A new method for optimal placement of phasor measurement units to maintain full network observability under various contingencies

Enshaee, Ali; Hooshmand, Rahmat‐Allah; Fesharaki, Fariborz Haghighatdar

doi:10.1016/j.epsr.2012.01.020

Cited by 127 publications

(118 citation statements)

References 13 publications

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“…Enshaee et al [23] implement the Binary Integer Programming method has been used again in a significant extension of earlier reliability studies. This research's importance comes from the treatment of several placement requirements such as achieving full observability; maximize measurement redundancy and addressing three cases of contingency, which are: single-line outage, single-PMU loss, and limited channel case.…”

Section: Pmu's Placement Approaches With Simulation Resultsmentioning

confidence: 99%

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Recent Development in Power System Dynamic State Estimation

Khandelwal¹,

Saxena²,

Tandon³

2017

IJERMT

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Abstractue to the expansion of power systems there is a challenge of real time monitoring and control by placing important parameters at various points for efficient functioning of the power system. There can be drastic changes during hourly load fluctuations, component outages, or network switching. In such conditions, the inclusion of predicted values could degrade the power system state estimation hence the need of state estimation is must. Synchronized Phasor Measurement Units (PMU) are being placed along with the GPS (global positioning system) to enhance the monitoring of the power system. It depends on the measurement numbers, types and locations. This paper aims to present the main categories for PMUs placement strategies which considers main factors that required for enhancing the performance of a state estimation process such as reliability, accuracy and the special requirements of the modern distribution grid.Keywords-Phasor Measurement Unit, Global Positioning Systems, State Estimations, Weighted Least Square Method, Observability I. INTRODUCTIONThe enlargement of power system has made more and more intricacy so real time checking and control became very significant for the consistent operation of power system and which the Energy Management System (EMS) cures. In this system, the State estimation forms the spine of the energy management system by providing a database of the real time state of the system for using in other EMS functions [1]. Therefore, efficient and accurate state estimation is a prerequisite for an efficient and consistent operation of the power system.State estimation is a technique where the voltage, magnitude and angles at all the buses of a power system are recorded from the existing measurements. These measurements are obtained through a suitable communication standard and are processed through state estimation procedures, to obtain the voltage magnitude and angle at all the buses of the network under consideration. As we know that the power system is a quasi-static system, hence changes slowly but progressively which are driven by dynamic load so the generations also have to be adjusted accordingly, which in turn changes the flows and injections across the system, which makes the entire system dynamic in nature. Therefore, in order to have a continuous monitoring of the power system, we must perform the state estimation at regular interval of time. However, by the expansion of power system, along with addition of generations and loads, this system becomes more complex for such monitoring, which requires heavy computing resources. Hence, static state estimators may not capably capture this dynamic behavior of the power system. This lead to the necessity of another set of procedures called the "Dynamic State Estimation" (DSE) techniques, where we use the actual physical modeling of the time varying nature of the power system. These procedures have dual advantages of being more accurate and possessing the ability to predict the state of the system at next step. That is, from the ...

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Section: Pmu's Placement Approaches With Simulation Resultsmentioning

confidence: 99%

“…method [23]. State vector (x) should be computed as accurately as possible, which means a minimum value for the variance of the error (σ2).…”

Section: Khandelwal Et Al International Journal Of Emerging Researcmentioning

confidence: 99%

Recent Development in Power System Dynamic State Estimation

Khandelwal¹,

Saxena²,

Tandon³

2017

IJERMT

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“…Table 1 shows the different cases of MOPP problems considered. Table 2 shows the specifications of test systems [3,22,23] and [25,26]. In MOPP problem, maximizing measurement redundancy is considered randomly on the buses.…”

Section: Case Studies and Discussionmentioning

confidence: 99%

“…When adjacent buses of the zero injection bus are observable except a bus, the unobservable bus will be observed by applying the KCL at zero-injection bus (i.e., zero injection bus effect) [10,[22][23][24]. Therefore, it is not needed to monitor all the zero injection buses or one of them adjacent buses.…”

Section: Minimizing Number Of Pmus With Zero Injection Bus Effectmentioning

confidence: 99%

A Multi-objective Placement of Phasor Measurement Units Considering Observability and Measurement Redundancy using Firefly Algorithm

Jeyaraj¹,

Rajasekaran²,

Kumar³

et al. 2015

Journal of Electrical Engineering and Technology

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-This paper proposes a multi-objective optimal placement method of Phasor Measurement Units (PMUs) in large electric transmission systems. It is proposed for minimizing the number of PMUs for complete system observability and maximizing measurement redundancy of the buses, simultaneously. The measurement redundancy of the bus indicates that number of times a bus is able to monitor more than once by PMUs set. A high level of measurement redundancy can maximize the system observability and it is required for a reliable power system state estimation. Therefore, simultaneous optimizations of the two conflicting objectives are performed using a binary coded firefly algorithm. The complete observability of the power system is first prepared and then, single line loss contingency condition is added to the main model. The practical measurement limitation of PMUs is also considered. The efficiency of the proposed method is validated on IEEE 14, 30, 57 and 118 bus test systems and a real and large-scale Polish 2383 bus system. The valuable approach of firefly algorithm is demonstrated in finding the optimal number of PMUs and their locations by comparing its performance with earlier works.

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“…Both classical and heuristic algorithms have been applied to this problem Abur, 2004, 2005;Chakrabarti et al, 2009;Enshaee et al, 2012;Momoh and Dekkar, 2001;Marin et al, 2003;Mori and Sone, 1999;Antonio and Torreao, 2001;Ahmadi et al, 2011). In Ahmadi et al (2010), MR has been introduced in the objective function with a weighting factor for getting any desired redundancy at any bus via the placement set under normal operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective artificial bee colony-based optimal PMU placement with considering zero injection bus in power network

Nayak

Mahapatra

Rout

2017

IJCAET

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Abstract:A novel multi-objective discrete artificial bee colony-based optimisation algorithm is proposed for the optimal phasor measurement units placement problem. The proposed algorithm is designed to incorporate a binary mutation step for efficient exploration and exploitation of any undiscovered region in the search space. A fuzzy-based mechanism is used to evaluate the best compromised solution from the final Pareto optimal set obtained by non-dominated sorting and crowding distance mechanism. A combination of two diverse objectives, i.e., minimisation of number of phasor measurement units and maximisation of N -1 measurement reliability are taken into consideration with complete system observability. The simulation results are obtained both with and without inclusion of the zero injection effect in the observability analysis on IEEE standard systems and are compared with the recently published papers to demonstrate the effectiveness of the proposed algorithm. All the programmes are implemented in standard MATLAB environment version R2010a.Keywords: phasor measurement units; PMUs; power system observability; measurement redundancy; artificial bee colony-based optimisation; multi-objective optimisation. Multi-objective artificial bee colony-based optimal PMU placement 103Reference to this paper should be made as follows: Nayak, M.R., Mahapatra, K. and Rout, P.K. (2017)

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A new method for optimal placement of phasor measurement units to maintain full network observability under various contingencies

Cited by 127 publications

References 13 publications

Recent Development in Power System Dynamic State Estimation

Recent Development in Power System Dynamic State Estimation

A Multi-objective Placement of Phasor Measurement Units Considering Observability and Measurement Redundancy using Firefly Algorithm

Multi-objective artificial bee colony-based optimal PMU placement with considering zero injection bus in power network

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