Phasor Measurement Units Placement Research Articles

Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer

Phasor measurement units (PMU) are currently considered as an essential step toward the future smart grid due to their capability in increasing the power system's situation awareness. Due to their high costs and limited resources, optimal placement of PMUs (OPP) is an important challenge to compute the minimum number of PMUs and their optimal distribution in the power systems for achieving full monitoring. The coronavirus herd immunity optimizer (CHIO) is a novel optimization algorithm that emulates the flock immunity strategies for the elimination of the coronavirus pandemic. In this research, the CHIO is adapted for the OPP problem for full fault observability. The proposed algorithm is implemented on power systems considering the zero injection bus impacts. A program is created in MATLAB® environment to implement the proposed algorithm. The algorithm is applied to different test systems including; IEEE 9-bus, 14-bus, 30-bus, 118-bus, 300-bus, New England 39-bus and Polish 2383-bus. The proposed CHIO-based OPP is compared to some exact and metaheuristic-based OPP techniques. Compared to these techniques, the promising results have proved the effectiveness and robustness of the proposed CHIO to solve the OPP problem for full fault observability.

Mathematical Models for the Single-Channel and Multi-Channel PMU Allocation Problem and Their Solution Algorithms

Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0/1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids.

Optimal PMU Placement for Fault Classification and Localization Using Enhanced Feature Selection in Machine Learning Algorithms

Machine learning (ML) algorithms are increasingly used in power systems applications. One important application is the classification and localization of various types of transmission line faults. Using voltage and current measurements from phasor measurement units (PMUs), a number of useful features can be extracted, which can form the basis of a ML-based prediction of the fault type, line, and distance on the line. This paper proposes a technique to find the optimal number and placement of PMUs by performing thorough feature selection. The features are selected to maximize the accuracy of the ML classification and regression algorithms. The results show that for the IEEE 14 bus system, the use of only five PMUs is sufficient to obtain high levels of accuracy. For example, a testing accuracy of 99.0% and 97.1% can be achieved for the fault type and fault line location, respectively. As for the fault distance along the line, the testing MAE of 3.1% can be obtained along with an R2 score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy.

Enhancing voltage profile in islanded microgrids through hierarchical control strategies

This paper presents a novel approach aimed at enhancing the voltage profile of an islanded microgrid (MG) through the utilization of Phasor Measurement Units (PMUs) and a hierarchical control system for Distributed Generators (DGs). Recognizing the criticality of maintaining an optimal voltage profile for essential loads within an MG, we assume that PMUs are strategically placed across the MG based on existing placement methodologies to ensure the observability of load buses. The synchrophasor measurements captured by these PMUs are leveraged within an offline-designed tertiary control layer of the hierarchical framework, which in turn sets the voltage reference for the secondary control layer of DGs across the MG. This approach effectively enhances the voltage profile of vital load buses within the MG. The proposed hierarchical strategy encompasses primary, secondary, and tertiary levels of control, responsible for stabilizing voltage and frequency, computing power references, and managing power flow, respectively. PMU measurements play a crucial role in the secondary control level by continuously monitoring deviations. By ensuring the effective placement of PMUs, the proposed framework enables efficient control, leading to notable improvements in the voltage profile and power management. To evaluate the effectiveness of our approach, five test networks were used: 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus test networks. Simulation results demonstrate an impressive over 80 % enhancement in the MG's voltage profile achieved by our proposed method. Additionally, our heuristic strategy efficiently assigns the closest load bus to each DG while aggregating equidistant bus deviations. Collectively, our research underscores the significant impact of PMU measurements and a well-structured hierarchical voltage control system in substantially improving the voltage profile of MGs.

Enhanced LA-HBO technique for optimal position of PMU with complete prominence

This manuscript proposes a hybrid technique for determining the optimal positioning of phasor measurement units (PMUs) in power systems. The PMUs play a crucial role in power system control, wide-area monitoring, and protection. The proposed hybrid method is the joint execution of the Lichtenberg algorithm (LA) and the heap-based optimization (HBO) technique. Hence, it is named the LA-HBO technique. The objective of the proposed method is to place the PMUs in the power system for observability. The goal is to enhance the efficiency and accuracy of PMU placement, ensuring optimal positioning for improved grid monitoring capabilities. The Lichtenberg Algorithm (LA) enhances PMU placement by addressing system observability challenges and ensuring that selected locations provide comprehensive coverage of the power grid. The heap-based approach optimizes PMU placement by efficiently managing the selection process, considering factors like computational efficiency and scalability. The proposed hybrid technique is implemented in IEEE-30 and -14 bus systems. The MATLAB-based simulation results are compared with the various existing methods, such as Sea Lion Optimization (SLO), Particle Swarm Optimization (PSO), and Ant Bee Colony Optimization (ABC). By then, the outcome reveals the efficacy of the proposed method for defining the optimum PMU locations. The proposed method shows a low computational time of 0.02348 sec for the IEEE-14 bus, and 0.03565 sec for the IEEE-30 bus compared with other existing methods.

