Hui Luan

To ensure the safe and stable operation of energy storage systems (ESS) during peak power supply periods, this study proposes an enhanced state of charge (SOC) estimation framework for lithium-ion batteries. By integrating an Improved Adaptive Forgetting Factor Recursive Least Squares (IAFFRLS) method with an adaptive extended Kalman filter (AEKF) optimized by an Improved Gray Wolf Optimizer (IGWO), the proposed method achieves superior dynamic adaptability. Specifically, the IAFFRLS employs a sliding-window root-mean-square error to dynamically adjust the forgetting factor, effectively mitigating the impact of single-point disturbances. Comparative results indicate that the average voltage estimation error is reduced by 58.62% compared to the conventional AFFRLS method. Four dynamic condition tests demonstrate that the proposed IAFFRLS–IGWO–AEKF method achieves average absolute errors of 0.186–0.209% in SOC estimation and 0.069–0.088% in voltage estimation, significantly outperforming two benchmark algorithms and enabling efficient, accurate, and stable SOC estimation for lithium-ion batteries in energy storage systems.

Under the backdrop of the “dual carbon” strategy, the rapid increase in renewable energy penetration has exacerbated challenges such as widening peak–valley load gaps and insufficient grid regulation capacity, highlighting the urgent need to establish a market-oriented collaborative dispatching mechanism. This paper proposes a peak-shaving and valley-filling dispatching approach based on a multi-agent system (MAS) to enhance both the regulatory capability and economic efficiency of power grids. A multi-agent collaborative architecture is established on the generation side, where behavioral modeling and interaction simulations of generation, load, and energy storage agents are conducted using the NetLogo platform to emulate dynamic responses under market conditions. On the grid side, dynamic electricity pricing and energy storage control strategies are implemented. An integrated time-of-use electricity pricing mechanism is designed that incorporates environmental pollution factors, supply–demand state factors, and price-smoothing factors to dynamically adjust tariffs. A price-responsive load demand model and a dynamic threshold-based energy storage control strategy are developed to facilitate flexible regulation. On the load side, an optimized dispatch model is formulated with dual objectives of minimizing system operating costs and reducing the standard deviation of the net load profile. The Beetle Antennae Search (BAS) algorithm is employed to solve the model, striking a balance between economic efficiency and stability. Case study results demonstrate that, compared with traditional dispatch methods, the coordinated optimization of the BAS algorithm and the dynamic pricing mechanism proposed in this paper achieves a dual improvement in solution efficiency and economy. This ultimately reduces the system’s peak-to-valley difference by 10.92% and operating costs by 66.2%, proving its effectiveness and superiority in power grids with high renewable energy penetration.

Subsurface structures in reality commonly have fractal characteristics. However, conventional magnetic exploration methods typically adopt anomalies with uniform physical properties for the forward modeling, which fails to capture the multiscale self-similarity of real geological bodies, and ultimately compromises the accuracy of subsequent inversion interpretations. The Menger sponge is a classic 3-D fractal structure that effectively characterizes the fractal properties of subsurface ore bodies. Therefore, focusing on the characterization of magnetic anomalies in complex fractal structures, this study conducted 3-D forward modeling of magnetic multi-quantities including magnetic anomaly, magnetic three components, and magnetic gradient tensor for Menger sponge magnetic anomaly bodies with different fractal stages. The generalized magnetic potential governing equation was derived based on the fundamental equations of a stable magnetic field, and the discretized format of the governing equation was derived using finite element method(FEM). The method’s validity was verified through comparison with analytical solutions, demonstrating a relative error below 5%. The Menger sponge fractal anomaly bodies with different stages were constructed within 3-D meshs, and the influence of different fractal stages on magnetic anomaly, magnetic three components, and magnetic gradient tensor was analyzed. Furthermore, the effect of magnetic multi-quantities on the recognition of fractal structures was discussed. This study effectively simulates the spatial distribution of magnetic multi-quantities for 3-D fractal structures, which provides a theoretical basis for high-precision inversion of complex geologic bodies, with significant applications in the analysis of ore body structures.

