Optimal Observer Synthesis for Microgrids With Adaptive Send-on-Delta Sampling Over IoT Communication Networks

Alavi, Seyed Amir; Mehran, Kamyar; Hao, Yang

doi:10.1109/tie.2020.3034853

Cited by 8 publications

(9 citation statements)

References 32 publications

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“…Thus, {v i } are exchanged in the communication network between battery controllers for the local bus voltage average consensus protocol. Global dynamics of distributed average consensus protocol can be given as:̇v =v − Lv (34) which can be realised using the event-based consensus protocol defined in Theorem 3.1.…”

Section: 1mentioning

confidence: 99%

“…Proof First

δ (t)

is defined as agents disagreement vector using the following substitution ([34]):

x (t) = a 1 + δ (t)

a

is the initial state average,

a = \frac{1}{N} \sum x_{i} false(t false)

. Input values to agents are then derived, using (5):

\begin{matrix} true \\ right \overset{δ}{true} (t) = - \sum_{i = 1}^{N} L_{i j} (a + {\overset{δ}{true}}_{j} false(t false)) = - a \sum_{i = 1}^{N} L_{i j} - \sum_{i = 1}^{N} L_{i j} {\hat{δ}}_{j} false(t false)) \\ right = - \sum_{i = 1}^{N} L_{i j} {\hat{δ}}_{j} false(t false)) \end{matrix}

To prove the stability of the proposed event‐triggered DAC protocol in (2), the following Lyapunov energy function is employed:

V false(δ (t) false) = \frac{1}{2} \sum_{i = 1}^{N} δ_{i}^{2} \geq 0

with its derivative along the dynamic trajectory (2) as:

\begin{matrix} true \\ right \overset{V}{true} (δ false(t false)) \end{matrix}

…”

Section: Event‐triggered Average Consensus With Communication Delaysmentioning

confidence: 99%

“…Proof. First 𝛿(t ) is defined as agents disagreement vector using the following substitution ( [34]):…”

Section: Average Consensus Protocolmentioning

confidence: 99%

See 2 more Smart Citations

Multilayer event‐based distributed control system for DC microgrids with non‐uniform delays and directional communication

Alavi

Rahimian

Mehran

et al. 2021

IET Generation Trans & Dist

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The secondary control layer of microgrids is often modelled as a multi-agent distributed system, coordinated based on consensus protocols. Convergence time of consensus algorithm significantly affects transient stability of microgrids, due to changes in communication topology, switching of distributed generations (DGs), and uncertainty of intermittent energy sources. To minimise convergence time in consensus protocol, this work proposes a multilayer event-based consensus control framework, which is resilient to communication delays and supports plug-and-play (P&P) addition or removal of DGs in DC microgrids of cellular energy systems. A novel bi-layer optimisation algorithm minimises convergence time by selecting an optimal communication topology graph and then adjusts controllers' parameters. Average consensus is achieved among distributed controllers using an eventbased consensus protocol, considering non-uniform delays between agents. A realisation method has also been introduced using the directional beamforming technique for topology assignment algorithm based on modern telecommunication technologies. Provided feasibility case study has been implemented on a real-time hardware-in-the-loop (HIL) experimental testbed, to validate the performance of the proposed framework for key purposes of voltage stabilisation and balanced power-sharing in DC microgrids. INTRODUCTIONFor a distributed microgrid with renewable energy sources (RES) and energy storage systems (ESSs), distributed control architecture is a natural choice compared to current centralised supervisory control and data acquisition (SCADA)-based approaches. The main advantages of distributed controllers are (1) increased reliability against controller failures, (2) distribution of computational complexity, and (3) robustness in the control system [1]. In microgrids, distributed control and estimation are mainly implemented in secondary and tertiary layers, due to distributed nature of RES, and limitations of the communication network. Optimal neighbour data sharing and multi-agent consensus protocols are problems of interest in proposed distributed strategies [1,2]. Among available consensus protocols in multi-agent systems, distributed average consensusThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: 1mentioning

confidence: 99%

“…Proof First

δ (t)

is defined as agents disagreement vector using the following substitution ([34]):

x (t) = a 1 + δ (t)

a

is the initial state average,

a = \frac{1}{N} \sum x_{i} false(t false)

