PMU Optimal Placement using sensitivity analysis for power systems fault location

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.

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.

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

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

One‐terminal algorithms utilise only measurements at one end of the transmission lines for fault location, and thus, the required number of phasor measurement units (PMUs) is less compared to two‐end algorithms. There have not been major studies in terms of reducing required number of PMUs to implement fault location algorithms based on single terminal data. This study develops a fault observability rule using the basic theories of power system observability. Then, this rule is used in some optimisation problems for determining the minimum number and optimal placement of PMUs to attempt the complete system observability in normal condition and complete or relative fault observability simultaneously. Moreover, here, a novel fault location algorithm is proposed, which uses one‐terminal voltage and current data. To enhance the accuracy of fault location, the novel algorithm utilises positive bus impedance matrix of the network along with the voltage and current equations of the faulted line. The performance of the proposed fault location algorithm and optimal placement method of PMUs for power system and fault observability is investigated on 39‐bus test system.

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

Phasor Measurement Unit (PMU) is a measurement devices capable of providing measurements with high accuracy, but due to its high cost it is desired to find the optimal placement for them. This paper tries to address the Optimal PMU Placement (OPP) for large power systems while including additional criteria like injection measurement, as well as, considering the Zero Injection Bus (ZIB) effect. The selected large power system is the Qatar Grid and the system is solved using Integer Linear Programming (ILP) techniques. The paper does not only focus on the number of PMU s required for full observability, it extends to take into consideration N−1 contingency. It also measures the ratio of conventional measurements to PMU s for the Qatar Grid in order to provide an estimate for this ratio. The paper is concluded with the obtained results as well as recommendations for practical application of OPP.

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.

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.

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.

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.

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).

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

This study presents a new approach for optimal placement of synchronised phasor measurement units (PMUs) to ensure complete power system observability in the presence of non‐synchronous conventional measurements and zero injections. Currently, financial or technical restrictions prohibit the deployment of PMUs on every bus, which in turn motivates their strategic placement across the power system. PMU allocation is optimised here based on measurement observability criteria for achieving solvability of the power system state estimation. Most of the previous work has proposed topological observability based methods for optimal PMU placement (OPP), which may not always ensure numerical observability required for successful execution of state estimation. The proposed OPP method finds out the minimum number and the optimal locations of PMUs required to make the power system numerically observable. The problem is formulated as a binary semi‐definite programming (BSDP) model, with binary decision variables, minimising a linear objective function subject to linear matrix inequality observability constraints. The BSDP problem is solved using an outer approximation scheme based on binary integer linear programming. The developed method is conducted on IEEE standard test systems. A large‐scale system with 3120 buses is also analysed to exhibit the applicability of proposed model to practical power system cases.

This paper presents techniques for the application of tertiary and secondary voltage control through the use of intelligent proportional integral derivative (PID) controllers and the wide area measurement system (WAMS) in the IEEE 39 bus system (New England system). The paper includes power system partitioning, pilot bus selection, phasor measurement unit (PMU) placement, and optimal secondary voltage control parameter calculations to enable the application of the proposed voltage control. The power system simulation and analyses were performed using the DIgSILENT and MATLAB software applications. The optimal PMU placement was performed in order to apply secondary voltage control. The tertiary voltage control was performed through an optimal power flow optimization process in order to minimize the active power losses. Two different methods were used to design the PID secondary voltage control, namely, genetic algorithm (GA) and neural network based on genetic algorithm (NNGA). A comparison of system performances using these two methods under different operating conditions is presented. The results show that NNGA secondary PID controllers are more robust than GA ones. The paper also presents a comparison between system performance with and without secondary voltage control, in terms of voltage deviation index and total active power losses. The graph theory is used in system partitioning, and sensitivity analysis is used in pilot bus selection, the results of which proved their effectiveness.