A novel nano-grade organosilicon polymer:Improving airtightness of compressed air energy storage in hard rock formations

Enhancing cavern sealing is crucial for improving the efficiency of compressed air energy storage(CAES)in hard rock formations.This study introduced a novel approach using a nano-grade organosilicon polymer(NOSP)as a sealant,coupled with an air seepage evaluation model that incorporates Knudsen diffusion.Moreover,the initial coating application methods were outlined,and the advantages of using NOSP compared to other sealing materials,particularly regarding cost and construction techniques,were also examined and discussed.Experimental results indicated a significant reduction in permeability of rock specimens coated with a 7–10μm thick NOSP layer.Specifically,under a 0.5 MPa pulse pressure,the permeability decreased to less than 1 n D,and under a 4 MPa pulse pressure,it ranged between4.5×10^(-6)–5.5×10^(-6)m D,marking a 75%–80%decrease in granite permeability.The sealing efficacy of NOSP surpasses concrete and is comparable to rubber materials.The optimal viscosity for application lies between 95 and 105 KU,and the coating thickness should ideally range from 7 to 10μm,applied to substrates with less than 3%porosity.This study provides new insights into air transport and sealing mechanisms at the pore level,proposing NOSP as a cost-effective and simplified solution for CAES applications.

Comparative analysis of thermodynamic and mechanical responses between underground hydrogen storage and compressed air energy storage in lined rock caverns

Underground hydrogen storage(UHS)and compressed air energy storage(CAES)are two viable largescale energy storage technologies for mitigating the intermittency of wind and solar power.Therefore,it is meaningful to compare the properties of hydrogen and air with typical thermodynamic storage processes.This study employs a multi-physical coupling model to compare the operations of CAES and UHS,integrating gas thermodynamics within caverns,thermal conduction,and mechanical deformation around rock caverns.Gas thermodynamic responses are validated using additional simulations and the field test data.Temperature and pressure variations of air and hydrogen within rock caverns exhibit similarities under both adiabatic and diabatic simulation modes.Hydrogen reaches higher temperature and pressure following gas charging stage compared to air,and the ideal gas assumption may lead to overestimation of gas temperature and pressure.Unlike steel lining of CAES,the sealing layer(fibre-reinforced plastic FRP)in UHS is prone to deformation but can effectively mitigates stress in the sealing layer.In CAES,the first principal stress on the surface of the sealing layer and concrete lining is tensile stress,whereas UHS exhibits compressive stress in the same areas.Our present research can provide references for the selection of energy storage methods.

Off-Grid Compressed Channel Estimation with Parallel Interference Cancellation for Millimeter Wave Massive MIMO

Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.

Fast compressed sensing spectral measurement with adaptive gradient multiscale resolution

We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.

Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.

Research and Application of Compressed Fermented Chestnut Leaf Feed in Fattening Mutton Sheep

Chestnut leaves and bran were evenly mixed according to different mass ratios,and the mixtures were sprayed and inoculated with a certain amount of prepared EM microbial liquid,and then compressed into 70 cm×40 cm×30 cm blocks by a fully automatic yellow-storage block compression and packaging machine.The obtained blocks were packaged and sealed with plastic film,and placed in a freely ventilated place for more than 15 d of anaerobic fermentation,so as to obtain compressed fermented chestnut leaf block feed.[Results]Through the detection and analysis of nutritional components in the compressed fermented chestnut leaf block feed in the laboratory,the protein content was equal to or greater than 10.2%,which was 44.68%higher than that of unfermented chestnut leaf feed;the tannin content was equal to or greater than 638 mg/kg,which was 18.41%lower than that of the original feed;and the crude fiber content was equal to or greater than 19.5%,which was 14.09%lower than that of the original feed.[Conclusions]This study improves the palatability of chestnut leaf feed,increases the use efficiency of feed and reduces feeding cost.It is worth popularizing.

