Commutation failures, which may lead to outage of HVDC systems, usually occur in line-commutated HVDC systems, especially in the inverter stations. As a method to reduce the risk of commutation failures, additional angle deducted from the firing order at the inverter station has its inherent limitations. In this paper, by analyzing the inverter transient process of normal commutation and commutation failure especially, it is shown that direct-current control can be used to inhibit the commutation failures and present an improvement strategy of control system on the inverter side. Based on the analysis of commutation failure prevention (CFPREV), an approach of direct current value setting is proposed, the selections of parameters in calculating process are CFPREV-based in order to implement it conveniently, and the dc current predictive control strategy is applied by modifying the conventional control system at the inverter. The validity is proved by simulation of the CIGRE HVDC model and multi-infeed HVDC model based on CIGRE HVDC, where various ac system faults in PSCAD/EMTDC are set manually. The results show that the proposed algorithm is very effective in inhibiting commutation failures or repetitive ones, and even would enhance fault-ride-through capability of HVDC system with commutation failures.

Direct-Current Predictive Control Strategy for Inhibiting Commutation Failure in HVDC Converter

Accurate runoff estimation is crucial for optimal reservoir operation and irrigation purposes. In this study, a novel hybrid method is proposed for monthly runoff prediction in Mangla watershed in northern Pakistan by integrating particle swarm optimization (PSO) and grey wolf optimization (GWO) with extreme learning machine (ELM) as ELM-PSOGWO. The proposed method was compared with the standalone ELM, hybrid of ELM-PSO, and binary hybrid PSOGSA (hybrid of PSO with gravitational search algorithm) methods. Monthly precipitation and runoff data were used as inputs to the models to examine their accuracy in terms of different statistical indexes. Test results showed that the proposed ELM-PSOGWO provided more accurate results than the standalone ELM, hybrid ELM-PSO, ELM-GWO nd binary hybrid PSOGSA methods in monthly runoff prediction. ELM-PSOGWO reduced the RMSE in prediction of ELM, ELM-PSO, ELM-GWO and ELM-PSOGSA by 38.2, 22.8, 22.4 and 16.7%, respectively. The PSO and GWO based ELM models also performed better than standalone ELM models, with an improvement in RMSE by 19.9 to 20.3%, respectively. Results also showed that adding precipitation as input enhanced the prediction accuracy of models. ELM-PSOGWO was also able to provide more precise estimates of peak runoff with the lowest absolute mean relative error compared to other methods. The results indicate the potential of ELM-PSOGWO model to be recommended for monthly runoff prediction.

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

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A comprehensive survey of sine cosine algorithm: variants and applications

GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems

This paper investigates the in-depth mechanism of commutation failure for a line-commuted converter-based high-voltage direct current (LCC-HVDC) system. The commutation failure prevention control (CFPREV) and the initial fault voltage angle (IFVA) are considered from the view of the voltage-time area (VTA) in the analysis. It is revealed that the IFVA is among the dominant factors for commutation failures when the voltage drop of the inverter bus is relatively small, and CFPREV further intensifies the impact of the IFVA on commutation failures, while the fluctuation of the direct current plays a dominant role in commutation failures under a greater voltage reduction at the inverter bus. A quantitative division of the severity of AC faults is proposed to determine dominant factors for commutation failures. The relationship between the chance of commutation failures to occur and the IFVA is built, and the method used for computing probability of commutation failures is proposed. The influence of the dynamic of CFPREV output on our research is studied. Simulations based on a typical monopole LCC-HVDC system using PSCAD/EMTDC software are conducted to verify the correctness of the theoretic analysis and the effectiveness of the proposed computing methods.

