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

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

Detection and isolation of faulty line in an active distribution network using intelligent numerical relay

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.

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 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.

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.

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

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.

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.

Population of Phasor Measurement Units (PMUs) in power systems are increasing day by day as PMUs measure the electrical quantities more accurately with time-stamping. The measurements done by PMU can be used for monitoring, controlling and for state estimation of the power system. Since the installation of PMUs demand high capital cost, their number and location to be chosen optimally is by minimizing investment without losing observability of the system. In this paper Integer Programming techniques used to solve Optimal Placement of PMU (OPP) problem. The OPP problem is solved for normal power system as well as for a few contingency conditions like one PMU outage, considering zero injection bus, outage of single line on various standard IEEE Bus Systems. The work is also trying to place PMUs under planned islanding in certain standard networks.

Phasor Measurement Units (PMUs) have potential role in power system monitoring, protection and control. PMUs directly measure and provide synchronized measurements of voltage and current phasors at various system locations. This offer numerous possibilities for ascertaining information related to the state and health of a power system. The aim of an optimal PMU placement is to provide minimal PMU installations to ensure complete observability of a power system. Several methods have been proposed in the past and significant work has been carried out till date to ensure observability of transmission networks. Penetration of new technologies at distribution level raises concern about system operation. These concerns can be addressed by synchrophasor applications. Hence, PMUs play an equally important role at distribution level also. Since, distribution systems are large in size as compared to transmission systems, the algorithms reported may take longer computational time for optimization. Hence, an approach towards optimal PMU placement for distribution system is required. This paper presents a simple and effective algorithm of PMU placement for distribution networks that saves considerable amount of computational time. The proposed algorithm is tested on IEEE-13, 34 and 123 Node Test Feeders and results show that computational time is considerably reduced without significantly affecting the number of PMUs.

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.

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.

In this paper, a stochastic-metaheuristic model is performed for multi-objective allocation of photovoltaic (PV) resources in 33-bus and 69-bus distribution systems to minimize power losses of the distribution system lines, improving the voltage profile and voltage stability of the distribution system buses, considering the uncertainty of PV units’ power and network demand. The decision-making variables, including installation location and the size of PVs, are determined optimally via an improved human learning optimization algorithm (IHLOA). The conventional human learning optimization algorithm (IHLOA) is improved based on Gaussian mutation to enhance the exploration capability and avoid getting trapped in local optimal. The methodology is implemented in two cases as deterministic and stochastic without and with uncertainties, respectively. Monte Carol Simulation (MCS) based on probability distribution function (PDF) is used for uncertainties modeling. The deterministic results proved the superiority of the IHLOA compared with conventional HLOA, particle swarm optimization (PSO), to obtain better values of the different objectives and faster convergence speed and accuracy. The results are clear that enhancing the conventional HLOA has increased the algorithm’s ability to explore and achieve the optimal global solution with higher convergence accuracy. Moreover, the stochastic results were clear that considering the uncertainties leads to correct and robust decision-making against existing uncertainties and accurate knowledge of the network operator against the exact values of various objectives compared to the deterministic case.

Reducing the cost of implementing phasor measurement units (PMUs) can be achieved by determining the minimum number of devices needed to provide the electrical network observability. This article describes the optimal PMU placement algorithm, based on the analysis of the topological properties of the electrical network. It was shown that the optimal placement of PMUs can be achieved by dividing the nodal admittance matrix into four submatrices, one of them will be a band submatrix, which can be reduced to a lower triangular matrix of maximum rank by elementary transformations. Results reflecting the number of installed PMUs were obtained for IEEE test bus systems with 9, 14, 19, 30 and 57 nodes. For all test bus systems, the proposed PMU placement algorithm provides complete observability of the system with the same or fewer nodes of installation in comparison with the results of other authors. There were carried out computations with the help of the algorithm accelerated calculations of state estimation performance in power systems, in order to confirm the correctness of this approach. There were determined conditions under which the equations of nodal voltages were solved linearly and nonlinearly (iteratively).