Phasor Measurement Units Placement Research Articles

Co-Optimal Placement of PMUs and Their Communication Infrastructure for Minimization of Propagation Delay in the WAMS

The phasor measurement unit (PMU) has evolved as an indispensable tool for real-time monitoring and control of the power system, thereby leading to its large-scale deployment in a wide-area measurement system (WAMS). Communication system plays a significant role for transferring measured real-time PMU data to the phasor data concentrator (PDC) for monitoring and control purposes. The propagation delay depends on the spatial position of the PMUs with respect to the PDC and the communication network. This paper deals with the problem of co-optimal placement of PMUs and their communication infrastructure for minimization of propagation delay in a WAMS. The novelty of the work is to explore the feasibility of microwave communication technology as an alternative to the existing fiber-based communication infrastructure in the WAMS. The proposed approach takes the link reliability and the geographical topological variations into consideration for the placement of the microwave links so as to minimize the propagation delay. The merit of the microwave communication technology in the framework of the WAMS is evaluated in terms of its cost and reliability. Simulation results and the propagation delay evaluations for the Eastern power grid of India validate the efficacy of the proposed approach in practical field applications.

Optimal PMU placement for pessimistic dynamic vulnerability assessment

This study proposes a novel optimal phasor measurement unit (PMU) placement method for dynamic vulnerability assessment with minimum number of PMUs. PMU measurements should reflect the change of power system status as earlier as possible when a contingency occurs. Therefore, those buses which are sensitive to the change of power system status have to be equipped with PMUs to monitor the fragile areas of power systems. First, a large power system is partitioned into several coherent clusters. Next, a probabilistic vulnerability index defined as the objective function and a binary quadratic programming model are proposed for this purpose. Finally, the proposed method is tested on IEEE 9-, 39-, and 145-bus systems. Results show that this method results in a PMU configuration with minimum number of devices and high vulnerability index. Such a PMU placement is able to estimate the vulnerability of power systems.

Optimal Location of Phasor Measurement Unit Using Ant Colony Optimization Under Contingency

Accurate and fast monitoring is achieved by using the real time synchronized phasor data obtained from Phasor Measurement Unit (PMU). As the cost of PMU is more expensive, it is not economical to install PMU at all the nodes of a power system. Therefore, it is very much essential to implement an optimal method for PMU location. In this research work, implementation of Ant Colony Optimization (ACO) for optimal PMU placement under normal and single PMU loss conditions is discussed. The PMUs are placed in such a manner that all voltage phasors and current phasors are measurable with a minimum number of PMUs. The proposed ACO is able to locate the PMUs optimally enabling the complete measurement of all voltage and current phasors with a minimum number of PMUs. The ACO is an efficient optimization algorithm to optimize the placement of the PMU for measuring the voltage and current phasors with minimum no of PMU. The proposed method is demonstrated in IEEE 14 bus, IEEE 30 bus, and IEEE 57 bus to find the optimal location of PMU under normal and for single PMU loss conditions.

Power system observability with minimum phasor measurement units placement

This paper presents optimal phasor measurement units (PMUs) placement algorithms for power system observability. The optimal placement problem (OPP) is formulated such that minimizing the number of PMU installations for full networkobservability. Three approaches, in this paper, are introduced aiming at reducing the computational burden in Optimal Placement problems. Depth First Search, Simulated Annealing and Minimum Spanning Tree as well as their differences and relations are discussed in details. The OPP methodologies applied include the system observablility during normal operating conditions, as well as during single branch forced outages. In order to improve the speed of convergence, an initial PMU placement is provided by graph-theoretic procedure. The IEEE 14-bus, 118-bus standard test power systems and New England 39-bus test systems are used for simulation purposes.Keywords: Phasor Measurement Unit (PMU); Observability; Depth First Search; Simulated Annealing Method; Minimum Spanning Tree; Optimal PMU Placement (OPP)

