Volume 25, Issue 3

The High-Speed Power Line Carrier Communication (HPLC) enables the connections among power metering devices in integrated energy systems, and thus their satisfying operations are indispensable for system reliabilities. In order to more precisely diagnose their conditions especially in real complex data scenes, a multi-model evaluation ensemble is proposed in this paper. Firstly, typical IoT application contexts of customer-side metering equipment are analyzed, thus the corresponding main impact factors along with their performance evaluation indices can be probed. Next, to handle the multi-source, heterogeneous, high-dimensional datasets during applications, the Kernel Independent Component Analysis (KICA) is established to diminish data dimensionalities, thus the individual weights of each index can be rated. On the other hand, the Component Importance Measure (CIM) model is built to differentiate the impact degree of each indicator on the overall IoT connection performance, where the influence of dissimilar index on the entire performance, rather than the proportion or frequency, will be directly assessed to determine their impact weights. Ergo, a comprehensive diagnosis can be achieved via these two-fold total weights accordingly. Finally, the feasibility and effectiveness of the proposed method can be verified by an empirical case study, which is conducive to further improving the accuracy and rationality of HPLC condition evaluations.

Due to the intermittence of renewable sources, reliable customer service is not guaranteed unless hybrid-energy systems emerged with energy storage is employed together with an appropriate energy management system (EMS). Usually integrating different renewable energy sources can able to supply a reliable power to consumers. In this paper, an efficient EMS for a four-wire, 1000 kW Microgrid system is presented. The studied system consists of three wind farms and three solar plants with a central link to battery banks, an electrolyzer, and a fuel-cell system. A hybrid optimization algorithm is adapted to track the maximum power of PV arrays under variable weather and partial shading conditions. The tracking algorithm merges the benefits of the Whale Optimization and Perturb-and-Observe (P&O) technique. To track the maximum available power of wind plants, a P&O algorithm is established by calculating the proper duty cycle of a boost dc/dc converter that regulates the load current. A TS-Fuzzy-based controller for the inverter is applied to provide an acceptable power quality level associated with the battery bank, electrolyzer, and fuel cell. The suggested inverter controllers provide multi tasks such as reactive power compensator, voltage regulator under unbalanced loads, and active filter. The validity of the proposed system is verified via Extensive Hardware-in-Loop (HIL).

This paper aims to introduce a new multilevel inverter topology less number of switches and gate drivers with a hybrid control method. In the proposed topology three kinds of voltage sources are utilized in the topology. A combination of these voltage sources, switches, and two H-bridges derived a new topology. Moreover, combining the nearest level and pulse width modulation created an efficient way to control the proposed topology which is described in this paper. Equations to determine the number of components and total standing voltage of the proposed multilevel is presented. A comprehensive comparison between the proposed topology and other best topologies is made. Additionally, the simulation results which are obtained by PSCAD/EMTDC and experimental results are in accordance with the theory. The proposed multilevel inverter with a hybrid control method has less number of switches, gate drivers, and low THD.

Power loss, voltage deviation and reliability are the issues that are widely encountered in the power system operation of developing countries. The Eastern region of Ethiopian Electric Power (EEP) is characterized by long transmission lines with no power generation units and large loads. Power loss and voltage profile deviations are experienced repeatedly in this region. The aim of this study is to present an extensive analysis of the role of Hybrid Renewable Energy Sources (HRES) used as Distributed Generation (DG) for power loss minimization, voltage profile and reliability improvement in EEP. The research work initially proves the superior performance of Manta Ray Foraging Optimization (MRFO) for the sizing and placement of DG. Optimal sizing and location of DG are then performed for the case study system using MRFO. The results indicate considerable power loss reduction and improvement in the voltage profile of the network. The reliability of the system is also improved by having an alternative energy source from RES. Wind and solar are considered as part of DG, the combination of which is then optimized through MRFO in the next part of the research. An economic feasibility analysis of the proposed result is also presented for this real-world problem. MATPOWER is used for executing the load flow whereas MATLAB programming is used for implementing MRFO. The result shows that the utility got economic benefits due to the introduction of DG to the network.

The Proton Exchange Membrane Fuel Cell (PEMFC) has found widespread use for regulated output voltage applications because of its quick response time and mobility. A different form of hydrogen is employed in fuel cell-based electric vehicles for smart transportation for the reduction of global warming and the development of smart cities. To properly manage the operation of Fuel Cells (FCs), there is a need for accurate modeling. One of the most common challenges is finding the exact values of unknown parameters in the PEMFC. In the current study, a new method called Dandelion Optimizer (DO) is used for parameter identification. DO is used to estimate the parameters of the PEMFC based on Current-Voltage ( I - V ) characteristics. The Ballard Mark V and BCS 500-W PEMFC stacks use the DO method to identify unknown parameters. The performance of the DO algorithm is compared to that of other optimization techniques and the Sum of Squared Errors (SSE) is used to represent the objective function of the current optimization problem. In contrast to traditional and other efficient techniques, the simulation results proposed by the DO algorithm have excellent accuracy in extracting the PEMFC optimal parameters.

In order to improve the effect and timeliness of ground fault monitoring and improve the effect of fault early warning, a DC ground fault monitoring method for electrical equipment in 110 kV smart substations based on improved rough sets is proposed. The characteristics of the DC ground fault current are analyzed, and the equivalent model of the DC system is constructed according to the analysis results. The low-frequency signal is enhanced according to the fuzzy rough set, and the low-frequency sampled transient zero-sequence current signal is enhanced preprocessing and wavelet packet decomposition according to the signal enhancement proportional coefficient, and the fault line is selected according to the principle of maximum energy. The wavelet packet transform method is used to decompose and analyze the waveform of the leakage current generated when a single-point grounding fault occurs in a branch of the DC system, and the grounding fault location of the DC system of the substation is realized by relative entropy operation. With the help of the cloud platform, a DC ground fault monitoring platform is built, and the ground fault monitoring is completed by using this platform. The experimental results show that the method can obtain the characteristic harmonics of the ground fault, accurately monitor whether the mutation occurs, and can realize the early warning of the ground fault of the electrical equipment in a faster time, indicating that it can realize the accurate and timely monitoring of the ground fault.

