properly as the uncompensated ones without using the pre-fault data.

A multi-objective grey wolf optimizer (GWO)-based multi-layer perceptrons (MLPs) trainer for optimal PMUs placement

PurposeThis paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line.Design/methodology/approachAn efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network (“GW-NN”) to place the PMUs in power grids optimally.FindingsFollowing extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches.Practical implicationsThe efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems.Originality/valueThis paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the cost and improve the observability of these grids in normal and faulty cases.

Realistic Method for Placement of Phasor Measurement Units through Optimization Problem Formulation with Conflicting Objectives

The Phasor Measurement Unit (PMU) is a modern metering device installed to monitor and control the power system network. However, the number of PMUs and their placements ought to be optimized to satisfy the techno-economic demand. In this paper, the PMU placement problem has been expressed as a multi-objective problem. Modified whale optimization algorithm (MWOA) is proposed to solve Optimal Placement of PMUs (OPP) problem formulation aiming at minimizing the required number of PMU installation subjected to complete network observability and maximizing the reliability for better operation of power systems. Here, the simultaneous optimization of the two conflicting objectives is performed using a fuzzy-based modified whale optimization algorithm (FMWOA). Besides, single objective optimization problems with and without considering the zero-injection bus constraint and multi-objective optimization problem with zero injection bus constraint have been investigated. The proposed method is validated on small and larger test systems, namely IEEE 14, 30, 57, 118 bus systems, and the polish 2383-bus system. Also, the proposed algorithm results are compared with those of previously published for better clarity.

An optimal placement of phasor measurement unit using new sensitivity indices

This paper presents an alternative approach to solve an optimal phasor measurement unit (PMU) placement by introducing two new sensitivity indices. The indices are constructed from the information measured from PMUs such as voltage magnitude and angle. These two parameters are essential for power system stability assessment and control. Therefore, fault analysis is carried out to obtain the voltage magnitude and angle deviations at all buses in order to construct the indices. An exhaustive search method is used to solve the linear integer programming problem where all possible combinations of PMU placement are evaluated to obtain the optimal solution. Unfortunately, the traditional objective function to minimize the total number of PMU placement leads to multiple solutions. The indices can be used to assess multiple solutions of PMU placement from the exhaustive method. In this work, an optimal solution is selected based on the performance of PMU placement in according to the indices. The proposed method is tested on the IEEE 14 bus test system. Only four PMUs are required to monitor the whole test system. However, the number of PMUs can be reduced to three PMUs after applying zero injection bus elimination.

Data-Driven Short-Term Voltage Stability Assessment Using Convolutional Neural Networks Considering Data Anomalies and Localization

Short-term voltage stability of power systems is governed by load dynamics, especially the proportion of small induction motors prevalent in residential air-conditioners. It is essential to efficiently monitor short-term voltage stability in real-time by detailed data analytics on voltage measurements acquired from phasor measurement units (PMUs). It is likewise critical to identify the location of faults resulting in short-term voltage stability issues for effective remedial actions. This paper proposes a time-series deep learning framework using 1D-convolutional neural networks (1D-CNN) for real-time short-term voltage stability assessment (STVSA), which relies on a limited number of phasor measurement units (PMU) voltage samples. A two-stage STVSA application is proposed. The first stage comprises a 1D-CNN-based fast voltage collapse detector. The second stage comprises of 1D-CNN-based regressor to quantify the severity of the short-term voltage stability event. Two novel indices are presented, and their predicted future values are used to quantify the severity of short-term voltage stability events. This work also considers DB-SCAN clustering-based fault detection and physics-based fault localization for effective short-term voltage stability assessment and remedial actions by identifying the most critical PMUs. A bad data pre-processing technique is also included to mitigate the impact of missing data and outliers on short-term voltage stability assessment accuracy. The proposed framework is validated using the standard IEEE test systems and compared against other machine learning models to demonstrate the superiority of 1D-CNN-based time-series deep learning for short-term voltage stability assessment.

Research on the model of smart grid wide-area measurement cyber-physical fusion system based on data fusion

Due to price and technical reasons, it is currently impossible to install phasor measurement units in the entire network. How to use the minimum number of phasor measurement units to maximize the observation of the state or dynamic behavior of the power grid is the first consideration for the wide-area measurement system at this stage. This paper proposes a method to optimize the configuration of the phasor measurement unit while ensuring that the power grid is fully observable and can capture the dynamic behavior of the power grid under transient conditions. This paper proposes a load modeling method based on wide-area measurement. Aiming at the problems of traditional large-area power grid load modeling based on load characteristic measurement devices and the characteristics of the current wide-area measurement system, a load modeling method based on local PMU observations is proposed. This method combines particle swarm optimization algorithm and time. Domain simulation calculation can identify the load models at all load nodes of the system at the same time. This paper proposes a CPPS information flow transmission model based on graph theory and information flow dynamics. This model considers the coupling effect of CPPS, the processing capacity of communication equipment and the transmission mode of CPPS information flow, and constructs a CPPS dynamics model from the perspective of communication transmission. In the analysis of the calculation example, by applying the information flow transmission model to CPPS topologies generated by different scales and different modeling methods, the influence of network scale, key nodes and network topology structure on the efficiency of CPPS information transmission was explored, and the effectiveness of the model was verified.

