Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field.

Although particle swarm optimization (PSO) in its standard form performs extremely well for less complicated convex optimization problems involving reduced search space, it fails in finding global optimal solutions for more complicated nonconvex optimization problems with multiminima functions, thus exploring the promising search space less efficiently to ensure solution with superior quality. Guaranteeing the location of the global optimum through PSO becomes strenuous. The inherited premature convergence problem of PSO becomes more prominent while handling, especially the complex nonconvex problems. However, PSO has the ability to hybrid with other optimization techniques to ensure optimal global solution, better convergence characteristics, computational efficiency, and so on, while dealing with complex nonconvex problems. After presenting a detailed survey of the variants of PSO (involving variations in the basic structure of PSO) in part I, part II of this paper now comprehensively details all the hybrid forms (purely) of PSO applied to a constrained economic dispatch problem. How PSO overcomes its premature convergence problem while hybridizing with other optimization techniques is well-highlighted.

This paper deals with a multiterminal voltage source converter (VSC)-based high voltage dc transmission system (M-HVDC) connecting offshore wind farms to onshore ac grids. Three M-HVDC configurations studied, each with different control strategies. The voltage-current characteristics of VSCs are presented and VSC converter operation with different output powers from offshore wind farms is assessed. A generalized droop control strategy is mainly used to realize autonomous coordination among converters without the need of communication. Operation of the three configurations with respect to their control system is analyzed through PSCAD/EMTDC simulations and experimentation. Results show control performance during wind power change, eventual permanent VSC disconnection, and change in power demand from the ac grid side converter.

Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs.

We develop a probabilistic model for determining the location of dc-link faults in MT-HVdc networks using discrete wavelet transforms (DWTs), Bayesian optimization, and multilayer artificial neural networks (ANNs) based on local information. Likewise, feedforward neural networks (FFNNs) are trained using the Levenberg–Marquardt backpropagation (LMBP) method, which multi-stage BO optimizes for efficiency. During training, the feature vectors at the sending terminal of the dc link are selected based on the norm values of the observed waveforms at various frequency bands. The multilayer ANN is trained using a comprehensive set of offline data that takes the denoising scheme into account. This choice not only helps to reduce the computational load but also provides better accuracy. An overall percentage error of 0.5144% is observed for the proposed algorithm when tested against fault resistances ranging from 10 to 485 Ω. The simulation results show that the proposed method can accurately estimate the fault site to a precision of 485 Ω and is more robust.

High voltage direct current (HVDC) power transmission is becoming increasingly important due to steadily rising need for bulk power delivery and interconnected power transmission and distribution systems. DC grids are vulnerable to dc faults, which lead to a rapid rise in dc fault currents. The dc faults must be cleared within timeframe of milliseconds to avoid collapse of the HVDC system. In the event of primary protection (PP) failure, back-up protection (BP) must be applied to clear the fault. In this paper, a novel algorithm based on a Naïve Bayes classifier is proposed to determine threshold levels and operational time frames for primary and back-up protection in multi-terminal voltage source converter based HVDC (VSCHVDC). Local voltage and currents are measured to detect and identify the kind of fault. A four terminal HVDC transmission system is developed in PSCAD/EMTDC and is subjected to lineline faults at different locations and time, to assess the designed protection schemes. Results show that relaying algorithm effectively detects the fault and expedite the primary protection operation. On malfunctioning of PP, BP is accelerated in a short delay of 0.2ms. Furthermore, the relaying algorithm provides faster protection compared to techniques available in the literature. The resulting reduced fault clearance time truncates the maximum fault current and inevitably, leads to reduced power ratings required for dc grid equipment.