The DC bias may affect the transient transfer characteristics of the current transformer (CT), threatening the correct action of the relay protection and the safe operation of the power system. Compared with the high voltage direct current (HVDC) transmission in the unipolar-earth loop operation mode and geomagnetically induced current (GIC), the amplitude of the stray current is smaller, and the randomness and fluctuation are larger and accompanied by sudden changes. Combining the characteristics of stray current and the CT equivalent model considering the eddy current loss of the iron core, the mechanism of local transient saturation under the influence of bias magnetization is explained, and the CT saturation phenomenon caused by stray current and its effect on line distance protection are simulated and analyzed. The research shows that the influence of low amplitude and high duration of stray current will make the magnetic flux of CT grow and affect the transmission of steady-state and transient-state so that the measurement impedance appears amplitude-phase shift, which leads to under-range rejection of distance protection.

Severe issues such as degradation of power quality and efficiency have surfaced in past few years due to extensive use of power electronic converters for conversion and control in power system. The shunt active power filter (SAPF) has gained attention as a competent solution among researchers. However, most SAPF control schemes render ineffective results under non-ideal supply conditions. A novel single-phase p – q scheme based on high selectivity filter (HSF) for extraction of reference compensation currents is proposed in this paper. A three H-bridges (3HB) voltage source inverter (VSI) topology of SAPF is considered and its dc-link voltage is regulated using an Enhanced Bacterial foraging optimization (BFO) based PI controller. This topology along with the proposed SAPF control scheme can guarantee improved APF performance as each phase of the three-phase four-wire system can be controlled independently. Extensive investigations are carried out using MATLAB/Simulink simulations followed by real-time performance verification in Opal-RT Lab. The effectiveness is assessed in terms of current harmonics, excessive neutral current and current unbalance compensation. The results obtained with proposed scheme when compared with conventional three-phase and single-phase p – q schemes employing low-pass filters (LPF) yield remarkably superior results under ideal, distorted and unbalanced supply and unbalanced loading conditions.

In recent times, as many people have started using an electric vehicle (EV) as it provides benefits such as low fuel economy, reduced emission, etc., the deployment of fixed electric vehicle charging stations has been a popular method for supplying EVs with charging services. The charging requirement of EVs varies from vehicle to vehicle. Various schemes have been introduced recently regarding electric vehicles’ energy trading, charging station deployments, etc. Selecting an optimal charging station for booking charging slots by electric vehicle is one of the main concerns to avoid inappropriate services. To address this challenge, we proposed an optimal charging station selection scheme to book charging slots by EV. The proposed method used a multi-criteria decision-making technique called technique for order performance by similarity to ideal solution (TOPSIS) based on various parameters of electric vehicle charging stations (EVCSs). In this, the performance score of each EVCS is determined, and the ranking process is made according to their performance score. Then, EV selects the EVCS with the highest ranked and books the charging slot. Further, an analysis of the proposed scheme is carried out for 10 EVCSs and 3 EVs.

The complexity raised due to the exponential growth of renewable power generators in modern power system evokes the necessity of detecting the potential power quality threats. With this context, the article proposes a novel power quality (PQ) detection method in PV and wind generator integrated distribution networks. In the first stage, the variational mode decomposition (VMD) technique is utilized to pull out the characteristic modes out of the voltage signal for nine different PQ events. In the later stage, a set of features are extracted from the decomposed signal to train a novel Feature Enabled Random Forest Classifier (FERFC). The feature enabling capability of Random Forest classifier is reckoned with the help of a single decision tree. It has been proved that the proposed novel VMD-FERFC has the ability to accurately identify and classify the PQ disturbances in the PV-wind-based distribution network. The performance of the suggested technique is verified in presence of noise. Further the supremacy of the method is established by comparing with other competitive reported PQ detection methods.

As Electric Vehicles (EVs) are considered as the future of the automobile industry, the demand for EV charging stations immensely rises. Though grid support can be expected for a microgrid (MG), it is highly advantageous to be self-reliant and be prepared for standalone operation for a long duration. This paper proposes a hybrid ac/dc microgrid (HMG) with an EV charging station in standalone mode with a smart power management strategy that increases the reliability of operation. Different modes of operation are designed according to power consumption levels and the operation of MG is switched between these modes considering the solar irradiance and battery bank state of charge. The decision-making system uses a combination of (a) fuzzy logic approach based on the state of charge of the energy storage system (ESS) and solar irradiance, adding to the intelligence of the system and providing smart reasoning capability and (b) logical decisions based on solar power and battery state of charge, considering all possible operating scenarios. With the proposed power management strategy in place, the standalone hybrid microgrid system is found to be able to prolong the discharge of the energy storage system under critical conditions of low battery state of charge and PV power availability and also under highly critical situations will be able to meet the critical load demand making use of the power available in the EV connected. Emphasis has also been given to maintaining the stability of the system while performing shifts in operating modes and during sudden changes in the scenario. Different operating conditions are recognized by the power management module in the model and an appropriate power management strategy is applied. Real-time simulation of the model was done in OPAL-RT real-time simulator using RT-LAB software and simulation results were reproduced.