The use of distributed generation has received wide attention due to low maintenance costs, reduced transmission line losses and network congestion, as well as minimal impact on climate change and global warming. However, the distributed generation integrated to the distribution network introduces various protection problems that cannot be solved by conventional protection systems. These obstacles include the bi-directional power flow and the variation in the fault current level during the topology change. Thus, appropriate fault detection and protection scheme are strongly recommended to increase safety and reliability of the distributed network. This paper presents a comprehensive overview of the distributed network fault detection and protection strategies incorporated with distributed generation. This review also investigates the various fault detection approaches concerning types, communication methods, operation mode, constraints and benefits. Additionally, numerous island detection techniques are explored, focusing on generation types, parameters, cost and advantages. Moreover, the review outlines the various protection schemes highlighting categories, operation, constraints, strength and shortcomings. The key issues and challenges are discussed along with selective proposals for future research. All the highlighted viewpoints of this research will hopefully be beneficial to power system engineers and researchers for the advancement of distributed network fault and protection strategies for suitable operation and management of future distributed generation systems.

Due to the increased integration of distributed generations in distributed networks, their development and operation are facing protection challenges that traditional protection systems are incapable of addressing. These problems include variations in the fault current during various operation modes, diverse distributed network topology, and high impedance faults. Therefore, appropriate and reasonable fault detection is highly encouraged to improve the protection and dependability of the distributed network. This paper proposes a novel technique that employs an improved Hilbert–Huang Transform (HHT) and ensemble learning techniques to resolve these challenges in a photovoltaic distributed network. First, improved HHT is utilized to extract energy features from the current signal. Second, variational mode decomposition (VMD) is applied to extract the intrinsic mode function from the zero component of the current signal. Then, the acquired energy feature and intrinsic mode function are input to the ensemble learning technique for fault detection and classification. The proposed technique is implemented using MATLAB software environment, including a classification learner app and SIMULINK. An evaluation of the results is conducted under normal connected mode (NCM) and island mode (ISM) for radial and mesh-soft normally open point (SNOP) configurations. The accuracy of the ensemble bagged trees technique is higher when compared to the narrow-neural network, fine tree, quadratic SVM, fine-gaussian SVM, and wide-neural network. The presented technique depends only on local variables and has no requirements for connection latency. Consequently, the detection and classification of faults using the proposed technology are reasonable. The simulation results demonstrate that the proposed technique is superior to the neural network and support vector machine, achieving 100%, 99.2% and 99.7% accurate symmetrical and unsymmetrical fault detection and classification throughout NCM, ISM, and dynamic operation mode, respectively. Moreover, the developed technique protects DN effectively in radial and mesh-SNOP topologies. The suggested strategy can detect and classify faults accurately in DN with/without DGs. Additionally, this technique can precisely detect high and low impedance faults within 4.8 ms.

The increasing integration of renewable sources into distributed networks results in multiple protection challenges that would be insufficient for conventional protection strategies to tackle because of the characteristics and functionality of distributed generation. These challenges include changes in fault current throughout various operating modes, different distribution network topologies, and high-impedance faults. Therefore, the protection and reliability of a photovoltaic distributed network relies heavily on accurate and adequate fault detection. The proposed strategy utilizes the Variational Mode Decomposition (VMD) and ensemble bagged trees method to tackle these problems in distributed networks. Primarily, VMD is used to extract intrinsic mode functions from zero-, positive-, and negative-sequence components of a three-phase voltage signal. Next, the acquired intrinsic mode functions are supplied into the ensemble bagged trees mechanism for detecting fault events in a distributed network. Under both radial and mesh-soft normally open-point (SNOP) topologies, the outcomes are investigated and compared in the customarily connected and the island modes. Compared to four machine learning mechanisms, including linear discriminant, linear support vector mechanism (SVM), cubic SVM and ensemble boosted tree, the ensemble bagged trees mechanism (EBTM) has superior accuracy. Furthermore, the suggested method relies mainly on local variables and has no communication latency requirements. Therefore, fault detection using the proposed strategy is reasonable. The simulation outcomes show that the proposed strategy provides 100 percent accurate symmetrical and asymmetrical fault diagnosis within 1.25 ms. Moreover, this approach accurately identifies high- and low-impedance faults.