Optimal placement of phasor measurement units using topology transformation method based on Grey Wolf optimization approach

Optimal placement of phasor measurement units using topology transformation method based on Grey Wolf optimization approach

A Heuristic Solution Algorithm for a Comprehensive Optimal Phasor Measurement Unit Placement Considering Zero-Injection Buses and Practical Constraints in Power System State Observability Problem

A Heuristic Solution Algorithm for a Comprehensive Optimal Phasor Measurement Unit Placement Considering Zero-Injection Buses and Practical Constraints in Power System State Observability Problem

PMU placement for fault line location using neural additive models—A global XAI technique

In recent years, machine learning (ML) techniques have shown impressive results in many power system applications, including line fault location using phasor measurement units (PMU). Despite this success, such techniques may be hard to use in real life scenarios, if their results are not well understood by power system experts. In this light, this work proposes to use a global Explainable Artificial Intelligence (XAI) model for the problem of line fault location. Using such a model allows the user to examine which bus measurements are used for classifying different line faults, increasing user understanding and trust. Another benefit of this technique is that it provides a method for PMU placement in cases where the system is partially observable. The XAI method used is a Neural Additive Model (NAM), which provides a global explanation. Using this model and a novel evaluation technique where norm weights in the model are used to determine which bus measurements are most impactful, the most important measurements are determined, allowing for better PMU placement. This technique is compared to existing methods and matches benchmark performance while being computationally cheaper.

Optimal Placement of μPMUs in Distribution Networks with Adaptive Topology Changes

With the increasing integration of energy sources and the growing complexity of distribution networks, it is crucial to monitor and early detection of topological changes to ensure grid stability and resilience. Current methods, for optimizing the placement of micro Phasor Measurement Units (μPMUs) focus on achieving observability and efficient monitoring. These algorithms aim to minimize the number of μPMUs needed while maintaining system observability or meeting criteria for observability. However, they may not consider all real-world constraints and uncertainties. In this study, we introduce a strategy for placing μPMUs with the objective of enhancing observability and monitoring capabilities. Our proposed algorithm employs a technique that makes optimal decisions at each step to approximate the global optimum. To determine the locations for μPMUs our algorithm takes into account parameters such as network structure, key nodes, and system stability. One distinguishing feature is its adaptability to distribution networks, including changes, in topology or potential device failures. Unlike classical approaches, our algorithm can continuously provide optimal placement solutions even in evolving network conditions. We have demonstrated that our suggested method achieves better results in terms of observability value and the required number of μPMUs compared to the state-of-the-art. By strategically placing μPMUs, operators can improve system observability, quickly detect and locate faults, and make informed decisions for effective network operations. This research helps improve optimal placement strategies for μPMUs by providing practical and effective solutions to improve distribution network reliability, resilience, and performance in the face of changing dynamics.

Optimization methods to solve the set covering problems

Phasor Measurement Unit Placement aims to decrease the cost this phasor equipment while maintaining the observability of the electrical distribution systems. This placement can be modeled as a set covering problem. Synchronized phasor data provides electrical systems’ information with higher quality and frequency than Remote Terminal Units. These data enable the implementation of faster than real-time state estimators. Thus, supervisory control and data acquisition systems can be replaced by automatically monitored systems that add reliability, efficiency, and resilience to smart grid systems. Due to the flexibility and large-scale in the distribution systems, heuristic methods have frequently been applied to this placement problem. Since the use of Chemical Reaction Optimization (CRO) algorithm is not recurrent, we have decided to implement it together with a topological system observability analysis. Initial population creation, parameterization, elementary reactions of CRO, and local search strategies are presented in this study. Computational results were performed in IEEE power system test cases. The optimization method proposed has obtained optimal results at a lower computation time than the literature. To test in large-scale, we have applied another optimization method to Set Covering Problems from Beasley library, with different parameters. However, the results were not satisfactory indicating that the CRO might not be suitable to some large-scale instances of the Set Covering Problems this library. In this case, further studies are required.

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.

