Islanded AC microgrids (IACMGs) is the optimal solution for power supply in areas inaccessible to transmission infrastructure. It is known that the normal operation of its controllers in IACMGs is the key to ensuring system power balance and voltage and frequency stability. However, controller software faults occur occasionally, which will deteriorate system performance and threaten normal system operation. To address these issues, a robust hierarchical cooperative control strategy based on universal droop control, distributed fault and state estimation, and an adaptive fault-tolerant mechanism is proposed for IACMGs with arbitrary lines impedance. This approach achieves precise reactive power sharing, zero-error bus voltage regulation, and frequency support even when controller soft faults occur, thereby improving the stability, reliability, and robustness of IACMGs. Both theoretical analysis and experimental results validate the effectiveness and feasibility of the proposed hierarchical control framework.

To accurately and timely detect the status of the low-voltage circuit breaker switch trip circuit, ensure the safe and stable operation of the power system, and reduce economic losses caused by circuit breaker failures, a method for detecting the status of the low-voltage circuit breaker switch trip circuit based on a current signal feature extraction algorithm is proposed. First, a current sensor is used to collect the real-time working current signal of the low-voltage circuit breaker switch tripping circuit, and the empirical wavelet transform method is applied to suppress the harmonics in the current signal, solving the problem of signal distortion caused by nonlinear loads. Then, based on the variational mode decomposition method, extract the features of the current signal; compared with traditional decomposition methods such as empirical mode decomposition, it avoids mode mixing and improves feature extraction accuracy. Finally, the extracted current signal features are used as input data, and a Bi LSTM neural network model is adopted to achieve state detection of the low-voltage circuit breaker switch tripping circuit. Bi LSTM can capture time information in both forward and reverse directions, enhancing the model's ability to recognize complex states. Experiments show that this method can effectively suppress harmonics in the current signal, accurately extract current signal features, and reliably detect normal, overload, short-circuit, and other statuses of the low-voltage circuit breaker switch trip circuit. The detection sensitivity is as high as 92%, with both the missed detection rate and false detection rate being low-less than 1% and 2%, respectively. The detection efficiency is high, and the application performance is satisfactory.

In this paper, Artificial Neural Network (ANN) is integrated with data processing, input variable selection, and external optimization techniques to forecast the day ahead output power of a PV system. Variational mode decomposition (VMD) is used to decompose the highly fluctuating original data into relatively stable components with periodic characteristics which can be logically interpreted. The VMD parameters are optimally set through a methodology that involves index of orthogonality (IO) and correlation measures. The input variable selection is accomplished through mutual information (MI). A neural network with technically decided architecture is the core of the forecasting model. The weights and biases of the ANN are externally optimized through Ant colony optimization (ACO) during training. The forecasted components are used as input for a second level forecasting of the PV power through another ANN. The proposed hybrid method, labeled as VMD-ACO-2NN, was evaluated based on a 100kW PV system in Beijing, China. It is compared against NN, GA-NN, ACO-NN and VMD-ACO-NN and proved to outperform all with each of the added features contributing a part. The forecasting model is able to outstandingly explain 97.68% of the total variation in the forecasted PV power.

This article proposes a fault detection and location strategy based on cognitive edge computing to harvest the benefits of cognitive edge computing and address the special needs of active distribution networks (ADNs). In the proposed strategy, an ADN smart gateway is used to compile data in a central repository where it will be processed and analyzed. The intermediary smart gateway includes a protection unit where the fault detection, location, and isolation are accomplished through a combination of virtual mode decomposition (VMD), support vector machine (SVM,) and long short-term memory (LSTM)–type deep machine learning tools. The local measurements of branch currents and bus voltages are processed through VMD, and the informative decomposed components are provided as inputs to the SVM-based fault detection unit and LSTM-based fault location unit. The smart digital relay passes trip commands to the respective circuit breaker/s and submits compiled data regarding the history of faults and protection actions to the upper-level units. The findings from simulation results demonstrate the effectiveness of the proposed strategy to provide fast and accurate fault detection and protection against all types of faults and locations in the ADN.

Microgrids are configured with hierarchal and higher-level monitoring and controlling systems, such as Supervisory Control and Data Acquisition, and equipped with advanced protection systems that need more measurements. For those control and protection systems to function properly, communication system should be deployed. Thus this chapter is targeted at discussing the communication requirements and the available communication media and protocols for application in the microgrids.

