Conventional wind power forecasting (WPF) methods adopt deterministic forecasting methods to produce a definite value of wind power output at a future time instant. However, any forecasting involves ...inherent uncertainty, and the uncertainty in WPF cannot be described by deterministic forecasting methods. Because WPF has the properties of time series data and long short-term memory (LSTM) is a time recursive neural network, the latter has significant advantages in forecasting the time series events. Therefore, in this study, a short-term WPF method based on the improved LSTM model is proposed, and the output power of a wind farm is calculated. The results show that the 4-h, 24-h, and 72-h forecasting accuracies of LSTM are higher than those of the back propagation (BP) neural network, the Particle swarm optimization and back propagation neural network (PSO-BP) hybrid model, and the wavelet neural network (WNN) at different time scales and seasons. The uncertainties in WPF performed by different forecasting models at different time scales are qualitatively described by the expectation, entropy, and hyper-entropy of cloud model. The uncertainties in WPF are quantitatively calculated by the confidence intervals based on the non-parametric kernel density estimation (NPKDE). The calculated results show that the proposed method can accurately predict the uncertainties in WPF at different confidence levels. The optimal operation results of reserve capacity based on the uncertainty in WPF and the optimal operation of the distribution network containing wind power and electric vehicles show that the proposed method can further improve the economic benefits of wind farm and distribution network.
•An improved long short-term memory network is proposed for wind power forecasting.•Cloud model is presented to qualitatively describe wind power forecasting error.•Non-parametric kernel density estimation is proposed for confidence intervals.•The results can provide valuable insights for wind power forecasting investigation.
Short-term wind power forecast (WPF) depends highly on the wind speed forecast (WSF), which is the prime contributor to the forecasting error. To achieve more accurate WPF results, this article ...proposes a wind speed correction method to improve the WSF result obtained by using the weather research and forecasting (WRF) model. First, the WRF model is constructed to forecast the wind speed, and its performance is analyzed. Second, a novel hidden Markov model (HMM) is developed to explore both the temporal autocorrelation of WSF error and the nonlinear correlation between the WSF result and the error. In the model, the fuzzy C-means cluster is introduced to properly divide the hidden state space of HMM and the emission probability of HMM is improved as continuous by the kernel density estimation (KDE) to make full use of the observation information. The proposed HMM model is better at wind speed correction through modification. Third, the HMM is solved by the Viterbi algorithm and the minimum mean-square error regulation to correct the predicted wind speed. Finally, the deterministic and probabilistic WPF results are obtained by using another KDE model, the proposed method is demonstrated to be superior to the benchmarks in case studies.
The accuracy of short-term wind power forecasting (WPF) can be improved by effective mining of numerical weather prediction data. In this article, a novel short-term WPF approach is proposed by ...combining wave division (WD), improved grey wolf optimizer based on fuzzy C-means clusters (IGFCM), and Seq2Seq model with attention mechanism based on long short-term memory model (LSTMS), named the WD-IGFCM-LSTMS model. Based on the fluctuation trend, the wind speed sequences of NWP are divided into a series of waves. Six fluctuation features that reflect the shape characteristics are extracted to quantify the partitioned waves. A new strategy is proposed to improve the global searching ability of the GWO to select the initial clustering center of FCM more effectively. The Seq2Seq deep learning model based on LSTM, named LSTMS, is applied for wave-oriented forecasting. The proposed approach outperforms the traditional point-to-point forecasting and realizes continuous sequence forecasting. The simulation results demonstrate that the WD-IGFCM-LSTMS model can perform better than other benchmark forecasting models.
Different from individual wind power forecast and regional wind power forecast (RWPF), one of the most significant research articles for alleviating negative influence on power systems aims to ...estimate the generation of multiple wind farms in the specific region, which is a valuable complement of the wind power forecast. This article proposes a nonparametric probabilistic method for RWPF, a quantile regression neural network (QRNN), enhancing the abilities of nonlinear mapping and massive data dealing. On this basis, the deep quantile regression is proposed to improve the performance of the QRNN. In this approach, the local-connected method is applied to the input layer of the neural network for tackling the challenge of the massive data. A ramp function is designed to avoid multiple quantile curves crossing problem. To improve the model's generalization capability, a smoothing method is applied to the loss function for achieving differentiability everywhere. By properly constructing the model, the approach provides a specific solution for RWPF with the massive input information. The test results on a region with ten wind farms demonstrate the effectiveness of the proposed approach.
