Saviztky–Golay Filtering for Solar Power Smoothing and Ramp Rate Reduction Based on Controlled Battery Energy Storage

Atif, Ammar; Khalid, Muhammad

doi:10.1109/access.2020.2973036

Cited by 69 publications

(44 citation statements)

References 30 publications

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“…We consider y(n) = x(n) + w(n) as the observed time series where w(n) denote the noise of y(n) signal. The output of SG filter denoted byx is equal [35]:…”

Section: Savisky Golay Filter (Sg)mentioning

confidence: 99%

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PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

et al. 2020

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This paper proposes an effective deep learning framework for Short-Term Load Forecasting (STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional neural network-Bidirectional Long Short-Term Memory (CBiLSTM) based on the Evolution Strategy (ES) method and the Savitzky–Golay (SG) filter (SG-CBiLSTM). The adopted methodology incorporates the virtue of different prepossessing blocks to enhance the performance of the CBiLSTM model. In particular, a data-augmentation strategy is employed to synthetically improve the feature representation of the CBiLSTM model. The augmented data is forwarded to the Partial Least Square (PLS) method to select the most informative features above the predefined threshold. Next, the SG algorithm is computed for smoothing the load to enhance the learning capabilities of the underlying system. The structure of the SG-CBiLSTM for the ISO New England dataset is optimized using the ES technique. Finally, the CBiLSTM model generates output forecasts. The proposed approach demonstrates a remarkable improvement in the performance of the original CBiLSTM model. Furthermore, the experimental results strongly confirm the high effectiveness of the proposed SG-CBiLSTM model compared to the state-of-the-art techniques.

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“…We consider y(n) = x(n) + w(n) as the observed time series where w(n) denote the noise of y(n) signal. The output of SG filter denoted byx is equal [35]:…”

Section: Savisky Golay Filter (Sg)mentioning

confidence: 99%

“…where h(n) and M denote the filter impulse response and the SG filter parameter, respectively. The polynomial p(n) and the squared error between the smoothed and noisy signal E are given as follows [35]:…”

Section: Savisky Golay Filter (Sg)mentioning

confidence: 99%

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

et al. 2020

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“…Although the obtained V OC (k) represents the optimal OCV for the ECM at step k, the V t errors of R 0 (k)I and V C (k), which are dependent on current changes, are also inherent within the V OC . To minimize these current dependent errors and prevent V OC from overfitting to the used data, the Savitzky-Golay filter [44] with a third-order polynomial was used to normalize the V OC . The filtered V OC thus includes both the OCV and the model uncertainty for the SOC.…”

Section: A Ocv-soc Curve Estimationmentioning

confidence: 99%

Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions

et al. 2020

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Load currents of batteries undergo real-time change depending on the operations of battery-powered devices, such as acceleration, deceleration, and sudden braking of electric vehicles. With the variation in current magnitude, parameters in an equivalent circuit model of the battery change depending on its chemical characteristics. Therefore, accurately identifying model parameters offline for various sizes and patterns of load currents with few experiments is difficult. Besides, there is a model uncertainty due to the difference between the linearity of the model structure and the nonlinearity of the real battery; hence, an experimentally obtained open-circuit voltage (OCV)-state-of-charge (SOC) curve has difficulty in providing an accurate OCV value for the model. Consequently, these difficulties lead to inaccuracies in the model and SOC estimation. In this paper, novel methods are proposed to reduce these inaccuracies under timevarying current conditions, where the magnitude of the load current changes in real time. These are (1) an OCV-SOC curve estimation for reducing the model uncertainty and (2) a current-adaptive extended Kalman filter algorithm for decreasing the effect of inaccurately identified model parameters. The experimental results show that the maximum-absolute-errors of the proposed SOC estimation were reduced to <1.5%. Consequently, battery-powered applications can obtain highly accurate SOC values even the magnitude of the load current changes in real time. INDEX TERMS Battery management system, electric vehicles, open-circuit voltage-state-of-charge curve estimation, adaptive extended Kalman filter, state-of-charge estimation.

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“…Authors in [11], show that a better battery utilisation whilst smoothing power fluctuations could be obtained with an LPF integrated with a battery's SOC feedback control. Authors in [12], proposed that using Savitzky–Golay filtering to reduce the solar power ramp rate whilst reducing the changes in the level of charge of energy storage by performing a polynomial curve fitting over the smooth power signal. Optimal battery storage sizing has been obtained with a monotonic charging/discharging methodology to reduce renewable power variability [13].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy logic controller for solar power smoothing based on controlled battery energy storage and varying low pass filter

Atif

Khalid

2020

IET Renewable Power Generation

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Matching of the colossal energy demand with carbon‐free resources originates a global challenge to produce and innovate advanced energy technologies that generate clean and friendly energy supplies without threatening our environment. Nevertheless, with the increase in the penetration from renewable energy resources like solar and wind farms many technical issues are presented typically; power stability, power quality, and frequency fluctuations. The aforementioned problems stem from the intermittent nature of renewable resources which makes the generated power unpredictable and requires controlling and smoothing before dispatching. Conventionally low pass filter (LPF) is used with a controlled energy storage system to smoothen the fluctuated power due to its simplicity, but a delay problem increases with higher values of the filtering the time constant. This study suggests a smoothing controller‐based fuzzy logic that determines the value of the filtering time constant based on the present solar power ramp rate to avoid the battery overworking, which results in the predetermined configuration of the LPF. The presented simulation results prove the smoothing performance of the proposed controller and compare its performance to the LPF during clear, normal, and cloudy irradiance profiles.

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Saviztky–Golay Filtering for Solar Power Smoothing and Ramp Rate Reduction Based on Controlled Battery Energy Storage

Cited by 69 publications

References 30 publications

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions

Fuzzy logic controller for solar power smoothing based on controlled battery energy storage and varying low pass filter

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