In this article, a novel third-order cumulant (TOC) method based on quadratic frequency coupling (QFC) constraints is proposed to enhance the accuracy of parameter extraction for magnetic resonance sounding (MRS) signals. As a geophysical exploration technology, MRS signals are often affected by environmental noise and human interference, leading to reduced signal-to-noise ratio (SNR) and increased difficulty in parameter extraction. To address this issue, a new signal processing technique is introduced in this study. By combining the third-order accumulated main diagonal slices with the QFC, it is possible to identify and suppress random and harmonic noise in an effective manner. This method is not only suitable for traditional harmonic noise processing but also capable of handling complex noise environments with multiple fundamental frequencies and time-varying fundamental frequencies. The simulation results show that for the noisy signal with an initial SNR of about −20 dB, the SNR can be increased to more than 20 dB after processing by the QFC, with an increase of at least 40–50 dB, and the fitting error of the characteristic parameter extraction is less than ±5%. Compared with the traditional HMC and SSA algorithms, the QFC algorithm obtains the minimum root mean square error of 0.72 nV; besides, the computational efficiency of the QFC has obvious advantages, which is 3.3 times that of the HMC and 9.4 times that of the SSA. Therefore, the QFC has higher accuracy in parameter extraction and computational efficiency. The successful application of this method will provide strong technical support for hydrogeological exploration, environmental monitoring, and underground engineering detection.

Magnetic resonance sounding is a geophysical method for quantitatively determining the state for groundwater storage that has gained international attention in recent years. However, the practical acquisition of magnetic resonance sounding signals, which are on the nanovolt scale, is susceptible to various types of interference, such as power-line harmonics, random noise, and spike noise. Such interference can degrade the quality of magnetic resonance sounding signals and, in severe cases, be completely drowned out by noise. This paper introduces an adaptive Gaussian filtering algorithm that is well-suited for handling intricate noise signals due to its adaptive solving characteristics and iterative sifting approach. Notably, the algorithm can process signals without relying on prior knowledge. The adaptive Gaussian filtering algorithm is applied for the envelope extraction of noisy magnetic resonance sounding signals, and the reliability and effectiveness of the method are rigorously validated. The simulation results reveal that, even under strong noise interference (with original signal-to-noise ratios ranging from −7 dB to −25 dB), the magnetic resonance sounding signal obtained after algorithmic processing is compared to the ideal signal, with 16 sets of data statistics, and the algorithm ensures an initial amplitude uncertainty within 4nV and restricts the uncertainty of the relaxation time within a 6 ms range. The signal-to-noise ratio can be boosted by up to 53 dB. The comparative assessments with classical algorithms such as empirical mode decomposition and the harmonic modeling method confirm the superior performance of the adaptive Gaussian filtering algorithm. The processing of the field data also fully proved the practical application effects of the algorithm.

The small-loop time-domain electromagnetic (TDEM) method has been widely used in urban underground space detection in recent years due to the small workspace requirements. Due to the small side length of the transmitting coil of the small-loop electromagnetic method, more difficulties have been introduced in modeling and instrument development. The traditional modeling method cannot include source calculation, and the error becomes significantly large when calculating the initial field of the small-loop, and the cross iteration of electric and magnetic fields also increases the modeling error of the small-loop. Therefore, a high-precision 3-D small-loop source TDEM modeling method need to be proposed to provide the theoretical basis for feature analysis, inversion, and instrument parameter design. In this article, the electromagnetic wave equations are adopted as the controlling equations and discretized based on the Crank–Nicolson finite-difference time-domain (CN-FDTD) method. The entire computational space, including air and ground, is subdivided into sources to support 3-D small-loop TDEM modeling for shallow anomalous body conditions. Furthermore, the iterative formulas of the electromagnetic wave equations in the complex frequency-shifted perfect match layer (CFS-PML) are derived, the reflection errors are largely suppressed, and the modeling accuracy is significantly improved. Finally, the effectiveness of the improved method is verified by homogeneous models, layered models, and complex anomaly models. In addition, analyzing the propagation characteristics of the small-loop can guide the setting of the receiver sampling rate and improve the accuracy of detection. The results show that the improved method can achieve stable, low-memory, and high-precision 3-D small-loop source TDEM modeling, which can provide theoretical support for the application of the TDEM method in urban and shallow detection.