Robust dynamic and algebraic state estimation for microgrids: A generalized approach

AbstractNetwork control, optimization, and security analysis are facilitated by accurate microgrid state estimation. By utilizing phasor measurement units (PMUs), a combined robust centralized dynamic algebraic approach is proposed in this paper for estimating algebraic states (voltage phasors) as well as dynamic states (associated with synchronous generators and wind turbines). Assuming that a load is placed on each bus, an innovative PMU placement method is also presented to provide measurements for all dynamic and algebraic state variables. As a robust link between algebraic state estimation and dynamic state estimation, the output results of the least absolute value (LAV) estimator, which is robust against bad data, are fed to dynamic state estimators as pseudo measurements. Next, to address the nonlinearity of synchronous generator and wind turbine models, an unscented Kalman filter (UKF) is applied to estimate dynamic states precisely. The numerical tests on a microgrid test system show that the suggested approach is suitable for both algebraic and dynamic state estimation. Synchronous generators and wind turbines are modeled using nonlinear models of the 9th and third orders, respectively, and static loads are modeled as voltage‐frequency dependent ones.

Multi‐objective precise phasor measurement locations to assess small‐signal stability using dingo optimizer

AbstractSmall‐signal stability is an important task and key research in electrical engineering for networks. This research article focuses on the implementation of a multi‐objective approach for choosing an optimal location for Phasor Measurement Units (PMUs) to quantify a power system's small‐signal stability by maximizing the signal‐to‐noise ratio (SNR) in the system. The novelty of this research lies in the implementation of Dingo Optimization (DOX) technique along with the Prony Analysis (PA) approach for the assessment of small‐signal stability in standard grid networks. The voltage angle, amplitude and the range of frequencies are measured by the optimal placement of PMUs, which primarily focus on the multi‐signal PA. To achieve the objective of this research, DOX integrated with the multi‐signal PA approach is used to determine the ideal position for PMU placement by considering maximum redundancy and optimizing the signal to noise ratio to a maximum level. The effectiveness of the DOX strategy is established with improved accuracy and fewer disturbances by optimizing the electromechanical oscillations of the system. The implementation of the DOX approach for attaining the best value of the maximized SNR is obtained by analyzing a wide set of conditions, perturbations, and additive noise, which provides an accurate assessment of damping ratio (DR) and frequency (f) of electromechanical oscillations. Numerical results obtained from the standard IEEE test systems (14, 39, 57, 118, and 300 bus systems) are compared with the existing methods in the literature. The statistical indices demonstrate that under the highly limited optimization context selected, the intended optimizer functions satisfactorily.

A Graph-Theoretic Approach for Optimal Phasor Measurement Units Placement Using Binary Firefly Algorithm

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability.

Allocation of synchronized phasor measurement units for power grid observability using advanced binary accelerated particle swarm optimization approach

Large-scale power grid observability is still a challenge because of deteriorating infrastructure and the incorporation of renewable energy sources. A smart grid that makes use of cutting-edge technology, such as a phasor measurement unit (PMU), is an excellent option for monitoring and bringing networks up to speed with the latest information. Latterly, the considerable investment required for the deployment locations has slowed down the adoption of PMU. Therefore, because PMUs are expensive, it is necessary to deploy them in the best possible places on large-scale power grids. The most significant share of optimal PMU placement problems (OPPP) is defined as 0–1 knapsack problems. Considering this, the development of an effective optimization technique that can handle difficulties has emerged as an appealing topic in recent years. In this paper, a meta-heuristic algorithm based on the binary particle swarm algorithm (BPSO), a binary accelerated particle swarm optimization (BAPSO), is offered for solving OPPP. Since earlier research has shown that BPSO is likely to stick to local optima, the majority of them evaluated their suggested technique using small-scale test systems. The technique that has been suggested searches for the optimal solution by employing two topologies—one global and one local—that are analogous to BPSO. This work determines the optimal PMU position for a large network in a reasonable amount of time by fine-tuning the acceleration factor. Additionally, in order to employ fewer PMUs, an integration strategy was put into place for the radial buses. The OPPP solutions are provided by the suggested method within a reasonable period with prior solutions published in reliable publications, according to computational findings.

