In recent years, exploration and exploitation of renewable energies are turning a new chapter toward the development of energy policy, technology and business ecosystem in all the countries. Distributed energy resources (DERs) are being largely interconnected to electrical power grids. This dispersed and intermittent generational mixes bring technical and economic challenges to the power systems in terms of operations, stability, reliability, interoperability and the policy making. In additional, DERs cause the significant impacts to the operation of traditional centralized generation power plants and the dispatch control centers. Under such circumstances, the accuracy of DERs power forecasting is one of the critical problems for TSO and DSO such as unit commitment, smooth fluctuations, peak load shifting, demand response, etc. In this paper, a simplified LSTM algorithm built over the architecture of Machine Learning methodology to forecast one day-ahead solar power generation is introduced. Through the machine learning processes of data processing, model fitting, cross validation, metrics evaluation and hyperparameters tuning, the result shows that the proposed simplified LSTM model outperform the MLP model. Moreover, the forecast of LSTM model can successfully capture intra-hour ramping on different weather scenarios. The average RMSE is 0.512 which is quite promising to inspire that the proposed methodology and architecture can best fit the short-term solar power forecasting applications.

With the skyrocketing growth in global photovoltaic (PV) power capacity, fault detection of PV systems has gained prodigious importance in recent years. It has been known that in PV system the DC series arc fault is more difficult to be detected than parallel arc fault. With the proposed technologies of characteristic extraction, filtering and fault identification, a novel efficient detection algorithm protecting a PV system against DC series arc fault is proposed. The detection algorithm is realized on an arc fault detector (AFD) based on a cost-effective TMS320F28335 digital signal processor (DSP). To verify the feasibility and effectiveness of the proposed AFD, a relevant experiment PV system is conducted to compare with two commercial AFDs by various testing states. A great deal of empirical evidence shows that the detection performance of the proposed AFD outperforms the commercially available AFDs. The research may be of some reference value for similar work in the future.

Creatine supplementation has an ergogenic effect in an acute complex training bout, but the benefits of chronic creatine supplementation during long-term complex training remain unknown. The study aimed to evaluate the effects of 4-week complex training combined with creatine supplementation on sport performances and muscle damage biomarkers. Thirty explosive athletes were assigned to the creatine or placebo group, which consumed 20 g of creatine or carboxymethyl cellulose, respectively, per day for 6 days followed by 2 g of the supplements until the end of the study. After 6 days of supplementation, subjects performed tests of one repetition maximum (1-RM) strength of half squat and complex training bouts to determine the optimal individual post-activation potentiation time. Thereafter, all subjects performed a complex training programme consisting of six sets of 5-RM half squats and plyometric jumps 3 times per week for 4 weeks. Body composition, 30-m sprint and jump performances were assessed before and after the training period. Moreover, blood creatine kinase (CK) activity was analysed at the first and the last training bout. After the training, the 1-RM strength in the creatine group was significantly greater than in the placebo group (p < 0.05). CK activity after the complex training bout in the creatine group was significantly reduced compared with the placebo group (p < 0.05). No differences were noted for other variables. This study concluded that creatine supplementation combined with complex training improved maximal muscular strength and reduced muscle damage during training.

High impedance faults(HIFs) often draw too little current to activate conventional overcurrent protection, making the detection of a HIF event a difficult task. A practical and implementable solution to HIF detection and location is presented. In the proposed methodology, wavelet transform used for extracting features of HIFs signals and neural network used for HIF detection are used to develop a novel HIF detector called enhanced FTU(feeder terminal unit). The designed enhanced FTU is a high-performance HIF detector that is the retrofitting of the ready-made FTU with an embedded digital signal processor(DSP) and a high-frequency current sensor. The enhanced FTU monitors the operation of distribution feeder system and sends a fault flag signal to Feeder Dispatch and Control Center(FDCC) when a HIF occurs. Based on the Feeder Automation(FA) mechanism, the HIF detection, isolation, and service restoration(FDIR) can be activated. Consequently, the power reliability can be significantly improved by reducing the outage area and repair time. The validation of the presented technique was evaluated through staged fault testing. Compared with the existing related research, the proposed enhanced FTU is available and feasible.

In power systems, determining the values of time dial setting (TDS) and the plug setting (PS) for directional overcurrent relays (DOCRs) is an extremely constrained optimization problem that has been previously described and solved as a nonlinear programming problem. Optimization coordination problems of near-end faults and far-end faults occurring simultaneously in circuits with various topologies, including fixed and variable network topologies, are considered in this study. The aim of this study was to apply the Nelder-Mead (NM) simplex search method and particle swarm optimization (PSO) to solve this optimization problem. The proposed NM-PSO method has the advantage of NM algorithm, with a quicker movement toward optimal solution, as well as the advantage of PSO algorithm in the ability to obtain globally optimal solution. Neither a conventional PSO nor the proposed NM-PSO method is capable of dealing with constrained optimization problems. Therefore, we use the gradient-based repair method embedded in a conventional PSO and the proposed NM-PSO. This study used an IEEE 8-bus test system as a case study to compare the convergence performance of the proposed NM-PSO method and a conventional PSO approach. The results demonstrate that a robust and optimal solution can be obtained efficiently by implementing the proposal.

Creatine plays an important role in muscle energy metabolism. Postactivation potentiation (PAP) is a phenomenon that can acutely increase muscle power, but it is an individualized process that is influenced by muscle fatigue. This study examined the effects of creatine supplementation on explosive performance and the optimal individual PAP time during a set of complex training bouts. Thirty explosive athletes performed tests of back squat for one repetition maximum (1RM) strength and complex training bouts for determining the individual optimal timing of PAP, height and peak power of a counter movement jump before and after the supplementation. Subjects were assigned to a creatine or placebo group and then consumed 20 g of creatine or carboxymethyl cellulose per day for six days. After the supplementation, the 1RM strength in the creatine group significantly increased (p < 0.05). The optimal individual PAP time in the creatine group was also significant earlier than the pre-supplementation and post-supplementation of the placebo group (p < 0.05). There was no significant difference in jump performance between the groups. This study demonstrates that creatine supplementation improves maximal muscle strength and the optimal individual PAP time of complex training but has no effect on explosive performance.