Optimal Placement Of Phasor Measurement Units 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.

Joint Optimal PMU Placement and Data Pruning for Resource Efficient Smart Grid Monitoring

Joint Optimal PMU Placement and Data Pruning for Resource Efficient Smart Grid Monitoring

Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality

The optimal PMU placement problem is placing the minimum number of PMUs in the network to ensure complete network observability. It is an NP-complete optimization problem. PMU placement based on cost and critical nodes is solved separately in the literature. This paper proposes a novel approach, a degree of centrality in the objective function, to combine the effect of both strategies to place PMUs in the power network optimally. The contingency analysis and the effect of zero-injection buses are solved to ensure the reliability of network monitoring and attain a minimum number of PMUs. Integer linear programming is used on the IEEE 7-bus, IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, and IEEE 118-bus systems to solve this problem. The results are evaluated based on two performance measures: the bus observability index (BOI) and the sum of redundancy index (SORI). On comparison, it is found that the proposed methodology has significantly improved results, i.e., a reduced number of PMUs and increased network overall observability (SORI). This methodology is more practical for implementation as it focuses on critical nodes. Along with improvement in the results, the limitations of existing indices are also discussed for future work.

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.

Deep Reinforcement Learning-Based Optimal PMU Placement Considering the Degree of Power System Observability

Deep Reinforcement Learning-Based Optimal PMU Placement Considering the Degree of Power System Observability

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.

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.

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.

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.

Optimal placement of PMU and PDC in power systems by considering the vulnerabilities against cyber-attacks

Optimal placement of PMU and PDC in power systems by considering the vulnerabilities against cyber-attacks

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

Optimal PMU Placement for Türkiye 400 kV Interconnected Power System Observability with Dragonfly Algorithm

Optimal PMU Placement for Türkiye 400 kV Interconnected Power System Observability with Dragonfly Algorithm

Wide area measurement systems based Power System State Estimation using a Robust Linear-Weighted Least Square method

The accuracy and robustness of Power System State Estimation (PSSE) are paramount for monitoring and surveillance of Energy Management Systems (EMS). To enhance the PSSE accuracy, phasor measurement units (PMUs) are widely used in Wide-area measurement systems (WAMS). The essential aspects of enhancing PSSE accuracy are systems observability and redundancy. These can be accomplished through an optimal PMU placement strategy. Iterative-based algorithms for estimating power system states using PMU and traditional Supervisory Control and Data Acquisition (SCADA) measurements face challenges such as non-linearity and lack of convergence. In this paper, a Robust Linear Weighted Least Square (RLWLS) estimator is proposed by combining conventional SCADA data with phasor measurements. Further, The RLWLS state estimator overcomes the issues mentioned above. Extensive simulations on various test systems, including traditional IEEE 14, 30, 57, and IEEE-118 bus systems and the real-time Indian Northern Region Power Grid (INRPG)-246 bus system in India, have shown to prove the resilience of this technique. Finally, compared to the prior PSSE approaches, the RLWLS methodology significantly improves the efficacy and performance of the state estimate solution.

A novel comprehensive optimal PMU placement considering practical issues in design and implementation of a wide-area measurement system

• Several WAMS-based applications are considered in the OPP problem. • Concept of the line-wise observability is presented. • Observability of multi-TSO power systems in the OPP problem is investigated • The proposed OPP model is originally a MILP one and its global solution can be easily found. Since phasor measurement units (PMUs) are increasingly installed in power systems, many research works have investigated the optimal PMU placement (OPP) problem with the sake of maximizing observability over the power grid, while minimizing the cost of purchasing PMUs. However, there are still some practical challenges that have not been considered in the previous research studies, and are needed to be addressed for a real wide-area measurement system (WAMS) implementation. In this paper, a comprehensive OPP model is proposed which employs a set of basic and novel constraints for a WAMS design. In this regard, the line-wise observability concept is proposed for the first time to consider the requirements of observability for a range of WAMS-based applications such as restoration management, model validation of power system components, and dynamic line rating (DLR) monitoring. In addition, observability constraints for multi-transmission system operator (multi-TSO) power systems that are operated by different legal entities are provided, which can considerably affect the OPP result. The proposed model is formulated based on a mixed-integer linear programming (MILP) formulation which enables the optimization problem to be solved easily and effectively by existing commercial optimization tools, without any need for linearization. The performance of the proposed OPP model is first verified through simulation results on an 86-bus real power system. Then, simulations are applied to IEEE 118-bus and 300-bus test systems, to certify the performance of the proposed model in comparison with other OPPs in the literature.

