In conventional power systems, stability is ensured successfully because of large synchronous generators, where, during transient conditions, the inertia and dynamics of the synchronous generators help to retain stability. On the contrary, inverter-interfaced distributed energy resources suffer from maintaining stability because of their quick dynamics (opposite to the concept of inertia). Therefore, there exists a trade-off between response and stability aspects and this is referred to as transient performance problem in microgrids. Conventionally, this problem is addressed from the controller side by increasing its bandwidth thereby leading to better disturbance rejection and immunity against parameter changes. Since the classification of the disturbances (as small or large) is not straightforward and uncertain, an increase in the controller's bandwidth beyond a certain limit is not possible. Therefore, it is understood that the transient performance improvement can't be achieved from the controller alone. Hence, the chances of improving performance from other approaches in the case of grid-connected mode and advanced control strategies for the controller in the case of islanded mode need to be investigated. With this intent, a comprehensive investigation of state-of-the-art approaches and methods to improve transient performance in microgrids is carried out in this paper. This work is carried out in three stages. Initially, it explores various technical challenges that are involved and possible approaches to address these challenges. Next, a critical review of each approach is carried out based on their philosophy to improve transient performance. Further, a comparative analysis that projects the scope of each approach is elucidated. Finally, some future research directions are proposed to enhance the effectiveness of key approaches.

The cascaded droop-voltage-current controller plays a key role in the effective operation of microgrids, where the controller performance is critically impacted by the desigheme, a constant value n of the droop controller. Moreover, in critical loading (e.g.: connection/disconnection of large inductive load), the pre-set value of the droop coefficient brings asymmetry in transient performance leading to instability. Hence, to improve symmetry by reducing the trade-off between transient response and stability margin, this paper proposes a state machine-based droop control method (SMDCM) aided with droop coefficients’ tuning through in-feasible range detection. Here, to realize the issues and the role of the droop controller’s dynamics on the microgrid’s stability, a small-signal stability analysis is conducted, thereby, an in-feasible range of droop values is identified. Accordingly, safe values for droop coefficients are implemented using the state machine concept. This proposed SMDCM is compared with the conventional constant droop control method (CDCM) and fuzzy logic-based droop control method (FLDCM) in terms of frequency/power/voltage characteristics subjected to different power factor (PF) loading conditions. From the results, it is seen that CDCM failed in many metrics under moderate and poor PF loadings. FLDCM is satisfactory under moderate PF loading, but, showed 54 Hz/48 Hz as maximum/minimum frequency values during poor PF loading. These violate the standard limit of ±2%, but SMDCM satisfactorily showed 50.02 Hz and 49.8 Hz, respectively. Besides, FLDCM levied an extra burden of 860 W on the system while it is 550 W with SMDCM. System recovery has taken 0.04 s with SMDCM, which completely failed with FLDCM. Similarly, voltage THD with FLDCM is 58.9% while with SMDCM is 3.08%. Peak voltage due to capacitive load switching is 340V with FLDCM and 150 V with SMDCM. These findings confirm that the proposed SMDCM considerably improved the transient performance of microgrids.

Parkinson’s disease (PD) is a neurodegenerative illness that affects the central nervous system and leads to a gradual degeneration of neurons that results in movement slowness, mental health problems, speaking difficulties, etc. In the past 20 years, the frequency of PD has doubled. Global estimates revealed that over 8.5 million cases have been identified so far. Thus, early and accurate detection of PD is crucial for treatment. Traditional detection methods are subjective and prone to delays, as they are reliant on clinical evaluation and imaging. Alternatively, artificial intelligence (AI) has recently emerged as a transformative technology in the healthcare sector, showing decent and promising results. However, an effective algorithm needs to be investigated for the most accurate prediction of a particular disease. Thus, this paper explores the ability of different machine learning algorithms in regard to the effective detection of PD. A total of 26 algorithms were implemented using the Scikit-Learn library on the Oxford PD detection dataset. This is a collection of 195 voice measurements recorded from 31 individuals, of which 23 have PD. The implemented algorithms are logistic regression, decision tree, k-nearest neighbors, random forest, support vector machine, Gaussian naïve bayes, multi-layered perceptron (MLP), extreme gradient boosting, adaptive boosting, stochastic gradient descent, gradient boosting machine, extra tree classifier, light gradient boosting machine, categorical boosting, Bernoulli naïve bayes, complement naïve bayes, multinomial naïve bayes, histogram-based gradient boosting, nearest centroid, radius neighbors classifier, logistic regression with elastic net regularization, extreme learning machine, ridge classifier, huber classifier, perceptron classifier, and voting classifier. Among them, MLP outperformed the other algorithms with a testing accuracy of 95%, precision of 94%, sensitivity of 100%, F1 score of 97%, and AUC of 98%. Thus, it successfully discriminates healthy individuals from those with PD, thereby helping for accurate early detection of PD for new patients using their voice measurements.