Optimization of Phasor Measurement Unit (PMU) Placement in Supervisory Control and Data Acquisition (SCADA)-Based Power System for Better State-Estimation Performance

Present-day power systems are mostly equipped with conventional meters and intended for the installation of highly accurate phasor measurement units (PMUs) to ensure better protection, monitoring and control of the network. PMU is a deliberate choice due to its unique capacity in providing accurate phasor readings of bus voltages and currents. However, due to the high expense and a requirement for communication facilities, the installation of a limited number of PMUs in a network is common practice. This paper presents an optimal approach to selecting the locations of PMUs to be installed with the objective of ensuring maximum accuracy of the state estimation (SE). The optimization technique ensures that the critical locations of the system will be covered by PMU meters which lower the negative impact of bad data on state-estimation performance. One of the well-known intelligent optimization techniques, the genetic algorithm (GA), is used to search for the optimal set of PMUs. The proposed technique is compared with a heuristic approach of PMU placement. The weighted least square (WLS), with a modified Jacobian to deal with the phasor quantities, is used to compute the estimation accuracy. IEEE 30-bus and 118-bus systems are used to demonstrate the suggested technique.

Contingency constrained phasor measurement units placement with n − k redundancy criterion: a robust optimisation approach

This study presents a novel approach for the contingency constrained phasor measurement units (PMUs) placement. The proposed approach is based on n − k redundancy criterion using robust optimisation. This security criterion ensures observability of the network under any contingency state, containing the loss of up to k PMUs. In the proposed method, the effects of zero injection and power flow measurements as well as possible contingency states (such as branch outage and single or multiple PMU loss) are considered. The non-linear modelling of observability function for measurements is reformulated by linearisation process. The resulting bi-level programming model is solved by its evolution to an equivalent single-level mixed-integer programming problem. The objective function of the optimal PMU placement is aimed at minimising the number of PMUs in a way that guarantees economic goals and the observability of all network buses. The advantage of this model is to significantly reduce the computational burden compared with other methods. The proposed method is tested on modified 7-bus test network, 118- and 2383-bus IEEE test networks. The results of the case studies clearly demonstrate the simplicity and efficiency of the proposed robust optimisation method in different cases.

Detection and location of faults in large transmission networks using minimum number of phasor measurement units

This paper presents an algorithm for detection and location of faults in large transmission network using the minimum number of phasor measurement units (PMUs). The transfer impedance between the fault point and the respective PMU buses, may be far away from the fault point, are used to define a non-linear set of voltage and current equations, which is then transformed into linear least square estimation problem where fault location is the unknown quantity. The algorithm starts by reducing the search area using a faulted bus identification index so as to speed-up the search process. Then considering all the transmission lines within the search area, a fault location identification index together with the fault location algorithm is used to identify the faulted line along with fault location. A PMU placement scheme, to find the minimum number of PMUs and their location, required for the proposed algorithm is also presented here. The proposed algorithm is tested and validated on the IEEE 14, 30, 39, and 118-bus systems for various fault conditions. Comparative results confirm that the number of PMUs obtained by the proposed placement scheme is minimum and sufficient for detection and location of faults irrespective of fault resistance and fault type.

Redundant placement of phasor measurement unit using multi objective evolutionary algorithms based on modified spectral clustering

Optimal placement of phasor measurement unit (OPPMU) considering controlled islanding is a high dimensional constrained optimization problem that determines optimum location of PMU. Controlled islanding can be used as an effective emergency action to split a large scale power system into islands for avoiding blackouts. This paper proposes an optimal PMU placement model considering power system controlled islanding. So that the power network remains observable under controlled islanding as well as normal operating condition. A new method based on spectral clustering is proposed to handle multiple constraints in order to guarantee the stability of generated islands. The objective function used in this controlled islanding algorithm is the minimal power flow disruption. Constraints such as generator coherency, load–generation imbalance are considered for spectral clustering. OPPMU is formed for the obtained islands using two conflicting objectives of minimizing the number of PMU and maximizing the measurement redundancy using conventional mixed integer linear programming, real coded genetic algorithm(RGA), non dominated sorting differential evolutionary algorithm, and modified non dominated sorting GA II (MNSGA II). RGA with simulated binary crossover and non dominated sorting differential evolution with improved crowding distance and mutation is implemented. In MNSGA II proposed a variant which reduces the run-time complexity using the technique space–time-trade-off. Simulation results using NE 39 and IEEE 118 bus test systems show that the proposed method is computationally efficient when solving controlled islanding based OPPMU problem.

