Optimal PMU placement for power system observability using BICA, considering measurement redundancy

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

In this paper, a novel hybrid optimization method to solve the phasor measurement unit (PMU) placement problem is presented. The optimal PMU placement (OPP) problem is formulated to guarantee both full observability of the network and minimal number of PMUs. In the hybrid optimization method, the mechanism of simulated annealing (SA) is involved into original particle swarm optimization (PSO) to prevent the method from premature convergence; crossover and mutation operators are applied to increase the diversity of swarm. The proposed method keeps the advantage of PSO to find the optimum result quickly, and is still simple to implement, and can further improve the ability of jumping out of local optimum results. Simulation results on several IEEE systems show the effectiveness of the method. (5 pages)

Application of heuristic algorithms to optimal PMU placement in electric power systems: An updated review

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.

This paper presents a study on optimal placement of phasor measurement units (PMU) with Global Positioning System (GPS) receivers for more accurate monitoring of a power system. The optimal PMU placement (OPP) problem is, in general, expressed as a combinatorial optimization problem subjected to the observability constraints of the studied power system, which consumes much time to solve. Three approaches, in this paper, are proposed aiming at reducing computational burden in OPP problems. First, a modified simulated annealing (MSA) method, is proposed. The conventional simulated annealing method is slightly modified in setting the initial temperature and in cooling procedure to consider the current state of solution sets. Second, a direct combination (DC) method is suggested using a simple but very effective heuristic rule. When selecting test and neighboring solution sets, this heuristic rule is used to select most effective sets in the observability sense. And the last one is tabu search (TS) method, in which the heuristic rule used in DC method is also used to reduce the searching spaces effectively. Suggested optimization methods are applied, in this paper, to solve OPP problems in IEEE 14, 30 and 57 bus systems. The simulation results show that the proposed methods can find the optimal solutions in an efficient manner.

Phasor measurement units (PMUs) are becoming popular and populating the power system grids rapidly due to their wide range of benefits and applications. This research paper proposes a comprehensive, effective, and revised formulation of the optimal PMU placement (OPP) problem with a view to minimizing the required number of PMU and ensuring the maximum number of measurement redundancy subjected to the full observability of the distribution grids. The proposed formulation also incorporates the presence of passive measurements/zero injection buses (ZIB) and the channel availability of the installed PMU. Additionally, the formulation is extended to various contingency cases i.e., the single line outage and single PMU loss cases. This paper solves the proposed OPP formulation employing a heuristic technique called backtracking search algorithm (BSA) and tests its effectiveness through different IEEE standard distribution feeders. Additionally, this study compares the obtained results with the mixed integer linear programming (MILP)-based approach and the referenced works. The obtained results demonstrate the superiority of the proposed formulation and solution methodology compared to other methodologies.

This paper presents a detailed bibliographic analysis of papers concerning the optimal PMU placement (OPP) problem that have more than ten citations and sorted in descending order by the number of citations. The results from this analysis show that IEEE journal and IEEE conference papers are the most influential. The most productive and high-impact authors, institutions, and countries/regions are also identified. According to this analysis, U.S.A. researchers and institutions dominate the research on OPP problem, whereas the most productive and influential authors are A. Abur and B. Gou. Moreover, Texas A&M University, Virginia Polytechnic Institute & State University, and University of Cyprus are the three most productive and influential institutions in OPP research.

Post-mortem studies carried out after recent power system blackouts have shown that time-stamped measurements from Phasor Measurement Units (PMUs) would have helped to avert these blackouts if they were available. Consequently, there is the need to optimally locate these PMUs to provide information about the system’s state. This paper proposes a novel method incorporating modal participation factors computed from the eigenvalues and eigenvectors of the reduced Jacobian matrix obtained from the system’s power flow with Binary Integer Programming (BIP), in solving the Optimal PMU Placement (OPP) problem while considering the system’s voltage stability. The proposed method identifies the critical buses prone to voltage instability, determines the minimum number of PMUs required for complete topological observability, and the best locations to site the PMUs. The proposed method was tested on benchmark IEEE 14-bus, 30-bus, and 57-bus test networks. Analyses of the results show that the proposed method provides a better procedure for the strategic placement of PMUs in practical networks for voltage stability monitoring especially for the cases of phased (multi-stage) PMU placement limited by the availability of funds and communication channels. Thereby making it necessary to give priority to critical/voltage weak buses during the phased installation process.