/pdf/probabilistic-analysis-of-commutation-failure-in-lcc-hvdc-44is5lgboh.pdf

Probabilistic Analysis of Commutation Failure in LCC-HVDC System Considering the CFPREV and the Initial Fault Voltage Angle

The conception of electromechanical oscillations initiates in the power network when there is an installation of the generator in parallel with the existent one. Further, the interconnection of multiple areas, extension in transmission, capricious load characteristics, etc. causes low-frequency oscillations in the consolidated power network. This paper proposes variants of a booming population-based grey wolf optimization (GWO) algorithm in the tuning of power system stabilizer parameters of a multi-machine system in damping low-frequency oscillations. The parameters have been tuned by framing an objective function considering the improving damping ratios for the system states with lesser damping ratios and shifting the system eigenvalues towards the left-hand side of s-plane for the improved settling characteristics for the oscillations in the system. The requisites of stabilizer strategy are mapped with the hallmarks of prevalent algorithms and designed hybrid versions of GWO for the enhancement of the multi-machine power system stability. Four variants of GWO technique are nominated based on the competent stabilizer performance namely, modified grey wolf optimization (MGWO), hybrid MGWO particle swarm optimization (MGWOPSO), hybrid MGWO sine cosine algorithm (MGWOSCA) and hybrid MGWO crow search algorithm (MGWOCSA) for the designed multi-machine power network. The proposed methods have been realized with the statistical analysis on the 23 benchmark functions. Nonparametric statistical tests, namely, Feidman test, Anova test and Quade tests, have been performed on the test system, further analysed in detail. A detailed comparative analysis under the self-clearing fault is presented to illustrate the suitability of the proposed techniques. For the analysis purpose, the location of system eigenvalues has been observed along with their oscillating frequencies and corresponding damping ratios. Further, the damping nature offered with considered system uncertainty for the system states also presented with the PSS parameters obtained by the proposed algorithms.

Amended GWO approach based multi-machine power system stability enhancement.

A hybrid modified grey wolf optimization‐sine cosine algorithm‐based power system stabilizer parameter tuning in a multimachine power system

An intelligent EGWO‐SCA‐CS algorithm for PSS parameter tuning under system uncertainties

Grid‐connected mode of microgrid (MG) operation is the most economical and reliable mode of service because the grid participates in the buying and selling of power with the MG, which reduces the generation cost of the MG system. Various types of electricity market pricing strategies are available in the literature with which the grid buys and sells power to the MG system. These strategies are implemented in turns on two different low voltage (LV) MG systems, and the generation cost of the systems are recorded to distinguish the cheapest strategy among them. A novel hybrid of recently developed superior optimization algorithms viz. grey wolf optimizer (GWO), sine cosine algorithm (SCA), and crow search algorithm (CSA) is used to minimize the overall generation cost of the MG system. It was evident from the results that generation cost was minimum when electricity market pricing was done using Strategy 2. Also, active participation of the grid incurred 15% savings in the system generation cost for the same strategy compared to the case when the grid was operating passively. Furthermore, statistical analysis corroborates to the superiority of the proposed hybrid MGWO‐SCA‐CSA over other algorithms used.

A novel hybrid algorithm for solving emerging electricity market pricing problem of microgrid

This paper proposes the modified version of a novel Harris hawks optimization in improving power system stability by optimal parameter tuning of power system oscillation damping devices. Harris Hawks Optimization is the latest nature-inspired metaheuristic optimization technique, which is based on the hunting behaviour of Harris hawks in nature. The mathematical model of a sample power network with static synchronous compensator (STATCOM) as power oscillation damping device has been considered for the study of the proposed technique. The performance characteristics of the proposed algorithm in improving power system stability are compared with whale optimization algorithm, and ant lion optimization on the considered power system model. An eigenvalue analysis and performance characteristics of system states during disturbances are presented to comment on the system performance with the proposed method.

Ramesh Devarapalli

Papers

Amended GWO approach based multi-machine power system stability enhancement.

A hybrid modified grey wolf optimization‐sine cosine algorithm‐based power system stabilizer parameter tuning in a multimachine power system

An intelligent EGWO‐SCA‐CS algorithm for PSS parameter tuning under system uncertainties

A novel hybrid algorithm for solving emerging electricity market pricing problem of microgrid

Application of Modified Harris Hawks Optimization in Power System Oscillations Damping Controller Design