Optimal PMU placement approach for power systems considering non-Gaussian measurement noise statistics

This paper investigates how to add a limited number of Phasor Measurement Units (PMUs) to the existing monitoring system so as to improve the estimation accuracy further. The existing methods are usually based on Gaussian noise assumption and the weighted least squares (WLS) estimator is taken into account. However, the Gaussian noise assumption is not always true in reality and the WLS is non-robust in this case. This paper proposes a new optimal PMU placement approach where the distribution of measurement noise can be non-Gaussian or Gaussian and many robust estimators such as the maximum likelihood estimator, Multiple-Segment, Quadratic-Linear, Square-Root and Schweppe-Huber Generalized-M estimator are considered. Based on the new Gain matrix obtained from the influence function approximation, the D-optimal and E-optimal experiment criterions are exploited in the optimal PMU placement problem. A convex relaxation in conjunction with an optimization improvement method based on the Fedorov exchange algorithm is utilized to solve the optimizing problem. Simulations on the IEEE 57-bus system and the Polish 2383-bus system are carried out to evaluate the effective performance of the proposed approach.

Smart Grid State Estimation with PMUs Time Synchronization Errors

State Estimation (SE) is one of the essential tasks to monitor and control the smart power grid. This paper presents a method to estimate the state variables combining the measurement of power demand at each bus with the data collected from a limited number of Phasor Measurement Units (PMUs). Although PMU data are usually assumed to be perfectly synchronized with the Coordinated Universal Time (UTC), this work explicitly considers the presence of time-synchronization errors due, for instance, to the actual performance of GPS receivers and the limited stability of the internal oscillator. The proposed algorithm is a recursive Kalman filter which not only estimates the state variables of the power system, but also the frequency deviations causing clock offsets which eventually affect the timestamps of the measures returned by different PMUs. The proposed solution was tested and compared with alternative approaches using both synthetic data applied to the IEEE 123 bus distribution feeder and real-field data collected from a small-size medium-voltage (MV) distribution system located inside the EPFL campus in Lausanne. Results show the validity of the proposed method in terms of state estimation accuracy. In particular, when some synchronization errors are present, the proposed algorithm can estimate and compensate for them.

Reverse harmonic load flow analysis using an evolutionary technique

In the present paper, a new approach to estimate the harmonic distortions is proposed by considering the existence of harmonic sources in the power system. The algorithm employs a differential evolution technique by formulating the reverse harmonic load flow problem for each harmonic order and by considering a limited number of phasor measurement units (PMUs) deployed across the power system. The technique utilizes the measured voltage and current harmonics obtained using synchronized PMUs with known transmission network as input data. The proposed technique estimates the harmonic voltage phasors at the unmonitored buses of the power system with reasonable accuracy. The effectiveness and efficiency of the proposed method is validated by applying it to five bus and IEEE 14-bus test systems by considering uncertainty in measurement noise. It is shown that the proposed approach yields optimum solution and provides reliable and accurate harmonic state estimates.

Data compression techniques for Phasor Measurement Unit (PMU) applications in smart transmission grid

Abstract The advances in Wide Area Measurement Systems (WAMS) and deployment of a huge number of phasor measurement units (PMUs) in the grid are generating big data volume. This data can be used for a variety of applications related to grid monitoring, management, operation, protection, and control. With the increase in this data size, the respective storage capacity needs to be enhanced. Also, communication infrastructure readiness remains bottleneck to transfer this big data. One of the probable solutions could be transmitting compressed data. This paper presents techniques for data compression in the smart transmission system using singular values decomposition (SVD) and the eigenvalues decomposition (EVD). The SVD and EVD based principal component analysis (PCA) techniques are applied to the real-time PMU data collected from extra-high voltage (EHV) substations of transmission utility in the western regional grid of India. Adequacy of data is checked by Kaiser-Meyer-Olkin (KMO) test in order to have the satisfactory performance of these techniques towards achieving the objective of efficient data compression. Results are found satisfactory gives compression more than 80% using real time data.