The small-loop transient electromagnetic (TEM) method has made great contributions in urban underground space detection in recent years due to its small working space. For the problems of single sampling rate and low sampling rate of traditional receivers, a dual sampling rate receiver is designed to record electromagnetic response with high accuracy. In addition, this paper employs the passive clamping technique and oscillation absorption technique to realize the falling edge linear fast shutdown of the triangle and trapezoidal wave emission currents, and to improve the excitation capability of the shallow target. In order to avoid the saturation of the early response, the primary field intensity of the center point is reduced by bucking coil. In order to solve the problem of high precision data interpretation in shallow layer, this paper adopts the method of remaining primary field removal and response splicing to splice the pure secondary field response of triangle wave and trapezoidal wave current waveform, and realizes the joint resistivity imaging of electromagnetic data. According to laboratory test results, the transmitter can output triangle wave and trapezoidal wave with adjustable parameters; the sampling rate of the receiver is 64 kHz and 256 kHz, which can effectively record the electromagnetic response and current waveform. The field experimental results exhibit that the small-loop electromagnetic detection system can effectively measure the shallow anomalies in urban underground space, which demonstrates the effectiveness of the dual waveform combined small-loop detection system for improving the accuracy of shallow exploration and promoting the development of geophysical refinement detection process.

Magnetic resonance sounding (MRS) is a geophysical method that can determine groundwater content directly and quantitatively. However, as MRS uses the Earth’s magnetic field as the background field, MRS signals are weak and cannot be shielded. Reliably extracting MRS signals in a strong noise environment is difficult. In this study, a data processing scheme using the adaptive local iterative filtering (ALIF) algorithm is proposed to extract MRS signal envelopes accurately. Based on the uncertainty of the initial amplitude and relaxation time, the decomposition order and mask coefficient of the ALIF algorithm are selected via traversal. Simulation results show that in the case of Gaussian noise and power frequency harmonic noise, the ALIF algorithm can reliably extract the MRS signal envelopes, and the correlation coefficient between the extracted and noiseless envelopes is 0.97. Under various noise types, amplitudes, and relaxation times, the average SNR increases by 30 dB∼42 dB. The ALIF algorithm is also suitable for extracting multi-exponential MRS signal envelopes. A comparative analysis between harmonic modeling cancellation and ensemble empirical mode decomposition shows the superiority of the ALIF algorithm, and the processing of the field data further verifies the effectiveness and practicability of the algorithm.

Magnetic resonance sounding (MRS) is a non-invasive, direct, and quantitative geophysical method for detecting groundwater, and has been widely used in groundwater survey, water resource assessment, and disaster water source forecasting. However, the MRS signal is weak (nV level) and highly susceptible to environmental noise, such as random noise and power-line harmonics, resulting in reduced quality of received data. Achieving reliable extraction of MRS signals under strong noise is difficult. To solve this problem, we propose a matching pursuit algorithm based on sparse decomposition theory for data noise suppression and MRS signal extraction. In accordance with the characteristics of the signal and noise, an oscillating atomic library is constructed as a sparse dictionary to realize signal sparse decomposition. A two-step denoising strategy is proposed to reconstruct the power-line harmonics and then extract the MRS signal. We simulated synthetic data with different signal-to-noise ratios (SNRs), relaxation times, and Larmor frequencies. Our results show that the proposed algorithm can effectively remove power-line harmonics and reduce random noise. SNR is significantly improved by up to 35.6 dB after denoising. The effectiveness and superiority of the proposed algorithm are further verified by the measured data and through comparison with the singular spectrum analysis algorithm and harmonic modeling cancellation algorithm.