A Mixed WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology

To address the issue that the phasor measurement units (PMUs) of wide area measurement system (WAMS) are not sufficient for static state estimation in most existing power systems, this paper proposes a mixed power system weighted least squares (WLS) state estimation method integrating a wide-area measurement system and supervisory control and data acquisition (SCADA) technology. The hybrid calculation model is established by incorporating phasor measurements (including the node voltage phasors and branch current phasors) and the results of the traditional state estimator in a post-processing estimator. The performance assessment is discussed through setting up mathematical models of the distribution network. Based on PMU placement optimization and bias analysis, the effectiveness of the proposed method was proved to be accurate and reliable by simulations of different cases. Furthermore, emulating calculation shows this method greatly improves the accuracy and stability of the state estimation solution, compared with the traditional WLS state estimation.

Multistage hybrid evolutionary computing-based optimal PMU placement for large scale power grid network

Phasor measurement unit (PMU) is one of the most significant grid components that plays vital role in ensuring reliable power transmission and distribution. The optimal PMU placement (OPP) in power system can not only ensure grid cost reduction, real-time monitoring and control, but can reduce the operational complexities and overheads significantly. This paper proposes a novel multistage hybrid evolutionary computing scheme for OPP solution. Our proposed model applied adaptive genetic algorithm (AGA) for initial state point retrieval for OPP, which was then fed as input to the pattern search (PS)-based PMU placement optimisation. Our proposed AGA-PS scheme ensures OPP solution by retrieving minimum number of PMUs and its optimal location across grid network to make power system completely observable under varied cases. The simulation results with IEEE 14, IEEE 39, IEEE 118 and KPTCL 155 bus networks has exhibited that the proposed AGA-PS outperforms major existing approaches in terms of optimal OPP solutions.

Multistage hybrid evolutionary computing-based optimal PMU placement for large scale power grid network

Phasor measurement unit (PMU) is one of the most significant grid components that plays vital role in ensuring reliable power transmission and distribution. The optimal PMU placement (OPP) in power system can not only ensure grid cost reduction, real-time monitoring and control, but can reduce the operational complexities and overheads significantly. This paper proposes a novel multistage hybrid evolutionary computing scheme for OPP solution. Our proposed model applied adaptive genetic algorithm (AGA) for initial state point retrieval for OPP, which was then fed as input to the pattern search (PS)-based PMU placement optimisation. Our proposed AGA-PS scheme ensures OPP solution by retrieving minimum number of PMUs and its optimal location across grid network to make power system completely observable under varied cases. The simulation results with IEEE 14, IEEE 39, IEEE 118 and KPTCL 155 bus networks has exhibited that the proposed AGA-PS outperforms major existing approaches in terms of optimal OPP solutions.

An Optimal PMU Placement With Reliable Zero Injection Observation

Zero injection (ZI) is an inherent characteristic of a power grid. The effect of ZI (ZI effect) is usually utilized as an auxiliary measurement termed ZI observation for simplification. We discover that the ZI observation can transmit. The reliability of ZI observation becomes weaker, as ZI observation transmits “deeper.” In this paper, a unified phasor measurement unit (PMU) placement model is proposed with considering the reliability of ZI observation. To improve the reliability of ZI observation, the depth of ZI observation is limited, and the ZI utilization rate is minimized. The optimality problem which considers reliable ZI observation is studied, which has not been discussed before. Five working conditions, i.e., base case, $N-1$ PMU outage, $N-1$ line outage, $N-1$ PMU outage, or $N-1$ line outage, and the PMU measurement channel limitation are studied. The proposed method is tested on six IEEE test systems, i.e., IEEE 14-, 30-, 39-, 57-, 118-, and 300-bus systems, the Polish 2383-bus test system, and a practical power system in China. Results indicate that the proposed method not only reduces the number of PMUs by employing ZI effect but also ensures the reliability of ZI observation while making full observation of the power system.