Optimisation of Thermal Comfort of Building in a Hot and Dry Tropical Climate: A Comparative Approach between Compressed Earth/Concrete Block Envelopes

Compressed earth blocks (CEB) are an alternative to cement blocks in the construction of wall masonry. However, the optimal architectural construction methods for adequate thermal comfort for occupants in hot and arid environments are not mastered. This article evaluates the influence of architectural and constructive modes of buildings made of CEB walls and concrete block walls, to optimize and compare their thermal comfort in the hot and dry tropical climate of Ouagadougou, Burkina Faso. Two identical pilot buildings whose envelopes are made of CEB and concrete blocks were monitored for this study. The thermal models of the pilot buildings were implemented in the SketchUp software using an extension of EnergyPlus. The models were empirically validated after calibration against measured thermal data from the buildings. The models were used to do a parametric analysis for optimization of the thermal performances by simulating plaster coatings on the exterior of walls, airtight openings and natural ventilation depending on external weather conditions. The results show that the CEB building displays 7016 hours of discomfort, equivalent to 80.1% of the time, and the concrete building displays 6948 hours of discomfort, equivalent to 79.3% of the time. The optimization by modifications reduced the discomfort to 2918 and 3125 hours respectively;i.e. equivalent to only 33.3% for the CEB building and 35.7% for the concrete building. More study should evaluate thermal optimizations in buildings in real time of usage such as residential buildings commonly used by the local middle class. The use of CEB as a construction material and passive means of improving thermal comfort is a suitable ecological and economical option to replace cementitious material.

Theoretical insights into thermal transport and structural stability mechanisms of triaxial compressed methane hydrate

The heat transfer and stability of methane hydrate in reservoirs have a direct impact on the drilling and production efficiency of hydrate resources,especially in complex stress environments caused by formation subsidence.In this study,we investigated the thermal transport and structural stability of methane hydrate under triaxial compression using molecular dynamics simulations.The results suggest that the thermal conductivity of methane hydrate increases with increasing compression strain.Two phonon transport mechanisms were identified as factors enhancing thermal conductivity.At low compressive strains,a low-frequency phonon transport channel was established due to the overlap of phonon vibration peaks between methane and water molecules.At high compressive strains,the filling of larger phonon bandgaps facilitated the opening of more phonon transport channels.Additionally,we found that a strain of0.04 is a watershed point,where methane hydrate transitions from stable to unstable.Furthermore,a strain of0.06 marks the threshold at which the diffusion capacities of methane and water molecules are at their peaks.At a higher strain of0.08,the increased volume compression reduces the available space,limiting the diffusion ability of water and methane molecules within the hydrate.The synergistic effect of the strong diffusion ability and high probability of collision between atoms increases the thermal conductivity of hydrates during the unstable period compared to the stable period.Our findings offer valuable theoretical insights into the thermal conductivity and stability of methane hydrates in reservoir stress environments.

Restoration of the JPEG Maximum Lossy Compressed Face Images with Hourglass Block-GAN

In the context of high compression rates applied to Joint Photographic Experts Group(JPEG)images through lossy compression techniques,image-blocking artifacts may manifest.This necessitates the restoration of the image to its original quality.The challenge lies in regenerating significantly compressed images into a state in which these become identifiable.Therefore,this study focuses on the restoration of JPEG images subjected to substantial degradation caused by maximum lossy compression using Generative Adversarial Networks(GAN).The generator in this network is based on theU-Net architecture.It features a newhourglass structure that preserves the characteristics of the deep layers.In addition,the network incorporates two loss functions to generate natural and high-quality images:Low Frequency(LF)loss and High Frequency(HF)loss.HF loss uses a pretrained VGG-16 network and is configured using a specific layer that best represents features.This can enhance the performance in the high-frequency region.In contrast,LF loss is used to handle the low-frequency region.The two loss functions facilitate the generation of images by the generator,which can mislead the discriminator while accurately generating high-and low-frequency regions.Consequently,by removing the blocking effects frommaximum lossy compressed images,images inwhich identities could be recognized are generated.This study represents a significant improvement over previous research in terms of the image resolution performance.

Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

Air tightness of compressed air storage energy caverns with polymer sealing layer subjected to various air pressures