Developing an Optimal Framework for PMU Placement Based on Active Distribution System State Estimation Considering Cost-Worth Analysis

Due to low levels of observability and automation in active distribution networks (ADNs), the deployment of accurate measurement devices would be inevitable to increase the network observability. This work develops an optimal framework for phasor measurement unit (PMU) placement considering the accuracy of the distribution system state estimation (DSSE) process. In this regard, first, considering the significance of active power in supplying network loads, an active power sensitivity analysis is conducted in which the accuracy of active power injection at each bus is multiplied by its value of lost load. In this way, the accuracy of active power estimation could be transformed into a monetary value. Then, based on the determined sensitivity criterion, the optimal placement of PMUs has been performed with the aim of appropriate accuracy for the DSSE. On the other hand, the accuracy of the results obtained in the state estimation procedure can affect the estimation of line-loadings and lead to load shedding due to the low accuracy of estimated line-loadings. It is noteworthy that due to the high integration of distributed energy resources in distribution systems, ADNs would be prone to congestion issues. Therefore, in the next stage, this perspective is also used for determining the optimal number and location of PMUs in ADNs to improve their observability based on the operational conditions of the network. Finally, the developed algorithm is applied on the 77-bus-UK-test distribution network to investigate its effectiveness in improving the DSSE accuracy and mitigating interrupted loads. The numerical results indicate that implementing the proposed framework not only increases the DSSE accuracy but also decreases the cost of compensating the accuracy of active power by 7% compared to the typical network without PMUs. Further, the results show that the proposed model significantly mitigates the total curtailed loads due to the low accuracy of estimated line-loadings.

Robust PMU Placement in Branch Switching Power Networks for Minimum Variance State Estimation

Phasor Measurement Units (PMUs) are measurement devices that measure voltage phasor data of a bus and current phasors of adjacent branches in a power network. With information on the power network impedance, a finite number of PMUs are used to estimate the voltage on all buses, also known as the state of the power network. Traditionally, the problem of Optimal PMU Placement (OPP) focuses on having complete observability of the network state by strategically placing a minimum number of PMUs. A change in network topology caused, for example, by network switches, changes the solution to the OPP problem. Since it is not feasible to change the locations of PMUs after every change in topology, the contribution of this paper is a PMU placement optimization algorithm which minimizes the variance on the estimated voltages and is also robust to the changes to the network topology. The optimization guarantees robustness to the change in network topology. Since it considers each change in the switch position as a different network topology, the optimization criterion is a multi-objective optimization function for different network topologies caused by changing the switch positions of the same network. The proposed robust optimal placement algorithm can be used for single current-channel PMUs and multi-current channel PMUs. The algorithm is tested on IEEE 14 bus network. The results show that the robust optimal placement can significantly reduce the effects of PMU measurement noise on the estimated states on average for different switch positions.

Optimal Placement of Phasor Measurement Unit in Smart Grids Considering Multiple Constraints

The distribution of measurement noise is usually assumed to be Gaussian in the optimal phasor measurement unit (PMU) placement (OPP) problem. However, this is not always accurate in practice. This paper proposes a new OPP method for smart grids in which the effects of conventional measurements, limited channels of PMUs, zero-injection buses (ZIBs), single PMU loss contingency, state estimation error (SEE), and the maximum SEE variance (MSEEV) are considered. The SEE and MSEEV are both obtained using a robust <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -distribution maximum likelihood estimator (MLE) because <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> -distribution is more flexible for modeling both Gaussian and non-Gaussian noises. The A- and G-optimal experimental criteria are utilized to form the SEE and MSEEV constraints. This allows the optimization problem to be converted into a linear objective function subject to linear matrix inequality observability constraints. The performance of the proposed OPP method is verified by the simulations of the IEEE 14-bus, 30-bus, and 118-bus systems as well as the 188-bus practical distribution system in China.

Optimal placement of PMU for enhancing power system monitoring

Phasor Measurement Units (PMUs) have emerged as crucial tools in the field of power system monitoring, control, and safety. Extensive research has been conducted on enhancing the capabilities of PMUs. Traditional SCADA techniques, which have been employed for many years, gather data on power system parameters at intervals of 4 to 10 seconds, offering a static perspective of the system's behavior. However, relying solely on static data may limit the effectiveness of the monitoring and control. On the other hand, PMUs provide real-time information about the dynamic state of the system, enabling more comprehensive insights. For the optimal and cost-effective installation of PMUs, it is vital to strategically position them in locations that effectively monitor the state of the system. The Instruction Level Parallelism (ILP) approach was employed to identify the ideal PMU placement for power system observability. The ILP approach determines the most suitable positions for PMUs by considering factors such as the redundancy of measurements and cost efficiency. To validate the effectiveness of the ILP approach, it was tested using the widely recognized IEEE 14 bus system, which serves as a standard benchmark for power-system analysis. Through the ILP approach, the optimal placement of the PMUs can be determined, thereby enhancing the observability of the power system. This in turn improves situational awareness, enables effective fault detection, and facilitates coordinated control strategies. By integrating PMUs and the ILP approach, the power system monitoring and control can be significantly enhanced. Ongoing research and development in PMU technology and placement methodologies continues to advance the field, enabling more efficient and effective management of modern power grids.