An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.publishedVersio

Optimal sizing and placement of capacitors in the isolated microgrid throughout the day considering the demand response program

Reactive power compensation (RPC) is a big problem during power system operation. Parenthetically, capacitor allocation and sizing may be the only convenient solution for RPC of power systems. The loss sensitivity factor (LSF) is applied here for finding the optimum capacitor position. This paper presents quasi-oppositional fast convergence evolutionary programming (QOFCEP), fast convergence evolutionary programming (FCEP), and evolutionary programming (EP) for the optimum location and sizing of shunt capacitors in the isolated microgrid (MG) for minimizing total real power loss throughout the day with and without the demand response program (DRP). The 33-node, 69-node, and 118-node isolated MGs have been studied to authenticate the efficacy of the suggested approach. Each MG includes small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), diesel generators (DGs), and plug-in electric vehicles (PEVs)

Chaos-infused wind power integration in the grey wolf optimal paradigm for combine thermal-wind power plant systems

This research presents a novel methodology for tackling the combined thermal-wind economic load dispatch (ELD) issue in contemporary power system. The proposed approach involves hybridizing active-set algorithm (ASA), interior point algorithm (IPA) and sequential quadratic programming (SQP) into grey wolf optimization (GWO) algorithm, while effectively incorporating the intricacies associated with renewable energy sources (RES). A more accurate model is made possible by hybridization for complex systems with memory and hereditary characteristics. The GWO is used as a tool for global search while ASA, IPA and SQP methods are used for rapid local optimization mechanism. The performance evaluation of the design heuristics is carried out on 37 thermal and 3 wind power generating units and outcomes endorse the effectiveness of the proposed scheme over state-of-the-art counterparts. The worthy performance is further validated on statistical assessments in case of thermal-wind integrated ELD problem in terms of measure of central tendency and variation on cost and complexity indices

A Comprehensive Review on the Modelling and Significance of Stability Indices in Power System Instability Problems

Many technological advancements in the modern era have made actual use of electrical power and the constrained operating of power systems within stability limits. Some expeditious load variations and rising power demands initiate complications in voltage stability and can put stress on performance, leading to voltage instability. Voltage Stability Indices can be used to perform voltage stability assessment. This review evaluates various VSIs based on mathematical derivations, assumptions, critical values, and methodology. VSIs determine the maximum loadability, voltage collapse proximity, stability margin, weak areas, and contingency ranking. Stability indices can also specify the optimal placing and sizing of Distributed Generators. Thus, VSIs play a vital role in power system voltage stability. This review is a comprehensive survey of various indices and analyses their accuracy in determining the instability of power systems. Voltage stability is a crucial concern in operating a reliable power system, and the systematic evaluation of voltage stability is essential in a power system. This review considered and analyzed 34 indices from 138 articles from the literature for their significant performance in various power system stability problems. Of 33 indices, were 22 derived from transmission line parameters, referred to as line indices, and 12 from bus and line parameters, referred to as bus indices

Developing a Hybrid Approach Based on Analytical and Metaheuristic Optimization Algorithms for the Optimization of Renewable DG Allocation Considering Various Types of Loads

The optimal location of renewable distributed generations (DGs) into a radial distribution system (RDS) has attracted major concerns from power system researchers in the present years. The main target of DG integration is to improve the overall system performance by minimizing power losses and improving the voltage profile. Hence, this paper proposed a hybrid approach between an analytical and metaheuristic optimization technique for the optimal allocation of DG in RDS, considering different types of load. A simple analytical technique was developed in order to determine the sizes of different and multiple DGs, and a new efficient metaheuristic technique known as the Salp Swarm Algorithm (SSA) was suggested in order to choose the best buses in the system, proportionate to the sizes determined by the analytical technique, in order to obtain the minimum losses and the best voltage profile. To verify the power of the proposed hybrid technique on the incorporation of the DGs in RDS, it was applied to different types of static loads; constant power (CP), constant impedance (CZ), and constant current (CI). The performance of the proposed algorithm was validated using two standards RDSs—IEEE 33-bus and IEEE 69-bus systems—and was compared with other optimization techniques