Optimal PMU Placement Considering Load Loss and Relaying in Distribution Networks

This paper studies the problem of optimal phasor measurement unit (PMU) placement considering the constraints of full system observability and load loss. The relaying functions of metering devices, such as feeder terminal units (FTUs) and dual-use line relays (DULRs), are taken into account to satisfy the maximum load loss coefficient limit (MLCL) and guarantee the operation of distribution system after the single-branch outage. A mixed-integer linear programming (MILP) is formulated to find the minimal load loss under the given configuration and branch outage with relaying functions of DULRs and pre-existing FTUs. A PMU deployment formulation considering the presence of metering devices and distributed generations (DGs) is solved by the genetic algorithm together with MILP. Various configurations of network and scenarios with different settings of conventional measurements and DGs are simulated in two test cases. Results show that the optimal PMU placement is greatly affected by the pre-existing traditional measurements and DGs as well as MLCL.

PMU Placement for Line Outage Identification via Multinomial Logistic Regression

We consider the problem of identifying a single line outage in a power grid by using data from phasor measurement units (PMUs). When a line outage occurs, the voltage phasor of each bus node changes in response to the change in network topology. Each individual line outage has a distinctive signature, and a multinomial logistic regression classifier can be trained to distinguish between these signatures reliably. We consider first the ideal case in which PMUs are attached to every bus. We then describe techniques from regularized optimization for placing PMUs selectively on a subset of buses, with the subset being chosen to allow discrimination between as many outage events as possible. Experimental results with synthetic 24-h demand profile data generated for IEEE 14-, 30-, 57-, and 118-bus systems are presented.

An optimal Phasor Measurement Unit placement techniques for achieving complete perceptibility of a network even when PMU failure

An optimal Phasor Measurement Unit placement techniques for achieving complete perceptibility of a network even when PMU failure

Integrated Mutation Strategy With Modified Binary PSO Algorithm for Optimal PMUs Placement

Optimal phasor measurement units (PMUs) placement refers to the strategic placement of PMUs to achieve the full observability of power systems with a minimum number of PMUs. A strategic placement is needed because of the economic or technical restriction that hinders the deployment of PMUs on every bus. A modified version of the binary particle swarm optimization method is proposed in this paper by integrating a mutation strategy and the V-shaped sigmoid function for placing the PMUs that maintains the full power system observability in the presence of zero-injection bus, single PMU loss, and PMU's channel limits while maximizing the measurement redundancy. The solution that has the highest measurement redundancy was selected as the best placement of PMUs. The use of mutation strategy and V-shaped sigmoid function in this paper improves the population diversity, thereby minimizing the chance of the particles being trapped in the local optima, consequently leading to a quality solution. In order to validate its effectiveness, the results obtained by the proposed method are compared with other published techniques to demonstrate the accuracy and validity of the proposed technique. The results of the IEEE 300-bus system show that the proposed method effectively managed to reduce the number of PMUs needed.

Observability of power systems with optimal PMU placement

Phasor Measurement Units (PMUs) are measuring devices that, when placed in electrical networks, observe their state by providing information on the currents in their branches (transmission lines) and voltages in their buses. Compared to other devices, PMUs have the capability of observing other nodes besides the ones they are placed on. Due to a set of observability rules, depending on the placement decisions, the same number of PMUs can monitor a higher or smaller percentage of a network. This leads to the optimization problem hereby addressed, the PMU Placement Problem (PPP) which aims at determining the minimum number and location of PMUs that guarantee full observability of a network at minimum cost.In this paper we propose two general mathematical programming models for the PPP: a single-level and a bilevel integer programming model. To strengthen both formulations, we derive new valid inequalities and promote variable fixing. Furthermore, to tackle the bilevel model, we devise a cutting plane algorithm amended with particular features that improve its efficiency. The efficiency of the algorithm is validated through computational experiments. Results show that this new approach is more efficient than state-of-the-art proposals.