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

This article presents a novel method for optimal phasor measurement unit placement (OPP) for full power system observability in the presence of conventional measurements. The method considers eligible PMU placements that may be omitted by existing OPP methods and hence may provide a better solution and can guarantee that the PMU placement can restore network observability. This is achieved by creating a new observability criterion and a network transformation scheme. The new observability criterion uses injection and zero injection measurements to improve the solution and the network transformation scheme reduces the size of the OPP problem and is a prerequisite for applying the proposed observability criterion. The OPP problem is solved using binary integer linear programming (BILP) or binary integer linear programming (BILP). Therefore, the proposed OPP method can be easily combined with other OPP methods, considering other constraints/scenarios using BILP, such as OPP considering PMU current channel limits. The new method is tested using simulations of the IEEE 14-, 118-, and 2736-bus test systems. These results show that the proposed new method is feasible in terms of execution time, is free of solutions that fail to make the system fully observable, and is capable of identifying optimal placement solutions that may be overlooked by existing OPP methods.

Phasor measurement unit (PMU) is among the most important measurement devices in modern power systems. It measures the voltage and current phasors which have both magnitude and phase angle using a common timing reference. These measurements are utilized in real time applications of electrical power systems. The main drawback of PMUs is its high cost so if PMUs are used to collect or send online real data from a system to a data centre or a decision maker, the used number of PMUs should be minimum with full observability of system measurements. This paper proposes a flower pollination algorithm (FPA) to find the optimal number and locations of PMUs in power systems. The optimization objectives of the work are the minimization of PMUs number, the achievement of complete observability of power system states, and the maximization of measurement redundancy. Existence of zero-injection buses in the system decreases the number of PMUs that is required to fulfil the complete observability of system states. Furthermore, additional constraints for remaining system full observable following failure of single PMU and single line outage are also included to increase the system reliability. The performance and efficiency of the proposed FPA is tested on IEEE standard networks such as 14-bus, 30-bus, 57-bus and 118-bus and new England 39-bus network. The obtained simulation results approve the ability of the proposed optimization method to find the optimal allocation of PMUs in different cases with fulfilling complete observability and reliability of electric power systems. The superiority of FPA is also verified by comparing its results of other optimization algorithms.

The Phasor Measurement Unit (PMU) is an important instrument for power system monitoring and control. It provides continuous, synchronized voltage measurements at the buses, as well as current stage esteem for such buses in which this PMUs can be found. The aim of the best possible PMU location (OPMUL) problem is to choose a small number of PMUs that will allow the entire network to be visible. To solve the optimal PMU placement (OPMUP) problem, several meta-heuristic optimization methods have been proposed. The proposed description is put to the test on IEEE 14 test systems, and the findings for full observability of the system under normal conditions are presented in this paper. In IEEE 14 bus systems, the proposed PMU placement has been tested.

The invention of phasor measurement unit (PMU) is considered to be one of the most important breakthroughs in the field of electrical grids monitoring and control. PMUs can be used to obtain accurate voltage and current phasors' measurements. The high cost of installation related to PMU limited its usage and increased the need for optimal allocation of PMUs. A binary bat optimization algorithm is proposed in this paper for solving the optimal PMU placement (OPP) problem to obtain a fully observable system. Simulations are carried on various IEEE bus test systems to ensure the algorithm's effectiveness and feasibility. A comparison is made between the obtained results and the other algorithms' results. The execution time of the proposed algorithm is also compared with other algorithms to illustrate the algorithm's ability to obtain the problem solution with lower execution time and better convergence curve.

This paper proposes an algorithm that finds the optimal sets of phasor measurement units (PMUs) to achieve a fault observable system, while it addresses the multi‐estimation issue. The optimal PMU placement (OPP) problem here is to find a set of PMU locations with minimum number of members that enables fault observability in a system and satisfies a defined minimum sensitivity requirement in measurements. The proposed algorithm generalizes the impedance method in fault analysis and optimizes PMU utilization to maintain a required minimum sensitivity in each set of measurements, given the required fault detection accuracy. Also, the set of measurements is unique and distinctive for each fault scenario, preventing multi‐estimation. A fault is referred to as a set of affected faulty line, fault location, and fault impedance. A sensitivity analysis is performed and sensitivity indices are derived to evaluate measurements quality to detect changes in fault line, location, or resistance. The algorithm is executed on IEEE 7‐bus, 14‐bus, and 30‐bus test systems. Subsequently, artificial neural networks (ANN) are employed to build fault locators through offline training. ANN use an optimal PMU set obtained by the proposed algorithm to uniquely map between the corresponding measurements set and the faults.

Phasor Measurement Unit (PMU) technology is increasingly used for real-time monitoring applications, especially line outage detection and identification (D&I) in the power system. Current outage D&I schemes either assume a full PMU deployment or a partial deployment with fixed PMU placement. However, the placement of the PMUs has a fundamental impact on the effectiveness of the D&I scheme. Building on a dynamic relationship between the substation voltage phase angle and active power, we formulated the optimal PMU placement problem for outage D&I as an optimization problem readily solvable by any heuristic algorithm. We tested the formulation using a genetic algorithm and simulated outages of IEEE 39 bus system. The optimal placement found produces a better D&I result of single-line outages than a randomly scattered, tree-like, and degree-based placements.