Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting

Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting

HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting

Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization

Water, a renewable but limited resource, is vital for all living creatures. Increasing demand makes the sustainability of water resources crucial. River flow management, one of the key drivers of sustainability, will be vital to protect communities from the worst impacts on the environment. Modelling and estimating river flow in the hydrological process is crucial in terms of effective planning, management, and sustainable use of water resources. Therefore, in this study, a hybrid approach integrating long short-term memory networks (LSTM) and particle swarm algorithm (PSO) was proposed. For this purpose, three hydrological stations were utilized in the study along the Orontes River basin, Karasu, Demirköprü, and Samandağ, respectively. The timespan of Demirköprü and Karasu stations in the study was between 2010 and 2019. Samandağ station data were from 2009–2018. The datasets consisted of daily flow values. In order to validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the three FMSs. Statistical methods such as linear regression and the more classical model autoregressive integrated moving average (ARIMA) were used during the comparison process to assess the proposed method’s performance and demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the PSO-LSTM model provided promising accuracy results and presented higher performance compared with the benchmark and linear regression models.

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Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting

A novel electricity consumption forecasting model based on kernel extreme learning machine-with generalized maximum correntropy criterion

Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are 1) accurately determining the peak load days and hours and 2) quantifying and reducing uncertainties associated with the forecast in probabilistic risk measures for dispatch decision-making. In this study, we develop a supervised machine learning approach to generate 1) the probability of the next operation day containing the peak hour of the month and 2) the probability of an hour to be the peak hour of the day. Guidance is provided on preparation and augmentation of data as well as selection of machine learning models and decision-making thresholds. The proposed approach is applied to the Duke Energy Progress system and successfully captures 69 peak days out of 72 testing months with a 3% exceedance probability threshold. On 90% of the peak days, the actual peak hour is among the 2 h with the highest probabilities.

https://www.frontiersin.org/articles/10.3389/fenrg.2022.944804/pdf

Predicting peak day and peak hour of electricity demand with ensemble machine learning

At present, the small resistance to ground system (SRGS) is mainly protected by fixed-time zero-sequence overcurrent protection, but its ability to detect transition resistance is only about 100 Ω, which is unable to detect single-phase high resistance grounding fault (SPHIF). This paper analyzes the zero-sequence characteristics of SPHIF for SRGS and proposes a SPHIF feeder detection method that uses the current–voltage phase difference. The proposed method is as follows: first, the zero-sequence current phase of each feeder is calculated. Second, the phase voltage root mean square (RMS) value is used to determine the fault phase and obtain its initial phase as the reference value. The introduction of the initial phase of the fault phase voltage can highlight the fault characteristics and improve the sensitivity and reliability of feeder detection, and then CVPD is the difference between each feeder ZSC phase and the reference value. Finally, the magnitude of CVPD is judged. If the CVPD of a particular feeder meets the condition, the feeder is detected as the faulted feeder. Combining the theoretical and practical constraints, the specific adjustment principle and feeder detection logic are given. A large number of simulations show that the proposed method can be successfully detected under the conditions of 5000 Ω transition resistance, –1 dB noise interference, and 40% data missing. Compared with existing methods, the proposed method uses phase voltages that are easy to measure to construct SPHIF feeder detection criteria, without adding additional measurement and communication devices, and can quickly achieve local isolation of SPHIF with better sensitivity, reliability, and immunity to interference.

/pdf/a-single-phase-high-impedance-ground-faulty-feeder-detection-2iczjhnn.pdf

A Single-Phase High-Impedance Ground Faulty Feeder Detection Method for Small Resistance to Ground Systems Based on Current-Voltage Phase Difference

Abstract—In order to resolve the problem about weak sensitivity of high-resistance ground fault in low-resistance grounding system, this paper analyzes the characteristics of zero-sequence (ZS) network when single phase grounding fault occurs in distribution network. It can be concluded that neutral point ZS current is approximately equal to ZS current of fault feeder and always much larger than ZS current of non-fault feeder. And the phase angle difference between neutral point ZS current and fault feeder ZS current is greater than that between neutral point ZS current and non-fault feeder ZS current. Based on this, a comparison method of ZS current with double judgement based on 5G technology is proposed. This method ensures that the information of the ZS current of the neutral point can be accurately transmitted to each protection device on time, forming a local protection. Through theoretical and simulation analysis, this method improves the sensitivity and reliability of high resistance grounding fault protection without changing the existing protection devices.

Zequan Hou

Papers

A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting

A novel electricity consumption forecasting model based on kernel extreme learning machine-with generalized maximum correntropy criterion

Predicting peak day and peak hour of electricity demand with ensemble machine learning

A Single-Phase High-Impedance Ground Faulty Feeder Detection Method for Small Resistance to Ground Systems Based on Current-Voltage Phase Difference

High Sensitivity Protection Method of Low-resistance Grounding System Based on 5G Technology