A predictive control strategy for mitigation of commutation failure in LCC-based HVDC systems

High-voltage direct-current (HVDC) systems are being widely employed in various applications because of their numerous advantages such as bulk power transmission, efficient long-distance transmission, and flexible power-flow control. However, line-commutated-converter-based HVDC systems suffer from commutation failure, which is a major drawback, leading to increased device stress and interruptions in transmitted power. This paper proposes a predictive control strategy, deploying a commutation failure prevention module to mitigate the commutation failures during ac system faults. The salient feature of the proposed strategy is that it has the ability to temporarily decrease the firing angle of thyristor valves depending on the fault intensity to ensure a sufficient commutation margin. In order to validate the performance of the proposed strategy, several simulations have been conducted on the CIGRE Benchmark HVDC model using PSCAD/EMTDC software. Additionally, practical performance and feasibility of the proposed strategy are evaluated through laboratory testing, using the real-time Opal-RT hardware prototyping platform. Simulation and experimental results demonstrate that the proposed strategy can effectively inhibit the commutation failure or repetitive commutation failures under different fault types, fault impedances, and fault initiation times

A novel intelligent detection schema of series arc fault in photovoltaic (PV) system based convolutional neural network

Series Arc Fault (SAF) can be defined as the failure that occurs between any electrical contact and any electrical circuitry. However, it considered one of the common failures that affect the operation of the PV system and causes serious problems such as fires and electrical shock hazards. Several reasons increase the possibility of this type of failures such as incorrect installation, irregular maintenance, and some environmental effects. This paper presents a new intelligent and accurate detection method of SAF in the PV system. In this method, Convolutional neural networks (CNN) which is a discriminative (supervised) deep learning algorithm used for the process of fault detection. In normal cases, the signal consists of DC component, inverter component and noise of Network. In the case of SAF, a new component will add to the signal; therefore, CNN used to discriminate against the new component to accurately detect the SAF. PSCAD is used to generate the Arc fault model; Performance evaluation and the results of the proposed method implemented using Python. The achieved accuracy of the proposed detection method is 98.9%.

A controllable thyristor-based commutation failure inhibitor for LCC-HVDC transmission systems

Commutation failure is a serious malfunction in line-commutated high voltage direct current (HVdc) converters which is mainly caused by the inverter ac faults, and results in a temporary interruption of transmitted power and damage to the converter equipment. In this article, a controllable commutation failure inhibitor (CCFI) is developed which obviates the main drawbacks of the existing power electronic based and fault current limiting based strategies. Under normal circumstances, the developed CCFI improves the steady-state stability and the power transfer capability of the inverter ac lines, while it does not cause excessive voltage stress on the converter valves. In addition, it would reduce the risk of commutation failure occurrence, since it does not lead to any voltage drop in the commutation circuit. When a fault occurs at one of the inverter ac systems, its corresponding CCFI limits the fault current depending on the reduced extinction angle. This would not only inhibit the successive commutation failures on the HVdc converter, but also extend the lifetime of components in the inverter ac systems. The practical feasibility of the developed CCFI is assessed through laboratory testing, using a real-Time Opal-RT hardware prototyping platform. The obtained results indicate that the developed CCFI can reliably inhibit the commutation failures during various types of faults

Integration of time of use (TOU) tariff in net energy metering (NEM) scheme for electricity customers

The introduction of Net Energy Metering (NEM) scheme for electricity customers in Malaysia is seen as an improvement from the previous Feed-In Tariff (FIT). However, the new NEM scheme only benefited the large residential customers but not to medium and small residential customers. Due to electricity tariff blocks structure, the large customers can avoid paying expensive tariff and hence reducing their electricity bill. This is not the case for medium and small customers since they are already paying lower tariff blocks due to their lower electricity consumption. This issue will discourage most residential customers to install solar PV system in their home and affect the Malaysia’s renewable energy target. This paper proposed a NEM scheme that integrates Time of Use (TOU) electricity tariff to the scheme. The proposed NEM-TOU scheme will be simulated, tested and compared to the new NEM scheme by using practical small, medium and large residential customers’ data. The results show that the proposed TOU-NEM scheme able to overcome the weakness of the current scheme where all customers (large, medium and low) can benefited by installing solar PV system in their home

Low-voltage ride-through for a three-phase four-leg photovoltaic system using SRFPI control strategy

With the innovative progresses in power electronics in recent years, photovoltaic (PV) systems emerged as one of the promising sources for electricity generation at the distribution network. Nonetheless, connection of PV power plants to the utility grid under abnormal conditions has become a significant issue and novel grid codes should be recommend. The low-voltage ride-through (LVRT) capability is one of the challenges faced by the integration of PV power stations into electrical grid under abnormal conditions. This work firstly provides a discussion on recent control schemes for PV power plants to enhance the LVRT capabilities. Next, a control scheme for a three-phase four-leg grid-connected PV inverter under unbalanced grid fault conditions using synchronous reference frame proportional integral (SRFPI) controller is proposed. Simulation studies are performed to investigate the influence of the control strategy on the PV inverter

Experimental study for the double-stage savonius blade with different overlap ratios for rain water harvesting (RWH) system

