An improved moth swarm algorithm based fractional order type-2 fuzzy PID controller for frequency regulation of microgrid system

Prusty, Umesh Chandra; Nayak, Pratap Chandra; Prusty, Ramesh Chandra; Panda, Sidhartha

doi:10.1080/15567036.2022.2038735

Cited by 21 publications

(13 citation statements)

References 28 publications

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“…Non-conventional sources such as a biodiesel engine generator (BDEG), biogas turbine generator (BGTG), wind turbine generator (WTG) plant, solar photovoltaic (SPV) power plant, aqua-electrolyzer (AE)based fuel-cell (FC), etc. are preferred to meet load demand [5][6][7][8][9][10][11][12]. Among these sources, SPV plants and WTG plants are highly stochastic in nature and weather-dependent.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, energy storage devices (ESDs) are incorporated into the model to store the excess power and to deliver the stored power during switching or emergencies. In [2,5,6,9], the authors considered a flywheel energy storage system (FESS), battery energy storage system (BESS), and ultra-capacitor as main storage units. In addition to this, superconducting magnetic energy storage (SMES) is also incorporated in the microgrid model due to its fast dynamic response [10].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, uncertainty occurs in the system, caused by variations in load, wind power output, and solar power output leading to frequency oscillations. These oscillations are compensated by decreasing or increasing the active power of controllable sources [6,11,[16][17][18][19][20][21]. In [11], a micro-turbine generator (MTG) is utilized as a controllable source to compensate for power variations of solar and wind sources.…”

Section: Introductionmentioning

confidence: 99%

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Reaction Curve-Assisted Rule-Based PID Control Design for Islanded Microgrid

Bashishtha,

Singh,

Yadav

et al. 2024

Energies

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In a renewable energy-based islanded microgrid system, frequency control is one of the major challenges. In general, frequency oscillations occur in islanded microgrids due to the stochastic nature of load and variable output power of distributed generating units (DGUs). In the presented research proposal, frequency oscillations are suppressed by implementing the proportional integral derivative (PID) controller-based control design strategy for an islanded microgrid. The modeling of the islanded microgrid is firstly presented in the form of a linearized transfer function. Further, the derived transfer function is approximated into its equivalent first-order plus dead time (FOPDT) form. The approximated FOPDT transfer function is obtained by employing the reaction curve method to calculate the parameters of the FOPDT transfer function. Furthermore, the desired frequency regulation is achieved for the manifested FOPDT transfer function by incorporating PID control design. For PID controller tuning, different rule-based methods are implemented. Additionally, comparative analysis is also performed to ensure the applicability of the comparatively better rule-based tuning method. The Wang–Chan–Juang (WCJ) method is found effective over other rule-based tuning methods. The efficacy of the WCJ method is proved in terms of transient response and frequency deviation. The tabulated data of tuning parameters, time domain specifications, and error indices along with responses are provided in support of the presented control strategy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reaction Curve-Assisted Rule-Based PID Control Design for Islanded Microgrid

Bashishtha,

Singh,

Yadav

et al. 2024

Energies

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“…An accurate STLP also contributes significant savings economically and also ensures power grid reliability and security [ 8 ]. Accurate forecasting is essential for the system controller to maintain grid system stability [ 9 , 10 , 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features

Chan,

Yiu,

Kim

et al. 2024

Sensors

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Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data. To perform STLP, existing DNNs use time-varying dynamics of either past load consumption or past power correlated features such as weather, meteorology or date. However, the existing DNN approaches do not use the time-invariant features of users, such as building spaces, ages, isolation material, number of building floors or building purposes, to enhance STLF. In fact, those time-invariant features are correlated to user load consumption. Integrating time-invariant features enhances STLF. In this paper, a fuzzy clustering-based DNN is proposed by using both time-varying and time-invariant features to perform STLF. The fuzzy clustering first groups users with similar time-invariant behaviours. DNN models are then developed using past time-varying features. Since the time-invariant features have already been learned by the fuzzy clustering, the DNN model does not need to learn the time-invariant features; therefore, a simpler DNN model can be generated. In addition, the DNN model only learns the time-varying features of users in the same cluster; a more effective learning can be performed by the DNN and more accurate predictions can be achieved. The performance of the proposed fuzzy clustering-based DNN is evaluated by performing STLF, where both time-varying features and time-invariant features are included. Experimental results show that the proposed fuzzy clustering-based DNN outperforms the commonly used long short-term memory networks and convolution neural networks.

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“…For its simplicity and reliability, CDM is considered one of the important approaches that are still used until this day. In addition, a classic optimizer called 'Jaya' [37][38][39], where it's proposed in this work to determine the optimal value of the integral controller due to its simplicity and speed computational time as introduced in [40,41] to control the flexible loads (i. e. EVs and HPs) according to system dynamics.…”

Section: Introductionmentioning

confidence: 99%

Adaptive frequency control in smart microgrid using controlled loads supported by real-time implementation

et al. 2023

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The operation of the system’s frequency can be strongly impacted by load change, solar irradiation, wind disturbance, and system parametric uncertainty. In this paper, the application of an adaptive controller based on a hybrid Jaya-Balloon optimizer (JBO) for frequency oscillation mitigation in a single area smart μG system is studied. The proposed adaptive control approach is applied to control the flexible loads such as HPs and EVs by using the JBO which efficiently controls the system frequency. The suggested technique uses the power balance equation to provide a dynamic output feedback controller. The main target is to regulate the frequency and power of an islanded single area μG powered by a PV and a diesel generator with integrations of smart bidirectional loads (HPs and EVs) that are controlled by the proposed adaptive controller in presence of electrical random loads. Moreover, the JBO is designed to minimize the effect of the system load disturbance and parameter variations. For a better assessment, the proposed controller using JBO technique is compared with two other methods which are the coefficient diagram method (CDM) and adaptive one using classical the Jaya technique. In the obtained results, the frequency deviation is found as 0.0015 Hz, which is fully acceptable and in the range of the IEEE standards. The MATLAB simulation results reveal that the suggested technique has a substantial advantage over other techniques in terms of frequency stability in the face of concurrent disturbances and parameter uncertainties. The real-time simulation tests are presented using a dSPACE DS1103 connected to another PC via QUARC pid_e data acquisition card and confirmed the MATLAB simulation results.

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An improved moth swarm algorithm based fractional order type-2 fuzzy PID controller for frequency regulation of microgrid system

Cited by 21 publications

References 28 publications

Reaction Curve-Assisted Rule-Based PID Control Design for Islanded Microgrid

Reaction Curve-Assisted Rule-Based PID Control Design for Islanded Microgrid

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features

Adaptive frequency control in smart microgrid using controlled loads supported by real-time implementation

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