A new fuzzy framework for the optimal placement of phasor measurement units under normal and abnormal conditions

This paper presents a new procedure for finding the optimal placement of the phasor measurement units(PMUs) in modern power grids to achieve full network observability under normal operating conditions,and also abnormal operating conditions such as a single line or PMU outage, while considering the availabilityof PMU measuring channels. The proposed modeling framework is implemented using the fuzzybinary linear programming (FBLP) technique. Linear fuzzy models are proposed for the objective functionand constraints alike. The proposed procedure is applied to five benchmark systems such as theIEEE 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus. The numerical results demonstrate that the proposedframework is capable of finding a fine-tuned optimal solution with a simple model and acceptable solutioncharacteristics compared with early works in the literature. Besides, four evaluation indices areintroduced to assure the various criteria under study such as the observability depth, measurement redundancy,and robustness of the method under contingencies. The results show that full network observabilitycan be met under normal conditions using 20% PMUs penetration; however, under contingencies,approximately 50% PMUs penetration is required. The novelty of the proposed procedure has proven thecapability of the proposed linear fuzzy models to find better optimal number of PMUs with lower numberof channels compared to other algorithms under various operating conditions. Hence, the proposedwork represents a potential tool to monitor power systems, and it will help the operators in a smart gridenvironment.

Optimal PMU Placement Using Modified Greedy Algorithm

Phasor measurement units (PMUs) provide synchronized measurements of real-time phasors of voltages and currents. It is considered as an important element of the smart wide area measurement system used in advanced power system monitoring, protection, and control applications. This paper proposes a new approach based on a greedy algorithm to solve the optimal phasor measurement unit placement (OPP) problem in the power network. The main purpose of proposed approach is to find out a high-quality solution in a reasonable time that ensures the practicability when applying for a real power network. The OPP problem is solved under both normal operating and contingency conditions. Moreover, some other realistic aspects that may affect the OPP problem, such as PMU channel limitation, zero injection bus, the presence of conventional measurements, are also considered to solve simultaneously. The simulations on IEEE 14-bus, 30-bus, 57-bus, 118-bus test systems, and especially on a large-scale network—the Polish 2383-bus system, are presented for evaluating the feasibility of proposed approach. The results of this study showed that the proposed method is effective and feasible to solve the OPP problem for a real power network.

A Multi-objective PMU Placement Method in Power System via Binary Gravitational Search Algorithm

This paper presents a Binary Gravitational Search Algorithm (BGSA) methodology for the optimal placement of Phasor Measurement Units (PMUs) to achieve full and maximum observability of the power system. The gravitational search technique has been extended in this investigation to incorporate binary variables for this purpose. The objective of proposed methodology is to minimize the total number of PMUs installed at various buses, which in turn minimize installation cost of the PMUs and improves observability redundancy by including an additional objective. Thus, the PMU placement problem has been expressed as a multi-objective problem. Besides, single PMU outage or single line outage cases in the presence of zero injection buses have been investigated. In this algorithm, the searcher agents are the collection of masses, which interact with each other using Newton's laws of gravity and motion. The proposed BGSA has been applied to the IEEE 14-bus, -30-bus, -118-bus, Northern Regional Power Grid 246-bus Indian system, and Polish 2383-bus system. Case studies reveal that the lower number of PMUs or equal number of PMUs has been produced by the proposed method compared to methods reported in the literature. In cases of equal number of PMUs, the observability produced by proposed method is higher or at least equal.