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Renewable energy (RE) sources play an important role that not only reduces the pressure on fossil fuels but also produces safe and clean energy by developing the microgrid (MG) technology that fulfills the excess demand for power throughout the world. The technological development and the blessing of information and communication technology converts the MG technology to a smarter one, termed as smart grid (SG) and virtual power plant, by establishing a two‐way communication between the consumers and service provider with the aid of smart metering infrastructure, dynamic pricing scheme, energy management system, smart transmission and distribution system. The transformation of the traditional power system to SG enhances the efficiency, flexibility, resiliency, and robustness of the power system due to the increasing level of cyber security and automation. A directional pathway from conventional to smart power system has been carried out in this paper by addressing the present status of the power system, challenges during the operations, and possible solutions. Initially, the control operation of MG with RE and energy storage system has been investigated which is essential to understand the operation complexity and challenges due to the variable nature of the RE, load dynamics, and uncertainties. But the facilities of end‐user feedback, dynamic pricing scheme, cyber security, and smart metering system are not present in MG technology that hampers the proper regulation of controlling operation, generating and distributing energy as well as energy losses. The development of SG with smart technologies overcomes these problems by proper controlling of power usage. Finally, this paper provides a path to move the MG toward SG by analyzing the technical and critical challenges of smart devices, equipment integration, and their control issues and provides possible solutions to overcome the integration and control challenges for a reliable and sustainable operation. [ABSTRACT FROM AUTHOR]

Although the use of a series capacitor increases the transmission line (TL) capacity, it inverts the voltage/current and creates a sub-harmonic frequency that results in unintended operation of the protection devices, which further decreases the transient stability under fault conditions. Restoring the transient stability requires an accurate diagnosis of faults in TLs, which further requires exploration of in-depth features. This imposes challenges for the existing approaches as they demand a large amount of data. In this paper, a self-attentive weight-sharing capsule network (WSCN) is proposed to achieve robust and high classification performance while utilizing a limited amount of data. The capsule network, with the weight-sharing mechanism delivers robust performance in detecting and classifying the faults in the TL domain. Simultaneously, the self-attention layer highlights the more dominant features that make the network work upon the limited data effectively. A Western-System-Coordinating-Council WSCC 9-bus and 3-machine test model, which was modified with the series capacitor was studied to quantify the robustness of the self-attention WSCN. The results were compared with those of others networks to confirm the explicit classification ability of the proposed network. A study on real-world data obtained from power network was also conducted to validate the proposed self-attention WSCN. [ABSTRACT FROM AUTHOR]

• Proposed an intelligent approach for transmission line fault analysis based on capsule network. • An unsupervised model is developed which requires no manual labeling of the data. • The performance against line parameter variation and high impedance faults is assessed. • Noises are considered to justify the robustness of the proposed approach. • The performance is compared with the baseline capsule network and existing approaches. Detection and classification of transmission line (TL) faults are key factors for the fault root cause analysis and rapid restoration of the power network. Deep learning can extract representative features automatically from big data, thus becoming an important breakthrough in fault analysis. Existing deep learning-based fault diagnosis models rely on a large number of data obtained from a variety of fault conditions to be generalized. Indeed, in the TL domain, it is difficult to define and, therefore to collect the fault incepted features of all possible fault scenarios. This paper proposes an unsupervised framework for fault detection and classification (FDC) of TL based on a capsule network (CN). Instead of using the baseline CN, an extension to this with a sparse filtering technique is adopted in this work. The capsule network with sparse filtering (CNSF) voluntarily learns the expensive fault features and significantly improves the model performance without involving a large number of data. The proposed scheme receives 1 / 2 cycle post-fault three-phase signals and encodes them into a single image that is defined as the input for the proposed CNSF model. The effectiveness of the proposed CNSF model is corroborated by four different TL topology confirming the model's adaptability to a topology change in response to intended control action or the switching actions due to cascading faults. Further assessment of the model's performance against noise, high impedance faults (HIF), and line parameter variations are also carried out in order to confirm the high reliability of the proposed model. In addition, a rigorous comparative study is conducted to guarantee the state-of-the-art performance of the proposed model. [ABSTRACT FROM AUTHOR]