The energy demand kept increasing, in line with the increment of world population. The price of fossil fuel is kept rocketing. Renewable energy is the alternative that had been approached to make sure the sustainability of the energy. The example of sustainable energies is rain, solar, tides, wind, biomass, and hydro. In general, the climate in Malaysia is characterized as high rainfall throughout the year. The collection of rain water can be used to generate an electricity under the concept of rain water harvesting (RWH) system. The purpose of this study is to vary the overlap ratio, ß (0.15 and 0.3) of the Savonius blades in the proposed RWH system. From the analysis, it is shown that for the Double-Stage Savonius blade rotors, higher electricity can be generated when ß is increased. From the finding, it is found that the Double-Stage Savonius blade rotor of 0.15 ß produces power output of 0.12 Watt while 0.3 ß produces 0.13 W, respectively

Comparison analysis of different classification methods of power quality disturbances

Good power quality delivery has always been in high demand in power system utilities where different types of power quality disturbances are the main obstacles. As these disturbances have distinct characteristics and even unique mitigation techniques, their detection and classification should be correct and effective. In this study, eight different types of power quality disturbances were synthetically generated, by using a mathematical approach. Then, continuous wavelet transform (CWT) and discrete wavelet transform with multi-resolution analysis (DWT-MRA) were applied, which eight features were then extracted from the synthesized signals. Three classifiers namely, decision tree (DT), support vector machine (SVM) and k-nearest neighbors (KNN) were trained to classify these disturbances. The accuracy of the classifiers was evaluated and analyzed. The best classifier was then integrated with the full model, which the performance of the proposed model was observed with 50 random signals, with and without noise. This study found that wavelet-transform was effective to localize the disturbances at the instant of their occurrence. On the other hand, the SVM classifier is superior to other classifiers with an overall accuracy of 94%. Still, the need for these classifiers to be further optimized is crucial in ensuring a more effective detection and classification system

Models, detection methods, and challenges in DC arc fault: A review

The power generation of solar photovoltaic (PV) technology is being implemented in every nation worldwide due to its environmentally clean characteristics. Therefore, PV technology is significantly growing in present applications and usage of PV power systems. Despite the strength of the PV arrays in power systems, the arrays remain susceptible to certain faults. An effective supply requires economic returns, the security of the equipment and humans, precise fault identification, diagnosis, and interruption tools. Meanwhile, the faults in unidentified arc lead to serious fire hazard to commercial, residential, and utility-scale PV systems. To ensure a secure and dependable distribution of electricity, the detection of such hazard is crucial in the early phases of the distribution. In this paper, a detailed review on modern approaches for the identification of DC arc faults in PV is presented. In addition, a thorough comparison is performed between various DC arc-fault models, characteristics, and approaches used for the identification of the faults

Energy management strategy and capacity planning of an autonomous microgrid: Performance comparison of metaheuristic optimization searching techniques

Electricity generation using renewable energy-based microgrid (REM) is a prerequisite to achieve one of the objectives of sustainable development goal (SDG 7- Affordable and Clean Energy). Nonetheless, the optimum design of the REM is challenging due to fluctuating demand and intermittent nature of the renewable energy sources. The optimum sizing of the REM is also associated with several non-convexities and nonlinearities, thereby precluding the application of deterministic optimization searching techniques for the sizing problem. This paper, therefore, proposes a rule-based algorithm and metaheuristic optimization searching technique (MOST) for the energy management (EM) and sizing of an autonomous microgrid, respectively. The purpose of the energy management scheme (EMS) is to provide power delivery sequence for the different components that compose the microgrid. Afterward, the EMS is optimized using MOST. For benchmarking, the paper compares the success of six different MOSTs. The simulation is performed for the climatic conditions of Maiduguri, Nigeria. The comparative results indicate that grasshopper optimization algorithm yields a better result relative to other studied MOSTs. Remarkably, it outperforms the grey wolf optimizer, the ant lion optimizer, and the particle swarm optimization by 3.0 percent, 5.8 percent, and 3.6 percent (equivalent to a cost savings of $8332.38,$4219.87, and $5144.64 from the target microgrid project). Results also indicate that the EMS adopted for the control of the microgrid has led to the implementation of a clean and affordable energy system. Moreover, the proposed microgrid configuration has minimized CO2 emission (by 92.3 %) and fuel consumption (by 92.4 %), when compared to the application of a fossil fuel-based diesel generator

Optimization of FACTS devices : classification, recent trends, and future outlook

Since the inception of industrialization, power system has been an indispensable aspect of economy. With the progression of time, technology has impalpably commingled into our lifestyle. Alongside blooming technologies, energy demand is proliferating and power companies are begetting energy at their best to quench it. Growing reliance on power system has brought its quality into more advertence. Various electronic devices and topologies have been invented to enhance power quality and reliability; numerous others are still underway. During the course, power system has grown to an intricate network of sources, loads and control devices, leading to various issues such as transmission congestion and high losses. This paper discusses ways to ameliorate congestion and gives an overview of relationship between our present energy resources and ecological threats like global warming. Moreover, it points out various power system problems such as energy losses and transients. The necessity of FACTS devices has also been elaborated alongside their classification and comparison. Finally, numerous topologies and optimization methods proposed in the technical literature have been classified and analyzed to alleviate power system conundrums, and a glimpse into future energy trends is presented