During the operation of compressed air storage energy system,the rapid change of air pressure in a cavern will cause drastic changes in air density and permeability coefficient of sealing layer.To calculate and properly evaluate air tightness of polymer sealing caverns,the air-pressure-related air density and permeability must be considered.In this context,the high-pressure air penetration in the polymer sealing layer is studied in consideration of thermodynamic change of the cavern structure during the system operation.The air tightness model of compressed air storage energy caverns is then established.In the model,the permeability coefficient and air density of sealing layer vary with air pressure,and the effectiveness of the model is verified by field data in two test caverns.Finally,a compressed air storage energy cavern is taken as an example to understand the air tightness.The air leakage rate in the caverns is larger than that using air-pressure-independent permeability coefficient and air density,which is constant and small in the previous leakage rate calculation.Under the operating pressure of 4.5-10 MPa,the daily air leakage in the compressed air storage energy cavern of Yungang Mine with high polymer butyl rubber as the sealing material is 0.62%,which can meet the sealing requirements of compressed air storage energy caverns.The air tightness of the polymer sealing cavern is mainly affected by the cavern operating pressure,injected air temperature,cavern radius,and sealing layer thickness.The cavern air leakage rate will be decreased to reduce the cavern operating pressure the injection air temperature,or the cavern radius and sealing layer thickness will be increased.

Regularization by Multiple Dual Frames for Compressed Sensing Magnetic Resonance Imaging With Convergence Analysis

Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual,which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering(BM3D)denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging(CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.

Airborne sparse flight array SAR 3D imaging based on compressed sensing in frequency domain

In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection(BP) and the data extraction method based on modified uniformly redundant arrays(MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing(CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally,by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.

Optical image encryption algorithm based on a new four-dimensional memristive hyperchaotic system and compressed sensing

Some existing image encryption schemes use simple low-dimensional chaotic systems, which makes the algorithms insecure and vulnerable to brute force attacks and cracking. Some algorithms have issues such as weak correlation with plaintext images, poor image reconstruction quality, and low efficiency in transmission and storage. To solve these issues,this paper proposes an optical image encryption algorithm based on a new four-dimensional memristive hyperchaotic system(4D MHS) and compressed sensing(CS). Firstly, this paper proposes a new 4D MHS, which has larger key space, richer dynamic behavior, and more complex hyperchaotic characteristics. The introduction of CS can reduce the image size and the transmission burden of hardware devices. The introduction of double random phase encoding(DRPE) enables this algorithm has the ability of parallel data processing and multi-dimensional coding space, and the hyperchaotic characteristics of 4D MHS make up for the nonlinear deficiency of DRPE. Secondly, a construction method of the deterministic chaotic measurement matrix(DCMM) is proposed. Using DCMM can not only save a lot of transmission bandwidth and storage space, but also ensure good quality of reconstructed images. Thirdly, the confusion method and diffusion method proposed are related to plaintext images, which require both four hyperchaotic sequences of 4D MHS and row and column keys based on plaintext images. The generation process of hyperchaotic sequences is closely related to the hash value of plaintext images. Therefore, this algorithm has high sensitivity to plaintext images. The experimental testing and comparative analysis results show that proposed algorithm has good security and effectiveness.

A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

The video compression sensing method based onmulti hypothesis has attracted extensive attention in the research of video codec with limited resources.However,the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging task.To resolve this problem,this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction optimizationmethod.Itmainly includes the optimization of prediction blocks(OPBS),the selection of searchwindows and the use of neighborhood information.Specifically,the OPBS consists of two parts:the selection of blocks and the optimization of prediction blocks.We combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video sequence.In addition,most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on performance.Therefore,Block-level search window(BSW)is constructed to cover the position of the optimal hypothesis block as much as possible.To maximize the availability of reference frames,Nearby reference frame information(NRFI)is designed to reconstruct the current block.The proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction performance.Experimental results showthat the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

Optimized Three-Dimensional Cardiovascular Magnetic Resonance Whole Heart Imaging Utilizing Non-Selective Excitation and Compressed Sensing in Children and Adults with Congenital Heart Disease

