Modal Participation Factor-Constrained Optimal PMU Placement in Power System Networks

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

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.

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)

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

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.

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.

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.

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

Phasor measurement units (PMUs) become a necessity in the modern distribution system especially with the direction towards smart grid. The PMUs should be placed appropriately in the distribution network. The problem of optimal PMU placement for full observability is analyzed in literature. Distribution network reconfiguration for loss reduction is also analyzed in literature. This paper studies the placement problem of PMUs in distribution system considering the system reconfiguration. System reconfiguration is achieved using the ant colony optimization (ACO) algorithm to solve the minimum losses problem. Greedy algorithm is used as an optimization tool to obtain the minimal number of PMUs and their corresponding locations. The 33-node distribution system is tested for optimally locating the PMUs with different network reconfiguration.

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.

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

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 addresses various aspects of optimal phasor measurement unit (PMU) placement problem. We propose a procedure for multistaging of PMU placement in a given time horizon using an integer linear programming (ILP) framework. Hitherto, modeling of zero injection constraints had been a challenge due to the intrinsic nonlinearity associated with it. We show that zero injection constraints can also be modeled as linear constraints in an ILP framework. Minimum PMU placement problem has multiple solutions. We propose two indices, viz, BOI and SORI, to further rank these multiple solutions, where BOI is <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bus</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">observability</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">index</i> giving a measure of number of PMUs observing a given bus and SORI is <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">system</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">observability</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">redundancy</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">index</i> giving sum of all BOI for a system. Results on IEEE 118 bus system have been presented. Results indicate that: (1) optimal phasing of PMUs can be computed efficiently; (2) proposed method of modeling zero injection constraints improve computational performance; and (3) BOI and SORI help in improving the quality of PMU placement.

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

This paper presents a two-stage method for the placement of Phasor Measurement Units (PMUs) to achieve a fully observable power system while considering the PMUs channel limitation. The first stage uses the Binary Integer Linear Programming (BILP) to find the initial optimum locations of PMUs which ensure full system observability under normal conditions. The second stage uses state enumeration based on probabilistic failure of different components to ensure full system observability under single and double outages. The proposed method is validated using the IEEE 14-Bus system, IEEE 30-Bus system and IEEE 57-Bus system. The simulation results show that the method is efficient in providing detailed information about the observability of the power system under different operating conditions.