Long short‐term memory‐based robust and qualitative modal feature identification of non‐stationary low‐frequency oscillation signals in power systems

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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A Novel OLTC Fault Diagnosis Method Based on Optimized Long Short-Term Memory Parameters

10.3233/faia210458 ◽

2021 ◽

Author(s):

Yan Yan ◽

Hongzhong Ma

Keyword(s):

Fault Diagnosis ◽

Power Systems ◽

Short Term Memory ◽

Permutation Entropy ◽

Short Term ◽

Term Memory ◽

Mode Decomposition ◽

Diagnosis Method ◽

Long Short Term Memory ◽

Grasshopper Optimization

Recently, long short-term memory (LSTM) networks have been widely adopted to help with fault diagnosis for power systems. However, the parameters of LSTM networks are determined by prior knowledge and experience and thereby not capable of dealing with unexpected faults in volatile environments. In this paper, we propose and apply an improved grey wolf optimization (IGWO) algorithm to optimize the parameters of LSTM networks, aiming to circumvent the drawback of empirical LSTM parameters and enhance the fault diagnosis accuracy for on-load tap changers (OLTCs). The composite multiscale weighted permutation entropy and energy entropy yielded by the grasshopper optimization algorithm and variational mode decomposition (GOA-VMD) method are used as the inputs of LSTM networks. The IGWO algorithm is applied in an iterative manner to optimize the relevant super arithmetic of the LSTM. In this way, an IGWO-LSTM combination model is constructed to classify different faults diagnosed in OLTCs. Experimental results verify the diagnosis performance superiority of the proposed method over several widely used comparison benchmarks

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An Improved Long Short-Term Memory Neural Network for Macroeconomic Forecast

Revue d intelligence artificielle ◽

10.18280/ria.340507 ◽

2020 ◽

Vol 34 (5) ◽

pp. 577-584

Author(s):

Lipeng Wang

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Low Frequency ◽

Forecast Model ◽

Evaluation Index System ◽

Short Term ◽

Term Memory ◽

Macroeconomic Forecast ◽

Long Short Term Memory ◽

Missing Items

The statistics and cyclical swings of macroeconomics are necessary for exploring the internal laws and features of the market economy. To realize intelligent and efficient macroeconomic forecast, this paper puts forward a macroeconomic forecast model based on improved long short-term memory (LSTM) neural network. Firstly, a scientific evaluation index system (EIS) was constructed for macroeconomy. The correlation between indices was measured by Spearman correlation coefficient, and the index data were preprocessed by interpolating the missing items and converting low-frequency series into high-frequency series. Next, the corresponding mixed frequency dataset was constructed, followed by the derivation of the state space equation. Then, the LSTM neutral network was optimized by the Kalman filter or macroeconomic forecast. The effectiveness of the proposed forecast method was verified through experiments. The research results lay a theoretical basis for the application of LSTM in financial forecasts.

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Application of Long short-term Memory Neural Networks in Dynamic State Estimation of Generators Subjected to Ageing in Complex Power Systems

2019 9th International Conference on Power and Energy Systems (ICPES) ◽

10.1109/icpes47639.2019.9105597 ◽

2019 ◽

Author(s):

Hadi Ariakia ◽

Kianoush Emami

Keyword(s):

Neural Networks ◽

Power Systems ◽

State Estimation ◽

Short Term Memory ◽

Dynamic State ◽

Short Term ◽

Term Memory ◽

Complex Power ◽

Dynamic State Estimation ◽

Long Short Term Memory

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Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting

Energies ◽

10.3390/en15010130 ◽

2021 ◽

Vol 15 (1) ◽

pp. 130

Author(s):

Gwo-Ching Liao

Keyword(s):

Neural Network ◽

Power Generation ◽

Power Systems ◽

Short Term Memory ◽

Search Algorithm ◽

Load Forecasting ◽

Short Term ◽

Term Memory ◽

Short Term Load Forecasting ◽

Long Short Term Memory

Load forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation and energy management systems. The dynamic balance between power generation and load in the optimization of power systems is receiving increasing attention. The intellectual development of information in the power industry and the data acquisition system of the smart grid provides a vast data source for pessimistic load forecasting, and it is of great significance in mining the information behind power data. An accurate short-term load forecasting can guarantee a system’s safe and reliable operation, improve the utilization rate of power generation, and avoid the waste of power resources. In this paper, the load forecasting model by applying a fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network (ILSTM-NN), and then establish short-term load forecasting using this novel model. Sparrow Search Algorithm is a novel swarm intelligence optimization algorithm that simulates sparrow foraging predatory behavior. It is used to optimize the parameters (such as weight, bias, etc.) of the ILSTM-NN. The results of the actual examples are used to prove the accuracy of load forecasting. It can improve (decrease) the MAPE by about 20% to 50% and RMSE by about 44.1% to 52.1%. Its ability to improve load forecasting error values is tremendous, so it is very suitable for promoting a domestic power system.

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