Being multivariable in nature, voltage and current control loops have controllers in the forward and cross-coupling paths. Most methods discussed in the literature focus on tuning the controllers in the forward paths to reduce the dq coupling. A modified pole-zero cancellation (MPZC) technique has recently been discussed, which uses the concepts of pole-zero cancellation and particle swarm optimization to effectively tune the forward path controllers. However, given the fixed gains in the cross-coupling paths, it is not possible to realize a superior transient response from this technique. Therefore, to achieve enhanced vector control of VSIs under transient conditions, this paper proposes a hybrid MPZC (HMPZC) method, which incorporates multivariable control along with the MPZC technique for both voltage/current control loops. In the proposed HMPZC method, the MPZC method is used to tune the forward path controllers, and multivariable control-based PI controllers are assigned in the cross-coupling paths of dq-axes loops rather than fixed gains. In this paper, these multivariable control-based PI controllers are designed using direct synthesis method-based internal model control (IMC). From the simulation results, it is verified that the proposed HMPZC method has reduced the coupling between the d- and q-axes loops of the current/voltage, leading to the improved transient response and power delivery capability of VSIs.

Multiloop voltage-controlled virtual synchronous generator (VSG) based control scheme is recently popular in forming stable microgrids. This scheme deploys traditional VSG (TVSG) control for power controllers in the outermost loop and proportional-integral synchronous reference frame-based voltage-current (PIVA) controllers in the inner loop (named TVSG-PIVA scheme). But, this scheme usually exhibits larger deviations and longer settling times in transient response under large active power demands. This poor transient response would further lead to unnecessary tripping which is referred to as nuisance tripping in the literature. This hampers the system’s stability even though there is no genuine fault. Thus, to address this problem, this paper proposes a modified VSG (MVSG) power controller based on adjusting the gain constant of the speed governor in the TVSG through an equalizing factor. From simulations, it is found that this MVSG supported by PIVA controllers (named MVSG-PIVA scheme) successfully avoided undesired trips under large active power demands, but, is susceptible to nuisance tripping under large reactive power demands. Thus, to effectively improve transient response and reduce the chances of nuisance trips under both large active/reactive power loads, this paper proposes a hybrid control scheme by deploying the MVSG power controller and internal model control-based VA controllers (named MVSG-IMCVA scheme). The efficacy of the proposed MVSG-IMCVA scheme is compared with the MVSG-PIVA scheme and conventional TVSG-PIVA scheme under different power factor loadings. From the results, it is proved that the proposed scheme improved the transient response and reduced unnecessary trips. Thus, the proposed modifications demonstrate the empowerment of the multiloop voltage-controlled VSG scheme, thereby ensuring system stability during dominant load changes.

Synchronous generator-based power stations, with their inherent inertia, can maintain frequency stability during sudden load switching, while distributed generating station-driven microgrids suffer from a lack of natural inertia. Cascaded power, voltage, and current controllers are a widespread control strategy used to regulate the power output of distributed generating stations to maintain frequency and voltage within stable limits. Virtual synchronous generator (VSG) control for the power controller is used as a potential solution to emulate inertia. To derive maximum benefit from VSG, proper tuning of its multiple parameters is required. In this direction, earlier works proposed the equivalence between the droop and VSG schemes, which suggested that the droop coefficient value could be directly used in the design of VSG. As an improvement to these conventional works, the proposed work in this paper identifies that VSG delivers a better response when an equalizing constant is used to adjust the droop coefficient value than using it directly. This paper proposes implementing the VSG with an equalizing constant as a new design parameter. A description of designing the parameters of this improved VSG considering the equalizing constant is also discussed in this paper. The performance of the conventional VSG and the proposed improved VSG are compared. From the results, it is observed that, at load switching, the output frequency of the proposed method in all test cases has settled at less than 3 s, while the conventional method took a maximum of 6 s in critical cases. Further, the output frequency’s maximum peak with the proposed method is 3 Hz less than the conventional method. These, along with other metrics, validate the importance of the proposed improved VSG-based control scheme for the enhancement of transient responses in microgrids.