. Input values to agents are then derived, using (5):

\begin{matrix} true \\ right \overset{δ}{true} (t) = - \sum_{i = 1}^{N} L_{i j} (a + {\overset{δ}{true}}_{j} false(t false)) = - a \sum_{i = 1}^{N} L_{i j} - \sum_{i = 1}^{N} L_{i j} {\hat{δ}}_{j} false(t false)) \\ right = - \sum_{i = 1}^{N} L_{i j} {\hat{δ}}_{j} false(t false)) \end{matrix}

To prove the stability of the proposed event‐triggered DAC protocol in (2), the following Lyapunov energy function is employed:

V false(δ (t) false) = \frac{1}{2} \sum_{i = 1}^{N} δ_{i}^{2} \geq 0

with its derivative along the dynamic trajectory (2) as:

\begin{matrix} true \\ right \overset{V}{true} (δ false(t false)) \end{matrix}

…”

Section: Event‐triggered Average Consensus With Communication Delaysmentioning

confidence: 99%

See 1 more Smart Citation

Multilayer event‐based distributed control system for DC microgrids with non‐uniform delays and directional communication

Alavi

Rahimian

Mehran

et al. 2021

IET Generation Trans & Dist

Self Cite

View full text Add to dashboard Cite

show abstract

“…Distribution of the forecasting tasks to the local controllers has been recently proposed due to advances in the area of edge computing and distributed control systems, which has enlightened new approaches to solving the energy management issues in microgrids [4], [5]. For a distributed microgrid with multiple sources, distributed control architecture is a natural choice compared to the current centralized supervisory control and data acquisition (SCADA) based approaches.…”

Section: Introductionmentioning

confidence: 99%

Forecast-Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

Alavi

Mehran

Vahidinasab

et al. 2021

IEEE Trans. Smart Grid

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“…The main disadvantages of this strategy are insufficient response time for load profile changes and steadystate voltage offsets. These shortages ultimately might lead to instability of the microgrid in certain scenarios [11][12][13]. In the distributed control strategies [14], the local information and the information from the neighbours over a sparse communication network are used by autonomous agents to achieve cooperative objectives.…”

Section: Introductionmentioning

confidence: 99%

Distributed control strategy for DC microgrids based on average consensus and fractional‐order local controllers

et al. 2021

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A novel distributed secondary layer control strategy based on average consensus and fractional-order proportional-integral (FOPI) local controllers is proposed for the regulation of the bus voltages and energy level balancing of the energy storage systems (ESSs) in DC microgrids. The distributed consensus protocol works based on an undirected sparse communication network. Fractional-order local controllers increase the degree of freedom in the tuning of closed-loop controllers, which is required for DC microgrids with high order dynamics. Therefore, here, FOPI local controllers are proposed for enhanced energy balancing of ESSs and improved regulation of the bus voltages across the microgrid. The proposed control strategy operates in both islanded and grid-connected modes of a DC microgrid. In both modes, the average voltage of the microgrid converges to the microgrid desired reference voltage. The charging/discharging of ESSs is controlled independent of the microgrid operating mode to maintain a balanced energy level. The performance of the proposed distributed control strategy is validated in a 38-V DC microgrid case study, simulated by Simulink real-time desktop, consisting of 10 buses and a photovoltaic renewable energy source.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Optimal Observer Synthesis for Microgrids With Adaptive Send-on-Delta Sampling Over IoT Communication Networks

Cited by 8 publications

References 32 publications

Multilayer event‐based distributed control system for DC microgrids with non‐uniform delays and directional communication

Multilayer event‐based distributed control system for DC microgrids with non‐uniform delays and directional communication

Forecast-Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

Distributed control strategy for DC microgrids based on average consensus and fractional‐order local controllers

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