• Microgrid is a small grid that uses renewable energy sources to generate power. • Performance of microgrid may deteriorate due to the uncertainty in load dynamics. • A controller is proposed to control the voltage, current and power of a microgrid. • The controller is designed for both single and three phase islanded microgrid. • Performance of the controller is measured and compared against various loads. Future grids will consist of many small and large microgrids that will require complex control of grid interaction as well as islanding operation. This paper addresses the control of islanded microgrids under dynamic system conditions. In this paper, a robust model reference modified adaptive proportional integral derivative (PID) controller is designed for voltage and current control augmentation of islanded microgrid against various load dynamics. The variation of load dynamics may cause the unsafe operation of the microgrid. The objective of the designed controller is to confirm the safe operation against voltage and current control under different load dynamics. The controller is designed based on selecting the desired reference model and controlling parameters. The effectiveness of the proposed controller is evaluated against the presence of load dynamics, harmonic sources, asynchronous machines, nonlinear and unknown loads. The results show that the proposed controller is capable of providing high tracking performance and safe operation for both the single and three phase islanded microgrid system. [ABSTRACT FROM AUTHOR]

The reliable operation of power systems becomes a formidable job these days due to the high amount of complexities in the expanded power system networks. The power system networks often comprise microgrids that encounter over 80% of faults due to their exposure to unpredictable weather conditions, which reduce the insulation strength of the conductors which damaged the distribution system. Therefore, detection, classification, and location of such faults in the distributing systems are a must to ensure the flawless operation of the power systems. Machine learning methodologies are getting more attention to detect these types of faults due to their capability to handle complex fault information. Nevertheless, the obstacles are even now on the board as the traditional machine learning techniques rely on oversimplified frameworks that are incapable of analyzing a wide range of latent and explicit parameters and are also time-consuming. In this work, a unique defect diagnosis technique based on a multiblock deep belief network (DBN) and the fundamental discrete wavelet transform (DWT) is proposed, allowing the architecture to identify the deterministic reconstructing throughout its inputs. This method enables a strong multilevel generative network to utilize fault-related properties, decodes high variability functionalities, and requires minimal previous knowledge. The suggested approach is validated using a wide set of input data at various sampling frequencies. To evaluate the efficiency of DBN, a benchmark methodology based on the International Electrotechnical Commission (IEC) standard was used. White Gaussian Noise (WGN) was also implemented to test the envisioned network's resilience. The findings show that the approach is capable of executing exact diagnosis procedures. [ABSTRACT FROM AUTHOR]

Cyberattacks (CAs) on modern interconnected power systems are currently a primary concern. The development of information and communication technology (ICT) has increased the possibility of unauthorized access to power system networks for data manipulation. Unauthorized data manipulation may lead to the partial or complete shutdown of a power network. In this paper, we propose a novel security unit that mitigates intrusion for an interconnected power system and compensates for data manipulation to augment cybersecurity. The studied two-area interconnected power system is first stabilized to alleviate frequency deviation and tie-line power between the areas by designing a fractional-order proportional integral derivative (FPID) controller. Since the parameters of the FPID controller can also be influenced by a CA, the proposed security unit, named the automatic intrusion mitigation unit (AIMU), guarantees control over such changes. The effectiveness of the AIMU is inspected against a CA, load variations, and unknown noises, and the results show that the proposed unit guarantees reliable performance in all circumstances. [ABSTRACT FROM AUTHOR]