Microgrids have special dynamic characteristics compared to the conventional grid and hence require special treatment. Large-scale penetration of renewable sources with largely uncertain, intermittent and fluctuation power output, continuous change of configuration and mode of operation, and low system inertia are some of the situations and challenges uniquely observed in microgrids. Such and other conditions result in the need for special way of addressing issues in the designing and implementation of microgrid dynamic control. This is covered in detail in this chapter. Causes, characteristics, and ways of handling dynamic disturbances in microgrids are discussed in this chapter. The chapter starts with an introductory description of dynamic disturbance and control. Possible causes of dynamic disturbance and the resulting changes in waveforms and system parameters are described. Some state-of-the-art techniques are presented with detailed formulations. Some advanced and innovative control strategies which can precisely fit the special requirements of microgrids are presented with the simulated and experimental results. The chapter ends with presenting experimental evaluation procedures and the results are included to verify the applicability of the presented techniques.

The concept of the microgrid has become more familiar in recent years with the technology being applied widely in both developing and developed countries for different reasons. However, the shortage of standards and published works on the microgrid has caused some confusion regarding what a microgrid is and what it is not. This chapter presents an introduction of the microgrid and related terminologies and concepts in such a way that some misconceptions can be cleared, and definitions which comply with international standards are provided. The chapter further explains different ways of classifying types of microgrids and gives brief explanations about the different types and components of microgrids.

Causes, characteristics, and ways of handling transient disturbances in microgrids are discussed in this chapter. It starts with the description of transient disturbance and control. Possible causes of transient disturbance and resulting changes in waveform and system parameters are described. It then follows by giving theoretical and mathematical explanations of how to design control systems to handle such disturbances. To address one of the challenges in the protection and control of microgrids due to the similarity in initial characteristics of faults and transient disturbances, the chapter dedicates a subtopic on discussing how the two events shall be identified from each other and treated accordingly. Discussions on frequency ride-through and voltage ride-through are found in the latter part of the chapter. The chapter ends with presenting application examples for implementation of the control techniques discussed in the earlier sections of the chapter with simulation and field test results.

Planning and optimizing the way of generating and consuming power are important practices in today's power system. To achieve optimal generation and utilization of energy, we need to know not only what we have and what we need now but also what we can generate and what we will consume in the future. Hence, short-term forecasting of generation and demand is a very important part of the optimal energy utilization demanded in the smart grid and microgrids as well. Depending on the forecasting horizon, we can have short-term, medium-term, and long-term forecasts. The short-term forecast which targets forecasting the generation for the next few hours to few days range is the focus of this chapter. Load forecasts of varying time horizon and step size are also important for different reasons, including economic operation, security, and planning. This chapter presents the basic concepts, classification, and different techniques of short-term forecasting of renewable generation and load. A review of the forecasting models in the literature and the industry and the accuracy enhancement techniques are provided. The chapter also presents some practical application examples for short-term forecasts of solar power, wind power, and load.

This chapter presents application cases of two microgrid projects in China. A demonstrational microgrid and a commercial one constructed for an industry park are discussed. The chapter outlines the brief introduction of the projects' significance, general characteristics of the projects, the operation principles, and the core technologies used. Detailed elaborations of the topology, constituting elements, tested techniques of protection and control systems in the microgrid projects are provided. The operational results from the protection and control systems deployed in the microgrids are also presented.

This chapter addresses one of the challenging issues in microgrid operation which is their protection. The chapter first introduces what a protection system is in general and what the main requirements for the protection of microgrids are. The differences between the protection of microgrids and a conventional distribution network are also elaborated. The conventional protection schemes and their applicability in microgrids are discussed with a special focus on overcurrent, differential, and distance protections. The relatively new and unseasoned voltage-based and centralized protection schemes are also briefly discussed. Then there is a discussion of advanced approaches such as adaptive protection and machine learning-based methods. The commonly used configurations and impacts of earthing system in the protection of microgrids are also covered in the later stages of the chapter.

While the introduction of the microgrid is believed to have a lot of technical advantages, including improved availability and reliability, some challenges do come with it. The main challenges are related with protection and control. The size, composition, structure, and nature of components result in the requirements and nature of control and protection of microgrids that are different from the conventional grid. This chapter addresses those technical issues that are faced in deploying a microgrid.

Microgrids can have different topologies during operation and are composed of various distributed energy resources. Such systems may be vulnerable to faults and different types of disturbances. The faults and disturbances in microgrids could have similarities to such events in the conventional grid. However, they also have some special characteristics due to the nature of the microgrid. Those special characteristics of microgrids could lead to the conventional protection system and fault analysis techniques becoming less effective. In this chapter, discussions on how to identify faults from other types of disturbances in microgrids are provided. The chapter further discusses the basic concepts of fault analysis and special feature of fault analysis in microgrids. Symmetrical components analysis is briefly elaborated and followed by a discussion on other techniques of detecting and locating faults in microgrids applied in the different literature in the field. Advanced algorithms based on Park transformation, total harmonic distortion (THD) and wavelet transform and the procedures involved in signal processing and extracting features (fault detection signals) are also discussed.