Background:In congenital heart disease(CHD)patients,detailed three-dimensional anatomy depiction plays a pivotal role for diagnosis and therapeutical decision making.Hence,the present study investigated the applicability of an advanced cardiovascular magnetic resonance(CMR)whole heart imaging approach utilizing nonselective excitation and compressed sensing for anatomical assessment and interventional guidance of CHD patients in comparison to conventional dynamic CMR angiography.Methods:86 consecutive pediatric patients and adults with congenital heart disease(age,1 to 74 years;mean,35 years)underwent CMR imaging including a freebreathing,ECG-triggered 3D nonselective SSFP whole heart acquisition using compressed SENSE(nsWHcs).Anatomical assessability and signal intensity ratio(SIR)measurements were compared with conventional dynamic 3D-/4D-MR angiography.Results:The most frequent diagnoses were partial anomalous pulmonary venous drainage(17/86,20%),transposition of the great arteries(15/86,17%),tetralogy of Fallot(12/86,14%),and a single ventricle(7/86,8%).Image quality of nsWHcs was rated as excellent/good in 98%of patients.nsWHcs resulted in a reliable depiction of all large thoracic vessels(anatomic assessability,99%–100%)and the proximal segments of coronary arteries and coronary sinus(>90%).nsWHcs achieved a homogenously distributed SIR in all cardiac cavities and thoracic vessels without a significant difference between pulmonary and systemic circulation(10.9±3.5 and 10.6±3.4;p=0.15),while 3D angiography showed significantly increased SIR for targeted vs.non-targeted circulation(PA-angiography,15.2±8.1 vs.5.8±3.6,p<0.001;PV-angiography,7.0±3.9 vs.17.3±6.8,p<0.001).Conclusions:The proposed nsWHcs imaging approach provided a consistently high image quality and a homogeneous signal intensity distribution within the pulmonary and systemic circulation in pediatric patients and adults with a wide spectrum of congenital heart diseases.nsWHcs enabled detailed anatomical assessment and three-dimensional reconstruction of all cardiac cavities and large thoracic vessels and can be regarded particularly useful for preprocedural planning and interventional guidance in CHD patients.

Leaky Cable Fixture Detection in Railway Tunnel Based on RW DCGAN and Compressed GS-YOLOv5

The communication system of high-speed trains in railway tunnels needs to be built with leaky cables fixed on the tunnel wall with special fixtures.To ensure safety,checking the regular leaky cable fixture is necessary to elimi-nate the potential danger.At present,the existing fixture detection algorithms are difficult to take into account detection accuracy and speed at the same time.The faulty fixture is also insufficient and difficult to obtain,seriously affecting the model detection effect.To solve these problems,an innovative detection method is proposed in this paper.Firstly,we presented the Res-Net and Wasserstein-Deep Convolution GAN(RW-DCGAN)to implement data augmentation,which can enable the faulty fixture to export more high-quality and irregular images.Secondly,we proposed the Ghost SENet-YOLOv5(GS-YOLOv5)to enhance the expression of fixture feature,and further improve the detection accuracy and speed.Finally,we adopted the model compression strategy to prune redundant channels,and visualized training details with Grad-CAM to verify the reliability of our model.Experimental results show that the algorithm model is 69.06%smaller than the original YOLOv5 model,with 70.07%fewer parameters,2.1%higher accuracy and 14.82 fps faster speed,meeting the needs of tunnel fixture detection.

A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.

Reduced Imaging Time and Improved Image Quality of 3D Isotropic T2-Weighted Magnetic Resonance Imaging with Compressed Sensing for the Female Pelvis

This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images.

Design and Development of Wind-Solar Hybrid Power System with Compressed Air Energy Storage for Voltage and Frequency Regulations

The intermittent nature of wind and solar photovoltaic energy systems leads to the fluctuation of power generated due to the fact that the power output is highly dependent upon local weather conditions, which results to the load shading issue that led to the voltage and frequency instability. In additional to that, the high proportions of erratic renewable energy sources can lead to erratic frequency changes which affect the grid stability. In order to reduce this effect, the energy storage system is commonly used in most wind-solar energy systems to balance the voltage and frequency instability during load variations. One of the innovative energy storage systems is the compressed air energy storage system (CAES) for wind and solar hybrid energy system and this technology is the key focus in this research study. The aim of this research was to examine the system configuration of the CAES system through modelling and experimental approach with PID controller design for regulating the voltage and frequency under different load conditions. The essential elements and the entire system have been presented in this work as thorough modelling in the MATLAB/Simulink environment for different load conditions. The developed model was tested through an experimental workbench using the developed prototype of the compressed air storage in the Siemens Lab at DeKUT and explored the consequence of the working parameters on the system proficiency and the model accuracy. The performance of the system for the developed prototype of CAES system was validated using results from an experimental workbench with MATLAB/Simulink R2022b simulation. The modeling and experimental results, shows that the frequency fluctuation and voltage drop of the developed CAES system during load variations was governed by the I/P converter using a PID_Compact controller programed in the TIA Portal V17 software and downloaded into PLC S7 1200. Based on these results, the model can be applied as a basis for the performance assessment of the compressed air energy storage system so as to be included in current technology of wind and solar hybrid energy systems.