• Development of a new approach for transmission line faults detection and classification based on the convolutional neural network combined with wavelet transform which makes the network to efficiently train and extract the relevant features from the three phase faulty data. • Integration of the self attention mechanism with proposed CNN to identify the particular type of fault more accurately that enables the model to perform the precise classification. • Variation of input signals like voltage signal, current signal, and combined with voltage and current signal are employed at various sampling frequency to verify the performance of proposed model. • Noises are considered to prepare the input data to confirm the robustness of proposed SAT-CNN model. • Performance comparison between proposed and some other state-of-the-art model is done to confirm the high performance of proposed SAT-CNN model. This paper introduces a novel self-attention convolutional neural network (SAT-CNN) model for detection and classification (FDC) of transmission line faults. The transmission lines continuously experience the number of shunt faults and its effect in the practical system rises the instability, line restoration cost and damages the load. Therefore, a robust and precise model is needed to detect and classify the faults for the rapid restoration of faulty phases. In this paper, we propose a SAT-CNN framework with time series imaging based feature extraction model for FDC of a transmission line. To ensure the noise immunity performance, the discrete wavelet transform (DWT) has been used to denoise the faulty voltage and current signals. The effectiveness of the proposed SAT-CNN framework is tested by varying the input signals namely voltage, current, and combined voltage and current signal, under the various sampling frequencies. The robustness of the proposed model is verified by adding the noises to the input data. Results show that the proposed model is capable to perform precise classification and detection of transmission line faults with high accuracy. A comparison between the proposed and other state-of-the-art FDC model is also studied to show the superiority of the proposed SAT-CNN model. [ABSTRACT FROM AUTHOR]

This paper presents a deep generative model-aided intelligent framework for effective health condition evaluation of power grid transformers. The health assessment of a power transformer is required to guarantee the stable and sustainable operation of the grid and to precisely convert the electrical energy. A power transformer must undergo a series of tests to determine its state of health and identify its health index. In this paper, we develop a novel approach to identify and classify the health condition of power transformers using a machine learning approach. The proposed framework is structured by using a multi-layer perception generative model with a logistic regression classifier. The developed model uses the twelve input layers which enables the model to effectively compressed the dataset and eight categories in the output classification layers. The effectiveness of the proposed model is examined on the real-world testing data set of 31 categories of six hundred and eight transformers. The obtained performance using the proposed framework confirms its efficacy in precisely evaluating the transformer's health condition. The obtained results have also been compared with the existing machine-learning models. The comparisons show that the proposed model outperforms the state-of-the-art models by achieving 99% of accuracy. [Display omitted] • An MLP-LR-based ML model is proposed for transformer health condition evaluation. • Real-time tested data is used to measure the proposed model efficacy. • Qualitative and quantitative comparative results are given with existing methods. • Real-time data and experts' comments for the qualitative assessment are examined. [ABSTRACT FROM AUTHOR]

Modernization in the area of smart energy equipment, are forming the perception of an interlinked energy network in subsequent times. This energy network is contemplated to govern real-time energy consumption and optimization in the future. Traversing a prolonged period of development, the energy industry has reached the landmark of Virtual Power Plant (VPP) and still going onward to this newfangled energy network, also can be called the next generation VPP. This paper demonstrates the challenges and opportunities in executing the transpiring aspects of the next generation VPP. Based on essential components and requisites, detailed descriptions are depicted from different technological viewpoints such as electrical and communication, computing technologies, energy management, economic profit, geographical factors, and adoption of emerging technologies. We also present the recent updates of existing VPP projects. Finally, considerations towards implementing the next generation VPP are marked out, along with necessary frameworks, regulations, and policies. This paper can provide prudent support for researchers and practitioners in the field of VPP. [Display omitted] • Review of development aspects in virtual power plants (VPP). • Critical requirements and necessary components for VPPs are presented. • Emerging technologies that can be integrated with next generation VPP are discussed. • Challenges and opportunities in the road of developing the next generation VPP are characterized. • Next generation VPP's framework, policies, and regulations are outlined. [ABSTRACT FROM AUTHOR]