A Novel Bus-Ranking-Algorithm-Based Heuristic Optimization Scheme for PMU Placement

This article presents a scheme for cost-optimized phasor measurement unit (PMU) placement and associated communication infrastructure (CI) design. For this purpose, a bus ranking algorithm is proposed, which ranks the buses in terms of connectivity, associated branch lengths, and supplementary network coverage ability that incentivizes better redundancy and lesser CI requirement. Next, a genetic algorithm (GA) is employed to search for minimum-cost optimal PMU placement solutions within the search space defined by the ranked-bus order. The search space is expanded by adding buses in the ranked order till GA finds a suitable solution satisfying the requisite constraints. The proposed scheme's performance is validated through comparison with existing state-of-the-art algorithms for IEEE 14-, 30-, and 118-bus networks under normal, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N-1$</tex-math></inline-formula> PMU contingency, branch loss contingency, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N-1$</tex-math></inline-formula> PMU/branch loss contingency conditions. In addition, considering real-world applications, the performance of the proposed scheme under channel-limit conditions is reported in this article.

Contingency-resilient PMU placement using Fuzzy Logic and Discrete Artificial Bee Colony algorithm for comprehensive network observability

In transforming traditional grid systems into intelligent and microgrid-based systems, the Phasor Measurement Units (PMUs) play a prominent role in enhancing monitoring, assessment, control, and protection by estimating voltage and current phasors correctly and quickly. However, since PMUs are not cost-effective, the decision to take the requisite number and their placement is a significant issue and needs to focus on a better approach. This study addresses the solution methodology to compute the optimal locations of a reduced PMU number to have complete network observability. A new hybrid optimization strategy based on the Fuzzy Logic (FL) and Discrete Artificial Bee Colony (DABC) algorithm has been suggested. The suggested method is titled FL-DABC algorithm, which has been applied to determining the optimal placement of PMUs in microgrid-based systems. The suggested approach tackles the problem of fully network-observable cost-effective PMU deployment. The effectiveness of the Algorithm is evaluated through simulations under various operating limitations, such as zero injection buses, a single PMU, and line outage scenarios. A topological observability analysis validates full network observability with the computed PMU placement. The study also includes branch PMU placement set computation and a comparative analysis with other techniques using standard IEEE test networks for simulations.

Algebraic Approach to PMU Placement for Minimum Variance Linear State Estimation in Power Networks

Phasor Measurement Units (PMUs) are used to measure voltages and currents in a power network. Optimal PMU placement in a given network is an important problem since installing PMU at every node of a network is expensive. This paper proposes an algebraic approach to the PMU placement problem with a focus on complete voltage state estimation and PMU measurement noise, with the objective to minimize the variance of the state estimation. The approach determines the minimum number of PMUs needed for full voltage state observability in a network based on the number of buses with known nodal currents. Combined with the information on PMU measurement noise and the network admittance matrix, an optimal PMU placement algorithm is formulated that minimizes the variance of the voltage estimation. This algorithm groups nodes of the network into four different sets based on the nodal voltages and nodal/line currents measured by the PMUs deployed in the network. The proposed placement algorithm considers both single current-channel PMU case and multi-current channel PMU case. This proposed placement algorithm is tested on IEEE 14 bus and IEEE 39 bus network. The results show that optimal placement can significantly reduce the effects of PMU measurement noise on the estimated states.

On the PMU Placement Optimization for the Detection of False Data Injection Attacks

The graph signal processing (GSP) approaches could facilitate the design of detectors for false data injection attacks (FDIAs) on smart grids. However, enhancing the performance of FDIA detection by optimizing the placement of phasor measurement units (PMUs) on smart grids remains an open problem. We formulate herein the detection of FDIAs as a binary hypothesis testing problem within the Grid-GSP framework and develop a subspace-based projection detection (SPD) algorithm, incorporating a quasi-optimal PMU placement strategy and two double thresholds. The proposed quasi-optimal PMU placement strategy enhances the performance of the SPD in FDIA detection. We further propose to enhance the SPD with the dynamic double threshold for high dynamic load profiles beyond a simple yet efficient static double threshold. Illustrative simulations based on the IEEE 118 bus test grid and the ERCOT dataset validate the superior performance of the proposed quasi-optimal PMU placement strategy along with the two double thresholds.

A Review of Various Optimization Strategies for Handling Optimal PMU Placement Problems

Abstract: The Phasor Measurement Unit (PMU) is the most fundamental power system technology that is utilized to protect, monitor, and control power networks. It not only allows real-time, synchronized voltage measurements on the buses but also the current phasor of lines connected to the buses where these PMUs are placed. The positioning of PMUs at each bus node is unnecessary and is restricted by the installation's high cost and complexity. As a result, the fundamental goal of Optimal PMU placement (OPP) is reducing the total no. of PMUs along with maximum observability, while simultaneously considering measurement redundancy. There are several methods for fixing this problem, and they fall into two categories: Metaheuristic methods and mathematical approaches. This study examines a variety of optimization strategies for handling OPP problems