Improvement of voltage stability by optimal capacitor placement using sensitivity matrix

Rajendar, Gogu; Banakara, Basavaraja

doi:10.1109/scopes.2016.7955796

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…The voltage-to-power sensitivity matrix should include the active and reactive components of the voltage to power [26]. For an n-node radiation distribution network, the sensitivity matrix is given by…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The top layer communication network is shown in Figu teracts with its neighbors, where degree di = 2. Agent i measures (2,5,6,9,10,12,14,16,17,20,21,23,25,26,27).…”

Section: System Architecturementioning

confidence: 99%

“…Figure 1. Low-voltage Distribution Network Model with 27 nodes, where circled nodes represent a set of photovoltaic (PV) inverters and loads(2,5,6,9,10,12,14,16,17,20,21,23,25,26,27).…”

mentioning

confidence: 99%

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Distributed Event-Triggered Voltage Control with Limited Re-Active Power in Low Voltage Distribution Networks

et al. 2021

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A high proportion of photovoltaic (PV) connections to a low-voltage distribution network (LVDN) causes serious voltage problems. In order to ensure voltage stability for renewable energy networks, we propose a distributed reactive voltage control strategy that is event-triggered. The voltage information of the PV nodes is transmitted to the upper layer of the communication network, where the agent calculates the output set value of the PV inverter. The underlying control strategy is based on the voltage sensitivity matrix, and the upper-level control strategy is based on an event-triggered consensus protocol. This strategy can accommodate the requirements for multi-time modeling and control. We verified the convergence of the event-triggered control algorithm using numerical analysis and proved the reduction of the communication times. We conducted case studies and simulation experiments to verify the effectiveness of our proposed voltage control strategies.

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Section: Sensitivity Analysismentioning

confidence: 99%

“…The top layer communication network is shown in Figu teracts with its neighbors, where degree di = 2. Agent i measures (2,5,6,9,10,12,14,16,17,20,21,23,25,26,27).…”

Section: System Architecturementioning

confidence: 99%