An Improved Bald Eagle Search Algorithm for Parameter Estimation of Different Photovoltaic Models

Clean energy resources have become a worldwide concern, especially photovoltaic (PV) energy. Solar cell modeling is considered one of the most important issues in this field. In this article, an improvement for the search steps of the bald eagle search algorithm is proposed. The improved bald eagle search (IBES) was applied to estimate more accurate PV model parameters. The IBES algorithm was applied for conventional single, double, and triple PV models, in addition to modified single, double, and triple PV models. The IBES was evaluated by comparing its results with the original BES through 15 benchmark functions. For a more comprehensive analysis, two evaluation tasks were performed. In the first task, the IBES results were compared with the original BES for parameter estimation of original and modified tribe diode models. In the second task, the IBES results were compared with different recent algorithms for parameter estimation of original and modified single and double diode models. All tasks were performed using the real data for a commercial silicon solar cell (R.T.C. France). From the results, it can be concluded that the results of the modified models were more accurate than the conventional PV models, and the IBES behavior was better than the original BES and other compared algorithms

Tachism: Tri-Port Antenna with Triple Notching Characteristic and High Isolation System for MIMO Application

A novel ultra-wideband (UWB) KAYI-shaped and common KITE-shaped ground plane tri-port antenna is proposed. The proposed research work has a small size of (30 × 30 × 1.6 mm3). The MIMO antenna elements are placed in a KAYI-shaped (Y-shaped) with a symmetric phase shift of 120∘ between the nearby MIMO antennas element improving the isolation. The antenna’s gain is more than 5 dBi for the entire bands of WiMax, WLAN, and X-band satellite communication. The suggested work includes notches at 3.2 GHz, 5.2 GHz, and 8.9 GHz, respectively. The notching characteristics are made possible by L-shaped slits for the WiMax band, the inverted U-shaped slot for WLAN, while the third is created by the interaction between the L-shaped and U-shaped notching elements. Results were measured after making the prototype antenna on the FR-4 substrate. The proposed antenna has good impedance matching for 2–20 GHz and three notching characteristics with high isolation among the MIMO elements. Mean effective gain (MEG), envelope correlation coefficient (ECC), and total active reflection coefficient (TARC) are the diversity metrics of MIMO antennas which are in good comparison to the proposed antenna. The antenna is a good candidate for deployment in wireless communication and MIMO applications

A Poisson Process-Based Random Access Channel for 5G and Beyond Networks

The 5th generation (5G) wireless networks propose to address a variety of usage scenarios, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Due to the exponential increase in the user equipment (UE) devices of wireless communication technologies, 5G and beyond networks (B5G) expect to support far higher user density and far lower latency than currently deployed cellular technologies, like long-term evolution-Advanced (LTE-A). However, one of the critical challenges for B5G is finding a clever way for various channel access mechanisms to maintain dense UE deployments. Random access channel (RACH) is a mandatory procedure for the UEs to connect with the evolved node B (eNB). The performance of the RACH directly affects the performance of the entire network. Currently, RACH uses a uniform distribution-based (UD) random access to prevent a possible network collision among multiple UEs attempting to access channel resources. However, in a UD-based channel access, every UE has an equal chance to choose a similar contention preamble close to the expected value, which causes an increase in the collision among the UEs. Therefore, in this paper, we propose a Poisson process-based RACH (2PRACH) alternative to a UD-based RACH. A Poisson process-based distribution, such as exponential distribution, disperses the random preambles between two bounds in a Poisson point method, where random variables occur continuously and independently with a constant parametric rate. In this way, our proposed 2PRACH approach distributes the UEs in a probability distribution of a parametric collection. Simulation results show that the shift of RACH from UD-based channel access to a Poisson process-based distribution enhances the reliability and lowers the network’s latency