This article proposes a dual-mode (DM) nested radio frequency (RF) rectifier for ambient wireless powering. The proposed architecture utilizes a DM nested feedback circuit to enhance the conductivity ...of the rectifier at low power while reducing the reverse leakage current at high power by generating supply voltages at the gates of the pMOS rectifying transistors. The proposed rectifier is fabricated in a 65-nm CMOS technology and occupies an area of <inline-formula> <tex-math notation="LaTeX">6480~\mu \text{m}^{2} </tex-math></inline-formula>. The measurement results show a peak power conversion efficiency of 86%, 10.1-dB dynamic range, and −19.2-dBm 1-V sensitivity when operating with a 100-<inline-formula> <tex-math notation="LaTeX">\text{k}\Omega </tex-math></inline-formula> load at the industrial, scientific, and medical band 433 MHz. Moreover, the enhanced low-power performance is achieved by reducing the effective threshold voltage of the rectifier by about 37%, compared with a low-threshold transistor in the 65-nm technology. This reduction in the threshold voltage allows the rectifier to operate with efficiency exceeding 10% for input power ≥ −40 dBm.
In this paper, a novel ensemble method consisting of neural networks, wavelet transform, feature selection, and partial least-squares regression (PLSR) is proposed for the generation forecasting of a ...wind farm. Based on the conditional mutual information, a feature selection technique is developed to choose a compact set of input features for the forecasting model. In order to overcome the nonstationarity of wind power series and improve the forecasting accuracy, a new wavelet-based ensemble scheme is integrated into the model. The individual forecasters are featured with different mixtures of the mother wavelet and the number of decomposition levels. The individual outputs are combined to form the ensemble forecast output using the PLSR method. To confirm the effectiveness, the proposed method is examined on real-world datasets and compared with other forecasting methods.
Wind power forecasting (WPF) is significant to guide the dispatching of grid and the production planning of wind farm effectively. The intermittency and volatility of wind leading to the diversity of ...the training samples have a major impact on the forecasting accuracy. In this paper, to deal with the training samples dynamics and improve the forecasting accuracy, a data mining approach consisting of K-means clustering and bagging neural network (NN) is proposed for short-term WPF. Based on the similarity among historical days, K-means clustering is used to classify the samples into several categories, which contain the information of meteorological conditions and historical power data. In order to overcome the over fitting and instability problems of conventional networks, a bagging based ensemble approach is integrated into the back propagation NN. To confirm the effectiveness, the proposed data mining approach is examined on real wind generation data traces. The simulation results show that it can obtain better forecasting accuracy than other baseline and existed short-term WPF approaches.
The nonlinearity in the consumption does not maintain steady voltage or frequency; therefore, in this article, a circular limited cycle oscillator frequency locked loop with prefilter (CLO-FLL-WPF) ...based control for voltage source converter control is used to achieve voltage and frequency regulation in an islanded microgrid (MG). This control is implemented to compensate local load reactive power, harmonic currents, and load unbalance. Besides, the active power of local loads is shared among the multiple energy sources. The CLO-FLL-WPF control technique performance is validated experimentally and through simulations by comparing it with the existing control algorithm in a MG. This MG is a combination of the solar photovoltaic array, pico-hydro turbine-driven synchronous reluctance generator, permanent magnet brushless dc generator based wind energy conversion, and battery storage. In traditional battery control, a proportional-integral control approach is used, which can cause a stability problem. Thereby, in this article, the bidirectional dc-dc converter control method is used, which provides improved stability and makes the controller design straightforward. Test results validate the effectiveness of the control algorithm under different dynamic and steady-state conditions.
Wind power forecasting has gained significant attention due to advances in wind energy generation in power frameworks and the uncertain nature of wind. In this manner, to maintain an affordable, ...reliable, economical, and dependable power supply, accurately predicting wind power is important. In recent years, several investigations and studies have been conducted in this field. Unfortunately, these examinations disregarded the significance of data preprocessing and the impact of various missing values, thereby resulting in poor performance in forecasting. However, long short-term memory (LSTM) network, a kind of recurrent neural network (RNN), can predict and process the time-series data at moderately long intervals and time delays, thereby producing good forecasting results using time-series data. This article recommends a hybrid forecasting model for forecasting wind power to improve the performance of the prediction. An improved long short-term memory network-enhanced forget-gate network (LSTM-EFG) model, whose appropriate parameters are optimized using cuckoo search optimization algorithm (CSO), is used to forecast the subseries data that is extracted using ensemble empirical mode decomposition (EEMD). The experimental results show that the proposed forecasting model overcomes the limitations of traditional forecasting models and efficiently improves forecasting accuracy. Furthermore, it serves as an operational tool for wind power plants management.
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