Communication delays in wide area measurement systems

Naduvathuparambil, Biju; Valenti, Matthew C.; Feliachi, A.

doi:10.1109/ssst.2002.1027017

Cited by 275 publications

(149 citation statements)

References 12 publications

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“…A random communication delay of τ i seconds was added, with 0.1 ≤ τ i ≤ 0.5. This delay corresponds to the total delay including: fixed delay of transducer, data processing, multiplexing, link propagation delay, random delay jitter, and data rate of the link [30]. This delay range was chosen to cover a mix of communication links present in todays power system infrastructure (ranging from telephone lines to fiber-optic cables [30]).…”

Section: A Test Systemmentioning

confidence: 99%

“…This delay corresponds to the total delay including: fixed delay of transducer, data processing, multiplexing, link propagation delay, random delay jitter, and data rate of the link [30]. This delay range was chosen to cover a mix of communication links present in todays power system infrastructure (ranging from telephone lines to fiber-optic cables [30]). It was also considered that measurements in the i-th substation are collected at different time instants, leading to a delay of δ ji seconds for the j-th measurement, with 0.1 ≤ δ ji ≤ 0.9.…”

Section: A Test Systemmentioning

confidence: 99%

See 1 more Smart Citation

Electric Power Network State Tracking From Multirate Measurements

Alcaide-Moreno

Fuerte‐Esquivel

Glavić

et al. 2018

IEEE Trans. Instrum. Meas.

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This paper proposes a novel tracking state estimator to process both fast-rate synchronized phasor and slowrate SCADA measurements. The former are assumed to be in limited number. The latter are exploited as and when they arrive to the control center. In order to restore observability, after each execution of the tracking state estimator, forecasted SCADA measurements are used as pseudo-measurements in the next estimation. An event detection analysis allows assessing if the system is in quasi steady-state. If so, an innovation analysis is performed to identify and eliminate erroneous SCADA measurements. The system state is computed by Hachtel's augmented matrix method. The option of exploiting time-tagged SCADA measurements is also considered. The method is illustrated through detailed dynamic simulations of a test system evolving towards voltage collapse, with and without emergency control.

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Section: A Test Systemmentioning

confidence: 99%

Section: A Test Systemmentioning

confidence: 99%

Electric Power Network State Tracking From Multirate Measurements

Alcaide-Moreno

Fuerte‐Esquivel

Glavić

et al. 2018

IEEE Trans. Instrum. Meas.

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“…But the real system condition is dynamic and oscillation occurs on the flow. By using PMUs the time-stamped measurements can be relayed on a continuous basis and the state vector can follow the dynamics of the system [14,15]. PMUs offer a number of possible benefits to the state estimation application including: direct calculation of the state using phasors, faster solution convergence, enhanced observability, improved solution accuracy and robustness, bad data detection and topology error correction, etc [16].…”

Section: Power System Monitoringmentioning

confidence: 99%

“…While phasor measurements in addition to remote control of the power systems, could provide direct feedback to these controllers and enable dynamic control of the power systems. Using PMUs to damp the low frequency inter-area oscillations is one of the effective applications of WAMS [15,17].…”

Section: Power System Controlmentioning

confidence: 99%

Laboratory-based deployment and investigation of PMU and OpenPDC capabilities

Golshani

Taylor

Ashton

2012

10th IET International Conference on AC and DC Power Transmission (ACDC 2012)

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Power systems operate in a more complicated condition than they used to. Changes in the wholesale electricity market alongside the difficulties in upgrading the transmission system have caused transmission systems to face more challenging network wide issues. Therefore, having a Wide Area Measurement System (WAMS) is vital with regard to ensuring secure and reliable operation. Traditionally, Supervisory Control and Data Acquisition (SCADA) systems were designed for monitoring of the power systems by polling the Remote Terminal Units (RTUs) at all the substations. However, the gathering of data is done at a slow rate of every few seconds so the monitoring is relatively static and infrequent. By using Phasor Measurement Units (PMUs) data can be provided in higher rates, commonly once every cycle, and with higher accuracy. The measured data from PMU is transmitted to a central location called Phasor Data Concentrator (PDC), where data can be collected, managed and processed. PMU-based WAMS has a significant role to play in enabling "Smart Grids" at the transmission level. Brunel University has deployed a PMU and local PDC using openPDC to visualize, process and archive grid measured data locally in the laboratory. In this paper we present an investigation on openPDC features and a preliminary study that has been performed based on the PMU deployment.

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“…As noted in [21], power system SCADA communications and measurements can experience delays of several hundred milliseconds, depending on the communication medium. Such large delays can have a substantial impact on the stability and settling time of network control systems [22].…”

Section: Introductionmentioning

confidence: 99%

A practical integration of automatic generation control and Demand Response

Shiltz

Annaswamy

2016

2016 American Control Conference (ACC)

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Abstract-For a power grid to operate properly, electrical frequency must be continuously maintained close to its nominal value. Increasing penetration of distributed generation, such as solar and wind generation, introduces fluctuations in active power while also reducing the natural inertial response of the electricity grid, creating reliability concerns. While frequency regulation has traditionally been achieved by controlling generators, the control of Demand Response resources has been recognized in recent smart grid literature as an efficient means for providing additional regulation capability. To this end, several control methodologies have been proposed recently, but various features of these proposals make their practical implementations difficult. In this paper, we propose a new control algorithm that facilitates optimal frequency regulation through direct control of both generators and Demand Response, while addressing several issues that prevent practical implementation of other proposals. In particular, i) our algorithm is ideal for control over a large, low-bandwidth network as communication and measurement is only required every 2 seconds, ii) it enables Demand Response resources to recover energy lost during system transients, and iii) it accommodates both measured disturbances and unmeasured disturbances. We demonstrate the viability of our approach through dynamic simulations on a 118-bus grid model.

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Communication delays in wide area measurement systems

Cited by 275 publications

References 12 publications

Electric Power Network State Tracking From Multirate Measurements

Electric Power Network State Tracking From Multirate Measurements

Laboratory-based deployment and investigation of PMU and OpenPDC capabilities

A practical integration of automatic generation control and Demand Response

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