A Design and Optimization Tool for Inverter-Based Microgrids Using Large-Signal Nonlinear Analysis

Kabalan, Mahmoud; Singh, Pritpal; Niebur, D.

doi:10.1109/tsg.2018.2864208

Cited by 52 publications

(46 citation statements)

References 35 publications

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“…The graph theory was applied in [7] to study the integration of new DGs into a MG and the effect of inappropriate DG allocation on MG stability was scrutinized. A non-linear large-signal mathematical model for a MG including dc-side dynamics was developed in [8] which was applied to draw the stability domain. The research in [9] reports that cascading lead compensators in power-frequency control loop can extend the stability margin of an island MG.…”

Section: B Relevant Literaturementioning

confidence: 99%

Microgrid Stability Analysis Considering Current State-Feedback

Pournazarian

Saeedian

Lehtonen

et al. 2020

2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems (PEDG)

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A converter-based microgrid including several distributed generations (DG) and energy storage systems (ESS) embodies a small power system consisting of several synchronous machines and loads. The stability issue and the coordination between generating units is an essential challenge, either in island or in grid-connected microgrids. The instability of individual DGs cause the microgrid instability as a whole. So, the individual unit stability and the microgrid stability both must be ensured simultaneously. The state feedback concept which is considered in control of converters has a substantial effect on the converter stability and microgrid stability, subsequently. This study will introduce a small-signal model for the microgrid including converter-based DGs and loads considering the previous studies in this field. A phase locked loop (PLL) is required for the control strategy. The effect of current state feedback is scrutinized and analyzed in this study as the main contribution. The effectiveness of the proposed control strategy to enhance the microgrid stability using chosen state feedback is examined through simulation studies. The simulation results ensure that the microgrid stability using the proposed control method and state-feedback is strengthened.

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Section: B Relevant Literaturementioning

confidence: 99%

Microgrid Stability Analysis Considering Current State-Feedback

Pournazarian

Saeedian

Lehtonen

et al. 2020

2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems (PEDG)

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“…For the dc microgrid model shown in Figure 11 with CPL (p/ ), a suitable operating point for the CPL defined by inductor current and capacitor voltage with constant load power can be represented [21] as 12 system can be studied at its operating points. Using these change of v system in Figure 11 can be linearized and studied at its operating point The two local models resulting from the single nonlinear function of the system can be represented with two rules using the activation functions F 1 1 (z) and F 1 2 (z) ; and they are respectively represented [21] as (28)…”

mentioning

confidence: 99%

“…TS membership function can be derived such that for the nonlinear part of the dc microgrid equation in (23), membership function can be written as already discussed in (25), leading to (30) where (31) and F max and F min are computed based on (32) The fuzziness of the TS method implies that if z is in the positive (+) region of the TS membership model shown in figure 12, then and the matrix of the dc microgrid can be evaluated as shown in (28). If z is in the negative region of TS membership of figure 12, then dc microgrid can be evaluated as shown in (29).…”

mentioning

confidence: 99%

“…The fuzziness of the TS method implies that if z is in the positive (+) region of the TS membership model shown in figure 12, then and the matrix of the dc microgrid can be evaluated as shown in (28). If z is in the negative region of TS membership of figure 12, then dc microgrid can be evaluated as shown in (29).…”

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confidence: 99%

“…System The domain of attraction for the system using varying dc-bus capacitance and varying cloud server loads modeled as CPLs are shown in Figure 13 and Figure 14 respectively. Since acceptable level of dc bus capacitance for dc microgrid is still an area of research study worldwide [3], [27], [28], the impact of allowed capacitance values for the 380 V dc microgrid on the dc bus link is shown in Figure 13. It is shown that the size of domain of attraction, and hence system stability increases with increasing dc bus capacitance.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Inference Based Stability Optimization for Iot Data Centers Dc Microgrids: Impact of Constant Power Loads on Smart Grid Communication Over the Powerline

Oyekanlu

2019

joe

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Direct Current (dc) microgrids due to their efficiency and energy savings are being deployed to provide power for servers in Internet of Things (IoT) data centers, in more electric aircrafts (MEA), electric ships and in rail systems round the word. In this paper, Takagi-Sugeno fuzzy inference method is used to establish a Lyapunov stability candidate for a 380 V ring bus dc microgrid modeled with Matlab. To determine suitability of using powerline communication (PLC) to monitor stability condition on the 380 V dc microgrid, impact of distortion caused by microgrid constant power loads (CPL) on signals transmitted over the dc microgrid PLC channel is examined. It is shown in this paper that while Lyapunov asymptotic stability is maintained on the dc bus, increasing CPL on the microgrid causes the dc microgrid PLC channel to experience growing signal distortion.

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The islanded micro‐grid large signal stability analysis based on neuro‐fuzzy model

Ahmadi

Kazemi

2020

Int Trans Electr Energ Syst

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Summary The study and identification of stability condition in an islanded micro‐grid are critical due to the lack of generation resources and load changes. On the other hand, the output power of the renewable units and the load level in a micro‐grid are uncertain and the system does not have a dominant operating point. Therefore, small signal analysis methods results have small validity range and cannot be generalized to the whole system. Therefore, a Lyapunov function (LF) based large signal stability method is proposed in this article to address the problem of small signal methods. Also, a Neuro‐Fuzzy system is proposed to consider uncertainties and network modeling. In this study, the Takagi‐Sugeno (T‐S) fuzzy system is used. Artificial neural network is also applied to provide an optimal tool for identifying system uncertainties, while T‐S rules are used to provide a framework of previous knowledge of the system. The main purpose is to propose an identification and modeling process to study the stability condition and boundaries in an islanded micro‐grid. After the uncertain system is modeled, the system LF is calculated using Linear Matrix Inequality and the system domain of attraction is determined.

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A Design and Optimization Tool for Inverter-Based Microgrids Using Large-Signal Nonlinear Analysis

Cited by 52 publications

References 35 publications

Microgrid Stability Analysis Considering Current State-Feedback

Microgrid Stability Analysis Considering Current State-Feedback

Fuzzy Inference Based Stability Optimization for Iot Data Centers Dc Microgrids: Impact of Constant Power Loads on Smart Grid Communication Over the Powerline

The islanded micro‐grid large signal stability analysis based on neuro‐fuzzy model

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